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CALIBRATION OF DETERMINISTIC PARAMETERS FOR
REASSESSMENT OF OFFSHORE PLATFORMS IN THE ARABIAN GULF
USING RELIABILITY-BASED METHOD
By: Hassan Zaghloul
This thesis is presented for the degree of Doctor of Philosophy
University of Western Australia School of Mechanical Engineering
2008
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DEDICATION
To the loving memory of my father
To my mother who had the arduous task of raising four children by herself after
the sudden death of my father
To my beloved wife Mine’ who endured my absences and supported my passion
for this research
To our sons Taha and Zaccaria who make our life beautiful
To all those who have contributed to this thesis
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ABSTRACT
The Arabian Gulf oil and gas production reserves have made it one of the world’s
strategic producers since early 1960s, with many of the existing platforms stretched
beyond their original design life. Advances in drilling technology and reservoir
assessments have extended the requirement for the service life of those existing
platforms even further. Extension of the life span of an existing platform requires
satisfactory reassessment of its various structural components, including piled
foundations.
The American Petroleum Institute Recommended Practice 2A (API RP2A) is
commonly used in the Arabian Gulf for reassessment of existing platforms. The
API guidelines have been developed for conditions in the Gulf of Mexico, the
waters off Alaska and the Pacific and Atlantic seaboards of the USA. However, the
Arabian Gulf conditions are fundamentally different to those encountered in US
waters. Hence, there is a need to develop guidelines for reassessment of existing
offshore structures to account for the specific conditions of the Arabian Gulf.
This thesis performs statistical analyses on databases collected during this research
from existing platforms to calibrate relevant load and resistance factors for the
required guidelines. The developed guidelines are based on established approaches
used in developing international codes and standards such as API RP2A-LRFD.
The outcome of this research revolves around the following three main issues:
1. Calibration of resistance factors for axial capacity of piles driven in the
carbonate soils
API RP2A (1993, 2000) does not quantify limiting soil parameters for piles driven
in carbonate soils and provides a single factor to predict the capacity of piled
foundations. This research identifies a set of limiting engineering parameters and
calibrates corresponding capacity reduction factors to predict axial capacity of
driven piles in the carbonate soils of the Arabian Gulf.
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Further, this thesis shows that the use of a single capacity reduction factor of 0.7, an
approach that is adopted in API RP2A-LRFD (1993), does not consider that axial
pile capacity in existing platforms is influenced by many parameters identified in
this research, including implied risk level, manning levels, variation in pile wall
thickness along its depth, soil composition, hammer type, installation method,
penetration ratio and the level of optimization in the original design. The
reassessment guidelines developed in this research recommends a set of capacity
reduction factors within a range of 0.4-1.0 to reflect the influence of the factors
discussed above.
2. Development of open area live loads (OALL) on offshore platforms
API RP2A-LRFD (1993) refers to ASCE Standard 7-05 to quantify live loads.
However, ASCE Standard 7-05 is only applicable to building structures and does
not quantify values for OALL on offshore platforms. This thesis reveals that, unlike
building structures, the magnitude of OALL on an offshore platform deck is not
independent of loading conditions.
OALL values on offshore platforms are rather affected by factors such as platform
size, safe working load (SWL) of materials handling equipment, expected life span
of the platform, deck location on the platform (upper deck versus other decks) and
the selected influence surface (pile, primary beams, secondary beams or topside
columns). This research investigates those factors and recommends a simplified
formula to calculate OALL. The proposed formula is a function of the SWL of the
material handling equipment which dominates the magnitude of the OALL.
Reassessment applications require a combination factor for OALL, which is a
function of the coefficient of variation (COV) of the mean lifetime maximum live
loads. This thesis proposes a combination factor of 1.5 on the basis that the COV =
10% to 20% of the mean lifetime maximum live loads on offshore platforms, which
is calculated in this research, is similar to the COV (14%) used to develop the live
load combination factor (1.5) in API RP2A-LRFD (1993).
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3. Effect of extreme storm conditions on the reliability of existing platforms in
the Arabian Gulf
In the process of calibrating pile resistance factors and development of OALL, this
research develops a set of statistical parameters for load and resistance factors. The
statistical parameters are used to perform reliability analysis on a selected platform
in the Arabian Gulf. The platform is selected such that the outcome of the reliability
analysis is applicable to other platforms in that region.
The outcome of the reliability analysis reveals that operating overload conditions
dominate the failure mechanism in the Arabian Gulf. The reliability analysis
resulted in an insignificant (10-71) probability of failure under extreme storm
conditions compared to the higher value (10-6) under operating overload conditions.
Such extreme values are only possible in a mathematical model and have little
physical meaning. Nevertheless, and despite lack of a physical meaning to such
extremely low failure probability value, it demonstrates that operating overload
dominates the failure mechanism in the Arabian Gulf. The extremely low
probability of failure is partly a result of no wave-in-deck as the wave heights are
lower than the deck at high return periods.
Consequently, reassessment of existing platforms in the Arabian Gulf would be
sufficiently addressed by considering operating overload conditions only. This
contrasts with Section ‘R’ of API RP2A (1993, 2000), which focuses on extreme
environmental conditions when performing reassessment.
The probabilities of failure considered in this research do not include errors and
omissions (controlled by quality assurance procedures) or material deterioration
(controlled by choice of materials, detailing, protective devices, and inspection and
repair procedures) or reliability-based maintenance.
Addressing operating overload conditions requires attending to two issues, namely
the capacity of piles driven in carbonate soils and OALL, which have been
addressed in this research. The operational overload situation is likely to occur
during shutdown condition or during drilling or work over activities where
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significant OALL are usually applied to platform decks. Such operational overload
can be managed by placing signs at various open areas on the platform nominating
the maximum load limits (kPa), introducing procedures that ensure that maximum
load limits are not exceeded during operation and management of human behavior
by reinforcing the importance of following the procedures.
The outcomes of this research are expected to have a profound influence on
reassessment of existing platforms in the Arabian Gulf.
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TABLE OF CONTENTS
CHAPTER 1. 1
INTRODUCTION ................................................................................................................................1
1.1. BACKGROUND 1 1.2. PROBLEM STATEMENT 3 1.3. METHODOLOGY 3 1.4. JUSTIFICATION OF THE RESEARCH 4 1.5. OUTLINE OF THE THESIS 7
CHAPTER 2. 8
RESEARCH ISSUES ............................................................................................................................8
2.1. INTRODUCTION 8 2.2. ROAD MAP 9 2.3. REASSESSMENT APPROACHES 9 2.3.1. Screening Level Check 10 2.3.2. Design Level Check 10 2.3.3. Structural Reliability Analysis Method 12 2.3.4. Probabilistic Approach 17 2.4. DEVELOPMENT APPROACH 18 2.5. SELECTION OF CALIBRATION CODE 18 2.5.1. Assessment of WSD 18 2.5.2. Assessment of LRFD 20 2.6. HISTORICAL BACKGROUND OF LRFD CODES 21 2.6.1. LRFD for Steel Building Structures 21 2.6.2. LRFD for Offshore Piled foundations 23 2.7. AXIAL PILE CAPACITY IN CARBONATE SOILS 27 2.7.1. Deposition History of Carbonate Soils in the Arabian Gulf 28 2.7.2. Characteristics of Carbonate Sediments 29 2.7.3. Installation Experience of Piles Driven in Carbonate Soils 31 2.7.4. Review of Loading Tests in Carbonate Soils 33 2.7.5. Sources of Difficulty in establishing Engineering Parameters 35 2.7.6. Approach used in Industry Practice 37
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2.8. OPEN AREA LIVE LOADS (OALL) 38 2.8.1. Background 38 2.8.2. Live Load Surveys for ASCE Standard 7-05 39 2.8.3. Probabilistic Model in ASCE Standard 40 2.8.4. Applicability of Probabilistic Model to Offshore Structures 41 2.8.5. Reduction Factors in ASCE Standard 42 2.9. CHARACTERIZATION OF THE CLIMATOLOGY IN THE ARABIAN GULF 43 2.10. SUMMARY 46 2.10.1. Code to be used in Calibration 46 2.10.2. Axial Pile Capacity in Carbonated Soils 46 2.10.3. Target Reliability Level 47 2.10.4. Open Area Live Load (OALL) 47 2.10.5. Dominant Failure Mechanism 47
CHAPTER 3. 60
METHODOLOGY ............................................................................................................................. 60
3.1. INTRODUCTION 60 3.2. OUTLINE OF THE METHODOLOGY 60 3.3. DATA COLLECTION AND GROUPING 62 3.3.1. Challenges 62 3.3.2. Sub-grouping the Data 62 3.4. STATISTICAL ANALYSIS 63 3.4.1. Distribution Type 63 3.4.2. Distribution Properties 64 3.5. APPLICATION OF RELIABILITY- BASED METHOD 66 3.5.1. Calculation of Probability of Failure 66 3.5.2. Bayesian Update 69 3.6. PREDICTION OF AXIAL PILE CAPACITY 72 3.7. CALIBRATION OF PILE RESISTANCE FACTORS 73 3.8. DERIVATION OF OPEN AREA LIVE LOADS (OALL) 76 3.8.1. Influence Surface Concept 76 3.8.2. Extreme Value Analysis 77 3.9. COMPUTER SOFTWARE PROGRAMS 78 3.9.1. Risk Analysis Software - @RISK 78
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3.9.2. APIPILE - Pile Capacity Spreadsheet 79 3.9.3. GRLWEAP-Wave Equation Analysis Program 79 3.9.4. Structural Analysis Computer Software (SACS) 80 3.9.5. Pile Driving Analyzer (PDA) 82 3.9.6. Case Pile Wave Analysis Program (CAPWAP) 83 3.10. SCOPE DELIMITATION AND KEY ASSUMPTIONS 85 3.11. JUSTIFICATION OF THE METHODOLOGY 86 3.11.1. Reliability-Based Method 86 3.11.2. Extent of the Database 86 3.11.3. Bayesian Updating 87 3.11.4. Wave Equation Analysis Method 87 3.11.5. Influence Surface Method 88 3.12. SUMMARY 89
CHAPTER 4. 102
CALIBRATION OF PILE RESISTANCE FACTORS .......................................................................102
4.1. INTRODUCTION 102 4.2. CALIBRATION MECHANICS OF AXIAL PILE RESISTANCE FACTORS 102 4.3. PILE INSTALLATION DATABASE 103 4.4. PREDICTED AXIAL PILE CAPACITY 105 4.4.1. Limiting Engineering Parameters 105 4.4.2. Input Data to APIPILE 108 4.4.3. Output from APIPILE 109 4.5. ACTUAL AXIAL PILE CAPACITY 109 4.5.1. Methodology 110 4.5.2. Input Data for Wave Equation Analysis 110 4.5.3. Short Term Axial Pile Capacity 115 4.5.4. Time Effect - Computing Setup Factors 116 4.5.5. Back-Analysis Procedure 120 4.5.6. Sensitivity Analysis of the Computed Actual Pile Capacity 126 4.6. STATISTICAL PARAMETERS OF BIAS FACTORS 127 4.6.1. Statistics of the Complete Database 128 4.6.2. Grouping by Installation Method 129 4.6.3. Grouping by Soil Type 131
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4.6.4. Grouping by Optimized Design 132 4.6.5. Grouping by Penetration Ratio 133 4.7. BENCHMARKING STATISTICS OF BIAS FACTOR 133 4.7.1. Bias Factors in API RP2A-LRFD (1993) 134 4.7.2. Bias in the Statistics of Bias Factors 135 4.8. BAYESIAN UPDATE OF BIAS FACTOR STATISTICS 138 4.8.1. “Prior” Distribution 138 4.8.2. “Likelihood” Distribution 139 4.8.3. “Posterior” Distribution 139 4.9. TARGET RELIABILITY INDEX 139 4.10. CALIBRATION OF RESISTANCE FACTORS 140 4.10.1. Target Reliability Levels 141 4.10.2. Soil Types 141 4.10.3. Installation Methods 142 4.10.4. Penetration Ratio 142 4.11. SUMMARY 143
CHAPTER 5. 202
CALIBRATION OF OPEN AREA LIVE LOADS............................................................................. 202
5.1. BACKGROUND 202 5.2. DEFINITIONS USED IN DEVELOPMENT OF OALL 203 5.3. CALIBRATION MECHANICS OF OALL 204 5.4. NATURE OF LIVE LOADS ON OFFSHORE PLATFORMS 204 5.5. EQUIPMENT LOAD DATABASE 206 5.6. SUBGROUPING THE EQUIPMENT DATABASE 207 5.6.1. Subgrouping by Platform Function 207 5.6.2. Subgrouping by Location on Platform 208 5.6.3. Subgrouping By Crane and Monorail SWL 208 5.7. STATISTICAL PARAMETERS OF EQUIPMENT WEIGHTS 209 5.7.1. On Lower Decks 209 5.7.2. On Upper Deck 210 5.7.3. Conclusion 210 5.8. CALCULATION OF MEAN LIFETIME MAXIMUM PILE LOAD 210 5.8.1. Extreme Axial Pile Load Using Normal Distribution 212
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5.8.2. Extreme Axial Pile Load using Lognormal Distribution 213 5.8.3. Minimum Separation Distance 215 5.10. BENCHMARKING THE STATISTICS OF OALL 219 5.11. SENSITIVITY ANALYSIS OF OALL PARAMETERS 220 5.11.1. Varying the Deck Area 220 5.11.2. Minimum Separation Distance 221 5.11.3. Varying the Crane Capacity 222 5.12. LIVE LOAD FACTORS 222 5.13. SUMMARY 224
CHAPTER 6. 248
DOMINANT FAILURE MECHANISM............................................................................................248
6.1. OBJECTIVE 248 6.2. PERFORMANCE MODEL 249 6.3. APPROACH 249 6.4. LOGIC OF PLATFORM SELECTION 249 6.5. MATHEMATICAL MODEL 251 6.5.1. Structural Modeling 251 6.5.2. Foundation Modeling 255 6.5.3. Loading Model 257 6.6. PUSHOVER ANALYSIS 258 6.6.1. Extreme Storm Conditions 259 6.6.2. Operating Overload Conditions 260 6.6.3. Analysis of the Results 261 6.7. LOAD AND RESISTANCE STATISTICS 262 6.7.1. Resistance Statistics 262 6.7.2. Dead Load Statistics 263 6.7.3. Live Load Statistics 263 6.7.4. Environmental Load Statistics 263 6.8. PROBABILITY OF FAILURE CALCULATIONS 265 6.8.1. Using Pushover Analysis Results 266 6.8.2. Validating Probability of Failure under Operating Overload Condition 269 6.9. CALIBRATION OF ENVIRONMENTAL PARTIAL LOAD FACTORS 274 6.10. SUMMARY 277
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CHAPTER 7. 307
CASE STUDY .................................................................................................................................. 307
7.1. BACKGROUND 307 7.2. DESCRIPTION OF THE PLATFORM 308 7.3. MATHEMATICAL MODEL 309 7.4. ANALYSIS OF THE RESULTS 310 7.5. SUMMARY 312
CHAPTER 8. 323
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ......................................................... 323
8.1. SUMMARY 323 8.2. METHODOLOGY 323 8.2.1. Open Area Live Load (OALL) 324 8.2.2. Axial Pile Capacity in Carbonate Soils 324 8.2.3. Dominant Failure Mechanism 325 8.3. CONCLUSIONS 326 8.3.1. Dominant Failure Mechanism in the Arabian Gulf 326 8.3.2. Applicability of Section ‘R’ to the Arabian Gulf Conditions 327 8.3.3. Specifications for OALL 327 8.3.4. Limiting Engineering Parameters of Carbonate Soils 328 8.3.5. Bias Factors of Axial Pile Capacity in the Arabian Gulf 329 8.3.6. Target Reliability Levels 330 8.3.7. Specifications for Axial Pile Capacity in Carbonate Soils 330 8.3.8. Modification to Deterministic Method for Reassessment 332 8.3.9. Limitations of API RP2A Prediction Model 333 8.3.10. Modeling Pile-Soil Interaction 334 8.4. RECOMMENDATIONS FOR FUTURE RESEARCH 334 8.4.1. Technical Issues 334 8.4.2. philosophical issues 336
BIBLIOGRAPHY ............................................................................................................................. 343
AUTHOR’S PUBLICATIONS ......................................................................................................... 389
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APPENDICES..................................................................................................................................390
APPENDIX A: EQUIPMENT DATABASE 391
APPENDIX B: PILED FOUNDATION DATABASE 392
APPENDIX C: SOIL PROFILE DATABASE 393
APPENDIX D: PREDICTED STATIC CAPACITY OF PILES IN THE DATABASE 394
APPENDIX E: “ACTUAL” PILE CAPACITY USING BACK ANALYSIS PROCEDURE DEVELOPED
IN THIS RESEARCH 395
APPENDIX F: COMMON STATISTICAL DISTRIBUTIONS USE IN THIS RESEARCH 396
APPENDIX G: STRUCTURAL RELIABILITY ANALYSIS (SRA) 397
APPENDIX H: PREDICTION METHODS FOR AXIAL PILE CAPACITY IN “NORMAL” SOILS 398
APPENDIX I: APIPILE MANUAL 399
APPENDIX J: SACS INPUT FILES AND EXTRACTS FROM SACS OUTPUT FILES 400
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ACKNOWLEDGMENTS
I owe an immense debt of gratitude to my supervisor, Professor Beverley Ronalds,
for her support from the formative stages of this thesis to its final draft. Her sound
advice, careful guidance and conscientious reviews of several drafts of this thesis
were invaluable as I travelled through the journey of this research.
I extend my appreciation to Dr. Geoff Cole and Dr. Manolis Fakas for providing
valuable suggestions during this research and to Dr. Mostafa Ismail for the
conscientious proof reading and making valuable suggestions to the last draft of this
thesis. Thanks to Dr. Krystyna Haq for reviewing an earlier draft of this thesis.
This acknowledgment must include all scholars and academics listed in the
Bibliography for providing the necessary background to this research. In particular,
the direct and indirect contribution of Professor Robert Bea to this research was
immensely useful and greatly appreciated. This also extends to Professor Bengt
Fellenius and Dr. Frank Rausche, who provided guidance at the start of this research
and offered software free of charge.
Last but not least, I appreciate the outstanding support from UWA library and
administration staff at the School of Oil and Gas Engineering.
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DEFINITIONS
Definitions adopted by researchers are often not uniform, so key and controversial
terms are defined in this section to establish positions taken in the research.
Fellenius (1999) recommended some of the following definitions adopted in this
research.
Term Definition
Allowable Load Maximum load that may be safely applied to a foundation unit under expected loading and soil conditions and determined as the Capacity divided by the Factor of Safety.
Applied (Service) Load Load actually applied to a foundation unit
Axial, Bearing, Shaft and Toe Capacity
Ultimate Resistance of the unit.
Blow count During pile driving, the blow count represents the count of blows for a specified penetration of the pile into the soil. Typically, the count of blows is measured for a pile driven one foot into the soil and the blow count is recorded in a pile driving record.
Capacity The maximum or ultimate soil resistance mobilized by a foundation unit. It is used as a stand-alone term and is synonymous with Ultimate Resistance.
Capacity, bearing The maximum or ultimate soil resistance mobilized by a foundation unit subjected to downward loading. It is the sum of the shaft resistance and the toe or ‘end bearing’ resistance.
Dynamic Monitoring The recording of strain and acceleration induced in a pile during driving and presentation of the data in terms of stress and transferred energy in the pile as well as of estimates of capacity.
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Term Definition
Factor of Safety The ratio of maximum available resistance or of the capacity to the code allowable stress or load.
Loading Test Refers to the situation of a test performed by loading a pile while Load Test is a test for finding out what load is applied to a pile.
Limit State A state that defines the boundary between a safe and unsafe situation
Penetration Resistance Effort required in advancing a pile. When quantified, it is either the number of blows required for the pile to penetrate a certain distance or the distance penetrated for a certain number of blows.
Pile Head The uppermost end of a Pile
Pile Impedance A material property of a pile cross-section determined as the product of the Young's modulus (E) and area (A) of the cross section divided by the wave speed (c).
Pile Point A special type of pile shoe.
Pile Shaft The portion of the pile between the pile head and the pile toe.
Pile Shoe A separate reinforcement attached to the pile toe of a pile to facilitate driving, to protect the lower end of the pile and/or to increase the toe resistance of the pile.
Pile Toe The lowermost end of a pile.
Pore Pressure Pressure in the water and gas present in the voids between the soil grains minus the atmospheric pressure.
Probability of failure This is an unfortunate choice of wording because it can be mistakenly treated as being synonymous with the actual rate of failure. The prefix “nominal” or “notional” is often applied to the probability of failure to emphasize its formal nature (CIRIA, 1977, Ellingwood et al., 1980, Melchers, 1999). An alternative would be to use the reliability index, β, which is mostly free of such connotation.
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Term Definition
Quantitative Risk Assessment (QRA)
Formal and systematic approach for identifying potentially hazardous events and estimating likelihood and consequences of accidents developing from these events to people, environment and resources.
Ultimate Load Capacity evaluated from the results of a static loading test.
Set Penetration for one blow, sometimes penetration for a series of blows.
Setup or Soil Setup Describes the effect of resistance increase with time after driving. This term is sometimes referred to as Soil Freeze but this term will not be used in this thesis as it has a different meaning for cold regions of the world.
Shaft Resistance Calculated as the integral over the embedded pile area of the unit skin friction value
Structural Analysis Refers to the technique of making stiffness or stress calculations while Structural Assessment includes the whole process of modeling the problem, analysis and interpretation of the results.
Structural Reliability Analysis (SRA)
SRA aims at determining the probability of failure of a Limit State that, in its basic form, attains an unsafe situation. In SRA, a Limit State is represented, again in its basic form, by a Limit State equation which attains a negative value for unsafe situations and a positive value for safe situations. The Limit State equation incorporates basic random variables defined by probability density functions through (a) statistical analysis of existing sample data and (b) by experience and theoretical considerations. The representation of real structural systems may involve a number of Limit States (such as buckling, yielding, fatigue or excessive deformation under various loading conditions), some of which may be represented by a number of different failure equations.
In this case, the analysis needs to incorporate statistical correlation effects between the basic random variables as well as between Limit State equations (a set of basic random variables only affect the outcome of different
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Limit State equations).
In this thesis, SRA is primarily concerned with calculating the probability of ultimate collapse of the total substructure due to extreme environmental storm loading. It does not treat all possible hazards to the structure from a QRA viewpoint.
Toe Resistance Soil resistance acting on the pile toe
Transferred Energy The energy transferred to the pile head and determined as the integral over time of the product of force, velocity, and pile impedance.
Wave Speed The speed of strain propagation in a pile.
Wave Trace A graphic representation against time of a force or velocity measurement.
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NOMENCLATURE
Symbol Definition
A Side surface area of pile or a factor to account for cyclic or static loading
BOR beginning of restrike
BS base shear
c Constant that accounts for the errors associated with simplification of the equation describing reliability of pile groups
C Wave speed in m/s
CAPWAP Case Pile Wave Analysis Program
COV Coefficient of variation
COVQ Coefficient of variation of load
COVQD Coefficients of variation for dead load (QD)
COVQL Coefficients of variation for live load (QL).
COVR Coefficient of variation of resistance
COVχ Coefficient of variation of system effect
COVζ Coefficient of variation of group efficiency
CS Soil type dominated by clayey soils overlain by sandy soils
CC Carbonate content
C(x,y) Influence coefficient
Cdb Hammer damping factor
d Mean water depth
D Diameter of a pile or hammer damping input value
Dn Nominal dead load
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Symbol Definition
DOE Department of Energy
d/gT2app Dimensionless relative depth
EOD End of driving
E(W) Mean of the equipment weights
Eh Hammer efficiency
Er Manufacturer rated hammer energy
ETR Energy transfer efficiency
F Unit skin friction capacity or total axial force on the column using influence surface diagram
F(x) A value used to approximate Cumulative Distribution Function at each value of x
FORM First order reliability method
fs,si Limit on unit friction value for a silica sand with a carbonate content (CC) of 20% or lower
fs,80 Limit on unit friction value applicable with carbonate content (CC) of 80% or greater
FOS Factor of safety
g Acceleration of gravity
H/gT2app Dimensionless wave steepness
H Wave height
HAT Highest astronomical tide
Hs Significant wave height
Hb Breaking wave height
Js Damping constant for skin friction
Jp Damping constant for end bearing
kram Hammer cushion or impact block or ram stiffness
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Symbol Definition
k Initial modulus of subgrade reaction in force per volume units
K Coefficient of lateral earth pressure
kN Kilo Newton = Unit of pressure measurement
L Length of a pile or wave length
LAT Lowest astronomical tide
Ln Nominal live load
LT Lifetime of T years
LRFD Load Resistance Factor Design
m Shape parameter for Weibull distribution
MHHW Mean higher high water
MHLW Mean higher low water
MLHW Mean lower high water
MLLW Mean lower low water
MN Mega Newton = 1000 * kN
MPa Mega Pascal
mram Ram mass
MSL Mean sea level
N Bearing capacity factor
OALL Open area live load
Pa Pascal
p-y curve Lateral soil resistance-deflection curve
( )BAP j Posterior distribution on A
( )jABP
Likelihood function of the data
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Symbol Definition
P(Aj) Prior distribution on A
psf or lb/ft2 Pounds per square foot
PH Horizontal load
PV Vertical load
pu Ultimate bearing capacity at depth X in units of force per unit length
Pf Probability of failure
Pf a Annual probability of failure
Pf L Probability of failure for a lifetime of L years
PDA Pile Driving Analyzer
q-z curve Relation between mobilized end bearing resistance & axial tip deflection
Q Load as described in reliability formulation
Qt Total capacity of a pile
Qs Skin friction capacity of a pile
QP End bearing capacity of a pile
Qi Nominal load effect
Qmean Mean load
q Unit end bearing capacity
Q80 Limit on end bearing applicable to carbonate content of 80% or higher
Qsi Limit on end bearing applicable to silica sand with a carbonate content of 20% or lower
R Resistance as described in reliability formulation
R2 Correlation coefficient value – a measure of correlation between two sets of data.
RP Return period
Rmean Mean resistance
Rm Measured value of resistance
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Symbol Definition
Rn Nominal resistance or predict nominal pile capacity using API RP2A
Sb Set per blow
su Undrained shear strength
SRD Soil resistance to driving
SRA Structural reliability analysis
SACS Structural analysis computer software
SC Soil type dominated by sandy soils overlain by clayey soils
SPT Standard penetration test
t Mobilized soil adhesion
tmax Unit skin friction capacity
t-z curve Axial load transfer relationship
Tapp Apparent wave period
Tp Peak period
Tz Mean zero-crossing period
V Current speed
W Wind Load
WSD Working Stress Design
Wn Nominal wind load
Wram Hammer ram weight
x and y Normalized spatial variables ranging from zero to one
X and Y Dimensions to define tributary area for a column or a pile
z Local pile deflection
αn Dispersion parameter
βT Target reliability index
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Symbol Definition
θ Scale parameter for Weibull distribution
Φ Standardized Normal cumulative distribution function.
εc Strain which occurs at one-half the maximum stress on laboratory undrained compression tests of undisturbed soil samples
γi Load factor for load condition i
un Characteristic largest value (mode) of the extreme value Fn
φ' Angle of internal friction
δ Angle of skin friction
V Ram impact velocity
Z Variable
φ Resistance factor
λR,λQD &λQL Bias factors for the resistance, dead load and live loads, respectively
λζ Bias factor of the group effect
λχ Bias factor of the system
λR Bias factor of the resistance
λμ p,l,u Mean of the bias factor in the posterior, likely and updated conditions
λσ p,l,u Standard deviation of bias factors in the posterior, likely and updated conditions
μ Mean of the normal variables
μp, l, u Estimate of the mean in the prior, likelihood and updated distributions
ζ Standard deviation of the logarithms of the variables
σ Standard deviation of the normal variables
σ p, l, u Estimate of standard deviation in the prior, likelihood and updated distributions
σw Standard deviation of the equipment weights
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LIST OF FIGURES
Figure 2-1: A description of reassessment methods shows gaps in the body of knowledge in determining
axial capacity of piles in carbonate sands, OALL on offshore structures and the effect of environmental
loads on the dominant failure mechanism in the Arabian Gulf 54 Figure 2-2: The process of reassessment of existing platforms outlined in Section ‘R’ of API RP2A-LRFD
(1993). The process requires attending to extreme storm conditions only and does not address other
conditions such as accidental or operating conditions 55 Figure 2-3: Water depth profile in the Arabian Gulf showing that the maximum water depth is 100m 56 Figure 2-4: Installation experience in the Arabian Gulf and the Mediterranean showing free fall of a pile
as evident from the zero blow count in the charts (Nauroy and Le Tirant, 1986) 57 Figure 2-5: Photo of an actual platform floor deck in the Arabian Gulf showing that the open area is
mainly unloaded except for some pipes that are used as scaffolding for painting and maintenance works
58 Figure 2-6: A plot of load versus time showing the nature of sustained and transient (or extraordinary)
loads and the total live load 59 Figure 3-1: Analytical approach used to calibrate pile resistance factors and OALL for the conditions of
the Arabian Gulf. The calibration of OALL established the statistical parameters of the database and
employed influence surface method and extreme value analysis to define a uniformly distributed load.
Calibration of the pile resistance factors utilized a database to calculate bias factors and employed
FORM to calibrate resistance factors for axial capacity of driven piles in the Arabian Gulf 91 Figure 3-2: Flowchart showing the approach adopted in this research to perform reliability analysis on a
representative platform from the Arabian Gulf with the objective of defining the dominant failure
mechanism in the Arabian Gulf 92 Figure 3-3: Schematic showing the basis for calculating the probability of failure 93 Figure 3-4: An example of large scatter in a set of data which is intended for calibration. The chart
shows that the calibration of a uniform set of factors requires the data to be sub-grouped. The number of
subgroups can be increased without limits 94 Figure 3-5: Influence Surface for column axial load. Note that the influence area is 2X * 2Y (McGuire
and Cornell, 1974) or four times the tributary area for a column 95 Figure 3-6: Distribution palette in @RISK enables a choice of distribution type that best fits the data
being analyzed 96 Figure 3-7: Force and velocity fall measurements versus time for a free end condition. This illustration is
typical for a free situation where the pile “runs” under the hammer blow. In the chart, A is the pile cross
sectional area, E is the pile elastic modulus, C is the wave speed and F is the force generated at the
impact surface of the pile (Hannigan et al., 1997) 97
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Figure 3-8: Schematic of CAPWAP Analysis Method 98 Figure 3-9: Factors that have a dominant effect on the accuracy of CAPWAP prediction (Hannigan et al.,
1990) 99 Figure 3-10: Schematic of CAPWAP analysis method showing CAPWAP iteration matching process
(Hannigan et al., 1990). The trial and error iteration adjustment process results in refinement in the soil
model to obtain the best agreement between the measured and computed pile head forces. The resulting
soil model can then be considered to represent the best estimate of the static pile capacity. In this
example, initial capacity (1667kN) was refined to derive a final value of 2187kN in step 5 100 Figure 3-11: One of the platforms used in this research. It functions as living quarters on the upper deck
and process facilities on lower decks. The platform includes a helideck 101 Figure 4-1: Diagrammatic representation of the soil/ pile database showing a pile that was driven using
various hammers in soil strata. The graph shows the relationship between the resistance of the pile and
penetration depth. The diagram shows that various hammers (Menck 4600/150, 3000/150) were used to
drive the pile to the desired penetration depth. Firstly, Menck 3000/150 hammer was used to drive the
pile from the seabed to the desired penetration depth of 98m 167 Figure 4-2: A comparison of the calculated driving force (left) and stresses (right) against measured
values as suggested by Tagaya et al. (1979) method. The measured response was taken from an actual
offshore installation in the Arabian Gulf, which was reported by Tagaya et al. (1979) 168 Figure 4-3: Input parameters used for a demonstration pile are shown here. This 1219mm diameter steel
pile is 105m long and penetrates 79.9m into the soil. The pile was driven by MENCK MRBS4600
hammer with an assumed efficiency of 67% and 1.5m stroke. The water depth measured from the water
surface to the mudline is around 25m 169 Figure 4-4: This chart shows the complete pile driving record for a demonstration pile. The soil profile
and the blow count at the pile tipping depth are of interest for the back-analysis calculations 170 Figure 4-5: This chart shows the results of the GRLWEAP Bearing Graph analysis. The output screen
shows all input parameters that were assumed in the design such as the assumed efficiency in addition to
stresses in the pile and the relationship between blow count and predicted capacity 171 Figure 4-6: Estimate of setup factor was made using various start/stop data of the pile driving record.
The graph shows the relationship between SRD to elapsed time during driving. Inspection of the trend in
the chart indicated that a setup factor of 2 fairly represented long term effect of setup 172 Figure 4-7: Scatter plot of the ratio of GRLWEAP capacity to static loading test capacity at the
Beginning of Restrike (BOR) and End of Driving EOD) conditions (Thendean et al., 1996) 173 Figure 4-8: Blow count versus depth diagrams for four piles which were plotted by an installation
contractor in during actual pile driving installation in the Arabian Gulf. The pile driving records were
collated in this research. The pile driving records show that all piles were driven to around 56m with
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blow count of approximately 25 blows per foot 174 Figure 4-9: GRLWEAP input data screen for the analysis of a pile to validate the back-analysis results.
The pile has a cross section of 1193cm2 and penetrates 56m into the soil stratum. The pile was driven by
a Menck MHU 600 hammer. The soil parameters used in this analysis were selected from Table 4-9. The
computed pile capacity from GRLWEAP represented short term capacity. The long term capacity was
computed by allowing for a setup factor of 2 and compared to the output from the dynamic monitoring
results for the pile in question 175 Figure 4-10: Predicted axial capacity using GRLWEAP of the pile that had been subject to dynamic
monitoring. This pile was used to validate the back-analysis procedure 176 Figure 4-11: Pile makeup of the dynamically monitored pile. The pile makeup consists of 9 sections with
a uniform outside diameter of 1219mm. The minimum wall thickness used in the pile makeup was 20mm
and the maximum wall thickness was 44mm at the mudline 177 Figure 4-12: Modeling of the pile-soil interaction in GRLWEAP requires a breakdown of the system at
each layer and at each change in pile section 178 Figure 4-13: Results of GRLWEAP analysis when average pile wall thickness across the whole pile
length was assumed for the pile instead of using the actual wall thickness for every pile section 179 Figure 4-14: Mechanics of wave propagation in a pile (Cheney and Chassie, 1993). The mechanics of
driving a pile was used to explain the reason behind the divergence in results when an average pile wall
thickness - instead of actual variable thickness - was used when modeling pile wall thicknesses in
GRLWEAP 180 Figure 4-15: Sensitivity analysis of the computed ultimate capacity in GRLWEAP as a result of changing
hammer efficiency. The curves show that the computed capacity is relatively insensitive to the assumed
efficiency for low blow count such as those experienced in the Arabian Gulf 181 Figure 4-16: Influence of changing segment length on the computed pile capacity showing that pile
capacity is insensitive to the segment length for the range of pile capacities experienced in the Arabian
Gulf 182 Figure 4-17: Sensitivity of using various cushion types and manufacturers on the computed ultimate
capacity. The plot shows that pile capacity is relatively insensitive to the selected cushion type as soil
resistance, rather than pile characteristics, dominates the axial capacity 183 Figure 4-18: Bias factors for the complete set of data comprising 138 piles. The trend shows similar
number of piles with bias factor above and below 1.0. Hence, the trend implies no bias in the overall
database 184 Figure 4-19: Statistical analysis of the complete set of data shows that a normal distribution is most
appropriate. The statistical analysis produced bias and coefficient of variation, λ = 0.93, COV = 0.36
for axial capacity of piled foundations in carbonate soils 185
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Figure 4-20: Statistical analysis of the complete set of data using the parametric method shows that a
normal distribution is most appropriate as described in Section 3.4.2. The statistical analysis produced
bias and coefficient of variation, λ = 0.93, COV = 0.36 for axial capacity of piled foundations in
carbonate soils 186 Figure 4-21: Scatter plot showing bias factors for piles installed using supplementary installation
methods. The plot shows that, in situations when piles are installed using supplementary methods, the
use of API RP2A to predict pile capacity significantly (40% to 80%) overestimated the capacity and
therefore is on the unsafe side 187 Figure 4-22: This chart shows a histogram of all piles that were installed using supplementary
installation methods. An assumption of normal probability distribution provided the best fit to the data as
shown above. The statistical parameters of the drilled/ grouted piles were computed as λ = 0.65 and
COV = 0.40 188 Figure 4-23: An assumption of normal probability distribution for piles installed using supplementary
methods resulted in linear probability plot. The statistical parameters of the drilled/ grouted piled
foundations were computed as λ = 0.65 and COV = 0.40 189 Figure 4-24: Scatter plot for soil Type SC which describes predominant cohesive soil profile underlain by
cohesionless soil 190 Figure 4-25: Scatter plot for soil type CS which describes predominant cohesionless soil profile
underlain by cohesive soil 191 Figure 4-26: Probability plot of soil type SC showing that a normal distribution fits the data with mean =
0.77, standard deviation = 0.26, COV = 0.34 192 Figure 4-27: Probability plot of soil type SC confirming that the assumed fitted normal distribution is
appropriate with mean = 0.77 and COV = 0.34. This is in line with the non-parametric analysis using
@RISK which indicated similar statistical parameters 193 Figure 4-28: Statistical parameters of the bias factors for piles driven in soil type CS was subgrouped
further to piles with optimum design against those with overconservative design. This plot shows
statistical parameters of bias factors for piles which are optimally designed 194 Figure 4-29: Statistical parameters of the bias factors for piles driven in CS soils. This plot shows the
scatter diagram of the bias factor for those piles with an overconservative design. The definition of
overconservative design in this research describes piles with a factor of safety of 4 or more according to
API RP2A-WSD (2000). The API RP2A-WSD (2000) only requires piles to be designed for a factor of
safety of 2 under operating conditions 195 Figure 4-30: Probability plot for soil type CS with conservative design, The analysis showed that a
Normal distribution provided the best fit to the data with statistical parameters λ = 0.97, COV = 0.34
196
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Figure 4-31: probability plot for soil type CS with conservative design, λ = 0.97, COV = 0.34. The
results of this non-parametric statistical analysis are consistent with the parametric analysis described in
Figure 4-30 197 Figure 4-32: Probability plot of soil type CS with optimum design. The probability plot shows that a
normal distribution provides the best fit to the data with statistical parameters λ = 1.12, COV = 0.26. 198 Figure 4-33: Probability plot of soil type CS with optimum design, λ = 1.12, COV = 0.26 199 Figure 4-34: Calibrated resistance factors for the various subgroups identified in this research. The
chart shows that, for a certain target reliability level, the resistance factor for a pile driven using
supplementary methods should be lower than that for a pile driven without the need for drilling or jetting.
The plot also shows that API RP2A-LRFD (1993) recommends a single value for the resistance factor
and does not address the various conditions that affect the value of the resistance factor 200 Figure 4-35: A plot showing the effect of penetration ratio on the calibrated resistance factors. The
analysis shows that the calibrated resistance factors are insensitive to various penetration ratios 201 Figure 5-1: An example of an unloaded open area during normal operations of an offshore platform and
at times other than shutdown. The photo shows that there is usually minor loads and personnel on open
areas of offshore platforms 232 Figure 5-2: An example of a skid frame supporting a compressor skid unit which includes a compressor
driven by a turbine. In this example, a motor could be scheduled for maintenance during a shutdown,
which would require various components of the skid to be disassembled on the open area of the platform
233 Figure 5-3: Scatter plot showing equipment weight and their corresponding footprint for every piece of
equipment in the database that was used in this research. The scatter plot shows that the majority of the
surveyed equipment weighs less than 20 tonnes (200kN) with an approximate linear relationship between
an equipment weight and its footprint 234 Figure 5-4: Schematic of a platform elevation showing that equipment with larger size (and weight) tends
to be located on the upper decks to facilitate removal and maintenance. This is usually preferred by
operation and maintenance personnel to facilitate and optimize operations and maintenance costs 235 Figure 5-5: This chart shows histogram of the truncated equipment weights on lower decks. The
statistical parameters were calculated as mean = 36.1kN and standard deviation = 18.4kN 236 Figure 5-6: Histogram of the equipment weight on lower decks and an assumed fitted Exponential
distribution. Using the assumed distribution, the statistical parameters were calculated as mean =
135.4kN and standard deviation = 134.8kN 237 Figure 5-7: This chart shows a histogram and fitted distribution of the truncated database for equipment
weights on the upper deck. The fitted distribution is lognormal with mean = 81.5kN and a standard
deviation = 30.6kN 238
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Figure 5-8: This chart shows histogram of the equipment weight on the upper deck fitted an Exponential
distribution with mean = 627kN and standard deviation = 638kN 239 Figure 5-9: Plan view of the model platform decks used to demonstrate the application of the influence
surface concept for a 15m square floor deck 240 Figure 5-10: The relationship between the maximum lifetime live load on a pile and the number of
shutdown events for lower decks. The plot shows an asymptotic relationship with a maximum lifetime live
load on the pile of 275kN for a lognormal distribution and 250kN for a normal distribution 241 Figure 5-11: Relationship between number of shutdown and the standard deviation of the mean lifetime
live load for lower decks showing that the standard deviation ranges between 10%-20%. An average of
15% is used in this research 242 Figure 5-12: The relationship between the maximum lifetime live load on a pile and the number of
shutdown events for the upper deck shows an asymptotic relationship 243 Figure 5-13: The relationship between the number of shutdowns and the standard deviation of the mean
lifetime live load effect for the upper deck showing that the standard deviation ranges between 15%-22%
and depends on the distribution type and the number of occurrences 244 Figure 5-14: Sensitivity analysis results showing the effect of varying the deck area on the number of
sectors for lower decks. The analysis revealed that OALL is not sensitive to varying deck areas.
Doubling the floor area from 200m2 to 400m2 results in 11% reduction in OALL 245 Figure 5-15: Relationship between minimum separation distance and OALL for n = 50 on lower floors.
The chart shows that the calculated OALL is sensitive to the selected minimum distance when the
minimum distance is 3m or less as discussed in Section 5.8.3. 246 Figure 5-16: Relationship between crane capacity and OALL on lower decks showing that OALL is
sensitive to the SWL of the crane used on the deck 247 Figure 6-1: Illustration of the dominant failure mechanism under various conditions. The chart shows
that the dominant failure mechanism is determined by comparing the probability of failure under
operating overload against extreme storm conditions. When the probability of failure under operating
conditions is much lower than the probability of failure under extreme storm condition, then operating
conditions dominate the failure mechanism. Conversely, when the probability of failure under extreme
storm conditions is lower than that under operating conditions, then extreme storm dominate the failure
mechanism. When the probability of failure under extreme storm condition is similar to that under
operating overload condition, the dominant failure mechanism is determined on the basis of interaction of
both conditions 285 Figure 6-2: Performance model for the cases of dominant extreme storm (left) and operating overload
(right) conditions. In the dominant operating condition, the effect of horizontal load PH on the pile
system is relatively small when compared to the effect of the vertical load PV 286
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Figure 6-3: Plan view of the model platform decks used in the pushover analysis. The platform is 15m by
15m between the gridlines 287 Figure 6-4: The requirement for large open area on wellhead platforms is driven by the dimensions of
drilling rig. This figure shows a jack-up drilling rig adjusted for one well and also shows alternative
locations of the rig to drill other wells 288 Figure 6-5: Illustration of the probability of failure for the selected platform in this research under
various conditions. The direction of the arrows indicates increasing/ decreasing probability of failure.
The “X” indicates the selected platform positioning in relation to the population. This demonstrates that
the selected platform provided a lower bound solution for the extreme storm condition as a result of
choosing the deepest water in the Arabian Gulf and an upper bound solution for the operating condition
as a result of choosing a wellhead platform with large open areas 289 Figure 6-6: SACS computer model geometry showing the 4-legged jacket structure in 100m water depth
and the topside structure. The piles are driven 70m into the soil 290 Figure 6-7: A description of the nonlinear SACS computer model used in the static pushover analysis 291 Figure 6-8: A plot of the soil shear strength of two boreholes at a given site. The plot shows the variation
of the interpreted shear strength along the depth, but also within the same layer. In this analysis, the
average shear strength value in each layer was adopted 292 Figure 6-9: Input p-y curves for the soils in the case research platform. The curves were computed in
SACS using API RP2A procedure described 293 Figure 6-10: Input t-z curves for the various layers in the case research platform. The curves were
computed in SACS using API RP2A procedure but a reduction factor was applied to the calculated spring
stiffness to reflect the findings of this research showing the reduced axial capacity of piles in carbonate
soils 294 Figure 6-11: Assessing applicable wave theory for use in the analysis (Source: API RP2A-LRFD, 1993).
The vertical axis is entered with the maximum wave height and apparent wave period. The horizontal
axis is entered with the mean water depth and the apparent wave period. The outcome of the analysis
defines the applicable wave theory to be used to derive hydrodynamic loading on the structure 295 Figure 6-12: Results of the static pushover analysis showing the collapse mechanism to be shear
dominated, where the piles are subject to critical failure. The deflected shape is shown only for the
framed structure (in red) and not for the piles. The discontinuity shown between the piles and the frame
represents the deformation when the frame collapsed 296 Figure 6-13: The pushover analysis in the vertical direction was carried out to assess the dominant
failure mechanism in the Arabian Gulf under operating overload. The COLLAPSE analysis shows that
the dominant failure mechanism is in the piles 297 Figure 6-14: Extrapolation of maximum wave height in the Arabian Gulf shows its long term distribution
xxxii
follows a Weibull distribution 298 Figure 6-15: Extrapolation of current speed in the Arabian Gulf shows that its long term distribution
follows a Weibull distribution 299 Figure 6-16: Fitting the calculated base shears computed from long term maximum wave heights
indicated that a Weibull distribution provided the best fit compared to other distributions as evident from
the straightness of the trend line 300 Figure 6-17: The probability of failure under extreme storm condition was calculated from the
relationship between the long term base shear values and the corresponding return periods. Using the
collapse load calculated from pushover analysis, the relationship provides the return period which
corresponds to the collapse load. The probability of failure was (2.3*10-71) calculated as the inverse of
the return period 301 Figure 6-18: A comparison of the severity of environmental data in the Arabian Gulf to other parts of the
world. The graph shows normalized extreme environmental load versus return period and demonstrates
the dependence of platform reliability level on its environment (Van de Graaf et al., 1994). The
normalized extreme environmental load versus return period for the Arabian Gulf was derived in this
research 302 Figure 6-19: Effect of dead to live load on reliability index showing insensitivity of the D/L ratio on the
reliability index 303 Figure 6-20: Effect of changing factor of safety and resistance COV on the calculated reliability index for
single piles 304 Figure 6-21: The effect of system factor on the computed group reliability index. The green horizontal
line presents the reliability index for a single pile and the red curve presents the reliability index for a
group 305 Figure 6-22: Effect of changing the system and group coefficient of variation on the computed reliability
index 306 Figure 7-1: Results of the geotechnical analysis carried out by the consultant to calculate the capacity of
piled foundations using API RP2A LRFD (1993). In deriving the ultimate axial capacity along the depth
of the pile, the consultant used the capacity reduction factors as per API RP2A-LRFD (1993) but
employed subjective limiting parameters to predict the pile capacity 319 Figure 7-2: An isometric view of the platform analyzed in this research 320 Figure 7-3: Pile assembly of the platform investigated in this research. The diagram is extracted from
the pile drawing and shows pile diameters and penetration lengths in mm 321 Figure 7-4: Mathematical model showing the finite elements used to study the behavior of the structure in
the case study 322 Figure 8-1: Flowchart showing an outline of the specifications that can be used for reassessment of
xxxiii
existing offshore platforms in the Arabian Gulf under operating overload conditions 339 Figure 8-2: Flowchart showing a proposed method developed in this research to calculate OALL 340 Figure 8-3: Flowchart showing a proposed method developed in this research to predict axial capacity of
piles driven in carbonate soils in the Arabian Gulf 341 Figure 8-4: A proposed flowchart to identify the requirement and steps required to collect a future
database for pile capacities 342
CHAPTER 1: INTRODUCTION 1
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Chapter 1.
INTRODUCTION
1.1. BACKGROUND
Advances in reservoir assessment and recovery techniques, subsea technology,
seismic and directional drilling techniques extend field life and impose higher
demands on existing platforms to support additional vertical and lateral loads. The
increase in vertical loads results from the need to support heavier and/or additional
pieces of equipment. The increase in lateral loads could be due to higher wave and
wind loads resulting from installation of additional risers on the platform.
Increasing vertical and horizontal loads on an existing platform entails reassessment
of the integrity of the platform to carry such additional loads.
In particular, reassessment of existing pile capacity is of interest in light of the
increased knowledge in the field of soil-structure interaction and unique behavior of
certain offshore soils. For example, the behavior of pile-foundations in carbonate
soils was not fully understood in the early 1960s during installation of the first series
of platforms in the Arabian Gulf. Later experience, ongoing research and full scale
test results showed significantly lower skin friction for driven piles in carbonate
soils than in siliceous sand (Kolk, 1999).
The outcome of reassessment determines the subsequent course of action. For
example, if a pile is reassessed and found to be “unsafe”, structural intervention may
be necessary or a new platform may be required. Both scenarios are very costly and
this may eventually compromise the economic viability of a development. A
rational reassessment of piles of existing platforms is therefore necessary to avoid
costly solutions while ensuring that the underlying risk is as low as practically
acceptable (ALARP).
A complete reassessment specification for offshore platforms requires attendance to
several conditions, including:
CHAPTER 1: INTRODUCTION 2
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
• Ultimate Limit States (ULS), arising from pre-service or in-service conditions,
including extreme events potentially leading to loss of the structure or of a major
component of it,
• Serviceability Limit State (SLS), arising from undesirable motions or
deflections, which may impinge on the functionality of key equipment or
discomfort of the crew,
• Fatigue Limit State (FLS) arising by cumulative effects of day-to-day operation
and leading to unsatisfactory performance and need for repairs, and
• Accidental Limit State (ALS), arising from accidental events such as due to ship
impact of Authorized and passing vessels or explosion and fire scenarios.
Attending to these conditions requires modeling of the physical processes that
govern the response of the structure and applying analytical methods to arrive at a
decision for each model. When these models are linked together, the combination
predicts the overall response of the system under consideration, enabling overall
decision regarding the reliability of the platform.
However, every model is usually valid within a certain range and for a specific use.
Consequently, models need to be chosen for their particular utility with regards to
the failure mechanism (e.g. corrosion, fatigue and overload) and cause of failure
(e.g. environmental loads, operating, earthquake and ship collision).
Treatment of all the models of an offshore structure in this research for every
conceivable loading condition, failure mechanism, failure mode, structural
configuration and type of structure would be impractical. Fortunately, for the
predominant class of fixed steel space-frame structures, which is the subject of this
research, only one specific scenario usually results in the dominant failure
mechanism. For example, a number of failure mechanisms may be of no major
concern in reassessment of existing platforms due to association with adequate
warning time (e.g. fatigue failures) or because these are of a strictly local nature and
pose no threat to the survival of the platform.
This research adopts a similar approach to that used in international codes and
standards and identifies a dominant failure mechanism for use in developing the
required guidelines for reassessment.
CHAPTER 1: INTRODUCTION 3
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
1.2. PROBLEM STATEMENT
Guidelines in international codes and standards such as Section ‘R’ of the API
RP2A-LRFD (1993), known as Section 17 in API RP2A-WSD, only consider
extreme storm condition on the basis that such condition dominates the failure
mechanism. A review of the state-of-the-knowledge in reassessment of existing
offshore platforms reveals that:
• Operating overload conditions, with specific reference to the selected values of
live loads and axial pile capacity in carbonate soils, are not covered by API
RP2A-LRFD (1993) or any other international codes or standards, and
• There are no studies to examine the effect of benign environments on the
dominant failure mechanism of offshore platforms.
• The thrust in this research is to develop specifications that can be employed for
reassessment of existing offshore structures in the Arabian Gulf. Development
of such specifications requires addressing the issues identified above since these
are not covered in international codes and standards such as API RP2A (1993,
2000).
1.3. METHODOLOGY
Solution to the core research problem is carried out within the framework of
reliability theory and employs calibration techniques similar to those used in the
development of API RP2A-LRFD (1993).
The research inherently assumes that the basic API RP2A formulation, which has
been based on many years of experience in US waters and combines the experience
of its expert Committee, is suitable for the Arabian Gulf. The focus of this research
is on calibrating deterministic parameters for loads and resistance factors, which can
be used with API RP2A-LRFD (1993) formulation, but employing a database
specific to the Arabian Gulf.
API RP2A is commonly used in the Arabian Gulf to perform design and
reassessment of existing platforms. The Author’s experience indicates that the
uptake of ISO Standards to perform design and reassessment in the Arabian Gulf
has been very slow. This is driven by weight of familiarity with API RP2A and the
fact that ISO Standard contains no specific data for offshore platforms in the
CHAPTER 1: INTRODUCTION 4
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Arabian Gulf, hence providing little benefit and no incentive for a change.
Calibration of load and resistance parameters can only be carried out when load and
resistance factors are separated so that research efforts can be rationally focused to
calibrate for conditions not previously covered in the literature. It is reasonable to
treat these two entities (foundation and structural frame) as two separate
components of a weakest-link model. The probability of failure of each of these
components is obtained by a separate model capable of taking into account the
redundancy within each component. The foundation component may be further split
into two components: (1) Laterally loaded piles; and (2) Axially loaded piles. Such
approach was adopted as the basis for calibrating the API RP2A-LRFD (1993) and
was also used by Marshall and Bea (1976).
A “first-order” probabilistic procedure is used to determine the values of the
resistance and load factors. The “first-order” probabilistic procedure is a simplified
method that uses only two statistical parameters, namely mean values and
coefficient of variation, of the relevant variables and a relationship between them
termed the “safety index” or β.
1.4. JUSTIFICATION OF THE RESEARCH
The importance of this research is underpinned by economic and safety reasons
relating to the strategic importance of the world’s supply of oil and gas in general
and in the Arabian Gulf region in particular.
Re-assessment of existing fixed structures around the world is a major issue as there
are now over 1,200 existing platforms exceeding 20 years of age (Morandi, 2006).
These platforms were designed according to the practices of their time, which are
different from the improved standards adopted in the design of new structures.
Reassessment and requalification criteria were incorporated into Section ‘R’ in API
RP2A-LRFD (1993).
While new designs often achieve Reserve Strength Ratio (RSR defined as ultimate
mean capacity over 100 year mean load) values of 1.6 and over, Section ‘R’ requires
values of 0.6, 0.8 and 1.2 under extreme storm conditions. However, Section ‘R’
does not address operating overload conditions but the Author’s experience in the
Arabian Gulf indicates that these dominate the failure mechanism in that region.
CHAPTER 1: INTRODUCTION 5
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
The Arabian Gulf oil and gas production rate makes it one of the strategic producers
in the world. In 2003, oil production from the Arabian Gulf amounted to about 27%
of the world's oil with a total reserve of about 57% (715 billion barrels) of the
world's crude oil. Besides oil, the Arabian Gulf also has huge reserves (2,462
trillion cubic feet) of natural gas, accounting for 45% of total proven world gas
reserves. Given the large reserves, it is important to develop specific parameters for
the Arabian Gulf to avoid excessive conservatism or unsafe offshore platforms.
Currently, reassessment of existing platforms in the Arabian Gulf is based on
subjective parameters, which may have safety and/or cost implications. A
conservative reassessment may suggest unnecessary need for strengthening or
providing additional foundation supports, which is very expensive for offshore
platforms. For example, foundation intervention in North Rankin “A” platform in
the North West Shelf of Australia ended up costing Woodside Petroleum close to
US$350 million (Haggerty and Khorshid, 1989). Such excessive costs could
compromise the feasibility of oil and gas developments, thereby resulting in losses
to society as a whole. On the other hand, unsafe reassessment could lead to
excessive deflection or even catastrophic collapse of platforms and loss of life,
environmental pollution, reputation damage and monetary losses.
Generally, standards for structural engineering contain requirements to ensure that
structures perform satisfactorily under the effect of various loads. These provisions,
which include load factors, resistance factors, allowable stresses and deflection
limits, have evolved more or less subjectively through extensive successful and
unsuccessful professional experience in the Gulf of Mexico, examination of
available experimental data, theory and judgment. As a consequence, these criteria
do not necessarily ensure consistent levels of safety and performance in other
geographical regions and may be inappropriate for structural schemes or geographic
locations where little basis for judgment may exist.
The demand for development of the North Sea since the 1970s required that the Gulf
of Mexico practice and experiences be extended and backed by extensive research
and development programs on issues of particular importance to the North Sea,
including fatigue, dynamics, large-scale tubular joints, large diameter and high
capacity foundation piles. However, application of the Gulf of Mexico practices has
not been examined to determine their suitability to the Arabian Gulf conditions.
CHAPTER 1: INTRODUCTION 6
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
This research addresses load and resistance factors using the specific conditions of
the Arabian Gulf. In particular, API RP2A (1993, 2000) does not cover carbonate
soils, which dominate the geology of the seabed in the Arabian Gulf. Hence, the
need for a set of specifications to address the specific conditions of the Arabian Gulf
is underlined.
There is no consensus between researchers on suitable parameters to calculate axial
capacity of piled foundations in carbonate soils. There is virtual agreement amongst
researchers that pile loading tests are the only satisfactory method to establish the
capacity of piles in carbonate soils (Angemeer et al., 1975; 1984; Dutt et al., 1984 &
1985; Gilchrist, 1985). Unfortunately, unlike onshore projects which confirm the
static analysis by performing load testing on some piles to verify the reliability of
the design methods, the costs of performing loading tests on offshore projects are
prohibitively high. Hence, reliance on prediction methods to assess axial capacity
has a higher profile and wider acceptance in the offshore industry, because the
consequence of a prediction method being in error is significant for offshore
structures.
In the absence of deterministic parameters in codes and standards that address the
specifics of the Arabian Gulf, reassessment of piles of existing platforms in the
Arabian Gulf could generally be performed using reliability or probabilistic
methods. However, appreciation of inadequacies in system reliability procedures
has caused concern over their routine use and operators have generally favored
essentially deterministic decision making procedures, backed by approximate
reliability reasoning (Stewart et al., 1988). Hence, a deterministic procedure for
reassessment of existing structures in the Arabian Gulf is required.
The probabilistic approach to structural safety embodied in this research continues
to be adopted in the structural engineering community.
To calibrate design codes, the traditional practice of setting safety factors and
revising codes based solely on experience is insufficient in an environment where
such trial and error approaches to managing uncertainty and safety may have
unacceptable consequences. In an era where standards for public safety are set in an
increasingly public forum, more systematic and quantitative approaches to
engineering for public safety are essential.
CHAPTER 1: INTRODUCTION 7
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
1.5. OUTLINE OF THE THESIS
This section outlines the path towards the conclusion of the thesis.
Chapter 2 outlines several issues affecting the research and sheds light on key
aspects of the Arabian Gulf to identify gaps in the body of knowledge when
reassessment of existing platforms is required. A detailed methodology to fill those
technological gaps is presented in Chapter 3.
Chapters 4, 5 and 6 address the gaps in the body of knowledge through calibrating
deterministic parameters for reassessment of offshore platforms in the Arabian Gulf.
Chapter 4 describes the calibration of resistance factors for piled foundations in
carbonate soils. Chapter 5 identifies the nature of live loads on offshore platforms
and quantifies the magnitude of live loads on open areas of offshore platforms.
Chapter 6 employs the statistical parameters developed in chapters 4 and 5 and
derives statistical parameters for environmental loads, which are then used to define
the dominant failure mechanism in the Arabian Gulf.
Chapter 7 applies the findings from Chapters 4, 5 and 6 to a real life case to
demonstrate the value of this research.
The conclusions from analysis of the research problem, their place in the body of
knowledge and areas for future studies are summarized in Chapter 8.
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Chapter 2.
RESEARCH ISSUES
2.1. INTRODUCTION
During the course of this research, a number of issues were identified which pointed
to gaps in the body of knowledge. Those issues are specifically related to the use of
API RP2A in reassessment of existing platforms in the Arabian Gulf.
API RP2A permits the use of either working stress design (WSD) or load and
resistance factor design (LRFD) methods to reassess the adequacy of platform
elements for specific performance requirements. These methods introduce factors
for nominal demand and/ or resistance to achieve a level of reliability, implying
safety levels that are not explicitly stated. The factors included in API RP2A-WSD
(2000) were derived from experience in US waters but were optimized by
minimizing the differences between the target reliability levels and the achieved
reliabilities over a sufficiently large and representative number of “calibration
points”. Consequently, actual reliabilities achieved by the code recommendations
may vary from situation to situation and may deviate substantially from the targets
for cases other than the calibration points.
The Arabian Gulf conditions deviate from those “calibration points”. This Chapter
identifies and discusses the following issues as related to the Arabian Gulf
conditions:
• Characterization of offshore soil in the Arabian Gulf and its effect on methods to
determine axial capacity of piled foundations;
• Identification of the nature of open area live loads (OALL) on offshore
platforms and determination of OALL that can be used with API RP2A
formulation; and
• Characterization and effect of extreme storm conditions on the reliability of
offshore platforms in the Arabian Gulf.
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2.2. ROAD MAP
To answer the research question identified in Section 1.2, there was a need to
investigate current methods used in reassessment of existing platforms to define
gaps in the body of knowledge. Figure 2-1 shows that API RP2A (1993, 2000)
identifies four main methods for reassessment of existing platforms. This Chapter
evaluated each method to establish a suitable framework that can be implemented to
answer the research question. The outcome of the evaluation revealed that the
design level check method is most appropriate for use in industry practice. The
screening level method is too coarse and the reliability-based and probabilistic
methods have limitations when used in reassessment.
However, the design level check only treats extreme storm conditions and does not
consider operating overload conditions. Reassessment of existing platforms under
operating overload conditions requires attendance to open area live loads (OALL)
and prediction of axial capacity of piles in carbonate soils. Hence, there was a need
to fill these gaps by developing OALL and calibrating resistance factors for piled
foundations in carbonate soils.
Calibration techniques and development methods were identified in this research.
Reliability-based method was employed to calibrate axial capacity of offshore piles
in carbonate soils. Extreme value analysis method was used to develop
deterministic values of OALL.
The outcome of this research is a set of specifications that can be used for
reassessment of existing platforms in the Arabian Gulf.
2.3. REASSESSMENT APPROACHES
Reassessment of existing platforms in accordance with API RP2A-LRFD (1993)
categorizes platforms according to life-safety and consequences of failure into high
(L1), medium (L2) and low (L3) consequences of failure. The categorization is
used to assign metocean parameters for the relevant platform category. For
example, API RP2A-LRFD (1993) recommends the use of current speed equal to
1.6 knots for reassessment of category L1 platforms in the Gulf of Mexico but
reduces the recommended current speed to 0.9 knots if the platform category is L3.
The API RP2A (1993, 2000) recommended approach for reassessment of existing
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platforms is mapped out in Figure 2-2. The process starts with a screening level
check and follows on to a design level check if the results of the former indicate that
the platform in question does not pass reassessment. If the design level check also
indicates that the platform does not pass reassessment criteria, potential sequential
analysis checks include ultimate strength check, structural reliability analysis and a
probabilistic approach. A description of the various checks is outlined in the
following subsections.
2.3.1. SCREENING LEVEL CHECK
In the screening level check, reassessment is only required if triggered by any one of
the initiators defined in API RP2A (1993, 2000). In such case, reassessment may be
carried out using design level check, reliability based or probabilistic methods.
2.3.2. DESIGN LEVEL CHECK
The design level approach is simple yet more conservative than other advanced
approaches. Figure 2-1 outlines the components of a design level check and the
corresponding codes used to apply the method.
The design level check is applied in stages. First, loads (actions) on the various
structures of the platform (jacket, piles and decks) are calculated using an
appropriate code. Second, a mathematical model of the platform which represents
the geometry, loads and characteristics of the platform is constructed to derive
action effects on each member in the platform structure.
The mathematical model is then analyzed to generate internal forces (axial force,
shear forces in two planes, bending moments in two planes and twisting moments)
in every member of the structure. The internal forces are combined in each member
and checked against the capacity of the member using either working stress design
(WSD) or load resistance factored design (LRFD) methods.
2.3.2.1. Working Stress Design (WSD) Method
The deterministic provisions in structural codes and standards for load combinations
and strength address the risks and uncertainties in structural performance as code
and standard-writers have historically understood them.
To account for uncertainties in the load effects and the resistance on offshore
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platforms, API RP2A-WSD (2000) and its previous editions require the use of a
factor of safety to account for the various factors that impact on the system
performance. Those factors include variations in the loads and material strengths,
inaccuracies in design equations, errors arising from poorly supervised construction,
possible changes in the function of the structure from the original intent,
unrecognized loads and unforeseen in situ conditions. Using the safety factor
approach, API RP2A-WSD (2000) has been formulated to achieve a specified level
of safety without the need to consider each of these factors separately and in explicit
detail.
In the working stress design (WSD) method, the design level check has the form:
ELDSFRn ++= Equation 2-1
Where Rn = Nominal resistance
SF = Safety factor
D = Dead load
L = Live load
E = Environmental load
The factor of safety is typically applied to the calculated ultimate capacity in routine
member design using working stress design (WSD). For example, from the first
edition in 1969, the recommended safety factors for pile design were 1.5 and 2.0 for
the extreme and operating conditions, respectively, and these have remained
unchanged to the present day. In fact, these factors of safety were based on even
earlier Gulf of Mexico engineering practice dating back to the 1950s.
2.3.2.2. Load and Resistance Factored Design (LRFD) Method
In the LRFD approach, the boundary between acceptable and unacceptable
performance is explicitly defined by a set of limit state equations. This approach
permits the identification of key factors affecting failure which may be somewhat
obscure within the traditional empirical rules of WSD. The limit state approach
lends itself for use within a probabilistic framework, allowing partial safety factors
to be developed based on rational risk analysis.
The general format of the LRFD specification is given by the formula:
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nii RQ φγ ≤∑ Equation 2-2
Where Σ = Summation
i = Type of load (dead, live, wind, wave)
Qi = Nominal load effect
γi = Load factor corresponding to iQ
Rn = Nominal resistance
φ = Resistance factor corresponding to nR
φ Rn = Design strength
The right hand side of the formula relates to the resistance (capacity) of the structure
while the left hand side characterizes the loading effect acting on that structure.
The purpose of the load and resistance factors is to account for unforeseen and
unfavorable deviations from their specified values and for variations due to
uncertainties in the analysis. Those load and resistance values vary for different
conditions. For example, API RP2A-LRFD (1993) recommends a resistance factor
of 0.8 for piles in compression under extreme storm conditions but reduces the
resistance factor to 0.7 for piles in tension.
The fundamental premise in the LRFD approach is that risks can be analyzed
explicitly within a consistent and rational framework. Loading and resistance
factors are used to explicitly account for variances of the criteria, making it possible
to rationally account for differences between US regional conditions and other
localities by calibrating the load and resistance factors to provide more consistent
safety levels for data points other than the calibration points used for US waters.
2.3.3. STRUCTURAL RELIABILITY ANALYSIS METHOD
Structural Reliability Analysis (SRA) is one of the advanced methods that may be
implemented in reassessment applications if the platform does not pass the design
level check. The SRA approach calculates the so-called Reserve Strength Ratio
(RSR), which is then used in reliability calculations. Many researchers, including
Bea, Efthymiou, Van de Graaf and Tromans (1994) used this method in the
reassessment of platforms in US waters and the North Sea. A description of SRA is
included in Appendix G.
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The reliability-based method does constitute a significant improvement over the
traditional approach of using experience alone to establish safety factors for
foundations in at least three ways:
• Foundation design becomes more cost-effective if the level of reliability can be
maintained at a consistent target value,
• It enables minimization of the incompatibility between structural and foundation
design which creates the undesirable effect of having to apply different loadings
on the structure and the foundation and may lead to potential confusion and
mistakes in design, and
• Probabilistic methods help to relieve the foundation engineer from the task of
having to intuitively assess the complex relationship between uncertainties and
risks. At the same time, it emphasizes the importance of engineering judgment
and experience on the other design aspects that are currently beyond the scope of
mathematical analysis.
In spite of its rational basis, the reliability-based method continues to have certain
limitations when used in reassessment as described below.
2.3.3.1. Prediction of “Actual” Reliability Level
Application of the reliability-based method in reassessment produces a quantitative
assessment of reliability levels. However, comparing the theoretical probability of
failure derived from reliability computations with a value established by actual case
histories is not straightforward.
A number of Authors (e.g., CIRIA, 1977; Smith, 1981; Livingstone, 1989) noted
that the theoretical probability of failure is usually significantly smaller than the
actual failure probability of failure, and that there will always be a gap between
predicted and experienced risks. This gap is generally 1 to 3 orders of magnitude
(CIRIA, 1977). Consequently, the predicted reliability levels are best referred to as
notional, rather than absolute, levels and are better suited to comparison purposes.
The discrepancy between the predicted reliability level and “actual” reliability level
can be due to the presence of a number of uncertainties which can be classified into
three categories: physical, knowledge based and human (Bea, 1993).
The first category (known as objective, Type I or aleatory) represents natural
randomness intrinsic to a variable, such as wind loading, and its uncertainty. It
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cannot be reduced with additional information.
The second category (known as subjective, Type II or epistemic) uncertainties arise
from limitations in knowledge, including measurement, statistical and modeling
uncertainty. It can be reduced at a cost by collecting more data or adopting models
that are more realistic. Knowledge based uncertainty can be further subdivided into
statistical, model and phenomenological uncertainties. Statistical uncertainty arises
due to a limited number of observations being used to make up a sample which is
then taken to represent a population. Modeling uncertainty is caused by the use of
simplified relationships between variables to represent real behavior. Methods used
to simplify loads and structural responses, such as the limit state equations are
examples of modeling uncertainties. Phenomenological uncertainty arises because
unimaginable phenomena occur, which cause structural failure. They are
particularly important for novel structures or those which attempt to extend the
state-of-the-art.
The third category (Type III) is the hardest to quantify and modify. It is associated
with human and organizational factors. This category is mainly attributable to
human error and modeling uncertainty. Around 80% of accidents involving marine
structures are due to unanticipated actions of people that have undesirable outcomes
(Bea, 2000) and not due to overloads or damage accumulation.
Structural reliability methods have generally not included Type III (Human and
Organizational Factors) uncertainties and remained focused on well-defined loading
and failure conditions such as member failure under extreme storm loading or
fatigue of welded connections. By contrast, the actuarial reliability, as expressed by
statistics of failure, highlights that Type III uncertainties can result in blowouts,
collisions, fires or explosions.
SRA principles are also not well suited to incorporate gross errors since they usually
alter the very nature of the problem by changing the probabilistic models of the
basic variables and even the form of the limit state equation (Ellingwood, 1992).
Therefore, failure probabilities predicted by SRA are not likely to converge with
actuarial data, since SRA method generally excludes gross “human” errors.
Another main reason for the mismatch between SRA results and failure probabilities
reported in databases lies in the different root causes for the failures. Failures
reported in databases tend to be caused by poor design, poor construction and
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inadequate operational practice as opposed to extreme events covered by SRA.
Consequently, comparisons of failure probabilities derived from SRA against those
entered into databases may not be relevant and failure probabilities should be
carefully interpreted.
Further, one main limitation with SRA is that data will be mostly available to
validate the probability density function (PDF) around their mean value, while the
design point from SRA will usually be the tail of the PDF, where less data is
available. However, this is an inherent problem in predicting long-term extreme
events by any method.
2.3.3.2. Target Reliability Levels
The outcome of any reliability analysis can only be meaningful in the presence of a
target value. A number of investigators have discussed selection criteria for target
reliability. The Author identified two main approaches to establish target reliability
levels, namely implicit and explicit approaches.
The first and more traditional approach identifies the theoretical reliability index
that is implicit in existing design codes (Ellingwood et al., 1980). The implicit
target reliability approach fundamentally relies on what has happened in recent past
with similar types of structures and in similar types of environments.
A quantitative assessment of implicit target reliability levels in different codes and
standards is presented in Table 2-1. Various committees also published their own
recommended target reliability levels. The American Society of Civil Engineers
(ASCE) Task Committee recommended target reliability indices between 2.3 to 3.4
for structural design, but considered that those target levels serve a useful function
of defining an approximate lower bound on the target reliability level. Nordic
Committee on Building Regulations (NKB, 1978) report gives a set of suggested
values for target reliability index for various failure types and consequences. NKB
recommended values range from 2.0 to 4.7 for annual target reliability levels.
Other researchers also provided recommendations on target reliability levels.
Meyerhof (1970) indicated that foundation probability of failure should be between
10-3 and 10-4, which corresponds to values of target reliability index values between
3.1 and 3.7. Bea (1983) reported that the reliability indices of offshore piles were 3
to 4 on an annual basis and 2 to 3 on a lifetime basis. From a calibration research
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involving six representative structures, Criswell and Vanderbilt (1987) concluded an
acceptable target reliability index in the range of 2.7 to 3.2. Tang et al. (1989)
reported reliability indices ranging from 1.4 to 3.0 for offshore piles. Barker et al.
(1991) selected target reliability index of 2.0 to 2.5 in their resistance factor
calibration for single driven piles. Withiam et al. (1998) confirmed that this range
of target reliability index is within a reasonable conformity with the reliability
indices evaluated for the current design practice, considering that piles are usually
used in a group. Cornell (1995) suggested a target annual structural failure
probability of 10-4 for new designs (βT = 3.7), and indicated that values one order of
magnitude in either direction seem unreasonable.
The discrepancy in the implicit target reliability levels suggests that these should not
be specified without giving extensive guidance as to how assessment should be
performed, and what assumptions are reasonable regarding uncertainty of loading
and strength. Further, a quantitative target assumes almost perfect technical
knowledge and complete understanding of the socio-political factors that play a role
in target setting including such factors as exposure time (annual or lifetime),
expected consequence level (low, medium, high), calculation method, failure mode
or nature of failure and level of society perception to risk (ISO2394, 1998, Melcher,
1999, Keese et al., 1982).
The second, and perhaps most controversial approach, is to compute explicit target
failure probabilities. This method requires high technical competence with many
technical challenges. It can be applied using cost-utility evaluation.
The fundamental premise of the cost-utility evaluation is that the most attractive “or
best” development alternative is the one that produces the highest utilities or
measures of worth. In principle, the cost-utility evaluation is the only way to
determine a rational value for target probability of failure. By researching the
variation of the initial cost, maintenance costs and the expected failure, it is
theoretically possible to compute the most economical target probability of failure
for design (Baecher, et al., 1980; Mortensen, 1993). At present, such approach is
impractical due to the difficulties in evaluating failure costs (e.g., cost of human
lives and environmental safety) and the effect of component failure on the system
(Criswell and Vanderbilt, 1987; Whitman, 1984; Vick, 1992).
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2.3.3.3. Uniqueness of Mapping Events
MacFarlane and Parry (1994) identified a factor which may undermine the
rationality of the reliability-based method, pointing to the potential non-uniqueness
of the mapping of events on to consequences. It is a general assumption that the
mapping process, which involves the process of structural analysis, is complete and
will always provide a consistent result. If the analysis method is not in fact unique,
the mapping may be flawed. This would be the case when any of the parameters in
the mapping process, for example, is not complete and the dependence and
interactive nature of events is not included in the mapping process, i.e. the analysis.
2.3.3.4. Bias in Prediction Models
Another limitation relates to the treatment of foundation failures, which is due to the
significant conservative bias in existing prediction models for foundation behavior
and failure. Bias is defined as the ratio of the true value of a random variable to the
predicted or nominal value. This bias may lead to predicted risk levels that are
perhaps unrealistically high.
The ‘separation’ between structural and foundation failure in fixed structures is a
helpful strategy for calibration. However, when a more accurate foundation model
is included, system failure may occur not due to a sequence of structural component
failures, but due to a progressive loss of foundation stiffness and excessive deck
displacements.
2.3.3.5. Simplification
Omission of the transient, dynamic non-linear effects on the loading and response of
the structure is a common simplification adopted in Structural Reliability Analysis.
This may be an implicit conservative bias in the static pushover analysis not
explicitly accounted for, as it will depend on many factors such as the type of
structure and soil conditions. It can be noted that dynamic effects may play an
important part in the response of a jacket undergoing softening effect due to yield.
However, dynamic effects are not significant for offshore structures in the Arabian
Gulf due to the benign environment and the built-in conservatism in the designs.
2.3.4. PROBABILISTIC APPROACH
The probabilistic approach achieved limited application in practice, partly due to
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some inherent drawbacks, although there are growing efforts to overcome them.
Some drawbacks include the high sensitivity of the small risk values to the assumed
probability distribution and its dependency on the model used, which makes the
probabilistic approach outside traditional engineering education.
2.4. DEVELOPMENT APPROACH
Section 2.3 presented the various methods used in reassessment of existing
platforms and revealed limitations associated with the use of the reliability-based
and probabilistic methods in reassessment.
Despite those limitations, the reliability-based and probabilistic methods have been
primarily used in the development of design criteria since their conception. The
logic is that there is little justification for incorporating these methods as an integral
part of the design process, because everyday design procedures must remain simple
and easy to use so as to allow the engineer to focus on the complex detailing of the
elements of an offshore platform.
In summary, it would seem reasonable to continue having procedures, parameters
and safety factors that are deterministic in nature to maintain both simplicity and
practicality but use a probabilistic basis to provide uniformity of reliability among
similar components and structures to minimize costs and optimize safety.
As a result, the reliability-based method was used in the calibration of limit state
codes around the world, including the Canadian, American, British, Norwegian and
Australian codes. This gave credence to the use of the reliability-based method in
this research to perform the required calibrations and answer the research question.
2.5. SELECTION OF CALIBRATION CODE
Section 2.3.2 presented two the deterministic approaches used in industry practice to
perform reassessment of existing platforms. This section evaluates both methods
with an objective of selecting an approach to calibrate its deterministic parameters.
2.5.1. ASSESSMENT OF WSD
When API RP2A-WSD was first introduced, the failure rate of offshore structures
constructed in the Gulf of Mexico was relatively high. For example, in 1965 the
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historical chance of platform loss was 1 in 200 per year (NRC, 1981). Such failures
were partly triggered by technology limitations, especially in relation to tubular and
their connections, in addition to the limited database dealing with environmental
conditions of wave heights, wave force effects, current and soil and foundation
characteristics.
With the widespread experience gained using API RP2A, the introduction of new
technology and availability of data, API continued to revise and update API RP2A-
WSD as a matter of policy to reflect experience and technology. Such policy
resulted in a significant reduction in the rate of failures. Today, the offshore
industry feels confident that platforms classified as high consequence that are
designed to meet current recommendations of API RP2A-WSD have acceptable
safety margins and that the use of API RP2A-WSD has produced platform structures
with high historical reliability levels. This may not be the case for platforms
classified as medium (L2) or low (L3) consequence with low (0.8 and 0.6) reserve
strength ratios (RSR) as was demonstrated under the forces of hurricanes Katrina
and Rita.
Despite the success associated with the use of API RP2A-WSD in practice, many
Authors (e.g., Simpson et al., 1981; Burland et al., 1981; Kulhawy, 1984) discussed
shortcomings and limitations associated with the single factor of safety approach
that forms the basis of the WSD method.
Fundamentally, the factor of safety is not unique and, depending on its definition,
can vary significantly over a wide range of situations. A particular factor of safety
is meaningful only with respect to a given assumption and equation (Kulhawy,
1984). Aside from non-uniqueness, the single safety factor approach makes less
efficient use of past experience. Since it is not defined within a consistent and
common framework, engineers cannot communicate and share their experiences
effectively. Furthermore, the approach does not distinguish between model and
parameter uncertainties, making it difficult to justify any reduction in the safety
level if there is additional information or advances in the state-of-the-art. There is
also currently no way to extrapolate the factor of safety rationally and consistently
to accommodate new scenarios.
Another significant source of ambiguity lies in the relationship between the factor of
safety and the underlying level of risk. A higher factor of safety does not
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necessarily imply a smaller level of risk, because its effect can be negated by the
presence of greater uncertainties in the environment or soil variability.
In addition to the above limitations, the safety factor approach is typically not
accompanied by a carefully prescribed procedure for defining capacity (e.g. net or
gross capacity), for carrying out the analysis (e.g. empirical or rational method) and
for deriving the pertinent design soil parameters (e.g. correlations or direct
measurements). As a result, the same numerical factor of safety can imply very
different safety margins for a structure.
2.5.2. ASSESSMENT OF LRFD
Shortcomings of the factor of safety approach associated with the WSD method
necessitated the search for more sophisticated treatment of safety levels in design
(e.g., Rojiani et al., 1991; Berger and Goble, 1992; Becker et al., 1993; Ovesen,
1993). As a result, API Committee conducted its own research to examine implied
risk levels in API RP2A-WSD (2000) and found considerable scatter in the implied
component reliabilities. The API Committee found that the LRFD method would
provide many benefits, including an increased uniform reliability and therefore
implied economy (Moses and Larrabee, 1988), especially for platforms with high
gravity loads compared to environmental loads. Moses and Stahl (1998) considered
LRFD reliability-based format to be more flexible with regard to rationally and
systematically incorporating new technological findings, interpretation of observed
platform survival and failure experiences and reformulation for new geographic
areas as well as formulating design rules in frontier areas consistent with present
practices, platform types, new threats such as ice, earthquake and collisions and the
requirements for evaluation of older structures.
The LRFD method was also considered to provide a rational approach to distinguish
between various conditions (termed limit states) that affect structural performance,
to ensure safety under rare but high-hazard conditions and to maintain function
under day-to-day conditions.
In addition, advances in the field of structural reliability such as the first-order
reliability analysis, stochastic load modeling and availability of supporting statistical
databases enable many of the uncertainties in loads and strengths to be modeled
probabilistically.
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The LRFD is a semi-probabilistic method, which is termed level 1 in structural
reliability analysis (SRA) approach. In Level 1 criteria, a single set of load factors
and another single resistance factor are applied to all situations, regardless of the
relative importance of the nominal loads in the load equation. Level 2 criteria are
known as First Order Reliability Method (FORM) and are based on the use of the
mean and coefficient of variation to calculate the reliability index which is a
measure of the safety level. Level 1 and 2 criteria can be made equivalent if the
resistance and load factors on level 1 are allowed to vary for different parameters
such as live-to-dead load ratios and beam span length. However, this would be
impractical and was therefore not adopted in the calibration of AISC or API RP2A.
2.6. HISTORICAL BACKGROUND OF LRFD CODES
Given the shortcomings associated with the traditional factor of safety approach and
the rationale offered by the LRFD method, API RP2A-LRFD (1993) was employed
as the base code to calibrate parameters for reassessment of existing platforms in the
Arabian Gulf.
This section provides a brief background of the API RP2A-LRFD (1993)
specifications with an objective of identifying the basis used in its calibration and
applicability to the specific conditions of offshore platforms in the Arabian Gulf.
2.6.1. LRFD FOR STEEL BUILDING STRUCTURES
API RP2A-LRFD (1993) only covers the resistance of tubular members and refers
to ANSI/AISC 360-05 for treatment of non-tubular members. For loading criteria,
ANSI/ AISC 360-05 refer to ASCE Standard 7-05. This section describes the history
of developing ANSI/AISC 360-05 and ASCE Standard 7-05 (formerly known as
ANSI A58).
Development research for steel buildings began in several industry projects in the
1970s, a time of marked activity in code development with increased understanding
in mathematics and physics, as the effects of loads on structures could be calculated
and knowledge of material and component behavior was developed through testing.
At that time, the Secretariat for American National Building Standard Committee
A58 on Minimum Design Loads for Buildings and Other Structures was
administered in the Structures Division of the Centre for Building Technology
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(CBT). The antecedents at the National Bureau of Standards (NBS) for this
standard dated back to 1924 when the Building and Materials Division published a
report under the auspices of the Department of Commerce Building Code
Committee on Minimum Live Loads.
Research on probabilistic methods in structural codes was a central thrust in the
CBT throughout the 1970s. Ellingwood and Culver (1977) researched the
probabilistic analysis of live and snow loads while Ellingwood (1979) investigated
the load combinations for reinforced concrete design. This work stood at the
intersection of research and practice and various standard-writing groups in the
United States agreed that the ANSI A58 Standard was the logical place for material-
independent load criteria to facilitate the development of one loading code that can
be used with different construction materials.
The development of the first draft of ANSI A58 criteria had to deal with loads and
load factors about which there was no general agreement, since ANSI A58 code
included no provisions for load factors and the statistical make-up (type of
probabilistic distribution, mean values, coefficients of variation) of some of its load
types was not clearly specified in the standards.
To enable compatible ANSI A58 loads with material codes, it was necessary to
develop specific basis for ANSI A58 and use this basis to develop material codes.
With lack of coordination between loading and material codes, there was a risk that,
as different standard-writing groups moved toward probability-based limit states
design, each would develop load requirements independently and that these load
requirements would be mutually incompatible in structural engineering practice
where construction technologies usually are mixed.
The ANSI A58 loading code was reworked such that:
• The statistical characteristics of its loads would be defined, and
• Common load factors and load combinations as applied to all types of structural
materials would be provided.
The reworked ANSI A58 was developed on the basis of the load and resistance
statistics reported by Galambos (1979) and identified in Table 2-2.
In 1979, Ellingwood was joined by T.V. Galambos, J.G. MacGregor and C.A.
Cornell to develop a set of common probability-based load requirements for limit
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states designs that would be compatible with all common construction technologies
using advanced structural reliability analysis methods and statistical databases. The
objectives of this joint effort were to:
• Recommend a set of load factors and load combinations for inclusion in the
ANSI A58 Standard that would be appropriate for all types of building
construction (e.g., structural steel, reinforced and pre-stressed concrete,
engineered wood, masonry, cold-formed steel and aluminum); and
• Provide a methodology for various material specification groups to select
resistance criteria consistent with the A58 load requirements and their own
specific performance objectives.
The outcome of this joint effort was NBS Special Publication 577, Development of a
Probability-based Load Criterion for American National Standard A5, which was
published in June 1980 but was first implemented through the voluntary consensus
process in the 1982 edition of American National Standard Institute ANSI A58. The
load provisions in the ANSI A58 standard has been published as American Society
of Civil Engineers (ASCE) Standard 7 since 1985 and is now called ASCE Standard
7-05.
The probability-based load criteria have appeared in all editions of that Standard
since then and have remained essentially unchanged since 1982. Subsequent
developmental work on probability-based codes in the United States in such diverse
applications as buildings, bridges, offshore structures, and nuclear power plants in
the intervening two decades can all be traced back to this one seminal document.
This research accounted for the development history of ANSI A58 and employed a
similar approach to develop live loads and calibrate resistance factors.
2.6.2. LRFD FOR OFFSHORE PILED FOUNDATIONS
Predicting the ultimate strength of piles supporting offshore platforms has been
under development since the 1950s. Pelletier et al. (1993) provided a
comprehensive history of those developments spanning over half a century. This
section summarizes the development history of pile resistance factors and the
parameters governing the computations of pile capacity in API RP2A (1993, 2000).
Prior to the 1950s, the prediction of the capacity of onshore driven piles was most
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commonly based on dynamic driving formulas such as the Engineering-News
Formula (Bowles, 1988). The availability of static loading test programs gradually
shifted design practice more towards the use of static design equations (using
laboratory sample strength data or estimated skin friction) and these were adopted in
the first API RP2A (1969). Initially, estimated values of skin friction based on
general descriptions of various soil types and consistencies were used with these
equations. Later, laboratory strength data played a more important role in
establishing engineering parameters.
The design guidance for the axial capacity of piles in clay was initially based on
engineering practice that had previously been followed for about 30 years in the
Gulf of Mexico and largely followed the practice of McClelland Engineers. This
guidance was unchanged until the 6th edition in 1975 when it was replaced by the
so-called API method 2. The introduction of API method 2, which was more
conservative than the original method, was a substantial change leading to a
significant increase in design of driven piles. Due to industry concerns, the previous
method was reinstated a year later in the 7th edition for highly plastic clays such as
those found in the Gulf of Mexico. The API method 2 was categorized for use with
other types of clay. The design guidance for clays remained almost unchanged until
the 17th edition in 1987 when it was completely over-hauled and a new method
introduced; however methods 1 and 2 were retained in the commentary.
For sands and silts, bearing capacity factors and soil friction angles were
recommended for a limited range of soil types, along with limiting values, for the
first two editions of RP2A. In the 3rd edition, the limiting values were removed and
this guidance remained almost unchanged until the 15th edition was introduced in
1984.
Following an extensive review of all available test data the guidance was changed
extensively. One of the most significant changes was for piles under tension where
the earth pressure coefficient was increased from 0.5 to 1.0 for full displacement
piles (plugged or closed-ended). Other changes included expanding the range of
soil types covered by guideline parameters and to re-introducing limiting values on
end bearing and skin friction.
Pelletier et al. (1993) attribute the changes in engineering parameters between
various editions of API RP2A to two main reasons. First, lack of high quality data
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from the results of full-scale testing of piles under axial loading precluded the
derivation of an accurate mathematical model. Various procedures were often used
in tests reported in the technical literature, leading to difficulty in evaluating results.
Also, many techniques were used to obtain properties of in situ soil and the results
of soil tests could often not be interpreted comparably. Furthermore, in only a few
instances, measurements were made using the necessary instrumentation to reveal
the detailed manner in which the foundation interacts with the supporting soils.
Second, the interaction between a pile and the supporting soil involves many
complexities, including influence of both installation method and construction
details on soil behavior.
Advanced Mechanics and Engineering Ltd (1999) investigated the effect of these
changes on the calculated ultimate axial capacity of piles. A one meter diameter
pile in a homogeneous profile of dense clean sand characterized by an internal
friction angle of 35 degrees was considered for that research. The conclusions
pointed to very little difference between the ultimate capacities predicted by the 1st
Edition and the 15th Edition. For the 3rd to the 10th Editions (1972 to 1984) there
was a reduction in ultimate compressive capacity of up to 30% for a given pile
length if the coefficient of lateral pressure was taken as 0.5. In terms of pile length
for a given pile load, the 3rd to 10th Edition would lead to a pile length up to 7m
longer than the 15th Edition for this example.
The historical background described above covered the prediction of axial capacity
of piled foundation using WSD format. However, for compatibility with structural
codes, there was a need to develop LRFD format.
Historically, the development of Geotechnical LRFD method took place in an
environment where the relevance of probabilistic design was still being debated
(Committee on Reliability Methods, 1995, Whitman, 2000) and predominantly
involved rearrangement of existing global factors of safety into a new design format.
The debate on using probabilistic methods in Geotechnical LRFD is due to a variety
of reservations, which can be categorized into three general classes (Kulhawy et al.,
2002). The first class of reservations concerns the relevance of using statistics to
model the variability of soil properties on the basis of the following arguments:
• Soil properties do not belong to a uniform statistical population;
• Probability distributions are not supported by sufficient data;
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• Statistical parameters (mean and variance) cannot characterize extreme soil
properties,
• Soil heterogeneity is not modeled adequately, and
• “Random” soil properties might take values that do not exist in nature (Boden,
1981; Simpson, et al., 1981; Driscoll, 1984; Oliphant, et al., 1988).
In fact, most of these objections point toward limitations of classical statistics rather
than probability theory as a whole. For example, soil properties that do not belong
to the same population and soil heterogeneity can be modeled using the random
field theory (Vanmarcke, 1983). Insufficient information can be supplemented
consistently by subjective judgment using Bayesian statistics.
The second class of reservations concerns the higher complexity of the design
calculations associated with the use of probability theory and includes the following
issues:
• Statistical information is not sufficiently well-defined to warrant sophisticated
treatment,
• There is a greater risk of making computational errors, and
• A research into soil behavior prediction is reduced to a mere mathematical
exercise, diverting attention from the proper characterization of the ground mass
and appreciation of the physical, chemical and mechanical processes taking
place in it (Beal, 1979; Semple, 1981; Simpson et al., 1981; Boden, 1981).
These reservations are not without merit. Excessive preoccupation with maintaining
simplicity would ultimately be a disservice to the Geotechnical engineering
profession. Historical hindsight showed that the judicious use of rational methods,
as initiated by Terzaghi in 1943, primarily caused most of the significant advances
in soil mechanics. The cost of rationality is more complicated calculations.
However, this cost is more than offset by the benefits associated with the use of
rational methods. For example, the improvement in soil behavior prediction allows
less conservatism to be applied in the design.
The third class of reservations concerns the difficulty of interpreting the theoretical
probability of failure and its usefulness in design. Many Authors have criticized the
notion of trying to quantify safety explicitly (e.g., Mortensen, 1983; Driscoll, 1984;
Beal, 1979) due to the difficulty of handling gross errors. This reservation gives
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merit to the debate on using LRFD in geotechnical engineering. CIRIA (1977)
identified that the probability of failure derived from theoretical analysis is smaller
than the actual failure probability because other important sources of uncertainties
such as gross errors and unpredictable “Acts of God” were not included in the
analysis.
Despite those reservations, the calibration of axial pile capacity in API RP2A-LRFD
(1993) was carried out using probabilistic methods and similar approach was
adopted in this research.
2.7. AXIAL PILE CAPACITY IN CARBONATE SOILS
The prediction of the axial capacity of offshore piles can be carried out using API
RP2A-LRFD (1993) guidelines. In parallel with this, Morgan and Finnie (2006)
identified other methods and their required soil input to predict axial capacity of
piles including University of Western Australia, Imperial College Pile Design
Method, Norwegian Geotechnical Institute and Kolk and van de Welde equations.
With any method, there are various caveats (often in the form of text rather than
equations) that limit its use, or there may be a need for modifications to the method
to make it applicable to unusual soils or situations (e.g. high carbonate content,
volcanic soils). Limitations are imposed on the method to reflect the approach used
to calibrate that method.
For example, Section 6.4.3 in API RP2A-LRFD (1993) limits the use of its method
to predict axial capacity of piles in carbonate soils, stating that “to date, general
design procedures for foundations in carbonate soils are not available”.
Other international codes and standards also do not provide, exploit or quantify
guidance for driven piles in carbonate soils. Angemeer (1973, 1975) and others
found that general design procedures for foundations in siliceous sands dramatically
overestimate the capacity in regions with carbonate sediments.
This section presents a background to carbonate soils and identifies the required
engineering parameters to predict axial capacity of piles driven in carbonate soil.
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2.7.1. DEPOSITION HISTORY OF CARBONATE SOILS IN THE
ARABIAN GULF
The Arabian Gulf measures approximately 1000 kilometers in length and 200 to 300
kilometers in width. The water depth profile is generally rather shallow, with the
deepest water (about 100m) being located near Iran as shown in Figure 2-3.
The area represents a shallow tectonic depression which was formed in the late
tertiary period (approximately 7 million years ago) by down warping of the earth’s
crust. At this time, the sea reached a maximum elevation of 150 meters above
present levels with subsequent deposition of typical ‘shallow carbonate Shelf Sea’
materials including limestone, marls and clastics (Agarwal et al., 1977).
Stevenson and Thompson (1978) described the formation of carbonate soils starting
18000 years ago when global cooling and the onset of glacial conditions in higher
latitudes marked the beginning of the Flandrian period. During this time, the sea
reached a minimum elevation of approximately 120 meters below its current level,
exposing the previously deposited marine sediments to sub-aerial and fluvial
weathering processes. As the climate steadily moderated towards the end of this
period and into the beginning of the Holocene (10,000 years ago), the Gulf
gradually became inundated again. However, this process was far from regular and
large zones of the region experienced irregular periods of sub-aerial and marine
sedimentation. Post-glacial sedimentation included shelly sands in the shallow
zones, clean carbonate mud in deeper depressions and impure carbonate mud or
marls along the axis of the Gulf.
The presence of gypsum and carbonate cementation in the Arabian Gulf soil profile
is common. Gypsum is one of the less soluble of these salts and is therefore
precipitated in relatively large amounts. It is precipitated in pore spaces of onshore
deposits when seawater is drawn inland as groundwater and is then evaporated on
rising to the ground surface.
Gypsum can be present in various forms including the thick accumulations of
gypsum developed on tidal flats or areas periodically flooded by saline water or the
thin crusts of gypsum usually inter-bedded with the prevailing sediments. These
sediments may be precipitated from standing water in highly restricted lagoons.
Cementation of loose carbonate particles requires little environmental change in
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temperature or carbon dioxide concentration and can be concurrent with submarine
deposition. Often precipitation of cement in coarse-grained sediments is much
localized laterally and vertically, and recently cemented carbonate layers may
overlie uncemented material. Thus, some carbonate sediments are able to support
high overburden pressure long before consolidation occurs if cementation is
contemporaneous with deposition. This may result in high void ratios at depth but,
with the increasing overburden pressures, creep and re-crystallization will be
induced and porosity will eventually be reduced. Cementation may also occur under
hot, arid sub-aerial and super-saline lagoonal conditions, similar to those imposed
during the sea recessions in the Arabian Gulf. Upward movement of saline
groundwater induced by high evaporation rates at ground level transports salts to the
surface where they are precipitated, binding loose sediments to form a hard
cemented layer or duricrust.
Groundwater movement through, and therefore cementation of, fine grained
sediments is relatively restricted but an additional effect of the sub-aerial period on
these soils was to impose overconsolidation characteristics by the processes of
desiccation and removal of overburden by erosion.
In conclusion, the deposition history of the Arabian Gulf soils results in extremely
variable sediments. In particular, the distribution and occurrence of the
predominantly carbonate materials commonly encountered in the top 100m of the
sediment column tend to be laterally and vertically variable. Occasionally this
lateral variability can occur over relatively short distances with attendant
significance for offshore foundation capacity.
2.7.2. CHARACTERISTICS OF CARBONATE SEDIMENTS
The deposition history of the Arabian Gulf established that its soils are dominated
by sediments of carbonate origin. This section presents an overview of the nature of
carbonate sediments.
Carbonate sediments are biologically derived gravel, sand, silt and clay sized
sediments, which may have undergone post depositional alteration such as
cementation and chemical replacement (McClelland, 1974). Carbonate sediments
may be found in a variety of forms from soft microscopic oozes through to large
complex structures of coral to massive strong rock.
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The word carbonate is used as a generic description to indicate a soil that contains a
significant proportion of carbonate material. The term ‘significant’ is somewhat
subjective. For example, the content of calcium carbonate could be up to 100
percent.
Carbonate sediments are distinctive in that they are composed of material of marine
origin, which are significantly different from the more-familiar land-based
(terrigeneous, often siliceous) materials and often have different engineering
properties from such soils. In general, it may be said that the mineral of which these
materials are formed is significantly softer than most terrigeneous materials and is
susceptible to crushing. However, high interparticle forces can develop due to
interlocking of unusually shaped particles and more significantly cementation. The
minerals are to some extent soluble in the pore water. This solubility can alter the
structure of the deposit with time and in some cases within the economic life of the
foundation. The particle shape is often hollow, resulting in unusual physical
characterization of the deposits. Finally, and as a consequence of these
characteristics, the deposits often have very high void ratios. As a result of these
distinctive features, carbonate soils exhibit unexpected behavior compared to
terrigeneous soils which form the basis of classical theory of soil mechanics.
Indeed, the difference can be so dramatic as to lend the theory inapplicable.
This group of soils has received special attention due to its worldwide distribution
along the continental shelves of the oil-rich zones and the increased need to develop
in these zones.
Carbonate sediments are often encountered in the relatively shallow water
environment of the tropics between the latitude 30 degrees North and 30 degrees
South including the Red Sea, the southern part of the Arabian Gulf, the Continental
Shelf of Western Australia and Bass Strait at the southern tip of Australia, the Java
Sea, in North America off the west coast of Florida, in central America off the
Yucatan peninsula and in Barbados (Stevenson and Thompson, 1978).
Geologic processes described in Section 2.7.1 control the soil structure within these
deposits and therefore its mechanical behavior. Celestino and Mitchell (1983)
suggested that grain hardness and intra-granular porosity, soil fabric, cementation
and carbonate content account for differences in behavior between carbonate
deposits and their terrigeneous counterparts.
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The fabric of carbonate soils is an important characteristic feature. The term “soil
fabric” covers a variety of sediment characteristics including the arrangement,
shape, and size distribution of grains, the intra-granular porosity or void ratio and
the relative amount of carbonate materials. These are interrelated parameters in the
sense that carbonate soils with highly angular particles often have a high in situ void
ratio due to particle orientation. Due to these features, grains of carbonate soil crush
readily under relatively low compressive and shear stresses. It is that crushability
that is responsible for mobilization of low lateral stress, which is required to develop
skin friction of driven piles.
Cementation is perhaps the most distinguishing feature of carbonate soils, and its
effect on the frictional soil-pile response and on the lateral stresses that are
mobilized against the pile wall has been much discussed in the literature. The
degree of cementation may range from weak fragile bonds at particle interfaces to
highly cemented concretions in which the matrix voids are virtually filled with
calcite. Cementation may increase the strength but it causes the soil to respond to
load in a brittle manner and undergo degradation due to crushing and
compressibility of the material leading to strain softening and “unzipping” type
failure. Cementation may promote arching following shear-induced volume
reduction, resulting in reduced lateral pressures on a driven pile.
2.7.3. INSTALLATION EXPERIENCE OF PILES DRIVEN IN
CARBONATE SOILS
Due to the complex nature of carbonate soils, installation experience of piled
foundations in carbonate soils has been highly unpredictable. For installation in
very weakly cemented and very compressible formations, it is not unusual to
experience the free fall of a pile for several meters or even several tens of meters as
shown in Figure 2-4. The free hammer fall can be under the own weight of the pile
or as a result of only a few blows of a hammer.
The first awakening to the unique behavior of carbonate materials came not from
borings but from pile driving operations during the construction of a platform in
Iranian waters in 1968 (McClelland, 1988). In this case, offshore borings identified
layers of calcarenite and thick layers of sand containing visible shell fragments but
the significance of the high carbonate content of the sands was not recognized.
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During installation, a 30-inch (760mm) diameter pile fell freely for 17m after first
penetrating 8.5m of variably cemented material. Fortunately, lithified sediments at
50m provided high end bearing capacity.
Surprises continued as carbonate sediments were encountered in construction of
piles in other parts of the world. In May 1980, the first Garoupa field platform in
the Campos Basin offshore Brazil was installed in 120m water (Matos and Mello,
1982). When the 48-inch (1219mm) diameter pile was placed, no penetration
occurred under the combined weight of the pile, the pile follower and the Vulcan-
560 steam hammer. After five blows, the pile suddenly broke through and ran out
from under the hammer to an uncontrolled penetration of 50 m. The plunge was so
rapid that the Vulcan 560 hammer was suspended in mid-air and continued driving
upon itself. This led to failure of an auxiliary hook which supported the hammer
and the hammer plunged to the sea bottom where it was lost. Subsequent
investigation revealed that there was a thin grout layer over the sea-bottom as a
result of spill over from pre-drilling well and cemented zones of carbonate subsoil
underlay this grout layer.
The installation of the North Rankin ‘A’ structure (NRA) on the Northwest shelf of
Australia in the 1980’s added yet another dimension to a rather unique engineering
problem. The large 72-inch (1800mm) diameter piles required high capacities and
deep penetration into carbonate sediments. A very comprehensive site investigation
and engineering program was undertaken. In spite of this, a surprise occurred once
again after installation and the piles would not meet their axial design requirements.
Limited remedial measures were first investigated but these ultimately were judged
ineffective leading to an exhaustive research of many strengthening alternatives.
The final price tag reached around US$350 million (Haggerty and Khorshid, 1989).
The free fall of piles was found to be due to crushing of the tiny shells constituents
of the carbonate soil thus exerting almost no effective lateral pressure against the
pile wall. Piles were thus driven very easily but developed little capacity in either
downward bearing or uplift. On the other hand, driving may prove to be ineffective
in strongly cemented levels and drilling (cleaning out the inside of hollow pipe) may
be the only solution for running through the hard but relatively thin layer.
Alternatively, an insert pile is set in place in the case of a thicker layer, with
subsequent grouting of the annulus.
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In the process of experiencing the unique behavior of carbonate soils, extensive
basic knowledge was added through fundamental and detailed investigations into
almost every aspect of foundation design and analysis for carbonate sediments,
ranging from site investigation procedures to material characterization as presented
in the following sections. However, the prediction of axial capacity of piled
foundations in carbonate soils remains highly subjective and site-specific.
2.7.4. REVIEW OF LOADING TESTS IN CARBONATE SOILS
Stemming from the failure of conventional methods to predict pile behavior in
carbonate sediments, several loading tests were performed and reported in the
literature.
The first full-scale pile loading tests in carbonate soils were performed by Angemeer
et al. (1973) in the Bass Strait, offshore south-eastern Australia. In 1977, Agarwal
et al. reported the results of Geotechnical site investigation that was performed at
three sites offshore India.
The research was conducted to understand the effect of varying percentages of
carbonate content on the engineering properties of the soils. The soil carbonate
content at the site ranged from 15% to 96%. The main conclusions from the
research pointed to the significant influence of carbonate content on stress-strain
characteristics of soil, particularly at higher normal stress and at carbonate contents
higher than 45%. Further, it was shown that the effect of quartz sand becomes
prominent if the carbonate content is less than 30% and that the strength of the soil
reduces for sands with a carbonate content above 45%. The research also concluded
that carbonate content in clay appears to have beneficial effects on strength
properties. Further, the sensitivity (ratio of the undisturbed undrained shear strength
to the remoulded undrained shear strength) of carbonate clay was reported to vary
mostly between 4 and 5.
Datta et al. (1979) described results of an experimental investigation designed to
determine the amount of crushing in carbonate sands under low and elevated cell
pressures and to assess the influence of crushing on the shear characteristics of such
sands. They found that the extent of crushing is influenced by particle
characterization and suspected the significance of intraparticle voids. They
concluded that the significant reduction in the maximum principal effective stress
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CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
ratio with crushing partly provides an explanation for the low skin friction around
piles observed in the field for piles in carbonate sands. The research used
dimensionless crushing coefficients to describe crushing of sands but was unable to
relate changes on shear behavior induced by crushing to a quantitative measure of
crushing.
In 1980, Datta et al. confirmed that particle crushing and cementation in carbonate
sands affect the axial load capacities of piles. Particles of these sands differ in their
nature, shape and form and exist in both cemented and uncemented states.
Based on field and model tests on piles in carbonate soils, Nauroy and LeTirant
(1983) suggested an inverse correlation between end bearing and compressibility
index. However, this correlation seems to apply best to uncemented soils that are
actually rather atypical.
In 1984, Dutt and Cheng reported the results of several Geotechnical studies on soils
from the Gulf of Suez with similar skeletal carbonate sands to that investigated by
Nauroy and Le Tirant (1983).
The soils were primarily weak to moderately cemented carbonate sands and silt with
carbonate contents greater than 90%. A subsequent series of twelve pullout tests
was used to define long term pile capacity parameters. The research concluded that
analyses that consider the lateral earth pressure coefficient K and the soil friction
angle δ as one variable provided a better evaluation of frictional response of piles in
carbonate soils with high carbonate content. For the high carbonate content sands
and silts encountered at the site under investigation, a limiting value of K.tanδ of
0.14 was found to be a best fit of the pullout test data. Incidentally, this value
coincided with the limiting value range (10kPa-14.4kPa) observed by Angemeer et
al. (1975) in the Bass Strait of Australia.
Nauroy and Le Tirant (1985) described a series of laboratory tests combined with
full-scale tests undertaken by the research institute AGREMA to determine the skin
friction and end bearing of driven piles and grouted piles installed in carbonate soils.
They reported a decrease in the skin friction of driven piles with the increase in the
compressibility of the soil. They suggested that the compressibility of carbonate
sand caused the very low horizontal stresses acting on the pile shaft which then
accounts for the very low values of friction observed on driven piles in carbonate
soils.
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Since then, many examples of low, and often erratic, measured pile capacities were
published (Dutt and Cheng, 1984; Dutt et al., 1985; Gilchrist, 1985; Puyuello et al.,
1983). A summary of the reported limiting skin friction based on static loading test
interpretation is shown in Table 2-3.
The wide range (1.0kPa to 33.1kPa) of limiting values shown in Table 2-3 may be
attributed to the test or research conditions in the investigations. In the well-
controlled tests conducted by Nauroy and LeTirant (1983), the shaft resistance is
negligible, whereas the test by Angemeer et al. (1975) represents the other extreme.
By comparison, API RP2A-LRFD (1993) recommends a limiting shaft resistance
for medium dense silica sand of about 80kPa.
The main finding of these tests showed that skin friction for steel piles could be very
low and appears to be uniform over the entire pile length. The conclusion was that
field loading test was the only satisfactory method of supporting capacity
calculations.
Most researchers advise caution in extrapolating any of their test results to other
sites and recommend the use of full-scale pile loading tests at the site where piles
are to be installed. Unfortunately, loading tests in offshore sites are rarely carried
out due to the prohibitive costs although Murff (1987) believes that such loading
tests could be essential to produce cost effective designs through full understanding
of the soil behavior at that site.
2.7.5. SOURCES OF DIFFICULTY IN ESTABLISHING
ENGINEERING PARAMETERS
Difficulties associated with predicting the behavior of carbonate sediments stem
from different sources, including deposition history, difficulties associated with in
situ testing, ambiguity associated with degree of cementation and disagreement over
in situ testing, the severe changes in the properties of soils caused by pile driving
and reconsolidation and drag down of soil from one layer into another and pile-soil
interaction during loading. As a result, there is potentially a significant margin of
error in the predicted pile capacity. If a conservative approach is adopted, the piled
foundation is safe against failure but may become uneconomical. In the terrestrial
environment, prediction difficulties could be minimized by performing loading tests.
For large diameter offshore piles, there have been very few loading tests thus
CHAPTER 2: RESEARCH ISSUES 36
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
requiring careful consideration of prediction methods.
This section provides a discussion of a number of the difficulties identified above.
2.7.5.1. Deposition History
Environmental conditions at the time of deposition control the quantitative
relationships between these key sediment components at a particular location.
Water depth, temperature and energy level (wave and currents) influence the
selection of grain types, the sorting and shredding of particles and the degree of
cementation. Thus, carbonate sediments in one part of the world may be totally
different to another part, resulting in lack of worldwide consensus on a methodology
for the calculation of the capacity of driven piles in carbonate sediments.
Hence, a general method for predicting the axial capacity of piles in carbonate soils
can only be developed to cover a specific geographic location. Such method can not
be generalized to cover carbonate soils around the world.
2.7.5.2. Difficulties associated with In Situ Testing
Great variability of the cementing agents in carbonate soils creates difficulties in
identification, categorization of unique properties and behavior, sampling, handling,
testing and employing empirical correlations. The brittle, crushable nature of
carbonate sands complicates site investigation and laboratory testing procedures.
Inserting a sampler, particularly by offshore percussion methods can damage
cementation bonds and cause crushing of the soil grains. Sample trimming also
results in additional disturbances. Furthermore, changes in temperature, pressure
and carbonate concentration that occur during sampling may give rise to changes in
the sample cementation during retrieval (Beringen et al., 1982).
2.7.5.3. Ambiguity associated with Degree of Cementation
Due to the complex nature of carbonate soils, it has not been possible so far to
identify a parameter to quantify the degree of cementation. This is largely due to
current offshore drilling and sampling practice which tends to destroy the
cementation bond. However, even if a parameter is developed, the degree of
cementation in any deposit is usually not uniform which makes it difficult to
interpret test data and conclusively identify trends. Most investigators (Angemeer et
al. 1973, 1975; Beringen et al., 1982; Datta et al., 1985; Hagenaar, 1982; Nauroy
CHAPTER 2: RESEARCH ISSUES 37
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and LeTirant 1985) agree that cementation is very important but proposed no clear
explanation of the mechanism by which it acts. Several researchers (Angemeer et
al., 1975; Beringen et al., 1982; Datta et al., 1980; Dutt and Moore, 1985) argued
that uniform and well-cemented carbonate sands give high shaft resistance while
weak and partial or irregular cementation is a cause of low shaft resistance. It is
argued that partial cementation can cause arching around the pile and can cause the
soil in the annulus created by driving to develop incomplete, low-pressure and
irregular contact with the pile. While these hypotheses may be plausible, they
generally leave much unsaid.
To date, little success has been achieved in obtaining high quality samples that
might provide insight into these effects. Consequently, an assessment of in situ
conditions based on sample properties has met with little success (Angemeer et al.,
1973; Beringen et al., 1982; Dutt and Moore, 1985).
The above reasons demonstrate the ambiguity and uncertainty associated with the
behavior of carbonate soils, which makes it difficult to establish relevant
engineering parameters. The difficulty is due to the fact that conventional soil
mechanics theory and experience rely on data developed primarily from terrestrial
sediments with hard particles that do not crush but displace during pile installation
thus packing more densely. In quartz type sands overburden influence is highly
significant, whereas it appears to have little effect on the offshore carbonate
sediments encountered to date, especially when cemented.
2.7.6. APPROACH USED IN INDUSTRY PRACTICE
The inability of classical soil models to provide adequate design guidelines for
carbonate soil-structure interaction problems makes it necessary for current pile
design practice to rely on limited correlations with published loading tests in these
soils rather than on any consistent guidelines.
The reliance on correlations for carbonate soils generally involves a significant
amount of engineering judgment, often imposing a lower limit of soil parameters
(e.g., bearing capacity factor, pile-soil interface friction and coefficient of lateral
earth pressure) in addition to the use of large factors of safety to account for various
uncertainties and lack of knowledge. This practice is almost wholly empirical and
highly site-specific but is likely to continue until the effects of cementation and
CHAPTER 2: RESEARCH ISSUES 38
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
grain crushing are fully understood and sufficiently quantified so as to enable the
development of a rational modeling for piled foundations in carbonate soils.
This research provides an approach that can be used in industry practice to predict
axial capacity of piles driven in carbonate soils. The development leading to the
proposed approach is presented in Chapter 4.
2.8. OPEN AREA LIVE LOADS (OALL)
2.8.1. BACKGROUND
Relatively accurate techniques are available to assess structural behavior under
given loads, yet the loads themselves remain an estimate based in part on field
measurements, in part on professional logic and experience and in part on trial and
error.
One of the loading conditions prescribed in building design codes is equivalent
uniformly distributed live load (EUDL) which depends on the occupancy type. It is
multiplied by a reduction factor to account for the observed decrease in unit load as
the loaded or tributary area increases.
As discussed in Section 2.3.2, API RP2A-LRFD (1993) refers to ASCE Standard 7-
05 to estimate the live loads. The ASCE Standard 7-05 provides requirements for
general structural design but does not provide live load values for offshore topside
structures. To the best knowledge of the Author, quantifying live load on offshore
platforms has not been addressed by other researchers.
An example of an open area on an existing offshore structure is shown in Figure 2-5.
It can be described as the area covered by grating or floor plates and not supporting
fixed equipment.
Other international codes and standards including BS 6349 (2000), BS EN ISO
13819 (1998) and DOE (1990) also provide no guidance to establish OALL. The
only exception, however, can be found in DNV (2000) which disregards OALL for
substructure design but then requires that “Global load cases shall be established
based upon ‘worst case’ characteristic load combinations complying with the
limiting global criteria to the structure”.
Lack of guidance for offshore platforms can be attributed to the dominance of
CHAPTER 2: RESEARCH ISSUES 39
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
extreme storm conditions on the reliability of offshore platforms in the Gulf of
Mexico and US waters, where API RP2A originated. Under extreme storm
conditions, live loads are not generally combined with other load cases.
Consequently, the use of subjective OALL values has traditionally not resulted in
any concerns to complete a design or reassessment of a platform in regions where
the failure mechanism is dominated by extreme storm conditions.
This is evident in Section ‘R’ of API RP2A-LRFD (1993), which limits its guidance
to extreme storm conditions when performing reassessment of existing platforms.
However, the Author’s experience in the Arabian Gulf consistently demonstrated
that benign environmental conditions elevated the importance of operating overload
conditions and hence the need for realistic estimation of live loads.
In industry practice, there is no consensus on the OALL value to be adopted in
reassessment of existing platforms. Some operators suggest that open area live
loads need not be considered for piled foundation design while others stipulate
values as high as 17kPa.
The development of OALL for offshore platforms in this thesis employed similar
methodology to that used in the derivation of EUDL for building codes and
standards. A description of the live load survey data and the probabilistic model
used to derive EUDL in building codes and standards is presented in this section.
2.8.2. LIVE LOAD SURVEYS FOR ASCE STANDARD 7-05
A survey of the literature revealed that the ASCE Standard 7-05 nominal live
loading is based on surveys compiled by Chalk and Corotis (1980). Statistics of the
instantaneous sustained loads were obtained as area weighted averages from all
surveys for a particular use as shown in Table 2-4 (Chalk and Corotis, 1980).
Available live load data principally addressed offices, dwellings, school classrooms,
retail and merchant stores, hospitals and health clinics, storage areas and light and
heavy industries. Some surveys permitted extraction of data pertaining to other uses
incorporated in the preceding occupancies such as library rooms and office lobbies.
Inspection of the existing load surveys shown in Table 2-4 reveals no relevance to
offshore platforms. Hence, a specific database was required for the purpose of this
research. Such database was collated in this research and is presented in Chapter 5.
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CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
2.8.3. PROBABILISTIC MODEL IN ASCE STANDARD
Live load provisions in many building codes are found to correspond approximately
to the mean or a percentile of the lifetime maximum value (Ellingwood and Culver,
1977; McGuire and Cornell, 1974).
As such, it can be used with Load and Resistance Factor Design (LRFD) or
Working Stress Design (WSD) code provisions without the need to conduct
additional sophisticated numerical analyses.
Since all live load surveys are conducted over a short period of time, i.e., on an
arbitrary point-in-time basis, the statistics of the extremes have to be derived
analytically. The issue then was to relate the statistics (mean and standard
deviation) of instantaneous intensity of sustained live load to those of lifetime
maximum values.
Using the survey data for buildings structures, the magnitude of live loads in
structural codes such as ASCE Standard 7-05 was derived employing a probabilistic
model to the survey database.
The use of probabilistic approach provided a logical framework for incorporating
the effect of randomness in the magnitude and placement of individual live loads.
The code design load concept is based on the total mean lifetime maximum load
effect, which considers that live loads vary in time and space in a random manner.
This means that the load was represented as stochastic processes to determine
OALL, which will in turn be used to produce (statistically) the same load effect on a
structural member as the actual random set of loads.
Melchers (1999) described two probabilistic approaches to the “stochastic” or time-
dependent random variables. The first approach is termed the classical (also called
time-integrated) approach and the second approach is termed first-passage (also
called time-dependent) concept which is more general than the classical approach.
The first passage approach was used during the 1970s to describe spatially
distributed floor live loads and derive EUDL for various buildings. Examples of
such models are included in the research work by Pier and Cornell (1973), McGuire
and Cornell (1974), Ellingwood and Culver (1977), Corotis and Doshi (1977), Chalk
and Corotis (1980) and Wen (1979). In those live load models, the spatial
variations were assumed homogeneous in a first approximation. The variation in
CHAPTER 2: RESEARCH ISSUES 41
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
time is divided into two components, sustained load and transient (also called
extraordinary or intermittent) loads. Figure 2-6 illustrates the various loading types.
The sustained load includes furnishings and personnel normally found in buildings
and it is the load that is usually measured in live load surveys. The load magnitude
according to the model represents the time average of the real fluctuating load.
Sustained loads are spatial in character and remain fairly constant until a load
change takes place usually at Poisson arrivals such as at the beginning of a new
tenancy (Andam, 1986). These weights are usually recorded in live load surveys.
The extraordinary load represents all types of live loads which are not covered by
the sustained load. The extraordinary load is usually associated with special events
that lead to high concentrations of people, although it may be due to stacking of
furniture or other items. In general, transient loads have shorter duration than the
sustained load. Many Authors (Andam, 1990; Asantey and Andam, 1996; Chalk
and Corotis, 1980) regarded transient load data as very rare and considered it
unavoidable to adopt standard data.
The total load history is the sum of the two load components and its maximum
represents the largest load that occurs on a given floor area during the lifetime of the
structure.
2.8.4. APPLICABILITY OF PROBABILISTIC MODEL TO
OFFSHORE STRUCTURES
The nature and the characteristics of loading on platform open deck areas are
different to buildings and offices in various ways. Firstly, histograms produced
from existing office and building load surveys placed equal weight on all values of
the variables and averaged the loads per room (Corotis and Doshi, 1977). However,
on platform decks, heavier pieces of equipment on a platform produce the governing
load effects on piles. Consequently, the use of average loads to develop OALL was
considered to be unsuitable for the purpose of this research. Secondly, unlike live
loads in offices and buildings, which are generally spatially random, OALL deck
loads are generally carried out in designated areas which are termed open areas.
Consequently, the rationale behind the reduction according to area that is commonly
used in existing models may not be applicable to offshore platforms.
CHAPTER 2: RESEARCH ISSUES 42
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Thirdly, transient loads do not apply to offshore platforms. Andam (1990) explained
that transient load comprised of three forms: emergency, meeting and redecoration.
The emergency form results from the crowding of people in emergency situations.
The meeting form is due to assembly of an abnormally large number of people for a
meeting (such as an open day or a party). The redecoration form models the
stacking of furniture so that redecoration can take place or at the time of occupancy
changes.
Consequently, the first passage approach used in live loads in existing building
codes and standards such as ASCE Standard 7-05 is not suitable for open area live
loads on offshore platforms. An alternative probabilistic model was required to
derive OALL. The development of OALL is covered in Chapter 5 of this thesis.
2.8.5. REDUCTION FACTORS IN ASCE STANDARD
When a specific value of OALL is established, another question that is commonly
posed relates to the reduction in live loads due to the effect of multiple floors or
large open areas. The various load reduction factors currently recommended by
different standards are used in design of building structures. These load reduction
factors were tracked back almost 60 years ago where it was determined (Dunham,
1946), somewhat subjectively, from load survey data gathered in two federal office
buildings. While it is generally accepted that some reduction in live loads is
permitted, there is considerable uncertainty as to the level of reduction that can be
considered for offshore piles. The Australian Standards AS/NZS 1170.1 (2002)
allows 20% reduction in live loads for design of building columns supporting
tributary area of 35m2, increasing to 50% for areas above 200m2.
Such reduction becomes questionable when applied to offshore piles. Det Norske
Veritas (DNV) rules for fixed offshore platforms make no allowance for reduction,
while API RP2A-LRFD (1993) recommends a 60% carry-down factor.
Another interesting aspect is the reduction of drilling and supply loads during storm
conditions. Eri et al. (1977) stated that DNV found it somewhat disturbing that
philosophies vary greatly, with one company claiming the feasibility of a 30%
reduction based on “previous experience”, which, after some investigation, was
found to be improperly documented.
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CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
For that particular platform in question, the reduction amounted to a considerable
3000 tons. After meticulous investigation into the subject, DNV found no reason to
approve the reduction.
Eri et al. (1977) reported that DNV was skeptical to the use of this reduction and
considered that “a reliable proof appeared virtually impossible to obtain”.
Consequently, there was a need to develop live load reduction factors that
correspond to the required OALL. The treatment of live load reduction factors is
covered in Chapter 5.
2.9. CHARACTERIZATION OF THE CLIMATOLOGY IN
THE ARABIAN GULF
API RP2A-LRFD (1993) has been developed for the Gulf of Mexico (GoM) and US
waters on the basis that extreme storm conditions dominate the failure mechanism in
these regions. The dominance of extreme storm conditions on the failure
mechanism is a result of the large physical uncertainty in extreme storm loads.
Failure incidents in the Gulf of Mexico support this presumption. Botelho et al.
(1994) reported the toppling of 10 major platforms and 25 satellite platforms
(mostly caissons) during the period of August 24-26, 1992 when Hurricane Andrew
moved through the Gulf of Mexico with sustained winds of 140 miles per hour (62
m/s). In addition, Andrew caused significant damage to another 26 platforms and
140 satellite platforms. In 2005, the devastations caused by hurricane Katrina
provided another reminder of the dominance of extreme storm conditions in the Gulf
of Mexico.
However, the Author’s experience in reassessment of a large number of offshore
platforms in the Arabian Gulf has demonstrated that extreme storm conditions do
not govern the integrity of those platforms. Hence, there was a need to explore the
climatology in the Arabian Gulf with an objective of determining the dominant
failure mechanism in the Arabian Gulf.
The climate of the Arabian Gulf is distinguished by two well-defined principal
seasons separated by two transitional periods, and reflects the general pattern of
barometric pressure distribution over the Arabian Gulf. The principal seasons are
winter and summer. Winter extends from December through March with relatively
CHAPTER 2: RESEARCH ISSUES 44
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
mild weather, but most of the annual amount of precipitation falls during these
months. In the winter, a trough of low pressure extends from the Gulf of Oman
along the Iranian coast maintaining north-westerly winds over the area during much
of the season.
Whilst winter is mild, it is characterized by the frequent passage of “traveling”
depressions moving from the Mediterranean along the Iranian mountains and south-
eastwards down the Arabian Gulf. These often interrupt the settled weather and
bring sudden changes. Their approach is heralded by southeast winds which may
reach gale forces at times and their arrival is often preceded or accompanied by
clouds and sometimes by rain and thunderstorms. With their passage, fresh or
strong north-westerly winds known locally as “Shamals” set in. These Shamals may
persist for several days often reaching gale force and are accompanied by
widespread sandstorms or dust storms and rough seas. In addition, characteristics of
the winter weather in the region are strong and gusty north-easterly winds, locally
called “Nashi”, which occur at times. Such winds do not travel along the Gulf as
“Shamals” but are more localized. Summer lasts from June to September and is
characterized by hot weather and almost permanently cloudless skies. In summer,
the atmospheric pressure is normally low over Iran but higher over Arabia.
Metocean criteria were retrieved using several records which were analyzed and
presented in confidential reports for the Arabian Gulf. The reports present extremes
of winds, waves, currents and associated periods for various water levels in different
directions.
The records contained in those confidential reports were obtained from several
sources, including:
• Observation of wave height, period and direction made by ships on passage or
voluntary observing fleet (VOF). The observations were collected and
processed by national meteorological services under the auspices of the World
Meteorological Organization (WMO) and entered into logbooks. The data were
archived and made available for analysis and are fully described in Shearman
(1982). The VOF data represent long term climatology.
• Satellites with radar altimeters (Geosat; ERS-1; TOPEX) at different periods
(November 1986 to December 1989; April 1992 – October 1993; October 1992
to July 1996).
CHAPTER 2: RESEARCH ISSUES 45
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
• Monthly frequency tables of wind speeds and directions from the UK
Meteorological Office (UKMO) for the period 1986 to 2000.
• 3-hourly records of wind speeds and directions at different locations in the
Arabian Gulf from January 1988 to December 2000 from the Department of
Civil Aviation and Meteorology in Qatar.
• Wave data recorded by Hunting Survey Report from September 1985 to
February 1987.
• Proudman Oceanographic Laboratory (POL) Arabian Gulf Model of tidal level
and current in the form of 1 year of hourly tidal current predictions.
Analysis of the data and derivation of metocean criteria is presented in confidential
reports. Some measurements from wave rider buoys were used to adjust the data
before finalizing the criteria.
The metocean criteria were derived by analysis of independent maxima extracted
from samples of data. The distribution independent maximum (e.g. the maximum
wind recorded in each storm over a number of years) was fitted with an extreme
value distribution and this was used for the estimation of extreme values. Results of
the analysis and recommended metocean criteria are shown in Table 2-5.
In the deeper water of the North Sea and the Gulf of Mexico, wave height depends
on wind speed and on the distance (fetch) and duration over which the wind blows.
For example, tropical cyclones in the Gulf of Mexico are caused by winds which
grow with great velocity and generate ferocious and violent seas. Each year, around
100 tropical disturbances develop over the Atlantic Ocean. About 25 of these
disturbances develop into tropical depressions, of which 10 become tropical storms,
5 become hurricanes and 2-3 are likely to strike the US coast (Kaiser and Pulsipher,
2006). Storms that grow into 75 mile per hour sustained winds are classified as
hurricanes. Hurricanes are characterized by pressure, wind speed and storm surge.
However, there is not a one-to-one relationship between these elements, so
maximum wind speed is typically used to establish the so-called Saffir-Simpson
category as shown in Table 2-6.
A comparison of the maximum wind speeds for the Arabian Gulf shown in Table
2-5 against those categories shown in Table 2-6 reveals the benign environment in
the Arabian Gulf. Unlike deep waters, waves interact with the seabed in the shallow
waters of the Arabian Gulf resulting in a slow down and loss of energy and
CHAPTER 2: RESEARCH ISSUES 46
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
consequently a reduction in wave height. Further, large ocean swells are prevented
from entering the enclosed Arabian Gulf area.
As a result of the benign environment in the Arabian Gulf, one of the objectives of
this research was to examine the reliability of offshore platforms in the Arabian Gulf
under environmental conditions. This is covered in Chapter 6 of this thesis.
2.10. SUMMARY
This Chapter identified and elaborated on a number of issues related to components
addressed in the course of reassessment of piled foundations of existing platforms.
In-depth analysis of those issues revealed gaps in the body of knowledge worthy of
research. A summary of those issues are identified in this section.
2.10.1. CODE TO BE USED IN CALIBRATION
This Chapter evaluated methods used in reassessment of existing platforms and
concluded that the design level check method is most suitable for use in industry
practice, while reliability-based and probabilistic methods would be most
appropriate to develop deterministic parameters.
The design level check method can be carried out using WSD or LRFD methods.
This Chapter shows that the LRFD method is appropriate for development of
deterministic parameters.
2.10.2. AXIAL PILE CAPACITY IN CARBONATED SOILS
This Chapter identified that API RP2A-LRFD (1993) provides no guidance to
predict the axial capacity of piles driven in carbonate soils, a key issue in the
Arabian Gulf. The need for guidance stems from poor foundation performance in
carbonate soils and the financial consequences of the remedial measures.
To predict axial capacity of piles driven in carbonate soils, some design methods
have evolved but these remain highly site specific and dependent on local
experience. The range of opinions on limiting soil parameters (shaft resistance and
end bearing) is so wide that use of a single objective value for each parameter is
likely to be either unconservative or uneconomical.
The characteristics of carbonate soils differ between geographic locations so there
CHAPTER 2: RESEARCH ISSUES 47
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
was a need to define limiting soil parameters for the Arabian Gulf region.
Most researchers recommend the use of site-specific loading tests to avoid costly
foundation installations problems and the frequent remedial strengthening
experienced in these soils. However, site-specific loading tests offshore are very
expensive and are sometimes impractical.
Hence, the need to develop specifications addressing axial pile capacity in carbonate
soils was underlined and is presented in Chapter 4 of this thesis.
2.10.3. TARGET RELIABILITY LEVEL
This Chapter revealed that the selection of target reliability level has been a subject
for debate and highlighted the need to specify target reliability levels for the purpose
of this research. It was necessary for the specified target reliability levels to be
compatible with existing practice. The selection of appropriate target levels for this
research is presented in Chapter 4.
2.10.4. OPEN AREA LIVE LOAD (OALL)
Reassessment of existing platforms requires quantifying OALL to enable calculation
of load effects on piles. This Chapter revealed lack of specifications to determine
live loads on open areas on offshore platforms. To overcome this limitation in
current international codes and standards and guide the research efforts, this Chapter
provided a historical background describing the process used by various researchers
and code committees to develop live loads in existing codes and standards. Similar
process was adopted in this research and is described in Chapter 5.
2.10.5. DOMINANT FAILURE MECHANISM
The guidelines for reassessment of existing offshore structures contained in Section
‘R’ of API RP2A-LRFD (1993) attend to the effect of extreme storm loads only.
However, the Author’s experience shows that gravity conditions dominate the
failure mechanism in the Arabian Gulf. Chapter 6 presents an investigation of the
dominant failure mechanism in the Arabian Gulf using a rational approach.
CHAPTER 2: RESEARCH ISSUES
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Table 2-1: Implicit target reliability levels βT in various structural codes and standards (Bhattacharya et al., 2001)
Probability of Failure
Standard Remarks βT 30-yr life
Lfp Annual
afp
Failure Type
Gravity (D + S + L) 3.0 2.0*10-3 6.7*10-5 Component
Gravity + Wind 2.5 8.0*10-3 2.7*10-4 Component AISC LRFD
Gravity + EQ 1.75 6.0*10-2 2.0*10-3 Component
Implicit 5.9*10-5 Component API RP2A
4.2*10-6 System
Implicit 3.5 4.0*10-4 1.3*10-5 Component AASHTO LRFD 5.5 3.3*10-8 1.1*10-9 System
Great Risk to life or environment 3.5 4.0*10-4 10-5
Small risk to life or environment 10-4 CAN/CSA
Impaired function only 10-3
Eurocode Normal distribution 3.5 4.0*10-4 1.3*10-5
Dangerous 10-6 CIRIA
Onshore 10-6 to 10-7
CHAPTER 2: RESEARCH ISSUES 49
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Table 2-2: Load statistics for ANSI A.58 load code (Reference: Galambos et al., 1982). The subscript n denotes nominal values
Load Type Mean COV Distribution
Dead load 1.0 Dn 0.10 Normal
Live Load 50 year lifetime value Ln 0.25 Type I
Arbitrary point-in-time live load value
0.25 Ln 0.4 to 0.8 Gamma
Wind load 0.78 Wn 0.37 Type I
CHAPTER 2: RESEARCH ISSUES 50 50
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Table 2-3: Limiting skin friction values derived from various static pile loading tests in sand. The tests were conducted during the 1970s and 1980s in various locations around the world
Peak Values
Range Mean
Standard Deviation COV
Reference Year No. of Tests
kPa kPa kPa
Angemeer et al. 1973 7 9.2-18.3 13.4 3.3 0.25
Angemeer et al. 1975 1 33.1 - - -
Hagenaar et al. 1981,2 5 16.7-22.5 20.3 2.2 0.11
Dutt and Cheng 1984 12 9.8-18.2 13.3 2.5 0.19
Dutt et al. 1985 4 9.5-17.3 - - -
Gilchrist 1985 4 11.5-21.0 17 4.3 0.25
Nauroy&Le Tirant 1983 1 1.0 - - -
CHAPTER 2: RESEARCH ISSUES 51
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Table 2-4: Result of surveys of instantaneous sustained loads for occupational groups which was used to derive the design live loads in ASCE Standard 7-05 (Chalk and Corotis, 1980)
Occupational Group Total Surveyed Area in ft2 (m2)
Offices 3,800,000 (353 031)
Offices lobbies 17,000 (1 580)
Residences 204,000 (18 952)
Patient rooms 79,000 (7 339)
Hospital surgeries 34,000 (3 159)
Health clinics 173,000 (16 072)
School classrooms 31,000 (2 880)
Libraries – stack rooms 6,000 (557)
Hotel guest rooms 670,000 (62 245)
Warehouse and storage 197,000 (18 302)
Industrial heavy 74,000 (6 875)
CHAPTER 2: RESEARCH ISSUES 52
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Table 2-5: Metocean criteria in the Arabian Gulf for the worst conditions from the northwest called “Shamal” (Source: Basis of Design Documents in the Arabian Gulf and from confidential reports). The 100 year return period wave was calculated using extreme value analysis and has a 1% probability of exceedance in every given year
Return Period (Years) Engineering parameter
100 50 10 1
One minute mean wind speed (m/s) 32.0 31.0 29.0 25.0
Significant Wave height (m) 5.4 5.2 4.7 3.8
Maximum wave height (m) 9.8 9.6 8.7 7.1
Surface current speed (m/s) 1.00 0.98 0.96 0.91
Crest to Crest Period (s) 9.9 9.7 9.1 8.2
Tidal rise (MHHW) (m) 1.3 1.3 1.3 1.3
Storm surge (m) 0.6 0.6 0.5 0.4
CHAPTER 2: RESEARCH ISSUES 53
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Table 2-6: Saffir – Simpson Hurricane Scale (Kaiser and Pulsipher, 2006)
Scale Number Category Winds m/s Damage
1 33 – 42 Minimal
2 43 – 45 Moderate
3 46-58 Extensive
4 59 – 69 Extreme
5 > 69 Catastrophic
CHAPTER 2: RESEARCH ISSUES 54
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 2-1: A description of reassessment methods shows gaps in the body of knowledge in determining axial capacity of piles in carbonate sands, OALL on offshore structures and the effect of environmental loads on the dominant failure mechanism in the Arabian Gulf
CHAPTER 2: RESEARCH ISSUES 55
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 2-2: The process of reassessment of existing platforms outlined in Section ‘R’ of API RP2A-LRFD (1993). The process requires attending to extreme storm conditions only and does not address other conditions such as accidental or operating conditions
CHAPTER 2: RESEARCH ISSUES 56
CALIBRATION OF DETERMINISTIC PARAMETERS FOR OFFSHORE PLATFORMS IN THE ARABIAN GULF
Figure 2-3: Water depth profile in the Arabian Gulf showing that the maximum water depth is 100m
CHAPTER 2: RESEARCH ISSUES 57
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Figure 2-4: Installation experience in the Arabian Gulf and the Mediterranean showing free fall of a pile as evident from the zero blow count in the charts (Nauroy and Le Tirant, 1986)
CHAPTER 2: RESEARCH ISSUES 58
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Figure 2-5: Photo of an actual platform floor deck in the Arabian Gulf showing that the open area is mainly unloaded except for some pipes that are used as scaffolding for painting and maintenance works
CHAPTER 2: RESEARCH ISSUES 59
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Figure 2-6: A plot of load versus time showing the nature of sustained and transient (or extraordinary) loads and the total live load
CHAPTER 3: METHODOLOGY 60
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Chapter 3.
METHODOLOGY
3.1. INTRODUCTION
Chapter 2 discussed a number of research-related issues and furnished background
for development of guidelines that can be used for reassessment of existing
platforms. This Chapter presents methodologies used in this research in order to
develop such guidelines.
3.2. OUTLINE OF THE METHODOLOGY
Using the conditions of the Arabian Gulf, which were outlined in Chapter 2, the
following elements were developed to provide specifications for reassessment of
existing platforms in the Arabian Gulf:
• Establishment of limiting engineering parameters that can be used to calculate
axial pile capacity in the Arabian Gulf;
• Calibration of axial pile capacity resistance factors;
• Development of OALL values;
• Investigation of live load factors; and
• Examining the dominant failure mechanism of offshore platforms in the Arabian
Gulf.
Figure 3-1 describes the tasks to develop items 1 to 4, while Figure 3-2 describes
development of item 5.
These developments were accomplished using established probabilistic and
reliability-based methods. In particular, the development of pile resistance factors
was conducted using a similar approach to the one described by Ellingwood et al.
(1982) to develop nominal member capacities. Firstly, the levels of reliability
implied by the use of the then current design standards and specifications were
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estimated and developed by optimization such that the material-oriented
specifications had a reasonable choice of resistance factors φ to meet a target
reliability index β. Secondly, a format for the proposed criteria that balances
theoretical consistency and appeal with ease of use in practice was selected.
Thirdly, a set of load combination factors were selected in order to make it possible
for material specification writers to prescribe resistance criteria that would result, on
the average, in designs similar to those currently obtained. Lastly, calculation aids
were provided to enable material specification writing groups to develop resistance
factors corresponding to desired reliabilities without further computer operations.
The use of probabilistic and reliability-based methods required a database which
was developed in this research and is described in Section 3.3. The database
included over 400 pieces of equipment and installation record data for 138 piles and
Geotechnical data at 33 locations in the Arabian Gulf. The collected database was
employed in the calibration of live loads and axial pile capacities using the
methodology described in Figure 3-1.
Calibration of resistance factors for driven piles required calculation of bias factors.
The bias factor was obtained by dividing the predicted capacity over the “actual”
capacity of each pile. The capacity of each pile was predicted using the empirical
approach described in API RP2A-LRFD (1993) and presented in Appendix D. The
development of axial pile capacity was carried out using Wave Equation Analysis,
which is described in Appendix E. The statistical parameters of the bias factors
were derived and employed in First Order Reliability Method (FORM) to calibrate
the resistance factors.
The development of OALL utilized an equipment database that was collated from
the Arabian Gulf during the course of this research. The statistical parameters of the
equipment weights were calculated and used to compute the maximum live load on
a pile using the influence area concept. To derive the mean of the lifetime
maximum live load on a pile, extreme value analysis was implemented.
The effect of extreme storm loading on the implied risk level on offshore platforms
in the Arabian Gulf was examined using reliability analysis as described in Figure
3-2. The probability of failure under extreme storm conditions was compared to that
under operating overload conditions. The outcome defined the dominant failure
mechanism in the Arabian Gulf.
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3.3. DATA COLLECTION AND GROUPING
A common need for calibration of codes and standards is a readily accessible
database. Essentially, every author who has presented recommendations for a new
or revised design method established a data bank and compared predictions made
using the new method with measured test results. For example, calibration of pile
resistance factors in API RP2A-LRFD (1993) employed a database collated by
Olson and Dennis (1982) for the Gulf of Mexico conditions. Consequently, it was
necessary to obtain a database that covers the Arabian Gulf conditions in order to
determine the required statistical values of parameters for that region.
3.3.1. CHALLENGES
A crucial limitation at the start of this research was lack of published data relating to
the Arabian Gulf conditions. Two databases were required for the purpose of this
research. One database was required to calibrate resistance factors for piles driven
in the carbonate soils of the Arabian Gulf, since the database used in the calibration
of API RP2A-LRFD (1993) excluded carbonate soils. A second database was
required to develop OALL since the calibration of live loads in ASCE Standard 7-05
was based on surveys with no relevance to offshore platforms.
Fortunately, the Author had the advantage of accessing a significant amount of data
through work in the offshore industry with an owner/ operator in the Arabian Gulf,
which enabled compilation of the required databases. The Author identified that
engineering calculations and installation reports of existing platforms would offer
the required source of data. Typically, those calculations and installation reports are
found in project dossiers.
Live load data were collated using equipment lists of more than sixty (60) platforms
and pile installation data were identified for 33 out of those 60 platforms. The
salient features of the platform structures are listed in Table 3-1, which identifies the
function, water depth, number of piles and jacket legs, pile diameter and pile
penetration for each platform. The database is formed from 138 piles.
3.3.2. SUB-GROUPING THE DATA
The use of a single set of data would provide consistency with the approach used in
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calibrating axial pile capacity resistance factors in API RP2A-LRFD (1993).
However, inspection of the data revealed distinct advantage in subgrouping the data
into sub-populations. The question then was the determination of factors to consider
in stratifying the data and the extent of which the data could be separated, since sub-
grouping of the data could be influenced by a large number of parameters. For
example, the database could be grouped to account for platform configuration,
Jacket configuration (monopod, tripod, 4 or more piles), installation method (float-
over, lifting), supplementary pile driving methods and hammer type (impact or
vibratory). In this research, the data points were subgrouped but a limitation on the
number of subgroups was introduced.
3.4. STATISTICAL ANALYSIS
Calibration of deterministic parameters employed the reliability-based method,
which rely on availability of statistical parameters. The basic information required
in the statistical analysis is the probability distribution of the data and estimates of
its mean and standard deviation or coefficient of variation. This section presents the
approach used in the statistical analysis of the data.
3.4.1. DISTRIBUTION TYPE
Distribution types expressing input uncertainty can be derived from either statistical
parameters or subjective probabilities. When input factors are based on historical
data, the first approach is appropriate. In cases where input factors are not derived
from historical data or when the back-up data are not available, the analysis must
utilize subjective probabilities to describe input uncertainties.
The selection of a probability distribution must be based on a skewness of the
distribution (left, symmetric or right) and a variance or degree of uncertainty (low,
medium or high).
Once a distribution that can model the occurrence of the observed data is found, the
statistical parameters can be derived to perform a reliability analysis and to study the
effect of specified risk levels. Due to the nature of the loading process, including
the element of human control and the large number of variables (leading to a high
degree of uniqueness for any given circumstance), it is not likely that a single
distribution can be found to precisely predict observed values in a particular
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situation. However, a distribution that provides a good model to the character of the
data was considered sufficient to provide load and resistance parameters that lead to
a realistic assessment of reliability.
In this research, the basic resistance variable was taken as the strength of the
structural element in question. The basic load variable was the load effect,
dimensionally consistent with the resistance. These can be used directly in the
reliability analysis when the failure criterion is formulated as a linear combination of
resistance and load variables.
3.4.2. DISTRIBUTION PROPERTIES
The properties of an underlying distribution, from which the data have been drawn,
are of interest. To infer the properties, two approaches may be employed, namely
nonparametric and parametric.
In the nonparametric analysis, no assumption is made regarding the distribution
from which the sample data has been drawn. The construction of histograms from
the sample data is a common form of nonparametric analysis. The sample mean,
variance and other statistics can be obtained from the data without reference to a
specific distribution.
Nonparametric analysis allows identification of the nature of the distribution from
which the data has been drawn without selecting one particular distribution. When
there is sufficient number of data points, representation of the distribution by a
histogram or with sample statistics can be quite helpful. The sample statistics are
estimates of random variables properties that do not require the form of the
underlying probability distribution to be known.
In many situations, the amount of data may be insufficient to construct a realistic
histogram with enough resolution to enable sample statistics. Such situations occur
frequently in reliability engineering. Under such circumstances, rank statistics
provide a powerful graphical technique for viewing the cumulative distribution
function (CDF). They also serve as a basis for the probability plotting for use in the
parametric analysis.
The Parametric analysis encompasses both the choice of the probability
distribution and the evaluation of the distribution parameters. Probability plotting is
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used to provide parameter estimates and also aids as a visual representation of how
well the selected distribution describes the data. Construction of probability plots is
performed using rank statistics technique.
To employ this technique, the random variable is ranked in ascending order. The
cumulative distribution function (CDF) is then approximated at each value of x and
the CDF could reasonably be approximated as (Lewis, 1996):
( ) NiNixF i ....,,2,1, == Equation 3-1
Where F(0) = 0 if the variable is defined only for x > 0
If N is not a large number, say less than 15 or 20, the above equation may seriously
overestimate F(x) and can be improved as follows (Lewis, 1996):
( ) NiN
ixF i ....,,2,1,1
=+
= Equation 3-2
This quantity can be derived from a rigorous statistical argument and is known in
the statistical literature as the mean rank.
Another statistical argument that may be used to obtain a slightly different
approximation for F(x) is the median rank. The form of the median rank is:
( ) NiNixF i ....,,2,1,
4.03.0
=+−
= Equation 3-3
In practice, the randomness and limited amounts of data introduce more uncertainty
than the particular form that is used to estimate F (Lewis, 1996). For large number
of data, both expressions yield identical results for F(x) after the first few samples.
In this research, Equation 3-2 was used when ranking statistics was employed.
Using the ranked statistics, reduced variate can be obtained from a table of standard
normal distribution in Microsoft Excel by using the NORMINV function. The
reduced variate can be plotted against the data to derive the quantile plot. A straight
line is then constructed through the data and the distribution parameters are
determined in terms of the slope and intercept.
The goal of the quantile plot is to determine the values of parameters for a function
that best fits the database. Quantile plots visually portray the quantiles, or
percentiles, of the distribution of simple data. As sample sizes increase, the quantile
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plot would more closely mimic the underlying population cumulative distribution
function.
The probability plot is one variation of the quantile plot, which is commonly used to
determine how well the data fit a theoretical distribution, such as Normal,
Lognormal or Gumbel. By expressing the theoretical distribution as a straight line,
departures from the distribution are more easily identified. Probability plots are
therefore plots of the quantiles of sample data versus the quantiles of the
standardized theoretical distribution.
3.5. APPLICATION OF RELIABILITY- BASED METHOD
Within the offshore industry, the term reliability has different interpretations. For
example, reliability of components (number of failures per year) is catalogued in
several databases such as Oreda offshore reliability handbook (1995). Structural
reliability analysis (SRA) is related to but distinct from the catalogued database in
that it applies a quantified probabilistic framework to evaluate the “probability of
failure” of a structure component or a system due to functional or other
superimposed loads. The term “failure” in this thesis implies system collapse
mechanism as opposed to local yielding or component failure.
3.5.1. CALCULATION OF PROBABILITY OF FAILURE
In structural reliability analysis (SRA), the structural resistance R is compared with
the applied load Q to provide a measure of the safety of the structure. For level 2
reliability, two measures of the distribution can be utilized. The first is a measure of
central tendency (mean or μ) and the second is a measure of variability (coefficient
of variation or COV).
The probability of failure is governed by the form of the probability distributions
and their separation as shown in Figure 3-3. The area of overlap between the two
curves provides a qualitative measure of the probability of failure and depends on
three factors:
• The relative position of the two curves: as the distance between the two curves
increases, the probability of failure decreases. The positions of the two curves
may be represented by the means (μQ or μR) of the two variables,
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• The dispersion of the two curves: if the two curves are narrow, the overlap and
the probability of failure are small. The standard deviation (σR or σQ)
characterizes the dispersion of the two variables and
• The probability density functions (fR(r), fQ(q)) represent the shapes of the curves
for the resistance and load, respectively.
The equations for these distributions are then combined mathematically to derive
another equation which describes the distribution of possible outcomes.
This approach requires a description of the distributions as equations followed by a
method to combine distributions analytically as described below. However, the
information about the probability density function is usually difficult to obtain and
common practice is to formulate an acceptable design methodology using only the
information on the means and standard deviations as described in Section 3.4.2.
When the global load (Q) and ultimate structure resistance (R) are normally
distributed, it can be shown that a new variable, Z, can be defined, which is also
normally distributed and has the following description:
QRZ −= Equation 3-4
QRZ μμμ −= Equation 3-5
22QRZ σσσ −= Equation 3-6
Z
Z
σμ
β = Equation 3-7
The probability of failure may then be obtained from the β value using the following
relationship:
( )β−Φ=fp Equation 3-8
Where β = Safety index
Ф( ) = Standardized normal cumulative distribution function
When both global load (Q) and ultimate structure resistance (R) are lognormally
distributed, the expression for the safety index β is based on first-order second-
moment reliability methods (Thoft-Christensen and Baker, 1982) and is given by:
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( )( )[ ]222
2
22
11ln
11
ln
QLQDR
R
QLQD
mean
mean
COVCOVCOV
COVCOVCOV
QR
+++
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
+
++
=β Equation 3-9
In using this equation, variables which are not lognormally distributed are
transformed into equivalent lognormal variables with the same values of density
function and cumulative distribution function at the design point. The
transformation can be carried out as follows (Thoft-Christensen and Baker, 1982):
2
21ln ξμκ −= Equation 3-10
⎟⎟⎠
⎞⎜⎜⎝
⎛+= 2
22 1ln
μσξ Equation 3-11
where μ = Mean of the normal variables
σ = Standard deviation of the normal variables
κ = Mean of the logarithms of the variables
ζ = Standard deviation of the logarithms of the variables
The reliability index can be related to the traditional working stress design (WSD)
with factors of safety, FS, through the following equation (McVay et al., 2000):
( )( )[ ]222
2
22
11ln
11
1ln
QLQDR
R
QLQD
QLQD
R
COVCOVCOV
COVCOVCOV
QLQD
QLQDFS
+++
⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜
⎝
⎛
+
++
⎟⎟⎠
⎞⎜⎜⎝
⎛+
⎟⎟⎠
⎞⎜⎜⎝
⎛+
=
λλ
λ
β Equation 3-12
n
mR R
R=λ Equation 3-13
where Rmean = Mean of the resistance
Qmean = Mean of the load
COVR = Coefficient of variation of the resistance
COVQD, COVQL = Coefficient of variation of dead and live load effects, respectively
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where λR = Bias factor for the resistance
Rm = Measured value of resistance = back-calculated capacity
Rn = Predicted capacity by API RP2A method
λQD = Bias factor for the dead load
λQL = Bias factor for the live load
FS = Factor of Safety
QD, QL = Nominal value of dead and live loads
λQD, λQL = Bias factor for the dead and live loads
COVR = Coefficient of variation for the resistance
COVQD = Coefficient of variation for the dead load
COVQL = Coefficient of variation for the live load
3.5.2. BAYESIAN UPDATE
One of the main difficulties, if not the main difficulty, with the use of failure
probabilities evaluated using the reliability-based method is the interpretation of the
result. Philosophers have struggled for decades over the question of what
probability exactly means, and this resulted in two philosophical schools in modern
theory. The first is based on a Frequentist Interpretation and the other is based on
a Bayesian Interpretation or degree of belief. These are also known as the
objective and the subjective interpretation, respectively.
Both approaches acknowledge that our limitations in sampling and measurement,
along with variability, result in uncertainty. Consequently, both methods approach
the problem of making inferences about unobservable quantities in different ways
both philosophically and mathematically. They converge to the same answer as the
amount of data increases (Dakins and Goodrum, 2004).
In the frequentist philosophy, a probability is an objective property of some event.
A structure with an annual failure probability of 0.01 cannot ‘fail’ by 1%; a structure
either fails or it does not fail. A frequentist interpretation implies that for 1000
nominally identical, but uncorrelated structures, on average 10 will fail in any year.
The frequentist interpretation of probabilities can be further subdivided into:
• Priori probabilities, e.g. games of chance - poker, roulette, baccarat, etc., where
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the odds of an outcome can be derived exactly from a knowledge of the system;
and
• Empirical probabilities, where probabilities, or rather statistics, are obtained
from past data. Complete knowledge of the system or sample space rarely
exists, and such statistics must often be determined from sample data.
The Bayesian or degree of belief interpretation was named after Thomas Bayes
(1702 -1761), an English clergyman and mathematician. Bayesian interpretation is
applied to decision making and inferential statistics that deal with probability
inference using the knowledge of prior events to predict future events. Bayes first
proposed his theorem in his 1763 work, which was published two years after his
death, An Essay towards Solving a Problem in the Doctrine of Chances. Bayes'
theorem provides a mathematical method that could be used to calculate, given
occurrences in prior trials, the likelihood of a target occurrence in future trials. Any
evidence from the universe is considered as conditional to the priors. According to
Bayesian logic, the only way to quantify a situation with an uncertain outcome is
through determining its probability.
Bayesian interpretation of probability is epistemic – a degree of belief, as against the
frequentist tradition where it is a limiting ratio of a repeatable phenomena (Howie,
2002). In situations where it is difficult or impossible to obtain repeatable events
like undertaking offshore pile loading test, the probability formed will be a belief
about something that one is uncertain about (von Plato, 1994). This ‘degree-of-
belief’ interpretation is why the calculated probabilities are referred to as notional.
This belief in a Bayesian sense is based on prior information and conditional
evidence or statistically calculated posteriors that are reciprocal to priors and
conditional evidence (Zellner, 1987). In other words, prior information based
hypothesis and conditional evidence can through Bayes’ rule update the prior to
posterior hypothesis such that the posterior can be a new prior, which with further
conditional evidence will lead to subsequent updating (Lange, 1999).
The knowledge about an existing unique event may be more or less uncertain. It
may range from the purely subjective (i.e. professional judgment with no
qualification) to a classical case that reflects the degree to which available
information supports a given assumption. Such uncertainty may conveniently be
modeled in probabilistic terms. This type of model does not describe properties of
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the event, but properties of the knowledge about the event.
The Bayesian interpretation has proved to be the most fruitful approach for
structural reliability analysis as it is possible to introduce model and statistical
uncertainties into the analysis. With the Bayesian interpretation, the evaluated
safety measure, or reliability, changes with the amount and quality of the
information on which it is based. Thus, rather than being a scientific approach
aiming at a description of the “truth of nature”, structural reliability theory is
considered to be a comparative tool; one of its main uses is in decision analysis.
The Bayesian updating procedure combines a “prior” distribution of the bias factor.
This research adopted historical records to represent the “prior” distribution and the
results of this research to represent the “likelihood” distribution. By combining the
two using standard Bayesian methods, the updated or “posterior” distribution of the
bias factor is obtained, which can then be used to calibrate the resistance factors.
The Bayes’ rule states:
( ) ( ) ( )( ) ( )∑ =
=
= ni
i ii
jjj
APABP
APABPBAP
1
Equation 3-14
where ( )BAP j = Posterior distribution on A
( )jABP
= Likelihood function of the data
( )jAP = Prior distribution on A, where A can take on a finite number of
values (the summation is over the possible values of A)
Bayes’ rule can be solved mathematically if the sets of probability distributions can
be used in combination with each other, which are referred to as conjugate
distributions.
The “posterior” distribution of the bias factor was obtained in this research using the
Bayesian Theorem of probability theory. Bayesian updating yields the mean and
variance of the updated (posterior) distribution according to the following formula
(Ang and Tang, 1975):
22
22
lp
pllpu σσ
σμσμμ
+
+= Equation 3-15
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22
222
lp
lpu σσ
σσσ
+= Equation 3-16
where μ = Mean
σ = Standard deviation
subscript p, l & u = Stands for prior, likelihood and updated (posterior) estimate
3.6. PREDICTION OF AXIAL PILE CAPACITY
API RP2A-LRFD (1993) adopts an empirical approach to predict pile capacity
driven in “normal” soils. In the “empirical” approach, simplified models, coupled
with experience or judgment factors to the prediction of pile behavior, are used.
This research introduced modifications to the limiting engineering parameters
within the API RP2A-LRFD (1993) approach in order to account for the effect
of carbonate soils.
Numerous procedures have been suggested for the empirical approach which can
generally be divided into static and dynamic methods. Briaud and Tucker (1988)
presented several static methods for predicting the ultimate load of a pile, including
Coyle (Coyle & Costello, 1981), Alpha (Tomlinson, 1971), Beta (Burland, 1973),
Lambda (Vijayvergiya and Focht, 1972), Meyerhof (Meyerhof, 1976), Mississippi
State Highway Department (MSHD, 1972) and Schmermann (Schmermann, 1978)
methods.
Another approach that can be used to predict axial capacity of piles is termed
“engineering mechanics”. In this approach, the designer attempts to develop an
accurate model for the behavior of the whole system. Appropriate data are fed into
the solution algorithm and pile-soil response is predicted.
In addition to the analytical approaches, the capacity of piles can also be established
directly using field measurements employing a pile driving analyzer (PDA).
The empirical approach must be pursued vigorously because it deals with the
immediate need to find simple methods of analysis suitable for use in the immediate
term. It may uncover field effects that need to be taken into account in the
engineering mechanics approach. Appendix H presents details of the various
methods used to predict axial pile capacity of driven piles.
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3.7. CALIBRATION OF PILE RESISTANCE FACTORS
The process of replacing the single factor of safety in the API RP2A-WSD method
with load and resistance factors in the API RP2A-LRFD approach is commonly
known as “code calibration” and includes the choice of a desired level of reliability
or “target reliability”.
Ellingwood and Galambos (1980) were probably the first to introduce rationally
calibrated load factors for buildings using First Order Reliability Method (FORM)
and available statistical data. Reliability-based code calibration has been formulated
by several researchers, such as Ravindra and Galambos (1978), Ellingwood et al.
(1980) and Rosenblueth and Esteva (1972).
Calibration of resistance factors may be performed using one or more of the
calibration methods:
• Calibration by professional judgment, which is a subjective method as it is based
on professional and personal judgment,
• Calibration by fitting with a working stress design (WSD), an example of which
is the use of the existing WSD method to calibrate LRFD factors. This method
results in resistance and load factors that provide the same results for LRFD as
for WSD. With this method, there is no advantage in using LRFD over WSD.
Situations and conditions that fall outside the database inherent in the WSD
method cannot be handled, and
• Reliability-based calibration, which uses the statistics of a given database to
calibrate the resistance factor. For this method, a set of target reliability indices
can be considered in the calibration of the resistance factors. This approach
produces factors that best represent the resistance of a structure and was used in
the calibration of API RP2A-LRFD (1993) as well as in this research.
Calibration of the load and resistance factors is based on the use of reliability index
β method, in which uncertainties are described by means and coefficients of
variation (standard deviation divided by the mean). The reliability index is a
measure of reliability and is related to the number of standard deviations that makes
the mean safety margin fall in the safe region as described in Figure 3-3.
A piled foundation was treated in this research as a structural element in the
calibration process in a lumped resistance model, which is a similar approach to that
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used in the calibration of API RP2A-LRFD (1993).
As mentioned earlier, the API code committee utilized reliability-based methods to
calibrate API RP2A-LRFD (1993) and applied a lumped parameter to represent the
resistance. The use of a lumped parameter approach for the resistance side of the
LRFD equation arises, in part, from tradition. Historically, LRFD format was
introduced by committees that were concerned primarily with structural loading
(e.g., Allen, 1975; Ellingwood et al., 1980). In the calibration of loads, the rationale
for applying more than one load factor is that the uncertainties involved in
estimating dead and live loads are significantly different. The same situation applies
to foundation capacity where the uncertainties underlying different components such
as side resistance and tip bearing may be significantly different.
Therefore, separation of the various uncertainties and assignment of various factors
would be statistically more appealing. However, until sufficient pile loading tests
that show a breakdown of the side and tip bearing capacities are available, the only
method to calibrate resistance factors is to use one factor.
In this model, all uncertainties associated with the resistance are lumped into one
parameter. Those uncertainties include capacity prediction method, effect of
uncertainty in soil parameters, loading rate and post-installation consolidation
effects. Moses and Larrabee (1988) adopted this approach and so did ASCE Manual
and Report 74 (Task Committee on Structural Loading, 1991) with the aim of
preserving a common calibration scheme between the structure and foundation.
Hence, the resistance side was left as a generic lumped parameter that could be
easily tailored to suit the diverse strength formulae for different materials.
For a given target reliability index βT and considering only dead plus live loads,
Barker et al. (1991) and Withiam et al. (1997) established that the resistance factor,
φ, can be calculated as follows using the statistical parameters for loads and
resistance and assuming lognormal distributions:
( )( )[ ]{ }222
2
22
11lnexp
11
QLQDRTQLQD
R
QLQDQLQDR
COVCOVCOVQLQD
COVCOVCOV
QLQD
+++⎟⎟⎠
⎞⎜⎜⎝
⎛+
+
++⎟⎟⎠
⎞⎜⎜⎝
⎛+
=βλλ
λλλφ Equation 3-17
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Where: βT = Target reliability level
λR, λQD, λQL = Bias factor for the resistance, dead and live loads
γQD, γQL = Load factors for the dead and live loads
COVQD,COVQL = Coefficient of variation of dead and live loads
QD, QL = Dead and live loads
It can be seen that the resistance factor is a function of load statistics, load factors,
resistance statistics, ratio between dead to live load and the target reliability index.
The above calibration procedure includes certain implicit assumptions regarding its
objectivity and variance with time. First, the calibration procedure is not a wholly
objective exercise. For example, the required degree of uniformity in the reliability
level is subjective as shown in Figure 3-4. The size of the calibration domains will
have to be reduced if minor deviations from the target reliability level cannot be
tolerated. In the extreme, there would be so many different domains and sets of
resistance factors that the analysis becomes impractical, although the ideal condition
of uniform reliability is achieved. Judgment is required in this regard to ensure that
the resulting format does not become overly cumbersome to use.
Another subjective aspect of the calibration procedure is the use of constraints to
ensure that the resistance factors emerging from the optimization process are
physically meaningful and not too different from those currently being used in
foundation design.
Thirdly, the time element of the loading is modeled (in part) using random variables
with extreme value distributions. In reality, the resistance of the structure is being
continuously degraded through corrosion, fatigue, wear and tear, abrasion or
erosion, denting and accidental damage. This continuing reduction in structural
resistance as the structure ages leads to an increase in the probability that the
resistance of the structure will be exceeded at some point and that the structure will
fail. However, assessment of time-variant reliability is more involved than time
invariant analysis. The problem can be analyzed using stochastic process theory, in
which case it is termed a time-variant reliability analysis. An alternative approach is
to use smaller intervals for the exposure period, such that the resistance can be
assumed constant over the interval, and the time effect on reliability can be
accounted for. Generally, in the case of corrosion for instance, it is necessary to
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assume some function for corrosion rate with time - typically this is obtained from
field measurements of typical installations, experience or theory, and is uncertain.
For each interval the resistance can be assessed, and the reliability for a short
reference period, typically an annual period can be evaluated. That is, calculations
are repeated at increments of time with a reducing resistance.
In this research, the calibration assumed time-invariant resistance which is
consistent with assumptions made in the calibration of API RP2A-LRFD (1993).
3.8. DERIVATION OF OPEN AREA LIVE LOADS (OALL)
This research examined the approach used in ASCE Standard 7-05 to derive EUDL
for building structures and concluded that the first passage approach, described in
Section 2.8.3, is unsuitable for deriving OALL on offshore platforms and that an
alternative model was required.
According to Melchers (1999), perfect models are not possible owing to insufficient
data, imperfect understanding and the necessity to predict future loading. Melchers
(1999) considered that efforts spent on data collection of the loads and on load
modeling might be more productive than refinement of the reliability estimation
techniques. Further, because of the large degree of variability in the loads, Corotis
(1972) considered that highly sophisticated analysis was not warranted and utilized
a classical approach to show the influence of load location on the supporting pile.
Hence, it was sufficient to employ a classical approach to reflect the influence of
random loading on the supporting piles. Application of the classical approach
employed the influence surface concept (Corotis, 1972), and extreme value theory
(Ang and Tang, 1984) to develop the extreme axial load on the pile during its
lifetime.
3.8.1. INFLUENCE SURFACE CONCEPT
The influence surface concept is used to indicate the effect of a unit load, randomly
located anywhere on the deck, on the axial load of a pile (McGuire and Cornell,
1974, Corotis and Doshi, 1977). The influence area of the axial load on the pile is
four times the conventional tributary area. Influence surfaces for other
configurations (such as beams or the influence surface associated with the several
columns) are covered by Pucher (1977). This research is only concerned with
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open area live loads on piles due to the uncertainty associated with axial
capacity of piles in carbonate soils.
Corotis (1972), McGuire and Cornell (1974), Corotis and Doshi (1977) and Chalk
and Corotis (1980) described application of the influence surface concept in
deriving loads on columns. Consequently, the influence surface concept was
utilized in this research to model the axial pile load effect.
The influence surface approach was derived from the Navier solution for plate
bending to determine the effect of substituting concentrated loads with equivalent
uniform loads. For a column and beam configuration, the influence surface of the
load on the pile axial loading is shown in Figure 3-5 (McGuire and Cornell, 1974,
Corotis and Doshi, 1977, Chalk and Corotis, 1980).
The load position with respect to the column influences the extent of loading
supported by the pile. Intuitively, a closer load to the pile has more significant
effect on that pile, and vice versa.
Corotis (1972) provided the following equation to describe the influence surface:
( ) ( )( )3232 2323, yyxxyxC −−= Equation 3-18
Where C(x,y) = Influence coefficient
x and y = Normalized spatial variables ranging from zero to one.
The normalized spatial variables of x and y can be obtained by dividing the
coordinates of each sector by the sector length or width. A sector area is assumed to
represent a random load. The locations of the force in terms of the normalized
coordinate pairs and the corresponding influence coefficients were computed as
described above.
3.8.2. EXTREME VALUE ANALYSIS
Statistics of the lifetime maximum load effect was of interest, as it represents the
load magnitude specified in design codes. In fact, the live load provisions in many
building codes are found to correspond approximately to the mean of the lifetime
maximum value (Ellingwood and Culver, 1977; McGuire and Cornell, 1974). The
lifetime maximum load effect on a pile was derived in this research using extreme
value analysis (Ang and Tang, 1984).
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Statistically, the largest value of the pile axial load pertains to the maximum values
from a number of events. An event describes a survey of all loads on open areas at
any given time. Conceivably, if several events (sample of size n) were repeated,
other maximum (and minimum) values will be obtained; thus, the possible largest
value comprises populations of their own (Ang and Tang, 1984).
Consequently, the maximum extreme value of the column axial load may also be
modeled as a random value with respective probability distribution. Such
distribution and its associated parameters have special characteristics that are unique
to the extreme value. The asymptotic theory of statistical extremes as presented by
Ang and Tang (1984) was used to develop the required statistics (mean and
coefficient of variation) of the maximum axial load effect on a pile.
3.9. COMPUTER SOFTWARE PROGRAMS
To process the databases collected in this research, it was necessary to utilize a
number of computer software programs. This section provides a brief description of
those programs.
3.9.1. RISK ANALYSIS SOFTWARE - @RISK
Section 3.4 provided a background of the methodologies to derive statistical
parameters. @RISK is a commercially available software program which was used
in this research to calculate statistical parameters of the variables in this research.
@RISK was used to perform nonparametric analysis by fitting distribution to the
data. The software includes a “BestFit” module. BestFit is Windows-based
software, which finds the distribution that best fits the data from a set of 38 different
continuous and discrete distribution functions. The different functions modelled in
@RISK are shown in Figure 3-6.
Automatic goodness of fit testing displays the accuracy of BestFit's answers.
BestFit also calculates the standard distribution type and parameter values that best
fits the data.
@RISK software is comprised of three main components:
• @RISK Model window for listing inputs and outputs, viewing input
distributions, fitting distributions, and defining correlations. @RISK Model also
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allows pop-up graphical definition of distributions for components of cell
formulas,
• @RISK add-in to Excel, which includes distribution functions, statistics
functions, output functions and simulation reports in Excel, and
• @RISK Results window for interactive graphs of simulation results, statistics,
data and sensitivity and scenario reports.
Each of the three components shares a common user interface including an
“Explorer-style” listing of simulation inputs and outputs and customizable toolbars
and icons.
3.9.2. APIPILE - PILE CAPACITY SPREADSHEET
Appendix D describes the API RP2A-LRFD (1993) method to predict axial capacity
of driven piles. Analysis of pile capacity for a large number of load tests is tedious
and there was the danger of occasional errors when hand analyses were performed.
To minimize these problems, a computer software coded APIPILE was developed
by the Author for this research. The program uses Microsoft Excel and adopts API
RP2A-LRFD (1993) empirical formulation. The computer program also performs
analysis for pile resistance to driving. A description of the required data under each
entry for a circular pile is listed in Appendix I.
3.9.3. GRLWEAP-WAVE EQUATION ANALYSIS PROGRAM
After the rationale of the WEA approach was recognized, several researchers
developed a software program using WEA. For example, the Texas Department of
Highways supported research at the Texas Transport Institute (TTI) in an attempt to
reduce concrete pile damage. FHWA sponsored the development of both TTI
program (Hirsch et al., 1976) and the WEAP (Goble and Rausche, 1976) which
stands for wave equation analysis program. FHWA supported the WEAP
development to obtain analysis results backed by measurements taken on
construction piles during installation for a variety of hammer models. The WEAP
program was updated several times under the FHWA sponsorship. Later, and based
upon assumptions of hammer efficiency and soil properties, GRL Inc. (Goble
Rausche Likins and Associates Inc., 1996) developed the software coded
GRLWEAP. GRLWEAP uses the wave equation method and takes into account
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quake and the damping of the soil. GRLWEAP was used in this research to predict
axial pile capacity using blow count information.
3.9.4. STRUCTURAL ANALYSIS COMPUTER SOFTWARE
(SACS)
The computer software SACS, which was developed by Engineering Dynamics, Inc.
(EDI, 1996), was used to undertake a series of linear elastic analyses in addition to
the non-linear pushover analyses using its module COLLAPSE. The complete
description of the software utilities and capabilities is included in the software
Manual (EDI, 1996). The main compatible SACS files to run the analysis were:
• Model input file which contains the general information of the computer model
viz., the geometry, member sizes, materials, loads and load combinations and
analysis options.
• Pile Soil Interaction or PSI input file which was used to model the soil in the
form of P-Y, T-Z & Q-Z curves. Non-linear springs were modeled to support
the pile and the surrounding soil. The PSI input file was obtained from the soil
report.
The following SACS program modules were used in this research:
• SEASTATE module was used to generate the dead weight and buoyancy of the
modeled members and to compute the environmental loadings (wave, current
and wind) on the structure. The SEASTATE runs combined basic load cases to
form various load combinations required in the analysis.
• SACS IV which refers to three of the program modules of the SACS system,
namely the pre-processor, the solver and the post-processor modules perform the
general purpose static structural analysis.
• COLLAPSE was used to perform pushover analysis of offshore type platform
structures.
COLLAPSE employs basic energy variation principles. Forms of these methods
have been used as tools for the analysis of engineering structures of forces and
displacements for more than a century. For an in-depth description of the
COLLAPSE module formulation, reference is made to the theory Manual of the
program (EDI, 1996). This section provides a qualitative description of three
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aspects of particular interest for validation and application in this research, namely
the elastic element, the plasticity model and the failure criteria.
The elastic element in this program follows an updated Lagrange (incremental-
iterative) procedure and uses a nonlinear Green strain formulation with the von
Karman approximation. Thus, the COLLAPSE beam element is valid for large
displacements, but restricted to moderate strains. The stiffness formulation of
COLLAPSE is derived from potential energy consideration. Total and incremental
equilibrium equations are established by taking the first and second variation of the
internal strain energy and the potential of the external work of the elastic beam-
column. The influence of axial force on the bending stiffness of the element is
introduced by the nonlinear terms in the Green strain formulation. The tangent and
secant stiffness matrices are then obtained by introducing interpolation functions for
the element displacements. The shape function in COLLAPSE is taken as the exact
solution of the 4th order differential equation for a beam-column. With these shape
functions, all integration in the element stiffness expressions is carried out
analytically, giving closed-form solutions of the nonlinear elastic stiffness matrix.
The plasticity model is represented by concentrated yield hinges, which reflect the
nonlinear material behavior. Hinges may be introduced at element ends and/ or
element midspan. The plasticity model is formulated in stress resultant (‘force’)
space based on the bounding surface concept. Two interaction surfaces are used,
one yield surface representing first fiber yield and one bounding surface
representing the full plastic capacity of the cross section. When the cross section is
loaded, the force point travels through the elastic region until it reaches the yield
surface. At this stage a yield hinge is introduced. When further loading takes place,
the yield surface travels with the force point, such that the force point stays on the
yield surface. This approach allows for an explicit formulation of the beam-element
stiffness matrix, including geometrical nonlinearities and nonlinear plastic behavior
with material hardening and gradual plastification of the cross section. The plastic
behavior of the member is thus defined by the (nonlinear) elastic stiffness, the strain
hardening and a parameter describing the elastic-plastic transition for each cross
sectional force component (axial force, bending moment etc.). COLLAPSE uses
self-equilibrating plastic forces to correct for the elastic stress distribution for each
sub area in addition to employing a kinematic strain hardening formulation with a
moving plastic surface.
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Failure is predicted in SACS COLLAPSE module in accordance with recognized
failure criteria and design formulation. The formation of a yield hinge is not the
limit of the load-carrying capacity and structural failure is indicated once a sufficient
number of plastic hinges has formed to make a kinematic mechanism. The peak
force in the member P-δ behavior defines column buckling. The non-linear
formulations automatically calculate the total (1st + 2nd order) bending moments and
also include the effects of cross-section geometry, boundary conditions and loading.
3.9.5. PILE DRIVING ANALYZER (PDA)
PDA is a computerized system that applies Case Method (Goble et al., 1975)
equations on measured pile dynamic data in order to determine, among other
quantities, the ultimate bearing capacity of a pile.
The dynamic testing system consists of a minimum of two strain transducers and
two accelerometers bolted to diametrically opposite sides of the pile to monitor
strain and acceleration and account for non-uniform hammer impacts and pile
bending. The reusable strain transducers and accelerometers are generally attached
two to three diameters below the pile head. The data acquisition system, such as the
Pile Driving Analyzer (PDA), conditions and converts the strain and acceleration
signals to force and velocity records versus time.
The most useful and convenient quantities for measurement are force and
acceleration at the pile top. As the transducer is deformed by the passing stress
wave, signals proportional to the strain magnitude are generated. Acceleration
measurements can be made using any of a number of commercially available
accelerometers modified to be attached to the pile. The results of the measurement
activity are matching records of force and velocity along the pile in the ground. The
force is computed from the measured strain (ε) times the product of the pile elastic
modulus (E) and cross sectional area (A). The velocity is obtained by integrating the
measured acceleration record (a).
Prediction of the capacity of piles from PDA is carried out in several steps. First,
reasonable estimates of the soil resistance distribution and quake and damping
parameters are made. Then, the measured acceleration is used to set the pile model
in motion. The program then computes the equilibrium pile head force which can
be compared to the PDA determined force. Initially, the computed and measured
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pile head forces will not agree with each other. Adjustments are made to the soil
model assumptions and the calculation process repeated.
The case shown in Figure 3-7 is a typical illustration of a free fall situation, where
the pile “runs” under the hammer blow. The free fall is characterized by a small
force and large velocity during pile installation.
3.9.6. CASE PILE WAVE ANALYSIS PROGRAM (CAPWAP)
The PDA data may be further evaluated by the rigorous numerical analysis program
CAPWAP to determine static bearing capacity and to distinguish between the toe
resistance and the distribution of the skin resistance along the pile (Hannigan, 1990).
In the CAPWAP model depicted in Figure 3-8, the pile is modeled by a series of
continuous pile segments and the soil resistance modeled by elasto-plastic springs
(static resistance) and dashpots (dynamic resistance).
The force and acceleration data from the Pile Driving Analyzer (PDA) are used to
quantify pile force and pile motion, which are two of the three unknowns. The
remaining unknown is the boundary condition which is defined by the soil model.
It is not possible to determine the soil response from the measured force and
velocity records. However, it is possible to analyze a pile under the action of either
the force or the velocity record with an assumed soil model. The other unused
record is then plotted and compared against an equivalent computed plot.
Differences between the measured and the computed curves lead an experienced
engineer to conclusions regarding the differences between the actual soil behavior
and the assumed set of soil parameters. The parameters may then be modified to
obtain a better match in a second iteration.
CAPWAP was written to facilitate this type of analysis. Soil reaction forces can be
accurately expressed as a function of pile motion only. It is generally assumed that
the soil reaction consists of an elasto-plastic component, and a linear viscous
component. In this way, the soil model has at each point three unknowns: the
ultimate static resistance, the quake or elastic soil deformation, and a damping
constant. An error minimization procedure is used to assess the differences between
the measured and computed curves, and quantify the sum of these differences with
the so-called Match Quality Number (MQN).
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( )∑
⎭⎬⎫
⎩⎨⎧ −
=i
jmjc
Fff
ABSMQN Equation 3-19
Where fjc = Computed pile top variables at time step j
fjm = Measured pile top variables at time step j
Σ = Summation over a time period
Fi = Top force at the time of the maximum pile top velocity
Reducing the MQN to a minimum value subject to several constraints will result in a
unique solution.
In the CAPWAP matching process, the ability to match the measured and computed
waves at various times is controlled by different factors. Figure 3-9 illustrates the
factors that most influence match quality in a particular zone.
The assumed shaft resistance distribution has the dominant influence on match
quality beginning with the rise of the record at time t, before impact and continuing
for duration of 2L/C thereafter, where L is the pile length in meters and C is the
wave speed in m/s. This is identified as Zone 1 in Figure 3-9.
In zone 2, the toe resistance and toe model (toe damping, toe quake and toe gap) are
most influential in the wave match. Zone 2 begins where zone 1 ends and continues
for duration equal to the rise time (t) plus 3ms. During zone 3, which begins where
zone 1 ends and continues for duration of the rise time t, plus 5ms, the overall
capacity controls the match quality. A good wave match in zone 3 is essential for
accurate capacity assessments. Zone 4 begins at the end of zone 2 and continues for
duration of about 20ms. The unloading behavior of the soil most influences the
match quality in this zone.
With each analysis, the program evaluates the match quality by summing the
absolute values of the relative differences between the measured and computed
waves. The program computes a match quality number for each analysis that is the
sum of the individual match quality numbers for each of these four zones. An
illustration of the CAPWAP iteration process is shown in Figure 3-10.
Through this trial and error iteration adjustment process, the soil model is refined
until maximum agreement can be obtained between the measured and computed pile
head forces. The resulting soil model is then considered to represent the best
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estimate of the static pile capacity, the soil resistance distribution, and the soil quake
and damping characteristics. In this example, the initial computation indicated a
value of 1667kN for the pile capacity, which was refined to derive a final value of
2187kN.
3.10. SCOPE DELIMITATION AND KEY ASSUMPTIONS
This research has similar limitations to those implicit in API RP2A-LRFD
formulation. Moses and Stahl (1998) discussed some of those limitations, including
load effect uncertainties, historical risk and cost of failure.
This research is applicable to reassessment of existing offshore structures located in
the Arabian Gulf. Developments in this research rely on the use of existing platform
data. The results may not be applicable for designing new platforms. Further, the
results of this research may not be applicable to other geographic locations around
the world since data collection only covered offshore platforms in the Arabian Gulf.
This research is limited in scope to changes from original design condition which is
described in Section ‘R’ of the API RP2A-LRFD (1993) as “reassessment
initiators”. Other changes in a platform such as fatigue or accidental damage are not
considered by API RP2A-LRFD (1993) under Section ‘R’ and similar approach is
adopted in this research.
An implicit assumption in the calibration of the resistance factors in this research is
that resistance remains constant for the life of the platform. Deterioration in the
capacity can be treated using physical surveys and updating of the reliability model
while human intervention through maintenance can be used to halt the deterioration.
A specific treatment of human and organizational factors is excluded from this
research. This follows a similar approach used to calibrate API RP2A–LRFD
(1993).
Piles can be installed using various installation methods and techniques, including
driving, jacking, drilling and grouting. In the drilled and grouted technique, the
steel pipe is grouted into pre-drilled oversize hole. Over the last two decades,
alternative construction methods emerged such as drilling the formation and then
pressure-grouting along the drilled lengths through pre-installed valves. However,
the majority of installations carried out in the 1960s, 1970s and 1980s used driven
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piles. This research addressed the driving technique only because it represents the
most common technique used to install existing platforms in the Arabian Gulf.
In API RP2A-LRFD (1993), calibration of resistance factors for piled foundation
revolved around the selection of resistance factors that can be used with the ultimate
strength calculations. Further, those pile resistance factors were calibrated from API
RP2A-WSD without explicit reference to the loads, definition of the
characteristic/nominal soil parameters, the method of transforming soil parameters
to engineering parameters and the bias in the calculation methods (Kulhawy et al.,
2002). Similar approach was adopted in this research, except that the developed
parameters are calibrated to account for the specific geological conditions in the
Arabian Gulf.
3.11. JUSTIFICATION OF THE METHODOLOGY
This section justifies the use of various methodologies adopted in this research.
3.11.1. RELIABILITY-BASED METHOD
Despite the limitations associated with using the reliability-based method in
reassessment as discussed in Section 2.3.3, it was used in the calibration of codes
such as API RP2A-LRFD (1993). Hence, its use was justified in this research.
The use of the reliability-based method in calibration has the benefit of repeatability
and familiarity as long as the standards do not radically alter. When such alterations
are under consideration there is an onus on the standard makers to ensure that the
new product is soundly based. In addition, and despite being empirical, this
approach does possess a major advantage of keeping the new design methodology
compatible with the existing experience base. This approach is also consistent with
the evolutionary nature of codes and standards that require changes to be made
cautiously and deliberately. The simplicity of the method also permits a new format
to be readily fine-tuned to local conditions (Turner et al., 1992).
3.11.2. EXTENT OF THE DATABASE
The target population in this research is existing platforms in the Arabian Gulf.
Figure 3-11 shows one of the platforms used in this research. The database in this
research was undertaken using real data from actual offshore installations in the
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Arabian Gulf. The extent of the database in this research was guided by databases
used to develop other codes such as API RP2A-LRFD (1993).
Calibration of the API RP2A-LRFD (1993) was based on 44 pile loading tests. By
comparison, the database in this research comprised 138 piles. Hence, the extent of
data was considered of appropriate size for the purpose of this research.
Similarly, the total surveyed area in this research was compared to those used to
develop ANSI A58, which is described in Table 2-4, and found to comparable. For
example, ANSI surveyed 20,400m2 to derive live loads for residential buildings and
67,000m2 to derive live loads for hotel guest rooms. By comparison, the surveys
conducted in this research covered approximately 35,000m2 of combined topside
deck area.
3.11.3. BAYESIAN UPDATING
Bayesian updating of the “prior” statistical parameters was employed in this
research. Sundarajan (1995) recognized that updating statistical parameters using
observations would be limited if the analysis deals with component risk while the
observations are actually system risk. In this research, both analytical calculations
and observations deal with component risk. Hence, the use of Bayesian updating
was justified.
3.11.4. WAVE EQUATION ANALYSIS METHOD
For the most part, wave equation analysis has been utilized to research potential
installation problems in the field and to select appropriate hammers. McClelland et
al. (1969) and Holloway et al. (1978) discouraged the use of the Wave Equation
Method to predict axial capacity of piles based on blow count information. A major
criticism of this approach had to do with the uncertainties regarding hammer and
cushion properties at the time of installation, uncertainty about the soil resistance to
pile driving, the viscous damping forces that are mobilized and the difficulty of
predicting factors affecting soil rheology (load deformation behavior) in the vicinity
of a pile, especially under dynamic loading conditions. Attempts to relate known
static resistance to the dynamic resistance phenomenon introduced additional
uncertainties into the analysis and further inaccuracies can enter the problem
through potential misrepresentation of the pile driving system in the mathematical
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model. The shape of the impact stress wave transmitted to the pile is a prime factor
in the pile driving performance. Incorrect estimation of the actual hammer
efficiency in the field or improper representation of the mechanical process can lead
to significant prediction errors, except for low blow counts.
In spite of such criticism, pile-driving data (blow count versus depth) was used by
many researchers to predict the axial capacity of piles. Vijayvergiya et al. (1977)
utilized the wave equation results and blow counts before and after driving to
compute setup and estimate the static capacity in chalk. Agarwal et al. (1978) used
a similar concept to research soil setup effects in carbonate clays. Mothewell and
Husak (1982) utilized the wave equation method of analysis to conclude that the
lower driving resistance recorded in the field reflected the ‘true’ axial capacity.
Further, significant developments and correlation studies of the computer software
program GRLWEAP since the 1970s were able to circumvent some of the
inaccuracies in dynamic resistance idealization by relating a viscous damping force
to the static resistance by either correlating measured blow count with measured pile
capacity or by selecting damping parameters based on experience or from the
literature. Hussein and Rausche (1988) considered that the engineering mechanics
approach using a software coded GRLWEAP provided reliable results due to the
improved models for soil behavior and their ability to describe the process of pile
installation and provide means of estimating the relevant soil properties. The
availability of diagnostic tests in the laboratory and in the field validated the
reliability of the wave equation analysis approach.
Hence, evidence in the literature indicated practical significance of the wave
equation analysis approach which provided credence to its use in this research.
3.11.5. INFLUENCE SURFACE METHOD
To assess the accuracy of the influence surface model and justify its use in this
research, Corotis (1972) computed the theoretical column loading as a random
variable using the influence surface approach and compared this to the theoretically
correct approach, which considers the location of the load to be a random variable.
Corotis (1972) found that the contribution to the reduction in the coefficient of
variation as a result of lumping all loads within a section at the point of average
influence for that section to be small.
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3.12. SUMMARY
Chapter 2 identified key issues to answer the research question and determined that
the development of guidelines for reassessment of existing platforms in the Arabian
Gulf required attending to the following topics:
• Calibration of axial pile capacity driven in carbonate soils,
• Derivation of open area live loads on platforms in the Arabian Gulf, and
• Determination of dominant failure mechanism in the Arabian Gulf.
This Chapter provided methodologies to address every issue.
Calibration of axial pile capacity in this research followed a similar approach to that
used in calibrating API RP2A-LRFD (1993).
The approach used to develop live loads in this research adopted a probabilistic
approach termed the classical method which is more appropriate to offshore
platforms than the first passage approach used in AISC Standard 7-05 to derive live
loads on building structures. The application of the classical method utilizes
influence surface method and extreme value analysis.
Establishment of the dominant failure mechanism in the Arabian Gulf required
calculation of probability of failure under extreme storm as well as operating
overload conditions. The probability of failure calculations relied on the use of
SRA.
Application of the methodologies described above employed a number of computer
software which were identified and described in this Chapter.
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Table 3-1: Salient characteristics of platforms forming the pile database
SN Platform Type # Legs
# Pile
Year Depth m
Dia mm
Penetration m
1 Production Platform 8 8 1965 34 762 43
2 Wellhead Platform 3 3 1966 36 762 61 3 Wellhead Platform 4 4 2000 36 1219 52 4 Riser Platform 6 6 1982 19 1219 52 5 Compressor Platform 8 8 1978 20 1219 87 6 Wellhead Platform 3 3 1978 19 762 66 7 Wellhead Platform 3 3 1979 32 762 61 8 Wellhead Platform 3 3 1979 21 762 85 9 Glycol Generator 4 4 1978 20 762 72 10 Wellhead Platform 3 3 1980 39 762 57 11 Wellhead Platform 4 4 1991 29 914 50 12 Wellhead Platform 3 3 1993 22 762 60 13 Living Quarters 4 4 1978 20 762 80 14 Wellhead Platform 4 4 1995 21 914 66 15 Compressor Platform 8 8 1978 36 914 82 16 Wellhead Platform 4 4 1995 13 914 62 17 Glycol Generator 4 4 1978 36 762 63 18 Wellhead Platform 4 4 1997 30 1219 45 19 Water Disposal 4 4 1998 27 914 64 20 Wellhead Platform 4 4 1998 41 1067 51 21 Living Quarter 4 4 1978 36 762 67 22 Wellhead Platform 4 4 2000 25 1219 72 23 Wellhead Platform 4 4 2000 13 914 74 24 Wellhead Platform 4 4 1981 20 914 56 25 Wellhead Platform 4 4 1981 20 914 56 26 Wellhead Platform 4 4 2004 27 1219 56 27 Wellhead Platform 3 3 1992 35 762 84 28 Wellhead Platform 4 4 1981 36 914 62 29 Wellhead Platform 4 4 1999 36 1219 51 30 Riser Platform 6 6 1983 36 914 83 31 Wellhead Platform 3 3 1978 26 762 59 32 Wellhead Platform 3 3 1980 23 762 52 33 Wellhead Platform 3 3 1981 30 762 66
Number of Piles 138 138
CHAPTER 3: METHODOLOGY 91
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Figure 3-1: Analytical approach used to calibrate pile resistance factors and OALL for the conditions of the Arabian Gulf. The calibration of OALL established the statistical parameters of the database and employed influence surface method and extreme value analysis to define a uniformly distributed load. Calibration of the pile resistance factors utilized a database to calculate bias factors and employed FORM to calibrate resistance factors for axial capacity of driven piles in the Arabian Gulf
CHAPTER 3: METHODOLOGY 92
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Operating Conditions dominate the failure mechanism
Extreme Storm dominate the failure mechanism
NO
YES
Single Pile + Group / System Effect
Extreme StormPushover Analysis
Reliability AnalysisChapter 6
Pf Gravity>>>>
Pf Ext storm
Nonlinear Model DnV UltiGuide
Rreassessment of existing platforms considers
operating conditions only
Calibrate Environmental Loads or consider both
effects
STOP
Areas for Future Research
Mathematical Model
Statistical Parametersfrom Chapters 4 & 5
Calculate Probability of Failure
Reliability Analysis
GravityPushover Analysis
Probability of Failure under Operating Overload Conditions
Probability of Failure under Extreme Storm Conditions
Use similar procedure to that used in the GoM or the
North Sea
Figure 3-2: Flowchart showing the approach adopted in this research to perform reliability analysis on a representative platform from the Arabian Gulf with the objective of defining the dominant failure mechanism in the Arabian Gulf
CHAPTER 3: METHODOLOGY 93
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 3-3: Schematic showing the basis for calculating the probability of failure
CHAPTER 3: METHODOLOGY 94
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Figure 3-4: An example of large scatter in a set of data which is intended for calibration. The chart shows that the calibration of a uniform set of factors requires the data to be sub-grouped. The number of subgroups can be increased without limits
CHAPTER 3: METHODOLOGY 95
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
X X
YY
Pile or Column
Beam
Influence Surface for pile/ column
Figure 3-5: Influence Surface for column axial load. Note that the influence area is 2X * 2Y (McGuire and Cornell, 1974) or four times the tributary area for a column
CHAPTER 3: METHODOLOGY 96
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Figure 3-6: Distribution palette in @RISK enables a choice of distribution type that best fits the data being analyzed
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Figure 3-7: Force and velocity fall measurements versus time for a free end condition. This illustration is typical for a free situation where the pile “runs” under the hammer blow. In the chart, A is the pile cross sectional area, E is the pile elastic modulus, C is the wave speed and F is the force generated at the impact surface of the pile (Hannigan et al., 1997)
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Figure 3-8: Schematic of CAPWAP Analysis Method
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Figure 3-9: Factors that have a dominant effect on the accuracy of CAPWAP prediction (Hannigan et al., 1990)
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Figure 3-10: Schematic of CAPWAP analysis method showing CAPWAP iteration matching process (Hannigan et al., 1990). The trial and error iteration adjustment process results in refinement in the soil model to obtain the best agreement between the measured and computed pile head forces. The resulting soil model can then be considered to represent the best estimate of the static pile capacity. In this example, initial capacity (1667kN) was refined to derive a final value of 2187kN in step 5
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Figure 3-11: One of the platforms used in this research. It functions as living quarters on the upper deck and process facilities on lower decks. The platform includes a helideck
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Chapter 4.
CALIBRATION OF PILE RESISTANCE FACTORS
4.1. INTRODUCTION
The literature includes numerous publications investigating axial pile capacity in
carbonate soils in various parts of the world except in the Arabian Gulf. The results
of those publications cannot be directly applied to the Arabian Gulf conditions due
to the site-specific nature of the carbonate soils which preclude extrapolation of
results between geographic regions.
This Chapter describes a methodology to calibrate axial resistance factors for piles
driven in carbonate soils. The calibrated axial capacity factors may then be used in
reassessment of existing platforms in the Arabian Gulf.
4.2. CALIBRATION MECHANICS OF AXIAL PILE
RESISTANCE FACTORS
Calibration of resistance factors in this research followed a similar approach to that
used in the calibration of API RP2A-LRFD (1993). The calibration employed the
statistics of the bias factors. The bias factor was computed by dividing “actual”
capacity over predicted capacity for each pile. To calibrate resistance factors, the
First Order Reliability Method (FORM) was applied to the statistics of the bias
factors.
The prediction of pile capacities used the empirical formulation recommended in
API RP2A-LRFD (1993), which is described in Appendix E. However, API RP2A-
LRFD (1993) does not specify limiting soil parameters for carbonate soils. Hence, a
set of parameters was required for this research.
In the calibration of API RP2A-LRFD, “actual” pile capacities were derived using
loading tests. Survey of the literature revealed lack of pile loading tests in the
CHAPTER 4: PILE RESISTANCE FACTORS 103
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carbonate soils of the Arabian Gulf. Further, it was impractical to perform loading
tests due to the prohibitive costs. Such tests would have to be carried out offshore to
capture the characteristics of the geological settings described in Section 2.7. Even
if tests were to be performed, the reported pile capacity would not be a unique
number because it depends on the interpretation method. There are various
interpretation methods such as Davisson and Decourt methods. Fellenious, (1980)
reported that the “actual” pile capacity using Davisson Method could be as much as
40% less than “actual” pile capacity using Decourt Method.
To derive “actual” pile capacities in the carbonate soils of the Arabian Gulf, this
research employed an analytical approach of the one-dimensional wave equation
analysis (WEA) to analyze installation records collated in the course of this
research. The analytical procedure considered appropriate parameters and model
effects of soil plug, soil resistance to driving profile and time effects. The analytical
procedure was validated using the results of a field dynamic pile monitoring.
The statistical parameters of the bias factors were then calculated and supplemented
using Bayesian updating method. As discussed in Section 3.5.2, Bayesian update
requires a prior distribution and a likelihood distribution to derive the posterior
distribution. The prior distribution was based on the statistical parameters used to
calibrate API RP2A-LRFD (1993). The likelihood distribution was based on the
bias factor statistics calculated in this research.
Calibration of resistance factors also required determination of target reliability
levels. An investigation of target reliability level used in the calibration of API
RP2A-LRFD (1993) was conducted and a set of target levels was determined to
meet the objectives of this research.
Using the posterior bias factor distribution and the selected target reliability levels,
resistance factors were calibrated using the First Order Reliability Method (FORM)
employing the methodology described in Section 3.7.
4.3. PILE INSTALLATION DATABASE
The calibration of the API RP2A-LRFD (1993) was based on 44 pile loading tests,
with only 20 test piles having penetration greater than 100 ft (30.5m). The database
was compiled by Olson and Dennis (1982) but it excluded carbonate soils.
CHAPTER 4: PILE RESISTANCE FACTORS 104
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Due to lack of pile loading tests in the Arabian Gulf, this research used pile
installation records collected in this research and discussed in Section 3.3. The data
collection efforts enabled a well-documented pile installation case worthy for use in
research work. The installation data for each pile include:
• Pile configuration and splice schedule,
• Pile penetration resistance versus penetration depth, including installation delay
records due to welding add-on and hammer and cushion changes,
• Details of the hammers used in the operations,
• Details of the pile and follower make-up,
• Compilation of Geotechnical and installation data including shear strength
profile, soil reports identifying engineering parameters, and
• Pile dynamic monitoring results of four piles.
Figure 4-1 provides an example of one pile installation record. The pile is
composed of three (3) sections. The lowest section is called “pile shoe”. Many
piles are installed with an internal driving shoe to reduce internal skin friction.
Comparative data of piles in clay with and without a shoe indicate that an internal
shoe can reduce the driving resistance and the extent to which the pile plugs during
driving (Heerema, 1979). Opinions vary regarding the reduction in internal skin
friction caused by a shoe during continuous driving. Some believe that an internal
shoe completely eliminates internal skin friction in stiff clay whereas others assume
reductions of 30 to 50 percent (Toolan and Fox, 1977; Durning and Ernie, 1978;
Heerema, 1979).
Figure 4-1 also represents the driving record for that pile, which is usually
completed by the installation contractor during the installation campaign of any
offshore platform. During driving operation offshore, the installation personnel
record the number of blows for every foot for pile penetration into the soil together
with the time at the start of the driving operation. If there is a requirement to stop
the driving, the number of blow counts is recorded at the end of driving together
with the time at which the driving stopped. At the restart of driving operation, the
time and the initial number of blow counts are also recorded.
An example of the first sheet of a PDR for one pile is shown in Table 4-1, which
includes the date of installation, platform and pile identification, water depth,
hammer type and location of installation in latitude and altitude. Table 4-2 shows
CHAPTER 4: PILE RESISTANCE FACTORS 105
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
an intermediate sheet in the PDR and Table 4-3 shows the last sheet of the PDR.
The columns showing “penetration depth” and “blow count” formed the required
data for this research.
The first sheet of the PDR shows the blow count at each penetration and the hammer
type (3000/150) used at the start of the driving operation and time of starting the
driving operation (10th March 1978) at 5:00pm. The intermediate (pen-ultimate)
sheet of the pile driving record (PDR) provides part of the history during the driving
operation and shows that the operation was stopped when the penetration reached
201 ft at 5:04 am and restarted on the 22nd March 1978 at 11:53 pm. This sheet also
indicates a change in the hammer type (4600/150) from what was used at the start of
the driving operation (3000/150). The last sheet of the PDR indicates that the final
penetration depth was 262 ft (79.9m) and was achieved with 25 blow counts per
foot. This sheet also shows that the target penetration was achieved at 6:12 pm on
the 23rd March 1978, which means that the driving operation for this pile took
around 2 weeks to complete (from 10th March 1978 to 23rd March 1978).
The delay time, type of hammer and cushion, pile make-up and number of hammer
blows per foot of pile penetration – at end of driving (EOD) and beginning of
restrike (BOR) - were noted in the pile driving records.
Appendix B includes a summary of the final blow count for each pile in the
database. The soil layers for each pile are shown in Appendix C.
4.4. PREDICTED AXIAL PILE CAPACITY
The prediction of the capacity of each pile in the 138 pile database was carried out
using the APIPILE spreadsheet described in Section 3.9.2. However, Section 2.7
revealed that API RP2A-LRFD (1993) excludes the use of its limiting soil
parameters to predict the axial capacity of piles in carbonate soils. Hence, there was
a need to define limiting engineering parameters that can be employed in this
research to calculate axial capacity of piles in carbonate soils.
4.4.1. LIMITING ENGINEERING PARAMETERS
For engineering practice, researchers identified various limiting friction and bearing
values for driven piles in carbonate sands (McClelland, 1974, Agarwal et al., 1977,
Datta et al., 1980, Beringen et al., 1985, Denis and Olson, 1983).
CHAPTER 4: PILE RESISTANCE FACTORS 106
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Table 4-4 presents suggested limiting parameters by a number of authors.
McClelland (1974) suggested limiting value of 20kPa for the shaft resistance and
5MPa for the end bearing, but cautioned that generalization of results was highly
speculative. Agarwal (1977) recommended an increase to the values suggested by
McClelland, but only for high percentage of carbonate content, while Datta et al.
(1980) suggested limiting values of 15kPa and 3MPa for friction and end bearing
respectively for piles driven in uncemented carbonate sands. For cemented
carbonate sands, Datta et al. (1980) considered that a more precise statement of
appropriate magnitudes of engineering parameters was not possible, primarily due to
the absence of a factor which can give a quantitative idea of the degree of
cementation.
The limiting unit skin friction values (2.9kPa to 28.7kPa) shown in Table 4-4 are
similar to the results from pile loading tests (1kPa to 33.1kPa) shown in Table 2-3.
This apparent agreement may actually be a causal effect rather than a confirmatory
analysis.
Other Authors proposed specific analysis and design methods but most qualified the
results to pertain to a particular site. Beringen et al. (1982) recommended specific
tests such as CPT, while others (Datta et al., 1980, Dutt and Moore, 1985) focused
attention on particular soil properties such as cementation or compressibility
(Nauroy and LeTirant, 1983, 1985). Datta et al. (1980) proposed that more
emphasis be placed on the particle nature and suggested that the behavior of
carbonates with few intraparticle voids, which are therefore less crushable, approach
the behavior of non-carbonate soils.
Many of the recommendations for current design practice in carbonate cohesionless
soils are based on the carbonate content and on the compressibility of the soil.
Nauroy et al. (1996) demonstrated that limiting values of unit skin friction depend
on the compressibility of the material and the type of pile (open-ended or closed-
ended, unplugged or plugged) and the compressibility index which can be obtained
from the results of an odometer test. The odometer test is ideally performed on
intact undisturbed material which requires good quality samples at the site
investigation stage. Tirant et al. (1983) presented typical ranges of compressibility
indices for siliceous sands and for detrital and bioclastic carbonate sands and
recommended limiting values as a function of the limit compressibility index.
CHAPTER 4: PILE RESISTANCE FACTORS 107
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
In carbonate clays, it is unclear whether the carbonate content has a beneficial or
adverse impact on the properties of the clay. Agarwal et al. (1977) stated that
“carbonate content in clay appears to have beneficial effects on strength properties”
whilst Nauroy and LeTirant (1985) contends that “the skin friction of driven piles in
fine carbonate soils is probably lower than observed in non-carbonate cohesive soils
of the same undrained shear strength”. For calculating the skin friction of driven
piles in cohesive carbonate soils, Nauroy and LeTirant (1985) recommended that the
lowest of five methods (API RP2A α-method; API RP2A method 2; Semple and
Ridgen method; Randolph and Murphy method; λ method) be used for prediction of
axial capacity of piles in carbonate soils. Based on field and model tests on piles in
carbonate soils, Nauroy and LeTirant (1985) also suggested an inverse correlation
between shaft friction and compressibility index.
In 1999, Alba and Audibert reviewed the research and developments that have taken
place over the last thirty years and acknowledged that susceptibility to crushing and
degree of cementation govern pile soil interaction, but conceded that these
parameters are the most difficult to quantify.
In 1999, Kolk compared laboratory data with results from full-scale pile loading
tests and examined current analytical methods for driven piles in carbonate soils. In
the absence of compressibility data, Kolk (1999) defined the limiting skin friction
and limiting end-bearing values by the carbonate content of the material as follows:
( ) ( )4log20
log80,,,lim,
⎟⎠⎞
⎜⎝⎛
×−−=
CC
ffff ssississ Equation 4-1
( ) ( )4log20
log80lim
⎟⎠⎞
⎜⎝⎛
×−−=
CC
qqqq sisi Equation 4-2
Where: q80 = 3MPa is the limit taken for a carbonate content of 80% or higher
qsi = limit taken for a silica sand with a carbonate content of 20% or lower as specified in API (2000)
fs,80 = 15kPa is the limit taken for a carbonate content of 80% or greater
fs,si = Limit for a silica sands with CC <=20%
CC = Carbonate content
CHAPTER 4: PILE RESISTANCE FACTORS 108
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Clearly, there is no consensus regarding the acceptable reduction in those limiting
parameters. Nevertheless, there is an agreement that the following parameters affect
the limiting values for skin friction and bearing:
• Carbonate content,
• Degree of cementation,
• Relative density, and
• Cone penetration resistance.
Given the wide difference in opinions and lack of consensus for the limiting
parameters, Lacasse and Goulois (1989) polled the opinion of experts in
Geotechnical practice by sending a questionnaire that adopted API recommended
practice as a reference and concentrated on medium to very dense sands. Each
expert was asked to estimate limiting values for the engineering parameters for
different soil types of carbonate sand.
Analysis of the responses to the questionnaires is shown in Table 4-5. The experts
pointed to insufficient data on carbonate sands and the urgent need for in situ tests.
However, there was agreement on the appropriateness of considering the product of
two parameters, K.tanδ, as a single variable in addition to being the significant
variable for determination of skin friction in sand.
This research used the mean values of the expert opinions. A review of several
confidential engineering reports in the Arabian Gulf indicated presence of a weak
degree of cementation in the Arabian Gulf soils and these were employed in the
calibration. A summary of the employed parameters is shown in Table 4-6.
4.4.2. INPUT DATA TO APIPILE
Prediction of the static capacity of piled foundations in this research required the use
of several engineering parameters which were derived from soil borehole details and
soil investigation reports. The database collated in this research included a borehole
at or close to each pile. Derivation of the soil parameters such as soil shear strength
from these boreholes is beyond the scope of this research and is extensively covered
in the literature (Bowles, 1988; Tomlinson, 1980). The engineering parameters
were extracted from various Geotechnical reports for each layer and at each soil
borehole for each pile.
CHAPTER 4: PILE RESISTANCE FACTORS 109
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
The extraction of parameters to predict capacity from the soil boring required
considerable effort and judgment due to the extreme soil variability from one layer
to the other. Even within the same layer, the distribution and occurrence of the
predominantly carbonate materials in the Arabian Gulf tend to be laterally and
vertically variable. Occasionally, this lateral variability can occur over relatively
short distances with attendant significance for offshore foundation capacity
prediction. Hence, it was also necessary to account for these variations in selecting
soil parameters by carefully inspecting and plotting all available soil data at a given
site to select the most likely value for each parameter.
Table 4-7 shows input data from one pile in the database. The pile is 91m deep with
nine layers at this particular site. The upper layer mainly comprised of sand mixed
with clay layers. The soil profile in any layer was classified to either sand (S) or
clay (C), and the corresponding nominal engineering parameters were derived from
the soil report associated with the borehole at the location of the platform. The sand
layers extend from the mudline to approximately 32m. The first layer was
essentially clay (down to 62m) which is laid over a sand layer (down to 91m). The
limiting values shown in the spreadsheet printout were obtained from Table 4-6.
4.4.3. OUTPUT FROM APIPILE
Calculation of the pile capacity was performed by APIPILE in accordance with API
RP2A-LRFD (1993) methodology, which is described in Appendix D, but applying
the limiting engineering parameters shown in Table 4-6.
The spreadsheet calculates friction and bearing values along the shaft and at the pile
tip as shown in Table 4-8 for one pile. The output provides the capacity of the pile
at the bottom of each layer. The calculations were stopped at the tipping depth. In
this particular case, the predicted capacity was found to be 17326kN as indicated at
the bottom most right of Table 4-8. The complete set of calculations for all piles is
provided in Appendix D.
4.5. ACTUAL AXIAL PILE CAPACITY
As discussed in Section 4.2, derivation of the bias factors requires evaluation of the
analytical and actual capacity of each pile in the database. Section 4.4 presented the
methodology used to derive the analytical capacity of each pile. This section
CHAPTER 4: PILE RESISTANCE FACTORS 110
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
describes the methodology used to derive the ‘actual’ pile capacity for each pile in
the database.
4.5.1. METHODOLOGY
The derivation of actual capacity of piles was carried out in this research using
Wave Equation Analysis method and with the aid of the software coded GRLWEAP
described in Section 3.9.3.
The input data was obtained from the pile installation records described in Section
4.3. Input parameters to GRLWEAP were employed to construct a mathematical
model of the pile/ soil interaction. The computed capacity from GRLWEAP
represented short term capacity of piles. Setup factors were utilized to compute long
term capacity of each pile.
Validation of the approach used in this research to calculate long term pile capacity
was conducted using field measurement data of pile capacity from actual pile
installation in the Arabian Gulf.
4.5.2. INPUT DATA FOR WAVE EQUATION ANALYSIS
To perform back-analysis of pile capacities, this research employed Bearing Graph
module in GRLWEAP. The “Bearing Graph” module was considered by the
Author to be the most direct approach to back-calculate the pile capacity.
The bearing graph analysis required realistic inputs for the hammer, pile and soil
parameters in addition to the penetration resistance (blows per foot). The input data
for GRLWEAP were obtained from pile drawings and pile driving records described
in Section 4.3.
4.5.2.1. Hammer Parameters
The hammer used to drive each pile can be described by five parameters, namely:
• The rated hammer energy,
• The efficiency of the hammer,
• The weight of the ram,
• The cushion stiffness, and
• The coefficients of restitution for the ram hitting the cushion.
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The rated energy and weight of the ram are hammer-specific. Cushion properties
are based on average values of field data obtained by dynamic measurements (GRL
and Associates, 1995).
Hammer efficiency is more complicated due to the ever increasing number of
hammer systems, makes and models, starting from the simple cat-and-rope driven
drop hammer of an SPT rig to sophisticated, hydraulically powered hammers with
pneumatic accelerators and electronic controls. Naturally, the unknowns vary
widely for these hammers and efficiency values cannot possibly be assigned based
on hammer model evaluations or a few measurements at the time of a hammer
model's introduction into construction practice. This is because field performance of
a hammer will depend on a variety of operational factors such as its state of
maintenance, fuel or power supply. Yet, an estimate of hammer efficiency is
required for the solution using WEA.
In GRLWEAP, the performance of a given pile driving system is evaluated by
comparison of its energy transfer efficiency (ETR) to the statistical results of similar
hammer-pile systems compiled from numerous projects. The ETR results were
obtained from actual dynamic pile measurements (GRL and Associates, 1995). It
includes losses occurring during impact, in particular:
• Cushion compression, which is non-axial;
• Plastic pile top deformation or other energy losses occurring between hammer
and pile, and
• Ram impact energy losses such as a stroke less than maximum, friction, or an
inaccurate timing of motive fluid injection (pre-admission, pre-ignition).
GRLWEAP includes a default efficiency value for each hammer and these were
used in deriving the “actual” pile capacity for every pile in the database.
4.5.2.2. Pile Parameters
Pile parameters consist of a diameter, wall thickness schedule, modulus of elasticity
of the pile material, unit weight of the pile material, freestanding length of pile and
penetration below the seafloor. These parameters are presented in Appendix B for
each pile and were derived from the pile drawings that form part of the database
collated during this research.
Dashpots were used in GRLWEAP to model the difference between static and
CHAPTER 4: PILE RESISTANCE FACTORS 112
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
dynamic behavior in the pile material. Dashpots are inserted between masses and in
parallel with springs to represent the pile cushion, which transfers some of the
dynamic load by absorbing more energy when the pile is suddenly loaded. The pile
cushion is a relatively soft element, usually located underneath the helmet and
immediately above the pile top. In the Arabian Gulf, the pile cushion material is
usually wood, most frequently plywood. The primary function of the pile cushion is
to protect the pile both against high average stress levels and high contact or
bending stresses. Often, pile cushion properties change during driving. For
example, plywood may compress to only ½ of its initial thickness and its elastic
modulus may increase.
In addition, coefficients of restitution (COR) were specified to model energy losses
in cushion material and in all segments that can separate from their neighboring
segments by a certain slack distance. The COR ranges from one for a perfectly
elastic collision (which preserves all energy) to zero for a perfectly plastic condition
(which loses all deformation energy). Partial elastic collisions were modeled with
an intermediate COR value. The soil resistance along the embedded portion of the
pile and at the pile toe was represented by both static and dynamic components. The
static soil resistance forces were modeled by elasto-plastic springs and the dynamic
soil resistance by linear viscous dashpots.
4.5.2.3. Soil Parameters
The soil data include static soil resistance at driving profile, quake input and soil
damping input. The original soil damping model proposed by Smith (1960) was
followed by other models such as non-dimensional “case damping” approach
developed by Gibson and Coyle (1968) and Goble and Rausche (1976). The use of
Smith model was considered most appropriate for this research as it is a well-
established model and has been subjected to verifications by various Authors (GRL
and Associates, 1995).
In the Smith damping model, the dynamic soil resistance is proportional to a
damping factor multiplied by the pile velocity times the assigned static soil
resistance.
The dynamic soil resistance can be described by four basic parameters, namely:
• Shaft quake,
CHAPTER 4: PILE RESISTANCE FACTORS 113
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
• Toe quake,
• Shaft damping and
• Toe damping.
Quake is the elastic rebound of the soil and represents the amount the pile will
rebound once the impact force of the hammer has dissipated after each blow. It is
the displacement at which the soil changes from elastic to plastic behavior.
Damping is the dissipation of energy by the soil that reduces the effective energy for
driving the pile. These parameters are not derived from fundamental soil properties
or from in situ soil testing techniques but could be evaluated from dynamic pile
monitoring. However, dynamic monitoring is rarely available and various estimates
were recommended in the literature as shown in Table 4-9.
Roussel (1979) values shown in Table 4-9 were determined from comprehensive
correlation research performed for large diameter offshore piles in which the driving
records of 58 piles at 15 offshore sites in the Gulf of Mexico were analyzed.
GRL and Associates (1995) indicated that it was rarely necessary to vary quakes
along the shaft, and that a value of 2.5mm could be used for unplugged “coring”
driving. For plugged conditions, GRL and Associates (1995) recommended a value
of D/120 for the quake, where D is the nominal diameter of the pile in mm, and
indicated that input of one average value for shaft damping and one value for toe
damping using the standard Smith damping approach yielded sufficient accuracy.
Survey of the literature revealed that researchers made no distinction between Smith
soil parameters for carbonate and non-carbonate soils. Hence, this research adopted
default Smith parameters in GRLWEAP as shown in Table 4-9, as these were found
to compare well with measurements in Arabian Gulf reported by Tagaya (1979).
4.5.2.4. Soil Resistance to Driving (SRD)
SRD is one of the more sensitive inputs to wave equation analysis. The effect of
installing the pile causes each layer to be extensively remoulded, intermingled with
adjacent layers and either compacting or loosening depending on the method of
installation and soil type.
In general, SRD can be achieved by reducing skin friction capacity in clay by some
factor to account for remoulding during driving. However, nearly the full static
capacity for skin friction in granular soils is used. The static end bearing for both
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clay and granular soils is generally utilized.
Toolan and Fox (1977) described one method to calculate SRD, in which unit skin
friction at time of driving is equated to a remoulded shear strength value in cohesive
deposits and to the static friction capacity in cohesionless deposits. The remoulded
shear strength value can represent a combination of effects including an adhesion
factor between the soil and the pile. It can also incorporate reduction in the unit
friction using driving compared with the long term static value that can be obtained
after pore pressures have dissipated and setup has occurred. The unit friction during
driving can be equated to the undisturbed undrained shear strength divided by the
clay sensitivity for use in pile driveability analysis. For a range of carbonate soils
from offshore India, Agarwal et al. (1977) quoted average sensitivities of between 4
and 5. Other research reports covered the calculation of SRD (Semple and
Gemeinhardt, 1981; Stevens et al., 1982; Tagaya et al., 1979). The method of
Semple and Gemeinhardt (1981) was based on case studies evaluating pile
driveability in clay, relating clay unit friction to the clay stress history in terms of
over-consolidation ratio (OCR).
Stevens et al. (1982) developed a procedure for cohesionless soils, which is based
on case studies evaluating pile driveability for hard clay, very dense sand and rock.
According to this method, granular material was treated as silt, sandy-silt, silty-sand
or sand and particular soil parameters are used. Further, the friction angle for
carbonate material is reduced by 5 degrees to take account of potentially lower soil
resistance due to particle crushing.
Tagaya et al. (1979) estimated that the pile capacity immediately after pile driving is
½ to ¼ of the ultimate pile capacity calculated by API RP2A and presented data
showing that the longer the remoulding time, the less the unconfined compressive
strength becomes. Tagaya (1979) showed that the strength of the disturbed soil is ½
to 1/3 that of the undisturbed soil. This method showed good agreement with
instrumented actual pile driving installation in the Arabian Gulf when the measured
and calculated driving force and pile stresses are compared as shown in Figure 4-2.
The method proposed by Tagaya et al. (1979) was considered most relevant to the
research and was adopted to derive SRD for every pile. The Tagaya et al. (1979)
method was programmed in APIPILE spreadsheet to compute SRD and the
percentage of shaft resistance to the overall resistance.
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4.5.3. SHORT TERM AXIAL PILE CAPACITY
The input parameters described in Section 4.5.2 were used to derive short term axial
capacity of one pile in the database. The procedure was repeated for every one of
the 138 piles and the results are documented in Appendix E. The pile driving record
data for this demonstration pile was used to derive the pile capacity using the
computer software GRLWEAP.
A 48 inch (1219mm) diameter pile was selected to demonstrate the methodology.
The pile soil interaction model shown in Figure 4-3 represents GRLWEAP input
screen. The pile was driven to 79.9m into the soil using MENCK MRBS 4600
hammer and projected around 25m above mudline.
A pile wall thickness of 31.8mm was used for the full length except that 50mm was
used for the pile shoe. The percentage of shaft resistance shown in Figure 4-3
represents a fixed percentage of the skin friction to total pile resistance and the
remainder to end bearing. In this research, the percentage of shaft resistance and the
SRD were developed as part of APIPILE spreadsheet. Analysis results of the
complete database are shown in Appendix D. Generally, the percentage was found
to be in the range 93% to 97%.
The influence of pile damping is small in steel and was ignored in the computations.
The soil profile was segmented into one meter segments and the required soil
parameters were provided for every soil segment using data from soil reports
collated in this research. A hammer damping input of 2 was used in this research.
According to GRL and Associates (1995), the use of zero hammer damping
indicates some high frequency vibrations which had not been observed in
measurements while a value exceeding two results in an over-dampened response.
The GRLWEAP default efficiency factor for the Menck hammer of 0.67 was used in
this analysis.
The pile in question was driven in the specified soil stratum using Menck 3000/150
hammer as shown in Figure 4-4. As the soil resistance increased, it was necessary
to switch to a larger hammer, and the driving continued using Menck 4600/150
hammer to the tipping depth of 79.9m. At penetration depth, the PDR shows 33
blow counts per foot as depicted graphically in Figure 4-4.
Using the pile, soil and hammer parameters, the results of the bearing Graph (BG)
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analysis are shown in Figure 4-5 which describes the relationship between blow
count and ultimate capacity
The BG was entered with the blow count at penetration depth (33 blows per foot)
and the short term capacity from the Bearing Graph was read and found to be
around 14000kN.
4.5.4. TIME EFFECT - COMPUTING SETUP FACTORS
Full understanding of the basis of the API RP2A approach as well as the output
from WEA is essential for computing the bias factors and calibrating deterministic
parameters for reassessment of piles. In this research, pile capacities were predicted
using API RP2A formulation, which computes “long term” static pile capacity since
it utilizes soil parameters that represent natural conditions unaffected by the pile
driving process. On the other hand, conventional wave equation applications
generally assume that the resistance to pile penetration during driving represents the
static resistance during (or immediately after) driving plus a superimposed viscous
resistance component. For consistency, it was necessary to obtain the long term
actual capacity so as to be compatible with the capacity derived using API RP2A-
LRFD (1993) method. This was carried out by multiplying the short term capacity
derived using GRLWEAP by a setup factor.
The setup factor only describes the loss of shaft resistance and does not affect the
end bearing. Setup factors are calculated as total capacity from restrike tests divided
by the total pile capacity at the end of driving.
capacitydrivingofEndcapacityrestrikeofBeginningFactorSetup = Equation 4-3
Axial resistance at the end of driving (EOD) and axial resistance on resumption of
pile driving (also termed beginning of restrike or BOR) were obtained from the plots
of wave equation analyses by reading the capacity values corresponding to the
number of blow counts at end of driving (EOD) and beginning of restrike (BOR)
respectively.
4.5.4.1. Mechanics of Setup
Setup was first mentioned in the literature in 1900 by Wendel (Long et al., 1999),
and was documented for virtually all types of driven piles. It was reported to occur
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in organic silt, inorganic saturated clay, loose to medium dense silt, sandy silt, silty
sand and fine sand in fine-grained soils in most parts of the world (Soderberg,
1961).
Many researchers (Davie and Bell, 1991; Fellenius et al., 1989; Randolph et al.,
1979; Rice and Cody, 1992; Svinkin, 1996; Tavenas and Audy, 1972; Thompson
and Thompson, 1985; Tomlinson, 1971; Wardle et al., 1992; York et al., 1994)
studied the phenomenon of time-dependent strength changes in soils during pile
driving.
When saturated cohesive soils are compressed and disturbed due to pile driving,
large excess pore pressures develop. These excess pore pressures are generated
partly from the shearing and remoulding of the soil and partly from radial
compression as the pile displaces the soil. The excess pore pressures cause a
reduction in the effective stresses acting on the pile and thus a reduction in the soil
shear strength. This results in a reduced pile capacity during and for a period after
driving. After driving, the excess pore pressure will dissipate primarily through
radial flow of the pore water away from the pile. With the dissipation of excess
pore pressures, the soil reconsolidates and increases in shear strength. This
phenomenon is widely referred to as soil setup in which the change in soil strength
over time results in a higher capacity during restrike testing. It is predominately
associated with an increase in shaft resistance (Bullock, 1999; Chow et al., 1998;
Fellenius et al., 2000; Lukas and Bushell, 1989).
Most pile capacity tests used to define the empirical relationships incorporated into
pile capacity analyses are executed somewhere between a few hours and several
days after the pile is driven. Some loading test indicated substantial increases in the
pile capacity as the pile was allowed to ‘setup’. These ‘aging’ increases were still
evident after 18 months of driving the piles (Bea and Audibert, 1979; Bea, 1980;
1983). Extraction of the piles after loading tests indicated development of
electrochemical process, which had effectively bonded the cohesionless soil to the
faces of the steel piles. Titi et al. (1999) demonstrated that setup accounts for
capacity increases of up to 12 times the initial capacity at end of driving condition.
The rate and magnitude of setup is a function of a number of factors (Samson and
Authier, 1986), the interrelationship of which is not well understood but a
qualitative description is available.
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In cohesive soils, setup was found to be a function of soil and pile properties (Camp
and Parmar, 1999; Finno et al., 1989; Long et al., 1999). The shear strength of the
remoulded soil is higher than the soil’s undisturbed shear strength (Randolph et al.,
1979; Seed and Reese, 1955). In fine-grained granular soils, setup is related to soil
and pile properties in addition to creep-induced breakdown of driving induced
arching mechanisms. Since setup is related to dissipation of excess pore-water
pressures, the more permeable the soil the faster setup develops. Setup rate
decreases as pile size increases (Long et al., 1999; Wang and Reese, 1989).
Set-up effects calculated in this research represents short term effects (300 hours) as
opposed to longer term effects (20+ years). Such approach is consistent with API
RP2A pile capacity equations, which include the former effect but not the latter.
Setup time is known even less than the setup factors. In some geologic areas such
as in Louisiana (USA), setup materializes very slowly probably because of very
slow draining of pore water pressures in the fine-grained soils. Indeed, static
loading tests in Louisiana usually indicate unrealistically low capacities if the
waiting time between pile installation and static loading test is less than 6 weeks. In
other areas and particularly in coarse-grained soils, setup may occur much quicker.
A rough estimate would be that sands set up within one hour, fine sands or silts
within 1 day and clays within 7 days.
However, an estimate of setup time is only required for driving interruptions to
select appropriate hammer size during design stage. Hence, this is immaterial for
reassessment as long term capacity would have already been materialized for
existing platforms subject to reassessment.
4.5.4.2. Assessment of Setup in the Literature
Assessment of setup can be performed using measurements or empirical formulae.
Measurements can be done by installing Piezometers within an area of three
diameters of the pile to monitor pore pressure dissipation with time. Alternatively, a
number of empirical relationships were proposed to estimate or predict setup and
have demonstrated reasonable accuracy in a number of studies (Skov and Denver,
1998; Svinkin, 1996; Guang-Yu, 1988; Huang, 1988; Svinkin and Skov, 2000).
However, established relationships are difficult to generalize due to the combined
(shaft and toe) resistance determinations, inter-dependence of back-calculated or
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assumed variables in addition to the complexity of the mechanisms contributing to
setup.
Nauroy and LeTirant (1983) presented data from model and full-scale pile loading
tests which indicated that, for highly compressible carbonate sands with calcium
carbonate content in excess of 80 percent, unit values are not affected by setup.
Also, using data from the Philippines, Noorany (1985) found no setup after 34 days
of driving piles when the calcium carbonate content was in excess of 90 percent. If
this is a true characteristic feature of carbonate sands/ silts, which are high in
carbonate content with high compressibility, then any measurement of soil
resistance during driving should be a close approximation of the actual pile capacity.
However, results of pile loading tests that were carried out offshore India (Agarwal
et al., 1977) did not support such findings. Nauroy and Le Tirant (1983) showed
that in material with lower calcium carbonate content and lower compressibility, the
unit skin friction was five times higher than the original value in only a week of
setup.
In 1993, Ping et al. presented the results of an installation monitoring and
performance assessment program for offshore platform foundations in Campos
Basin on the Brazilian continental shelf. One of the main objectives of the program
was to evaluate the setup effects of soils at the site. The re-drive tests were
performed between 26 and 71 days after initial driving so as to provide information
on the gain of resistance with time. Ping et al. (1993) reported increases in skin
friction representing setup of 3 to 4 for the soils at Enchova and Namorado sites in
Brazil. Thus, the effect of setup on the long term capacity remains highly site-
specific.
4.5.4.3. Setup in this Research
Time effect was evident from inspecting the pile driving records in this research. In
the short term, and during pile installation, driving delays were caused either by the
need to weld an additional section of the pile or to change a hammer, and varied in
duration between 5 minutes and 10 days. The estimate of setup was based on actual
blow count at stoppage and at start-up. For each back analysis performed, an
assessment was made of the increase in time in soil resistance to driving (SRD) due
to these delays.
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The procedure for calculating the setup factor was repeated for each stoppage
penetration and for a number of piles to derive long term pile capacity. The setup
factors were plotted against the time period lapsed between End-of-Driving (EOD)
and Beginning-of-Restrike (BOR) as shown in Figure 4-6. Inspection of the trend in
the chart indicates that a setup factor of 2 fairly represented long term effect of
setup.
Large scatter in the data generally occurred at the shorter time delays and at shallow
penetration, which may suggest higher setup factors. This was partly due to the fact
that at shallow penetrations, the setup factor was based on a small difference divided
by a small number and was therefore very sensitive to the small changes in values.
The computed long term setup factor of 2 was compared with reported setup factors
in the literature. Tagaya et al. (1979) estimated the long term capacity of piles
driven in similar soils in the Arabian Gulf using a setup factors range from 1.5 to 4
with a final recommendation of 1.5 for that site. The choice of 1.5 is the lowest
value obtained through a literature review. Therefore, the value of setup factor of 2
was considered to be a fair representation which fell within the range of setup
factors reported in the literature. Rather than using a value of 2, the use of the
individual computed value for each pile was contemplated. However, the computed
setup factors for each pile represent short term setup which tends to underestimate
the long term capacity of the pile. Since the computed capacity using API RP2A
represents long term capacity, and in order to have consistent bias factors, the
representative value of 2 was used in this research.
4.5.5. BACK-ANALYSIS PROCEDURE
The “actual” axial pile capacity was derived in this research by multiplying the short
term axial pile capacity, which was described in Section 4.5.3, by a setup factor of 2,
as described in Section 4.5.4. The process of deriving “actual” pile capacity is
termed back-analysis procedure in this research.
Validation of the back-analysis procedure revolved around two issues. Firstly, there
was a need to validate the accuracy of GRLWEAP to predict the short term axial
pile capacity. Secondly, it was important to validate that the use of the back-
analysis procedure correctly predicted axial pile capacity.
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4.5.5.1. Validation of GRLWEAP Results
A survey of the literature showed that the level of reliability of GRLWEAP
realistically predicts the capacity of piled foundations. Zhou (1999) confirmed that
GRLWEAP accurately predicts axial pile capacity for the Beginning of Restrike
(BOR) condition when only soil profile, hammer and pile data is available.
Additionally, using the database presented by Likins et al. (1996), Thendean et al.
(1996) conducted a correlation research using 99 cases for which static pile loading
test was available in a variety of soil and pile types. It is noted that the predictions
are automated within GRLWEAP and are thus operator independent. In the
correlation of that research, the ratio of predicted static capacity in failure to actual
capacity was evaluated by the Davisson offset method for end of driving (EOD) and
beginning of restrike (BOR) tests. Thendean et al. (1996) showed the general
tendency of GRLWEAP EOD results to underpredict the load test capacities. On
the other hand, BOR results tend to be overpredicted as shown in Figure 4-7.
In the initial stages of this research, such trend made it hard to justify the use of
GRLWEAP given its tendency to underpredict the EOD and overpredict BOR
capacities. However, a closer look at the analysis in light of the interpretation of test
results provided a different view and supported the use of GRLWEAP. Fellenius
(1975, 1980) presented nine different definitions of pile capacity evaluated from
load-movement records of static loading test. Five of these were of particular
interest, namely, the Davisson Offset Limit, the DeBeer Yield Limit, the Brinch-
Hansen Ultimate Load, the Chin Kondner Extrapolation and the Luciano Decourt
methods.
The Davisson Offset Limit Load is probably the best known and widely used
method. The limit load was proposed by Davisson (1972) as the load corresponding
to the movement that exceeds the elastic compression of the pile (taken as a free
standing column) by a value of 0.15 inch (4mm) plus a factor equal to the diameter
of the pile divided by 120. The Offset Limit Load is not necessarily the ultimate
load.
The method proposed by DeBeer (1968) is based on plotting the load movement
data in a double-logarithmic diagram. If the ultimate load was reached in the test,
two line approximations will appear; one before and one after the ultimate load
(provided the number of points allow the linear trend to develop). The slopes are
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meaningless, but the intersection of the lines is useful, as it indicates where a change
occurs in the response of the piles to the applied load. DeBeer called the
intersection the Yield Load.
The Hansen 80% criterion was developed by J. Brinch Hansen (1963), who
proposed a definition for pile capacity as the load that gives four times the
movement of the pile head as obtained for 80 % of that load.
The Chin (1970; 1971) method can be applied by dividing each pile movement by
its corresponding load and plotting the resulting value against the movement. After
some initial variation, the plotted values will fall on straight line. The inverse slope
of this line is the Chin-Kondner Extrapolation of the ultimate load. As an
approximate rule, the Chin-Kondner Extrapolation load is about 20 % to 40 %
greater than the Davisson limit.
Decourt (1999) proposed a method which can be applied by dividing each load by
its corresponding movement and plotting the resulting value against the applied
load. A linear regression determines the line. The Decourt extrapolation load limit
is the value of load at the intersection.
Fellenious (1980) interpreted results of a pile loading test using the various methods
described above as shown in Table 4-12.
Inspection of the result shows that the Davisson Offset Limit Load method, which
was used to correlate GRLWEAP, is conservative while the Hansen Criterion is
more “liberal”. Maximum values of pile capacity can be estimated with the Chin
method from which results are about 20 % to 40 % greater than from the Davisson
limit.
Considering that API RP2A-LRFD (1993) pile load test results were interpreted
using Davisson Offset Limit, GRLWEAP results were considered to provide pile
capacity that is consistent with test results used to calibrate API RP2A-LRFD
(1993). Consequently, GRLWEAP pile capacity prediction was considered to
represent “actual” pile loading tests. Further, the automated parameters within
GRLWEAP were shown to represent the most likely conditions of pile driving
especially for BOR conditions.
4.5.5.2. Validation of the “Actual” Capacity
Validation of the back-analysis procedure was carried out in this research by
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comparing the predicted axial pile capacity described in Section 4.5.3 to the
dynamic measurement results of that pile.
The dynamic measurements were obtained from actual offshore installation in the
Arabian Gulf. The dynamic pile measurements were carried by a specialist
contractor in the Arabian Gulf during an offshore installation campaign of a
platform in 2003. Figure 4-8 shows pile driving records of the four piles supporting
that platform.
To validate the results of the back-analysis method, the piles were modeled using
GRLWEAP. The input data screen in Figure 4-9 shows the ultimate capacity range
that was specified to start from 2400kN and stepped up in equal steps to reach
24600kN. The selection of this range ensured that the bearing graph captured all
possible blow count range.
The input data sheet shown in Figure 4-9 describes a MENCK MHU 600 hammer to
drive the pile to the tipping depth. The assumed hammer efficiency was 67% which
was taken as the default from GRLWEAP for the Menck MHU600 hammer. The
GRLWEAP default parameters were used to represent soil parameters (quake and
damping). The pile is 90m long and penetrates 56m into the soil stratum, with a
sectional area of 1193cm2. The shaft resistance percentage used in the calculations
is 90%.
The output data in Figure 4-10 shows the relationship between the blow count and
the ultimate capacity in the GRLWEAP bearing graph. The ultimate capacity values
are defined at the bottom of Figure 4-10.
Figure 4-10 shows a residual skin friction component of 65% of pile capacity, which
is lower than 90% component shown in Figure 4-9 and calculated using API RP2A
methodology. The 90% skin friction component is based on bearing capacity
theory, which tends to over predict the true toe resistance of long piles. Typically,
the residual (post-peak) capacity has been shown to be 10% to 20% lower than the
peak capacity (Mirza, 1997).
The residual stress effect has been attributed to the flexibility of the pile and
differences between toe and shaft quakes. This is because the pile’s shaft resistance
tends to restrict the pile’s rebound at the end of the impact event. When the next
blow occurs, pile and soil are prestressed and therefore less energy is needed to
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compress and move the pile through the soil. GRLWEAP performs residual stress
analysis, which is more rational than the standard blow count calculation method
originally proposed by Smith, particularly for long or flexible piles.
To compute the ultimate capacity for each pile, Figure 4-10 was entered with the
blow count of the pile, which was read from the pile driving records. Using linear
interpolation, ultimate capacity for each pile was calculated to represent the short
term capacity as previously described. Applying a setup factor of 2 as discussed in
Section 4.5.4, the long term ultimate capacity was computed. The results are
summarized in Table 4-14.
The computed capacity at the target penetration was compared to the dynamic field
measurements (CAPWAP) in Table 4-14. Comparison of the calculated capacity
using back-analysis procedure to the CAPWAP results was very encouraging (less
than 5% difference).
Initial attempt to check the validity was not so encouraging but was found to be due
to inaccurate modeling of the pile. This was driven by the desire to shortcut
modeling of the pile wall thicknesses due to the large amount of data entry that was
required for that pile.
The make-up of the pile in question is shown in Figure 4-11. To model the pile
cross sections shown in Figure 4-11 for GRLWEAP analysis, a large number of
sections were required for modeling the pile accurately, which made the data entry
quite tedious.
The input required a change in the calculated properties not only at each wall
thickness but also at each change in the soil layer within that thickness as shown in
Figure 4-12. In this example, the soil boring identified eleven (11) soil layers up to
the tipping depth, and the structural drawings identified 10 pile sections. When the
pile sections were imposed on the soil layers, it was necessary to subdivide the pile
into twenty one (21) sections to model the changes in soil profile within each pile
section. This required considerable effort to model each pile in the database
containing 138 piles. A shortcut was contemplated to reduce the complexity of the
input data in GRLWEAP by using the average pile wall thickness instead of
modeling the various pile sections.
Using an average wall thickness, instead of the actual pile wall thicknesses, the
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computed short term pile capacity was 15.6MN as shown in Figure 4-13. The
computed pile capacity (15.6MN) at 25 blows per foot was factored by applying a
setup factor of 2. The computed capacity (31.2MN) is almost double the
corresponding capacity (15.9 MN at BOR) from dynamic monitoring which is
shown in Table 4-14.
In order to understand the reason for the large discrepancy in results when average
pile wall thickness was used, full understanding of wave propagation was necessary.
This section presents an overview of the mechanics of driving a pile.
When a hammer first strikes a pile, it is only compressed at the ram pile interface as
shown in Figure 4-14. This compressed zone, or force pulse, expands into the pile
toward the pile toe at a constant wave speed which depends on the pile’s elastic
modulus and mass density. When the force pulse reaches the embedded portion of
the pile, its amplitude is reduced by the action of static and dynamic soil resistance.
Depending on the magnitude of the soil resistance along the pile shaft and at the pile
toe, the force pulse will generate either a tensile or a compressive force pulse, which
travels back to the pile head. Both incident and reflected force pulses will cause a
pile toe motion and produce a permanent pile set if their combined energy and force
are sufficient to overcome the static and dynamic resistance effects of the soil.
During an elastic loading process, the pile penetrates into the ground and the
activated soil resistance increases until it reaches, and then exceeds, the maximum
(i.e., ultimate) values. The excess energy then works on advancing the pile and
securing permanent penetration. Typically, piles are installed with permanent set of
30 to 3mm/blow (i.e. driving blow count of 10 to 100 blows per foot). Pile refusal
criterion (sometimes defined as penetration resistance exceeding 250 blows per foot)
occurs if the driving system is incapable of producing sufficient pile displacement
beyond the elastic and into the plastic soil deformation ranges. Under such
conditions, dynamic pile testing can only measure the mobilized portion of the
ultimate pile capacity activated by the limited pile movement. Dynamic testing
during restrike is performed under a limited number of hammer blows. The same
original pile driving hammer or another one may be used to restrike the pile. The
pile should experience permanent penetration under the restrike hammer blow if the
ultimate pile capacity is to be reached and measured.
Hence, soil resistance to driving depends on the transferred energy which in turn
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depends on the pile modulus. As the pile modulus is a function of the wall
thickness, using inaccurate wall thickness in the model results in computing an
incorrect value for the transferred energy and hence the wrong capacity prediction.
This explains the reason for the inaccurate prediction when the incorrect wall
thickness was used in the analysis.
This was an interesting observation as it demonstrated the importance of pile wall
thickness in the static prediction of ultimate pile capacity. However, API RP2A-
LRFD (1993) formulation does not account for this effect. Hence, the observation
in this research could lead to a very useful future research requirement to refine the
API RP2A prediction and reduce the bias and error reported by Tang (1988) in the
application of the method to predict axial pile capacity.
As a result of the above, the use of the above back-analysis procedure with
GRLWEAP one-way equation analysis to derive pile capacity was considered
representative of loading test results. Furthermore, the model for every pile in the
entire pile database (138 piles) accounted for variations in the wall thicknesses of
each pile to ensure accurate results.
4.5.6. SENSITIVITY ANALYSIS OF THE COMPUTED ACTUAL
PILE CAPACITY
The use of GRLWEAP to perform back analysis requires a number of input
parameters, some of which are related to the physical characteristics but others are
default values within GRLWEAP. The parameters associated with the physical
characteristics include the pile diameter, penetration length and the pile wall
thickness. The sensitivity of the default parameters used to calculate the “actual”
capacity was examined and is reported in this section.
4.5.6.1. Hammer Efficiency
The sensitivity of using various hammer efficiencies was examined by executing a
number of Bearing Graph analyses for various hammer efficiency values. The
analysis was performed on the pile shown in Section 4.5.3, which used a MENCK
MRBS 4600 hammer. For this hammer, the default efficiency value in GRLWEAP
was 0.67. The analysis investigated various hammer efficiencies, ranging from
0.40 to 0.8. The computations resulted in curves for the ultimate capacity against
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blow count (blows per foot) and these were plotted in Figure 4-15.
The plot indicates that the choice of hammer efficiency value is relatively
insensitive for up to 100 blow count (blows per ft). This is the range experienced in
most offshore pile driving in the Arabian Gulf as revealed from examining pile
driving records in the database. Hence, the use of default values for the hammer
efficiency in GRLWEAP was shown to produce acceptable results when the number
of blow counts is less than 100 blows per foot.
4.5.6.2. Segment Length
In this research, each pile was divided into one-meter segment lengths, and each
segment was modeled as a weight and a spring. The sensitivity of the assumed
segment length was checked by executing the analysis with various segment lengths.
The analysis was executed for segment lengths of 0.5 m, 1.0m and 5m.
Figure 4-16 demonstrates that the selection of segment length has almost no effect
on the computed capacities (around 17,000kN) experienced in the Arabian Gulf.
Hence, the use of one meter segment length produced reliable results and was
adopted in this research.
4.5.6.3. Cushion Effects
Cushion type is not usually specified in pile driving records and the selection of
cushion type is user-defined in GRLWEAP. Several Bearing Graph analyses were
executed to examine the effect of changing cushion type and thickness. For the
demonstration pile, the default cushion type for the Menck MRBS 4600 hammer
was a Bongossi wood model 42 (1065)-72 (1830). To investigate the sensitivity of
using a different cushion type, two other cushion types were analyzed. The first one
was a Mitsubishi MH 80B Micarta and the second was Delmag 42(1070)-48(1220)
Offshore 43%alim+57% conbest.
As can be seen from Figure 4-17, the computed capacity is highly insensitive to the
selected cushion type or manufacturer. This is because soil resistance, rather than
pile characteristics, dominates the axial capacity.
4.6. STATISTICAL PARAMETERS OF BIAS FACTORS
This section presents the statistical parameters of the bias factors. For each pile in
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the database, the axial pile capacity was predicted using API RP2A-LRFD (1993)
procedure as described in Section 4.4. A basic assumption in this research was that
the statistics of the bias factors represented all sources of errors such as SPT testing,
pile loading tests and the static pile capacity prediction models.
The short term axial capacity for each pile was also derived using WEA as described
in Section 4.5.3 and time effects were incorporated as described in Section 4.5.4.
The bias factor for each pile was calculated using the following relationship:
CapacityCalculatedARPAPIFactorSetupCapacityGRLWEAPFactorBias
2×
==λ Equation 4-4
A bias factor greater than unity indicates that the limiting soil parameters shown in
Table 4-6 underpredicts the capacity and vice versa.
The statistics of the bias factors were calculated in this research for the following
subgroups:
• Database subgrouped by installation method,
• Database subgrouped by soil profile, and
• Database subgrouped by degree of optimization in design.
The statistical parameters were also calculated for the complete database. Further
grouping of the data was considered impractical as it would have resulted in too
many groups, which would make it impractical for engineering applications.
Further, the size of each group would have also been reduced to a level possibly
leading to gross inaccuracies in the calculations of the statistical parameters. In
addition, the use of three groups has already extended the analysis beyond the use of
a single group used in the calibration of API RP2A-LRFD (1993) and was therefore
considered sufficient refinement to the API RP2A-LRFD (1993) yet still within
practical bounds.
4.6.1. STATISTICS OF THE COMPLETE DATABASE
Figure 4-18 presents a scatter plot of the computed bias factors versus the
penetration ratio for the complete database. The penetration ratio is defined as the
ratio of the pile length over its diameter. Inspection of the trend in Figure 4-18
shows an almost equal distribution around a bias factor of 1.0. This implies no bias
in the overall database when predicting capacity using API RP2A-LRFD (1993)
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formulation with the limiting engineering parameters defined in Table 4-6.
The statistical parameters of the bias factors were derived using the method
described in Section 3.4 and with the aid of the @RISK software. The histogram of
the data was plotted and several distributions were examined to fit the data. The
normal probability plot generated the best fit shown in Figure 4-19. The associated
statistical parameters were calculated as 0.93 and 0.36 for the mean and coefficient
of variation, respectively.
To confirm the selected distribution, probability plots of the bias factors were
generated for several competing distributions to arrive at the best fit. Figure 4-20
represents a probability plot for an assumed normal distribution using the parametric
method described in Section 3.4.2. The correlation coefficient associated with the
linear fit to the data in the probability plot provided a measure of the goodness of the
fit. The calculated correlation coefficient for a normal distribution produced the
highest correlation coefficient (0.98) and therefore confirmed the assumption of
normal distribution. The calculated statistical parameters (mean = 0.93, coefficient
of variation = 0.36) of the data were found to be identical to the values using non-
parametric method.
4.6.2. GROUPING BY INSTALLATION METHOD
Availability of installation records for existing platforms reduces uncertainty
associated with predicting axial pile capacity when reassessment of a platform is
required. On the other hand, installation data are unavailable during design, which
makes it allow higher margin of safety to deal with uncertainties associated with
installation circumstances. Hence, it was prudent to subgroup the database by
installation method in order to represent the effect of various installation
circumstances on the predictive method.
Inspection of the pile driving records revealed that the method used to install the pile
could be broadly divided into two groups. The first group includes piles that were
driven without supplementary methods and the second group includes piles that
were installed using supplementary installation methods such as pre-drilling/
grouting or jetting. Out of the 138 piles in the database of this research, 20 piles
were installed with jetting, and the balance was installed without the need for
supplementary methods.
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The statistical analysis results indicated that API RP2A-LRFD (1993) significantly
(40% to 80%) overestimate the capacity as shown in Figure 4-21 when piles are
installed using supplementary methods. The computed capacities were therefore on
the unsafe side.
McClelland et al. (1969) reported similar (50% to 85%) decrease in the shaft
resistance for piles installed using supplementary methods compared to those driven
without the need for supplementary methods. Further, Poulos and Davis (1980)
recommended a reduction of 50% for the ultimate shaft resistance from the
originally calculated capacity in the jetted zone for jetted piles driven to the final
penetration. The reduction depends on the size of the predrilled hole.
The excessive (80%) reduction in capacity for some piles was disturbing and
required an explanation. One explanation relates to the hammer type used for those
piles. Mosher (1987, 1990) summarized the results from five sites where piles were
installed by both impact and vibratory hammers. The conclusion was that, for a
significant majority of the cases, piles installed in sand with a vibratory hammer had
a lower ultimate capacity than impact driven piles at the same site. Interestingly,
piles with low bias factor of 0.20 were found to have been driven with a VULCAN
530, which is a vibratory hammer. Hence, data could be further broken according to
the hammer type (impact or vibratory), but this was not adopted in this research.
Mosher (1987) also concluded that time-dependent soil strengths occurred equally
for both installation methods but that, with time, impact driving increased the
capacity of the installed pile as compared to a vibratory driven pile. Inspection of
pile driving records in this research revealed that piles were driven using the same
hammer type (vibratory or impact) for the full penetration depth. Hence, for the
database collated in this research, no increase in pile capacity would be materialized
as there was no change in hammer type during driving.
To derive the statistical parameters, histogram of the data was generated using
@RISK and a number of probability distributions were fitted. The normal
distribution provided the best fit for this set of data as shown in Figure 4-22. For the
normal distribution, the mean (λ = 0.65) and the coefficient of variation (COV =
0.40) corresponding to a normal quantile of 0 and 1 respectively were calculated for
piles that were installed using supplementary methods.
Due to the small number of data points, the parametric method described in Section
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3.4.2 was employed. The normal probability plot for this set of data generated a
straight line as shown in Figure 4-23 and confirmed that that the normal distribution
type and statistical parameters (λ = 0.65 and COV = 0.40) provided good
representation of the data.
4.6.3. GROUPING BY SOIL TYPE
Further subgrouping of the data was inspired by an approach adopted by Tomlinson
(1971) using a large number of loading tests. This subgrouping accounts for the
high variability in soil formations in the vertical as well as in the horizontal
direction. In this research, two types were identified. The first type represents piles
in soils dominated by cohesionless soils underlain by cohesive soils (Type CS) and
the second type represents piles in soils that are dominated by cohesive soils
underlain by cohesionless soils (Type SC).
The bias factor distribution for the first soil type SC is shown in Figure 4-24, while
Figure 4-25 shows the distribution for the soil type CS.
Inspection of Figure 4-24 revealed that the use of API RP2A-LRFD (1993) with the
limiting parameters shown in Table 4-6 tends to overpredict pile capacities for SC
soil type, which is unsafe. On the other hand, when piled foundations are installed
on soils dominated by cohesionless soils over cohesive soils, the trend has not been
as conclusive as can be seen in Figure 4-25 but appears to underpredict the capacity.
Interestingly, the trend of overpredicting the capacity for soil type SC and
underprediction for soil type CS is consistent with the pile loading test results
reported by Tomlinson (1971). Tomlinson showed that the adhesion factors for
piles driven through granular materials into stiff or very stiff clays were
considerably higher than those piles driven through soft clays into the stiffer soils.
Similar results were reported if piles were driven into stiff to very stiff clays without
other overlain strata.
Tomlinson (1971) provided an explanation of such a trend by examining the soil
movement in the upper layer and the separation of the pile surface from the
surrounding undisturbed soil. During driving, the upper sand layers in soil type CS
are carried down to a limited depth forming a skin of compacted sand or sand/ clay
mixture around the shaft. This skin has a high friction value such that piles driven
to penetrations of less than 20 diameters into the cohesive soils can have an ultimate
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skin frictional resistance exceeding 1.25 times the undrained shear strength of the
soil. On the other hand, when piles are driven through soft clays - as the case with
type SC soils - a soft skin is carried down, which has a considerable weakening
effect on the frictional resistance of the shaft.
Development of the statistical parameters for the bias factors for both soil types was
performed using @RISK and using the method described in Section 3.4.2. For the
SC soils, the bias and standard deviation were calculated as 0.77 and 0.26,
respectively as shown in Figure 4-26. The computed COV is 0.34.
The development of the statistical parameters for the bias factors for both soil types
was confirmed using probability plots. For the SC soils, the bias and coefficient of
variation were calculated as 0.77 and 0.34, respectively, as shown in Figure 4-27.
4.6.4. GROUPING BY OPTIMIZED DESIGN
The bias factor distribution for piles driven in soils CS (shown in Figure 4-25)
indicates, though not conclusively, that the use of API RP2A-LRFD (1993) with the
limiting parameters shown in Table 4-6 underpredicts pile capacity. To explain the
inconsistent trend of bias factors in Figure 4-25, a number of deterministic analyses
from historical designs were reviewed and an interesting trend was detected. Some
piles appeared to be grossly over-designed as was evident from the large factor of
safety identified in the deterministic analyses using API RP2A-WSD (2000). Under
operating conditions, API RP2A-WSD (2000) requires a minimum factor of safety
of 2.0 for pile design. The deterministic analyses of the database that constituted the
data in Figure 4-25 broadly identified two groups according to the calculated factor
of safety. The first group is termed “Optimized” as shown in Figure 4-28 and
represents piles with factor of safety around 2.0 under operating conditions. The
other group, termed “overconservative” represents piles with factor of safety around
4.0 or more and is shown in Figure 4-29.
Inspection of Figure 4-29 reveals that the application of API RP2A-LRFD (1993)
formulation with the limiting parameters defined in Table 4-6 tends to overpredict
the capacity (bias < 1.0) for overconservative designs (FOS > 2.0) when the
penetration ratio exceeds 80. However, for lower penetration ratios (up to 50), the
API RP2A formulation tends to underpredict the capacity (bias > 1.0). The bias
factor pattern is generally opposite to the pattern shown in Figure 4-28 for optimum
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designs.
To obtain the statistical parameters of the overconservative group, the non-
parametric approach described in Section 3.4.2 was applied using @RISK. The
statistical parameters (mean = 0.97, COV = 0.34) of the bias factors for the
conservative designs were computed as shown in Figure 4-30.
Using the parametric approach described in Section 3.4.2, the probability plot shown
in Figure 4-31 confirmed the results of the non-parametric analysis.
For the optimum design of type CS soil, an analysis was performed using @RISK.
The statistical parameters were computed using non-parametric approach. The bias
factor for the optimum design was computed as 1.12 with COV of 0.26 as shown in
Figure 4-32.
Using the parametric approach described in Section 3.4.2, the statistical parameters
for the optimum design (λ = 1.12, standard deviation = 0.29, COV = 0.26) were
computed as shown in Figure 4-33.
4.6.5. GROUPING BY PENETRATION RATIO
Penetration ratio is defined as the ratio of the pile length (L) over its diameter (D).
The effect of penetration ratio was investigated by grouping the complete database
into four groups as shown in Table 4-15 to represent penetration ratios less than 50,
between 50 and 75, between 75 and 100 and larger than 100.
Table 4-15 reveals little change in the mean of the penetration ratio but indicates
larger variability in the coefficient of variation. The coefficient of variation is
relatively high (more than 0.40) for low and high penetration ratios. For
intermediate penetration ratios, the coefficient of variation is lower (less than 0.35)
and is consistent with the mean of the complete database.
4.7. BENCHMARKING STATISTICS OF BIAS FACTOR
In order to tie the work back to the API RP2A-LRFD (1993), the statistical
parameters of the complete database derived in this research were compared to those
used in the calibration of API RP2A-LRFD (1993).
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4.7.1. BIAS FACTORS IN API RP2A-LRFD (1993)
The statistical parameters used in the calibration of API RP2A-LRFD (1993) are not
explicitly stated in API RP2A-LRFD (1993) but were traced to a research project
titled “Offshore Axial Pile Design Reliability”. In that research project, Tang
(1988) used 44 load tests to evaluate the statistical parameters and found substantial
bias (mean = 0.78) and error (COV = 0.68) in the predicted model. On further
examination of the database, Tang (1988) excluded piles driven in stratum
consisting of carbonate sand containing shells and/ or signs of carbonate
cementation which reduced the database to 33 piles only. Exclusion of piles in
carbonate sand improved the capacity prediction and yielded mean bias of 1.01 and
error of 0.46.
Hence, the bias and error computed for carbonate soils in this research (0.93, 0.36)
were essentially similar to those reported by Tang (1988) for the non-carbonate soils
(1.01, 0.46). This implies that the large scatter (0.78, 0.68) reported by Tang (1988)
was mainly due to combining carbonate soils and non-carbonate soils in one
database which points to the large difference in soil behavior between carbonate
soils and other soil types.
Prior to the publication of API RP2A-LRFD (1993), Hamilton and Murff (1992)
presented a set of bias factors and coefficients of variation of loads and resistance
which could be used in calibration work of piled foundations and these are shown in
Table 4-18. However, those proposed values were not adopted in the calibration of
API RP2A-LRFD (1993).
For piles in normally consolidated or soft clays, Hamilton and Murff (1992)
considered that there was considerable evidence to support a capacity bias of around
1.3. Such bias was considered to be mainly due to rapid loading rate effect, which
would apply to the extreme loading condition. Likewise, piles primarily supporting
gravity loads were assigned a bias of 0.9 because of the very slow loading rate.
Piles in sand are not as sensitive to loading rate. Lacasse (1988) demonstrated
consensus among industry experts that API RP2A-WSD guidelines are conservative
for dense sand. Accordingly, and based on results reported by Tang (1988),
Hamilton and Murff (1992) used bias of 1.2 for axial capacity of piles in sand.
Hamilton and Murff (1992) reported coefficients of variations of 30% and 40% for
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piles in sand and clay respectively on the basis of the work reported by Tang and the
survey of industry experts by Lacasse (1988). Hamilton and Murff (1992)
supported the contention that axial pile capacity in sand has a higher degree of
uncertainty than axial pile capacity in clay.
The discussion above implies that piles in clay have a higher reliability than piles in
sand due to the difference in bias and uncertainty. However, current specifications
(WSD and LRFD) used in industry practice do not address the apparent discrepancy
between piles in different soil types.
4.7.2. BIAS IN THE STATISTICS OF BIAS FACTORS
The bias factors computed in this research (0.93) and those (1.01) reported by Tang
(1988) were found to be considerably lower than the bias value of three (3) reported
by Bea (1983) and others.
The identification and characterization of “biases” associated with API RP2A-
LRFD (1993) formulation was of particular importance to clarify the discrepancy.
These biases represented explicit and implicit conservatism that were intentionally
and unintentionally integrated into the current API RP2A-LRFD (1993) guidelines.
To illustrate the magnitude of bias that can be present in a traditional design, Bea
(1983) studied the failure of a platform located at South Pass Block 70, Mississippi
River Delta. The platform was designed according to API RP2A guidelines to
withstand a 58 ft high (17.7m) design wave. The most heavily loaded pile (i.e.
critical pile) was designed for a factor of safety of 1.5 against a combination of
dead, live and extreme wave loads. The ultimate shaft capacity for the critical pile
was calculated using the API RP2A method and found to be 3100 kips (13790kN).
However, based on a detailed case research after a sea-floor slide at the site, the
actual capacity of the critical pile was estimated to be 10,000 kips (44484kN). This
represented a bias factor of more than 3.
In 1984, Bea considered the results of an older pile loading test in silty sand and
reported large difference (bias = 2.0) between the pile axial capacities based on API
static capacity guidelines and the pile test capacity. Bea (1984) explained that the
large bias are accumulated from extrapolations and interpretations involved in all
phases of the data gathering and design process methods used in the ‘crude’ but
industry practice (Bea, 1983; Wu et al., 1989; Tang, 1988; Tang and Gilbert, 1990;
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1992) as well as the pile capacity model including:
• Performing the soil boring,
• Accomplishing the sampling,
• Performing the testing (in situ and laboratory),
• Characterizing the soil strength and stiffness characteristics,
• Defining the pile loading (dead, live, environmental),
• Analyzing the individual pile and pile system performance, and
• Defining the age of the pile at the time that the capacity was to be evaluated.
Bea (1983) quantified the factors contributing to this large bias and suggested that a
bias of 2.7 could easily result from a mere 10% on each of the elements presented in
Table 4-17. Even if the lower bound of all elements is considered, there is still bias
of 2.5.
Similar analysis was carried out by Tang (1988) who evaluated the overall bias and
error associated with the API recommended procedures for the design of offshore
piles driven in clay. Tang (1988) identified significant parameters affecting the
reliability of axial pile capacity through a complete uncertainty analysis. The
analysis considered the following:
• Capacity model error,
• Systematic bias in undrained shear strength as a result of using driven samples.
The use of driven sample method is more common in practice than the pushed
sample method which formed the basis of the design method in RP2A. The use
of pushed sample generally reduces the scatter of error and is more reliable but
driven samples were mostly used for older platforms to define strength
parameters,
• Systematic bias through the use of nominal or “interpreted” strength instead of
using the average of the measured undrained shear strength,
• Systematic bias due to insufficient soil samples at the site,
• Spatial variability of soil properties,
• Reconsolidation and time effect,
• Rate of loading effect,
• Compressibility effect, and
• Error in jacking and determination of ultimate load.
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A high bias is particularly applicable to older platforms due to the effect of older
soil boring techniques. There has been a dramatic improvement in the methods used
to perform borings and obtain, preserve, transport and test samples as a function of
time (Dover, et al., 1981; McClelland, Ehlers, 1986; Young, et al., 1983; Wu, et al.,
1989). Hence, soils could be envisioned as being near their lower limit of strength
in these conditions and characterizations. For example, it is not uncommon for the
soil boring of an existing platform to have had been carried out using rudimentary
methods with no heave compensation, wire line sampling method, unconfined
compression testing and lower bound characterization of available data. In such
cases, the bias could be expected to be in the range of 2 or more (Emrich, 1971,
Young, et al., 1983, Quiros, et al., 1983, Dover, et al., 1981, McClelland and Ehlers,
1986).
To understand the effect of sampling, Tang (1988) investigated the undrained shear
strength (su) as it is the most predominant parameter defining foundation capacity in
clay. The undrained shear strength values depend on sampling techniques (driven or
pushed), type of tests performed in the field or in the laboratory and the degree of
disturbance of the soil sample. Soil strength sampling/ test method are generally
performed using any one of the following methods:
• Unconfined compression strength on pushed sample (UCP),
• Unconsolidated undrained strength on pushed sample (UUP),
• Unconsolidated undrained strength on driven sample (UUD),
• Miniature vane strength on driven sample (MVD),
• Miniature vane strength on pushed sample (MVD), and
• Unconfined compression strength on driven sample (UCD)
Tang (1988) reported a bias in the calculated pile capacity ranging from 1.3 to 3.7
and an error varying from 28 to 53%. The unconfined compression (UC) tests of
driven samples were generally found to produce low shear strength values as they
are significantly affected by soil disturbance. Tang (1988) calibrated the API pile
test database to the UCP samples and reported an average bias in undrained shear
strength with a mean of 1.18 and COV 27% due to the use of UCP samples.
Thus, older borings could be expected to have inherently greater biases and the use
of these values without any adjustments may be overly conservative. The boreholes
used in this research were taken between 1965 and 2001, and UC tests on driven
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samples were performed for a majority of cases.
To account for the high bias inherent in API RP2A-LRFD (1993) as described
above, the Author considered the use of a bias factor (2.5), or higher, instead of the
calculated (0.93) bias factors to calibrate the required resistance factors. However,
this would have deviated from the statistical parameters adopted in the calibration of
API RP2A-LRFD (1993) and was therefore not adopted in this research.
The mean bias of 0.93 is used for calibration purposes only for consistency with the
approach adopted in calibrating API RP2A LRFD. The true bias in piled
foundations may be higher as evident by lack of any clear foundation failure during
hurricanes Katrina, Ivan and Rita.
4.8. BAYESIAN UPDATE OF BIAS FACTOR STATISTICS
Calibration of the resistance factors requires sufficiently large number of
observations. However, partitioning of the database reduced the size of each
sample. To supplement the limited size of the partitioned database and provide a
more robust estimate of bias factors, Bayesian updating was employed to enable
inference. In statistics, inference refers to the process of drawing conclusions or
making predictions on the basis of limited information.
4.8.1. “PRIOR” DISTRIBUTION
Due to paucity of the data related to the Arabian Gulf, the ‘prior’ distribution of the
overall bias in pile axial capacity was represented by statistics in the literature.
A survey of the literature to identify suitable statistical parameters revealed
disagreement between researchers on the topic. Olsen (1984) suggested a bias of
0.79 and a range of COV between 20% to 40%, but Moses and Larrabee (1988)
reported that the commonly quoted database was a bias of 1.0 and the COV of 20%.
Moses (1986) explained that piles in platforms are stronger than that of Olsen’s
research (1984) because “pile capacity predictors” presented in Olsen’s research did
not behave the same as “pile capacity consultants” even though they might be the
same individuals.
Using a database of 44 load tests (out of the original databank that comprised 1004
load tests), Tang (1988) estimated a range of overall bias in the 16th Edition of the
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API RP2A between 1.3 and 3.7 and a range for the overall error (COV) ranging
from 32% to 53%. The range was affected by the methodology used to determine
the undrained shear strength at the given site. For the 17th Edition of API RP2A,
Tang (1988) estimated a range between 1.6 and 2.9 for the overall bias, while the
overall error (COV) ranged between 0.3 and 0.4.
In this research, a mean value of 1.3 with COV of 0.3 was used for the ‘prior’ of the
bias factor. The mean value of 1.3 was considered to provide a reasonable balance
to represent expert opinions.
4.8.2. “LIKELIHOOD” DISTRIBUTION
The ‘likelihood’ distribution of the bias factor distribution in this research simulated
the results of the back analyses presented in Section 4.6 and summarized in Table
4-16.
4.8.3. “POSTERIOR” DISTRIBUTION
Using the approach described in Section 3.5.2, the updated statistics of the bias
factors were computed and are summarized in Table 4-19.
Inspection of the statistical parameters reveals that the mean ranges from 0.85 to
1.18. The coefficient of variation ranges from 0.20 to 0.26, which is lower than the
value (0.46) employed in calibrating API RP2A-LRFD (1993). Hence, grouping the
data in the manner described above reduced the error in the data, which enabled a
more refined resistance factors compared to those in API RP2A-LRFD (1993).
4.9. TARGET RELIABILITY INDEX
Section 2.3.3 identified limitations in the reliability-based method and revealed that
the selection of a specific target level, to reflect the many variables that should be
considered, has not been resolved.
For the purpose of this research, the Author considered the use of target reliability
level β that was employed in the calibration of API RP2A-LRFD (1993). However,
using a single target reliability index implies similar risk to different types of
facilities and may result in uneconomical or non-optimized solutions. For
reassessment applications, the Author did not justify adopting a single target
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reliability level, especially in view of recent advances in risk and reliability analyses
and the need to develop a rational cost effective yet safe approach. Hence, a range
of target values was selected for this research to reflect the many variables.
In view of the variety of circumstances for a single pile, target reliability index
values of 1.5, 2.0, 2.11, 2.5, 3.0 and 3.5 were selected for the calibration of the
resistance factors. The selection of a target reliability level of 2.11 in this research
maintains consistency with the target level selected by Moses (1980) in the
calibration of API RP2A-LRFD (1993). The use of this target reliability level ties
the work back to API RP2A-LRFD (1993).
Application of the calibrated resistance factors in reassessment of existing platforms
requires a method to identify the appropriate target reliability level for that platform.
Table 4-20 provides a qualitative approach to assess an appropriate target reliability
level. To nominate an appropriate target reliability level, the likelihood of adverse
events and consequence of those events are required.
Depending on the nature and the specifics of the platform, likelihood of occurrences
can be expressed as a probability that an event can happen, or a chance that the pile
existing status will change and will require mitigation steps to take place. The
consequence reflects the perceived or actual impact that may occur if the given risk
materializes. The Engineer should consider the description of the consequence
outlined below and ask the question: “if the event occurs, how would it impact the
integrity of the pile in question?”
Combination of these two values (likelihood of occurrence and consequence) will
enable the determination of the relative risk level for a given pile. Risk items that
plot in the upper left-hand corner of the risk matrix represent the greatest risk to a
pile and require assigning a high target reliability level value, while those on the
lower right-hand side are of lesser concern and qualify for a low target reliability
level.
4.10. CALIBRATION OF RESISTANCE FACTORS
Using the calibration equations shown in Section 3.7, calibration of the resistance
factor was carried out for the partitioned groups. The results are shown in Table
4-21. The resistance factors were calculated for the total (toe + skin) capacity, since
the one-way WEA does not separate the skin and toe capacities.
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The calibrated resistance factors are graphically presented in Figure 4-34. The
relationship demonstrated that driven piles require higher resistance factors than
those installed using supplementary methods. Meanwhile, piled foundations in soil
type CS require higher resistance factor than type SC soil.
The outcome of the calibration provided valuable insight into implicit assumptions
and limitations associated with the current empirical method included in the API
RP2A-LRFD (1993). The variation in resistance factors shown in Table 4-21
demonstrates that a single value for the resistance factor, which was used by Moses
(1980) to calibrate API RP2A-LRFD (1993), does not account for the various
parameters affecting the reliability of piled foundations. Those parameters are
identified and discussed in this section.
4.10.1. TARGET RELIABILITY LEVELS
Target reliability indices used in this research represents various consequences and
risk levels for the platform. For example, an unmanned wellhead platform jacket
that does not support storage or processing facilities and includes safety features
such as Emergency Shutdown Valves (ESDV) would represent relatively low
consequence of failure from a life and environmental point of view as its likelihood
of an adverse event and/or the consequence of failure is low. Hence, lower bounds
of the target levels may be considered for such a platform.
On the other hand, a manned compression platform with processing facilities for
sour gas service and storage facilities represents the other extreme and qualifies for
high target reliability levels. The use of a higher target reliability level for such a
platform is logical as it results in lower resistance factors. The use of lower
resistance factors limits any additional demand on an existing platform to produce
the desirable lower probability of failure. This approach resolves one of the
drawbacks of the deterministic method.
4.10.2. SOIL TYPES
Current static prediction methods including API RP2A formulation consider soil
type (cohesive or cohesionless) but do not consider that soils are usually made up of
combinations of cohesive and cohesionless soils, rather than one soil type. The
calibration studies conducted in this research highlighted the difference in behavior
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between two types. The calibrated resistance factors for SC soils are 20%-25%
lower than those factors for CS piles shown in Table 4-21.
4.10.3. INSTALLATION METHODS
This research showed that the use of supplementary installation methods has a
dramatic effect on the capacity of piled foundations in carbonate soils. Back-
analysis of drilled piles revealed that that the capacity of piles installed using
supplementary methods are much lower than those installed without the need for
supplementary methods. This finding agrees with many scholars recommending
reductions in the resistance factors for conditions such as jetting and drilling.
This research also showed that the reduction in capacity of piles installed by drilling
varies from site to site and depends on the degree of disturbance to the surrounding
soils. The level of disturbance is in turn a function of the jetting or drilling
procedure, which varies widely from project to project and depends on the
individual contractor’s operation. Currently, there are no specific guidelines in the
API RP2A-LRFD (1993) or other codes for acceptable supplemental installation
procedures, which is due to the fact that API RP2A-LRFD (1993) addresses design
rather than reassessment. During design, piles are assumed to reach penetration
depth without the need for drilling or grouting. During installation, the presence of
harder soil strata or a boulder may make it necessary to use supplementary methods.
4.10.4. PENETRATION RATIO
Calibration of resistance factors for various penetration ratios employed the
statistical parameters defined in Table 4-15 as “likelihood” parameters and the
statistical parameters defined in Section 4.8.1 as “prior" parameters. The posterior
parameters for the various penetration ratios are shown in Table 4-22.
Resistance factors were calibrated for the various penetration ratios using target
reliability index (βΤ) of 2. The calibrated resistance factors are reported in Table
4-22. Inspection of the results indicates that the calibrated resistance factors are
insensitive to changes in the penetration ratios.
The calculations were repeated using a higher reliability index value (βΤ = 2.7) to
examine the sensitivity of various reliability index values on the computed
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resistance factors. The results reported in Figure 4-35 reveal that resistance factors
are insensitive to penetration ratios but sensitive to target reliability index levels.
An increase of the target level from 2 to 2.7 (35% increase) resulted in up to 25%
reduction in the calibrated resistance factors.
4.11. SUMMARY
Despite the extensive knowledge gained from researching the behavior of piled
foundations driven in carbonate soils, international codes and standards such as API
RP2A-LRFD (1993) provides no guidance for reassessment of piled foundations in
those soils.
This Chapter calibrated resistance factors for axial capacity of piles in the carbonate
soils of the Arabian Gulf.
A database from actual pile installations in the Arabian Gulf was collated in this
research to derive bias factors for the piles. The bias factors were then used to
calibrate resistance factors that can be used with API RP2A-LRFD (1993)
formulation.
The bias factor for each pile was calculated by dividing the “actual” capacity by the
predicted capacity. The “actual” capacity was derived by multiplying the short term
pile capacity, which was obtained using the bearing graph module of GRLWEAP
software, by a setup factor of two.
Derivation of the short term capacity employed default values in GRLWEAP.
Sensitivity studies were conducted to investigate the effect of using those default
values on the computed short term capacity. It was shown that, for the relatively
low blow counts experienced in the Arabian Gulf, the computed ultimate capacities
are relatively insensitive to the changes in the hammer efficiency, pile segment
length or the cushion type. Hence, it was considered appropriate to utilize the
default values in GRLWEAP when computing the “actual” pile capacities.
The resulting statistics of the bias factor (mean and coefficient of variation) for the
complete database (138 piles) were 0.93 and 0.36, respectively. The calculated bias
factors were then partitioned into four groups in order to reduce the error in the bias
factor statistics. The resulting groups represented various physical situations that
can be expected in practice. The grouping covered installation method (driven or
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drilled/ jetted), soil type (cohesionless underlain by cohesive soils and vice versa),
level of optimization in design and the penetration ratio.
However, partitioning the data reduced the size of the sample. To supplement the
limited size of the partitioned database and provide a more robust estimate of bias
factors, Bayesian updating was employed. The application of Bayesian update
required prior and likelihood distributions to derive the posterior distribution which
can be used in the calibration of resistance factors. The prior distribution was
evaluated through a literature survey. The likelihood distribution was derived using
the database collated in this research.
The mean values of the bias factors for the various groups were found to range from
0.65 to 1.12, and their error ranged from 0.26 to 0.40.
Using the statistics of the bias factors for each group, calibration of axial resistance
factors was conducted in this research using first order reliability method (FORM)
for the operating condition load case.
The methodology described in this research is valid for use in industry practice.
However, the bias factors and calibrated resistance factors should be viewed as
initial and only relevant to conditions in the Arabian Gulf where weakly cemented
carbonate soils are dominant. Further, these factors can be considered by
international committees such as ISO and the regulatory bodies in their further
development of guidelines and criteria for platform reassessment in the Arabian
Gulf. The use of the developed values in a different context can lead to significant
errors and incorrect prediction of capacities.
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Table 4-1: The first sheet of the pile driving record (PDR) for a demonstration pile is shown. The PDR is in tabulated form showing the blow count at each penetration, hammer type (3000/150) used at the start of the driving operation and time of starting the driving operation (10th March 1978) at 5:00pm
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Table 4-2: An intermediate (pen-ultimate) sheet of the PDR for the demonstration pile is shown. This sheet provides part of the history during the driving operation and shows that the operation was stopped when the penetration reached 201 ft at 5:04am and restarted on the 22nd March 1978 at 11:53 pm. This sheet also indicates a change in the hammer type (4600/150) from the initial hammer (3000/150)
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Table 4-3: The last sheet of the pile driving record for the demonstration pile is shown. The PDR sheet indicates that the penetration depth was 262 ft (79.9m) and was achieved with 25 blow count per foot. This sheet also shows that the target penetration was achieved at 6:12pm on the 23rd March 1978 which means that the driving operation for this pile took around 2 weeks to complete (from 10th March 1978 to 23rd March 1978 as shown on the pen-ultimate PDR sheet)
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Table 4-4: Recommended limiting skin friction and end bearing values by various researchers for design of piled foundations in carbonate soils
Author Year Limiting Skin Friction (kPa)
Limiting End Bearing (MPa)
McClelland 1974 20.0 5.0
Angemeer 1975 19.2 -
Agarwal 1977 28.7 6.7
Datta 1980 14.4 2.9
Abbs 1988 7.7 - 17.2 -
Nauroy & LeTirant
1983 2.9 -
Poulos 1988 5.7-9.6 -
LeTirant 1994 4.8 - 9.6 3.9 – 7.7
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Table 4-5: Summary of replies of a questionnaire was sent to various Geotechnical experts to recommend limiting skin friction and end bearing values for piled foundations in carbonate sands (Lacasse and Goulois, 1989)
Parameter Degree of Cementation
Expected Value
`Mean Value
Uncemented 2 – 8 4.1
Weakly cemented 3.5 - 8.1 5.1 Limiting unit end bearing (MPa)
Well cemented 6 - 12 7.6
Uncemented 5 - 50 22
Weakly cemented 5 - 30 22 Limiting unit skin friction (kPa)
Well cemented 7 - 76 24
Uncemented 20 - 30 24
Weakly cemented 32 - 50 42 Bearing Capacity Factor
Well cemented - -
Uncemented - -
Weakly cemented 10 - 40 23 Soil Pile Friction Angle (degree)
Well cemented 5 – 40 26
Uncemented - -
Weakly cemented 0.4 – 0.6 0.47 Coefficient of lateral earth pressure
Well cemented 0.2 – 0.8 0.46
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Table 4-6: Using the results of the questionnaire shown in Table 4-15, this table presents the limiting soil parameters selected for this research. Inspection of soil reports in the Arabian Gulf revealed weak degree of cementation
Parameter Units Mean Value used in Research
Limiting unit end bearing MPa 5.1
Limiting unit skin friction kPa 22
Bearing Capacity Factor 42
Soil Pile Friction Angle degree 23
Coefficient of lateral earth pressure 0.47
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Table 4-7: This table presents a printout of the APIPILE spreadsheet showing input data of one pile in the database. The printout shows all required parameters in red while the black color shows the calculations performed in the spreadsheet APIPILE. The input data reflects the limiting soil parameters identified in Table 4-6
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Table 4-8: Extract from APIPILE Excel spreadsheet developed in this research to predict axial pile capacity in accordance with API RP2A-LRFD (1993). The spreadsheet output shows the calculated friction capacity along the pile shaft and the bearing value at the pile tip at the bottom of each layer
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Table 4-9: Compilation of basic Smith soil model parameters by various Authors and the selected parameters that were used in this research
Damping Constant Quake
Reference Soil Type Skin Friction Js, s/m
End Bearing Jp s/m
Side mm
End1 mm
Clay 0.65 0.01-1.0 2.5 2.5
Sand 0.15 0.33-0.65 2.5 2.5 Tomlinson, 1995
Silt 0.33-0.50 0.33-1.5 2.5 2.5
Clay 0.098 0.492 2.54 2.54
Sand 0.262 0.492 2.54 2.54 Stevens et al., 1982
Silt - - - -
Clay 0.656 0.01 2.54 2.54
Sand 0.164 0.49 2.54 2.54 Tagaya, 1979
Silt - - - -
Soft Clay 0.26 0.66 5.08 5.08
Firm Clay 0.23 0.50 3.81 3.81
Stiff Clay 0.20 0.50 2.54 2.54
V. Stiff Clay 0.16 0.50 2.54 2.54
Hard Clay 0.10 0.50 2.54 2.54
Roussel, H.J., 1979
Sand 0.26 0.50 2.54 2.54
Clay 0.65 0.50 2.50
Silts 0.16 0.50 2.50 D/60 (2.5)
Sand 0.16 0.50 2.50 D/120 (2.5)
GRLWEAP (this research)
Rock 0.16 0.50 2.50
1 Values in brackets represent unplugged conditions
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Table 4-10: Extract from the spreadsheet APIPILE developed in this research showing calculation used to predict the Soil Resistance to Driving (SRD) at the top and bottom of each layer
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Table 4-11: Summary of reported soil setup factors for various soil types in various sites around the world (Rausche et al., 1996)
Predominant Soil Type along Pile Shaft
Range of reported
Setup Factors
Recommended Setup Factors
Number of Sites (% of database)
Clay 1.2-5.5 2.0 7 (15%)
Silt-Clay 1.0-2.0 1.0 10 (22%)
Silt 1.5-5.0 1.5 2 (4%)
Sand-Clay 1.0-6.0 1.5 13 (28%)
Sand-Silt 1.2-2.0 1.2 8 (18%)
Fine Sand 1.2-2.0 1.2 2 (4%)
Sand 0.8-2.0 1.0 3 (7%)
Sand-Gravel 1.2-2.0 1.0 1 (2%)
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Table 4-12: A list of the various methods commonly used to interpret pile-loading tests and an example application of these methods to interpret static loading test (Fellenious, 1980)
Method Development Year
Load Limit (kips)
Davisson Offset Limit Load 1972 375
Dr Beer Yield Load 1968 360
Brinch-Hansen 80% Criteria 1963 418
Chin-Kondner Extrapolation 1971 475
Decourt Method 1999 474
Average 420
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Table 4-13: Results of dynamic pile monitoring results obtained from a confidential report of a real life offshore installation
Penetration Actual SRD Setup Increase Pile ID
m Remarks
MN MN
A1 55.75 EOD 10.3
EOD 11.5 A2 56.05
BOR 15.9 15.9-11.5 = 4.4
EOD 9.5 B1 56.00
BOR 15.6 15.6-9.5 = 6.1
B2 56.00 EOD 10.0
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Table 4-14: Back analysis results from GRLWEAP and a comparison with CAPWAP results. The results demonstrate that the accuracy of the developed back-analysis method is within 5% of the accuracy using CAPWAP
PILE A1 A2 B1 B2
Blow count (blows/ft) 23 25 20 23
Short term Capacity (MN) 7.7 7.99 7.42 7.70
Setup Factor 2 2 2 2
Long term Capacity (MN) 15.4 15.9 14.8 15.4
Capacity from CAPWAP (MN) 15.5 15.9 15.6 15.5
Percentage of Error 0.3% 0.5% 4.9% 0.3%
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Table 4-15: The computed bias factors were subgrouped to represent various penetration ratios (<50, 50-75, 75-100, >100). Statistical parameters were calculated for each penetration ratios. The computed statistical parameters were considered as likelihood values when Bayesian updating was applied
L/D <50 50-75 75-100 >100 ALL
λ = mean 0.95 0.93 0.93 0.89 0.93
COV 0.42 0.34 0.32 0.47 0.36
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Table 4-16: Summary of the statistical parameters of the bias factors computed in this research. The bias factors shown in this table were considered as likelihood values when Bayesian updating was applied
Installation Method Soil Type Mean COV
Complete Database All 0.93 0.36
SC 0.77 0.34
CS – Effective Design 1.12 0.26 Driven
CS – Conservative Design 0.97 0.34
Drilled All 0.65 0.40
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Table 4-17: Major sources of bias and variability for a single pile (Bea, 1983)
Source Bias COV
Construction – pile penetrations 0.9-1.0 0.05-0.10
Soil sampling and testing (wire line) 1.5-2.0 0.10-0.20
Rate of cycling effects 1.3-2.0 0.20-0.30
Cyclic (one-way) loading effects 0.9-1.0 0.10-0.20
Pile compressibility and strain softening effects 0.9-1.0 0.10-0.20
Template pile system capacity versus most heavily loaded pile capacity
1.8-3.0 0.10-0.20
Engineering interpretation of shear strength data 1.1-2.5 0.10-0.30
Static capacity prediction (API) 1.1-1.3 0.30-0.50
Soil/ pile variability across site 1.0 0.10-0.35
Maximum loading annual 0.3 0.50-0.80
(lifetime) (0.8) (0.3-0.45)
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Table 4-18: Hamilton and Murff (1992) proposed the statistical parameters shown in this table. These parameters were published prior to the introduction of API RP2A-LRFD (1993) but were not used in the calibration of API RP2A-LRFD (Hamilton and Murff, 1992)
Function Parameter Clay Sand
Bias (Operating) 0.90 1.20
Bias (Extreme) 1.30 1.20 Pile Capacity
COV 0.30 0.40
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Table 4-19: Bayesian updating was employed in this research to arrive at the “posterior” statistical parameters for the resistance. To apply Bayesian updating in this research, “prior” and “likelihood” statistical parameters were required. The “prior” statistical parameters are based on analysis of data reported in the literature and described in Section 4.8.1. The “likelihood” parameters were determined on the basis of analysis of the database collated in this research and reported in Table 4-16
Prior Likelihood (Table 4-16)
Posterior Installation Method
Soil Type/ Condition
Mean COV Mean COV Mean COV
SC 1.30 0.30 0.77 0.34 0.93 0.23
CS Effective Design 1.30 0.30 1.12 0.26 1.18 0.20 Driven
CS Overconservative Design 1.30 0.30 0.97 0.34 1.11 0.23
Drilled All Soils 1.30 0.30 0.65 0.40 0.85 0.26
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Table 4-20: A proposed method to determine appropriate target reliability level for reassessment of an existing platform. The risk matrix shown in this table can be used for a qualitative assessment to establish the relative risk level for a given pile. To assess the target reliability level for a certain application, there is a need to answer two questions. The first question identifies the likelihood of an adverse event and the second question defines the consequences if this event takes places and its impact on the integrity of the pile in question. For example, if an event is almost certain to occur but its effect on the platform pile is insignificant; the table is entered with those criteria to define a target level of 2.5. This target level can then be used with Table 4-21 to define the appropriate resistance factor for reassessment of a pile
Consequence
Catastrophic damage in human life, environment and financial goals
Major threatening goals and objectives – requires close management
Severe and would require significant adjustment to function
Minor and would threaten element of a function
Insignificant and routine procedures are sufficient to deal with consequences
Lik
elih
ood
Manned Nonevacuated sour gas
Manned
nonevacuated
Manned
Evacuated
Unmanned
Nonevacuated
Unmanned
Evacuated
Almost Certain (>90%)
3.5 3.5 3.0 2.5 2.5
Likely
(65%-90%) 3.5 3.0 2.5 2.5 2.0
Moderate
(<65%) 3.0 3.0 2.5 2.0 1.5
Unlikely
(10%-35%) 3.0 2.5 2.0 1.5 1.5
Rare
(<10%) 2.5 2.5 2.0 1.5 1.5
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Table 4-21: Resistance factors were calibrated in this research using the “posterior” statistical parameters defined in Table 4-19 and employing various target reliability levels. Inspection of the results reveals that the calibrated resistance factors are sensitive to the target reliability levels. This finding highlights the importance of defining a target level when reassessment is conducted. By comparison, API RP2A-LRFD (1993) nominates a single value for the resistance factor, which is based on the use of a single target reliability level (β = 2.11). Further, this research also concluded that resistance factors are a function of other parameters such as soil type and installation method, whereas API RP2A – LRFD (1993) provides a single value for the resistance factor regardless of the driving method or the soil type
Calibrated Resistance Factor for βT
Installation Method Soil Type Mean COV
1.5 2.0 2.11 2.5 3.0 3.5
SC 0.93 0.23 0.80 0.69 0.64 0.60 0.52 0.45
CS Optimum 1.18 0.20 1.05 0.92 0.88 0.81 0.71 0.62 Driven
CS conservative
1.11 0.23 0.95 0.82 0.76 0.71 0.62 0.54
Drilled All 0.85 0.26 0.70 0.60 0.54 0.51 0.44 0.38
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Table 4-22: Calculated “posterior” statistical parameters for various penetration ratios using Bayesian approach. The “prior” statistical parameters are based on analysis of data reported in the literature and described in Section 4.8.1. The “likelihood” parameters were determined on the basis of analysis of the database collated in this research and reported in Table 4-15. The calibrated resistance factors were based on the use of a target reliability index level of 2 and were found to be insensitive to changes in the penetration ratio
L/D <50 50-75 75-100 >100 driven drilled ALL
Rλ 1.30 1.30 1.30 1.30 1.30 1.30 1.30
Prio
r
COVR 0.30 0.30 0.30 0.30 0.30 0.30 0.30
Rλ 0.95 0.93 0.93 0.89 0.96 0.76 0.93
Like
lihoo
d
COVR 0.42 0.34 0.32 0.47 0.34 0.43 0.35
Rλ 1.13 1.08 1.07 1.11 1.10 0.98 1.08
Post
erio
r
COVR 0.25 0.23 0.32 0.26 0.23 0.25 0.23
φ 0.82 0.81 0.81 0.79 0.83 0.71 0.81
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Figure 4-1: Diagrammatic representation of the soil/ pile database showing a pile that was driven using various hammers in soil strata. The graph shows the relationship between the resistance of the pile and penetration depth. The diagram shows that various hammers (Menck 4600/150, 3000/150) were used to drive the pile to the desired penetration depth. Firstly, Menck 3000/150 hammer was used to drive the pile from the seabed to the desired penetration depth of 98m
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Figure 4-2: A comparison of the calculated driving force (left) and stresses (right) against measured values as suggested by Tagaya et al. (1979) method. The measured response was taken from an actual offshore installation in the Arabian Gulf, which was reported by Tagaya et al. (1979)
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Figure 4-3: Input parameters used for a demonstration pile are shown here. This 1219mm diameter steel pile is 105m long and penetrates 79.9m into the soil. The pile was driven by MENCK MRBS4600 hammer with an assumed efficiency of 67% and 1.5m stroke. The water depth measured from the water surface to the mudline is around 25m
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Figure 4-4: This chart shows the complete pile driving record for a demonstration pile. The soil profile and the blow count at the pile tipping depth are of interest for the back-analysis calculations
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Figure 4-5: This chart shows the results of the GRLWEAP Bearing Graph analysis. The output screen shows all input parameters that were assumed in the design such as the assumed efficiency in addition to stresses in the pile and the relationship between blow count and predicted capacity
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0.0
0.5
1.0
1.5
2.0
2.5
0 50 100 150 200 250 300
Elapsed Delay During Driving (Hours)
SRD
Fric
tion
ratio
Figure 4-6: Estimate of setup factor was made using various start/stop data of the pile driving record. The graph shows the relationship between SRD to elapsed time during driving. Inspection of the trend in the chart indicated that a setup factor of 2 fairly represented long term effect of setup
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Figure 4-7: Scatter plot of the ratio of GRLWEAP capacity to static loading test capacity at the Beginning of Restrike (BOR) and End of Driving EOD) conditions (Thendean et al., 1996)
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Figure 4-8: Blow count versus depth diagrams for four piles which were plotted by an installation contractor in during actual pile driving installation in the Arabian Gulf. The pile driving records were collated in this research. The pile driving records show that all piles were driven to around 56m with blow count of approximately 25 blows per foot
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Figure 4-9: GRLWEAP input data screen for the analysis of a pile to validate the back-analysis results. The pile has a cross section of 1193cm2 and penetrates 56m into the soil stratum. The pile was driven by a Menck MHU 600 hammer. The soil parameters used in this analysis were selected from Table 4-9. The computed pile capacity from GRLWEAP represented short term capacity. The long term capacity was computed by allowing for a setup factor of 2 and compared to the output from the dynamic monitoring results for the pile in question
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CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-10: Predicted axial capacity using GRLWEAP of the pile that had been subject to dynamic monitoring. This pile was used to validate the back-analysis procedure
CHAPTER 4: PILE RESISTANCE FACTORS 177
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-11: Pile makeup of the dynamically monitored pile. The pile makeup consists of 9 sections with a uniform outside diameter of 1219mm. The minimum wall thickness used in the pile makeup was 20mm and the maximum wall thickness was 44mm at the mudline
CHAPTER 4: PILE RESISTANCE FACTORS 178
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-12: Modeling of the pile-soil interaction in GRLWEAP requires a breakdown of the system at each layer and at each change in pile section
CHAPTER 4: PILE RESISTANCE FACTORS 179
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-13: Results of GRLWEAP analysis when average pile wall thickness across the whole pile length was assumed for the pile instead of using the actual wall thickness for every pile section
CHAPTER 4: PILE RESISTANCE FACTORS 180
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-14: Mechanics of wave propagation in a pile (Cheney and Chassie, 1993). The mechanics of driving a pile was used to explain the reason behind the divergence in results when an average pile wall thickness - instead of actual variable thickness - was used when modeling pile wall thicknesses in GRLWEAP
CHAPTER 4: PILE RESISTANCE FACTORS 181
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0
200
400
600
800
2460 4920 7380 9840 12300 14760 17220 19680
Ultimate Capacity (kN)
Blo
wco
unt (
Blo
ws/
ft)
Figure 4-15: Sensitivity analysis of the computed ultimate capacity in GRLWEAP as a result of changing hammer efficiency. The curves show that the computed capacity is relatively insensitive to the assumed efficiency for low blow count such as those experienced in the Arabian Gulf
CHAPTER 4: PILE RESISTANCE FACTORS 182
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-16: Influence of changing segment length on the computed pile capacity showing that pile capacity is insensitive to the segment length for the range of pile capacities experienced in the Arabian Gulf
0
250
500
750
1000
1250
1500
2460 4920 7380 9840 12300 14760 17220 19680
Ultimate Capacity (kN)
Blo
wco
unt (
Blo
ws/
ft) 5m segment
2m segment
1m segment
CHAPTER 4: PILE RESISTANCE FACTORS 183
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0
200
400
600
800
1000
2460 4920 7380 9840 12300 14760 17220 19680
Ultimate Capacity (kN)
Blo
wco
unt (
Blo
ws/
ft)
Figure 4-17: Sensitivity of using various cushion types and manufacturers on the computed ultimate capacity. The plot shows that pile capacity is relatively insensitive to the selected cushion type as soil resistance, rather than pile characteristics, dominates the axial capacity
CHAPTER 4: PILE RESISTANCE FACTORS 184
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-18: Bias factors for the complete set of data comprising 138 piles. The trend shows similar number of piles with bias factor above and below 1.0. Hence, the trend implies no bias in the overall database
CHAPTER 4: PILE RESISTANCE FACTORS 185
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
( , )
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0.0
0.5
1.0
1.5
2.0
2.5
< >5.0% 5.0%90.0%0.381 1.474
@RISK Trial VersionFor Evaluation Purposes Only
Figure 4-19: Statistical analysis of the complete set of data shows that a normal distribution is most appropriate. The statistical analysis produced bias and coefficient of variation, λ = 0.93, COV = 0.36 for axial capacity of piled foundations in carbonate soils
CHAPTER 4: PILE RESISTANCE FACTORS 186
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
y = 2.9599x - 2.7457R2 = 0.9802
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4
Bias Factors
Red
uced
Var
iate
Figure 4-20: Statistical analysis of the complete set of data using the parametric method shows that a normal distribution is most appropriate as described in Section 3.4.2. The statistical analysis produced bias and coefficient of variation, λ = 0.93, COV = 0.36 for axial capacity of piled foundations in carbonate soils
CHAPTER 4: PILE RESISTANCE FACTORS 187
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
30 40 50 60 70 80 90 100 110
Pile Tip Penetration Ratio (L/D)
Bia
s Fa
ctor
Figure 4-21: Scatter plot showing bias factors for piles installed using supplementary installation methods. The plot shows that, in situations when piles are installed using supplementary methods, the use of API RP2A to predict pile capacity significantly (40% to 80%) overestimated the capacity and therefore is on the unsafe side
CHAPTER 4: PILE RESISTANCE FACTORS 188
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
< >5.0% 5.0%90.0%0.219 1.296
@RISK Trial VersionFor Evaluation Purposes Only
Figure 4-22: This chart shows a histogram of all piles that were installed using supplementary installation methods. An assumption of normal probability distribution provided the best fit to the data as shown above. The statistical parameters of the drilled/ grouted piles were computed as λ = 0.65 and COV = 0.40
CHAPTER 4: PILE RESISTANCE FACTORS 189
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
y = 0.324x + 0.7576R2 = 0.9209
0.0
0.4
0.8
1.2
1.6
-3 -2 -1 0 1 2 3
Normal Quantile
Bia
s Fa
ctor
Figure 4-23: An assumption of normal probability distribution for piles installed using supplementary methods resulted in linear probability plot. The statistical parameters of the drilled/ grouted piled foundations were computed as λ = 0.65 and COV = 0.40
CHAPTER 4: PILE RESISTANCE FACTORS 190
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
30 40 50 60 70 80 90 100 110
Pile Tip Penetration Ratio (L/D)
Bia
s Fa
ctor
Figure 4-24: Scatter plot for soil Type SC which describes predominant cohesive soil profile underlain by cohesionless soil
CHAPTER 4: PILE RESISTANCE FACTORS 191
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
30 40 50 60 70 80 90 100 110
Pile Tip Penetration Ratio (L/D)
Bia
s Fa
ctor
Figure 4-25: Scatter plot for soil type CS which describes predominant cohesionless soil profile underlain by cohesive soil
CHAPTER 4: PILE RESISTANCE FACTORS 192
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0.0
0.5
1.0
1.5
2.0
2.5
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
< >5.0%90.0%0.345 1.199
@RISK Trial VersionFor Evaluation Purposes Only
Freq
uenc
y (x
10-3
)
Figure 4-26: Probability plot of soil type SC showing that a normal distribution fits the data with mean = 0.77, standard deviation = 0.26, COV = 0.34
CHAPTER 4: PILE RESISTANCE FACTORS 193
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
y = 0.2575x + 0.7719R2 = 0.9459
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
-3 -2 -1 0 1 2 3
Normal Quantile
Bia
s Fa
ctor
Figure 4-27: Probability plot of soil type SC confirming that the assumed fitted normal distribution is appropriate with mean = 0.77 and COV = 0.34. This is in line with the non-parametric analysis using @RISK which indicated similar statistical parameters
CHAPTER 4: PILE RESISTANCE FACTORS 194
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
30 40 50 60 70 80 90 100 110
Pile Tip Penetration Ratio (L/D)
Bia
s Fa
ctor
Figure 4-28: Statistical parameters of the bias factors for piles driven in soil type CS was subgrouped further to piles with optimum design against those with overconservative design. This plot shows statistical parameters of bias factors for piles which are optimally designed
CHAPTER 4: PILE RESISTANCE FACTORS 195
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
30 40 50 60 70 80 90 100 110
Pile Tip Penetration Ratio (L/D)
Bia
s Fa
ctor
Figure 4-29: Statistical parameters of the bias factors for piles driven in CS soils. This plot shows the scatter diagram of the bias factor for those piles with an overconservative design. The definition of overconservative design in this research describes piles with a factor of safety of 4 or more according to API RP2A-WSD (2000). The API RP2A-WSD (2000) only requires piles to be designed for a factor of safety of 2 under operating conditions
CHAPTER 4: PILE RESISTANCE FACTORS 196
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
( , )
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
< >5.0% 5.0%90.0%0.425 1.518
@RISK Trial VersionFor Evaluation Purposes Only
Figure 4-30: Probability plot for soil type CS with conservative design, The analysis showed that a Normal distribution provided the best fit to the data with statistical parameters λ = 0.97, COV = 0.34
CHAPTER 4: PILE RESISTANCE FACTORS 197
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
y = 0.3234x + 0.9713R2 = 0.9153
0.0
0.4
0.8
1.2
1.6
2.0
2.4
-3 -2 -1 0 1 2 3
Normal Quantile
Bia
s Fa
ctor
Figure 4-31: probability plot for soil type CS with conservative design, λ = 0.97, COV = 0.34. The results of this non-parametric statistical analysis are consistent with the parametric analysis described in Figure 4-30
CHAPTER 4: PILE RESISTANCE FACTORS 198
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-32: Probability plot of soil type CS with optimum design. The probability plot shows that a normal distribution provides the best fit to the data with statistical parameters λ = 1.12, COV = 0.26.
CHAPTER 4: PILE RESISTANCE FACTORS 199
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
y = 0.2798x + 1.1192R2 = 0.9067
0.0
0.4
0.8
1.2
1.6
2.0
-3 -2 -1 0 1 2 3
Normal Quantile
Bia
s Fa
ctor
Figure 4-33: Probability plot of soil type CS with optimum design, λ = 1.12, COV = 0.26
CHAPTER 4: PILE RESISTANCE FACTORS 200
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-34: Calibrated resistance factors for the various subgroups identified in this research. The chart shows that, for a certain target reliability level, the resistance factor for a pile driven using supplementary methods should be lower than that for a pile driven without the need for drilling or jetting. The plot also shows that API RP2A-LRFD (1993) recommends a single value for the resistance factor and does not address the various conditions that affect the value of the resistance factor
0.2
0.4
0.6
0.8
1.0
1.2
1.5 2 2.5 3 3.5
Target Reliability Index
Res
ista
nce
Fact
or
Drilled - All Soil Types
Driven - Soil Type SC
Drilled - Soil Type CS Optimum
Drilled - Soil Type CS Conservative
Target reliability index (βT = 2.11) used in calibrating API-RP2A-LRFD (1993) and its corresponding resistance factor (φ = 0.7)
CHAPTER 4: PILE RESISTANCE FACTORS 201
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 4-35: A plot showing the effect of penetration ratio on the calibrated resistance factors. The analysis shows that the calibrated resistance factors are insensitive to various penetration ratios
0.60
0.70
0.80
0.90
<50 50-75 75-100 >100
Penetartion Ratio
Res
ista
nce
Fact
or
β = 2.7
β = 2.0
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Chapter 5.
CALIBRATION OF OPEN AREA LIVE LOADS
5.1. BACKGROUND
Section 2.8.1 revealed that open area live load (OALL) on offshore platforms has
not been explored, quantified or addressed in international codes and standards.
Hence, there was a need to develop OALL that can be used in reassessment of
existing platforms.
Open areas on offshore platforms provide areas where equipment can be laid during
normal operation and during shutdown. Open areas have the following benefits:
• Permit access for normal operation and maintenance,
• Locate wells, production and pipeline facilities to reduce the risk from potential
events,
• Permit access for operators to perform necessary emergency shutdown actions in
an emergency situation,
• Facilitate personnel escape, evacuation and rescue procedure in the event of an
emergency,
• Permit access for fire fighting or other emergency response,
• Protect critical facilities from damage during normal operations or emergency
situations,
• Segregate toxic or highly reactive materials and high risk facilities,
• Separate continuous ignition sources from probable points of release of
flammable materials,
• Separate air intakes (building pressurization, combustion air to turbine or
heater),
• Separate equipment to minimize involvement of or escalation to adjacent
facilities in a fire or explosion, and
• Provide site security
CHAPTER 5: OPEN AREA LIVE LOADS 203
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Calibration of OALL required a database that models the nature of offshore
platforms. However, the literature survey conducted in this research identified lack
of a suitable database to assess live loads on offshore platforms. Available live load
databases only covered buildings and structures other than offshore platforms as
discussed in Section 2.8.2. Hence, there was a need for a relevant database for the
purpose of this research.
In addition to the requirement for an appropriate database, the discussions in Section
2.8.3 revealed that the probability model used to derive live loads for buildings in
codes and standards was not suitable to derive OALL for offshore platforms and an
alternative probability model was required.
This Chapter identifies the nature of the database required to develop OALL,
describes the data collection efforts in this research and defines the probabilistic
model used in this research to derive OALL effect on piles of offshore platforms.
5.2. DEFINITIONS USED IN DEVELOPMENT OF OALL
The OALL concept adopted in this research is consistent with the uniformly
distributed live loads used in design codes such as ASCE Standard 7-05 for
buildings. As such, OALL developed in this research can be used with the API
RP2A-LRFD (1993) code provisions without the need to conduct additional
sophisticated numerical analyses.
The process of developing OALL in this research was to calculate the mean lifetime
maximum live load on a pile given the arbitrary point-in-time survey data.
The mean lifetime maximum live load effect considers the variables involved in the
live load process and is distinct from the data obtained in surveys, which represent
arbitrary point-in-time. Whereas the loads measured by live load surveys give an
instantaneous picture of the loading on a floor, the mean lifetime maximum load
effects account for spatial and temporal variations in the load components.
Using the survey data collated in this research, statistical parameters of OALL were
developed and can be divided into 2 sets:
• Statistical parameters associated with arbitrary point-in-time survey data, and
• Statistical parameters of the lifetime maximum live load on a pile, which is
obtained using influence coefficient and extreme value analysis (Ang & Tang).
CHAPTER 5: OPEN AREA LIVE LOADS 204
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
5.3. CALIBRATION MECHANICS OF OALL
The derivation of OALL started with identifying the nature of live loads on offshore
platforms, which was then used to conduct arbitrary point-in-time surveys. The
survey results produced a database that was used to derive OALL on offshore
platforms. The database consisted of instantaneous loads from survey data, which
are termed arbitrary point in time values, since it was not feasible to conduct load
surveys lasting several years. Consequently, it became necessary to employ a
stochastic process to these load values.
The arbitrary point-in-time survey data points were subjected to a statistical analysis
to identify the most suitable distribution and obtain their statistical parameters. This
step was required because the statistical parameters heavily rely on the distribution
type as described in Section 3.4. To obtain the statistical parameters, the data was
plotted using a scatter plot to check for linearity, presence of outliers and unusual
points.
Using the arbitrary point-in-time statistical parameters, an appropriate stochastic
process was employed to treat the arbitrary-point-in-time data and derive the
maximum load effects.
Using the statistical parameters of the database and the maximum load effect,
extreme value analysis was employed to produce the mean lifetime maximum value
for a single “realization” using the methodology described in Section 3.8.2. The
mean lifetime maximum load effect on a pile was developed using the asymptotic
distribution of the extreme values from the single “realization” case.
The OALL effect on a pile was converted to equivalent uniformly distributed load
using tributary area. The derived OALL covers loads on open deck areas around
equipment supported by floor grating and plating. This approach is compatible with
that used in calibrating EUDL in ASCE Standard 7-05.
5.4. NATURE OF LIVE LOADS ON OFFSHORE PLATFORMS
Unlike office and residential building surveys, which target furniture and persons to
represent live loads on the floors of building structures, the nature of live loads on
offshore platform topsides was not easily identifiable.
The Author conducted a number of surveys while on job assignments on offshore
CHAPTER 5: OPEN AREA LIVE LOADS 205
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
platform sites to define the nature of live loads on offshore platforms. The Author’s
surveys represent the arbitrary point-in-time data, which is similar in nature to the
furniture surveys used by ANSI to derive load data for buildings as described in
Section 2.8.2. The surveys revealed that open areas on offshore platforms are
usually loaded with equipment or parts of equipment or tools used in maintenance
such as scaffolding. The Author recorded all items such as scaffolding and minor
pieces of equipment that were found on various platform open areas on platform
deck structures.
On examining the magnitude of the surveyed data against the platform deck areas, it
was evident that a probabilistic analysis would result in relatively minor OALL
design values. Figure 5-1 shows an example of live loads on an offshore platform
during normal operating conditions. The small OALL magnitude would be due to
the large open deck areas in addition to the relatively small surveyed weights.
Several discussions with offshore maintenance and operations personnel in the
Arabian Gulf were conducted to identify if other equipment would normally be
located on open areas. The discussions were conducted with a number of senior
level personnel with a combined experience of over 100 years in maintenance and
operations.
The discussions revealed that during operation, minor items would be expected on
open areas to comply with house-keeping rules on offshore platforms. House-
keeping rules for operation and maintenance of offshore platforms address safety
issues which prevents items on open areas during normal operating conditions.
However, the maintenance and operation personnel agreed that this would not be the
case during shutdown. In such case, various activities could take place
simultaneously which would make it possible for the open areas to be completely
occupied by various pieces of equipment. Further, the discussions revealed that
floor live loads on offshore platforms should account for operation and maintenance
of equipment and possible modifications or changes in use during shutdown.
During installation or maintenance, portions of equipment may be laid down at
various locations on the floor.
The discussions also indicated that the characteristic of those pieces of equipment
could generally be considered similar to the population of equipment on offshore
platform decks but need not be correlated to each other. For example, the
CHAPTER 5: OPEN AREA LIVE LOADS 206
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
replacement or maintenance of a motor, which is part of the compressor skid,
similar to that shown in Figure 5-2 could be scheduled during a shutdown. The
motor weight is related to the weight of the turbine itself, but other equipment pieces
that may be present on the open topside deck have no correlation to the motor
weight or the turbine weight or to each other’s weight. Other equipment in the same
area may include blasting equipment, scaffolding packs and welding machines or
other tools which are not correlated to the weight of the rotor or to each other.
The discussions also revealed that very large pieces of equipment or those with
heavy weights are usually not maintained offshore due to space or lifting
restrictions. Typically, a platform would have a crane with a limited lifting
capacity. The capacity of those cranes is usually limited to around 10 tonnes on the
main deck and would therefore be insufficient to lift equipment pieces heavier than
10 tonnes. When large pieces of equipment require maintenance offshore, they are
disassembled in their fixed location and their components are placed on the open
area. It is generally true that the total weight of the large and heavy equipment
occurs only in its fixed location on the floor and can only be considered in the
structural design as dead loads.
In summary, open area live loads on offshore platforms can be described by
equipment pieces with weights less than the crane capacity on the platforms.
5.5. EQUIPMENT LOAD DATABASE
Using the characterization of the live load on offshore platform decks, a survey of
actual pieces of equipment was conducted. The survey covered over 400 pieces of
equipment supported on over 35,000m2 of combined topside deck area on offshore
platforms in the Arabian Gulf.
The database of all equipment is presented in Appendix A and includes footprints
and weights for over 400 pieces of equipment from 60 platforms. Each point in this
database defines the equipment name, its weight, footprint and location on the
topside deck. The data was grouped to different deck usage (main, mezzanine and
cellar decks).
The equipment data were extracted from actual offshore visits, layout drawings,
vendor drawings, and equipment data sheets. Inspection of the records showed that
the surveyed equipment pieces were installed from the 1960s until 2003. Hence, the
CHAPTER 5: OPEN AREA LIVE LOADS 207
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
survey reflects the time-dependent nature of the live loads that could be present on a
platform deck.
Figure 5-3 presents a scatter plot of equipment weights against the respective
footprint and shows that the majority of the surveyed equipment weighs less than 20
tonnes (200kN).
5.6. SUBGROUPING THE EQUIPMENT DATABASE
Inspection of Table 2-4 reveals that surveys which were used to derive live loads in
the development of building codes such as ANSI A58 were only grouped to building
functions. For example, the surveyed areas for residences were 204,000 ft2 but no
further subgrouping was considered to determine the statistical parameters of
various room usages (open space such as living rooms against closed rooms such as
bedrooms). The approach used in building codes was examined for use with
offshore platforms but with the following two questions. The first question
addressed the subgrouping of the data to cover various platform functions and the
second question covered the treatment of the data within that function.
5.6.1. SUBGROUPING BY PLATFORM FUNCTION
In this research, it was not possible to divide the database in accordance with
platform function due to the large number of functions that can be assigned to
platforms. Table 3-1 presented seven functions associated with the database (e.g.
compression, wellhead, glycol, living quarters, riser and water disposal) but other
functions (e.g. utility, observation) could also be envisaged.
Due to the difficulty of obtaining data for offshore platforms, it was not possible to
obtain sufficient data for every conceivable platform function. It is difficult to
obtain real data for offshore platforms when compared to building structures due to
confidentiality issues. Owners and operators allow limited personnel to visit their
facilities. Hence, the amount of data that can be obtained is limited. Subdividing a
limited amount of data with platform functions would result in a small sample for
each group which would affect the calculated statistical parameters. Consequently,
it was considered impractical to divide the database of this research in a similar
manner to that used in building codes and a single database was contemplated
without further grouping the data.
CHAPTER 5: OPEN AREA LIVE LOADS 208
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
5.6.2. SUBGROUPING BY LOCATION ON PLATFORM
The Author’s experience with industry rules and best practices related to layout of
equipment on offshore platforms revealed that heavy and large pieces of equipment
are usually placed on upper decks. Such layout is commonly adopted to facilitate
maintenance and removal of those heavy equipment by independent cranes in the
event of any need to overhaul or replace those equipment pieces Hence, it was
considered appropriate to subgroup the equipment database in accordance with their
location on the platform deck, which reflected the philosophy usually adopted
during layout of equipment on offshore platforms. Equipment pieces on lower
decks are handled by monorails with relatively smaller safe working loads (SWL),
while larger equipment on the upper deck are usually handled by jib cranes with
larger SWL.
Figure 5-4 shows an elevation view of an actual platform in the Arabian Gulf.
Inspection of the layout reveals that larger pieces of equipment are located on the
upper deck while smaller size equipment pieces tend to be located on lower decks.
Hence, grouping the database into two groups reflects layout practice and is also
easy to comprehend when performing deterministic reassessment.
5.6.3. SUBGROUPING BY CRANE AND MONORAIL SWL
Handling equipment pieces on a platform requires use of cranes and monorails. On
each platform, there is usually a number of monorail beams and one or two cranes.
Determination of the lifting crane capacity is usually carried out in a “materials
handling” study during platform original design and crane capacity is usually
optimized to reduce capital expenditure (CAPEX) by placing heavier equipment on
the upper deck.
Inspection of the data revealed that crane Safe Working Load (SWL) ranges from 5
to 10 tonnes on upper decks, while SWL of monorails is usually limited to 5 tonnes
and can be as low as one tonne on lower decks.
In this research, a limitation was imposed on the database to reflect industry practice
and common crane and monorail SWL on various decks. The database was limited
to 10 tonnes and 5 tonnes for equipment on upper and lower decks, respectively.
CHAPTER 5: OPEN AREA LIVE LOADS 209
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
5.7. STATISTICAL PARAMETERS OF EQUIPMENT
WEIGHTS
Statistical analysis was carried out for two sets of equipment weights. The first
dataset is described in Section 5.7.1 and covered equipment weights on lower decks.
The second dataset covered equipment weights on the upper deck and is described
in Section 5.7.2.
A definition of the probability distribution of the data was required to establish the
statistical parameters of equipment weights for the upper deck and lower decks.
Several probability models such as Normal, Gumbell, Exponential and one-sided
Normal distribution for the equipment data were examined and the validity of each
distribution model was tested by performing a co-linearity test of the probability
paper graph. The selected distribution was confirmed by visual inspection.
5.7.1. ON LOWER DECKS
As previously discussed in Section 5.6.3, the complete database includes equipment
weights which can not be lifted during shutdown operations because of the
limitations on materials handling equipment (monorails) on lower decks of
platforms. As the interest in this research was focused on equipment weights that
can physically be placed on the open areas, the database only considered those
weights that can physically be lifted by a monorail hoist limited to SWL of 5 tonnes.
The statistical parameters of equipment on lower decks are shown in Figure 5-5 with
mean of 36.1kN and standard deviation of 18.4kN. The calculated COV for the
arbitrary point-in-time equipment weights is 0.51.
For comparison, the overall dataset on the lower deck was also examined to identify
the statistical parameters if the effect of monorail capacity was disregarded. Figure
5-6 shows a histogram of the complete dataset of the equipment on lower decks. An
assumed distribution was fitted to the data. The fitted distribution shows an
Exponential distribution and its parameters. Using the Exponential distribution, the
mean and standard deviation of the equipment weights on lower decks were
computed as 135.4kN and 134.8kN, respectively.
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5.7.2. ON UPPER DECK
When the equipment weights on the upper deck were analyzed to reflect a crane
SWL of 10 tonnes, the distribution shown in Figure 5-7 was representative of the
data with a mean of 81.5kN and a standard deviation of 30.6kN.
When the effect of cranes is disregarded, Figure 5-8 provides a histogram and a
fitted distribution for equipment on the upper deck distribution using @RISK and
the non-parametric approach described in Section 3.4.2. The analysis reveals that an
Exponential distribution provided the best fit to the equipment data on upper deck
with a mean of 627kN and a standard deviation of 638kN.
5.7.3. CONCLUSION
A comparison of the results above indicates the significant effect of including the
crane/ monorail capacity in determining the statistical parameters of the equipment
weights. Further, as the statistical parameters directly affect the calculated OALL, it
was clear that the effect of the crane/ monorail capacity would be dominant on the
outcome of this research and that disregarding the effect of crane would lead to
overconservative results.
Consequently, it was necessary to consider the effect of crane/ monorail effect on
the computed statistical parameters in this research.
5.8. CALCULATION OF MEAN LIFETIME MAXIMUM PILE
LOAD
The discussions with operation and maintenance personnel proved to be very
valuable to guide the efforts for collection of appropriate survey data and enable the
selection of an appropriate probabilistic model.
Using the statistical parameters for equipment weights on the upper and lower decks
developed in Section 5.7, this section applies the influence surface method to
calculate the maximum live load on piles. The influence surface method was
described in Section 3.8.1.
The total axial live load supported by one pile due to a random variable is given by
the following formula (Ang and Tang, 1984):
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∑ =
==
mi
i iiWCF1
Equation 5-1
Where F = Axial live loading on one pile due to the load on ¼ of the influence area
C = Influence coefficient
W = Random variable of equipment weights, assumed to be statistically independent with the same mean & standard deviation
m = Number of sectors
Discussions with maintenance and operation personnel revealed that loads applied
on platform decks during shutdown events were not necessarily similar to the
loading applied during another shutdown event. The discussions also revealed that
pieces of equipment on an open area were not necessarily related to the neighboring
equipment. Hence, pile axial load was assumed to vary (statistically) independently
between shutdown events. In such case, the mean value and standard deviation of
the maximum axial live load on one pile F are given by (Ang and Tang, 1984):
( ) ( )∑ =
==
mi
i ii WECFE1
Equation 5-2
Wmi
i iF C σσ ×= ∑ =
=12 Equation 5-3
Where E(W) = Mean of the equipment weight
σW = Standard deviation of the equipment weight
The mean and standard deviation of the axial live load on the pile are the load effect
statistics for one “realization” of a fully loaded deck. During the platform lifetime,
there will be a number of such realizations. Each realization represents one
shutdown event. Discussions with maintenance and operation personnel revealed
that it would be reasonably conservative to assume that other realizations, with
independent (different) weight combinations, will occur once every year during the
platform lifetime.
The interest is in the statistics of the lifetime maximum load effect. For this
purpose, the asymptotic distribution of the extreme values was used (Ang and Tang,
1984). To derive the lifetime maximum load effect, the distribution type for the
load effect was required. Ang and Tang (1984) described the derivation of the
lifetime maximum load effect using two distribution types, namely normal and
lognormal. Both distribution types were examined in this research.
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Assuming that the Central Limit Theorem is valid, the axial live load loading on one
pile (F) will approach a normal distribution. The Central Limit Theorem indicates
that any variable that can be physically modeled as a summation of several effects is
likely to be described by the normal distribution. Since the total load on a structural
sector arises from the combined additive effect of individual personnel and pieces of
equipment, the normal distribution is a logical model. As the size of the sector
increases, the number of individual contributions usually increases and the normal
model can be expected to provide a better fit.
However, the number of sectors in a platform deck is likely to be small. Since only
a small number of random variables are in the computation of the axial load and the
contribution of the closest sectors would tend to dominate, the distribution would be
somewhere between a normal and lognormal distribution. In this research, both
normal distribution and lognormal distributions were used and a comparison of their
respective extreme values was made.
To allow for the weight of personnel working on the deck, ten persons on the
platform deck during shutdown was assumed. An average weight of 667 N per
person (Ellingwood and Culver, 1977) added about 30N/m2 to the OALL from
analysis of the sustained loads. Thus, the effect of personnel is relatively
insignificant. This assumption is also likely to be conservative for large open areas
offshore but the variance in normal personnel load contributes negligibly to the
variance in the unit load (Wen, 1979).
5.8.1. EXTREME AXIAL PILE LOAD USING NORMAL
DISTRIBUTION
It can be shown that the extreme values of F (termed Fn in this research) approaches
Type 1 asymptotic distribution (Ang and Tang, 1984). Thus, using Cramer’s
approximation to the Type 1 asymptotic solution, it was possible to compute the
statistical parameters of Fn for a number of realizations (n) as n approaches infinity.
For a normal variable F with mean value of μF and a standard deviation of σF, the
extreme value Fn has a characteristic largest value (mode) of μn and a dispersion
parameter αn given by Ang and Tang (1984) as follows:
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FFn nnn μσπμ +⎟⎟
⎠
⎞⎜⎜⎝
⎛ +−=
ln224lnlnlnln2 Equation 5-4
Fn
nσ
α ln2= Equation 5-5
The mean and standard deviation of the extreme value Fn can then be calculated as
follows (Ang and Tang, 1984):
( )n
nn uFEαγ
+= Equation 5-6
nF α
πσ6
= Equation 5-7
Where γ = Euler number = 0.577216
By varying the values of n, the relationship between E(Fn) and σFn and the number
of shutdown events (n) could be established for an assumed number of sectors. The
relationship plot usually shows a unique second order polynomial converging to an
asymptotic value. The point at which this graph converges to an asymptotic value
can be regarded as the natural saturation point for that specific floor deck.
5.8.2. EXTREME AXIAL PILE LOAD USING LOGNORMAL
DISTRIBUTION
The lognormal distribution was also investigated because the normal model, which
is based on the Central Limit Theorem, may not be justified due to the relatively
small number of sectors in a typical platform deck and dominance of several
equipment weights on the load effect.
Having determined the values of μF and σF, the values of the lognormal distribution
parameters ζ and λ are determined from the following equation:
⎟⎟⎠
⎞⎜⎜⎝
⎛+= 2
22 1ln
μσζ Equation 5-8
where n = Sample size of the number of times (realizations) for which the deck is fully loaded
μF = Mean value of a normal variable F
σF = Standard deviation of a normal variable F
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2
21ln ζμλ −= Equation 5-9
From the relationship between a normal variable and a lognormal variable, the
variable F’=ln(F) is normal and its extreme values will converge to Type 1
asymptotic distribution assuming a sample size of n:
λζπζ ++
−=n
nnun ln224lnlnlnln2' Equation 5-10
ζα n
nln2' = Equation 5-11
According to the above logarithmic transformation, Fn will converge to the Type 2
asymptotic distribution with the parameters (Ang and Tang, 1984):
Un e=ν Equation 5-12
'nk α= Equation 5-13
Where νn = Characteristic largest value of the initial variate F
k = Shape parameter with 1/ k being a measure of dispersion
Hence, the mean and standard deviation of Fn was computed using the following
equations:
( ) ⎟⎠⎞
⎜⎝⎛ −Γ=
kFE nn
11ν Equation 5-14
21
2 1121 ⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ −Γ−⎟
⎠⎞
⎜⎝⎛ −Γ=
kknF νσ Equation 5-15
Where Γ = Represents the gamma function whose values can be obtained from a standard table (Ang and Tang, 1984) or as shown below
The logarithm of the gamma function is sometimes treated as a special function to
avoid additional ‘branch-cut” structures that are introduced by the logarithm
function (http://mathworld.wolfram.com/LogGammaFunction.html).
The log-gamma function can be defined by:
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( ) ∑∞
= ⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ +−+−−=Γ
11lnlnln
i kz
kzzzz γ Equation 5-16
Stirling series (in its normal form) also provides a solution, given by a simple
analytical expression:
( ) ( ) 53 12601
3601
121ln
212ln
21ln
zzzzzzz +−+−⎟
⎠⎞
⎜⎝⎛ −+=Γ π Equation 5-17
By varying the values of n, a plot of E(Fn) versus the sample size can be obtained
and the maximum lifetime live load on a pile can be established.
5.8.3. MINIMUM SEPARATION DISTANCE
In this research, the minimum distance between equipment pieces was of interest to
determine the required number of sectors on a platform deck, which is then used to
calculate influence coefficient. This enables the application of influence surface
method described in Section 3.8.1.
The minimum separation distance depends on the chemical and mechanical process
requirements. Processes differ from each other because of their inherent hazards.
During equipment layout stage at Front End Engineering Design (FEED), processes
and operations are usually grouped on a platform depending on their fire and
explosion hazard which can be classified into moderate, intermediate and high.
The moderate category includes processes, operations or materials having a limited
explosion hazard and a moderate fire hazard. This class generally involves
endothermic reactions and non-reactive operations such as distillation, absorption,
mixing and blending of flammable liquids. Exothermic reactions with no flammable
liquids or gasses also fit into this hazard group. Typical examples include acetic
anhydride and formaldehyde.
The intermediate category includes processes, operations or materials having an
appreciable explosion hazard and a moderate fire hazard. This class involves mildly
exothermic reactions such as alkylation in refinery and cyclohexane.
The high category includes processes, operations or materials having a high
explosion and moderate to heavy fire hazard. This class involves highly exothermic
or potential runaway reactions and high hazard products handling such as Ethylene
and Polyethelene.
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This classification is used to determine minimum spacing requirements but
additional factors and judgments can still affect the class to which the process is
assigned, including pressure vessel size, flammable liquid holdup, gas versus liquid
phase, loss history, interdependency, and lead time to rebuild. For example, loss
history shows that fires or explosions in congested areas of oil and chemical plants
can result in extensive losses.
Wherever explosion or fire hazard exists, proper plant layout and adequate spacing
between hazards are essential to loss prevention and control. Layout relates to the
relative position of equipment or units within a given site. Spacing pertains to
minimum distances between units or equipment. Further, vapor cloud calculations
could indicate greater separation distance between some units is needed because of
higher than normal explosion damage potential and business interruptions.
The plant layout and spacing necessary to limit loss size is determined on the basis
of worst case scenarios for water cloud, vessel and building explosions and for fires.
The analysis involves the calculation of overpressure circles and is typically carried
out by loss prevention engineers.
A good layout and sufficient spacing between hazards, equipment and units will
have the following benefits:
• Less explosion damage: Overpressures created by an explosion decrease rapidly
as the distance from the center of the explosion increases,
• Less fire exposure: Radiation intensity from a fire decreases as the square of the
separation distance,
• Higher dilution of gas clouds or plumes: Gas concentration decreases as the
distance from the emission source increases,
• Easier access to equipment for maintenance, inspection and fire fighting
purposes, and
• Easier spill and spill fire control in open areas.
Loss prevention engineers typically establish a probable maximum loss (PML) and
maximum foreseeable loss (MFL) estimates based upon a vapor cloud explosion
where such a hazard exists. An adequate spacing between explosion hazard areas
will lower the PML and MFL.
On the other hand, extensive spacing increases the initial investment costs required
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to build a given platform. The larger platform deck requires additional supporting
structural weight, larger foundations, more piping, cabling and larger drainage
systems. Additional or larger pumps or compressors may also be required as
friction loss increases with the piping length, which will in turn increases operating
costs.
Industrial Risk Insurers (1978) produced recommendations and guidelines for loss
prevention and protection. Those guidelines are based on experiences gained from
loss cases documented by the insurance underwriters. The guidelines cover onshore
and offshore plants that process oil and gas with an objective of keeping plants as
safe as possible from fire and explosion. For offshore facilities, the Industrial Risk
Insurers guidelines reproduced spacing charts for offshore facilities that require a
minimum distance of 3m.
Greater distances may be required where facilities are of very large size or contain
high pressures or toxic materials. Where spacing is significantly below the
minimum distances of 3m recommended by Industrial Risk Insurers, it is usually
necessary to compensate for the increased degree of risk. This can often be done by
providing more extensive safety features such as fire proofing, fixed water sprays,
additional fire fighting equipment and training, fire and/ or blast walls. However,
reduction below 3m is not usually permitted in industry practice.
In conclusion, a minimum separation distance between equipment of 3m was used
in this research, which reflects the recommendations of Industrial Risk Insurers
(1978) recommendation and industry practice.
5.9. APPLICATION
This section applies the procedure described in Section 5.7.3 to compute the mean
lifetime maximum load effect on a pile. For this purpose, an offshore platform with
a square plan dimension of 15m was considered to demonstrate the procedure and
sensitivity analyses were then performed on the parameters of this platform to derive
a generic solution for OALL. The plan of the platform is shown in Figure 5-9.
The number of sectors on a floor deck was established on the basis of two criteria.
First, the minimum distance between equipment was established at 3m as discussed
in Section 5.8.3. Hence, the size of each sector had to be 3m or more in plan.
Second, for equipment weighing less than or equal 10 tonnes, the average equipment
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footprint was found to be approximately 10m2 as shown in Figure 5-3, which also
translates to an approximate minimum plan dimension of 3m.
Hence, the plan dimension of the platform was divided into sectors as shown in
Figure 5-9. The plan dimension of 15m*15m was divided into 16 sectors, which
satisfies both conditions of minimum separation distance and average equipment
footprint.
The influence surface concept described in Section 3.8.1 was applied to determine
the axial load on piles for a random load located anywhere on the deck. To derive
the mean lifetime maximum load effect on a pile, the procedure described in Section
5.7.3 was employed. The calculations were conducted for 50 shutdown events and
are presented in Table 5-2. The number of shutdown selected for their application is
based on one shutdown per year for a lifetime of 50 years.
By varying the number of shutdowns n (also termed realizations or occurrences in
this thesis), the mean lifetime maximum load effect on piles against the number of
shutdowns is plotted in Figure 5-10 and Figure 5-12 for the lower and upper decks,
respectively.
Inspection of Figure 5-10 reveals that, at large number of occurrences, E(Fn)
displays an asymptotic distribution. Similar conclusion is also drawn from Figure
5-12.
A plot of the relationship of the number of occurrence (or realizations) against the
standard deviation of Fn is shown in Figure 5-11 and Figure 5-13 for the upper and
lower decks, respectively. It is clear that the assumed distribution has a marked
effect on the computed standard deviation.
The calculated mean lifetime maximum live load effect on piles for each deck was
divided by the tributary area of the deck to provide OALL as shown in Table 5-3.
The derived OALL is equivalent to EUDL nominated in design codes and standards.
Conversely, using EUDL stipulated in design codes multiplied by the tributary area
produces the mean of the lifetime maximum live load on a pile.
Inspection of Table 5-3 reveals that the range of OALL used in industry practice is
subjective and is hence not representative of the various parameters affecting live
loads. For example, the current specifications of one operator in the Arabian Gulf
apply 2.5kPa to all floors when calculating live loads but an older specification of
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the same operator applied 17kPa to calculate live load effects. An investigation of
the specifications of other operators in the Arabian Gulf revealed that the use of
5kPa for OALL is more common. The Author’s experience in South East Asia with
a major American contractor and review of calculations performed by major
Japanese contractors revealed that using 5kPa for all decks is not uncommon.
5.10. BENCHMARKING THE STATISTICS OF OALL
This section evaluates the results of this research with an objective of ensuring that
its findings are consistent with values used to develop API RP2A-LRFD (1993).
The computed coefficient of variation (10% - 20%) shown in Figure 5-11 and
Figure 5-13 for the maximum lifetime live load effect was compared to that used in
the development of API RP2A-LRFD (1993). Moses (1980) defined live loads on
offshore platforms as facility loads associated with the platform operation during
some portion of its lifetime. According to Moses (1980), live loads covered
applications such as floor loading, drill rig, drilling facilities and supplies, crane and
derrick hook loads and production facilities and supplies.
In deriving the statistical parameters for live loads, Moses (1980) considered that it
would seem reasonable to use no bias for live loads on the basis that live loads may
be overestimated or underestimated. Hence, this research adopted a bias factor of
1.0 for live loads. To derive COV for live loads, Moses (1980) argued that live load
placement and analysis would carry more uncertainty than dead loads, hence leading
to larger variability. Moses assumed 10% weight variability and a further 10%
analysis uncertainty leading to an overall value of about 14% for live load COV.
Hence, this research adopted bias factor of 1.0 and COV of 15% for OALL. The
use of no bias for live loads is consistent with the value used by Moses (1980) as
discussed above. The use of 15% for the COV is based on the results of this
research, which is also consistent with the value employed by Moses (1980) to
calibrate API RP2A.
The mean lifetime maximum live load effect developed in Section 5.9 was derived
from nominal arbitrary point-in-time statistics defined in Section 5.7. This section
investigates the relationship between the statistics of the lifetime maximum load
effect and the statistics of the arbitrary point in time values in view of available
knowledge in the field.
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A review of the literature revealed no specific relationship between the arbitrary-
point-in-time and the nominal load data statistics. However, ANSI/AISC 360-05
stipulates that the “mean value of arbitrary-point-in-time live load is in the order of
0.24 to 0.40 times the mean maximum lifetime live load” but that “its dispersion is
far greater”.
The ratio (0.24 to 0.40) nominated in ANSI/AISC 360-05 was found to be similar to
the ratios calculated in this research. For the lower deck, Table 5-3 provides a value
of 4.4kPa for OALL. Hence, the mean maximum lifetime live load on a pile is
4.4kPa*15*15/4 = 247kN. The mean value of 36.1kN for the arbitrary point-in-time
live loads was defined in Section 5.7. Hence, the ratio (0.15) computed in this
research for lower decks is similar to the lower bound of the ratio (0.24) stipulated
in ANSI/AISC 360-05.
The dispersion of the arbitrary point-in-time on lower and upper decks was defined
in Section 5.7 to be 0.51 and 0.38, respectively. These dispersion values are greater
than the ratio (0.15) between the mean lifetime maximum load effect and mean
value of arbitrary point-in-time.
Hence, the ratio of the statistics of the arbitrary point-in-time live load and the
maximum lifetime live load computed in this research is consistent with values
defined in ANSI/ AISC 360-05.
5.11. SENSITIVITY ANALYSIS OF OALL PARAMETERS
Section 5.9 presented an application of the methodology using the statistical
parameters developed in this research. The application considered one platform
deck size, a specific SWL and a specific number of sectors. The application resulted
in a nominated value for OALL. In order to derive generic values for OALL, there
was a need to investigate the effect of considering other deck sizes and study the
effect of varying the SWL and the number of sectors.
5.11.1. VARYING THE DECK AREA
The application described in Section 5.9 considered a floor plan of 15m*15m to
derive OALL values. This section identifies OALL values for other deck sizes
ranging from 100m2 to 1000m2. For every assumed floor area, the number of
sectors was determined based on the minimum separation distance of 3m defined in
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Section 5.8.3.
The relationship between the floor size and OALL is shown in Figure 5-14. The
analysis shows that OALL reduces with increasing floor areas. However, the
reduction is not significant. For example, an increase in the floor area from 200m2
to 400m2 (100% increase) results in 11% reduction in OALL.
5.11.2. MINIMUM SEPARATION DISTANCE
Varying the minimum separation distance between equipment changes the number
of sectors, which results in a change in the computed influence coefficients as
shown in Table 5-4. The calculations were repeated using a number of realizations
(n) of 50.
The sensitivity of changing the number of sectors on the computed OALL for lower
floors is depicted in Figure 5-15. Inspection of the trend indicates that, for a floor
area of 15m*15m, increasing the separation distance results in a smaller number of
sectors, leading to a reduction in the calculated OALL.
This analysis implicitly assumed that the deck is fully occupied with OALL.
However, it is unlikely that a platform deck is fully occupied with live load only due
to the presence of fixed equipment on the deck. The fixed equipment can be
considered as dead loads, so open areas only cover the spaces between fixed
equipment. The effect of fixed equipment can be modeled by eliminating some
sectors when calculating influence coefficients. For example, sectors 5 and 8 in
Figure 5-9 could be occupied by fixed equipment so the calculations for influence
coefficient would discount those sectors in the calculations.
In this research, the effect of fixed equipment on the computed influence
coefficients was not considered. This is a conservative assumption leading to an
upper bound to the computed OALL.
Inspection of the results showed that the computed OALL is sensitive to the
separation distance between equipment and to small (less than 3m) separation
distance in particular. However, as previously discussed in 5.8.3, fire and safety
considerations and regulations preclude the use of separation distances less than 3m.
For separation distances of 3m or larger, the computed OALL is not as sensitive to
changes in the minimum separation distances. The relationship between the
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separation distance and OALL was established using regression analysis as follows:
OALL (kPa) = 42.7*(separation distance in m)-1.8 Equation 5-18
5.11.3. VARYING THE CRANE CAPACITY
The statistical parameters defined in Section 5.7 are based on the use of 10 tonne
SWL for cranes on the upper deck and 5 tonne SWL for monorails on lower decks.
This section investigates the effect of changing the assumed crane capacity on the
computed OALL. The analysis was conducted for lower levels but is equally
applicable to upper deck.
Figure 5-16 depicts the sensitive relationship between monorails capacities and
OALL. Doubling the monorail SWL resulted in doubling the computed OALL.
Hence, analysis of OALL on any platform needs to include the monorail capacities
on that platform.
OALL (kPa) = 0.08 * Crane SWL (kN) + 0.28 Equation 5-19
5.12. LIVE LOAD FACTORS
Section 5.7.3 identified a procedure to calculate the magnitude of the OALL that can
be used with the strength check described in API RP2A-LRFD (1993). This section
quantifies a corresponding live load factor that can be used with the derived OALL
in the strength check equations.
The derivation of live load factor was performed in light of approaches previously
used to define live load factors in API RP2A-LRFD (1993) and ANSI/AISC 360-05.
However, a review of the literature describing the methodology used to derive live
load factors in API RP2A-LRFD (1993) identified a number of inconsistencies and
pointed to subjectivity in the selection of live load factors in API RP2A-LRFD
(1993). These inconsistencies are described below and a conclusion is drawn to
recommend live load factors for this research.
API RP2A-LRFD (1993) recommends a load factor of 1.5 to the computed action
effect on the structure resulting from the short duration live loads exerted on the
structure. The live load factor of 1.5 is based on a coefficient of variation equal to
14% (Moses, 1980). However, API RP2A-LRFD (1993) Commentary proposed a
simplified method to calculate the load factor in lieu of a detailed reliability analysis
CHAPTER 5: OPEN AREA LIVE LOADS 223
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if the statistical parameters are different to those used in deriving load factors in API
RP2A-LRFD (1993).
The simplified procedure is applicable to loads acting alone and requires an estimate
of the coefficient of variation. It employs Table 5-5 to determine a load factorγ .
The load factor is then multiplied by the bias to provide the load factor which is
applicable to the specified coefficient of variation. The bias is defined as the ratio of
the mean value to the nominal or design value.
API RP2A-LRFD (1993) demonstrated the method using the environmental
statistical data for environmental data (mean = 0.7 and coefficient of variation =
0.37) and established a load factor (1.393) which was considered to be close enough
to the environmental load factor (1.35) defined in Section C.3.1.1 of the API RP2A-
LRFD (1993).
On applying the above procedure to the live load statistical parameters (bias = 1 and
coefficient of variation = 0.14) reported by Moses (1980) and used to calibrate API
RP2A-LRFD (1993), the resulting live load factor was 1.306. However, such live
load factor does not match live load factors (1.5) nominated in API RP2A-LRFD
(1993). Therefore, this research concluded that the simplified procedure described
in the commentary of API RP2A-LRFD (1993) is possibly applicable to
environmental loads only.
A comparison of live load factors defined in API RP2A-LRFD (1993) and
ANSI/ASCE 360-05 revealed another inconsistency in the application of the load
factors in reassessment of the various components of existing offshore platforms.
As discussed in Section 2.3.2, API RP2A-LRFD (1993) addresses tubular sections
only and refers to ANSI/ASCE 360-05 for non-tubular members. However, API
RP2A-LRFD (1993) recommends the use of 1.5 for live load factor whereas
ANSI/ASCE 360-05 recommends the use of 1.6. API RP2A-LRFD (1993)
acknowledged the inconsistency but justified this inconsistency using a simple dead
plus live load combination to conclude that the error introduced by such decision is
small (less than 3.4%).
Further, a single value for live load factor (1.6) was nominated in ANSI/ ASCE 360-
05 despite the wide range of coefficient of variation (23% to 100%) shown in Table
5-6 for the arbitrary point-in-time live loads. However, the discussion in Section
CHAPTER 5: OPEN AREA LIVE LOADS 224
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5.10 identified a relationship between the statistics of the arbitrary point-in-time
loads and those of the maximum lifetime load effects. Further, Section C.1-1 of the
API RP2A-LRFD (1993) Commentary provides a relationship between the
coefficient of variation of live load effects and live load factor. Hence, a
relationship exists between the coefficient of variation of the arbitrary point-in-time
loads and live load factors. Such a relationship would result in a set of live load
factors that correspond to the various coefficients of variation identified in Table
5-6. However, as discussed above, ANSI/ AISC 360-05 nominates a single live load
factor. Consequently, the development of live load factors in API RP2A-LRFD
(1993) and ANSI/AISC 360-05 appears to disregard the variation in the statistical
parameters of the arbitrary point-in-time live loads.
Computation of live loads in other research projects without attending to changing
the load factors is not uncommon. For example, a similar approach was adopted in
the bridge live load research. Miao and Chan (2002) obtained extreme daily
moments and shears using 10 year weigh-in-motion (WIM) data as compared to the
traditional normal probability paper approach used in bridge codes and standards
such as AASHTO. In their study, Miao and Chan (2002) analyzed the WIM data to
understand the real traffic status for comparison with bridge design live load models
in use in Hong Kong and did not consider load factors while investigating the design
load.
Hence, there is justification to maintain a live load factor of 1.5 for offshore
platforms in the Arabian Gulf, especially given the similar coefficient of variation
(10-20%) derived in this research as described in Section 5.8.3 compared to that
(14%) used in the calibration of API RP2A-LRFD (1993).
5.13. SUMMARY
In the absence of specific guidance for open area live loads in international codes
and standards, industry practice uses subjective value for open area live loads when
performing reassessment of existing offshore structures. The use of an arbitrary and
subjective value for the live loads in geographic regions, such as the Gulf of Mexico
and the North Sea, may not to be critical for the outcome because extreme storm
conditions dominate the failure mechanism in the those geographical locations.
The Author’s industry experience pointed to the dominant effect of OALL on the
CHAPTER 5: OPEN AREA LIVE LOADS 225
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results of deterministic reassessment of offshore platforms in the Arabian Gulf. API
RP2A-LRFD (1993) in general and Section ‘R’ in particular do not cover OALL.
For a complete set of specifications, it was important to rationally determine OALL
values that can be used in reassessment of existing platforms in the Arabian Gulf.
This Chapter presented a rational procedure to establish the mean of lifetime
maximum load effect on piles, which can be used to derive OALL. Application of
the procedure required a set of statistical parameters for equipment weights, which
would be equivalent to the arbitrary point-in-time of furniture used in deriving live
loads in ANSI A58. The statistical parameters of equipment weights on upper and
lower decks are identified in Table 5-1.
The derived OALL was compared to industry practice from the Author’s experience
working for contractors and operators around the world. It was found that the
calculated OALL falls within the range used in industry practice, but that industry
practice values for OALL is too broad and is likely to result in either unsafe or
uneconomical reassessment outcome.
Further, OALL values used in industry practice do not consider that OALL is
function of a number of parameters and is therefore not a unique number as the case
with live loads on building structures. Some of the parameters affecting the
magnitude of OALL include the location of the load on the platform (lower decks or
upper deck), deck area, SWL of material handling equipment on the floor and the
separation distance between equipment.
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Table 5-1: Statistical parameters of the arbitrary point-in-time equipment weights on upper and lower decks for given crane or monorail SWL. These values were derived using the equipment weight database shown in Appendix A and collated in this research
Crane SWL (tonne)
Mean (kN) Standard Deviation
COV (%)
Lower 5 36.1 18.4 51
Upper 10 81.5 30.6 38
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Table 5-2: Derivation of the maximum lifetime live load on a pile for an assumed floor deck of 15m*15m in plan and using a number of realizations (n) = 50 shutdown events
Parameters Units Upper Deck Lower Decks
Mean (Section 5.7) 81.5 36.1
Standard Deviation (Section 5.7) 30.6 18.4
Number of Sectors 16 16
Σ Influence coefficients 4.00 4.00
Σ square of Influence coefficient. 2.21 2.21
E(F) 326 144
Fσ 45.5 27.3
n 50 50
Assuming normal variables
nu kN 442 214
nα kN-1 0.0615 0.102
Euler number 0.577216 0.577216
E(Fn) kN 451 220
σFn kN 21 13
COV 4.6% 5.7%
Assuming lognormal variables 2ζ 0.139 0.188
λ 5.78 4.95
υ’n 6.07 5.35
α’n 20.164 14.911
nv 432.1 210.4
k 20.16 14.91
E(Fn) kN 446 220
σFn kN 28.7 19.7
COV 6.4% 9%
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Table 5-3: Statistical parameters and magnitudes of OALL. The statistics are based on normal distribution for the mean lifetime maximum load effect. The statistical parameters are compared to the range of OALL used in industry practice. The range used in the industry is based on the Author’s experience while working in various parts of the world
OALL (kPa) Deck Reference Parameter This
Research Industry Practice
Figure 5-10 E(Fn) 250 4.4 2.5 to 17 Lower deck Figure 5-11 σ (Fn) 9% 9% 14%2
Figure 5-12 E(Fn) 500 8.9 2.5 to 17 Upper decks Figure 5-13 σ (Fn) 15% 15% 14%
2 Implicit in API RP2A-LRFD (1993) and is based on the value used by Moses (1980) to calibrate API RP2A-LRFD (1993)
CHAPTER 5: OPEN AREA LIVE LOADS 229
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Table 5-4: Sensitivity analysis showing the effect of changing minimum distance between equipment on OALL
# Sectors used in the analysis 4 9 16 25 36
Distance between equipment 15/2 15/3 15/4 15/5 15/6
Σci 0.66 2.25 4.00 6.25 9.00
Σci2 0.20 1.24 2.21 3.45 4.97
E(Fn) 55 158 245 351 474
OALL (kPa) 0.98 2.81 4.50 6.24 8.43
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Table 5-5: Recommended load factors for load statistics not covered in API RP2A specifications (reference: API RP2A-LRFD Table Comm. C.1-1, 1993)
COVQ % 5 10 15 20 25 30 40
γ 1.20 1.25 1.32 1.43 1.60 1.75 2.10
Where: QCOV = Coefficient of variation of the load effect
γ = Load factor assuming mean load is used
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Table 5-6: Instantaneous sustained load parameters for occupational groups for various occupancies and usage and the corresponding design live load value from ASCE Standard 7-05 (Reference: Chalk and Corotis, 1980)
Arbitrary Point-in-Time
Live Load Mean
Standard Deviation for 200ft2
COV Occupancy or use
kPa kPa kPa %
Hospitals: Operating rooms 2.9 0.68 0.39 57
Hospitals: Private rooms 1.9 0.35 0.31 89
Libraries 7.2 1.66 0.52 31
Stores: Retail first floor 4.8 0.86 0.24 28
Stores: Retail upper floors 3.6 0.57 0.46 81
Manufacturing: Light 6.0 0.91 0.91 100
Manufacturing: Heavy 12.0 2.88 1.63 57
Office buildings: Lobbies 4.8 0.52 0.28 54
Office buildings: Offices 2.4 0.22 0.16 72
Hotels: Private rooms and corridors
1.9 0.22 0.06 27
School Classrooms 1.9 0.57 0.13 23
Storage warehouses: Heavy 12.0 3.42 2.78 81
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Figure 5-1: An example of an unloaded open area during normal operations of an offshore platform and at times other than shutdown. The photo shows that there is usually minor loads and personnel on open areas of offshore platforms
CHAPTER 5: OPEN AREA LIVE LOADS 233
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Figure 5-2: An example of a skid frame supporting a compressor skid unit which includes a compressor driven by a turbine. In this example, a motor could be scheduled for maintenance during a shutdown, which would require various components of the skid to be disassembled on the open area of the platform
CHAPTER 5: OPEN AREA LIVE LOADS 234
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Footprint = 0.0501 * Equipment Weight + 7.706
0
30
60
90
120
150
0 300 600 900 1200 1500 1800 2100 2400
Equipment Weight (kN)
Foot
prin
t (m
2 )
Figure 5-3: Scatter plot showing equipment weight and their corresponding footprint for every piece of equipment in the database that was used in this research. The scatter plot shows that the majority of the surveyed equipment weighs less than 20 tonnes (200kN) with an approximate linear relationship between an equipment weight and its footprint
CHAPTER 5: OPEN AREA LIVE LOADS 235
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Figure 5-4: Schematic of a platform elevation showing that equipment with larger size (and weight) tends to be located on the upper decks to facilitate removal and maintenance. This is usually preferred by operation and maintenance personnel to facilitate and optimize operations and maintenance costs
CHAPTER 5: OPEN AREA LIVE LOADS 236
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0.0
0.1
0.2
0.3
0.4
0.5
0.6
0 5 10 15 20 25 30 35 40 45 50 55
@RISK Trial VersionFor Evaluation Purposes Only
Figure 5-5: This chart shows histogram of the truncated equipment weights on lower decks. The statistical parameters were calculated as mean = 36.1kN and standard deviation = 18.4kN
CHAPTER 5: OPEN AREA LIVE LOADS 237
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p ( )
Val
ues
x 10
^-3
Values in Thousands
0
1
2
3
4
5
6
7
8
-0.5 0.0
0.5
1.0
1.5
2.0
2.5
@RISK Trial VersionFor Evaluation Purposes Only
Figure 5-6: Histogram of the equipment weight on lower decks and an assumed fitted Exponential distribution. Using the assumed distribution, the statistical parameters were calculated as mean = 135.4kN and standard deviation = 134.8kN
CHAPTER 5: OPEN AREA LIVE LOADS 238
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0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.350 10 20 30 40 50 60 70 80 90 100
110
@RISK Trial VersionFor Evaluation Purposes Only
Figure 5-7: This chart shows a histogram and fitted distribution of the truncated database for equipment weights on the upper deck. The fitted distribution is lognormal with mean = 81.5kN and a standard deviation = 30.6kN
CHAPTER 5: OPEN AREA LIVE LOADS 239
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p ( )
Val
ues
x 10
^-3
Values in Thousands
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
-0.5 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
@RISK Trial VersionFor Evaluation Purposes Only
Figure 5-8: This chart shows histogram of the equipment weight on the upper deck fitted an Exponential distribution with mean = 627kN and standard deviation = 638kN
CHAPTER 5: OPEN AREA LIVE LOADS 240
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Figure 5-9: Plan view of the model platform decks used to demonstrate the application of the influence surface concept for a 15m square floor deck
CHAPTER 5: OPEN AREA LIVE LOADS 241
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Figure 5-10: The relationship between the maximum lifetime live load on a pile and the number of shutdown events for lower decks. The plot shows an asymptotic relationship with a maximum lifetime live load on the pile of 275kN for a lognormal distribution and 250kN for a normal distribution
200
225
250
275
300
50 400 800 1200 1600 2000 2400
Number of Occurences
Mea
n Li
fetim
e M
ax L
LE(
F n) (
kN)
Normal Distribution
Lognormal Distribution
CHAPTER 5: OPEN AREA LIVE LOADS 242
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Figure 5-11: Relationship between number of shutdown and the standard deviation of the mean lifetime live load for lower decks showing that the standard deviation ranges between 10%-20%. An average of 15% is used in this research
0
5
10
15
20
25
50 400 800 1200 1600 2000 2400
Number of Occurences
Std
Dev
of M
ean
Life
time
Max
LL
σ E
(Fn)
(kN
)
Normal Distribution
Lognormal Distribution
CHAPTER 5: OPEN AREA LIVE LOADS 243
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Figure 5-12: The relationship between the maximum lifetime live load on a pile and the number of shutdown events for the upper deck shows an asymptotic relationship
400
425
450
475
500
525
550
50 400 800 1200 1600 2000 2400
Number of Occurences
Mea
n Li
fetim
e M
ax L
LE(
Fn) (
kN)
Normal Distribution
Lognormal Distribution
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Figure 5-13: The relationship between the number of shutdowns and the standard deviation of the mean lifetime live load effect for the upper deck showing that the standard deviation ranges between 15%-22% and depends on the distribution type and the number of occurrences
0
5
10
15
20
25
30
50 400 800 1200 1600 2000 2400
Number of Occurences
Stan
dard
Dev
iatio
n of
Mea
n Li
fetim
e M
ax L
Lσ
E(F
n) (k
N)
Normal Distribution
Lognormal Distribution
CHAPTER 5: OPEN AREA LIVE LOADS 245
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OALL= -0.0029 A + 5.7159
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
0 100 200 300 400 500 600 700 800 900
Floor Area (m2)
OA
LL (k
Pa)
Figure 5-14: Sensitivity analysis results showing the effect of varying the deck area on the number of sectors for lower decks. The analysis revealed that OALL is not sensitive to varying deck areas. Doubling the floor area from 200m2 to 400m2 results in 11% reduction in OALL
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OALL (kPa) = 42.7 * Separation Distance in m-1.8
0.0
4.0
8.0
12.0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
Minimum Separation Distance (m)
OA
LL (k
Pa)
Figure 5-15: Relationship between minimum separation distance and OALL for n = 50 on lower floors. The chart shows that the calculated OALL is sensitive to the selected minimum distance when the minimum distance is 3m or less as discussed in Section 5.8.3.
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OALL = 0.08 * Crane Capacity (kN) + 0.28
1.0
2.0
3.0
4.0
5.0
6.0
7.0
20 25 30 35 40 45 50 55 60 65 70 75
Crane or Monorail Safe Working Load (kN)
OA
LL (k
Pa)
Figure 5-16: Relationship between crane capacity and OALL on lower decks showing that OALL is sensitive to the SWL of the crane used on the deck
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Chapter 6.
DOMINANT FAILURE MECHANISM
6.1. OBJECTIVE
Calibration of axial pile resistance factors and open area live loads presented in
Chapters 4 and 5 covered operating overload conditions only. To address the
problem statement identified in Section 1.2, there was a need to examine the effect
of extreme storm conditions on the reliability of existing platforms in the Arabian
Gulf.
This Chapter presents the results of a reliability analysis on a platform to evaluate
the dominant failure mechanism in the Arabian Gulf. The platform was selected to
identify the need for considering extreme storm conditions in conjunction with
operating overload conditions for the required reassessment specifications.
Including extreme storm conditions in the specifications would only be required if
the probability of failure under extreme storm conditions is of the same order of
magnitude or higher than the corresponding probability of failure under operating
overload conditions as illustrated in Figure 6-1.
On the other hand, if the probability of failure in the extreme storm condition is
much higher than the probability of failure in the operating overload, extreme storm
conditions would dominate the failure mode. In such case, parameters would only
need to be calibrated for the extreme storm conditions.
If the reliability analysis results show that the probability of failure under extreme
storm conditions is close to the probability of failure under operating overload, the
dominant failure mechanism would result from the interaction of both conditions
and parameters would be needed for both conditions.
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6.2. PERFORMANCE MODEL
The loading on an offshore structure may be resolved into a horizontal load, PH, and
a vertical load, PV. The horizontal load results from a combination of wave, current
and wind whereas the vertical loads include gravity (dead and live) loads. A
simplified schematic of the loads and moments applied to the pile system is shown
in Figure 6-2.
For a given direction of loading, the horizontal loading, PH, is applied to the
structure at distance above the sea floor to develop an overturning moment, which is
resolved to shear and axial forces in the foundation system. The vertical loading PV
develops mainly axial loads on the pile system. In real life situations, a dominant PH
represents an extreme storm condition and PV effect is relatively small, while a
dominant PV represents an operating overload condition in which the PH effect is
relatively small.
6.3. APPROACH
Assessment of the probability of failure under extreme storm conditions was carried
out using results of a pushover analysis. First Order Reliability Method (FORM)
was then employed using the results of the pushover analysis to estimate the
probability of failure of the piled foundation system.
The statistical parameters for the resistance (axial pile capacities) and for the loading
(open area live loads) derived in this research were employed in the reliability
analysis to determine the probability of failure under operating overload conditions.
To estimate the probability of failure under operating overload, a consistent
approach to that used for the extreme storm condition was required. To achieve
such consistency, a novel approach was employed in this research and is based on
incrementing the vertical loads.
Due to its novelty, the outcome of applying FORM to the results of the pushover
analysis in the vertical direction was verified by comparing its results with another
established method to validate the computed probability of failure.
6.4. LOGIC OF PLATFORM SELECTION
Reliability analysis was conducted on a selected platform in the Arabian Gulf. The
CHAPTER 6: DOMINANT FAILURE MECHANISM 250
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platform was selected such that the outcome of the reliability analysis could be
considered as generic and applicable to other platforms in the Arabian Gulf.
A survey of available platform data identified a four-legged unmanned wellhead
jacket, which was considered to meet the objective of this research. The selected
platform was designed in 1972 according to API RP2A-WSD. It is located in 100m
water depth with leg spacing of 15m in plan between the main gridlines. The
platform is supported by one pile at each corner of the jacket base, which coincides
with the main gridlines. The jacket was fabricated using mild steel to support nine
(9) conductors. The topside supports flow meters, low pressure (LP) and high
pressure (HP) manifolds, piping and control panels, pig trap, instrument air system,
risers, emergency shutdown (ESD) valves and fire and gas detection systems. These
facilities are typical for a wellhead platform in the Arabian Gulf and are shown in
Figure 6-3.
The choice of a four-legged platform served the purpose of this research. Since the
objective was to compare failure mechanisms, it was reasonable to use a
configuration that was likely to provide an upper bound to the failure probability for
extreme storm conditions, such that if extreme storm conditions were found not to
dominate the failure mechanism, similar conclusions could be drawn for six or eight
legged offshore platforms.
Under extreme storm conditions, the probability of failure associated with the use of
six or eight-legged platform structures would be lower due to the inherent reserve
strength and redundancy in the system. Tang and Gilbert (1993) studied offshore
pile system reliability considering a four-legged platform and an eight-legged
platform and found that the latter provided more than twice the redundancy of the
former when similar failure mechanisms were compared. Hence, the use of a four-
legged platform would neutralize the high reserve strength effect on the system, and
was therefore adopted as an appropriate choice for the purpose of this research.
The selection of a platform in 100m water depth also served the objective of this
research. This water depth is the deepest in the Arabian Gulf as shown in Figure
2-3. Since the objective was to confirm the premise that the operating overload
condition dominated the failure mechanism, the use of the deepest water maximized
the hydrodynamic loads on the platform, resulting in an upper bound probability of
failure under extreme storm conditions.
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CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
The choice of a wellhead platform rather than a compression platform or a
processing platform was also based on the objective of this research. Wellhead
platforms in the Arabian Gulf are generally associated with larger open areas on
topside decks, which are usually required for drilling reasons as shown in Figure
6-4. Large open areas result in an upper bound for the probability of failure under
operating overload conditions.
The above discussion is illustrated in Figure 6-5. The graph maps out the expected
probability of failure under operating overload and extreme storm conditions. The
probability of failure for the selected platform is positioned on the map.
Therefore, the reliability analysis of the selected platform would produce an upper
bound probability of failure for the operating overload condition and extreme storm
conditions. Consequently, the conclusion from this research would be applicable to
other platforms in the Arabian Gulf which would be either in shallower water or
with smaller open deck areas.
6.5. MATHEMATICAL MODEL
The nonlinear computer model used for the static pushover analysis of the 4-legged
steel jacket platform is shown in Figure 6-7. The model consists of a fully coupled
nonlinear jacket foundation system. Figure 6-7 also depicts the force-deformation
relationship used to model each of the primary members. The piles were modeled
using the Pile Soil Interaction (PSI) module in SACS. Nonlinear p-y (lateral), t-z
(axial skin friction) and q-z (axial end bearing) springs were attached to pile nodes
to model pile/ soil behavior.
Some of the important items to determine the estimates of platform capacity,
together with various factors that influence shear strength and estimates of lateral
and axial soil capacities are identified in this section.
6.5.1. STRUCTURAL MODELING
The structure was modeled with one finite element per physical member and
employed DNV guidelines (1999). The mathematical model is shown in Figure 6-6.
The geometry of the space frame was modeled in three dimensions. The nodes of
the model of the framework were selected as the intersection points of the
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CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
centerlines of the legs, diagonal members and plan braces. The offsets between the
intersection points of brace member/ chord centerlines at the joints which are less
than ¼ of the CAN diameter and the corresponding member end eccentricities were
not included in the structural model. This approach is in line with API RP2A-LRFD
(1993) recommendations.
The ultimate strength model included the primary framework in the vertical and
battered frames of the structure and the secondary framework that provides stiffness
to the plan framing and lateral support to the conductors and other appurtenances.
Often, the secondary framework, such as plan bracing, becomes part of the load
mechanisms as primary members yield and shed load.
The model of the non-linear characteristics of primary members reflected the plastic
performance at the ultimate load limit. An inelastic “beam-column” element was
used to represent the legs, piles and conductors while “strut” elements modeled the
horizontal and diagonal braces.
The stiffness and strength of appurtenances such as launch cradles, mudmats, J-
tubes, risers and skirt pile guides were not included. These elements do not to
contribute significantly to the overall global stiffness and structural strength.
The tension and compression failure of a member was described using a beam
element that includes a lumped plasticity formulation in COLLAPSE. An initial
out-of-straightness was included in the element properties so that the solution
accurately predicts the ultimate compressive strength of the element. An out-of-
straightness of 0.15% of the member length was assumed in modeling the members
of the space frame.
The rigid cross-section of the tubes implies that the cross section remains unchanged
during axial compression. However, at large compressive deformations, significant
local buckling or distortion of the cross-section may occur in the tubular walls. This
local buckling can reduce the maximum load carrying capacity and the post
buckling behavior. The following regions were implemented for the various
slenderness ratios (D/t):
• For D/t < 35, a tubular member will develop the full plastic bending capacity up
to high axial shortening deformations,
• For 35 < D/t < 60, industry experience showed that local buckling appears to
CHAPTER 6: DOMINANT FAILURE MECHANISM 253
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
reduce the member’s compressive strength but with an increase in the load
shedding in the post buckling range. However, this usually occurs at such large
axial shortening that the collapse strength of the frame has already been
achieved and only the framework’s post collapse behavior is affected. Thus, a
rigid-cross section for describing buckling behavior was assumed, and
• D/t > 60, local buckling may occur prior to global member buckling, which
limits the member’s compressive strength. Initial local shell buckling also
results in load shedding in the post buckling range more rapidly than when the
cross-section is assumed rigid. In the case of a structure in which the primary
braces involved in the failure modes with D/t > 60, more detailed evaluation
would be required using existing empirical equation (Van Langen 1995) or
detailed non-linear FE analysis. This is not the case in the selected platform or
generally for real offshore platform structures as it is the industry norm to select
low value of D/t.
The ultimate capacity of simple unstiffened and undamaged tubular joints was
established using API RP2A-LRFD (1993) formulae with all resistance factors set to
unity.
The calculations showed that ultimate axial load and ultimate moment capacities
provided by all joints exceeded the axial yield load and plastic moment capacity of
the brace cross-section. As the brace failed prior to the joint failure, there was no
need to include the joints in the failure mechanism. This enabled a more optimum
computer model size because inclusion of the joint in the COLLAPSE model
introduces additional degrees of freedom.
Joint flexibility was not modeled as it does not significantly affect the primary axial
loading in a framed structure. As previously discussed, joints were found to be
stronger than the incoming bracing members. Hence, member failures occurred in
tension or compression.
To ensure that a member’s tensile capacity is effectively mobilized, the model for
member tensile failure included a ductility criterion that identified disconnection in
the member ends after cracking and opening of joint welds. In lieu of a more
advanced analysis, a fracture criterion that limits the tensile strain in the member to
3% was applied. When such limit is exceeded in a member, the member is
disconnected and the analysis is repeated. This did not occur in the analysis and
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strains were below the limit of 3%.
The yield strength of the structure was based on survey of literature and industry
experience. A number of authors used the results of routine mill tests as a basis for
research of the variability in yield strength. Usually the results of mill tests carried
out on a large number of specimens show a skewed distribution since material
below the specified strength is generally detected in routine control tests and not
included in the sample. Julian (1957) used test results from 3124 specimens of low
carbon steel (ASTM A7) and reported mean of 20% above the nominal (minimum
specified) value of the yield strength and a coefficient of variation of 7.8%. Tall and
Alpsten (1969), Galambos and Ravindra (1978) and Baker (1972) reported similar
values.
Another aspect was also considered in the evaluation of member strength in this
research. The tension specimen test is normally applied as a routine acceptance test
for structural steels in most countries. It is performed at a specified loading or strain
rate. Usually, the upper yield point is recorded because reported yield point under
relatively high rate of loading may be considerably above the static yield level. In
laboratory tests, the static yields level is measured by including 2 minute stops in the
loading procedure after measuring the upper yield point. This enables the steel to
“relax” to the static yield level. Galambos and Ravindra (1978) reported that the
average static yield strength of steel is 10% lower than the upper yield point which
is recorded during the mill test. Even a “very slow” laboratory strain rate can raise
the apparent yield strength level by as much as 5%. The actual strain rate or loading
rate experienced during failure under extreme environmental loading may be
accounted for by increasing the apparent yield stress level. Galambos and Ravindra
(1978) and Tall and Alpsten (1969) suggested an increase of 10% for 250MPa steel
and 5% for 350MPa steel.
In this research, the strength of fabricated tubular was based on an increase of 10%
over the yield stress to account for the excess (+20%) of the mean value of yield
strength over the specified strength and a reduction to account for strain rate effects
during mill tests which increases the measured stress by 10%. An additional 10%
for 250MPa steel (5% for 350MPa steel) was applied to account for strain rate
effects during extreme environmental loading when members are failing. Hence, the
assumed yield strength was taken as 300MPa for this mathematical model.
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The increase in the assumed yield strength was applied to the material strength used
in the COLLAPSE model rather than carrying out any post corrections. The results
of the COLLAPSE analysis then represent the mean ultimate system strength Rmean.
This strength was directly used in the reliability calculations together with an
estimate of the variability in system strength.
The strain hardening property of steel is extremely important in spreading
“plasticity” along a yielding member or in the plastic hinge region and thereby
ensuring ductile response. However, the increased material strength due to strain
hardening is usually not significant in space frame structure. For compression
members strain hardening will only occur in the post-collapse range and reduce the
load shedding. For tensile members the yielding required for strain hardening to
become significant (above 3%) does not usually occur. For joints the strain
hardening, when relevant, is already included in the ultimate capacity criteria.
Influences of residual stresses and dimensional tolerances were identified in this
research and evaluated for consideration in the mathematical model. The models for
component strength, such as member compressive strength and joint strength are
semi-empirical. That is, they have a theoretical basis but are formulated to conform
to experimental data. When the components are assembled to form the framework,
the effects of fabrication stresses, misalignment and other irregularities were not
included due to their relatively minor effect on the collapse strength. An important
corollary of plasticity theory, developed in connection with portal frames, is that the
initial state of stress has no effect on the collapse load provided that global or local
instability is not governing. This is due to the fact that the initial state of stress is
‘by definition’ in self-equilibrium and hence no net work is done by these forces
when the structure deforms. This also applies in a generalized sense to the
evaluation of the ductile collapse strength of space structures. In space frames,
member slenderness is usually such that buckling is elasto-plastic and hence less
sensitive to the initial state of stress.
6.5.2. FOUNDATION MODELING
Survey of the literature identified that the majority of authors ignored soil pile
interaction when performing pushover analysis (Vughts and Edwards, 1992). For
example, Boon et al. (1993) did not consider foundation failure in their investigation
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on the basis that foundation bias is very high. Such an approach is consistent with
common practice in the North-Sea and the Gulf of Mexico.
In this research, ignoring the foundation model could not be justified for both
vertical and lateral directions due to lack of published research relevant to the
Arabian Gulf conditions. At the outset, it was not possible to establish a relative
load level that would cause failure in foundations. Hence, it was important to model
the foundation.
Foundation failure mechanism was captured using capacity models for lateral
failure, punch through and pull out. The foundation models included in the analysis
were based on definition of soil shear strength and modeling of soil pile interaction
in the horizontal and lateral directions.
Inspection of the database collated in this research revealed increases in the soil
shear strength of cohesive soils with depth. However, within the same layer, the
interpreted soil shear strength can vary depending on the consultant and on the
testing method. Figure 6-8 shows shear strength for one borehole. The lines
represent different interpretations by different consultants and at different times. At
intermediate soil layer between 24m to 56m below mudline, the shear strength
values ranged between 100 and 250kPa. In this research, the mathematical model
was based on the mean value of the shear strength using an unconfined undrained
(UU) test method in every layer.
Modeling soil elements in the lateral direction was based on the lateral failure
mechanism as a portal frame failure below mudline, with plastic hinges developing
below the jacket at mudline and at the counter curvature point in the piles. Most of
the energy dissipation will be associated with the plastic hinges in the pile steel and
the flexibilities and/ or local stiffness variations in the frame work should not
influence the pile lateral failure mechanism. Thus, modeling lateral foundations
collapse concentrated on the description of portal frame action below mudline and
on an accurate modeling of the ultimate capacity of the p-y curves to determine the
counter curvature point.
API RP2A-LRFD (1993) p-y curves were used to model the soil elements in the
lateral direction. In the derivation of the API RP2A-LRFD p-y curves, Tang, (1990)
did not exclude carbonate soils when deriving the lateral pile capacity. Hence, the
use of API RP2A-LRFD (1993) was considered appropriate for use in this research.
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The API RP2A-LRFD (1993) method was used in this research and p-y curve for
every soil layer is presented in Figure 6-9. The springs represent the soil resistance,
and are characterized by nonlinear lateral, axial load transfer and tip load
displacement curves.
The axial resistance model of the soil in API RP2A-LRFD (1993) is provided by a
combination of axial soil-pile adhesion, or load transfer along the side of the pile,
and end bearing resistance at the pile tip. The foundation compression and tension
capacity will usually be significantly less than that of the pile steel cross section
when API RP2A-LRFD (1993) methodology is used. This is the case for non-
carbonate soils due to the bias in the API RP2A formulation described above and as
evident by actual post-mortem analysis (Bea, 1983).
The relationship between mobilized soil-pile shear transfer and local pile deflection
at any depth is described using t-z curve, while the relationship between mobilized
end bearing resistance and axial tip deflection is described using q-z curve.
The t-z curves were generated in SACS for every soil layer using the relationship
outlined in API RP2A-LRFD (1993) and are presented in Figure 6-10, except that
the soil spring stiffnesses were reduced to reflect the reduction in limiting soil
parameters as discussed in Section 4.4.
6.5.3. LOADING MODEL
Loading on the platform can be divided into three load cases, namely dead loads,
live loads and environmental loads. The dead loads include self weight of the
platform in addition to equipment, appurtenances self weight and buoyancy. Live
loads on the platform decks were obtained from the analysis in Chapter 5. The live
loads were applied as uniformly distributed loads in the model.
For drag-dominated structures, the environmental loading arises from the combined
effect of waves, currents and winds. The probability that a certain combination of
these metocean parameters is exceeded is a key aspect affecting the platform
response statistics and hence the overall platform reliability. This problem was
tackled by transforming the metocean variables into a single structure response
variable (global base shear) and examining the long term statistics of this variable.
To perform reliability analysis, pushover analysis was executed using the mean
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value of the environmental conditions, which were obtained using the parameters
identified in Table 2-5. The associated wind and current loads should be used with
the maximum wave height but as the associated values were not available from the
metocean reports, this research used 100 year return period for wind and current.
The use of the 100 year return period instead of the associated values is conservative
but provided an upper bound to the probability of failure on the case research
platform. This approach is consistent with the strategy to provide upper bound of
probability of failure under extreme storm conditions as discussed in Section 6.3.
The loading exerted on the space frame structure for the given environmental
conditions was calculated using SACS. The reference design load was used as the
100 year return period load, which is the load with an annual exceedance probability
of 1%. Wave kinematics were obtained from appropriate wave theories and
adjusted (downwards) to account for directional spreading of wave energy. The free
stream current was also adjusted downwards to account for current blockage by the
structure. Force coefficients were selected based on the flow regime and an
assumed marine growth thickness of 100mm.
Wave kinematics on the platform was derived using Morison’s equation as provided
in Figure C.3.2-3 of API RP2A-LRFD (1993), which defines the regions of
applicability of the various functions. The analysis parameters were obtained from
Table 2-5 and entered in the API RP2A-LRFD (1993) graph. Using the above
parameters, two-dimensional regular wave kinematics showed that Stoke 5th order
wave theory was appropriate as shown in Figure 6-11.
6.6. PUSHOVER ANALYSIS
Pushover analysis is an ultimate strength analysis that includes member failure due
to yielding, buckling and soil-pile failure as well as joint failure. Whereas
conventional analysis for design purposes mainly focuses on the first failure of a
structural member, global non-linear pushover analysis accounts for possible
redistribution of forces and subsequent member failures until system collapse.
Pushover analysis follows an event-to-event strategy, tracing first fiber yield,
occurrence of plastic hinges and failure of each member up to and beyond the
maximum capacity of the whole structural system. The introduction of plasticity
reduces the stiffness of the structure and additional loads due to subsequent load
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increments will be re-distributed to members adjacent to the member that has gone
plastic. This procedure (progressive collapse of the member) is continued until the
structure as a whole collapsed or pushed over. Further description of the static
pushover method can be found in several references (Puskar et al., 1994, Bea et al.,
1988, Lloyd and Clawson, 1983).
Pushover analysis was executed in this research using the non-linear COLLAPSE
module in SACS. The model considered the behavior of the piled foundations,
fluid-structure interaction, combined structure foundation collapse mechanisms,
stiffness and the mass of the jacket and topside modules.
The COLLAPSE analysis provided the key failure mode (ultimate strength) under
the action of extreme storm and operating overload conditions and identified those
members that participated in each failure mode including parallel and series
subsystems.
The mathematical model was based on the mean (or best estimate) of the properties
and capacity models for structural steel and soil. The following SACS modules
were used to perform elasto-plastic analysis:
• COLLVUE to perform interactive collapse result processing,
• SEASTATE to generate environmental loads, and
• PSI to perform non-linear foundation analysis.
6.6.1. EXTREME STORM CONDITIONS
The static lateral pushover typically consists of a representative “snapshot” of lateral
wave forces acting on the platform structure. To execute pushover analysis under
extreme storm conditions, two types of loading were identified:
• Vertical operating load, and
• Lateral extreme storm load.
The vertical load is transferred from the deck to the jacket and acts as a constant
load. The vertical loads include dead loads, which are made up of self weight plus
equipment weights on the deck, and live loads, which were assessed based on
Section 5.10. The lateral load is the load that would push the structure to its
ultimate capacity.
To execute pushover analysis, vertical loads were first applied. The SEASTATE
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case was then executed with the modified basic model file to combine the loads and
convert to basic load cases. The storm loads were incrementally increased above a
reference value (100 year storm) to derive the collapse load. The load vector
includes wave, wind and current forces with a notional return period of 100 years.
COLLAPSE analysis was then executed using the output file of the SEASTATE and
the collapse input file. This implies that the wave height is fixed (at the 100 year
wave height) and the analysis determines how many multiples of this load can be
taken by the structure before collapse. This load application procedure does not
account for changes in the load pattern as the wave height is increased. This
approach provided reliable results as long as no additional horizontal elevations are
submerged (DNV, 1999).
Under extreme storm conditions, collapse could result in either of the two major
mechanisms: (1) shear dominated or (2) overturning dominated. In the former, the
critical piles would be subjected to lateral failure, whereas in the latter, the critical
piles would be subjected to axial failures. Tang and Gilbert (1993) evaluated the
reliability of offshore pile systems, and considered that the collapse mechanism
tended to be shear dominated in relatively shallow waters while overturning
dominated in greater water depths. The collapse strength of space structures was
expressed in this research in terms of global environmental loading base shear. The
platform collapse was defined by the maximum value of the total environmental
load applied to the structure just before collapse.
Figure 6-12 shows a shear dominated mechanism for the platform as the critical
piles failed laterally. This failure mechanism is consistent with observations made
by Tang and Gilbert (1993) for shallow waters. The output from the pushover
analysis produced a Reserve Strength Ratio (RSR), which is defined as the ratio
between the base shear at platform collapse and that of the 100 year wave load. The
calculated RSR is indicated in Figure 6-12 and denoted by Load Factor of 5.5.
6.6.2. OPERATING OVERLOAD CONDITIONS
To the Author’s best knowledge, all reported pushover analyses in the literature
address extreme storm conditions. The use of pushover analysis in the vertical
direction was required in this research such that the calculated probability of failure
under operating overload condition is consistent with that under extreme storm
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condition. Although having not been attempted in the past by other researchers, the
use of such an approach was employed. To validate calculated probability of failure
under operating overload conditions, an alternative method was used in this
research.
To model soil pile interaction in the vertical direction, the q-z curves recommended
in API RP2A-LRFD (1993) were scaled down in order to model the effect of
carbonate soils on the pile axial capacity. The reduced capacity represented the
reduction in limiting values between carbonate and non-carbonate soils.
To perform the pushover analysis in the vertical direction, dead loads and
environmental loads were first applied. Dead loads were represented by self weight
of the structure and the own weight of all equipment while environmental loads
were represented by a one year return period to model the environmental loads at
operating conditions. OALL values developed in this research were used in the
analysis.
To execute pushover analysis in the vertical direction, dead loads were first applied.
The SEASTATE case was then combined with the dead loads. The live loads were
incrementally ramped up above the OALL values to derive the collapse load.
Figure 6-13 shows that the platform dominant failure mechanism is in the piles with
a load factor of 6.8. The collapse load equals the computed load factor (6.8)
multiplied by the live load effect. The live loads were calculated in the analysis
using the values shown in Table 5-3. In Figure 6-13, the deformation is magnified
to identify the final step in the analysis before the platform collapsed. The piled
foundation system plunged into the soil. The slight lateral deformation is due to the
P-Delta effect.
6.6.3. ANALYSIS OF THE RESULTS
The high value of the load factors (5.5, 6.8) in both pushover analyses, when
compared to an industry norm of around 2.0, was due to the conservative design of
the platform structure and piles. The conservatism in the original design of the
platform was partly caused by considerations such as the need for extra stiffness due
to pre-service requirements such as transportation and lifting but could also be
attributed to two main reasons. Firstly, the use of conservative open area live loads
during design as opposed to the values derived in this research contributed to the
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conservatism in design. Secondly, the use of subjective but conservative limiting
soil parameters during pile design resulted in larger pile sizes than the case if
findings of this research were implemented.
6.7. LOAD AND RESISTANCE STATISTICS
In order to calculate the probability of failure using the pushover analysis results, the
statistical parameters for the resistance and loads established in this research were
employed. This section describes the statistical parameters used to calculate the
probability of failure under extreme storm and operating overload conditions.
6.7.1. RESISTANCE STATISTICS
The statistical parameters considered member and foundation strengths. The main
aspect of member strengths is variability in steel yield strength which has a COV of
approximately 10% (Cox, 1987). The influence of dimensional tolerances, such as
out-of-roundness and out-of-straightness, has smaller coefficients of variation since
they are statistically uncorrelated.
If all member strengths are fully correlated, the system collapse strength will have a
COV of 10% (Boon et al., 1993). If the member strengths are statistically
independent, the COV of the collapse strength will reduce (Tromans and van de
Graaf, 1992). In any case, Boon et al. (1993) found that the influence of structural
uncertainty is of minor importance. In this research, COV for steel member strength
was assumed to be 10%.
The statistical parameters for offshore piled foundations were also required to
determine the platform resistance. However, while the structural steel strength and
the mechanical behavior of steel structures are relatively well researched and can be
rationally quantified, this is significantly less so for the behavior of offshore piled
foundations. Van Langen (1995) indicated far more modeling and parameter
uncertainty in the description of axial pile capacity and lateral pile capacity when
compared to uncertainties in the strength of steel structure. Further, due to the
dominance of model and parameter uncertainties, pile capacities must be assumed to
be correlated and system effects do not reduce coefficient of variation. Even if all
parameters such as pile penetrations and soil strengths are measured accurately, this
modeling uncertainty cannot be reduced. It will affect the predicted pile capacities
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of all piles in a structure in the same manner and therefore assuming full correlation
for evaluating uncertainty in system failure of piled foundations appears appropriate.
The resistance has some Type I uncertainty and was modeled using COV of 10%
(Bea, 1990). In addition, modeling uncertainty would also be present. In the
Arabian Gulf, this research showed that the resistance is strongly dependent on the
behavior of the seabed soils. The carbonate sediments, which are highly variable
and imperfectly understood, increased this uncertainty. Hence, considerably higher
uncertainties were calculated (36%) as shown in Figure 4-19.
6.7.2. DEAD LOAD STATISTICS
The COV for dead loads was established in this research by reference to available
literature. Moses (1980) assumed a mean of 1.05 and COV of 8% for dead loads of
building structures. The Author used similar mean (1.05) for dead loads but
considered that a value of 8% for the COV to be high for existing offshore
platforms.
Generally, the weight of an existing platform is known within 3% accuracy which is
the tolerance that can be practically achieved by load cells. The use of load cells is
common practice in adopted similar the fabrication yard before load out and
transportation of any substructure or topside in the Arabian Gulf.
Further, the use of accurate weight reports before transportation and installation is
common practice in the offshore industry to ensure that the capacity of the derrick
barge crane is sufficient for the offshore lift. During operation, weight control is
normally updated to reflect any modification to the platform. Hence, the remainder
of the research considered COV of 5% for the dead load.
6.7.3. LIVE LOAD STATISTICS
The load statistics for OALL are presented in terms of the mean and coefficient of
variation (COV) of the lifetime maximum load effect. These statistics are presented
in Table 5-3.
6.7.4. ENVIRONMENTAL LOAD STATISTICS
Uncertainties in the environmental loads are due to inherent uncertainties in the
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environment itself (Type I), limitations in the mathematical models to replicate
measured storm data and extrapolation techniques to predict extreme events (Type
II). To account for Type I uncertainty, long term wave load distribution was
required. The long term wave load distribution is an expression that enables an
estimate of the wave load level for any return period (typically 100 years or higher).
The fit of data from this step was used to extrapolate to long return periods. The
shape of the fit beyond the range of available data may have a significant impact on
the calculated probability of failure.
Derivation of the statistics of wave loads required long term wave height parameters
to be established. This section describes the derivation of the long term wave
heights which was used to develop the statistical parameters of the wave loads.
Computing the long term maximum wave heights and current speeds employed the
metocean data defined in Table 2-5. To identify the most suitable distribution,
several competing models were tested and a Weibull distribution was found to
represent the data for both variables. For such distribution, the relationship between
the natural logarithm of the maximum wave height or current speed and the
logarithm of the natural logarithm of the return period is shown in Table 6-1. The
linear trend depicted in Figure 6-14 for maximum wave heights and in Figure 6-15
for current speed shows that a Weibull distribution is appropriate for the Arabian
Gulf data.
Using regression analysis, the maximum wave heights and current speeds for higher
return periods (1000 year and 10000 years) were extrapolated as shown in Table 6-1
and plotted in Figure 6-14 and Figure 6-15.
Using the return periods and extrapolated long term maximum wave heights and
current speeds, a number of linear static analyses were executed using SACS to
calculate the base shear for every return period. The calculated base shears are
shown in Table 6-2.
To identify a distribution that fit the base shear data points, the approach described
in Section 3.4 was employed and the calculations are shown in Table 6-3. A
Weibull distribution was found to provide the best fit to the data as evident by the
straight line shown in Figure 6-16.
To obtain the Weibull distribution parameters, the method described in Appendix F
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was employed. The scale and shape parameters were calculated using data from
Table 6-3. The resulting slope and intercept of the curve is 4.59 and -32.87,
respectively. The corresponding scale (θ) and shape parameters (m) were 1298 and
4.6, respectively.
For the computed Weibull shape and scale parameters, the mean value and standard
deviation of the base shear were found to be 1186kN and 11.7kN, respectively,
resulting in COV of 1.0% for the environmental loads.
The uncertainty discussed above is that due to inherent variability (Type I). Other
sources of uncertainty may also be incorporated into the assessment and these are
termed Type II or modeling uncertainty. This type stems from uncertainty in the
prediction of the extreme storm conditions and the hydrodynamic forces resulting
from the storm conditions.
To account for Type II effects in the reliability model, Efthymiou et al. (1996)
estimated a coefficient of variation value of 8% or less for jackets analyzed using
modern techniques. In this research, the coefficient of variation for the uncertainty
in hydrodynamic load calculation was taken as 6% on the basis of relatively small
Type I uncertainty.
The resultant uncertainty combines Type I and Type II and was required to calculate
the probability of failure. In the Arabian Gulf, the combined load COV of the base
shears was calculated as ( ) ( ) %1.606.001.0 22 =+ .
Full definition of the environmental load statistics requires evaluation of a bias
factor. Bias is defined as the mean of the maximum expected wave base shear
divided by the nominal design base shear. From the analysis above, the mean base
shear was calculated as 1186kN as described above and the nominal (100 year
return period) was 1281kN as shown in Table 6-2 resulting in a bias factor of 0.92.
6.8. PROBABILITY OF FAILURE CALCULATIONS
The annual probability of failure Pf for extreme storm conditions was estimated as
the annual probability of the load exceeding the structural resistance. If the
structural resistance could be denoted by a single value (i.e. deterministic), then the
target reserve strength ratio (RSR) to achieve a specified probability of failure under
environmental overload is read directly off the non-dimensional load versus return
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period graph. Conversely, if the failure load is known, the probability of failure of
the platform can be established by constructing a diagram using the relationship of
the normalized base shears for different return periods.
For operating overload conditions, a simple measure of the structural reliability may
be given by superimposing the expected ultimate strength of the system on the load
distribution and considering the region of overlap between the two curves to indicate
the probability of failure. This was performed using an analytical approach which
employed closed form equations to calculate the probability of failure for a complete
system. In this approach, FORM was applied and the input parameters were derived
from pushover analysis. Alternatively, the probability of failure was determined for
a single pile then a method was applied to establish the probability of failure of the
pile group. In FORM, the probability of failure was computed using the statistics of
the loads and resistance which are treated as normal variables.
For consistency with the API RP2A-LRFD (1993), a comparison of the method used
in this research to calculate the probability of failure against that used to calibrate
API RP2A-LRFD (1993) was conducted. The investigation revealed that the API
RP2A PRAC (such as API PRAC 80-22, API PRAC 86-29B and API PRAC 87-29)
reports did not consider system reliability in the calibration. In all API PRAC
documents, only members and components were considered. However, one
objective of this research was to evaluate system reliability so it was necessary to
employ a different approach and the methodology adopted in the industry to
compute the probability of failure is described in this section and was used to
estimate the probability of failure of the system.
6.8.1. USING PUSHOVER ANALYSIS RESULTS
Pushover analysis results were used to calculate the probability of failure of the
entire system by calculating the Reserve Strength Ratio (RSR). The RSR is denoted
by the ratio of expected ultimate strength to the design load. The probability of
failure was then estimated using Level 2 reliability method or FORM.
In calculating the RSR, the ultimate capacity was represented by the base shear at
collapse in the direction that causes failure. For extreme storm overload condition,
the direction of failure is associated with the highest wave height (shamal) as shown
in Figure 6-12. For the operating overload condition, Figure 6-13 shows the
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collapse for operating overload in the vertical direction.
Table 6-4 lists the input data used to calculate the probability of failure for the
extreme storm and operating overload conditions. The input data quantified the
loads (dead, live, storm), resistance (pile axial and pile lateral capacity) and the
statistical parameters of those loads and resistances.
The dead loads represent self weight plus equipment weight. The self weight was
generated by the software and the equipment weights were calculated from vendor
datasheets. The open area on the upper and lower decks constituted approximately
50% of the total area. Hence, the total live load was calculated by multiplying the
open area on the upper and lower decks by the values shown in Table 5-3, resulting
in a load effect on the pile equals to 1688kN. The live load values shown in Table
5-3 are applicable to the platform under consideration.
The capacity of piles in the vertical direction is defined as the vertical load causing
collapse and was computed by multiplying the load factor computed in Section 6.6.2
(6.82) by the reference live load (1688kN) and adding the dead load. This approach
is similar to that adopted in calculating the collapse load under lateral loads.
Similarly, pile capacity in the lateral direction was obtained by multiplying the
reference base shear for 100 year return period by the load factor (5.5) calculated in
Section 6.6.1.
The statistical distribution and parameters of the pile capacity in the vertical
direction was determined in Section 4.6.1, while those in the lateral direction
adopted values used by Moses (1980).
The combination of the loads for the case of operating overload excluded
environmental loads. The operating overload condition represents shutdown
conditions, which are usually planned under very mild (Hs<1m) weather conditions
because supply boats, which are required to service such operation, only operate
under mild conditions. Further, the load combination for extreme storm conditions
excluded live loads because operational guidelines on offshore platforms do not
allow live loads on open areas during storm conditions.
Table 6-4 presents the input data used in the calculations of the probability of failure
under operating overload and extreme storm conditions.
The probability of failure under operating conditions was calculated using Equation
CHAPTER 6: DOMINANT FAILURE MECHANISM 268
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
3-9 resulting in reliability index of 4.6, which corresponds to a probability of failure
of 2.4*10-6.
The probability of failure under extreme storm conditions employed the relationship
between the long term base shear values and corresponding return periods shown in
Table 6-2 and plotted in Figure 6-17. The probability of failure under extreme
storm condition was computed as the inverse of the return period corresponding to a
collapse load of 7045kN, which was calculated using the pushover analysis results
(Load Factor = 5.5, Base Shear for 100 year RP = 1218kN) as presented in Section
6.6.1. The resulting probability of failure under extreme storm condition was
2.3*10-71.
Results of the analysis demonstrate that the operating overload condition dominates
the failure mechanism due to its very low probability of failure (2.4*10-6) compared
to the probability of failure extreme storm condition (2.3*10-71).
The very low probability of failure under extreme storm conditions is due to the
dependency of the reliability of offshore structures on the environment. Van de
Graaf et al. (1994) studied extreme load normalized to its 100 year value against
return period for offshore platforms in a number of geographic areas around the
world. The study concluded that the reliability of offshore platforms in the
Northern, Central and Southern North Sea, which were designed to API RP2A-
WSD, is significantly higher than those in the Gulf of Mexico. The result of the
study is reproduced in Figure 6-18 which shows reliability levels for return periods
up to one million years. Van de Graaf et al. (1994) clarified that reliability levels
calculated beyond ten thousand years are of a “notional” nature as the extrapolation
beyond ten thousand years is so great. However, Van de Graaf et al. (1994) pointed
out that non-linear effects associated with very long return periods could be
expected to limit, rather than amplify, extreme conditions.
The normalized load against return period for the offshore platform in the Arabian
Gulf is imposed in Figure 6-18. Inspection of Figure 6-18 reveals the benign nature
of the long term environmental conditions in the Arabian Gulf compared to other
parts of the world especially that of the North West Shelf of Australia. A feature
particular to the Arabian Gulf is the relative insensitivity of extreme loads to return
period when compared to the results for other locations. The high reliability level
for platforms in the North Sea reported by van de Graaf et al. (1994) is consistent
CHAPTER 6: DOMINANT FAILURE MECHANISM 269
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
with findings of this research for platforms in the Arabian Gulf.
The long term statistics of the wave heights are central to the reliability calculations.
Hence, a description of the long term wave heights in various parts of the world was
of interest to justify the high reliability levels in the Arabian Gulf when compared to
the reliability of offshore platforms in the North Sea. The ratio of long term (ten
thousand year) maximum wave heights to the 100 year values for a number of
geographic locations around the world, including the Arabian Gulf, is shown in
Table 6-5. The long term wave heights in various parts of the world were derived
from research publications as outlined in Table 6-5, while the long term wave height
for the Arabian Gulf was based on findings of this research.
In conclusion, the long term maximum wave height in the Arabian Gulf explains the
high reliability for offshore platforms in the Arabian Gulf. This observation
provided a partial explanation of the consistent finding of the Author regarding the
dominance of operating overload when reviewing and designing offshore structures
in the Arabian Gulf.
6.8.2. VALIDATING PROBABILITY OF FAILURE UNDER
OPERATING OVERLOAD CONDITION
As previously mentioned in Section 6.6.2, pushover analysis has usually been
associated with lateral overload due to storm events. In this research, the probability
of failure was calculated using the results of pushover analysis in the vertical
overload condition. To the best knowledge of the Author, application of pushover
analysis in the vertical direction has not been contemplated in previous research
work. Hence, it was important to validate the results of applying pushover analysis
in the vertical direction.
The validation of the system probability of failure under operating overload
conditions was established using an analytical approach which computes the
reliability of a single pile and then incorporates system and group effects.
The reliability of single axially loaded piles was studied by several Authors
including Bea (1983), Sidi (1985), Orchant et al. (1988), Tang (1989) and Barker et
al. (1991).
If the only load effects to be considered are dead and live loads, the reliability index
CHAPTER 6: DOMINANT FAILURE MECHANISM 270
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
β associated with the linear performance function can be calculated using first order
reliability method or FORM (Whitman, 1984, Barker et al., 1984, Becker, 1996,
Withiam et al., 1997) as described in Section 3.5.
Equation 3-12 reveals that the reliability index is a function of the bias factors,
COV, dead to live load ratio and the safety factor. However, Moses (1980) used a
reliability index of 2.11 for the calibration of piles resistance factors in API RP2A-
LRFD (1993). The use of a single reliability index in code formulation implies
insensitivity to the many factors that affect the calculation of reliability index.
In this research, the reliability index βs of a single pile under operating overload for
the platform shown in Figure 6-6 was calculated using the following input
parameters:
• Factor of safety = 2.0, which was calculated from the deterministic analysis of
the platform,
• Ratio of dead to live load = 3, which was calculated from the deterministic
analysis of the platform,
• Dead load statistics as determined in Section 6.7.2,
• Statistical parameters (Bias = 1.0, COV = 0.15) for live loads as determined in
Section 5.10, and
• Statistical parameters for the resistance was based on the posterior distribution
values (Bias = 1.08, COV = 0.23) derived in Table 4-22.
( )( )
( ) ( )[ ]
( ) ( )( )
( ) ( )[ ]60.2
15.005.0123.01ln
23.0115.005.01
1305.113208.1ln
11ln
11
1ln
222
2
22
222
2
22
=++×+
⎥⎥⎦
⎤
⎢⎢⎣
⎡
+++
+×+××
=++×+
⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢
⎣
⎡
+
++
+
⎟⎟⎠
⎞⎜⎜⎝
⎛+
=QLQDR
R
QLQD
QLQD
R
sCOVCOVCOV
COVCOVCOV
QLQD
QLQDFS
λλ
λ
β
The calculated reliability index for a single pile (2.60) corresponds to a probability
of failure of 4.9*10-3 for a single pile using Microsoft Excel function
NORMDIST(β,0,1,TRUE).
Under operating conditions, the calculated probability of failure for a single pile
(4.9*10-3) is three orders of magnitude greater than the calculated probability of
failure using pushover analysis in Section 6.8.1 for the vertical direction (2.4x10-6)
because the pushover analysis was carried out for the complete structural system
CHAPTER 6: DOMINANT FAILURE MECHANISM 271
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
while the above analysis was performed for a single pile.
The derivation of system reliability from single pile reliability required
consideration of system and group effects. Survey of the literature identified a
method that was developed by Zhang et al. (2001) which derived groups and system
effects from single pile reliability index. The method of Zhang et al. (2001) was
used to compute the system reliability index for the system from the results of the
single pile reliability.
Zhang et al. (2001) measured group effect using group efficiency factor. This factor
is the ratio of the group ultimate capacity to the sum of individual capacities of all
piles. The group effect factor is a function of pile spacing and group size.
System effects in piled foundations arise due to pile-superstructure interaction. A
system factor was used to measure system effects and is defined as the ratio of the
load required for all the piles in a pile system to reach their ultimate computed
capacity and that required for the most heavily loaded pile to reach the same state
(Bea, 1983). For a 4-leg jacket platform, Zhang et al. (2001) suggested a unity
value for system effect to model no load distribution.
To account for group and system effects, Zhang et al. (2001) developed the
following relationship:
( )Β
⎟⎟⎠
⎞⎜⎜⎝
⎛
++
+Β
×+
ΒΑ
=2
2
11
ln21
ln RG
RS
SG
COVCOV
ζχ λλββ
Equation 6-1
In which:
RSRG λλλλ ζχ ××= Equation 6-2
222RSxRG COVCOVCOVCOV ++= ξ Equation 6-3
( ) ( )[ ]222 11ln QLQDRS COVCOVCOVA ++×+= Equation 6-4
( ) ( )[ ]222 11ln QLQDRG COVCOVCOV ++×+=Β
Equation 6-5
The group reliability index βG equation was simplified as
CHAPTER 6: DOMINANT FAILURE MECHANISM 272
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
( )c
BSG ++= ζχ λλββ
ln Equation 6-6
Where: βs = Reliability index of a single pile
ΒG = Reliability index of a pile group
COVRS = Coefficient of variation of resistance for a single pile
COVRG = Coefficient of variation of resistance for pile group
λζ = Bias factor of the group effect
λχ = System bias factor
c = Constant that accounts for the errors associated with simplification of the main equation
COVχ = Coefficient of variation of system effect
COVζ = Coefficient of variation of group efficiency
The bias factor of the group effect (λζ) is defined as the ratio of the measured and
nominal group efficiency factors and ranges from 1.19 to 1.4 (Zhang et al., 2001).
The system bias factor can be taken as 1.0, 1.25, 1.5 and 2.0, with unity indicating
no system effect and the constant was calculated as -0.20 (Zhang et al., 2001). The
system COV was estimated between 0.10 and 0.20 (Bea, 1983) and the COV of
group efficiency ranges from 0.10 to 0.14 (Zhang et al., 2001).
Once the overall bias factor λRG and coefficient of variation COVRG of a pile group
associated with a specific prediction method are determined, the reliability index of
the pile group associated with the allowable stress design could be calculated.
Using the method described above, the probability of failure under operating
overload was calculated. The load factors used in the reliability analysis were 1.25
for dead load and 1.50 for live load in accordance with API RP2A-LRFD (1993)
load combination requirements. The load statistics are presented in terms of mean
and coefficient of variation (COV) of the bias factors. The bias factor is defined as
the ratio of the actual load over the nominal load.
A lognormal distribution of the bias factors of both dead and live loads was
assumed. The use of a lognormal distribution for OALL agreed with assumptions
made by Moses (1980) and also agreed with the calibration of the resistance factors
for bridge foundations adopted by AASHTO (Barker et al., 1991).
CHAPTER 6: DOMINANT FAILURE MECHANISM 273
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
A dead to live load ratio of three was used in this research and reflects the actual
ratio for this platform on the basis of the derived live load magnitudes shown above.
The sensitivity of various dead to live load ratios was examined in this research and
is shown in Figure 6-19. It is clear that changing the dead to live load ratio is
insensitive to changes in the calculated reliability index.
For a constant dead to live load ratio of 3 and changing the factor of safety, the
relationship between the reliability index and the COV is Figure 6-20. The
relationship shows that the reliability index increases with increasing factor of safety
but reduces with increasing COV.
In the application of the method proposed by Zhang et al. (2001), a range of values
for the various parameters can be assumed. The values were selected so as to verify
the research premise that operating overload dominates the failure mechanism in the
Arabian Gulf. Consequently, the parameters were selected to provide a lower bound
and an upper bound for the reliability index.
To maximize the reliability index, upper limit values for the numerator (λχ = 2.0, λζ
= 1.4) and lower limit for the denominator (COVχ = 0.10, COVζ = 0.10) were
selected. To minimize the reliability index, the numerator values were minimized
(λχ = 1.0, λζ =1.19), and the denominator values were maximized (COVχ = 0.20,
COVζ = 0.14).
Hence, the maximum reliability index is:
386.036.01.01.0 222 =++=RGCOV
( )( ) ( )[ ] 4.52.0
15.005.01386.01ln4.12ln60.2
222=−
++×+
×+≈Gβ
( ) 8107.2,1,0, −×=−= TRUENORMDISTPf β
The minimum reliability index is:
43.036.014.02.0 222 =++=RGCOV
( )( ) ( )[ ] 85.22.0
15.005.0143.01ln19.11ln6.2
222=−
++×+
×+≈Gβ
A comparison of the probability of failure using the pushover in the vertical
direction (Pf = 2.4*10-6), which is described in Section 6.8.1, falls within the range
CHAPTER 6: DOMINANT FAILURE MECHANISM 274
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
of Pf = 2.2*10-3 to 2.7*10-8 calculated using the method developed by Zhang et al.
(2001).
To illustrate the system factor effect, the calculations were repeated for various
values of system bias factors while keeping all other parameters unchanged. A
value of 1.19 was assumed for the group effect λζ. The coefficient of variation
values of 0.40, 0.20 and 0.14 were assumed for the single pile reliability, system
effect and group effect, respectively. Figure 6-21 shows the significance of the
system effect on the calculated reliability index. For system factor bias of 1.50, as
in the case of four-legged platforms in the Gulf of Mexico (Agarwal et al., 1996),
the probability of failure is 1 to 2 orders of magnitude smaller than those of single
piles.
To check the sensitivity of the assumed coefficient of variation, the analysis was
repeated with various other values for the coefficient of variation for the group and
system factors. As can be seen from Figure 6-22, an increase in the group and
system coefficient of variation reduces the group reliability index for a specified
bias factor. At higher system bias factors, the higher the COV, the more
pronounced the reduction in group reliability.
6.9. CALIBRATION OF ENVIRONMENTAL PARTIAL LOAD
FACTORS
Despite the need to limit specifications for reassessment of existing platforms in the
Arabian Gulf to operating overload conditions, there was need to identify
environmental load factors to produce a complete set of specifications.
Partial load factors in API RP2A-LRFD (1993) offers plenty of scope for
influencing the outcome of reassessment by calibrating the partial factors to suit
regional environmental conditions. As previously mentioned, API RP2A-LRFD
(1993) was developed for the Gulf of Mexico and US waters and implicitly accounts
for the long term environmental conditions of the Gulf of Mexico as shown in
Figure 6-18. For other regions such as the North Sea and the North West Shelf of
Australia, several researchers identified that load factors should not be similar to
those for the Gulf of Mexico conditions.
Theophanatos et al. (1992) presented results from a joint industry project on the
CHAPTER 6: DOMINANT FAILURE MECHANISM 275
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
calibration of LRFD for Mediterranean Sea platforms while Turner et al. (1992)
described the joint industry project work to adapt LRFD for use in the North Sea.
The calibration of the partial factors was undertaken using a reliability-based
procedure. Firstly, a set of one or more target reliabilities were assessed from a
range of representative components which were designed to the API RP2A-WSD
code. The partial factors were then adjusted so that the average reliability is close to
the targets.
The most significant difference between components designed for the Gulf of
Mexico and those designed for the North Sea and the Mediterranean arises from the
uncertainty in the environmental loading. The probability distribution for the
uncertainty in the extreme environmental load i.e., F/Fdes (20 year maximum base
shear force/ design base shear force) was typically found to have the parameters
shown in Table 6-7 for various geographic locations.
In both joint industry projects, the probabilistic description of gravity loads was
based on similar data to that adopted for the calibration of API RP2A-LRFD (1993).
Theophanatos et al. (1992) justified this approach due to the paucity of data related
to the Mediterranean Sea platforms. For dead loads, a bias of 1.0 and a COV of
0.06 were used. The short term live loads were defined only for the operating
conditions and values (bias = 1.0, COV = 14%) from the calibration of API RP2A-
LRFD (1993) were used. Theophanatos et al. (1992) assumed that these values
represented the expected maximum during the 20 year reference period.
Inspection of Table 6-7 reveals that the coefficient of variation for environment in
the Gulf of Mexico conditions is lower than the North Sea and the Mediterranean.
Hence, there was a case to justify lower partial load factors for extreme storm
loading in both regions. Nevertheless, the calibrated environmental load factors
(1.3) for the North Sea and the Mediterranean was found to be close to that adopted
for the Gulf of Mexico (1.35) and both studies concluded the practicality of
maintaining the environmental load factor of 1.35.
An extension of this argument was applied to the Arabian Gulf conditions using the
wave height statistics developed in Section 6.7.4 and comparing these to the
statistics shown in Table 6-7. The coefficient of variation of wave loads in Arabian
Gulf is smaller (6%) compared to other geographical regions. Hence, there was a
case to reduce the environmental load factor for reassessment of offshore platforms
CHAPTER 6: DOMINANT FAILURE MECHANISM 276
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
in the Arabian Gulf. For the calculated COV of 6%, an environmental load factor of
1.21 would be recommended based on the application of the method described in
API RP2A-LRFD (1993) Commentary and discussed in Section 5.12.
However, in keeping with the approach adopted for the Mediterranean and the North
Sea, the Author considered it more justifiable to maintain the environmental load
factor (1.35) used in API RP2A-LRFD (1993) for reassessment of platforms in the
Arabian Gulf.
Nevertheless, the Author performed an analysis to determine the effect of reducing
the load factor on the computed reliability level. The use of a lower load factor
(1.25) results in higher return period compared to the use of a load factor of 1.35.
The relationship between the load factor and the return period is shown below (Bea,
2008):
SeLF ln**8.0 σβ= Equation 6-7
)ΤΡΥΕ,0,1,= βNORMDIST(fP Equation 6-8
Where: LF = Load Factor
0.8 = A coefficient used to separate the loading and capacity uncertainties
β = Target annual safety index
Slnσ = Standard deviation of the logarithms of the annual maximum
combined loading.
fP = Probability of failure, calculated using Excel spreadsheet function
TYPE = TRUE or FALSE as per Excel function
Rearranging the terms in Equation 6-7:
( ) SLF ln**8.0ln σβ= Equation 6-9
Hence, for the Arabian Gulf, a reduction in the load factor (from 1.35 to 1.25)
results in an increase in the reliability index, a reduction in the probability of failure
and an increase in the return period.
( )( ) 25.1
35.1
25.1
35.1
lnln
ββ
=LFLF
Equation 6-10
CHAPTER 6: DOMINANT FAILURE MECHANISM 277
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
( )( )
( )( ) 2485.17
25.1ln35.1ln
lnln
35.125.1
35.125.1 =∗=∗= ββ
LFLF
This leads to an extremely low ( )12710− probability of failure. Such extreme value is
only possible in a mathematical model and has little physical meaning. Such result
suggests that the reliability of offshore platforms in the Arabian Gulf is insensitive
to the selected environmental load factor.
6.10. SUMMARY
This Chapter presented the reliability analysis for a selected platform in the Arabian
Gulf under extreme storm and operating overload conditions. The selection criteria
for this platform were such that any conclusions from this reliability analysis could
be extended to cover other platforms in the Arabian Gulf.
A summary of the calculated probability of failure under operating overload and
extreme environmental conditions is shown in Table 6-6. The operating overload
conditions result in probability of failure that is many orders of magnitudes higher
than the corresponding storm overload conditions.
Inspection of Table 6-6 indicates that the pushover results with FORM in the
vertical direction described in this thesis to treat overload in the vertical direction
produced comparable results to the analytical method and can therefore be
considered appropriate for future use.
Given that operating overload conditions dominate the failure mechanism in the
Arabian Gulf, reassessment of existing platforms in the Arabian Gulf would be
sufficiently based on considering operating overload only.
CHAPTER 6: DOMINANT FAILURE MECHANISM 278
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Table 6-1: Extrapolation of maximum wave heights and current speeds in the Arabian Gulf to produce log term values for maximum wave heights and current speeds. The long term maximum wave heights and current speeds are employed in a series of linear static analysis to calculate base shear for the various return periods. The red colored bold figures indicate that the value is extrapolated. A plot of the maximum wave height values versus return period is shown in Figure 6-14 while Figure 6-15 extrapolates long term values for current speed
Ret
urn
Peri
od
Log
[ln(R
P)]
Max
imum
Wav
e H
eigh
t (H
max
) in
met
ers
Ln[
Hm
ax]
Cur
rent
Spe
ed (V
) in
m/s
L
n[V
]
5 0.2067 7.9 1.4586 0.93 -0.0758
10 0.3622 8.7 1.5476 0.96 -0.0373
50 0.5924 9.6 1.6487 0.98 -0.0186
100 0.6632 9.8 1.6864 1.00 -0.0003
1000 0.8393 10.7 1.7724 1.02 0.0243
10000 0.9643 11.3 1.8335 1.04 0.0430
CHAPTER 6: DOMINANT FAILURE MECHANISM 279
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Table 6-2: Using a series of linear elastic analyses of the platform model described in Section 6.5 and using the maximum wave heights and current speeds shown in Table 6-1, this table presents the output of the analyses (total base shear at every return period). The input and extracts from the output data for the SACS analyses are shown in Appendix J
Return Period (RP) (year)
Maximum Wave Height (m)
Total Base Shear (BS) (kN)
5 year 7.9 979
10 year 8.7 1090
50 year 9.6 1205
100 year 9.8 1281
1000 year 10.7 1392
10000 year 11.3 1540
CHAPTER 6: DOMINANT FAILURE MECHANISM 280
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Table 6-3: Tabulation of the parameters used to calculate the parameters of Weibull distribution for base shears in the Arabian Gulf
Empirical CDF RP BS x=ln(BS) i =
rank F(x)=i/(N+1) y=ln(ln(1/(1-F(x)))
1 850 6.745 1 0.125 -2.013
5 979 6.886 2 0.250 -1.245
10 1090 6.994 3 0.375 -0.755
50 1205 7.095 4 0.500 -0.366
100 1281 7.155 5 0.625 -0.019
1000 1392 7.239 6 0.750 0.326
10000 1540 7.339 7 0.875 0.732
CHAPTER 6: DOMINANT FAILURE MECHANISM 281
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Table 6-4: Input data for calculating the reliability index and corresponding probability of failure for the operating and extreme storm conditions. The reliability index and probability of failure for the operating condition was calculated using First Order Reliability Method (FORM) and employed the result of the pushover analysis described in Section 6.6. The resistance coefficient of variation of the piles in the vertical direction adopted the results of Section 4.6. The coefficient of variation for live load effects was based on the values developed in Section 5.7.3. The probability of failure for the extreme storm condition was computed using the long term base shear values developed in Figure 6-16. The probability of failure under extreme storm condition was defined as the inverse of the return period corresponding to the collapse load developed in Section 6.6
Operating Overload
Extreme Storm
Overload
Dead Load (kN) 9300 9300
Mean Value of Operating Live Load (kN) 1688 -
Base Shear (kN) for 100 year RP from
Table 6-2
- 1281
Reserve Strength Ratio (RSR) 6.82 5.50
Mean of Extreme Storm Load (kN) Section 6.7.4 - 1186
Collapse Load (kN) 9300+1688*6.82 = 20812
1281*5.50 = 7045
COV DEAD 5% 5%
COV LIVE Refer to Section 5.10 15% -
COVE Refer Section 6.7.4 - 6.1%
COVR Refer Section 6.7.1 36% 30%
CHAPTER 6: DOMINANT FAILURE MECHANISM 282
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Table 6-5: Comparison of the long term maximum wave height in the Arabian Gulf against other parts of the world showing the relatively benign environment in the Arabian Gulf that leads to the high reliability of offshore platforms in this region
Region Bias COV (H)
H10,000
/H100 References
North Sea 0.84 21% 1.32 DOE, 1990
North West Shelf, Australia 0.78 33% 1.55 Stroud, 1999
Gulf of Mexico 0.79 32% 1.37 Efthymiou et al., 1997
Arabian Gulf 0.91 6% 1.16 Table 6-1
CHAPTER 6: DOMINANT FAILURE MECHANISM 283
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Table 6-6: Summary of the calculated probability of failure under operating overload and extreme storm conditions using several method of calculation for the operating overload condition
Method of Analysis Operating Overload Storm Overload
FORM with Pushover Analysis Pf = 2.4*10-6 Pf = 2.3*10-71
FORM with Pile Group Method Pf = 2.2*10-3 to 2.7*10-8 -
CHAPTER 6: DOMINANT FAILURE MECHANISM 284
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Table 6-7: Environmental Partial Load Factors for the Gulf of Mexico conditions (API RP2A-LRFD, 1993) and factors proposed by LRFD for the Mediterranean and the North Sea for a reference period of 100 years (Moses and Stahl, 1998)
Location Bias COV (H)
Environmental Load Factor γW
Gulf of Mexico 0.79 32% 1.35
Central & Southern North Sea 0.84 21% 1.18
Northern North Sea 0.81 27% 1.25
South/ Eastern Mediterranean 0.87 25% 1.30
NW Australia 0.78 33% 1.36
CHAPTER 6: DOMINANT FAILURE MECHANISM 285
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10-1
10-2
10-3
10-4
10-5
10-6
10-21
10-22
10-40
PR
OB
AB
ILIT
Y O
F FA
ILU
RE
Operating Overloadcondition
Extreme storm conditions
HIG
HLO
W
Operating Conditions
Storm Conditions
Operating Conditions
Storm Conditions
Operating Conditions
Storm Conditions
Interaction of extreme storm and operating
conditions
Dominant failure
mechanism
U N
S A
F E
R
E G
I O
NS
A F
E
R E
G I
O N
Figure 6-1: Illustration of the dominant failure mechanism under various conditions. The chart shows that the dominant failure mechanism is determined by comparing the probability of failure under operating overload against extreme storm conditions. When the probability of failure under operating conditions is much lower than the probability of failure under extreme storm condition, then operating conditions dominate the failure mechanism. Conversely, when the probability of failure under extreme storm conditions is lower than that under operating conditions, then extreme storm dominate the failure mechanism. When the probability of failure under extreme storm condition is similar to that under operating overload condition, the dominant failure mechanism is determined on the basis of interaction of both conditions
CHAPTER 6: DOMINANT FAILURE MECHANISM 286
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 6-2: Performance model for the cases of dominant extreme storm (left) and operating overload (right) conditions. In the dominant operating condition, the effect of horizontal load PH on the pile system is relatively small when compared to the effect of the vertical load PV
CHAPTER 6: DOMINANT FAILURE MECHANISM 287
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 6-3: Plan view of the model platform decks used in the pushover analysis. The platform is 15m by 15m between the gridlines
CHAPTER 6: DOMINANT FAILURE MECHANISM 288
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Figure 6-4: The requirement for large open area on wellhead platforms is driven by the dimensions of drilling rig. This figure shows a jack-up drilling rig adjusted for one well and also shows alternative locations of the rig to drill other wells
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Figure 6-5: Illustration of the probability of failure for the selected platform in this research under various conditions. The direction of the arrows indicates increasing/ decreasing probability of failure. The “X” indicates the selected platform positioning in relation to the population. This demonstrates that the selected platform provided a lower bound solution for the extreme storm condition as a result of choosing the deepest water in the Arabian Gulf and an upper bound solution for the operating condition as a result of choosing a wellhead platform with large open areas
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Figure 6-6: SACS computer model geometry showing the 4-legged jacket structure in 100m water depth and the topside structure. The piles are driven 70m into the soil
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Figure 6-7: A description of the nonlinear SACS computer model used in the static pushover analysis
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Figure 6-8: A plot of the soil shear strength of two boreholes at a given site. The plot shows the variation of the interpreted shear strength along the depth, but also within the same layer. In this analysis, the average shear strength value in each layer was adopted
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Figure 6-9: Input p-y curves for the soils in the case research platform. The curves were computed in SACS using API RP2A procedure described
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Figure 6-10: Input t-z curves for the various layers in the case research platform. The curves were computed in SACS using API RP2A procedure but a reduction factor was applied to the calculated spring stiffness to reflect the findings of this research showing the reduced axial capacity of piles in carbonate soils
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Figure 6-11: Assessing applicable wave theory for use in the analysis (Source: API RP2A-LRFD, 1993). The vertical axis is entered with the maximum wave height and apparent wave period. The horizontal axis is entered with the mean water depth and the apparent wave period. The outcome of the analysis defines the applicable wave theory to be used to derive hydrodynamic loading on the structure
Where: 2
appgTH
=
29.9*81.98.9
= 0.010
2
appgTd
=
104.09.9*81.9
1002 =
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Figure 6-12: Results of the static pushover analysis showing the collapse mechanism to be shear dominated, where the piles are subject to critical failure. The deflected shape is shown only for the framed structure (in red) and not for the piles. The discontinuity shown between the piles and the frame represents the deformation when the frame collapsed
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LOAD STEP 69 LOAD FACTOR 6.82LOAD STEP 69 LOAD FACTOR 6.82
Figure 6-13: The pushover analysis in the vertical direction was carried out to assess the dominant failure mechanism in the Arabian Gulf under operating overload. The COLLAPSE analysis shows that the dominant failure mechanism is in the piles
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Figure 6-14: Extrapolation of maximum wave height in the Arabian Gulf shows its long term distribution follows a Weibull distribution
y = 0.4521x + 1.9901
1.0
1.3
1.5
1.8
2.0
2.3
2.5
2.8
3.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2
log(ln(RP))
ln(H
max
)
100
year
RP
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Figure 6-15: Extrapolation of current speed in the Arabian Gulf shows that its long term distribution follows a Weibull distribution
y = 0.1495x - 0.1012
-0.20
-0.16
-0.12
-0.08
-0.04
0.00
0.04
0.08
0.12
0.16
0.20
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2
log(ln(Return Period))
ln(C
urre
nt S
peed
)
100-
year
RP
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Figure 6-16: Fitting the calculated base shears computed from long term maximum wave heights indicated that a Weibull distribution provided the best fit compared to other distributions as evident from the straightness of the trend line
y = 4.3939x - 31.497
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
6.80 6.90 7.00 7.10 7.20 7.30 7.40
ln(Base Shear)
Empi
rical
CD
F =
ln(ln
(1/(1
-F(x
)))
100-
year
RP
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0
500
1000
1500
2000
2500
0.1 1 10 100 1000 10000 100000
Return Period
Bas
e Sh
ear
Figure 6-17: The probability of failure under extreme storm condition was calculated from the relationship between the long term base shear values and the corresponding return periods. Using the collapse load calculated from pushover analysis, the relationship provides the return period which corresponds to the collapse load. The probability of failure was (2.3*10-71) calculated as the inverse of the return period
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Gulf of M
exico
Southern North Sea
Northern North Sea
Central North Sea
NWS of Australia
Arabian Gulf
1
1.5
2
2.5
3
1E+02 1E+03 1E+04 1E+05 1E+06Return Period (Years)
Nor
mal
ized
Loa
d
Figure 6-18: A comparison of the severity of environmental data in the Arabian Gulf to other parts of the world. The graph shows normalized extreme environmental load versus return period and demonstrates the dependence of platform reliability level on its environment (Van de Graaf et al., 1994). The normalized extreme environmental load versus return period for the Arabian Gulf was derived in this research
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0.0
1.0
2.0
3.0
4.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
5.6
Factor of Safety
Rel
iabi
lity
Inde
xD/L=3 D/L=0.5
Figure 6-19: Effect of dead to live load on reliability index showing insensitivity of the D/L ratio on the reliability index
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Figure 6-20: Effect of changing factor of safety and resistance COV on the calculated reliability index for single piles
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70
Resistance Coefficient of Variation
Rel
iabi
lity
Inde
x
FoS = 2
FoS = 3
FoS = 4
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2.0
3.0
4.0
5.0
6.0
1 1.25 1.5 1.75 2 2.25 2.5
System Bias Factor
Gro
up R
elia
bilit
y In
dex
Figure 6-21: The effect of system factor on the computed group reliability index. The green horizontal line presents the reliability index for a single pile and the red curve presents the reliability index for a group
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2.0
2.8
3.5
4.3
5.0
5.8
1.00 1.25 1.50 1.75 2.00 2.25 2.50
System Bias Factor
Gro
up R
elia
bilit
y In
dex
COV=0.42 COV=0.49 COV=0.58 COV=0.69
Figure 6-22: Effect of changing the system and group coefficient of variation on the computed reliability index
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Chapter 7.
CASE STUDY
7.1. BACKGROUND
A real life situation is presented in this Chapter to demonstrate the value of this
research. In one of the projects with an operator in the Arabian Gulf, the field
development department requested an investigation of the possibility of installing a
new piece of equipment weighing 800 tonnes (8000kN) on an existing platform
deck. The distribution of the additional load was not uniform to all four piles that
supported the platform. The reaction on the heaviest loaded pile was 336 tonnes
(3365kN).
An external consultant was appointed to investigate the case. The consultant
applied API RP2A-LRFD (1993) using subjective parameters for OALL and for the
limiting soil parameters. The consultant concluded that the additional load would
overstress the platform piles. To mitigate the overstress, the consultant
recommended strengthening the foundation system of the platform.
The field development department compared the cost of installing a new platform
with the costs associated with strengthening the existing foundation system and
decided to install a new platform at a cost of approximately USD35 million. The
decision was not driven by the cost of strengthening the existing foundation system
alone, as there was a need for a shutdown to effect the strengthening with
consequent loss of production. The difficulty of offshore work, accessibility
problems and the need for more effort to ensure adequate quality of workmanship
were contributing factors to the excessive costs associated with strengthening the
existing foundation system. Strengthening of existing offshore piles can be
prohibitively costly as was demonstrated by the historical case of North Rankin “A”
platform in the North West Shelf of Australia.
An investigation of this historical case, using the results of this research, showed
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that the reserve capacity of the existing piled foundations could have safely
accommodated the additional pile load of 336 tonnes.
Consequently, the requirement for strengthening of the existing piles or the
subsequent decision to install a new platform was not necessary. The decision to
install a new platform was fundamentally driven by lack of appropriate parameters
to be used in the reassessment of existing platforms in the Arabian Gulf.
A description of the platform and the consultant’s approach is presented, and is
followed by applying outcome of this research to demonstrate its value in practice.
7.2. DESCRIPTION OF THE PLATFORM
The platform in question is an unmanned wellhead platform in the Arabian Gulf,
which was installed in 41m water depth. The platform substructure is a 4 legged
jacket with nine well conductors and a superstructure consisting of cellar (EL (+)
12.5) and upper (EL (+) 17.8) decks and a helideck. The substructure was designed
to support eight (8) risers. Three of the risers had an outside diameter (OD) of 6”,
one with 12” OD and for with 10” OD. The platform has one boat landing, risers,
riser guard, barge bumper and other appurtenances like anodes.
The cellar deck area had a grated area of 131m2 and the upper deck had a grated
area of 213m2. The open areas were approximately 50m2 and 35m2 on the cellar and
upper decks, respectively. The open areas allowed for the footprint of the required
new equipment.
The consultant performed calculation of the pile capacity using the soil stratigraphy
shown in Table 7-1 and adopted API RP2A-LRFD (1993) method but used
subjective engineering parameters for the limiting values. Table 7-1 identifies the
soil strata and provides a generic description of each layer from the mudline to
80.8m below mudline. The consultant provided a capacity curve against the pile
penetration as shown in Figure 7-1.
Figure 7-2 shows an isometric view of the platform. The platform foundation
system consisted of four (4) similar piles, which were driven to the design
penetration depth. The pile outside diameter (OD) is 1219mm with 25mm wall
thickness for the pile full length. The pile makeup is shown in Figure 7-3.
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7.3. MATHEMATICAL MODEL
Finite element models of the jacket together with the pile-soil properties were
developed to monitor the behavior of the structure. The mathematical model is
shown in Figure 7-4.
To model soil-structure interaction, each soil layer was substituted with an
equivalent tubular having identical soil-pile lateral characteristics. The equivalent
tubular non-linear properties were derived by the soil consultant from pile lateral
load (p-y) curves using similar approach to that described in Section 6.5.2. The (p-
y) curve data considered soil layer thicknesses, pile diameter and the equivalent
tubular sectional area.
The pile was modeled up to the actual penetration as a tubular inside the leg without
using grout in between the jacket leg and the pile. The analysis considered P-Delta
effects, with an initial imperfection of 0.5% of the member length for braces to
monitor the buckling and post-buckling behavior and a strain hardening of 3% for
all jacket steel components.
The loading input data to the computer model was grouped to vertical loads and
lateral loads. The vertical loads consisted of dead loads and OALL. The self weight
of the modeled members were generated by the software through assigning a value
for gravity (g = 9.81 m/s2). The non-generated dead loads were calculated and
entered into the model as uniformly distributed or concentrated loads on the
respective members. Table 7-2 summarizes the load reactions on the structure.
The analysis only investigated the operating overload as the objective of the
research was to assess the effect of adding more equipment loads on the platform
integrity. The additional piece of equipment did not affect the extreme storm
conditions.
Environmental parameters shown in Table 7-3 for the operating condition were used
in the calculation of the hydrodynamic lateral loads. A constant marine growth
thickness of 50mm radial from EL (+) 1.5 to EL (-) 16.5 was assumed to reduce
linearly to zero at mudline.
The hydrodynamic coefficients were in line with API RP2A-LRFD (1993). Wave
and current forces exerted on the anodes were accounted for by increasing the
coefficient of drag and inertia of the member at the location of anodes. Wave and
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current forces exerted on the gratings, handrails and non-modeled members were
also accounted for by increasing the drag and inertia coefficient values of the tubular
members by a factor of 1.2. The structural analysis of the platform was performed
using SACS program modules.
7.4. ANALYSIS OF THE RESULTS
The analysis was executed and an output file was produced in Table 7-4 to show the
reactions of the most loaded pile in two cases. The first case represented the soil
consultant’s calculations, which used subjective parameters and industry practice in
the Arabian Gulf. The second case implemented findings of this research to analyze
the case.
In Table 7-4, the existing dead loads (13096kN) on the piles were distributed
equally to the four piles. Applying a load factor of 1.3 in accordance with API
RP2A-LRFD (1993), the factored pile dead load was computed as 4256kN.
The factored OALL computed by the consultant was based on 17kPa with a 60%
carry down factor. The OALL of 17kPa was based on the project basis of design
(BOD) defined by the owner and operator of the platform. The 60% reduction
factor was based on Clause C.2.8 in the API RP2A-LRFD (1993) which allows a
reduction in the internal forces but only if operating practices provide adequate
safeguards to prevent loads from exceeding the reduced values. The 60% reduction
factor was not accompanied by measures to ensure that such load would not be
exceeded. Hence, the application of 60% reduction was questionable but was
nevertheless implemented by the consultant who subjectively considered that the use
of 17kPa was too conservative.
The OALL was computed using the tributary areas for the most heavily loaded pile.
The resulting factored OALL on one pile was 1300kN, which was obtained as the
product of OALL (17kPa) by the tributary area (85m2) and using 60% carry down
factor and a load factor of 1.5.
The reaction from the new equipment was not equally distributed to all piles due to
its position on the deck. The layout required the new equipment to be located off-
center and towards the edge of the platform. The most heavily loaded pile
supported an additional load of 3365kN. Using a load factor of 1.3, the factored
equipment weight transmitted to the pile is 4375kN. Hence, the consultant
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estimated a total load on the most heavily loaded pile of 11454kN.
The consultant computed the pile capacity using API RP2A-LRFD (1993) on the
basis of 50m penetration. The consultant did not investigate the pile driving records
to reflect the actual installation conditions. The consultant produced the capacity
chart presented in Figure 7-1. For 50m penetration, the capacity chart read an
ultimate compression pile capacity of 12589kN using the API RP2A-LRFD (1993)
design method. The ultimate capacity of the piles was factored using a capacity
reduction factor of 0.7 in accordance with API RP2A-LRFD (1993)
recommendation, resulting in a factored capacity of 8812kN.
The consultant compared the factored pile capacity (8812kN) with the factored load
effect (11454 – 4375 = 7079kN) on that pile without the additional piece of
equipment and concluded that the piles were safe in the inplace condition without
the addition of any more loads. To add a piece of equipment weighing 800 tonnes
(8000kN), the consultant estimated an increase in the pile load effect of 3365kN and
applied a load factor of 1.30 resulting in factored load effect of 4375kN on the most
heavily loaded pile. Since the combined factored load effect (11454kN) exceeded
the factored capacity (8812kN), the consultant concluded that the piles would be
overstressed and recommended construction intervention.
Using the outcome of this research, the analysis was repeated as described in Table
7-4. OALL was recomputed employing the findings described in Section 5.13. The
material handling equipment on the platform included a jib crane with SWL of 10
tonnes on the upper deck and a monorail with SWL of 5 tonnes on lower decks.
Using Equation 5-18, the OALL was computed as 4.28kPa and 8.28kPa on the
lower and upper decks, respectively. Hence, the total unfactored live load effects
were computed as 504kN (4.28kPa*50m2+ 8.28kPa*35m2=214kN+290kN).
Applying a load factor of 1.5 to the live loads in accordance with API RP2A-LRFD
(1993) recommendations produced live load of 756kN.
Using the findings of this research, the back-analysis procedure was applied. The
pile driving records (PDR) and soil reports were used to back-calculate the pile
capacity. The PDR showed final blow counts of 37, 30, 32 and 49. The blow
counts corresponded to capacities of 16722kN, 14647kN, 15769kN and 17135kN
for the four piles respectively. The pile in question was the most heavily loaded
pile, yet it had the lowest capacity (14647kN).
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The calibrated capacity factors derived in this research shown in Table 4-21 were
employed in these calculations. The applicable capacity reduction factor of 0.82
was used. Hence, the factored pile capacity was computed as 12010kN as shown in
Table 7-5.
Using the results of this research, Table 7-6 shows that the factored capacity
exceeded the factored load effect on the most heavily loaded pile. Consequently,
there was reserve capacity in the foundation system to accommodate the required
additional equipment (3365kN) load with no need for construction intervention.
7.5. SUMMARY
The value of this research was demonstrated using a real life situation showing that
considerable (US$35 million – 2005 prices) savings could have been achieved had
the results of this research been available. The results of this research would have
led to the conclusion that the capacity of an existing offshore platform was adequate
for the additional loading and that a new platform was not required.
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Table 7-1: Description of the soil stratigraphy
Depth (m) Layer #
From To Generic Description
I 0.00 1.00 CAPROCK
II 1.00 9.00 Silty carbonate SAND
III 9.00 21.00 Very weak CALCARENITE
IV 21.00 49.00 Very stiff calcareous CLAY
V 49.00 50.00 Crystalline GYPSUM
VI 50.00 54.50 Hard Silty calcareous CLAY
VII 54.50 61.00 Medium dense silica SAND
VIII 61.00 79.00 Dense carbonate SILT
IX 79.00 80.80 Dense carbonate SAND
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Table 7-2: Description of the gravity loads assumed in the computer model by the engineering consultant
Designation Description Load Effect (kN)
DL SACS Generated load: self weight + marine growth + buoyancy of jacket + topside weights
5280
DL Non-generated dead loads for Jacket: submerged weight of anodes + conductor guides + upending and lifting trunnions/ padeye + submerged weight of mudmat plates and sections + crown shim plate + pile spacers + grating and hand railing on boat landing + riser clamps
842
DL Non-generated dead loads on Topside: hatch covers + grating + handrail + stairway + Christmas tree platform
904
DL Riser dead load (1-12”+3-6”+4-10”+J-Tube) 464
DL Crane dead load 147
DL Equipment dry weight: instrument air receiver + survival craft + test separator + drain vessel + safety items + solar panel + battery + remote terminal units + cables + shutdown panel + miscellaneous E&I items + hydraulic power unit
1034
DL Piping dry weight: present and future 642
OPER Equipment operating content weight: present and future
245
OPER Piping operating content weight: present + future 14
LL OALL @ 17kPa * 60% pile rundown @ EL +12.5 2179
LL OALL @ 17kPa * 60% pile rundown @ EL +17.8 1342
TOTAL UNFACTORED LOADS ON ALL PILES 13096
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Table 7-3: Environmental data used in the analysis of the operating condition
Parameter Assumption
Wave Theory Stokes 5th
Maximum Wave Height (one year return period) 7.3m
Wind speed (1 year return period for 1-hour average) 13.9 m/s
Wave Period 8.2 seconds
Wave Length 117.2m
Angle from X Towards Y 0.0 Degrees
Mudline Elevation -40.8m
Wave Celerity 13.6 m/s
Unmodified Wave Period 7.9 seconds
Admiralty chart datum 40.8m
L.A.T 0.15m
Astronomical tide 1.85m
Storm tide 1.3m
Total water depth for operating overload 44.1m
Crest Position determined by Maximum Moment
Starting Crest Position -12.00
Number of Steps 18
Step Size 1.0m
Crest Water Depth 46.8m
Trough Water Depth 41.7m
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Table 7-4: Analysis of the pile loads for the case study. The results show the factored loads for the most heavily loaded pile
Pile Load Description Units Without results
of this researchWith
results of research
A=Dead load*1.3 kN 3274*1.3=4256
B=OALL*1.5 kN 887*1.5=1300 504*1.5=756
C=Environmental*1.35 kN 1128*1.35=1523
D=Additional Equipment on pile*1.3 kN 3365*1.3=4375
F=Total factored pile load (A+B+C+D) kN 11454 10910
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Table 7-5: Analysis of the pile capacity for the case study. The results show the factored capacity for the most heavily loaded pile. The design drawings showed one pile assembly covering all piles. Thus, the theoretical capacity, using API RP2A-LRFD (1993), of all piles is the same. However, pile driving records showed different penetrations for the various piles
PILE Without results of
this research
With results of research
G=Design Pile Penetration m 50.0 53.5
H=Blow count (blows per foot) at tip Bpf - 30
I=Pile Capacity kN 12589 14647
J=Capacity Reduction Factor φ 0.70 0.82
K=Factored Capacity Piles (I*J) kN 8812 12010
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Table 7-6: A comparison of the factored load effect on the pile against the factored capacity. The results show that using subjective parameters for the load effect and the capacity concluded that the pile capacity would be exceeded when subjected to an increase in load effect. Using the results of this research, this pile could have been justified to support the load and therefore saving USD 35 million which was the cost of construction of a new platform
PILE Units Without results of
this research
With results of research
F=Total factored pile load (A+B+C+D) kN 11454 10910
K=Factored Capacity Piles (I*J) kN 8812 12010
L=Reserve capacity in pile (K-F) kN -2642 +1100
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Figure 7-1: Results of the geotechnical analysis carried out by the consultant to calculate the capacity of piled foundations using API RP2A LRFD (1993). In deriving the ultimate axial capacity along the depth of the pile, the consultant used the capacity reduction factors as per API RP2A-LRFD (1993) but employed subjective limiting parameters to predict the pile capacity
CHAPTER 7: CASE STUDY 320
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Figure 7-2: An isometric view of the platform analyzed in this research
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Figure 7-3: Pile assembly of the platform investigated in this research. The diagram is extracted from the pile drawing and shows pile diameters and penetration lengths in mm
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Figure 7-4: Mathematical model showing the finite elements used to study the behavior of the structure in the case study
323
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Chapter 8.
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
8.1. SUMMARY
This research develops guidelines for reassessment of existing offshore platforms
which are not covered in international codes and standards such as Section ‘R’ in
API RP2A-LRFD (1993).
Section ‘R’ addresses extreme storm conditions only and is specific to the
conditions in the Gulf of Mexico and US waters. In these regions, extreme storm
conditions dominate the failure mechanism. The Arabian Gulf environment is much
more benign compared to that in the Gulf of Mexico and US waters. Consequently,
there is a need to assess the applicability of Section ‘R’ when conducting
reassessment of existing offshore platforms in the Arabian Gulf.
Further, Section ‘R’ lacks guidance on reassessment of axial pile capacity driven in
carbonate soils. This is because the behavior of piles driven in carbonate soils is
very different, and their capacity is much lower, than those driven in “normal” soils.
Since the Arabian Gulf soils are characterized by their high carbonate content, there
is a need to develop parameters to predict the axial capacity of piles in carbonate
soils.
Moreover, API RP2A-LRFD (1993) refers to ASCE Standard 7-05 to determine live
loads. However, live loads nominated in ASCE Standard 7-05 only cover building
structures and does not quantify live loads on offshore platforms. Therefore, one of
the objectives of this research is to provide a methodology to calculate live loads on
offshore platforms.
8.2. METHODOLOGY
This research follows similar approach to that used in the calibration of API RP2A-
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LRFD (1993) and borrows from decades of code development experience.
However, this research employs a database, which is collected in this research and is
pertinent to the conditions of the Arabian Gulf.
The output from this research is a set of specifications that address the following
topics:
• Prediction of axial capacity of piles driven in carbonated soils,
• Calculation of live loads on open areas of offshore platforms, which can be used
to develop load effect on piles, and
• Determination of dominant failure mechanism in the Arabian Gulf.
8.2.1. OPEN AREA LIVE LOAD (OALL)
Development of OALL is based on deriving lifetime maximum loads, which is
equivalent to EUDL adopted in building codes and standards such as ASCE
Standard 7-05.
The derivation of OALL for offshore platforms in the Arabian Gulf utilizes
statistical parameters of equipment weights. A suitable probabilistic model is
identified in this research and applied to the statistical parameters to calculate the
mean lifetime maximum axial load on a pile. The probabilistic model utilizes the
influence surface method, which is consistent with the approach used by ANSI A58
(now called ASCE Standard 7-05) to derive maximum load effect on a pile due to
the random nature of the loads. An extreme value analysis is then applied to the
statistical parameters of the live load effect to produce the mean of the lifetime
maximum load effect.
8.2.2. AXIAL PILE CAPACITY IN CARBONATE SOILS
Calibration of resistance factors for axial pile capacity in carbonate soils is
conducted in this research using a database of offshore piles installed in the Arabian
Gulf. The database comprised installation records of 138 offshore piles, which were
installed between 1960 and 2003. The installation records include pile driving
records, soil profile and pile drawings for each pile.
Calibration of axial pile capacity is conducted using bias factors of axial pile
capacities. The bias factor for each pile is computed by dividing actual pile capacity
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over its predicted capacity.
The predicted capacity of each pile is derived in this research using API RP2A-
LRFD (1993) formulation, except that limiting soil parameters identified in Table
4-6 are used in lieu of the specified values in API RP2A-LRFD (1993) in order to
model conditions in the Arabian Gulf. The prediction of axial pile capacities using
API RP2A-LRFD (1993) is tedious, with a higher likelihood of errors when hand
calculations are performed. To overcome potential errors, an Excel spreadsheet
coded APIPILE is developed in this research. The operating manual for APIPILE is
described in Appendix I.
To derive “actual” pile capacities, an approach termed back-analysis is
implemented. The back analysis approach is developed in this research and
employs installation records and time effect to calculate the “actual” pile capacity.
The installation records are analyzed using GRLWEAP to produce short term pile
capacity. To model the effect of time at the end of driving (EOD) and provide the
actual long term capacity, setup factor is applied to the short term capacity. The
back-analysis method results are validated using PDA and CAPWAP results, which
were obtained from actual offshore installation in the Arabian Gulf.
The computed bias factors are partitioned in this research to reduce the error in the
statistical parameters. The partitioned bias factors reflect installation methods, soil
profile (cohesive soils overlain by cohesionless soils or vice versa), and level of
conservatism in the original design.
Calibration of axial pile capacity in carbonate soils requires calculation of statistical
parameters of the bias factors and determination of target reliability levels. The
statistical parameters are derived using the parametric approach described in Section
3.4. The selection of target reliability levels is based on survey of literature and also
accounts for the target reliability level employed in calibrating axial pile capacity in
API RP2A-LRFD (1993).
8.2.3. DOMINANT FAILURE MECHANISM
Reliability analysis is applied to a specific platform in this research to determine the
dominant failure mechanism in the Arabian Gulf. The reliability analysis is
performed for extreme storm and operating overload conditions to calculate
probability of failure under each failure condition. The condition that corresponds
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to the highest probability of failure is defined as the dominant failure mechanism,
and the corresponding parameters for such dominant failure mechanism would then
be required to formulate reassessment guidelines.
To perform reliability analysis, a pushover analysis has been employed and the
resulting reserve strength ratio (RSR) is used in a First Order Reliability Method
(FORM) to derive reliability index, which is then used to compute probability of
failure. Pushover analysis is conducted using COLLAPSE module in SACS
software. The mathematical model employed DNV guidelines to model the
structure and pile-soil interaction. The statistical parameters for the loads and
resistance are derived in this research for the conditions of the Arabian Gulf.
8.3. CONCLUSIONS
A number of conclusions are derived in the course of this research and are presented
in this section. The value of this research is demonstrated using a case research
from a real life situation, showing that a saving of about US$35 million (2005
prices) could have been achieved if the results of this research were available at the
time of executing that project.
8.3.1. DOMINANT FAILURE MECHANISM IN THE ARABIAN
GULF
This research reveals that, unlike in the Gulf of Mexico and the North Sea where
extreme storm conditions dominate the failure mechanism, the failure mechanism in
the Arabian Gulf is dominated by operating overload conditions.
This research identifies a platform to examine its dominant failure mechanism. The
platform is selected such that the conclusion from the reliability analysis is
applicable to other platforms in the Arabian Gulf. The calculated probability of
failure of this platform under extreme storm conditions is infinitesimally (10-71)
small. On the other hand, the probability of failure under operating overload is
much higher (10-6).
The dominance of operating overload conditions is attributed to the combined effect
of benign environment, large open deck areas, water depth and the low axial
capacity of piled foundations in carbonate soils in the Arabian Gulf.
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The benign environment in the Arabian Gulf reflects the low (6%) COV of the base
shear calculated in Section 6.7.4. The required guidelines for reassessment of
existing platforms in the Arabian Gulf focus on operating overload conditions. An
outline of such procedure is shown in Figure 8-1.
8.3.2. APPLICABILITY OF SECTION ‘R’ TO THE ARABIAN
GULF CONDITIONS
Given that operating overload conditions dominate the failure mechanism in the
Arabian Gulf, this research concludes that guidelines contained in Section ‘R’ of
API RP2A-LRFD (1993) are inapplicable to the Arabian Gulf conditions. Section
‘R’ addresses extreme storm conditions only when performing reassessment of
existing offshore platforms on the basis that extreme storm conditions dominate the
failure mechanism. Section ‘R’ lacks guidance on axial pile capacity and open area
live loads, which are two primary requirements needed for reassessment of existing
offshore platforms in the Arabian Gulf.
8.3.3. SPECIFICATIONS FOR OALL
The Author’s experience reveals that industry practice employs subjective values for
OALL ranging from 2.5kPa to 17kPa when performing reassessment of existing
platforms. The use of an arbitrary value for the live load is likely to result in either
unsafe or uneconomic assessment.
The need to quantify OALL is actually applicable to all platforms around the world
but is critical for the rational reassessment of existing platforms in the Arabian Gulf
due to the dominant nature of the operating overload on the failure mechanism.
This is not the case in other parts of the world, such as the Gulf of Mexico, where
extreme storm conditions dominate the failure mechanism. In those regions, API
RP2A-LRFD (1993) does not require live loads to be combined with extreme storm
conditions when reassessment of existing platforms is carried out.
This research reveals that the magnitude of OALL is affected by various parameters
including:
• Platform size,
• Platform expected life span,
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• Location of the deck on the platform (upper deck, other decks),
• The selected influence surface (pile, primary beams, secondary beams or topside
columns),
• The safe working load (SWL) or capacity of the crane or monorail used to
handle equipment on a deck, and
• The separation distance between equipment on a deck.
The research examines the sensitivity of each parameter on the magnitude of OALL
and concludes that the magnitude of OALL effect on piles is dominated by SWL of
the crane or monorail on a deck if the distance between equipment on that deck
exceeds 3m. For situations when the separation distance between equipment pieces
is less than 3m, this research point to the dominance of the separation distance in
determining the magnitude of OALL effect on piles.
Figure 8-2 provides a methodology that can be used to determine OALL effect on
piles.
The use of the approach recommended in this research achieves significant cost
savings in the reassessment of existing platforms in the Arabian Gulf. These
savings can be brought about by either minimizing risk or by avoiding unnecessary
remedial work to existing piled foundations. Use of a rational OALL may also
provide development opportunities by allowing the addition of loads on existing
facilities without the need for construction intervention.
8.3.4. LIMITING ENGINEERING PARAMETERS OF
CARBONATE SOILS
International codes and standards, including API RP2A-LRFD (1993), do not
provide limiting values for pile end bearing and friction for carbonate soils. This
research reveals two issues associated with the selection of limiting engineering
parameters for carbonate soils.
Firstly, the limiting soil parameters for carbonate soils are site specific and currently
depend on experience. A survey of the literature revealed that limiting parameters
for carbonate soils have been suggested for various parts of the world such as
Australia and India, and are predominantly based on experience in these regions.
The literature lacks guidance on limiting parameters in the Arabian Gulf.
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Previous test results are sometimes used to evaluate limiting engineering parameters
but these are even more problematic to apply in practice due to the wide range of
recommended limiting values for the skin friction and the end bearing from those
test results as described in Section 2.7.4. This wide range is understood to be a
function of the geographic locations, but could also be a result of the loading test
method, interpretation method and the soil stratification. Further, researchers warn
against extrapolating their site-specific research findings to other parts of the world.
Secondly, specifying limiting parameters for carbonate soils is not independent of
resistance factors. Survey of the literature reveals that the suggested limiting
parameters by various researchers are derived independently with no relation to the
capacity reduction factors. However, this research shows that the capacity reduction
factors are heavily dependent on the selected limiting engineering parameters, as
these are used to predict axial pile capacity, which is used to calculate bias factors
which are in turn used to calibrate resistance factors.
Therefore, a study of the limiting parameters is not independent of capacity
prediction equations. Hence, this research selects a set of limiting soil parameters
which are representative of carbonate soils and focuses on calibrating resistance
factors for the selected limiting parameters.
The limiting soil parameters adopted in this research are taken from the study
reported by Lacasse and Goulois (1989), which were derived by averaging opinion
of experts in the field. The limiting parameters are shown in Table 4-5.
8.3.5. BIAS FACTORS OF AXIAL PILE CAPACITY IN THE
ARABIAN GULF
Calibration of API RP2A-LRFD (1993) initially considered a large (1004 load test)
database, with a large (0.786) bias and substantial (68%) error in the prediction
model. After screening out low quality load test data and those in carbonate soils,
Tang (1988) selected 44 pile load tests. For the 44 load tests, the prediction was
improved (1.007, 46%) and used to calibrate API RP2A-LRFD (1993).
The statistical parameters developed in this research for carbonate soils (0.93, 36%)
compares reasonably well with those (1.007, 46%) reported by Tang (1988) in non-
carbonate soils. Further reduction in the bias and error in the prediction model is
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achieved by subdividing the database into groups with similar characteristics. In
this research, the database (138 piles) is sub-grouped to reflect physical differences
in the database, resulting in a reduction (20%-26%) in the error and an improvement
to the prediction model.
The probability distribution of the bias factors for carbonate soils shows a normal
distribution, which is consistent with findings by other researchers (Efthymiou et al.,
1996 and Tang, 1988).
8.3.6. TARGET RELIABILITY LEVELS
Target reliability level represents the implied risk level for a platform. Despite the
vast amount of studies and literature, there is no consensus on target reliability
values that could be directly used in calibration of existing codes and standards.
Target reliability levels implicit in codes and standards range from 1.75 to 4.3 as
shown in Table 2-1. Yet, the calibration of resistance factors in API RP2A-LRFD
(1993) employed a single target level (β = 2.11).
This research shows that the resistance factors of piled foundations are heavily
influenced by the selection of target reliability levels. Consequently, it is crucial to
select appropriate target levels to ensure acceptable risk level for reassessment. In
this research, calibration of resistance factors for the axial capacity of piles
employed a range of target reliability values as shown in Table 4-21.
8.3.7. SPECIFICATIONS FOR AXIAL PILE CAPACITY IN
CARBONATE SOILS
This section defines a procedure to predict axial capacity of piled foundations in
carbonate soils. The procedure is mapped out in Figure 8-3.
The first step in reassessment of an existing platform piled foundation is to collect
available data from the list identified below:
• Pile configuration and splice schedule from engineering drawings,
• Pile driving records,
• Pile penetration resistance versus penetration depth,
• Installation delay records due to welding add-on and hammer and cushion
changes,
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• Details of hammers used in the operation,
• Details of the pile and follower make-up,
• Geotechnical information obtained for the site from soil consultant’s report,
which includes shear strength profile, soil reports identifying engineering
parameters to be used in the prediction of the static pile capacity, continuous
hammer steam pressure records and soil plug field log of soil exploration and
pile installation activities, and
• Pile Driving Analyzer (PDA) and CAPWAP analysis reports.
The application of the procedure depends on the available data. If available
installation records include PDA or CAPWAP results, the pile capacity can
generally be estimated without further work by directly reading results from the
dynamic measurement and CAPWAP report but applying setup factors as described
in section 4.5.4 to compute the long term capacity.
In practice, and especially for older platforms, dynamic pile monitoring was not
performed. However, it is not uncommon to find pile driving records with soil
profile data. In such case, the back-analysis method developed in this research and
described in Section 4.5 can be applied.
To apply the back-analysis procedure, the following procedure is recommended:
• Sketch a soil profile and pile diagram as shown in Figure 4-1 and classify soil
type in general terms to be either sand or clay.
• Assign dynamic soil parameters to each soil layer using the recommended
values in Table 4-9 for the Arabian Gulf. The use of site-specific dynamic soil
parameters is recommended, but these are typically not available, hence the need
for the generic values identified in Table 4-9.
• Model pile cross sections and the actual soil profile accurately. However,
accuracy must be weighed against robustness. The findings of this research
show that dividing the soil every 1.0m produces reliable results.
• Perform one-way wave equation analysis (WEA) using GRLWEAP. The input
data of the pile and hammer parameters may adopt the default values in the
software.
• The result of the WEA provides an estimate of the capacity at the end of driving,
also referred to as short term capacity.
• To incorporate time effects, use a setup factor of 2 as presented in Section 4.5.4.
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• Calculate long term capacity of the pile by multiplying the setup factor by the
short term capacity calculated above.
In the absence of pile driving records, the capacity can be derived using the soil
profile and the pile cross section and applying the engineering limiting values
included in Table 4-5 to API RP2A-LRFD (1993) formulation. The calculated
capacity represents the ultimate capacity and must be factored using one of the
resistance factors developed in this research and shown in Table 4-21. The use of
Table 4-21 requires a qualitative assessment of the soil type, a determination of the
level of conservatism in the design and a description of the installation method. The
capacity reduction factors are much lower for piles installed using supplementary
installation procedures (such as air or water jetting, drilling and grouting).
If soil profile data are not available, there may be a need for an offshore campaign to
identify the soil profile and provide the engineer with the required engineering
parameters for the reassessment. However, such a geotechnical campaign should
only be specified as a last resort and only when absolutely necessary. The cost of an
offshore geotechnical campaign in the Arabian Gulf was around USD500, 000
(2005 prices).
8.3.8. MODIFICATION TO DETERMINISTIC METHOD FOR
REASSESSMENT
Current practice of using the design level method to perform reassessment of driven
piles in existing platforms does not account for data accumulated during installation,
which would not normally be known at the time of design. For example, the
availability of pile driving records through installation records make the design level
inapplicable as it inherently assumes lack of such data. Further, current
deterministic methods do not consider risk levels in the assigned load and resistance
factors.
This research provides a set of guidelines that recognize that reassessment is
fundamentally different from and requires knowledge beyond the scope of design
codes. The guidelines allow for availability of installation records and consider the
various risk levels associated with the different platform types and functions.
Calibration of the resistance factor is a function of the bias factor statistics and target
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reliability levels. Consequently, the use of a single resistance factor of 0.7, as per
API RP2A-LRFD (1993), implies different safety levels for various platforms. To
produce consistent safety levels, a higher value for the target level should be used
with structures of high importance such as manned compression platforms or those
processing sour gas. On the other hand, lower target values may be used for
unmanned satellite platforms with no storage facilities. This approach would
harmonize and rationalize codes such that structures with similar functions and
similar manning levels have comparable safety levels. Such approach would
overcome one of the drawbacks associated with the use of deterministic methods,
typically used in design applications, as discussed in Section 2.5.
Hence, this research adopts a range (1.5 to 3.5) of target reliability levels and
calibrates corresponding resistance factors using First Order Reliability Method
(FORM).
The outcome of this research reveals that the calibrated resistance factors range from
0.38 to 1.00 and depend on the statistics of bias factors and the reliability index (risk
level). For example, the capacity reduction factor of a pile that is installed using a
supplementary installation method (drilling) is 0.38 if that pile supports a platform
with high consequences of failure (βT = 3.5) while a capacity reduction factor of
0.95 may be used if the pile is driven with no supplementary installation methods
and if it supports a platform with low consequences of failure (βT = 1.5).
8.3.9. LIMITATIONS OF API RP2A PREDICTION MODEL
The API RP2A-LRFD (1993) model used to predict axial capacity of piles requires
nominating a wall thickness. In practice, it is not uncommon for an offshore pile to
have a variable cross section along its length. Consequently, there is a question of
which wall thickness to use in the model.
This research reveals that, for a pile composed of many cross sections, the use of an
average wall thickness to model such pile poorly predicts its axial pile capacity.
This research also reveals that the API RP2A-LRFD (1993) model poorly predicts
the axial pile capacity for driven piles that are installed using supplementary
installation methods (such as drilling).
Therefore, for pile configuration with considerable variation in cross section along
its length, or for piles driven in carbonate soils using supplementary methods, the
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API RP2A-LRFD (1993) prediction model is inapplicable as it produces poor
results. In such cases, the Author recommends the use of the back-analysis
procedure, which has been shown in this research to produce reliable results.
8.3.10. MODELING PILE-SOIL INTERACTION
For the Gulf of Mexico and the North Sea platforms, modeling of piled foundations
has usually been ignored when conducting structural pushover analyses due to the
large bias in the design formulation of piled foundations in non-carbonate soils
(Bea, 1983; Edwards et al., 1984). However, disregarding pile modeling of offshore
platforms in the Arabian Gulf is questionable due to lack of consensus on
established limiting soil parameters for carbonate soils. This research provides a
mechanism to make a decision on whether foundation modeling can be ignored.
The application of the back-analysis procedure developed in this research provides
the means to determine the degree of bias in a piled foundation in carbonate soils.
Without ensuring that the degree of conservatism is sufficiently high, foundations
must be included in the analysis of offshore platforms in the Arabian Gulf.
8.4. RECOMMENDATIONS FOR FUTURE RESEARCH
During the course of this research, a number of issues were identified for future
research. These are classified into technical and philosophical issues.
8.4.1. TECHNICAL ISSUES
A number of technical issues have been identified during the course of this research,
which would complement the developments in this research to enable a generic set
of reassessment specifications for all geographic regions.
This research adds to the various findings on limiting values for piles
installed in carbonate soils, and is in line with the general approach that
recommends developing limiting values for each geographical location
(Kolk, 1999). However, there is a need for a constitutive model for
carbonate soils, enabling modeling of large geological units which are
detached from the assumptions of extrapolations based on small diameter
sampling and testing.
Significant research has been conducted to understand the behavior of piled
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foundations in carbonate soils. Despite the vast amount of literature and
expertise in the field, there is lack of an accepted method in the industry.
There is a need to consolidate recent knowledge on the subject with an
objective of gaining consensus on an acceptable design method that can be
used in industry practice.
Future projects that encounter carbonate soils should be undertaken with a
view to extending the knowledge. Areas where current knowledge is lacking
or where work could be undertaken include non-standard laboratory and
small scale in situ pile tests. Future research is required to assess the effects
of scale, develop better tests and standardize more effective procedures for
various types of model pile tests to enable correlation with prototype full-
scale tests.
There is a need to collect additional pile driving records to increase the
sample size and refine the calibrated resistance factors shown in this
research. Additionally, there is a need to promote more instrumentation and
monitoring of existing platforms such that reliable pile data can be collected.
This would also enable the development of bias factors for the toe and skin
separately and hence the calibration of resistance factors for skin and toe
instead of applying a single resistance factor for the total capacity.
Current API RP2A static prediction formulation does not include the effect
of wall thickness. However, during the course of this research, the one-way
WEA demonstrated that the calculated capacity of a pile with various wall
thicknesses along the pile length might be very different from the calculated
capacity using average wall thickness. Hence, there is a need to examine the
API RP2A formulation to verify and include the effect of using various wall
thicknesses in a pile cross section when calculating the pile capacity.
Current practice for predicting axial capacity of piles does not differentiate
between soil types when API RP2A-LRFD (1993) formulation is used to
predict axial pile capacity. However, Section 4.7.1 reveals that piles in clay
have a higher reliability than piles in sand. This discrepancy in apparent
reliability justifies the later consideration of separate resistance factors for
piles in sand and clay. Additionally, since piles in stiff, over consolidated
clay do not show the same loading rate effect as those in soft clay, they too
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may require a separate resistance factor.
There is a need to fully understand the effects of long term setup through an
analytical approach and the time duration required to achieve a specific
setup. Current reassessment methods of such effects can only consider short
term setup, which may be overconservative. Setup effect – normally taken at
the beginning of restrike – could be different to the long term setup.
This research employed @RISK functions to select best fit distributions to
the data. There is a need to repeat the work using different distributions to
the data to determine the effects of alternative distributions on the computed
parameters.
There is also a need to provide standard specifications for drilling and
jetting. The procedure should ideally be linked to a specified resistance
factor. If actual drilling or jetting conditions deviate from such procedures,
lower bound resistance factors should then be used to ensure reliable but
economic foundations. This would also partly explain the scatter in bias
factors for the drilled and jetted piled foundations.
OALL in this research used equipment database from the Arabian Gulf
platforms. An investigation of the statistical parameters in other geographic
parts of the world is an area for future research.
An increase in the probability of failure can also take place under structural
deteriorating conditions. Future research should be conducted to extend the
present work to model deterioration of capacities, time-dependent reliability
and reliability-based inspection and maintenance.
Further research is also needed to model structural deterioration due to boat
impact in conjunction with operational overload conditions.
8.4.2. PHILOSOPHICAL ISSUES
Related to the above technical issues there are a number of philosophical issues that
also need to be addressed within the context of a more consistent application of
system reliability techniques for offshore structures.
An important question associated with reliability assessments is the setting
of target safety levels. This question needs to be addressed in parallel with
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other technical developments if these methods are to be widely applied to
demonstrate the safety of new and existing structures. The historical
performance of existing structures and the application of the ALARP
principle provide an initial basis for setting such targets.
The integration of reliability methods into the design process will form an
important step towards the use of the methods in life cycle planning
optimization. The system reliability assessment should not be seen in
isolation, and it is important to be viewed within the context of an overall
hazard management strategy. An improved understanding of how it
interfaces with other hazard management systems is an important
development in this direction. This will enable a more rational use of the
methods within the overall strategy for design and life cycle planning of a
fixed platform.
Lack of sufficient test data for carbonate soils should be accounted for in
future projects, and a combined effort by operators would enable creation of
such database. There is no added cost to operators to contribute to such
database. The specification of dynamic monitoring is common practice in
many recent projects to avoid contractual issues. Hence, an international
effort to provide results of dynamic monitoring would produce an excellent
database for future research. The availability of such a database will enable
better understanding of piles driven in carbonate sediments. This approach
is consistent with recommendations made by Terzaghi (1927) who states that
“Foundation problems are of such character that a strictly theoretical
mathematical treatment will always be impossible. The only way to handle
them efficiently consists of finding out, first, what has happened on
preceding jobs of a similar character; next, the kind of soil on which the
operations were performed; and finally, why the operations have led to
certain results. By systematically accumulating such knowledge, the
empirical data being well defined by the results of adequate soil
investigations; foundation engineering could be developed into a semi-
empirical science”. Figure 8-4 presents a methodology to guide such future
database.
This research work has identified negligible risks related to environmental
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overload and that operating overload conditions are the dominant failure
mechanism in the Arabian Gulf. There are potentially significant gains from
future improvements of operational practices in the Arabian Gulf. Unlike
natural hazards that can not be controlled, operational loading can be
controlled by monitoring and management strategies. The likelihood of
overload condition in such case would result from human behavior and
failure to follow and rules. Failure of human behavior to follow rules and in
such cases is an area for future studies.
While extreme storm overload conditions are caused by natural events that
can not be controlled, this research shows that operational overload
conditions dominate the failure mechanism in the Arabian Gulf. A root
cause of such failure mechanism is in human and organization factor (HOF).
Hence, there is a need to study HOF causing operational overload.
The American Petroleum Institute has formed a Committee to oversee the
transition between API RP2A and ISO Standard for the design of fixed
structures. The latter will be the path forward for future designs. Future
research should include this work as a starting point for a Regional Annex
for the Arabian Gulf where it could be made widely available to practicing
engineers.
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Figure 8-1: Flowchart showing an outline of the specifications that can be used for reassessment of existing offshore platforms in the Arabian Gulf under operating overload conditions
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Figure 8-2: Flowchart showing a proposed method developed in this research to calculate OALL
CHAPTER 8: SUMMARY, CONCLUSIONS and RECOMMENDATIONS 341
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
PDA/CAPWAP available?
Apply setup factor as ratio BOR / EOD capacity and calculate ultimate axial pile capacity
Is PDR available?
Perform back-analysis procedure using GRLWEAP with hammer, soil and pile parameters using procedure identified in Section 4.5
NO
Soil data available
Use API RP2A-LRFD (1993) procedure with the limiting soil parameters defined in Table 4-5 and the capacity reduction factors In Table 4-21
Obtain soil borehole data from platform site
NO
NO
YES
YES
YES
Collect available installation data:Installation records: PDR/ PDA/ CAPWAPSoil borehole dataMean values of the soil parameters
STOP
STOP
STOP
Figure 8-3: Flowchart showing a proposed method developed in this research to predict axial capacity of piles driven in carbonate soils in the Arabian Gulf
CHAPTER 8: SUMMARY, CONCLUSIONS and RECOMMENDATIONS 342
CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF
Figure 8-4: A proposed flowchart to identify the requirement and steps required to collect a future database for pile capacities
BIBLIOGRAPHY
AASHTO, 1999, “Load Resistance Factored Design Bridge Design Specifications”, Washington, D.C.
ACI Committee 318, 1983, “Building Code Requirements for Reinforced Concrete”, ACI 318-83, American Concrete Institute, Detroit, 111 pages
Advanced Mechanics & Engineering, 1999, “Assessment of the Historical Development of Fixed Offshore Structure Design Codes”, Health and Safety Executive, Offshore Technology Report – OTO 1999 015.
Agarwal, S.L., Malhorta, A.K. and Banerjie, R., 1977, “Engineering Properties of Carbonate Spoils Affecting the Design of Deep Penetration Piles for Offshore Structures”, Proceedings of the 9th Annual Offshore Technology Conference, Houston.
Agarwal, S., Rawat, P., Paintal, S., 1978, “Problems in the Installation of Offshore Piles for Fixed Platforms”, Proceedings of the 10th Offshore Technology Conference, Paper No. 3274, Houston Texas.
Agarwal R.K., Dolan D.K. and Cornell C.A., 1996, “Development of Bias in Analytical Predictions Based on Behavior of Platforms during Hurricanes”, Proceedings of the Offshore Technology Conference, Paper No. 8077, Houston
Agarwal RK, Litton RW, Cornell CA, Tang WH, Chen JH, Murff JD, 1996, “Development of piled foundation bias factors using observed behavior of platforms during Hurricane Andrew”, Proceedings of the Offshore Technology Conference, Paper OTC
8078, Houston.
Aker, 1990, “PIA Theory Manual”, Aker Engineering, Oslo, Aldwinckle, D.S
Alba, J.L. and Audibert, J.M.E., 1999, “Pile Design in Carbonate and Carbonaceous Granular Materials: An Historical Overview”, Proceedings of the Second International Conference on Engineering for Carbonate Sediments, Bahrain.
Allen, D. E., 1975, “Limit States Design - A Probabilistic Research”, Canadian Journal of Civil Engineering, 2(1), March 36 – 49, American Institute of Steel Construction, Load & Resistance Factor Design.
Al-Shafei, K.A., Cox, W.R., Helfrich, S.C., 1994, “Pile Load Tests In Dense Sand: Analysis of Static Test Results” Proceedings 26th Annual Offshore Technology Conference Houston, Paper Number OTC 7381.
AISC Standard ANSI/AISC 360-05, (2005a), Specification for Structural Steel Buildings, American Institute of Steel Construction, Chicago, Il., 198 p.
AISC, American Institute of Steel Construction, AISC, 1986, “Load and Resistance Factor Design Manual of Steel Construction”, Chicago
AISC, American Institute of Steel Construction AISC, 1996, “Load and Resistance Factor Design Manual of Steel Construction”, Chicago
AISC LRFD MANUAL, 2001, “Manual of Steel Construction Load and Resistance Factor Design Third
Edition”, American Institute of Steel Construction, Inc. Chicago
American Petroleum Institute, 1977, “Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms RP-2A”, 9th Edition, American Petroleum Institute, Washington, D.C.
American Petroleum Institute API, 1979, “Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms, “API Recommended Practice 2A, 11th Edition.
American Petroleum Institute API, 1987, “Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms”, API Recommended Practice 2A, 15th Edition.
American Petroleum Institute API, 1993, “Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms - Load and Resistance Factor Design.” API RP2A-LRFD.
American Petroleum Institute; 1997, “Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms, Load and Resistance Factor Design, RP-2A-LRFD”. Supplement 1 to 1st Edition, American Petroleum Institute, Washington, D.C.
American Petroleum Institute, 2000, “Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms - Working Stress Design”, API RP2A-WSD, 21st Edition.
Andam, K.A., 1986, “Floor Live Loads for Office Buildings”, Building and Environment, Volume
21, No. ¾, pp 211-219.
Andam, K.A. and Asantey, S.B.A., 1998, “Stochastic Assessment of the Lifetime Maximum Live Load”, Engineering Structures, Volume 20, No. 9, pp 805-818, Elsevier Science Ltd
Anderson, W.D., Silbert, M.N., and Lloyd, J.R., 1982, “Reliability Procedure for Fixed Offshore Platforms”, Journal of the Structural Division, ASCE, ST11, pp 2517-2538
Angemeer, J, Carlson E. and Click, J., 1973, “Techniques and Results of Offshore Pile Load Testing in Carbonate Soils”, Proceedings of the 5th Annual Offshore Technology Conference, OTC 1894, Houston, Texas
Angemeer, Carlson & Klick, 1973, Techniques and Results of Offshore Pile Load Testing in Carbonate Soils, Offshore Technology Conference, Houston, USA.
Angemeer, J, Carlson, E, Stroud, S and Kurzeme, M, 1975, “Pile Load Tests in Carbonate Soils Conducted in 400 feet of Water from a Semi Submersible Rig”, Proceedings of the 7th Annual Offshore Technology Conference, OTC 2311, Houston, Texas
Ang, A. H.S., and Tang, W. H., 1975, Probability Concepts in Engineering Planning and Design I: Basic Principles, Wiley, New York.
Ang, A. H.S., and Tang, W. H., 1984, “Probability Concepts in Engineering Planning and Design II: Decision, Risk, and Reliability”, Wiley, New York.
ANSI A58.1-1973, “Building Code Requirements for Minimum Design
Loads in Buildings,” American National Standards Institute.
Asantey, S.B.A. and Andam, K.A., 1996, “Factory and Warehouse Live Load Survey”, Building and Environment, Volume 31, No. 2, pp 167-178, Elsevier Science Ltd
American Society of Civil Engineers, 2005, “ASCE 7-05 - Minimum Design Loads for Buildings and Other Structures”, ASCE/SEI 2005.
Asgarian, B and Lesani, M, 2006, “Effects of Pile-Soil Interaction on Push-Over Analysis of Jacket Type Platforms”, 25th International Conference on Offshore Mechanics and Arctic Engineering, OMAE2006-92249, Hamburg, Germany.
Aurora, R.P., 1981, Model Studies of Long Piers in Clay”, Proceedings, 13th Offshore Technology Conference, Houston, Volume 4, pp. 331-338.
Australian Standards, AS/NZS 1170.1, 2002, “Loading Code - Dead and Live Loads”, Standards Australia
Azadi, M.R.E., Moan, T. and Amdahl, J., 1995, “Dynamic Effects on the Performance of Steel Offshore Platforms in Extreme Waves”, Proc. EUROSTEEL, A.N. Kounadis, Rotterdam, Balkema.
Baecher, G. B., Pate, M. E., and De Neufville, R., 1980, “Risk of Dam Failure in Benefit-Cost Analysis”, Water Resources Research, 16(3), June, 449-456, Manual of Steel Construction, 1st Edition, AISC, Chicago, 1986, 1114 p.
Baecher, G.B., and Rackwitz, R., 1982, “Factors of Safety and Pile
Load Tests”, International Journal for Numerical and Analytical Methods in Geomechanics, Volume 6, pp 409-424.
Baecher, G.B., 1987, “Geotechnical Risk Analysis User’s Guide”, FHWA/RD-87-011, Federal Highway Administration, McLean, 55 pages
Baldi G., Bellotti, R., Ghionna, V., Jamiolkowski M. and Pasqualini, E., 1986, “Interpretation of CPT's and CPTU's - 2nd Part: Drained Penetration of Sands”, 4th International Geotechnical Seminar, Singapore, pp. 143-156.
Barker, R.M., Duncan, J.M., Rojani, K.B., Ooi, P.S.K., Tan, C.K. and Kim, S.G., 1991, “Load Factor Design Criteria for Highway Structure Foundations”, Final Report NCHRP 24-4 National Cooperative Highway Research Program, NCHRP Report 343, Transportation Research Board, Washington, 308 pages.
Bea, R.G., and Doyle, E.H., 1975, “Parameters affecting Axial Capacity of Piles in Clays”, Offshore Technology Conference, OTC 2307, pp 611-623
Bea, R.G., 1979, “Earthquake and Wave Design Criteria for Offshore Platforms”, Journal of the Structural Division, Proceedings of the ASCE ST 2, pp 401-419.
Bea, R.G., 1980, “Dynamic Response of Piles in Offshore Platforms”, Dynamic Response of Piled foundations: Analytical Aspects, Proceedings of Session Sponsored by Geotechnical Engineering Division, ASCE National Convention, pp 80-109.
Bea, R.G., 1980, “Reliability
Considerations in Offshore Platform Criteria”, Journal of the Structural Division, ASCE ST 2, pp 1835-1853.
Bea, R.G., 1982, “Soil Strain Rate Effects in Axial Pile Capacity”, Offshore Piling, College of Engineering, The University of Texas at Austin and the Institution of Civil Engineers, London, England, pp 107-132.
Bea, R.G., 1983, “Characterization of the Reliability of Offshore Piles subject to Axial Loadings”, Proceedings of the ASCE, Structural Congress, Houston, Texas
Bea, R.G., 1990, “Reliability-based Design Criteria for Coastal and Ocean Structures”, National Committee on Coastal and Ocean Engineering, Institution of Engineers, Australia
Bea, R. G., 1990, “Reliability Criteria for New & Existing Platforms”, Proceedings of the 22nd Offshore Technology Conference (2), Houston, 393 - 408.
Bea R.G., 1992, “Structural Reliability: Design and Requalification of Offshore Platforms - Reliability of Offshore Operations”, Proceedings of the International Workshop, Building and Fire Research Laboratory, National Institute of Standards and Technology, Gaithersburg, Special Publication 833, 41-67.
Bea, R.G. and Young, C.N., 1993, “Loading and Capacity Effects on Platform Performance in Extreme Storm Waves and Earthquakes”, Proceedings of the 25th Offshore Technology Conference, OTC 7140, Houston
Bea, R.G., 1993, “Reliability-based
Requalification Criteria for Offshore Platforms”, ASME, Proceedings of the 12th OMAE Volume 11, pp 351-361, New York
Bea, R.G., 1996, “Nonlinear performance of Offshore Platforms in Extreme Storm Waves”, ASCE, Journal of Waterway, Port, Coastal and Ocean Engineering, Vol. 122, No. 2, pp. 68-74
Bea R.G., 1996, “Reassessment and Requalification of Infrastructure: Application to Offshore Structures”, Journal of Infrastructure Systems 1996, 2(2):45±53.
Bea, R.G., Mortazavi, M. M., 1996, “ULSLEA: a Limit Equilibrium Procedure to determine the Limit State Loading of Template-Type Platforms”, Journal of Offshore Mechanics and Arctic Engineering, Transactions of the ASME, 118 (4), 267±75.
Bea, R.G., Mortazavi, M.M. and Loch, K.J., 1997, “Evaluation of Storm Loading on Capacities of Offshore Platforms”, Journal of Waterway, Port, Coastal and Ocean Engineering
Bea R G, 1997, “Human and Organization Errors in Reliability of Offshore Structures”, Journal of Offshore Mechanics and Arctic Engineering, Transactions of the ASME 119(1): 46±52.
Bea R.G. and Mortazavi, M.M., 1998, “Reliability-based Screening of Offshore Platforms”, Journal of Offshore Mechanics and Arctic Engineering, Transactions of the ASME 1998; 120(3):139±48.
Bea R.G., 1999, “Risk Assessment and Management (RAM) of Marine Systems: Past, Present and Future”, Proceedings State of the Art
Pipeline Risk Management Conference, Perth, Western Australia.
Bea, R.G., Xu, T., Ramos, R., Valle, O and Valdes, V, 1999, “Reliability-based Design Criteria for Floating Drilling & Production Structures in the Bay of Campeche”, Proceedings of the Offshore Technology Conference Paper Number 11065, Houston
Bea, R.G., Jin, Z., Valle, C. and Ramos, R., 1999, “Evaluation of Reliability of Platform Piled foundations”, Journal of Geotechnique and Geoenvironmental Engineering, ASCE, 125(8), 696–704
Bea, R., Puskar, F.J., Smith, C., and Spencer, J., 1988, “Development of AIM (Assessment, Inspection and Maintenance) Programs for Fixed and Mobile Platforms, Proceedings 20th Offshore Technology Conference OTC Paper Number 5703.
Bea, R.G, 2000, “Human and Organizational Factors in Design and Reliability of Offshore Structures”, PhD Thesis, University of Western Australia
Bea, R.G., 2008, Private email correspondence with the Author
Beal, A.N., 1979, “What's Wrong With Load Factor Design”, Proceedings of the Institution of Civil Engineers, 66 (Part 1), November, pp 595 - 604.
Beake & Sutcliffe - Marine Pile Testing in Carbonate Rocks, 1980, “Pipe Pile Derivability in Carbonate Rocks of the South Arabian Gulf”, International Conference on Foundations on Rock, Sydney.
Becker, D. E., Allen, D. E., Ho, K. S., & Law, K. T., 1993, “Development of Limit States Design for Foundations in National Building Code of Canada”, International Symposium on Limit State Design in Geotechnical Engineering (2) Danish Geotechnical Institute, Copenhagen, 479 - 489.
Becker, D. E., 1996, “Eighteenth Canadian Geotechnical colloquium: Limit States Design for Foundations Part II, Development for the National Building Code of Canada”, Canadian Geotechnical Journal, 33(6), 984–1007.
Been, K, Clark, J. I. and Livingstone, W. R., 1993, “Verification & Calibration Studies for New CAN/CSA-S472 Foundations of Offshore Structures”, Canadian Geotechnical Journal, 30(3), June, 515-525
Been, K. and Jefferies, M.G., 1993, “Determination of Sand Strength for Limit State Design”, International Symposium on Limit State Design in Geotechnical Engineering (1), Danish Geotechnical Institute, Copenhagen, 101-110
Bennett, R.M., 1993, “Structural Analysis Methods for System Reliability”, Journal of Structural Safety
Berger, J.A. & Goble, G.G., 1992, “Reliability-Based Specification for Driven Piles”, Proceedings of the 6th Specialty Conference on Probabilistic Mechanics & Structural & Geotechnical Reliability, Denver, preprint copy.
Billington, D.P, 1983, “The Tower and the Bridge – The New Art of Structural Engineering”, Basic Books, New York
Botelho, D.L.R, Petrauskas, C., Mitchell, T.J. and Kan, D.K.Y, 1994, “A detailed Research on the Failure Probability of ST130 “A” Platform during the Passage of Hurricane Andrew”, Proceedings of Offshore Technology Conference, Paper Number 7472, Houston, Texas.
British Standards Institution, 1972, “Code of Practice for Structural Use of Concrete”, CP110 Part 1, London, 154 p
Burnett, 1979, “The Engineering Geological Description of Carbonate Suite Soils and Rocks - Ground Engineering”.
Bengtson, P.E., Bergdahl, U. and Ottosson, E., 1993, “A Comparative Research on Limit State Design and Total Safety Design for Shallow Foundations”, International Symposium on Limit State Design in Geotechnical Engineering (1), Danish Geotechnical Institute, Copenhagen, 13 - 22.
Benjamin, J.R. and Cornell, C.A., 1970, “Probability, Statistics and Decision for Civil Engineering”, McGraw-Hill, New York, 684 p
Beringen, F.L., Kolk, H.J. and Windle, D., 1982, “Cone Penetration and Laboratory Testing in Marine Carbonate Sediments”, Symposium on Geotechnical Properties, Behavior and Performance of Carbonate Soils, ASTM, STP 777, pp 179-209
Bhattacharya, B., Basu, R. and Ma, K., 2001, “Developing Target Reliability for Novel Structures: the Case of the Mobile Offshore Base”, Elsevier, Marine Structures 14, pp 37-58.
Birkinshaw M, Kam J. and
McIntosh A.R., 1994, “The Application of Risk and Reliability Management to Offshore Structural Integrity Assessment”, ERA Conference, London
Birkinshaw, M. and Sharp, G., 1994, “Engineering the Risks”, Proceedings 3rd International Conference Offshore Structural Design – Hazards, Safety and Engineering, ERA, London.
Birkinshaw M, Smith D, 1996, “The Setting of Target Safety Level for the Assessment of Offshore Structures”, Proceedings of the International Offshore and Polar Engineering Conference, Los Angeles
Bjerrum L, 1973, “Problems of Soil Mechanics and Construction on Soft Clays”, State of the Art Report, Session IV, Proceedings 8th International Conference SMFE, Moscow 1973.
Bolt, H.M., Billington C.J. and Ward J.K., 1994, “Results from Large Scale Ultimate Load Tests on Tubular Jacket Frame Structures”, Proceedings of the Offshore Technology Conference, Paper OTC 7451, Houston.
Bolt, H.M., Billington C.J., Ward J.K., 1995, “A Review of the Ultimate Strength of Tubular Framed Structures”, Health and Safety Executive - Offshore Technology Report OTH 92 365, HSE Books, London.
Boon, M.S., Vanderschuren, L., Graaf, J.W. and Tromans, P.S., 1993, “Failure Probability of Southern North Sea Platform under Environmental Loading”, Proceedings of the 3rd International Offshore and Polar Engineering Conference ISOPE, Singapore
Borowski, W.S. and Paul, C.K., 1997, “The Gas Hydrate Detection Problem: Recognition of Shallow-Sub bottom Gas Hazards in Deepwater Areas”, Proceedings of the Offshore Technology Conference Paper Number 8297, Houston, Texas.
Bowles, J.E., 1988, “Foundation Analysis and Design”, 4th Edition, McGraw-Hill Inc., New York
Burnett, 1979, “The Engineering Geological Description of Carbonate Suite Soils and Rocks”, Ground Engineering.
Brand, E.W., Muktabhant, C. and Taechathummarak, A., 1972, “Load Tests on Small Foundations in Soft Clays”, Proceedings Performance of Earth and Earth Supported Structures, Volume 1, Part 2, ASCE, New York, 903–928.
British Standards BS 6349-1, 2000, “Maritime Structures-Part 1: Code of Practice for General Criteria”, British Standards Institution.
Buchan S.J. and Stroud S.A., 1999, “Extreme Metocean Criteria for Pipeline Design, Methodologies and Uncertainties”, Northwest Shelf Australia State of the Art Pipeline Risk Management Conference, November 1999.
Bullock, P.J., 1999, “Pile Friction Freeze: A Field and Laboratory Research, Volume 1”, PhD Dissertation, University of Florida.
Burland, J.B, 1973, “Shaft Friction Piles in Clay - A Simple Fundamental Approach, Ground Engineering, 6(3), 30–42.
Burland, J.B., Potts, D.M. and Walsh, N.M., 1981, “Overall Stability of Free and Propped
Embedded Cantilever Retaining Walls”, Ground Engineering, 14(5), July, 28 - 38.
Briaud, J. and Tucker, M., 1988, “Measured and Predicted Axial Response of 98 Piles”, Journal of Geotechnical Engineering, Volume 116, No. 9
Briaud L.M. and Tucker E., 1989, “Axially Loaded 5 Pile Group and Single Pile in Sand”, Proceedings 12th ICSMFE, Rio de Janeiro 1989, Volume 2, p l121
British Standards Code of Practice CP3, Chapter V, Loading: Part 1, 1990, British Standards Institution.
Brown, C.B, 1979, “A Fuzzy Safety Measure”, Proceedings of the ASCE, Journal of Engineering Mechanical Division Volume 105 Number EM5, pp 855-872.
BS EN ISO 13819-1, 1998, “Petroleum and Natural Gas Industries - Offshore Structures Part 1: General Requirements”, European Standard EN ISO.
Camp III, W.M. and Parmar, H.S, 1999, “Characterization of Pile Capacity with Time in the Cooper Marl: A Research of the Applicability of a Past Approach to Predict Long term Pile Capacity,” Emre, TRB, pp. 1-19
Camp III, Wright, W., William, B. and Hussein, M., 1993, “The Effect of Overburden of Pile Capacity in a Carbonate Marl,” Deep Foundations Institute 18th Annual Members’ Conference
Casagrande, A., “Role of Calculated Risk in Earthwork and Foundation Engineering”, Journal of Soil Mechanics and Foundations Division (ASCE), 91(SM4), Jul
1965, 1-40.
Castillo, E., 1988, “Extreme Value Theory in Engineering”, Academic Press Inc., Harcourt Brace Jovanovich Publishers, Statistical Modeling and Decision Science
Chalk, P.L., and Corotis, R.B., 1980, “Probability Model for Design Live Loads”, Proceedings of the ASCE, Journal of the Structural Division, 106(10), 2017–2033.
Chaterjee, S. and Biswas, A.K., 1971, “The Human Dimensions of Dam Safety”, Water Power, 23(12), December, pp 445 - 453.
Chen, W.F. and Ross, D.A., 1977, “Tests of Fabricated Tubular Columns”, Proceedings of the ASCE, Journal of Structural Division, Volume 103, Number ST3
Chen, Y.J. and Kulhawy, F.H., 1993, “Undrained Strength Interrelationships among CIUC, UU and UC Tests”, ASCE, JGE, Vol. 119, No. 11, Nov. 1993
Cheney, R.S. and Chassie, R.G., 1993, “Soils and Foundations Workshop Manual, Second Edition, Report No HI-88-009, U.S. Department of Transportation, Federal Highway Administration, Office of Engineering, Washington, D.C., 353-362
Chow F.C., 1996, “Investigations into Displacement Pile Behavior for Offshore Foundations.” PhD Thesis, University of London Imperial College.
Chow, F.C., Jardine, R.J., Brucy, F., and Nauroy, J.F., 1998, “Effects of Time on Capacity of Pipe Piles in Dense Marine Sand”, Journal of Geotechnical and Geoenvironmental Engineering, Vol. 124, No. 3,
ASCE, pp 254-264
Chopra, A.K, 1995, “Dynamics of Structures: Theory and Applications to Earthquake Engineering”, Prentice-Hall, Englewood Cliffs, N.J.
Clark, C.E., 1961, “The Greatest of Finite Set of Random Variables”, Operations Research, Bethseda, Md., pp 145-162.
Clark and Walker, 1977, “A Proposed Scheme for the Classification and Nomenclature for use in the Engineering Description of Middle Eastern Sedimentary Rocks”, Geotechnique
CIRIA, 1977, “Construction Industry Research and Information Association”, Rationalization of Safety & Serviceability Factors in Structural Codes”, Report 63, London, 226 p.
Coduto, P.D, 2001, “Foundation Design Principles and Practices”, 2nd Edition, Prentice-Hall Inc, Upper Saddle River, New Jersey, 883 pages
Construction Industry Research & Information Association, 1977, “Rationalization of Safety and Serviceability Factors in Structural Codes”, Report 63, CIRIA, London, 226 p.
Committee on Reliability Methods for Risk Mitigation in Geotechnical Engineering, 1995, “Probabilistic Methods in Geotechnical Engineering”, National Academy of Sciences, Washington
Cornell, C.A., 1969, “A Probability-Based Structural Code”, J. American Concrete Institute 66, 974-985.
Cornell, C.A., 1969, “Structural Safety Specifications Based on Second Moment Reliability Analysis”, IABSE Symposium on Concepts of Safety of Structures and Methods of Design, London
Cornell, C.A., 1995, “Structural Reliability - Some Contributions to Offshore Technology”, Proceedings of the 27th Offshore Technology Conference, Paper Number 7753, pp 535-542.
Cornell, C.A., Tang, W.H., Chen, J.H., and Murff, J.D., 1996, “Development of Piled foundation Bias Factors using Observed Behavior of Platforms during Hurricane Andrew, “Proceedings of the 28th Annual Offshore Technology Conference, Volume 2, Houston, TX, 445–455.
Corotis, R.B., 1971, “Statistical Analysis of Live Load in Column Design”, Journal of the Structural Division, ASCE, Volume 98, No. ST8, Paper 9123, pp 1803-1835.
Corotis, R. B, 1972, “Statistical Analysis of Live Load in Column Design”, Journal of the Structural Division, ASCE, 98(8), 1803–1815.
Corotis, R.B., and Doshi, V.A., 1977, “Probability Models for Live Load Survey Results”, Journal of the Structural Division, ASCE, 103(6), 1257–1274
Coyle, H.M., and Reese, L.C., 1966, “Load Transfer to Axially Loaded Piles in Clay”, Journal of Soil Mechanics and Foundations Division, ASCE, Vol. 92, SM2, pp. 1-26
Coyle, H.M. and Gibson, G.C., 1968, “Damping Constants related to Common Soil in Sands and Clays”, Texas Transportation
Institute, Research Report 125-1, Texas A&M University
Coyle, H.M. and Sulaiman, I.H., 1973, “Skin Friction for Steel Piles in Sand”, Journal of Soil Mechanics and Foundations Division, ASCE, Vol. 93, SM6, pp. 261-278.
Coyle, H. M. and Castello, R. R., 1981, “New Design Correlations for Piles in Sand,” Journal Geotechnical Engineering Division, ASCE, Vol. 107, Number GT7, pp. 956-986.
Chow, F.C., Jardine, R.J., Nauroy, J.F. and Brucy, F., 1997, “Time Related Increase in Shaft Capacities of Driven Piles in Sand,” Geotechnique, Volume 47, No. 2, pp. 353-361.
Cox, S. and Cheyne, A. 1998, “Assessing Safety Culture in Offshore Environments”, Loughborough University, UK
Criswell, M. E. & Vanderbilt, M., 1987, “Reliability-based Design of Transmission Line Structures: Methods”, Report EL-4793 (1), Electric Power Research Institute, Palo Alto, 473 p.
CSN 73 1401, 1995, “Structural Steel Design”, Prague: Czech Institute for Specifications
Cullen, the Honorary Lord, 1990, “The Public Inquiry into the Piper Alpha Disaster”, HMSO, London
Dahlberg, R. and Ronold, K.O., 1993, “Limit State Design of Offshore Foundations”, International Symposium on Limit State Design in Geotechnical Engineering (2), Danish Geotechnical Institute, Copenhagen, 491 - 500.
Dakins, M. and Goodrum, P., 2004,
“Introduction to Bayesian Methods”, Training Course on Probabilistic Risk Assessment, Syracuse Research Corporation, PRA Centre of CNY, Washington, DC
Dalane, J.I. and Haver, S., 1995, “Requalification of an Unmanned Jacket Structure using Reliability Methods”, Proceedings of the Offshore Technology Conference, Paper Number 7756, Houston
Danish Geotechnical Institute, 1978, “Code of Practice for Foundation Engineering”, Bulletin 32, DGI, Copenhagen, 52 p.
Danish Geotechnical Institute, 1985, “Code of Practice for Foundation Engineering”, Bulletin 36, DGI, Copenhagen, 53 p.
Das, P.C., 1998, “Application of Reliability Analysis in Bridge Management”, Engineering Structure; 20 (11):957±9.
Datta, M., Rao, G.V. and Gulhati, S.K., 1978, “The Nature of Carbonate Soils”, Proceedings of Geocon-India, Conference on Geotechnical Engineering, Volume 1
Datta, Guhatti and Rao, 1979, “Crushing of Carbonate Sands”, Proceedings of the 11th Offshore Technology Conference, Paper Number 3525, Houston, Texas
Datta, M., Guhatti S.K., and Rao, G.V., 1980, “An Appraisal of the Existing Practice of Determining the Axial Load Capacity of Deep Penetration Piles in Carbonate Sands”, Proceedings, 12th Annual Offshore Technology Conference, Houston, Texas, Paper No. 3867.
Datta M, Gulhati, S K and Rao G V,
1980, “Development of Pore Water Pressures in Dense Carbonate Sands repeated Compressive Stress Cycles”, Proceedings, 12th Offshore Technology Conference, Houston, Texas.
Datta M, Gutati S K and Rao G V, 1982, “Engineering Behavior of Carbonate Soils of India and Some Observations on Classifications for Such Soils”, Symposium on Geotechnical Properties, Behavior and performance of Carbonate Soils, ASTM Special technical Publications, STP 777, ASTM, Philadelphia, Pa. 113-140
Davie J.R. and Bell K.R., 1991, “A Pile Relaxation Case History”, Proceedings of the International Conference, Foundations Profondes, Paris, France, 421-429.
Davisson, M.T, 1972, “High Capacity Piles,” Innovations in Foundation Construction; Proceeding of ASCE Illinois Section and Illinois Institute of Technology Lecture Series, January 19, 1982-May 3, 1982, pp. 81-112
Decourt, L., 1999, “Behavior of Foundations under Working Load Conditions”, Proceedings of the 11th Pan-American Conference on Soil Mechanics and Geotechnical Engineering, Foz DoIguassu, Brazil, August 1999, Volume 4, pp. 453 488
Department of Energy, 1990, “Offshore Installations: Guidance on Design, Construction and Certification”, HMSO Publications, London.
Department of the Navy, 1982, “Foundations and Earth Structures Design Manual 7.2”, NAVFACDM-7.3, Alexandria, Virginia
De Mello, V.F.B, 1969, “Foundation of Buildings on Clay”, Proceedings of the 7th ICSMFE, Vol. 1, Mexico City, 49–136.
De Ruiter, J. and Beringen, F.L., 1979, “Piled foundations for Large North Sea Structures,” Marine Geotechnology, Volume 3, No. 3, Part 1, pp. 267-314.
De, R., 1989, “Research of Redundancy in Near-Ideal Parallel Structural Systems”, Proceedings of the 5th ICOSSAR, Volume 2: 975-982. New York: ASCE.
De SR., 1990, “Onshore Structural System Reliability: Wave-Load Modeling, System Behavior and Analysis”, Report Number RMS-6, the John A. Blume Earthquake Engineering Centre, Department of Civil Engineering, Stanford University, Stanford, California.
De, R.S., 1995, “Risk Analysis Methodology for Developing Design and Assessment Criteria for Fixed Offshore Structures”, Proceedings of the Offshore Mechanics Conference, OTC 7755, May, Houston TX.
Deep Foundations Institute, 1984, “Standard Testing Method for Deflection Characteristics of Pile Driving Cushion Material”, DFI, P.O. Box 359, Springfield, N.J., Proceedings of the Fourth International Conference on the Application of Stress-Wave Theory to Piles, the Netherlands, September 1992.
Dennis N.D. and Olson R.E., 1983, “Axial Capacity of Steel Pipe Piles in Clay”, Proceedings, Geotechnical Practice in Offshore Engineering, Austin, Texas.
Diamantidis D., 2001, “Assessment
of Existing Structures”, Joint Committee of Structural Safety JCSS, Rilem Publications S.A.R.L., The publishing Company of Rilem, 159 pages.
Digre K.A., Puskar F.J., Aggarwal R.K., Irick J.T., Kreiger W.F. and Petrauskas C., 1995, “Modification to and Applications of the Guidelines for Assessment of Existing Platforms contained in Section 17.0 of API RP2A”, Proceedings of the Offshore Technology Conference. Paper OTC 7779, Houston.
DiGioia, A.M., Jr. and Rojas-Gonzalez, L.F., 1990, “Application of Reliability-based Design Concepts to Transmission Line Structure Foundations”, Paper 90 SM 307-9 PWRD, IEEE Power Engineering Society Summer Meeting, New York, 7 p.
Ditlevsen O & Madsen H.O, 1996, “Structural Reliability Methods”, John Wiley & Sons, Chichester
DIN 1072, 1983, “Standard Design Loads for Buildings: Live Loads”, DIN Deutsches Institute fur Normung.
DNV, Det Norske Veritas, 1995, “Guidelines for Offshore Structural Reliability Analysis – General”, Final Draft, Report No. 95-2018, Oslo: Det Norske Veritas.
DNV, Det Norske Veritas, 1996, “Rules for Submarine Pipeline Systems”, Hovik, Norway
DNV – Sintef, Bomel, 1999, “Ultiguide Best Practice Guideline for Use of Non-Linear Analysis Methods in Documentation of Ultimate Limit States of Jacket Type Offshore Structures”, Det Norske Veritas, Norway.
DNV Classification Note 30.6, 1992, “Structural Reliability Analysis of Marine Structures”, Det Norske Veritas
DNV, Offshore Standard DNV-OS-C101, 2000, “Design of Offshore Steel Structures, General (LRFD Method)”
Duncan, J.M., Tan, C.K., Barker, R.M. and Rojiani, K.B., 1989, “Load and Resistance Factor Design for Bridge Foundations”, Symposium on Limit States Design in Foundation Engineering, Canadian Geotechnical Society, Toronto, 47-63.
Dunnicliff, J. and Deere, D.U., 1984, “Judgment in Geotechnical Engineering: The Professional Legacy of Ralph B. Peck”, Wiley, New York, 332 p.
Dunham, J.W., 1947, “Design Live Loads in Buildings”, Transactions ASCE, Volume 112, Paper No 2311, pp 725 744.
Dunlap, W.A. and Ibbs, C.W. (Editors), 1993, “Assessment and Requalification of Offshore Production Structures”, New Orleans, Proceedings of an International Workshop
Durning, P.J. and Rennie, I.A., 1978, “Determining Pile Capacity and Pile Driveability in Hard Over-consolidated North Sea Clay”, Abstract Book 8, European Offshore Petroleum Conference and Exhibition, London
Dutch National Environmental Policy Plan, 1989, “Premises for Risk Management, Second Chamber of the States”, General Session 1988-9, 21137, No. 5
Dutt, R.N. and Cheng, A.P., 1984,
“Frictional Response of Piles in Carbonate Deposits”, Proceedings, 16th Annual Offshore Technology Conference, Houston, Texas, paper No. 4838.
Dutt, R.N. and Moore, J.E., 1985, “Behavior of Piles in Granular Carbonate Sediments from Offshore Philippines”, Proceedings, 17th Offshore Technology Conference, OTC 4849, Houston, Texas
Dutta, M., Gulhati, S.K. and Rao, G.V., 1980, “An Appraisal of the Existing Practice of Determining the Axial Load Capacity: Deep Penetration Piles in Carbonate Sands”, Proceedings of the 12th Annual Offshore Technology Conference, Houston, Paper OTC 3867.
EDI, Engineering Dynamics, Inc., 1996, “SACS – Structural Analysis Computer System Program”
Edwards, G., Heidweiler, A., Kerstens, J and Vrouwenvelder, A., 1984, “Methodologies for Ultimate Limit Reliability Analysis of Offshore Jacket Platforms”, International Conference on Structural Safety and Reliability, Kobe, Japan.
Efthymiou M, Van der Graaf J.W., Tromans P.S. and Hines I.M., 1996, “Reliability-based Criteria for Fixed Steel Offshore Platforms”, Proceedings of the 15th International Conference OMAE, I, 129-141
Efthymiou, M., van de Graaf, J.W., Tromans, P.S., Hines, I.M., 1997, “Reliability-based criteria for Fixed Steel Offshore Platforms”, Journal of Offshore Mechanics and Arctic Engineering, Transactions of the ASME; 119 (2):120±4.
Efthymiou, M., Tromans, P.S. and
van de Graaf, J.W., 1996, “Reliability of a Jacket Structure in a Tropical Cyclone Environment”, 1996 Offshore Mechanics and Arctic Engineering – Volume I – PART A, Offshore Technology ASME 1996.
Efthymiou, M. and van de Graaf, J.W., 1997, “Reliability-based Design and Reassessment of Fixed Steel Platforms”, SIEP Report 97-5050 for Shell Expro Aberdeen (confidential report)
Eisenstein, Z., 1989, “Canadian Foundation Engineering Manual & Limit States Design”, Symposium on Limit States Design in Foundation Engineering, Canadian Geotechnical Society, Toronto, 65 - 76.
Ellingwood, B. R., and Culver, G. C, 1977, “Analysis of Live Loads in Office Buildings”, ASCE, Journal of the Structural Division, 103(8), 1551–1560.
Ellingwood, B., 1978, “Reliability Basis of Load and Resistance Factors for Reinforced Concrete Design”, National Bureau of Standards Building Science Series 110, Washington, D.C
Ellingwood, B., 1979, “Reliability-Based Criteria for Reinforced Concrete Design”, J. Structural Div., ASCE 105, 713-727.
Ellingwood, B., Galambos, T. V., MacGregor, J. G., & Cornell, C. A., 1980, “Development of Probability-Based Load Criterion for American National Standard A58”, Special Publication 577, National Bureau of Standards, Washington, 222 p.
Ellingwood, B., 1981, “Wind and Snow Load Statistics for Probabilistic Design”, J. Structural
Division, ASCE 107, 1345-1350
Ellingwood, B. and Galambos, T.V., 1982, “Probability-Based Criteria for Structural Design”, Structural Safety 1, 15-26.
Ellingwood, B., MacGregor, J., Galambos, T and Cornell, A, 1982, “Probability Based Load Criteria: Load Factors and Load Combinations”, ASCE, Journal of the Structural Division, Proceedings of the ASCE, Vol. 108, No. ST5.
Ellingwood B.R., 1996, “Reliability-based Condition Assessment and LRFD for Existing Structures”, Structural Safety, Volume 18, Number 2, pp.67-80.
Ellingwood BR. 1998, “Issues related to Structural Ageing in Probabilistic Risk Assessment of Nuclear Power Plants”, Reliability Engineering and System Safety; 62 (3):171±83.
Elms, D.G, 1985, “The Principle of Consistent Crudeness”, Proceedings Workshop on Civil Engineering Applications of Fuzzy Sets, Purdue University, October, pp 35-44.
Elms, D.G, 1992, “Consistent Crudeness in System Construction”, In B.H.V. Topping (Editor), Optimization and Artificial Intelligence in Civil Engineering, Kluwer Academic Publishers, Volume 1, pp 71-85.
Elms, D.G., 1997, “System Health Approach for Risk Management and Design”, Shiraishi, Shinozuka and Wen (editors), Structural Safety and Reliability: Proc. ICOSSAR ’97, Kyoto, Japan, November, Balkema, Rotterdam, pp 271-277.
Elms, D.G, 1998, “Indicator Approaches for Risk Management
and Appraisal”, in M.G. Stewart and R.E. Melchers (Editors) Integrated Risk Assessment: Applications and Regulations, Balkema, Rotterdam, pp 53-59.
Elms, D., 1998, “Owning the Future: Integrated Risk Management in Practice”, 330 pp, H/B, pub. ISBN 0-908993-17-X
Emami A., 1998, “Analysis of Static and Dynamic Pile Soil Behavior”, PhD thesis, Norwegian University of Technology and Sciences (NTNU), No. 1998:52, Trondheim
Emhaidy S., Gharaibeh A, Dan M. Frangopol B., Onoufriou T, 2002, “Reliability-Based Importance Assessment of Structural Members with Applications to Complex Structures”, Elsevier Computers and Structures 80 (2002) 1113–1131,
Engeling, P., 1974, “Driveability of Long Piles”, Proceedings 6th Offshore Technology Conference OTC 2084, Houston, Texas
Eurocode 1, 1993, “Basis of Design and Actions on Structures Part 1: Basis of Design”, Draft, Brussels, CEN/TC 250
European Committee for Standardization, 1993, “Geotechnical Design, General Rules”, Eurocode 7 (Part 1), Working Document for European Committee for Standardization, N.K. Ovesen - Convener, Copenhagen, February, 126 p
Eri, J., Ball, D.L. and Middleton, E., 1977, “Application of Regulations and Design Codes for Offshore Installations”, Proceedings of the 9th Annual Offshore Technology Conference, Houston, Texas, OTC Paper 2761
Estes AC, 1997, “A system reliability approach to the lifetime optimization of inspection and repair of highway bridges”, Ph.D. Thesis, Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, Colorado.
Estes AC, Frangopol DM, 1998, “RELSYS: a computer program for structural system reliability analysis”, Structural Engineering Mechanics, Techno Press; 6 (8): 901–19.
Evangelista, A., Pellegrino, A., and Viggiani, C., 1977, “Variability among Piles of the Same Foundation”, Proceedings, 9th International Conference on Soil Mechanics and Foundation Engineering, Vol. 1, Tokyo, pp 493-500
Faulkner, D, 1984, “On Selecting a Target Reliability for Deep Water Tension Leg Platforms”, Proc. 11th IFIP Conference on System Modeling and Optimization, Copenhagen, 25-29 July, pp490-513
Feld, J., 1965, “Failures in foundation”, Soil test, Inc., Evanston, Ill
Fellenius B. H., 1975, “Test Loading of Piles: Methods, Interpretation, and New Proof Testing Procedure”, ASCE, Volume 101, GT9, pp. 855 869
Fellenius, B.H., 1980, “The Analysis of Results from Routine Pile Load Tests, Ground Engineering”, Vol. 13, No. 6, pp. 19-31
Fellenius, B.H., 1984, “Ignorance is Bliss and that is Why we Sleep So Well”, Geotechnical News, 2 (4), pp 14-15.
Fellenius, B.H., Riker, R.E., O'Brien A.J. and Tracy G.R, 1989, “Dynamic and Static Testing in Soil exhibiting Set-up”, Journal of Geotechnical Engineering, Vol. 115, No. 7, 984-1001
Fellenius, B.H., 1991, “Piled foundations - Foundation Engineering Handbook”, Second Edition, H.S. Fang, ed., Van Nostrand Reinold, publisher, New York, 13: 511-536.
Fellenius, B.H., 1999, “On the Preparation of a Piling Paper”, A Guide prepared for the Deep Foundations Institute Annual Meeting October 14 – 16, 1999, Dearborn, Michigan.
Fellenius, B H., Brusey, W G., and Pepe, F, 2000, “Soil Set-up, Variable Concrete”, Modulus and Residual Load for Tapered Instrumented Piles in Sand,” Specialty Conference on Performance Confirmation of Constructed Geotechnical Facilities, University of Massachusetts, Amherst, April 9-12, ASCE, pp. 1-17
Fellenius, B.H., 2001, “We Have determined the Capacity, then What”, Deep Foundation Institute, New Jersey, http://web.pile.com/Education/fm/default.asp?company
Fellenius, B.H., 2001, “What Capacity Value to Choose from the Results of a Static Loading Test”, Deep Foundation Institute, New Jersey, http://web.pile.com/Education/fm/default.asp?company=
Feng YS, Moses F. 1986, “Optimum design, redundancy and reliability of structural systems”, Computer Structures 1986; 24(2): 239–54.
Ferry, B.J., 1997, “Basic Concepts of Structural Design in Probabilistic Methods for Structural Design”, Edited by C Guedes Soares, Kluwer, Dordrecht
Finno, Richard J., Cosmao, Tanguy, and Gitskin, Brett, 1989, “Results of Foundation Engineering Congress Pile Load Tests,” Predicted and Observed Axial Behavior of Piles, Geotechnical Special Publication No. 23, ASCE, pp. 338-355.
Fjeld, S., 1977, “Reliability of Offshore Structures,” Paper OTC 3028 presented at the 1977 Offshore Technology Conference, Houston.
Flaate K. and P. Seines, 1977, “Side Friction of Piles in Clay”, Proceedings of the 9th International Conference SMFE, Tokyo.
Fleming, W.G.K, 1992, “A New Method for Single Pile Settlement Prediction and Analysis”, Geotechnique 42: 411-425
Flint, A.K, 1976, “Design Objectives for Offshore Structures in Relation to Social Criteria”, Proceedings BOSS Conference Trondheim: Tapir.
Flint A.R. and Baker M.J., 1980, “The Derivation of Safety Factors for Design of Highway Bridges”, Proceedings Conference on the New Code for the Design of Steel Bridges, Cardiff
Focht, J.A., Jr. and O’Neill, M. W., 1985, “Piles and Other Deep Foundations”, Proceedings, 11th International Conference on Soil Mechanics & Foundation Engineering (1), San Francisco, 187-209
Focht, J. A., Jr., 1994, “Lessons Learned From Missed Predictions”,
Journal of Geotechnical Engineering (ASCE), 120(10), 1653-1683.
Fookes and Higginbottom, 1975, “The Classification and Description of Near Shore Carbonate Sediments for Engineering Purposes”, Geotechnique
Forbes V, Onoufriou T, 2000, “Structural Reliability Analysis Framework for Fixed Offshore Platforms – Offshore Application and Sensitivity Research”, Report No. JHA004, University of Surrey, Department of Civil Engineering
Forehand, P. W. and Reese, J. L., 1964, “Predictions of Pile Capacity by the Wave Equation”, Journal of the Soil Mechanics and Foundations Division, ASCE, Volume 90, No.SM2, Proceedings Paper 3820, March, pp. 125
Fu, S.L., C. Petrauskas, D.L.R. Botelho, E.W. Carter, E.A. Basaldua, and B.J. Abbott, 1992, “Evaluation of Environmental Criteria for Gulf of Mexico Platform,” Proceedings, 24th Annual OTC, Houston, TX, May 4-7, Paper OTC 6834
Frangopol, D.M., Gharaibeh, E.S., Hearn G, Shing, P.B., 1998, “System Reliability and Redundancy in codify Bridge Evaluation and Design”, International Srivastava NK, editor, Structural Engineering World Wide, Paper Reference T121-2, Elsevier, Amsterdam, 9 pages on CD-ROM
Freudenthal, A. M., 1947, “Safety of Structures”, Transactions, ASCE, 112, 125-159.
Frieze, P et al., 1991, Report of ISSC Committee Volume 1, Applied Design. Proceedings 11th ISSC, Amsterdam: Elsevier.
Frieze PA, Morandi AC, Birkinshaw M, Smith D, Dixon AT, 1997, “Fixed and Jack-up Platforms: Basis for Reliability Assessment”, Marine Structures; 10 (2±4):263±84.
Galambos, T.V. and Ravindra, M.K., 1973, “Tentative Load and Resistance Factor Design Criteria for Steel Buildings”, Research Report No. 18, Civil Engineering Department, Washington University, St. Louis, Mo
Galambos, T.V. and Ravindra, M.K., 1976, “Load factors for Wind and Snow Loads for use in Load and Resistance Factor Design Criteria”, Research Report No. 34, Civil Engineering Department, Washington University, St. Louis, Mo.
Galambos, T.V. and Ravindra, M.K., 1976, “Load and Resistance Factor Design Criteria for Steel Beam”, Research Report No. 27, Civil Engineering Department, Washington University, St. Louis, Mo.
Galambos, T.V. and Ravindra, M.K., 1976, “Tentative Load and Resistance Factor Design Criteria for Steel Plate Girders”, Research Report No. 29, Civil Engineering Department, Washington University, St. Louis, Mo.
Galambos, T.V. and Ravindra, M.K., 1976, “Tentative Load and Resistance Factor Design Criteria for Steel Beam-Columns”, Research Report No. 32, Civil Engineering Department, Washington University, St. Louis, Mo
Galambos, T.V. and Ravindra, M.K., 1976, “Tentative Load and Resistance Factor Design Criteria for Connections”, Research Report No. 33, Civil Engineering
Department, Washington University, St. Louis, Mo.
Galambos, T.V. and Ravindra, M.K., 1976, “Tentative Load and Resistance Factor Design Criteria for Composite Beams”, Research Report No. 44, Civil Engineering Department, Washington University, St. Louis, Mo.
Galambos, T.V. and Ravindra, M.K., 1977, “The Basis for Load and Resistance Factors Design Criteria for Steel Building Structures”, Canadian Journal of Civil Engineering, Vol. 4 No. 2
Galambos, T.V., 1979, “The AISC Load and Resistance Factor Design Criteria”, 3rd International Conference on Structural Safety and Reliability
Galambos, T.V., Ellingwood, B., MacGregor, J.G. and Cornell, C.A., 1982, “Probability-Based Load Criteria: Assessment of Current Design Practice”, J. Structural Engineering, ASCE 108, 959-977.
Gambino, S. J., and Gilbert, R. B., 1999, “Modeling Spatial Variability in Pile Capacity for Reliability-Based Design” Analysis, Design, Construction, and Testing of Deep Foundations, J. M. Roesset, ed., ASCE, Reston, 135–149
Gates, M, 1957, “Empirical Formula for Predicting Pile Bearing Capacity”, Civil Engineering, ASCE, 27(3), 65–66
Gebara J, Westlake H, DeFranco S, O'Connor P, 1998, “Influence of Framing Configuration on the Robustness of Offshore Structures”, Proceedings of the Offshore Technology Conference, Paper OTC 8736, Houston
Gebara J.M., D. Dolan, S. Pawsey, P. Jeanjean and K.H. Dahl-Stamnes, 2000, “Assessment of Offshore Platforms under Subsidence - Part I: Approach, Journal of Offshore Mechanics and Arctic Engineering, Vol. 122, November, p.260
Gharaibeh ES, 1999, “Reliability and Redundancy of Structural Systems with application to Highway Bridges”, Ph.D. Thesis, Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, Colorado
Ghosn M. and Moses F., 1998, “Redundancy in Highway Bridge Superstructures”, Transportation Research Board Report 406 Washington D.C.: National Academy Press.
Gharaibeh E.S., Frangopol D.M., Shing P.B., 1998, “Structural Importance Assessment of Bridge Members: A Reliability-Based Approach”, Dunaszegi L, editor. Developments in short and medium span bridge engineering’98, vol. 2 Montreal: Canadian Society of Civil Engineering. p. 1221–33
Gharaibeh E.S., McCartney J., Frangopol D.M., 2001, “Reliability-Based Importance Assessment of Structural Members”, In: Bathe KJ, editor. Computational Fluid and Solid Mechanics, Volume 1. Amsterdam: Elsevier. p. 198–200.
Gibbs, H.J. and Holtz, W.G., 1957, “Research on Determining the Density of Sand by Spoon Penetration Test”, Proceedings 4th International Conference on Soil Mechanics and Foundation, Engineering Vol.1, pp35-39
Gibson, G. C. and Coyle, H. M,
1968, “Soil Damping Constants related to Common Soil Properties in Sands and Clays”, Research Report No.125-1, Texas Transportation Institute, Texas A&M University
Gierlinsky JT, Sears RJ, Shetty NK, 1993, “Integrity Assessment of Fixed Offshore Structures: a Case Research using RASOS Software”, International Conference on Offshore Mechanics and Arctic and Engineering, Glasgow.
Gilchrist, J M., 1985, “Load Tests on Tubular Piles in Coralline Strata”, Journal of Geotechnical Engineering, ASCE, 111(5), 641-655
Goble, G., Likins, Garland E., and Rausche, Frank, 1975, “Bearing Capacity of Piles from Dynamic Measurements,” Final Report, Department of Civil Engineering, Case Western Reserve University, Cleveland, Ohio.
Goble, G. and Rausche, F., 1976, “Wave Equation Analyses of Pile Driving-Program Manuals”, Department of Transportation, Report Number FHWA IP-76-14.3
Goble, G. and Rausche, F., 1986, “Wave Equation Analysis of Piled foundations”, WEAP86 FHWA Contract DTFH61-84-C-00100
Goble Rausche Likins and Associates, 1999, GRLWEAP Wave Equation Manual; Cleveland, Ohio
Goble, G., 1999, Geotechnical related development and implementation of load and resistance factor design (LRFD) methods. NCHRP Synthesis 276, Transportation Research Board, Washington.
Golder, H.Q., 1966, Discussion of “Role of the Calculated Risk in Earthwork and Foundation Engineering”, Journal of the Soil Mechanics and Foundation Division, ASCE, 92(SM1): 188 189.
Green, R., 1991, “The Development of a LRFD code for Ontario Bridge Foundations”, Proceedings Geotechnical Engineering Congress (GSP27), ASCE, 1365-1376, New York
Green, R., “LSD Code for Bridge Foundations”, 1993, International Symposium on Limit State Design in Geotechnical Engineering (2), Danish Geotechnical Institute, Copenhagen, 459 - 468.
Guang-Yu, Z., 1988, “Wave Equation Applications for Piles in Soft Ground,” Proceedings 3rd International Conference on the Application of Stress-Wave Theory to Piles (B.H. Fellenius, Editor), Ottawa, Ontario, Canada, pp. 831-836
Gudmestad, O. T., 1998, “Challenges in Requalification and Rehabilitation of Offshore Platforms - on the Experience and Developments of a Norwegian Operator”, pp. 19-25 in. Proceedings of International Workshop on Platform Requalification, OMAE 1998, Lisbon
Gudmestad, O.T., Hansen, K., 2000, “On Some Research Issues related to Requalification of Fixed Steel Jacket Structures”, Proceedings of the 10th International Offshore and Polar Engineering Conference, ISOPE, Seattle, USA.
GRL and Associates, Inc., 1995, “GRLWEAP Manual”, Pile Dynamics, Inc., “Pile Driving
Analyzer Manual”
Guha, B., 1983, “Limit State Design of Foundations - Design Guide Part I, Shallow Foundations and Retaining Walls”, Report 83267, Ontario Hydro, Toronto, 93 p
Hadjian, A.H., 2000, “A Basic Limitation of Partial Load Factors”, 8th ASCE Specialty Conference on Probabilistic Mechanics and Structural Reliability
Haldar, A., 1981, “Statistical Methods in Numerical Methods in Geomechanics, NATO Advanced Research Institute Series, J.B. Martins, Ed. Boston: D. Reidel Publishing, pp 471-504.
Haldar, A. and S. Mahadevan, 2000, “Probability, Reliability and Statistical Methods in Engineering Design”, John Wiley and Sons, New York
Hallam, M G, Heaf, N J and Wootton, L R, 1977, “Rationalization of Safety and Serviceability Factors in Structural Codes”, CIRIA Report 63.
Hannigan, P.J., 1990, “Dynamic Monitoring and Analysis of Piled foundation Installations”, Deep Foundations Institute Short Course Text, First Edition, 69
Hannigan, P.J., Goble, G., Thendean, G., Likins, G. and Rausche, F., 1996, “Design and Construction of Driven Piled foundations”, U.S. DOT Federal Highway Administration Report, FHWA-41-96-033
Hannigan, P.J., Goble, G.G., Thendean, G., Likins, G.E., and Rausche, F., 1997, “Design and Construction of Driven Piled foundations: Workshop Manual,
Volume 1, Publication Number FHWA-HI-97-014 by the Federal Highway Administration, Washington, D.C.
Hannigan, P.J., Goble, G., Thendean, G., Likins, George E., and Rausche, F., 1997, “Design and Construction of Driven Piled foundations-Volume II”, Federal Highway Administration Report Number FHWA-HI-97-013
Hagenaar J and J van, 1981, “Structures in the Red Sea”, R den Berg -Installation of piles for Marine X ICSMF, Stockholm
Hagenaar J, 1982, “The Use and Interpretation of SPT Results for the Determination of Axial Bearing capacities of Piles Driven into Carbonate Soils”, Proceedings of the Second European Symposium on Penetration testing, Amsterdam, Netherlands, 51-55.
Hagenaar, J., Sijtsma, H. and Wolsleger, A., 1984, “Selection and Use of Piles for Marine Structures in Coral Formations and Carbonate Sediments”, Conference on Piling and Ground Treatment, Institution of Civil Engineering, Thomas Telford, London
Haggerty, B.C. and Khorshid, M.S., 1989, “Foundations and Structural Modifications to an Existing Offshore Platform”, Proceedings of the 21st Annual Offshore Technology Conference, OTC 6075
Hamilton, J.M. and Murff, J.D, 1992, “Selection of LRFD Resistance Factors for Piled foundation Design”, Structures Congress '92, American Society of Civil Engineers, San Antonio, Texas pp 788-791.
Hamilton, J.M. and Murff, J.D,
1995, “Ultimate Lateral Capacity of Piles in Clay”, Proceedings of the 27th Annual Offshore Technology Conference, OTC 7667
Hansen, J.B., 1963, “Discussion on Hyperbolic Stress-Strain Response of Cohesive Soils”, American Society of Civil Engineers, ASCE, Journal for Soil Mechanics and Foundation Engineering, Volume 89, SM4
Hansen, J.B., 1965, “Philosophy of Foundation Design: Design Criteria, Safety Factors & Settlement Limits”, Symposium on Bearing Capacity & Settlement of Foundations, Duke University, Durham, 1 - 13.
Hartikainen, J. and Heinonen, J., 1993, “Geotechnical Dimensioning of Footings Using Partial Safety Coefficients”, International Symposium on Limit State Design in Geotechnical Engineering (2), Danish Geotechnical Institute, Copenhagen, 501 - 511.
Harwood, R.G., 1996, “Comparative Ultimate Strength Analysis of the Kittiwake Structure”, HSE Workshop on System Measures of Strength of Offshore Structures
Hasofer, A.M. and Lind, N.C., 1974, “Exact and Invariant Second-Moment Code Format”, Journal Engineering Mechanics Division, ASCE, 100(EM1): 111-121.
Helberg, C., 1996, “Pitfalls of Data Analysis, Practical Assessment, Research & Evaluation”, 5(5) Retrieved from website http://PAREonline.net/getvn.asp?v=5&n=5.
Hellan, Ø, 1995, “Non-linear Pushover and Cyclic Analysis in Ultimate Limit State Design and
Reassessment of Tubular Steel Offshore Structures”, MTA-report 1995: 108, Trondheim, Dept Marine Technology, Norwegian Institute of Technology
Heaney, A.C., 1971, “A Reliability-Based Research Concerning Live Load and Codified Structural Design”, Thesis submitted to the University of Waterloo, in partial fulfillment of the requirements of the degree of Doctor of Philosophy.
Heerema, E.P, 1979, “Relationships between Wall Friction, Displacement Velocity, and Horizontal Stress in Clay and in Sand, for Pile Driveability Analysis”, Ground Engineering, Volume 12, No. 1.
Hendawi S, Frangopol DM, 1994, “System Reliability and Redundancy in Structural Design and Evaluation”, Structural Safety, 16(1–2):47–71.
Heideman, L.C. and Weaver, T.O., 1992, “Static Wave Force Procedure for Platform Design”, Proceedings Civil Engineering in the Oceans: 496-517. College station, Texas: ASCE.
Hirsch, T.J., Carr, L. and Lowery, L.L., 1976, “Pile Driving Analysis – Wave Equation”, Volume 1-4, FHWA HIP-76-13.1 through IP-76-13.4.
Hobbs, R., 1993, “A Review of the Design and Certification of Offshore Piles with Reference to Recent Axial Pile Load Tests”, Volume 28, Offshore Site Investigation and Foundation Behavior, Society for Underwater Technology, pp. 729-750
Holloway, D.M., Clough G.W. and Vesic A.S., 1979, “A Rational
Procedure for evaluating the Behavior of Impact-Driven Piles”, Special Technical Publication 670, ASTM, 335-357
Howie, D., 2002, “Interpreting Probability: Controversies and Developments in the Early Twentieth Century”, Cambridge: Cambridge University Press.
HSE, 1995, “Offshore Installations: Guidance on Design Construction and Certification”, Third Amendment to Fourth Ed., London.
Huang, S., 1988, “Application of Dynamic Measurement on Long H-Pile Driven into Soft Ground in Shanghai,” Proceedings 3rd International Conference on the Application of Stress-Wav Theory to Piles (B.H. Fellenius, editor.), Ottawa, Ontario, Canada, pp. 635-643
Hussein, M. and Rausche, F., 1988, “Wave Equation Analysis of Pile Driving: Methodology and Performance,” Proc., ASCE 6th National Conference on Microcomputers in Civil Engineering, November 9-11, Orlando, Florida.
Hussein, M. and Likins, G, 1995, “Dynamic Testing of Piled foundations during Construction,” Proc., ASCE Structures Congress XIII, Boston Massachusetts
Hussein, M., Sharp, M. and Knight, William, 2002, “The Use of Superposition for Evaluation Pile Capacity,” Proceedings ASCE Geo-Institutes International Deep Foundation Congress, Orlando, Florida.
Ham, 1961, “Classification of Carbonate Rocks”, American Association of Petroleum Geology
Conference
Heerema, E.P, 1979, “Relationships between Wall Friction, Displacement Velocity and Horizontal Stress in Clay and in Sand for Pile Drivability Analysis,” Ground Engineering. Volume 12, Number 1, pp.55-65
Helfrich, S.C., Wiltsie, E.A., Cox, W.R. and Al-Shafei, K.A., 1985, “Pile Load Tests in Dense Sand: Planning, Instrumentation and Results”. Proceedings 7th Annual Offshore Technology Conference, Houston, Paper Number OTC 4847
Herbich, J.B, 1987, Ed. “Handbook of Coastal and Ocean Engineering”, Gulf Publishing Company
IEC Technical Committee 11, 1991, “Loading and Strength of Overhead Transmission Lines”, Technical Report 826, International Electrotechnical Commission, Geneva, 227 pages
International Conference of Building Officials (ICBO), 1997, “Uniform Building Code”, Whittier, California
Karman, T., 1969, “Statistical Investigation on Live Loads on Floors”, CIB W23, International Council for Building Research, Studies and Documentation, Madrid, Spain
Industrial Risk Insurers, 1978, “Appendix A: Minimum Separation between Process Equipment”.
International Standards Organization, ISO 13822, 1997, “Assessment of Existing Buildings”, sixth draft, Document Number 18-2, Rotterdam
International Standards
Organization, Petroleum and Natural Gas Industries Offshore Structures – Fixed Steel Structures, ISO 13819-2, Draft C (1997)
International Standards Organization, ISO 2394, 1998, “General Principles of Reliability for Structures”, International Organization for Standardization, Geneva, Switzerland, 1998.
Iwan, W.D et al., 1993, “A Reliability-Based Approach to Seismic Reassessment of Offshore Platforms”, Proceedings 6th ICOSSAR, Rotterdam: Balkema.
Jardine, R.J. and R.F. Overy, 1996, “Axial Capacity of Offshore Piles Driven in Dense Sand”, OTC 7973, Houston, May. 585
Jardine, R.J. and Chow, F.C., 1996, “New Design Methods for Offshore Piles”, Publication 96/103, Marine Technology Directorate
Jardine R.J., Overy R.F. and Chow F.C., 1998, “Axial Capacity of Offshore Piles in Dense North Sea Sands”, Journal of Geotechnical and Environmental Engineering ASCE; 124 (2):171±7.
JCSS, 1982, “General Principles on Reliability for Structural Design”, Joint Committee of Structural Safety.
Jordan, I., 1988, “Safety Levels Implied in Offshore Structural Codes: Application to CSA Program for Offshore Structures”, Report, Memorial University of Newfoundland, St. John's - Prepared for the USA Program for Fixed Offshore Structures
Jordan, I.J. and Maes, M.A., 1991, “Rationale for Load Specifications and Load Factors in the New CSA
Code for Fixed Offshore Structures”, Civil Engineering 18(3): 454:464.
Kaiser, M.J. and Pulsipher, A.G., 2006, “Analysis of Extreme Weather Events Shows Vulnerability of Gulf of Mexico Production”, Oil and Gas Journal, Volume 104.17, PennWell
Kam J.C., Snell R.O. and Shetty N.K., 1995, “A Review of Structural System Reliability Analysis for Onshore Structures”, Proceedings 14th International Conference on Onshore Mechanics and Arctic Engineering OMAE, Volume II, 1995, p. 223–34.
Kanda, J. and Kinoshita, K., 1985, “A Probabilistic Model for Live Load Extremes in Office Buildings”, 4th International Conference on Structural Safety and Reliability, ICOSSAR’85
Kaplan, P., Murray, J. J. and Yu, W. C, 1995, “Theoretical Analysis of Wave Impact Forces on Platform Deck Structures”, Proceedings Offshore Mechanics and Arctic Engineering, OMAE, Volume, 1A, pp. 189-198, Copenhagen
Karlsud, K. and Haugen, T., 1985, “Behavior of Piles in Clay under Cyclic Axial Loading – Results of Field Model Tests”, Behavior of Offshore Structures, pp 589-600.
Karlsrud K., B. Kalsnes and Nowacki, F., 1992, “Response of Piles in Soft Clay and Silt Deposits to Static and Cyclic Axial Loading Based on Recent Instrumented Pile Load Tests”, Society of Underwater Testing, London, Sept. 1992.
Karamchandani A, 1987, “Structural System Reliability Analysis Methods”, Report no. 83, the John
A. Blume Earthquake Engineering Centre, Department of Civil Engineering, Stanford University, Stanford, California.
Keese, D.L. and Barton, W.R., 1982, “Risk Assessment and its Application to Flight Safety Analysis”, Sandia National Laboratories, SAND89
King, Van Hooydonk, Kolk and Windle, 1980, “Geotechnical Investigations of Carbonate Soils on the North-West Shelf Australia”, OTC, Reynolds & Kaderbek -Miami Limestone3 Foundation Design
Kirkemo, R, 1988, “Applications of Probabilistic Fracture Mechanics to Offshore Structures”, Applied Mechanics Review, 41: 61-84.
Kishida, H., 1967, “Ultimate Bearing Capacity of Piles Driven into Loose Sand”, Soils and Foundations, 7(3), 20–29
Kjeldsen, S. P., 1996, “Examples of Heavy Weather Damages caused by Giant Waves”, Technomarine, No. 820.
Kolk H.J. and Velde, E., 1996, “A Reliable Method to determine Friction Capacity of Piles Driven into Clays”, OTC Paper No. 7993, Houston, May 1996
Kolk, H.J., 1999, “Deep Foundations in Carbonate Sediments”, Proceedings of the Second International Conference on Engineering for Carbonate Sediments”, Bahrain
Kulhawy, F.H., 1984, “ASCE Drilled Shaft Standard: University Perspective”, Analysis & Design of Piled foundations, Ed. J. R. Meyer, ASCE, New York, 390-395.
Kulhawy, F. H., 1984, “Limiting Tip and Side Resistance: Fact or Fallacy”, Proceedings Analysis and Design of Piled foundations, ASCE, pp. 80-98.
Kulhawy, F.H., C.W. Jackson and P.W. Mayne, 1989, “First Order Estimation of Ko in Sands and Clays”, Foundation Engineering: Current Principles and Practices, Volume 1, Ed. F. H. Kulhawy, ASCE, New York, 121-134.
Kulhawy, F.H. and P.W. Mayne, 1991, “Relative Density, SPT and CPT Interrelationships - Calibration Chamber Testing”, Ed. A. B. Hyang, Elsevier, New York pp 197-211.
Kulhawy, F. H, 1992, “On the Evaluation of Static Soil Properties”, Stability and Performance of Slopes and Embankments II (GSP 31), Editors R.B. Seed & R.W. Boulanger, ASCE, New York, June, 95-115.
Kulhawy, F.H., 1994, “Some Observations on Modeling in Foundation Engineering”, Proceedings, 8th International Conference on Computer Methods & Advances in Geomechanics (1), Eds. H. J. Siriwardane & M. M. Zaman, Morgantown, 209 - 214.
Kulhawy F H., Phoon K, 1996, “Engineering Judgment in the Evolution from Deterministic to Reliability-Based Foundation Design”, Proceedings of Uncertainty ’96 Uncertainty in the Geologic Environment - From Theory to Practice (GSP 58), Eds. C. D. Shackelford, P. P. Nelson & M. J. S. Roth, ASCE, New York, 1996
Kulhawy, F.H., 1996, “From Casagrande’s ‘Calculated Risk’ to Reliability-Based Design in
Foundation Engineering”, J. Boston Society of Civil Engineers, 11(2), 43–56.
Kulhawy, F.H. and Phoon, K.K., 2002, “Observations on Geotechnical Reliability-Based Design Development in North America”, Foundation Design Codes and Soil Investigation in view of International Harmonization and Performance, 2002
Kraft, L.M., Ray, R.P., and Kagawa, T., 1981, “Theoretical T-Z Curves”, Journal of the Geotechnical Engineering Division, ASCE, Volume, 107, GTll, pp. 1543-1562
Kraft L.M., J.A. Focht and S.F. Amerasinghe, 1981, “Friction Capacity of Piles Driven Into Clay”, ASCE, JGED, Vol. 107, Number GTll, Nov. 1981
Kraft, L.M., Jr., 1982, “Effective Stress Capacity Model for Piles in Clay”, Journal of the Geotechnical Engineering Division, ASCE, Volume 108, GT11, pp 1387-1404.
Krieger, W.F. et al., 1994, “Process for Assessment of Existing Platforms to determine their Fitness for Purpose”, Proceedings 20th OTC: OTC 7482. Houston: Offshore Technology Conference
Kyfor, Z.G., Schnore, A.R., Carlo, T.A., and Baily, P.F., 1992, “Static Testing of Deep Foundations”, FHWA-SA-91-042, Federal Highway Administration, Washington, D.C., 174 pages
Lacasse, S., 1988, “Uncertainty in Offshore Geotechnical Engineering – International Survey of API RP2A Engineering parameters for Axial Pile Capacity of Driven Piles in Sand”, Report 85307-14, Norwegian Geotechnical Institute, Oslo,
Norway.
Lacasse, S., and Goulois, A, 1989, “Uncertainty in API Parameters for Predictions of Axial Capacity of Driven Piles in Sand”, Proceedings 21st Offshore Technology Conference, Society of Petroleum Engineers, Richardson, Tex., 353–358.
Lacasse S., Nadim F., 1996, “Model Uncertainty in Pile Axial Capacity Calculations”, Proceedings of the Offshore Technology Conference, Paper OTC 7996, Houston
Ladd, C.C., Moh, Z.C., and Gifford, D.G., 1971, “Undrained Strength of Soft Bangkok Clay”, Proceedings, 4th Asian Regional Conference on Soil Mechanics and Foundation Engineering, Volume 1, pp 135-140.
Lange, M., 1999, “Calibration and the Epistemological Role of Bayesian Conditionalization”, Journal of Philosophy, 96 (6), 294-324.
Lazaridis, A., and O’Neill, M.W, 1991, “Analysis of Geometric Mispositioning in a Vertical Pile Group”, Geotechnical Engineering Conference, F. G.Mc- Lean, ed., ASCE, New York, 519–530
Leemis LM, 1995, “Reliability and Probabilistic Models and Statistical Methods”, Englewood, New Jersey: Prentice-Hall; 1995.
Leonards, G.A., 1982, “Investigation of Failures”, Journal of Geotechnical Engineering Division (ASCE), 108(GT2), February, 187 - 246
Lewis, E.E., 1996, “Introduction to Reliability Engineering”, John Wiley & Sons Inc. ISBN 0471-01833-3
Liang, R.Y. and Zhou, J., 1997, “Probability Method applied to Dynamic Pile Driving Control”, Journal of Geotechnical Engineering, ASCE, Volume 123, no. 2, pp 137-144
Light J.M., Gebara J.M., DeFranco S.J., Stahl B., 1995, “Ultimate Strength analyses of a North Sea Offshore Platform”, SSRC Conference, USA.
Lindley, D.V., 1971, “Making Decisions”, Wiley Interscience.
Livingstone, W. R., 1989, “CSA Code for Design, Construction, & Installation of Fixed Offshore Structures”, Symposium on Limit States Design in Foundation Engineering, Canadian Geotechnical Society, Toronto, 77 - 89.
Liu, J. L., Yuan, Z. L. and Zhang, K. P, 1985, “Cap-pile-soil interaction of Bored Piled foundations”, Proceedings of the 11th ICSMFE, Balkema, Rotterdam, The Netherlands, Volume 3, 1433–1436
Liu Y, Moses F. 1991, “Bridge Design with Reserve and Residual Reliability Constraints”, Structural Safety; 11:29–42
Liu YW, Moses F., 1994, “Sequential Response Surface Method and its Application in Reliability Analysis of Aircraft Structural Systems, Journal of Structural Safety, 16 (1±2):39±46.
Litkouhi, S. and Poskitt, T.J, 1980, “Damping Constants for Pile Driveability Calculations”, Geotechnique 30, No. 1: 77-86.
Likins, G. and Rausche, F., 1988, “Hammer Inspection Tools”, Third Seminar on the Application of Stress
Wave Theory on Piles, Ottawa, Canada, pp 659-667.
Likins, G., F. Rausche, G. Thendean.& M. Svinkin, 1996, “CAPWAP Correlation Studies”, Proceedings of the Fifth International Conference on the Application of Stress-Wave theory to Piles, Orlando, pp 447-464.
Likins, Garland E., Rausche, Frank, and Goble, George G, 2000, “High Strain Dynamic Pile Testing, Equipment and Practice,” Proceedings, Sixth International Conference on the Application of Stress-Wave Theory to Piles, Sao Paulo, Brazil September 11-13, 2000.
Likins, G. and Rausche, F., 2004, “Correlation of CAPWAP with Static Load Tests”, Proceedings of the 7th International Conference on the application of Stress-wave Theory to Piles 2004 - Petaling Jaya, Selangor, Malaysia, August 9-11.
Lloyd, J.R., and Clawson, W.C., 1983, “Reserve and Residual Strength of Pile founded Offshore Platforms”, Proceedings of the symposium on the role of design, and redundancy in marine structural reliability, Washington, D.C., National Academic press, pp 157-195.
Lloyd, J.R. and Karsan, D., 1988, “Development of a Reliability-based Alternative to API RP2A”, Proceedings of the 20th OTC Vol. 4: 593-600, OTC 5882 Houston: Offshore Technology Conference.
Long, James H., Kerrigan, John A. and Whysockey, Michael H., 1999, “Measured Time Effects for Axial Capacity of Driven Piling,” Transportation Research Record 1663, Paper No. 99-1183, pp. 8-15.
Lotsberg I, Kirkemo F, 1989, “A Systematic Method for Planning In-Service Inspections of Steel Offshore Structures”, OMAE, Europe 89, The Hague, The Netherlands, 1989.
Lo, S C. R. and Li, K. S., 1993, “Issues in Reliability-Based Design in Geotechnical Engineering - A Discussion”, International Symposium on Limit State Design in Geotechnical Engineering (3), Danish Geotechnical Institute, Copenhagen, 659 - 663.
Lumb, P., 1978, “Precision and Accuracy of Soil Tests”, Proceedings, 1st International Conference on Applications of Statistics and Probability to Soil and Structural Engineering, University of Hong Kong, pp 330-345.
Lunne, T. and Christoferson, H.P., 1983, “Interpretation of Cone Penetrometer Data for Offshore Sands”, 15th Offshore Technology Conference, Houston, Texas, USA, Proceedings Vol.1, pp 181-192.
Lunne, T., Christoffersen, H.P., and Tjelta, T.I., 1985, “Engineering Use of Piezocone Data in North Sea Clays”, Proceedings, XIICSMFE, San Francisco, Vol.2, pp.907-912
Lunne, T., Robertson, P .K. and Powell, J.J.M., 1997, “Cone Penetration Testing in Geotechnical Practice”, Blackie Academic & Professional, First edition
Lukas, R.G. and Bushell, T.D., 1989, “Contribution of Soil Freeze to Pile Capacity”, Foundation Engineering: Current Principles and Practices, Volume 2, ASCE, pp 991-1001.
MacFarlane, C. and Parry, A C, 1994, “Some (critical) Comments on
Risk Analysis”, Proceedings of Conference on Offshore Safety in a Cost Conscious Environment, Stavanger
MacKinnon, A.G., 1986, “Presentation and Comments on DFI's Standard Testing Method for Deflection Characteristics of Pile Driving Cushion Materials”, Deep Foundation Institute, P.O.Box 359, Springfield, NJ, presented at the DFI Annual Meeting, Houston, TX.
Madsen, H.O., Krenk, S. and Lind, N.C., “Methods of Structural Safety”, Prentice-Hall, Englewood Cliffs, 1986, 403 p.
Maes, M.C., 1986, “Calibration of Partial Factors in the New SCA Code for Fixed Offshore Production Structures”, Technical Report No. 9. Calgary, Alberta: Environmental Protection Branch, Canada Oil and Gas. Lands Adm.
Maes M.A., Schueremans L. and Van Balen K., 1999, “Reliability-Based Assessment of Existing Masonry Structures”, ICASP 8th International Conference on Applications of Statistics and Probability in Soil and Structural Engineering, Editors Melchers R.E. and Stuart M.G., Sydney, Australia, pp. 689-695
Malhotra, S, 2002, “Axial Load Capacity of Pipe Piles in Sand: Revisited,” Deep Foundations Congress, Geotechnical Special Publication, No 116, Volume 2 ASCE, Reston, Va., pp 12230-1246.
Manzocchi, G.M., Shetty, N.K. and Gierlinski, J.T, 1999, “Implications of Wave-in-Deck Forces on the Reliability of offshore platforms”, Proceedings from HSE/E&P Forum Airgap Workshop, London July
Marshall, P.W., and Bea, R.G., 1976, “Failure Modes of Offshore Platforms”, Behavior of Offshore Structures (BOSS) ’76, Proceedings of the 1st International Conference, Tronheim, Norway, Volume II, pp 579-635
Marshall, P.W. and Banon, H, 2000, “Code Alternatives for ISO Earthquake Design”, OMAE-paper S&R 6137, OMAE 2000, New Orleans
Marley, M.J., and Etterdal, B., 1998, “Implications of Wave-in-Deck on Offshore Structural Reliability”, Paper T217-2, Structural Engineering Worldwide 1998, San Francisco
Marshall, R.D., Pfrang, E.O., Leyendecker, E.V., Woodward, K.A., Reed, R.P., Kasen, M.B. and Shives, T.R, 1982, “Investigation of the Kansas City Hyatt Regency Walkways Collapse”, NBSIR 82-2465. Washington D.C.: National Bureau of Standards.
Massel, S R, 1996, “On the Largest Wave Height in Water of Constant Depth”, Ocean Engineering, Volume 23, Number 7, pp553-573
Matousek, M, 1992, “Quality Assurance”, in D.I. Blockley (Editor), Engineering Safety, McGraw-Hill, UK, p.72-88
Matlock, H., 1970, “Correlations for Design of Laterally Loaded Piles in Soft Clay”, Proceedings, 2nd Offshore Technology Conference, Houston, Col. I, pp. 557-594
Matos, S F and Mello J R, 1982, “Piling of Garoupa Platform”, Offshore Technology Conference, OTC 4207, pp 495-510
McClelland, B., Focht, J.A., Jr. and
Emrich, W.J., 1969, “Problems in Design and Installation of Offshore Piles”, Journal of the Soil Mechanics and Foundation Engineering, ASCE, Volume 94, Number SM6
McClelland B, 1974, “Design of Deep Penetration Piles for Ocean Structures”, Journal Geotechnical Engineering Division ASCE, Volume 100, No. GT7.
McClelland Engineers Inc, 1980, “Geotechnical Consideration for Near Shore Foundation Design in Carbonate Materials”, US Dept of Navy, Civil Journal Engineering Laboratory, 1980.
McClelland, B. and Reifel, M.D, 1983, “Planning and Design of Fixed Offshore Platforms”, Van Nostrand Reinhold Company.
McClelland, B, 1988, “Carbonate Sediments: An Engineering Enigma”, Engineering for Carbonate Sediments, Rotterdam.
McLean, Va. Paikowsky, S.G. and Stenersen, K.L., 2000, “The Performance of the Dynamic Methods, their Controlling Parameters and Deep Foundation Specifications”, Proceedings of the 6th International Conference on Application of Stress-Wave Theory to Piles, S. Niyama and J
McGuire, R.K. and Cornell, C.A, 1974, “Live Load Effects in Office Buildings”, Journal of the Structural Division, ASCE, 100(7), 1351–1366.
McVay, M.C., Schmertmann, J., Townsend, F. and Bullock, P., 1999, “Pile Friction Freeze: A Field and Laboratory Research”, Florida Department of Transportation, Volume 1, pp. 192-195 Pile
Dynamics, Inc. (1998) - Pile Driving Analyzer Manual, Model PAK, Cleveland, Ohio.
McVay, M. C., Birgisson, B., Zhang, L. M., Perez, A. and Putcha, S, 2000, “Load and Resistance Factor Design (LRFD) for Driven Piles using Dynamic Methods - A Florida perspective”, Geotechnical Testing Journal, 23(1), 55–66
Mecklenburgh, J.C., 1985, “Process Plant Layout”, John Wiley & Sons, New York, NY
Melchers, R.E., 1999, “Structural Reliability Analysis and Prediction”, 2nd edition, John Wiley & Sons, Chichester, England, 437 pp
Meyer, P.L., Holmquist, D.V., and Matlock, H., 1975, “Computer Predictions for Axially-Loaded Piles with Nonlinear Supports “, Proceedings, 7th Offshore Technology Conference, Houston, Vol. 1, pp. 375-388.
Meyerhof, G. G., 1970, “Safety Factors in Soil Mechanics”, Canadian Geotechnical Journal, 7(4), Nov 1970, 349 - 355.
Meyerhof, G. G., 1976, “Factors of Safety in Foundation Engineering Ashore and Offshore”, Proceedings, 1st International Conference on Behavior of Offshore Structures (1), Trondheim, 901 - 911.
Meyerhof, G. G., 1976, “Bearing Capacity and Settlement of Piled foundations”, J. Geotechnical Engineering Division, ASCE, 102(3), 195–228.
Meyerhof, G.G., 1984, “Safety Factors and Limit States Analysis in Geotechnical Engineering”, Canadian Geotechnical Journal, 21(1), February, 1-7
Miao, T.J. and Chan, T.H.T., 2002, “Bridge Live Load Models from WIM Data”, Elsevier, Engineering Structures 24, pp 1071-1084.
Micic T.V., Chryssanthopolous M.K. and Baker M.J., 1990, “Reliability Analysis for Highway Bridge Deck Assessment”, Journal of Structural Safety; (2±4): 247±54
Ministry of Transportation of Ontario, 1991, Ontario highway bridge design code (OHBDC), 3rd Ed., Downsview, Ontario, Canada
Mirza U.A.A., 1995, “A Simple Approach for Calculating Pile Skin Friction in Clays,” Pre-print, ISOPE Conf., Holland 1995
Mirza, U.A.A., 1997, “Pile Skin Friction in Clays”, International Journal of Offshore and Polar Engineering, Volume 7 Number I. The International Society of Offshore and Polar Engineers
Moan, T., 1983, “Safety of Offshore Structures”, Proc. 4th ICASP: 41-85. Bologna: Pitagora Editrice.
Moan, T., 1988, “The Inherent Safety of Structures Designed According to the NPD Regulations”, SINTEF Report STF71 F88043. Trondheim.
Moan, T., 1990, “Development of Rational Structural Safety Criteria for Offshore Structures with an Emphasis on Ships”, Proceedings Sixth International Conference on Floating Production Systems, IBC, London.
Moan, T, 1993, “Reliability and Risk Analysis for Design and Operations Planning of Offshore Structures”, Proceedings 6th ICOSSAR Conference, Rotterdam: Balkema, 1994.
Moan, T., Hovde, G.O. and Jiao, G., 1993, “Fatigue Reliability Analysis of Offshore Structures Considering the Effect of Inspection and Repair”, Proc. 6th ICOSSAR Conf., Rotterdam: A.A. Balkema, 1994, pp. 519-526
Moan, T., 1995, “Safety Level across Different Types of Structural Forms and Materials - Implicit in Codes for Offshore Structures”, SINTEF Report STF70 A95210. Trondheim: Prepared for ISO/TC250/SC7.
Moan, T et al. (Editors), 1995, “Criteria for Design Safety Factors and Quality Assurance Expenditure, Proc. 3rd ICOSSAR, Amsterdam: Elsevier.
Moan, T., 1998, “Target Levels for Structural Reliability and Risk Analysis of Offshore Structures”, in Risk and Reliability in Marine Technology, C. Guedes Soares (Editor), Rotterdam: A.A. Balkema
Moan, T., 1999, “Review of Probabilistic Inspection Analysis Methods”, Offshore Technology Report OTO 061, Health and Safety Executive, UK.
Moan, T., Johannesen, J.M. and Vårdal, O.T., 1999, “Probabilistic Inspection Planning of Jacket Structures”, Proceedings 31st Offshore Technology Conference OTC 10848. Houston: Offshore Technology Conf.
Mohan, D., 1957, “Consolidation and Strength Characteristics of Indian Black Cotton Soil”, Proceedings 4th International Conference Soil Mechanics, London
Morandi, AC, Frieze PA, Birkinshaw M, Smith D, Dixon AT, 1997, “Reliability of Fixed and
Jack-up Structures: a Comparative Research”, 8th International Conference On Behavior of Offshore Structures, Delft.
Morandi, AC, 2006, “The Application of Reliability Assessment Techniques in the Offshore Industry”, 25th International Conference on Offshore Mechanics and Arctic Engineering OMAE2006-92632, Hamburg, Germany
Morgan, N and Finnie, I, 2006, “An Integrated Approach to Offshore Pile Axial Design in Sands and Clays”, 25th International Conference on Offshore Mechanics and Arctic Engineering OMAE2006-92130, Hamburg, Germany
Mortensen, K., 1983, “Is Limit State Design a Judgment Killer”, Bulletin 35, Danish Geotechnical Institute, Copenhagen, 16 p.
Mortensen, D, 1993, “Safety Requirements for Foundation Structures Determined by Economical Considerations”, International Symposium on Limit State Design in Geotechnical Engineering (3), Danish Geotechnical Institute, Copenhagen, 1993, 683 - 686.
Moses, F., Final reports for API PRAC 79-22 (1980), 80-22 (1981), 81-22 (1982), 82-22 (1983), 83-22 (1985), 85-22 (1986), 87-22 (1987), prepared for American petroleum Institute, Dallas.
Moses, F., 1980, “Guidelines for Calibrating API RP2A for Reliability-Based Design”, API PRAC 80-22
Moses, F. and Russell, L., 1980, “Applicability of Reliability
Analysis in Offshore Design Practice”, Final Report, API PRAC 79-22, American Petroleum Institute
Moses, F., 1982, “System Reliability Developments in Structural Engineering”, Structural Safety; 1(1):3–13.
Moses, F., 1986, “Load and Resistance Factor Design - Recommended Practice for Approval”, Final Report, API PRAC 86-22, Dallas
Moses, F., 1987, “Load and Resistance Factor Design - Recalibration LRFD Draft Report”, API PRAC 87-22, Dallas
Moses, F. and Larrabee, R. D., 1988, “Calibration of Draft RP2A-LRFD for Fixed Platforms”, Proceedings, 20th Offshore Technology Conference (2), Houston, 171 - 180.
Moses F, 1990, “New Directions and Research Needs in System Reliability Research”, Journal of Structural Safety; 7: 93±100.
Moses, F., 1991, “A Global Approach for Reliability-Based Offshore Platform Codes”, Proceedings 4th Integrity of Offshore Structures Symposium, D. Faulkner et al. (Editors), Glasgow, London: Elsevier
Moses, F. and Stahl, B., 1998, “Calibration Issues in Development of ISO Standards for Fixed Steel Offshore Structures”, 17th International Conference on Offshore Mechanics and Arctic Engineering, ASME.
Mosher, R.L., 1987, “Comparison of Axial Capacity of Vibratory Driven Piles to Impact Driven Piles”, USACEWES Technical Report ITL-
87-7
Mosher, R.L., 1990, “Axial Capacity of Vibratory-Driven Piles Versus Impact- Driven Piles,” Presented at the 69th Annual Meeting of the Transportation Research Board, Washington, DC, 7-11 January.
Motherwell, J.A. and Husak, A.D., 1982, “Axial Capacity and Driveability of Piles in Sand”, Proceedings 2nd International Conference on Numerical Methods in Offshore Piling, University of Texas at Austin
Ministry of Transportation of Ontario, 1991, Ontario Highway Bridge Design Code (OHBDC), 3rd Ed., Downsview, Ontario, Canada.
MSL Engineering, 1999, “Reliability of Fixed and Jack-up Structures”, Report to the Health and Safety Executive UK, OTO 1999 035.
Nacci, W. and Demar, S., 1975, “Engineering Behavior in Carbonate Soils”, Journal Engineering in the Oceans – ASCE conference
Nauroy, J.F. and LeTirant, P., 1983, “Model Tests of Piles in Carbonate Sands”, Proceedings of ASCE Specialty Conference on Geotechnical Practice in Offshore Engineering
Nauroy J.F., LeTirant, P., 1985, “Driven Piles and Drilled and Grouted Piles in Carbonate Sands”, Proceedings, 7th Offshore Technology Conference, Houston, Texas, 83-91.
Nauroy, J.F., 1985, “Side Friction of Piles in Carbonate Sands”, Proceedings of the 11th International conference on Soil Mechanics and
Foundation Engineering, Volume 3, San Francisco, California, 1611-1614.
Nauroy, J.F, Brucy, F, Le Tirant, P. and Kervadec, J.P, 1986, “Design and Installation of Piles in Carbonate Formations”, Proceedings 3rd International Conference on Numerical Methods in Offshore Piling, Nantes, Editor Technip, Paris, pp 461-480
Nicholas, N., Sharp, J., Kam, J., 1994, “Benchmarking of Collapse Analysis of Large Scale Ultimate Load Tests on Tubular Jacket Frame Structures”, Proceedings 3rd International Conference Offshore Structure Design, Hazards, Safety and Engineering, ERA, London
Nichols NW, Birkinshaw M, Bolt HM, 1997, “Systems Strength Measures of Offshore Structures”, Proceedings of the 8th International Conference on Behavior of Offshore Structures, Delft.
Nicholls D.B, 1997, “A Framework for Setting Risk Criteria in Aviation”, Proceedings of the ESREL'97 International Conference on Safety and Reliability, Lisbon, June
Niyama, S. and Beim, G., 2000, “Applications of Stress Wave theory to Piles – Quality Assurance on Land and Offshore Piling”, Proceedings of the Sixth International Conference, Sao Paulo, Brazil, A.A. Balkema Publishers, Rotterdam, Netherlands, 772 pages
NKB, Nordic Committee on Building Regulations, 1978, “Recommendations for Loading and Safety Regulations for Structural Design”, Report 36, NKB (Nordic Committee on Building Regulations), Copenhagen
Noble, G.S, 1995, “Commission of Inquiry into the Collapse of a Viewing Platform at Cave Creek near Punakaiki on the West Coast Wellington”, N.Z.: Department of Internal Affairs.
Noel, Smith D. Leeanne, and Williams, Gustavious P, 1989, “Summary of Pile Capacity Predictions and Comparison with Observed Behavior,” Predicted and Observed Axial Behavior of Piles, Geotechnical Special Publication NO. 23, ASCE, pp. 356-385
Noorany, I., 1985, “Side Friction of Piles in Carbonate Sands”, Proceedings, 8th International Conference on Soil Mechanics and Foundation Engineering, Sand Francisco, California
Nordal, H., 1991, “Application of Ultimate Strength Analysis in Design of Offshore Structural Systems”, Proceedings Conference on Integrity of Offshore Structures”, pp. 153-165, Glasgow
Norwegian Petroleum Directorate, 1999, “Acts, Regulations and Provisions for the Petroleum Activities”, Norwegian Petroleum Directorate, Stavanger, (Latest Issue)
Nowak A, Zhou J, 1990, “System reliability models for bridges”, Journal of Structural Safety; 7 (2±4):247±54.
Nowacki F., K. Kadsrud and P. Sparrevik, 1992, “Comparison of Recent Tests on OC Clay and Implications for Design”, Proceedings Large-Scale Pile Tests in Clay, ICE, London. Published by Thomas Telford Ltd and edited by J. Clarke.
NPD, 1992, “Regulations
Concerning Load bearing Structures in the Petroleum Activity”, Stavanger: Norwegian Petroleum Directorate.
NRC, 1981, “Safety and Offshore Oil”, Marine Board, National Research Council, Washington, D.C
Olagnon, M., Nerzic, R. and Prevosto, M, 1999, “Extreme Water Level from Joint Distributions of Tide, Surge and Crests: a Case Research”, pp. 95-100, Volume III of Proceedings of ISOPE 1999, Brest June 1999
Oliphant, J., Blockley, D. I., & Larnach, W. J., 1988, “Controlling Safety in Geotechnical Design”, Proceedings, Institution of Civil Engineers, 85(Pt 2), March, 67 - 88.
Olson, R.E., and Dennis, N.D, 1982, “Review and Compilation of Pile Load Test Results, Axial Pile Capacity”, Final Report to American Petroleum Institute on Project PRAC 81-29, University of Texas at Austin.
Olson, R.E., 1984, “Analysis of Pile Response under Axial Loads”, Final Report on PRAC 83-42B, University of Texas at Austin for American Petroleum Institute
Olson, R. E., 1988, “Comparison Of Measured Axial Load Capacities of Steel Pipe piles in Sand With Capacities Using the 1986 API Recommended Practice (RP2A)”, Final Report, project PRAC 86-29A, American Petroleum Institute
Olson, L., Berggren, B., Bengtsson, P-E., & Stille, H., 1989, “Reliability-Based Partial Coefficients - A Simplified Approach”, Proceedings, 12th International Conference on Soil Mechanics & Foundation
Engineering (3), Rio De Janeiro, 2081 - 2084.
Olson, R. E., 1990, “Axial Load Capacity of Steel Pipe Piles in Sand”, Proceedings, 22nd Offshore Technology Conference, Houston, Paper no OTC 6419.
O’Neill, M.W., and Murchison, J.M., 1983, “An Evaluation of p-y relationships in Sands”, Report to the American Petroleum Institute Report PRAC 82-41-1, University of Houston, Texas.
O’Neill, M.W, 1983, “Group Action in Offshore Piles.” Geotechnical Practice in Offshore Engineering, S. G. Wright, ed., ASCE, New York, 25–64.
O’Neill, M.W., and Gazioglu, S.M., 1984, “An Evaluation of p-y Relationships in Clays”, Report to the American Petroleum Institute Report PRAC 82-41-2, University of Houston, Texas
O’Neill, M.W., and Raines, R.D, 1991, “Load Transfer for Piles in Highly Pressured Dense Sand”, Journal of Geotechnical Engineering, 117, 8: 1206-1226
Onoufriou T, Smith D, Birkinshaw M, Smith JK, Forbes V., 1997, “Structural System Reliability Framework for Fixed Offshore Platforms”, Proceedings of the 8th International Conference on Behavior of Offshore Structures, Delft
Onoufriou T, 1999, “Reliability-based inspection planning for offshore structures”, Journal of Marine Structures; 12 (7±8):521±39.
Onoufriou T., 1999, “Reliability-based inspection optimization of
floating offshore structures”, Transaction of the Institution of Marine Engineers; 111(3):135±44.
Orhant, C.J., Kulhawy, F.H., and Trautmann, C.H., 1988, “Reliability-Based Foundation Design for Transmission Line Structures”, Volume 2 critical evaluation of in situ test methods, EL-5507 Final Report electrical power institute paloalto CA.
Oreda, 1995, Offshore Reliability Data Handbook, Oreda Participants, Det Norske Veritas Industri Norge AS, DNV Technical
Ovesen, N.K., 1989, “Geotechnical Limit States Design in Europe”, Symposium on Limit States Design in Foundation Engineering, Canadian Geotechnical Society, Toronto, 33 - 45.
Ovesen, N.K., 1989, “General Report/Discussion Session 30: Codes & Standards”, Proceedings, 12th International Conference on Soil Mechanics & Foundation Engineering (4), Rio de Janeiro, 1989, 2751 - 2764.
Ovesen, N.K., 1993, “Eurocode 7: A European Code of Practice for Geotechnical Design”, International Symposium on Limit State Design in Geotechnical Engineering (3), Danish Geotechnical Institute, Copenhagen, , 691 - 710.
Paikowsky, S.G., Regan, J.E., and McDonnell, J.J, 1994, “A Simplified Field Method for Capacity Evaluation of Driven Piles”, Publication No FHWA-RD-94-042 Federal Highway Administration, McLean, Virginia
Paikowsky, S.G., and Stenersen, K.L, 2000, “The performance of the dynamic methods, their controlling
parameters and deep foundation specifications”, Proceedings 6th International Conference on Application of Stress-Wave Theory to Piles, S. Niyama and J. Beim, eds., Balkema, Rotterdam, The Netherlands, 281–304.
Paloheimo, E. and Ollila, M., 1973, “Research in the Live Loads in Persons”, Ministry of Domestic Affairs, Finland
Paté-Cornell, M.E., 1993, “Risk Management for Existing Energy Facilities: A Global Approach to Numerical Safety Goals, in Ageing of Energy Production and Distribution Systems”, Applied Mechanics Reviews: Volume 46, Number 5. New York: ASME
Pate-Comell, M. E., 1994, “Quantitative Safety Goals for Risk Management of Industrial Facilities”, Structural Safety, Volume 13, pp. 145-157
Pawsey, S., Driver, D., Gebara, J. Bole, J. and Westlake, H., 1998, “Characterization of Environmental Loads on Subsiding Offshore Platforms”, Proceedings Offshore Mechanics and Arctic Engineering, OMAE, Lisbon, July
Pier, J.C., and Cornell, C.A., 1973, “Spatial and Temporal Variability of Live Loads”, Journal of the Structural Division, ASCE, 99(5), 903–922.
Pelletier, J.H., Murff, J.D. and Young, A.C., 1993, “Historical Development and Assessment of the Current API Design Methods for Axially Loaded Pipes”, Proceedings, 25th Annual Conference Offshore Technology Conference, Houston, Texas, USA
Petroski, H, 2000, “Vanities of the
bonfire”, American Scientist, v.88, pp486-490
Phoon, K.K., Kulhawy, F.H. and Grigoriu, M.D., 1995, “Reliability-Based Design of Foundations for Transmission Line Structures”, Report No. TR-105000, Electric Power Research Institute, Palo Alto, California
Phoon, K.K., Kulhawy, F.H. and Mircea, D.G., 2003, “Development of a Reliability-Based Design Framework for Transmission Line Structure Foundations”, Journal of Geotechnical and Geoenvironmental Engineering, ASCE
Ping, W.V and Locke, G.E., “Evaluation and Monitoring of Pile Performance Using Dynamic Measurements in Supporting Offshore Structures”, Geotechnical Special Publication No. 38, Design and Performance of Deep Foundations, pp. 124-140, Edited by P. Nelson, T. Smith, and E. Clukey, American Society of Civil Engineers (ASCE), October 1993
Poulos, H.G. and Davis, E.H., 1980, “Piled foundation Analysis and Design”, John Wiley and Sons, New York
Poulos, H G and Chan, K, 1984, “Model Pile Skin Friction in Carbonate Soils”, Unpublished Report.
Poulos, H.G, 1997, “Analysis of pile groups with defect piles”, Proceedings 14th ICSMFE, Balkema, Rotterdam, The Netherlands, 871–876.
Pucher, A, 1977, “Influence Surface of Elastic Plates”, Berlin” Springer
Puech, A, Poltlet, D and Boisard, P, 1990, “A Procedure to Evaluate Pile
Driveability in the Difficult Soil Conditions of the Southern Part of the Gulf of Guinea”, Proceedings 22nd OTC, Houston, OTC 6237
Pugsley, A., 1955, “Report on Structural Safety”, Structural Engineer, 33(5), May, 141 - 149.
Pugsley A.G., 1966, “The safety of structures”, Arnold, London
Pugsley, A.G., 1973, “The Prediction of Proneness to Structural Accidents”. The Structural Engineer, v.51 n.6, pp 195-196.
Puskar F J, Aggarwal R K, Cornell CA, Moses F U, Petrauskas C, 1994, “A comparison of analytically predicted platform damage to actual platform damage during Hurricane Andrew”, Proceedings of the Offshore Technology Conference. Paper OTC 7473, Houston, 1994.
Puyuello, J G, Sastre, J and Soriano, A, 1983, “Driven Piles in a Granular Carbonate Deposit”, Proceedings, Conference on Geotechnical Practice in Offshore Engineering, Austin, Texas
Quiros, G.W., Young, A.G., Pelletier, J.H., and Chan, J., H-C, “Shear Strength Interpretation of Gulf of Mexico Clays”, Geotechnical Practice in Offshore Engineering, Proceedings of ASCE Specialty Conference, Austin, Texas, pp 144-165.
Rackwitz, R. and Fiessler, B., 1978, “Structural Reliability under Combined Random Load Sequences”, Computers & Structures, 9, March, 489 - 494.
Rackwitz, R., 2000, “Optimization - the basis of code-making and reliability verification”, Elsevier Structural Safety 22 (2000) 27±60
Raiffa, F., and Schlaifer, R., 1968, “Applied Statistical Decision Theory”, MIT Press, Cambridge, Mass.
Ramos, R., Perez, F. and Ortega, R, 1998, “Mexico engineering experiences and developments”, pp. 27-39– Proceedings of International Workshop on Platform Requalification, OMAE 1998, Lisbon
Randolph, M.F., Carter, J.P. and Wroth, C.P., 1979, “Driven Piles in Clay - the Effect of Installation and Subsequent Consolidation”, Geotechnique, 29(4), 361-393.
Randolph, M.F., 1983, “Design Considerations for Offshore Piles”, Geotechnical Practice in offshore Engineering, Proceedings of ASCE Specialty Conference, Austin, Texas, pp 422-439.
Randolph M.F and B.S. Murphy, 1985, “Shaft Capacity of Driven Piles in Clay”, Proceedings 17th Offshore Technology Conference, Volume I, pp.371-378, OTC Paper 4883, Houston, May 1985
Randolph M.F., J. Dolwin & R. Beck, 1994, “Design of Driven Piles in Sand”, Geotechnique, Vol. 44, No 3, September, p.427
Randolph, M.F., Joer, H.A., Khorshid, M.S., Hyden, A.M., 1996, “Field and Laboratory Data from Pile Load Tests in Carbonate Soil”, Proceedings, 28th Offshore Technology Conference, OTC Paper 7992, Houston, May 1996.
Rausche, F., Goble, G. and Moses, F, 1972, “Soil Resistance Predictions from Pile Dynamics”, Journal of the Soil Mechanics and Foundations Division, Volume 98, No SM9, September 1972, ASCE,
pp. 917-987.
Rausche, F., Goble, G., Likins, G.E. and Miner, R., 1985, “The Performance of Pile Driving Systems”, FHWA Contract DTFH61-82-1-00059
Rausche, F., Goble, G., and Likins, G, 1985, “Dynamic determination of pile capacity”, J. Geotechnical Engineering., ASCE, 111(3), 367–383
Rausche, F., Likins, G.E., and Hussein, M., 1988, “Pile Integrity Evaluation by Impact Methods”, Third Seminar on the Application of Stress Wave Theory on Piles, Ottawa, Canada, pp 44-55.
Rausche, F, Hussein, M, Likins, G E., and Thendean, G, 1994, “Static Pile Load-Movement from Dynamic Measurements,” Proceedings, ASCE Geotechnical Engineering Division’s Vertical and Horizontal Deformations of Foundations and Embankments Conference, College Station, Texas.
Rausche, F, Richardson, B., and Likins, G E, 1996, “Multiple Blow CAPWAP Analysis of Pile Dynamic Records,” STRESS-WAVE’96 Conference, Orlando, Florida.
Rausche, F. and Hussein, M., 1999, “Pile Driving in Carbonate Sediments”, Proceedings of the Second International Conference on Engineering for Carbonate Sediments, Bahrain
Rausche, F., Robinson, B. and Liang, L., 2000, “Automatic Signal Matching with CAPWAP”, Proceedings 6th International Conference on the Application of Stress-Wave Theory to Piles, Sao Paulo, Brazil, September 11-13, 2000
Ravindra, M.K., Lind, N.C. and Siu, W., 1974, “Illustrations of Reliability-Based Design”, Journal of Structural Division of American Society of Civil Engineers 1—(St9):1789-1811
Ravindra, M.K. and Galambos, T.V., 1978, “Load & Resistance Factor Design for Steel”, Journal of Structural Division (ASCE), 104(ST9), September, 1337-1353.
Ravindra M.K., 1990, “System Reliability Considerations in Probabilistic Risk Assessment of Nuclear Power Plants”, Journal of Structural Safety; 7 (2±4):269±80.
Reese L.C. and Cox, W.R., 1976, “Pullout Tests of Piles in Sand”, Proc.8th Annual OTC, Houston, Texas
Reese. L.C., Cox, W.R., and Koop, F.D., 1974, “Analysis of Laterally Loaded Piles in Sand”, Proceedings 6th Offshore Technology Conference, Houston, Vol.3, pp. 473-483.
Reese L.C., Cox, W.R. and Koop, F.D., 1975, “Field testing and analysis of laterally loaded piles in stiff clay”, Paper OTC 2312, Offshore Technology Conference, Houston, Texas
Rettedal, W. and Gudmestad, Ove T., 1995, “Acceptance Criteria for Risk in Offshore Construction Projects”, Proceedings Offshore Mechanics and Arctic Engineering, OMAE, Volume I-B, pp.67-75, Copenhagen
Rice C.G. and Cody W.K, 1992, “Impact and Ramification of Setup for Piled Foundations”, Proceedings of 17th Annual Members' Conference, New Orleans, Louisiana, USA, 239-251.
Robertson, P.K. and Campanella, R.G., 1983, “Interpretation of Cone Penetration Tests: Sand”, Canadian Geotechnical J, 20 (4), 718-733.
Rojiani, K.B., Ooi, S.K., & Tan, C.K., 1991, “Calibration of Load Factor Design Code for Highway Bridge Foundations”, Geotechnical Engineering Congress (GSP 27), Eds. F.G. Mclean, D.A. Campbell, & D.W. Harris, ASCE, New York, 1353 - 1364.
Ronalds, B.F., Wong, Y., Tuty, S., Piermattei, E.J., 1998, “Monopod Reliability Offshore Australia”, 17th International Conference on Offshore Mechanics and Arctic Engineering, OMAE 98-1229.
Ronalds, B., Anthony, N.R., Tuty, S., Fakas, E., 2003, “Structural Reliability of Monopods Under Storm Overloads”, Transactions of the American Society of Mechanical Engineers ASME, Volume 125.
Roussel, H.J., 1979, “Pile Driving Analysis of Large Diameter High Capacity Offshore Pipe Piles”, PhD Thesis, Tulane University, New Orleans, LA
Rosenblueth, E. and Esteva, L., 1972, “Reliability Basis for some Mexican Codes”, ACI Publication SP-31, American Concrete Institute, Detroit, MI
Rubinstein, M.F., 1970, “Structural Systems – Static, Dynamics and Stability”, Prentice Hall, Inc
Samson, L. and Authier, J, 1986, “Change in Pile Capacity with Time: Case Histories,” Canadian Geotechnical Journal, 23(1), pp. 174-180
Sakai, T., Sawai, H. and Shioi, Y., 1996, “Theoretical Analysis of the
Pile Driving Formula”, Proceedings, 5th International Conference on the Application of Stress Wave Theory to Piles, F.C., Townsend, M. Hussein, and M.C. McVay (Editors), September 11-13, 1996, Orlando, FL, pp 81-88.
Scherf, I., Etterdal, B. and Monshaugen, T., 1999, “Cost-efficient Structural Upgrade and Life Extension of North Sea Jacket Platforms with Use of Modem Reassessment Techniques”, Offshore Technology Conference, OTC, paper 10851, Houston, May
Schmertmann, J.H, 1967, “Guidelines for use in the soils investigation and design of foundations for bridge structures in the State of Florida”, Res. Bulletin 121 (RB-121), prepared for the FDOT by the University of Florida, Gainesville, Florida
Schmertmann, J.H., 1975, “Measurement of in situ Shear Strength”, Proceedings of Conference on in situ Measurement of Soil Properties”, ASCE, New York
Schmertmann, J.H, 1978, “Guidelines for cone penetration test, performance, and design”, Report No FHWA-TS-78-209, Federal Highway Administration, Washington, D.C.
Schmertmann, John H, 1981, “A General Time-Related Soil Friction Increase Phenomenon”, Laboratory Shear Strength of Soil, ASTM STP 740, R.N. Yong and F.C. Townsend, Eds., American Society for Testing and Materials, pp. 456-484.
Shearman, R.J., 1982, “An Overview of Metrological Services, Scientific and Technical Studies Conference on North Sea Weather
and Environment”, Technology Developments for the Offshore Industry
Semple, R.M., 1981, “Partial Coefficients Design in Geotechnics”, Ground Engineering, 14(6), September, 47 - 48.
Semple, R.M. and Gemeinhardt, J.P., 1981, “Stress History Approach to Analysis of Soil Resistance to Driving”, Proceedings 13th OTC, Houston, V 01.1, pp.165-172
Semple, R.M. and Rigden, W.J., 1984, “Shaft Capacity of Driven Pipe Piles in Clay”, Proceedings, Symposium on Analysis and Design of Piled foundations, ASCE, edited by J.R. Meyer, p.59.
Sennesset, K., Janbu, N. and Svano, G., 1982, “Strength and Deformation Parameters from Cone Penetration Tests”, Proceedings, 2nd European Symposium on Penetration Testing, Amsterdam, Volume 2, pp. 863-870
Sentler, L., 1976, “Live Design for Transmission Line Structures: Uncertainties in Soil Property Measurement”, Report EL 5507(3), Electric Power Research. Institute Palo Alto, Oct 1988, 94 p.
Seed, H.B. and Reese, L.C, 1955, “The Action of Soft Clay Along Friction Piles”, Proceedings of the American Society of Civil Engineers 81, Paper 842.
Senner D., Cathie D., 1993, “Offshore Pile Design for Sites with Complex Soil Conditions”, Proceedings, 25th Offshore Technology Conference, Houston, OTC 7197
Shetty N.K, Gierlinski J.T, Smith JK and Stahl B, 1997, “Structural
system reliability considerations in fatigue inspection planning”, Proceedings of the Eighth International Conference on Behavior of Offshore Structures, Delft
Sherf, I, Etterdal, B and Monshaugen, T., 1999, “Cost-Efficient Structural Upgrade and Life Extension of North Sea Jacket Platforms with use of Modern Reassessment Techniques”, OTC Paper 10851, Houston, Texas
Shetty N.K, 1994, “Selective Enumeration Method for Identification of Dominant Failure Paths of Large Structures”, International Conference on Offshore Mechanics and Arctic and Engineering, Houston
Sidi, I. D, 1985, “Probabilistic Prediction of Friction Pile Capacities”, PhD thesis, University of Illinois at Urbana, Ill
Simpson, B., Pappin, J. W, & Croft, D. D., 1981, “An Approach to Limit State Calculations in Geotechnics”, Ground Engineering, 14(6), September 21 - 28.
Sigursdon G, Skjong R, Skallerud B, Amdahl J, 1994, “Probabilistic collapse analysis of jackets”, International Conference on Offshore Mechanics and Arctic and Engineering, Houston
Skempton, A.W. and Northey, R.D., 1952, “The Sensitivity of Clays”, Geotechnique, Volume 1, pp 30-53.
Svinkin M.R, 1996, “Discussion of Setup and Relaxation in Glacial Sand”, by York et al. Journal of Geotechnical Engineering, ASCE, 122(4), 319-321.
Skov R. and Denver H, 1988,
“Time-dependence of bearing capacity of piles”, Fellenius (ed.), Proceedings of the Third International Conference on the Application of Stress-Wave Theory to Piles, BiTech Publishers, Ottawa, Canada, 879-888.
Smith, E.A.L., 1960, “Pile-Driving Analysis by the Wave Equation”, ASCE, Journal of Soil Mechanics and Foundations Division, Volume 86, Number SM4, pp. 35-61.
Smith, G.N., 1981, “Probability Theory in Geotechnics - An Introduction”, Ground Engineering, 14(7), October
Smith D, Birkinshaw M, 1996, “Issues associated with the extreme weather hazard for Fixed offshore installations on the UK Continental Shelf”, International Conference on Offshore Mechanics and Arctic and Engineering, Florence.
Snell R.O, 1997, “ISO offshore structures standard. Proceedings of the Offshore Technology Conference”, Paper OTC 8421, Houston, 1997.
Soderberg, Lars O, 1961, “Consolidation Theory Applied to Foundation Pile Time Effects,” Geotechnique, London, Volume 11, No. 3, pp 217-225.
Sotbery, T. and Leira, B., 1994, “Reliability-Based Pipeline Design and Code Calibration”, 13th International Conference on Offshore Mechanics and Arctic Engineering, Volume 5, ASME, pp. 351-363.
Sowers, G. F., Martin, C. B., Wilson, L. L. and Fausold, M, 1961, “The bearing capacity of friction pile groups in homogeneous clay from model studies”, Proceedings
5th ICSMFE, Paris, 155–159
Standards Australia, 1995, “Piling-Design and Installation”, The Crescent, Homebush, NSW 2140, Australia.
Stahl, B. and Blenkarn, K.A., 1976, “Offshore Platform Reliability – A Parameter Research”, Methods of Structural Analysis, Proceedings of the National Structural Engineering Conference, ASCE, Madison, Wisconsin, Volume I, August, pp. 419-438
Stahl, B., 1986, “Reliability Engineering and Risk Analysis,” Chapter 5 in Planning and Design of Fixed Offshore Platforms, edited by B. McClelland and M.D. Reifel, Van Nostrand Reinhold Publishing Company, New York
Stahl, B. and Lloyd, L, 1995, “Debate on the Application of Structural Reliability Techniques for Offshore Structural Engineering”, London: The Institution of Structural Engineering.
Stevens, R.S., Wiltsie, E.A. and Turton, T.H, 1982, “Evaluating Pile Driveability for Hard Clay, Very Dense Sand and Rock”, Proceedings 14th Annual OTC, Houston, Paper OTC 4205
Stevens, R.F., and Al-Shafei, K.A, 1996, “The Applicability of Ras-Tanjib Pile Capacity Method To Long Offshore Piles”, Proceedings, 28th Offshore Technology Conference, Houston, Paper no OTC 7974.
Stevenson, C.A. and Thompson, C.D., 1978, “Driven Piled foundations in Coral and Coral Sand Formations”, 7th International Harbor Congress, K.V.I.V., Netherlands, 1.05/1-1.05/13
Stewart, G, Efthymiou, M and Vughts, J., 1988, “Ultimate Strength and Integrity Assessment of Fixed Offshore Platforms”, Conference on the Behavior of Offshore Structures, BISS, Norwegian Institute of Technology, Trondheim
Stewart G., Van de Graaf, J.W., 1990, “A Methodology for Platform Collapse Analysis”, Proceedings of the Offshore Technology Conference, Paper OTC 6311, Houston
Stewart, G., Moan, T., Amdahl, J. and Eide, O.I, 1993, “Non-linear Re-assessment of Jacket Structures under Extreme Storm Cyclic Loading”, Part 1 -Philosophy and Acceptance Criteria. Proceedings 12th OMAE Conf.: 491-502 S.K. Chakrabati et al. (Editors). New York: ASME
Stewart M, Rosowsky D., 1998, “Structural safety and serviceability of concrete bridges subject to corrosion”, Journal of Infrastructure Systems; 4 (4):146±55.
Stevens, R.S., Wiltsie, E.A. and Turton, T.H, 1982, “Evaluating Pile Driveability for Hard Clay, Very Dense Sand and Rock”, Proceedings 14th Annual OTC, Houston, Paper OTC 4205
Swan, C., Taylor. P. H., and van Langen, H., 1997, “Observations of wave-structure interaction for a multi-legged concrete platform”, Applied Ocean Research, Volume 19, pp. 309-327
Sundarajan, C.J., 1995, “Probabilistic Structural Mechanics Handbook – Theory and Industrial Applications”, Chapman and Hall, New York
Svinkin, M.R, 1996, “Setup and
Relaxation in Glacial Sand-Discussion,” Journal of Geotechnical Engineering, Volume 122, Number 4, ASCE, pp. 319-321
Svinkin, M.R., Skov R, 2000, “Set-Up Effect of Cohesive Soils In Pile Capacity,” Proceedings 6th International Conference on Application of Stress Waves to Piles, Sao Paulo, Brazil, Balkema, pp. 107-111.
Stroud, 1999, “Identification of Tropical Cyclone Sub-populations Northwest Shelf Australia”, Proceedings State of the Art Pipeline Risk Management Conference, Perth, Australia
Sundararajan C (Ed.), 1995, “Probabilistic Structural Mechanics Handbook – Theory and Industrial Applications”, Chapman & Hall, New York
Sullivan, W.R., Reese, L.C and Fenske, C.W., 1979, “Unified Method for Analysis of Laterally Loaded Piles in Clay”, Numerical Methods in Offshore Piling, ICE, London, pp. 135-146.
Tagaya, M., Heerema, E.P., Uchino, T. and Kusaka, T, 1979, “Pile Driveability Test on Actual Offshore Platform in Carbonate Clay for Qatar NGL Offshore Project”, Proceedings 11th Annual OTC, Volume.2, Houston
Tang, W.H., 1984, “Principles of Probabilistic Characterization of Soil properties”, Proceedings, Symposium on Probabilistic Characterization of Soil properties, ASCE National Convention, Atlanta, Georgia
Tang, W.H, 1988, “Offshore Axial Pile Design Reliability”, Research Report for Project PRAC 86-29B,
American Petroleum Institute
Tang, W.H., 1989, “Uncertainties in Offshore Axial Pile Capacity”, Foundation Engineering: Current Principles and Practices, Volume 2, Proceedings of the Congress, ASCE, pp 833-847 Edited by Fred H Kulhawy.
Tang, W.H., 1989, “Uncertainties in Offshore Axial Pile Capacity”, Proceedings Congress of Foundation Engineering Current Principles and Practice, F.H. Kullhawy, ASCE, pp. 833-847
Tang, W.H. and Gilbert, R.B., 1990, “Offshore Lateral Pile Design Reliability”, Research Report for Project PRAC 87-29, sponsored by the American Petroleum Institute
Tang W.H., Woodford, D.L. and Pelletier, J.H., 1990, “Performance Reliability of Offshore Piles”, OTC Paper 6379, Houston, May 1990.
Tang, W.H. and Gilbert, R.B., 1992, “Offshore Pile System Reliability”, Research Report for Project PRAC 89-29, American Petroleum Institute
Tang, W.H., and Gilbert, R.B, 1993, “Case research of Offshore Pile System Reliability”, Proceedings 25th Offshore Technology Conference, Society of Petroleum Engineers, Richardson, Tex., 677–686
Task Committee on Structural Loadings (J.D. Mozer, Chair), 1991, “Guidelines for Electrical Transmission Line Structural Loading”, Manual & Report on Engineering Practice 74, ASCE, New York, 139 p.
Tavenas F. and Audy R, 1972, “Limitations of the driving formulas for predicting the bearing capacities
of piles in sand”, Canadian Geotechnical Journal, Canada, 9(1), 47-62
Terzaghi, K. and Peck, R. B., 1948, “Soil Mechanics in Engineering Practice”, Wiley, New York, 384 p.
Theophanatos, A., Cazzulo, R., Berranger, I., Ornaghi, L. and Wittenberg, L., 1992, “Adaptation of API R2A-LRFD to Mediterranean Sea”, Proceedings of the 24th Offshore Technology Conference, Volume 2, pp 529-538, OTC 6932
Thendean, G., Rausche, Frank, Svinkin, Mark, and Likins, Garland E., 1996, “Combining Static Pile Design and Dynamic Installation Analysis in GRLWEAP”, STRESSWAVE’96 Conference, Orlando, Florida.
Thendean G., Rausche, F., Likins, G. and Svinkin, M., 1996, “Wave Equation Correlation Studies”, STRESSWAVE '96 Conference, Orlando, FL, 1996
Thoft-Christensen P, Murotsu Y, 1986, “Application of structural system reliability theory”, Berlin: Springer.
Thompson C.D. and Thompson D.E, 1985, “Real and apparent relaxation of driven piles”, Journal of Geotechnical Engineering, ASCE, Volume 111, Number 2, 225-237
Titi, Hani H. and Wathugala, G. Wije, 1999, “Numerical Procedure for Predicting Pile Capacity- Setup/Freeze,” Transportation Research Record 1663, Paper No. 99-0942, pp. 25-32
Tomlinson, M.J., 1971, “Some Effects of pile drilling on skin friction”, Proceedings conference on
behavior of piles, Institution of Civil Engineers (ICE), London pp 107-114
Toolan FE and Fox D A, 1977, “Pile Driving Analysis by the Wave Equation”, Transactions ASCE, Volume 127, Part 1, Paper 3306.
Toolan F.E. and Ims, B.W., 1988, “Impact of Recent Changes in the API Recommended Practice for Offshore Piles in Sand and Clays”, Preprint, Oceanology International 88.
Toolan F.E., Lings, M. and Mirza, U., 1990, “An Appraisal of API RP2A Recommendations for Determining Skin Friction of Piles in Sand”, OTC Paper 6422, Houston, May 1990.
Thoft-Christensen P and Baker M.J., 1982, “Structural reliability theory and its applications”, Springer-Verlag, Berlin
Tomlinson, M.J., 1980, “Foundation Design and Construction”, Fourth Edition, Pitman Advanced Publishing Program, Townsend.
Tromans, P. and Van de Graff, J., 1992, “A Substantiated Risk Assessment of a Jacket Structure”, Proceedings of the Offshore Technology Conference, Paper OTC 7075, Houston
Tromans, P.S., Hagemeijer, P.M and Wassink, H.R, 1992, “The Statistics of the Extreme Response of Offshore Structures”, Ocean Engineering, Volume 19, No 2, pp 161-181.
Turner, R.C., Ellinas, C.P. and Thomas, G.A., 1992, “Towards the Worldwide Calibration of API RP2A-LRFD”, Proceedings, 24th Offshore Technology Conference
(2), Houston, 513 - 520.S
Turkstra, C.J. and Madsen, H.O., 1979, “Load Combination in Codified Structural Design”, Journal of the Structural Division, Proceedings of the American Society of Civil Engineers, Volume 106, Number ST12
Vick, S.G., 1992, “Risk in Geotechnical Practice”, Geotechnical News, 10(1), March, 55 - 57.
Vijayvergiya V.N. & J.A. Focht Jr., 1972, “A New Way to Predict Capacity of Piles in Clay”, Offshore Technology Conference, Houston May, Paper OTC 1718.
Vijayvergiya, V.N., Cheng, A.P. and Kolk, H.J., 1977, “Design and Installation of Piles in Chalk”, Proceedings 9th Offshore technology Conference, Paper Number 2938
Vugts J.H & Edwards, 1992, “Offshore Structural Reliability Assessment from Research to Reality”, Proceedings BOSS'92 Conference
Van de Graaf, J.W., Tromans, P.S., Efthymiou, M., 1994, “The Reliability of Offshore Structures and its Dependence on Design Code and Environment”, Proceedings of the Offshore Technology Conference, Paper OTC 7382, Houston
Van de Graad, J.W., Efthymiou, M., Tromans, P.S., 1998, “Implied Reliability Levels for RP 2A-LRFD from Studies of North Sea Platforms”, unpublished
Valdes, V. and Ortega, R, 1998, “Issues and Challenges in the Requalification of Offshore Platforms in Mexico”, pp. 6-18 in
Proceedings of International Workshop on Platform Requalification, OMAE, Lisbon, July 1998.
Van Langen H, Swee JLK, Efthymiou M, Overy R, 1995, “Integrated foundation and structural reliability analysis of a north sea structure”, Proceedings of the Offshore Technology Conference. Paper OTC 7784, Houston.
Vanmarcke, E H, 1983, “Random Fields: Analysis and Synthesis”, M.I.T. Press, Cambridge, Massachusetts
Vanmarcke, E., 1983, “Risk Assessment for Offshore Structures: A Review”, ASCE, Journal of Structural Engineering, Volume 109, No. 2.
Vesic, A.S., 1967, “A Research of Bearing Capacity of Deep Foundations”, Final Report, Project B-189, Georgia Institute of Technology, pp. 729-750
Vesic, A, 1969, “Load transfer, lateral loads and group action of deep foundations”, Proceedings ASTM Symposium Journal Performance of Deep Foundation, 5TP 444, pp S-14
Vesic, A.S., 1975, “The Bearing Capacity of Shallow Foundations”, Foundation Engineering Handbook, Editors H.F. Wintlrkorn and H. Y. Fang, Van Nostrand Reinhold, NY
Vesic, A. S, 1977, “Design of piled foundations”, NCHRP Synthesis of Highway Practice 42, Transp. Res. Board, National Research Council, Washington, D.C
Von Plato, J., 1994, “Creating Modern Probability”, Cambridge:
Cambridge University Press.
Wang, ST, and Reese, L.C, 1989, “Predictions of Response of Piles to Axial Loading”, Predicted and Observed Axial Behavior of Piles, Geotechnical Special Publication No. 23, ASCE, pp. 173-187
Ward, E.G., Lee, G.C., Botelho, D, Turner, W, Dyhrkopp F and Hall, R.A., 2000, “Consequence-Based Criteria for the Gulf of Mexico: Philosophy and Results”, Proceedings of the Offshore Technology Conference, Houston, TX, May 1-4, 2000, Paper OTC 11885.
Wardle I.F., Price G. and Freeman T.J, 1992, “Effect of time and maintained load on the ultimate capacity of piles in stiff clay”, Piling: European practice and worldwide trends, ICE, London, 92-99.
Warke R.W., Ferregut C., Glover A.G. and Horsley D.J, 1997, “A Reliability-Based Method for Assessing the Fitness-for-Service of Pipeline Girth Welds”, PRCI-EPRG, 11th Biennial Joint Technical Meeting, Arlington, Virginia
Wei-Liang, 1996, “Reliability-based design for jacket platform under extreme loads”, China Ocean Engineering, 10(2): 145-160.
Wei-Liang, J, and Shun-Feng, Y.G., 2006, “System Reliability-Based Assessment on existing Jacket Platforms”, Proceedings of OMAE2006, 25th International Conference on Offshore Mechanics and Arctic Engineering, Paper 92562, June 4-9, 2006, Hamburg, Germany
Wen, Y., 1979, “Statistics of Extreme of Live Load on
Buildings”, Journal of the Structural Division, Proceedings of the American Society of Civil Engineers, Volume 105, No ST10
Wen, Y.K., and Yeo, G.L, 1999, “Design live loads for parking garages”, Rep., ASCE Structural Engineering Inst., Reston, Va
White, B.S. and Fomberg, B, 1998, “On the chance of freak waves at sea”, Journal of Fluid Mechanics, Volume 355, pp. 113-138, 1998
Withiam, J.L., Voytko, E.P., Barker, R.M., Duncan, J.M., Kelly, B.C., Musser, S.C. and Elias, V., 1997, “Load and Resistance Factor design (LRFD) for Highway Bridge Substructures”, FHWA Rep. DTFH61-94-C-00098, Federal Highway Administration, Washington, D.C.
Wisch DJ, 1997, “Fixed steel standard: ISO and API developments – ISO TC 67/SC 7/WG 3”, Proceedings of the Offshore Technology Conference. Paper OTC 8423, Houston.
Whitaker, T., 1960, “Some experiments on model piled foundations in clay”, Proceedings Symposium on Piled Foundation, International Association for Bridge and Structural Engineering., Stockholm, Sweden.
Whitman, R.V, 1984, “Evaluating Calculated Risk in Geotechnical Engineering”, Journal Geotechnical Engineering, ASCE, Volume 110, Number 2, pp. 145-188
Whitman, R.V, 1997, “Acceptable Risk and Decision-making Criteria”, In Risk-Based Dam Safety Evaluation, Proceedings International Workshop, Trondheim, Norway, September 1997
Whitman, R.V., 2000, “Organizing and evaluating uncertainty in Geotechnical engineering”, Journal of Geotechnique and Geoenvironmental Engineering, ASCE, 126(7): 583-593.
Wolff, T.F., 1993, “Probabilistic assessment of pile interference”, Journal Geotechnical Engineering, ASCE, 119(3), 525–542
Wu, T.H., Tang, W.H., Sangrey, D.A., and Baecher, G. B, 1989, “Reliability of offshore foundations: State of the art”, J. Geotechnical Engineering, ASCE, 115(2), 157–178.
Wu, Y.K. and Moan, T., 1989, “A Structural System Reliability Analysis of Jacket Using an Improved Truss Model”, Proceedings 5th ICOSSAR, Volume 2: 887-894. New York: ASCE.
Yang N.C, 1970, “Relaxation of piles in sand and inorganic silt”, Journal of Soil Mechanics and Foundation Division, ASCE, Volume 96, Number SM2, 395-409
York D.L., Brusey W.G., Clemente F.M. and Law S.K, 1994, “Setup and relaxation in glacial sand”, Journal of Geotechnical Engineering, ASCE, 120(9), 1498-1513
Yan-Kun, Z., Wei-Liang, J. and Zhuo-Dong, L., 2001, “System reliability analysis method for offshore jacket platforms under extreme loads”, Journal of Ocean Engineering, 19(4): 15-20
Yijayvergiya, Y.N., 1977, “Load-Movement Characteristics of Piles”, Ports '77 Conference, Long Beach, California.
Young, A.G., Quiros, G.W., and
Ehlers, C.J., 1983, “Effects of Offshore Sampling and Testing on Undrained Soil Shear Strength”, OTC 4465, Offshore Technology Conference.
Zaghloul, H M and Ronalds, B, 2004, “The Use of Implicit Safety Factors in the Reassessment of Existing Platforms in the Arabian Gulf”, OMAE 2003, Paper Number 51529, Vancouver, BC, Canada
Zaghloul H, Ronalds, B and Cole, G, 2005, “Development of Piled Foundation Bias Factors in the Arabian Gulf”, Proceedings of 24th International Conference on Offshore Mechanics and Arctic Engineering, Paper 67269, Halkidiki, Greece.
Zaghloul, H. and Parvindra, 2005, private email correspondence
Zellner, A., 1987, “Bayesian Inference”, In J. Eatwell, M. Milgate and P. Newman (Eds.) New Palgrave: A Dictionary of Economics (pp. 208-218), London: Macmillan
Zhang, L., Wilson H. Tang and Charles W.W. Ng, 2001, “Reliability of Axially Loaded Driven Pile Groups”, Journal of Geotechnical and Geoenvironmental Engineering
Zhao T-G, Ono T, 1998, “System reliability evaluation of ductile frame structures”, Journal of Structural Engineering 1998; 124(6):678±85.
Zhou, L., 1999, “Development of Semi-Empirical Equations for Estimating Smith Soil Parameters Based on Statistical Analysis of Pile Database”, Master Thesis, University of Florida, Civil Engineering.
Zimmerman J.J., Corotis RB, Ellis JH, 1992, “Structural System Reliability Considerations with Frame Instability”, Engineering Structures; 14 (6):371–8.
Zimmerman, T.J.E., Cosham, A., Hopkins, P. and Sanderson, N, 1998, “Can Limit States Design be Used to Design a Pipeline Above 80% SMYS”, Proceedings International Symposium Offshore Mechanics and Arctic Engineering', OMAE 1998, ASME
AUTHOR’S PUBLICATIONS
These conference papers have been published to address topics relevant to this
research:
Zaghloul H; Ronalds, B, 2004, “The Use of Implicit Safety Factors in the
Reassessment of Offshore Platforms in the Arabian Gulf”, Proceedings of 23rd
International Conference on Offshore Mechanics and Arctic Engineering, Paper
51529, Vancouver, Canada
Zaghloul H; Ronalds, B; Cole, G, 2005, “Probabilistic Analysis of Live Loads on
Offshore Platforms”, Proceedings of 24th International Conference on Offshore
Mechanics and Arctic Engineering, Paper 67266, Halkidiki, Greece.
Zaghloul H; Ronalds, B; Cole, G, 2005, “Development of Piled Foundation Bias
Factors in the Arabian Gulf”, Proceedings of 24th International Conference on
Offshore Mechanics and Arctic Engineering, Paper 67269, Halkidiki, Greece.
Zaghloul, H; Imms, K, 2004, “The Use of New Code to Qualify Existing Structures
– A Case Study”, Proceedings of the Second International Conference on Structural
Engineering, Mechanics and Computations, South Africa
APPENDICES
Appendix A
EQUIPMENT DATABASE
Operating ElevationWeight Length Width Area
kN m m m2 mmGlycol Drain Pump 6.0 0.7 0.5 0.4 Sub CellarClosed Drain Drum 5.0 0.7 0.4 0.3 Sub CellarOpen Drain Drum 4.0 0.7 0.4 0.3 Sub CellarHot Oil Drain Pump 6.0 0.7 0.4 0.3 SubcellarClosed Drain Pump 6.0 0.7 0.4 0.3 Sub CellarClosed Drain Pump 6.0 0.7 0.4 0.3 Sub CellarOpen Drain Pump 3.0 0.3 0.3 0.1 Sub CellarOpen Drain Pump 3.0 0.3 0.3 0.1 Sub CellarOpen Drain Vessel 75.0 1.6 1.6 2.6 92546Closed Drain Vessel 75.0 1.6 1.6 2.6 92546Open Drain Pump 4.0 1.0 1.0 1.0 92546Closed Drain Pump 4.0 1.0 1.0 1.0 92546Closed Drain Vessel 75.0 2.5 2.5 6.3 93460Closed Drain Pump 4.0 1.0 1.0 1.0 93460Solid Potable Water Filter 25.0 0.5 0.5 0.3 95725Carbon Potable Water Filter 25.0 0.7 0.7 0.5 95725Open Drain Vessel 75.0 1.4 1.4 2.0 94304Closed Drain Vessel 75.0 1.4 1.4 2.0 94304Open Drain Pump 4.0 0.8 0.8 0.6 94304Closed Drain Pump 4.0 0.8 0.8 0.6 94304Instrument Air Buffer Vessel 25.0 3.2 0.7 2.2 95545Instrument Air Buffer Vessel 25.0 3.2 0.7 2.2 95545Chemical Injection Pump Skid 50.0 2.0 0.8 1.6 96838Central Open Drain Vessel 93.0 4.0 2.4 9.6 94222Central Closed Drain Vessel 93.0 4.0 2.4 9.6 94222Closed Drain Pump 6.0 1.0 1.0 1.0 94222Closed Drain Pump 6.0 1.0 1.0 1.0 94222Drain Oil Pump 6.0 1.0 1.0 1.0 94222Closed Drain Vessel 75.0 1.2 1.2 1.4 93460Closed Drain Pump 4.0 1.0 1.0 1.0 93460Open Drain Vessel 75.0 1.2 1.2 1.4 94100Closed Drain Vessel 75.0 1.2 1.2 1.4 94100Open Drain Pump 4.0 0.3 0.3 0.1 94100Closed Drain Pump 4.0 0.3 0.3 0.1 94100Open Drain Vessel 75.0 1.2 1.2 1.4 94527Closed Drain Vessel 75.0 1.2 1.2 1.4 94527Open Drain Pump 4.0 0.3 0.3 0.1 94527Closed Drain Pump 4.0 0.3 0.3 0.1 94527Open Drain Vessel 93.0 4.0 1.4 5.6 97100Closed Drain Vessel 93.0 4.0 1.4 5.6 97100Closed Drain Pump 6.0 0.7 0.4 0.3 97100Closed Drain Pump 6.0 0.7 0.4 0.3 97100Open Drain Oil Pump 6.0 0.7 0.4 0.3 97100Open Drain Vessel 75.0 1.2 1.2 1.4 93460Closed Drain Vessel 75.0 1.2 1.2 1.4 93460Open Drain Pump 4.0 0.3 0.3 0.1 93460Closed Drain Pump 4.0 0.3 0.3 0.1 93460Solid Potable Water Filter 25.0 0.5 0.5 0.3 96639Carbon Potable Water Filter 25.0 0.7 0.7 0.5 96639U.V. Steriliser 5.0 0.4 0.4 0.2 96639Hypochlorite generator Package 80.0 2.5 2.0 5.0 CellarFirewater Pump 95.0 5.5 2.5 13.8 CellarFirewater Jockey Pump 6.0 4.0 3.0 12.0 CellarFirewaterJockey Pump 6.0 4.0 3.0 12.0 CellarFuel Gas Super Heater 29.0 11.0 1.5 16.5 CellarHP Fuel Gas Scrubber 65.0 2.0 2.0 4.0 CellarInstrument Air Receiver 45.0 2.0 2.0 4.0 CellarGlycol Regeneration Package 350.0 9.0 4.5 40.5 Cellar1st Stage Liquid Recycle Heater 5.0 10.0 1.5 15.0 Cellar1st Stage Liquid Recycle Heater 5.0 10.0 1.5 15.0 Cellar
DescriptionEquipment Footprint
Operating ElevationWeight Length Width Area
kN m m m2 mmDescription
Equipment Footprint
1st Stage Recycle Cooler 19.0 11.0 1.5 16.5 Cellar1st Stage Recycle Cooler 19.0 11.0 1.5 16.5 CellarArab 'C' Slug Catcher 1590.0 12.0 4.0 48.0 Cellar1st Stage Compressor Suction KO Drum 34.0 2.5 2.5 6.3 Cellar1st Stage Compressor Suction KO Drum 34.0 2.5 2.5 6.3 Cellar2nd Stage Comprerssor Suction KO Drum 211.0 2.5 2.5 6.3 Cellar2nd Stage Comprerssor Suction KO Drum 211.0 2.5 2.5 6.3 Cellar3rd Stage Comprerssor Suction KO Drum 105.0 2.0 2.0 4.0 Cellar3rd Stage Comprerssor Suction KO Drum 105.0 2.0 2.0 4.0 Cellar4th Stage Comprerssor Suction KO Drum 106.0 2.0 2.0 4.0 Cellar4th Stage Comprerssor Suction KO Drum 106.0 2.0 2.0 4.0 CellarGlycol Contactor 1110.0 3.0 3.0 9.0 CellarCondensate Flash Vessel 610.0 10.0 3.5 35.0 CellarCondensate Stabilizer 754.0 4.0 4.0 16.0 CellarCondensate/Condensate Exchanger 48.0 4.5 4.5 20.3 CellarStabilizer Reboiler 74.0 8.0 4.0 32.0 CellarStabilizer Reboiler 74.0 8.0 4.0 32.0 CellarStabilizer Side Exchanger 124.0 4.0 4.0 16.0 CellarCondensate Transfer Pumps 66.0 6.0 4.0 24.0 CellarCondensate Transfer Pumps 66.0 6.0 4.0 24.0 CellarTX Inlet K. O. Drum 258.0 7.0 2.0 14.0 CellarTX Outlet K. O. Drum 239.0 7.0 2.0 14.0 Cellar Flame Front Generator Package 12.0 7.0 2.0 14.0 CellarHP Flare KO Drum 120.0 3.0 10.0 30.0 CellarSea Water / Tempered Water Exchanger 61.0 10.0 2.0 20.0 CellarSea Water / Tempered Water Exchanger 61.0 10.0 2.0 20.0 CellarSea Water / Tempered Water Exchanger 61.0 10.0 2.0 20.0 CellarSeawater Lift Pumps 70.0 6.0 2.0 12.0 CellarSeawater Lift Pumps 70.0 6.0 2.0 12.0 CellarSeawater Lift Pumps 70.0 6.0 2.0 12.0 CellarSeawater Filters 25.0 8.0 2.0 16.0 CellarSeawater Filters 25.0 8.0 2.0 16.0 CellarSeawater Filters 25.0 8.0 2.0 16.0 CellarTempered Water Circulation Pumps 46.0 8.0 2.5 20.0 CellarTempered Water Circulation Pumps 46.0 8.0 2.5 20.0 CellarTempered Water Circulation Pumps 46.0 7.0 2.0 14.0 CellarInstrument Air Dryer Set 50.0 0.8 0.8 0.6 100000Electric Heater 5.0 1.0 1.0 1.0 100000Air Blower 5.0 1.0 1.0 1.0 100000Prefilter 5.0 1.0 1.0 1.0 100000Drying Vessel 5.0 1.0 1.0 1.0 100000Instrument Air Receiver 33.0 1.3 1.3 1.7 100000Whittaker Escape Capsule 25.0 6.2 3.7 22.9 100000Whittaker Escape Capsule 25.0 6.2 3.7 22.9 100000Chillers for Air Conditioning Unit 56.0 1.0 1.0 1.0 100000After Cooler for Inst. Air Comp. 5.0 1.0 1.0 1.0 100000Start-up Generator/Diesel Engine 90.0 6.6 3.0 19.8 100000New Package Glycol Unit 597.0 15.5 5.0 77.5 100000Air Handling Unit 50.0 1.0 1.0 1.0 100000Instrument Air Compressors 108.0 6.0 3.6 21.6 100000Air Pump 5.0 1.0 1.0 1.0 100000Diesel Oil Supply Pumps 2.0 0.3 0.3 0.1 100000Instrument Air Intake Filter 12.0 2.5 0.9 2.3 100000Duplex Filter/ Coalescer Diesel 20.0 1.4 1.0 1.4 100000Fuel Tank for G/T Driven Elec.Power Generator 490.0 2.6 1.3 3.3 100000Instrument Air Receiver 38.0 5.0 1.9 9.5 100000Low Pressure Fuel Gas KO Drum 28.0 1.8 1.8 3.2 100000Instrument Air Buffer Tank 16.0 3.9 2.0 7.8 100000Switch Board 270.0 12.6 2.0 25.2 100000Switch Board 60.0 15.6 1.6 25.0 100000Transformer 63.0 3.1 3.1 9.6 100000
Operating ElevationWeight Length Width Area
kN m m m2 mmDescription
Equipment Footprint
Transformer 63.0 3.1 3.1 9.6 100000Fuel Gas Scrubber 6.0 0.4 0.4 0.1 100000Fire Water Pump 175.0 7.0 3.5 24.5 100000Manifold 1175.0 16.0 3.5 56.0 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Formation Water Disposal Pump 22.0 3.4 1.4 4.8 100000Formation Water Disposal Pump 22.0 3.4 1.4 4.8 100000Formation Water Disposal Pump 22.0 3.4 1.4 4.8 100000Surge Vessel Skid 93.0 3.5 2.5 8.8 100000XX HP Separator 597.0 8.9 2.0 17.8 100000XX HP Separator 597.0 8.9 2.0 17.8 100000HP/MP Test Separator 426.0 10.2 2.2 22.4 100000Panametric 15.0 2.4 0.7 1.7 100000Hydrocyclone Skid 216.0 4.5 4.5 20.3 100000Manifold 758.0 15.2 4.0 60.8 100000Manifold 469.0 7.2 4.0 28.8 100000Manifold 864.0 15.0 3.9 58.5 100000K-143 Injection Unit 75.0 2.4 1.9 4.5 100000Glycol Absorber 101.0 2.2 2.2 4.8 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Fuel Gas Super Heater 13.4 5.9 0.6 3.5 100000Pipeline Gas Heater 15.0 5.2 0.3 1.6 100000Air Pump 2.0 0.1 0.1 0.0 100000Air Pump 2.0 0.1 0.1 0.0 100000Sea Water Cooler Pump 139.0 2.8 1.8 5.0 100000Sea Water Cooler Pump 139.0 2.8 1.8 5.0 100000Sea Water Cooler Pump 139.0 2.8 1.8 5.0 100000Temp. Water Cir. Pump 66.0 4.5 2.8 12.6 100000Temp. Water Cir. Pump 66.0 4.5 2.8 12.6 100000Temp. Water Cir. Pump 66.0 4.5 2.8 12.6 100000Temp. Water Cir. Pump 2.0 1.0 1.0 1.0 100000S.W. Cooling Filter 150.0 0.8 0.8 0.6 100000Temp. Water Storage Tank 949.0 4.9 3.0 14.7 1000001st Stage KO Drum 160.0 2.0 2.0 4.0 1000003rd Stage KO Drum 162.0 2.3 2.3 5.3 1000004th Stage KO Drum 118.0 1.7 1.7 2.9 100000Pipeline Gas KO Drum 45.0 0.8 0.8 0.6 100000Gas Turbine KO Drum 27.0 0.7 0.7 0.5 100000Liquid Blow Down Drum 33.0 1.0 1.0 1.0 1000002nd Stage KO Drum 231.0 2.8 2.8 7.8 100000Corrosion Inhibitor Dosing Package 126.0 4.3 3.3 14.2 100000Corrosion Inhibitor Dosing Package 101.0 4.0 2.5 10.0 100000Corrosion Inhibitor Dosing Package 101.0 4.0 2.5 10.0 100000Chemical Injection Skid 78.5 3.8 3.7 14.1 100000Fire Water Pump Drive 175.0 7.0 3.6 25.2 100000Diesel Transfer Pump 4.5 1.0 1.0 1.0 100000Diesel Transfer Pump 4.5 1.0 1.0 1.0 100000Fire Water Pump 175.0 1.0 1.0 1.0 100000Diesel Oil Tank 446.0 7.0 4.0 28.0 100000Black Start Meg Injection Package 75.0 4.0 1.4 5.6 100000Instrument Air Buffer Vessel 26.0 4.5 1.8 8.1 100000Instrument Air Surge Vessel 25.0 1.2 1.2 1.4 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000
Operating ElevationWeight Length Width Area
kN m m m2 mmDescription
Equipment Footprint
Formation Water Disposal Pump 51.0 4.2 1.6 6.7 100000Formation Water Disposal Pump 51.0 4.2 1.6 6.7 100000Formation Water Disposal Pump 51.0 4.2 1.6 6.7 100000Surge Vessel Transfer Pumps/ Skid 127.0 4.0 3.2 12.8 100000Scale Inhibitor Inection Skid 27.0 2.5 1.9 4.8 100000Intelligent Pig Launcher 25.0 13.8 0.7 9.7 100000Biocide Chemical Injection Skid Skid 27.0 2.0 1.9 3.8 100000Drain Vessel 75.0 2.0 2.0 4.0 100000Chemical Injection Skid 150.0 3.0 2.1 6.3 100000Reverse Demulsifier InjectionSkid 142.0 3.0 3.0 9.0 100000Demulsifier (Petrolite) Injection Skid 98.0 4.8 2.9 13.9 100000K-143 Chemical Injection Skid 5.0 2.3 2.0 4.6 100000Glycol Absorber 207.0 2.2 2.2 4.8 100000Seawater/Tempered Water Exch. 414.0 14.4 4.2 60.5 100000Fuel Gas Super Heater 10.0 5.9 0.6 3.5 100000Outlet Knock-Out Drum 72.0 0.9 0.9 0.7 100000Glycol Unit Feed Knock Out Drum 71.0 0.9 0.9 0.7 100000Glycol Absorber 113.0 0.8 0.8 0.6 1000003rd Stage Liquid Pump 204.0 2.8 1.8 5.0 1000003rd Stage Liquid Pump 204.0 2.8 1.8 5.0 1000003rd Stage Liquid Pump 204.0 2.8 1.8 5.0 100000Sea Water Cooling Pump 204.0 2.8 1.8 5.0 100000Sea Water Cooling Pump 204.0 2.8 1.8 5.0 100000Sea Water Cooling Pump 204.0 2.8 1.8 5.0 100000Tempered Water Circulation Pump 73.0 4.5 2.8 12.6 100000Tempered Water Circulation Pump 73.0 4.5 2.8 12.6 100000Tempered Water Circulation Pump 73.0 4.5 2.8 12.6 100000Tempered Water Make-Up Pump 2.0 1.0 1.0 1.0 100000Air Pumps 230.0 1.0 1.0 1.0 100000Sea Water Cooling Filters 115.0 2.5 2.5 6.3 100000Tempered Water Storage Tank 949.0 6.0 4.0 24.0 1000001st Stage Knock Out Drum 222.0 4.0 4.0 16.0 1000002nd Stage Knock Out Drum 129.0 3.0 3.0 9.0 1000003rd Stage Knock Out Drum 286.0 4.0 4.0 16.0 1000004th Stage Knock Out Drum 182.0 3.2 3.2 10.2 100000Glycol Unit Feed Knock Out Drum 169.0 2.5 2.5 6.3 100000Pipeline Gas Knock Out Drum 91.0 2.2 2.2 4.8 100000Gas Turbine Knock Out Drum 27.0 1.8 1.8 3.2 100000Liquid Blow Drum 34.0 2.0 2.0 4.0 100000Instrument Air Dryer Set 50.0 3.7 2.0 7.4 100000Electric Heater 5.0 1.0 1.0 1.0 100000Air Blower 5.0 1.0 1.0 1.0 100000Prefilters 5.0 1.0 1.0 1.0 100000Drying Vessel 5.0 1.0 1.0 1.0 100000Chillers for AC Unit 56.0 1.0 1.0 1.0 100000After Cooler for Instrument Air Dryer 5.0 1.0 1.0 1.0 100000Start Up Generator/ Diesel Engine 90.0 6.5 3.0 19.5 100000Package Gycol Unit 872.0 16.6 5.1 84.7 100000Instrument Air Receiver 33.0 1.3 1.3 1.7 100000Air Handling Units 50.0 1.0 1.0 1.0 100000Instrument air Compressor 108.0 6.8 3.5 23.8 100000Air Pump 5.0 1.0 1.0 1.0 100000Diesel Oil Supply Pumps 1.3 0.3 0.3 0.1 100000Insturment Air Intake Filter 12.0 2.5 0.9 2.3 100000Fuel Tank for G/T Elec.Power Generator 490.0 2.6 1.3 3.3 100000Instrument Air Receiver 43.5 5.0 2.0 10.0 100000Low Pressure Fuel Gas KO Drum 27.8 1.8 1.8 3.2 100000Instrument air Buffer Tank 16.0 2.7 1.0 2.7 100000Diesel Filter Unit for Power Generator 10.0 1.0 1.0 1.0 100000Escape Capsule 25.0 6.2 3.7 22.9 100000Switch Board 310.0 14.4 2.0 28.8 100000
Operating ElevationWeight Length Width Area
kN m m m2 mmDescription
Equipment Footprint
Switch Board 60.0 16.2 1.6 25.9 100000Transformer 40.0 3.2 3.2 10.2 100000Transformer 40.0 3.2 3.2 10.2 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Receiver 32.0 1.1 1.1 1.2 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Receiver 32.0 1.1 1.1 1.2 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Instrument Air Buffer 54.0 1.7 1.7 2.9 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Instrument Air Buffer 54.0 1.7 1.7 2.9 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Turbo Expander Precooler 140.0 5.0 3.0 15.0 Mezz1st Stage Compressor Discharge Cooler 78.0 11.0 1.5 16.5 Mezz1st Stage Compressor Discharge Cooler 78.0 11.0 1.5 16.5 Mezz2nd Stage Compressor Discharge Cooler 24.0 11.0 2.0 22.0 Mezz2nd Stage Compressor Discharge Cooler 24.0 11.0 2.0 22.0 Mezz3rd Stage Compressor Discharge Cooler 241.0 20.0 2.0 40.0 Mezz3rd Stage Compressor Discharge Cooler 241.0 20.0 2.0 40.0 Mezz4th Stage Compressor Discharge Cooler 32.0 10.0 1.5 15.0 Mezz4th Stage Compressor Discharge Cooler 32.0 10.0 1.5 15.0 MezzGas Scrubber 1.0 0.2 0.2 0.0 102438Gas Scrubber 1.0 0.2 0.2 0.0 103352Gas Scrubber 1.0 0.2 0.2 0.0 103353Gas Scrubber 1.0 0.2 0.2 0.0 103353Gas Scrubber 1.0 0.2 0.2 0.0 103353Instrument Air Buffer 64.0 1.7 1.7 2.9 104000Gas Scrubber 1.0 0.2 0.2 0.0 104384Instrument Air Buffer 64.0 1.7 1.7 2.9 104384Diesel Oil Storage Tank 544.0 4.0 2.4 9.6 105480Diesel Oil Transfer Pump 6.0 1.8 0.8 1.4 105480Diesel Oil Transfer Pump (Stand By) 6.0 1.8 0.8 1.4 105480Separator M.P. 408.0 9.0 2.8 25.2 105480Separator M.P. 408.0 9.0 2.8 25.2 105480Corrosion Inhibitor Pumps 10.0 1.5 3.0 4.5 105480Corrosion Inhibitor Skid 75.0 1.9 1.5 2.9 105480H.P. Separator 610.0 11.0 2.8 30.8 105480
Operating ElevationWeight Length Width Area
kN m m m2 mmDescription
Equipment Footprint
Hydrocyclone Skid 265.0 3.0 3.0 9.0 105480CK-352 Injection Pump Skids 30.0 1.5 3.0 4.5 105480Intelligent Pig Launcher 15.0 11.3 0.8 9.0 105480Test Separator 610.0 9.0 3.5 31.5 106663High Pressure Separator 1039.0 13.5 3.7 50.0 106663M.P. Separator 391.4 10.6 2.8 29.7 106663M.P. Separator 391.4 10.6 2.8 29.7 106663Degassing Vessel Skid 2079.0 11.9 4.5 53.6 106663Surge Vessel 870.0 13.9 3.8 52.8 106663Hydrocyclone Skid 618.0 4.5 4.5 20.3 106663M.P. Separator 640.0 9.4 2.8 26.3 106401Surge Vessel 1066.0 11.3 4.2 47.5 106401Degassing Vessel 588.0 10.0 4.5 45.0 106401Hydrocyclone Skid 49.0 4.0 2.0 8.0 106401Scale Inhibitor Skid 27.0 2.0 1.9 3.8 106401Biocide Injection Skid 29.0 2.0 1.9 3.8 106401Power Generator 95.0 2.5 2.3 5.8 107162Power Generator 95.0 2.5 2.3 5.8 107162Power Generator 95.0 2.5 2.3 5.8 107162Power Generator Gas Turbine 413.0 10.5 3.5 36.8 107162Power Generator Gas Turbine 413.0 10.5 3.5 36.8 107162Power Generator Gas Turbine 413.0 10.5 3.5 36.8 107162Air Blast Cooler GT-4201A 25.0 2.0 1.1 2.2 107162Air Blast Cooler GT-4201B 25.0 2.0 1.1 2.2 107162Air Blast Cooler GT-4201S 25.0 2.0 1.1 2.2 1071626.3T OHT Crane 160.0 1.0 1.0 1.0 107163Expansion Tank 10.0 0.5 0.3 0.2 107162Instrument Air Comp./ Drier Skid 75.0 11.2 3.2 35.8 107162Power Generator 95.0 2.5 2.3 5.8 107163Power Generator 95.0 2.5 2.3 5.8 107163Power Generator 95.0 2.5 2.3 5.8 107163Power Generator 95.0 2.5 2.3 5.8 107163Power Generator Gas Turbine 388.0 10.5 3.5 36.8 107163Power Generator Gas Turbine 388.0 10.5 3.5 36.8 107163Power Generator Gas Turbine 388.0 10.5 3.5 36.8 107163Power Generator Gas Turbine 388.0 10.5 3.5 36.8 107163Instrument Air Compressor/ Drier Skid 75.0 5.5 2.1 11.3 107163Demulsifier Injection Skid 150.0 2.3 1.0 2.3 108000Corrosion Inhibitor Skid 110.0 4.0 2.6 10.4 108000Fuel Gas Sphere Launcher 79.0 7.0 1.4 9.8 108000Scale Inhibitor Mixer 30.0 2.0 1.0 2.0 108000Chemical Injection Skid 95.0 5.0 2.6 13.0 108000Mixer for T-5208 20.0 1.0 1.0 1.0 108000Corrosion Inhibitor Unit 87.0 7.3 3.2 23.4 108000Chemical Injection Skid 27.0 2.0 1.7 3.4 108000MP Separator 484.0 14.2 3.8 54.0 108000Extra High Pressure Test Separator 727.0 8.5 2.4 20.4 108000High Pressure Separator 610.0 14.2 2.7 38.3 108000Inlet K.O. Vessel 171.0 1.5 1.5 2.3 108000Outet K.O. Vessel 171.0 1.5 1.5 2.3 108000Mixer for T-5209 50.0 1.9 1.6 3.0 108000Glycol Contactor 324.0 1.6 1.6 2.6 108000Glycol Contactor 184.0 4.0 4.0 16.0 108000Panametric (Train`A') 25.0 1.4 1.0 1.4 108000Glycol Heater 75.0 3.3 0.4 1.3 108000Pedestal Crane 81.0 15.1 1.2 18.1 108000Hydraulic Oil Tank 50.0 2.0 1.5 3.0 108000Chemical Injection Skid 50.0 2.1 0.9 1.9 108000Outet K.O. Vessel 90.0 1.0 1.0 1.0 108000Inlet K.O. Vessel 117.0 1.2 1.2 1.4 108000Reverse Demulsifier Injection Skid 83.0 5.4 1.9 10.3 108000
Operating ElevationWeight Length Width Area
kN m m m2 mmDescription
Equipment Footprint
CK-352 Injection for V-5246 2.0 0.5 0.3 0.2 108000Degassing Buffer Tank 50.0 3.7 1.5 5.6 110058Process Gas Compressor 634.0 12.6 4.0 50.4 110058Process Gas Compressor Turbine 1469.0 17.0 4.2 71.4 1100583rd Stage Separator 226.0 6.1 2.1 12.8 1100584th Stage Separator 1561.0 14.5 2.7 39.2 1100581st Stage Recycled Cooler 22.0 1.2 0.6 0.7 110058MP Gas Cooler 217.0 7.2 1.2 8.6 110058Nitrogen Generation Package 100.0 4.1 1.3 5.3 110058Crane Support M-4203 330.0 1.0 1.0 1.0 110058Pedestal on KT-4201 585.0 15.3 5.1 78.0 110058Pedestal on K-4201 285.0 12.8 4.8 61.4 110058H.P. Separator 1441.0 15.2 4.1 62.3 110000H.P. Separator 1441.0 15.2 4.1 62.3 110000Extra High Pressure Separator 862.0 9.2 2.3 21.2 110000Extra High Pressure Test Separator 821.0 8.5 2.4 20.4 110000Hydrocyclone Skid 696.0 6.6 5.0 33.0 110000Process Gas Compressor 1068.0 12.6 4.0 50.4 110059Process Compressor Gas Turbine 2152.0 17.0 4.2 71.4 1100593rd Stage Separator 370.0 7.2 3.5 25.2 1100594th Stage Separator 2053.0 13.5 4.1 55.4 1100591st Stage Recycle Gas Cooler 30.0 4.0 1.6 6.4 1100592nd Stage Suction Cooler 27.0 4.0 1.6 6.4 110059Nitrogen Generation Package 100.0 4.1 1.3 5.3 110059Turbo Expander Package 125.0 8.0 3.5 28.0 MainFlash Gas Compressor Package 1600.0 23.0 5.0 115.0 MainFlash Gas Compressor Package 1600.0 23.0 5.0 115.0 MainRe-Injection Compressor Package 3500.0 23.0 5.0 115.0 MainRe-Injection Compressor Package 3500.0 23.0 5.0 115.0 MainRe-Injection Compressor Package Lube Oil Skid 150.0 23.0 5.0 115.0 MainRe-Injection Compressor Package Lube Oil Skid 150.0 23.0 5.0 115.0 MainTempered Water Make-up Pump 4.0 7.0 2.0 14.0 MainChemical Injection Barrel Pump 1.0 7.0 2.0 14.0 MainHot Oil Circulation Pumps 29.0 5.0 3.0 15.0 MainHot Oil Circulation Pumps 29.0 5.0 3.0 15.0 MainHot Oil Filters 50.0 2.0 2.0 4.0 MainSphere Launcher 10.0 4.1 0.5 2.0 1164592nd Stage Condenser 230.0 7.0 1.2 8.4 1164593rd Stage Condenser 341.0 7.0 1.3 9.1 1164594th Stage Condenser 480.0 8.0 1.1 8.8 11645915th OHT Crane 270.0 79.3 0.4 31.7 116459Pedestal Crane 432.0 4.1 4.1 16.8 116459Temp. Water H. Tank 92.0 3.7 1.8 6.7 116459Seal Oil Tank 130.0 3.0 2.0 6.0 116459Sphere Launcher 22.0 4.5 0.5 2.3 1164592nd Stage Condenser 437.5 7.9 1.6 12.6 1164593rd Stage Condenser 507.5 8.9 1.6 14.2 1164594th Stage Condenser 910.0 9.7 1.1 10.7 116459OHT Crane 24T 270.0 79.3 0.4 31.7 116459Pedestal Crane 15T 432.0 4.1 4.1 16.8 116459Tempered Water Heater Tank 91.5 3.8 1.8 6.8 116459Lube Oil Storage Tank 130.0 3.0 2.0 6.0 116459Heat Recovery Unit 925.0 16.0 8.0 128.0 ElevatedHeat Recovery Unit 925.0 16.0 8.0 128.0 Elevated
419.0
Appendix B
PILED FOUNDATION DATABASE
Penetration Pile Wall above mudline Penetration Total Hammer Blow
S.N. Ratio Year W.D. Pile Dia. Thick L1 L2 L1+L2 Type Countmm mm m m bpm
1 56.0 1965 34.4 A1 762 16 39.6 42.7 82.3 Vulcan 140-C 410
2 56.0 1965 A2 762 39.6 42.7 82.3 Vulcan 140-C 446
3 56.0 1965 A3 762 39.6 42.7 82.3 Vulcan 140-C 446
4 56.0 1965 A4 762 39.6 42.7 82.3 Vulcan 140-C 482
5 58.0 1965 B1 762 39.6 44.2 83.8 Vulcan 140-C 348
6 55.6 1965 B2 762 39.6 42.4 82.0 Vulcan 140-C 469
7 56.0 1965 B3 762 39.6 42.7 82.3 Vulcan 140-C 351
8 56.0 1965 B4 762 39.6 42.7 82.3 Vulcan 140-C 476
9 90.7 1983 36.3 A1 914 25 41.0 82.9 123.9 Vulcan 560 174
10 90.4 1983 A2 914 41.0 82.6 123.6 Vulcan 560 144
11 90.0 1983 A3 914 41.0 82.3 123.2 Vulcan 560 269
12 90.7 1983 B1 914 41.0 82.9 123.9 Vulcan 560 292
13 90.0 1983 B2 914 41.0 82.3 123.2 Vulcan 560 148
14 91.0 1983 B3 914 41.0 83.2 124.2 Vulcan 560 220
15 84.1 1978 35.97 A1 914.4 32 41.1 76.9 118.0 Menck 3000 292
16 95.9 1978 A2 914.4 41.1 87.7 128.8 Menck 4600 958
17 93.8 1978 A3 914.4 41.1 85.7 126.8 Menck 4600 968
18 83.5 1978 A4 914.4 41.1 76.4 117.5 Menck 3000 197
19 92.6 1978 B1 914.4 41.1 84.7 125.8 Menck 4600 581
20 93.4 1978 B2 914.4 41.1 85.4 126.5 Menck 4600 659
21 92.8 1978 B3 914.4 41.1 84.9 126.0 Menck 4600 984
22 92.8 1978 B4 914.4 41.1 84.8 125.9 Menck 4600 958
23 82.0 1978 35.96 A1 762 32 41.1 62.5 103.6 MRBS 3000/150 85
24 82.2 1978 A2 762 41.1 62.6 103.7 MRBS 3000/150 75
25 82.1 1978 B1 762 41.1 62.6 103.7 MRBS 3000/150 98
26 82.1 1978 B2 762 41.1 62.6 103.7 MRBS 3000/150 66
27 86.0 1978 35.96 A1 762 32 41.1 65.5 106.6 Menck 3000 203
28 86.1 1978 A2 762 41.1 65.6 106.7 Menck 3000 210
29 92.9 1978 B1 762 41.1 70.8 111.9 Menck 3000 131
30 86.3 1978 B2 762 41.1 65.7 106.8 Menck 3000 180
31 80.1 1966 35.6 A1 762 16 40.7 61.0 101.7 Vulcan 040 364
32 81.0 1966 B2 762 40.7 61.7 102.4 Vulcan 040 292
33 80.6 1966 A3 762 40.7 61.4 102.1 Vulcan 040 554
34 59.9 2002 35.55 A1 1219 32 41.5 73.0 114.5 MHU500T 148
35 59.3 2002 A2 1219 41.5 72.2 113.7 MHU500T 210
36 60.0 2002 B1 1219 41.5 73.2 114.7 MHU500T 151
37 59.3 2002 B2 1219 41.5 72.2 113.7 MHU500T 213
38 42.5 1982 19.2 A1 1219 32 24.1 51.8 75.9 Menck3000 69
39 42.5 1982 A2 1219 24.1 51.8 75.9 Menck3000 69
40 42.5 1982 B1 1219 24.1 51.8 75.9 Menck3000 75
41 42.5 1982 B2 1219 24.1 51.8 75.9 Menck3000 59
42 42.5 1982 C1 1219 24.1 51.8 75.9 Menck3000 69
43 42.5 1982 C2 1219 24.1 51.8 75.9 Menck3000 75
44 65.5 1978 20.116 A1 1219.2 32 25.3 79.9 105.2 4600/150 85
45 65.0 1978 A2 1219.2 25.3 79.2 104.5 4600/150 112
46 70.5 1978 A3 1219.2 25.3 86.0 111.3 4600/150 121
47 65.3 1978 A4 1219.2 25.3 79.6 104.9 4600/150 95
48 81.0 1978 B1 1219.2 25.3 98.8 124.1 4600/150 942
49 80.5 1978 B2 1219.2 25.3 98.1 123.4 4600/150 525
50 81.0 1978 B3 1219.2 25.3 98.8 124.1 4600/150 866
51 64.8 1978 B4 1219.2 25.3 78.9 104.2 4600/150 102
52 100.8 1978 20.116 A1 762 32 25.3 76.8 102.1 3000/150 85
53 91.2 1978 A2 762 25.3 69.5 94.8 3000/150 92
54 78.8 1978 B1 762 25.3 60.0 85.3 3000/150 59
55 78.8 1978 B2 762 25.3 60.0 85.3 3000/150 59
56 105.2 1978 20.116 A1 762 32 25.3 80.2 105.5 3000/150 79
Penetration Pile Wall above mudline Penetration Total Hammer Blow
S.N. Ratio Year W.D. Pile Dia. Thick L1 L2 L1+L2 Type Countmm mm m m bpm
57 96.8 1978 A2 762 25.3 73.8 99.1 3000/150 89
58 102.4 1978 B1 762 25.3 78.0 103.3 3000/150 82
59 93.6 1978 B2 762 25.3 71.3 96.6 3000/150 85
60 69.4 1998 27.2 A1 914.4 varies 35.2 63.5 98.7 Vulcan 530 196
61 69.4 1998 A2 914.4 35.2 63.5 98.7 Vulcan 530 148
62 69.4 1998 B1 914.4 35.2 63.5 98.7 Vulcan 530 148
63 69.4 1998 B2 914.4 35.2 63.5 98.7 Vulcan 530 136
64 86.7 1978 18.6 A1 762 25 23.3 66.1 89.4 Vulcan 040 794
65 100.3 1978 B2 762 23.3 76.4 99.7 Vulcan 040 33
66 90.4 1978 A3 762 23.3 68.9 92.2 Vulcan 040 817
67 80.0 1979 32.1 A1 762 25 36.9 61.0 97.9 Vulcan 340 322
68 80.1 1979 B2 762 36.9 61.0 97.9 Vulcan 340 433
69 80.1 1979 A3 762 36.9 61.0 97.9 Vulcan 340 194
70 111.2 1979 20.9 A1 762 25 25.6 84.7 110.3 Vulcan 340 262
71 109.6 1979 B2 762 25.6 83.5 109.1 Vulcan 340 157
72 110.4 1979 A3 762 25.6 84.1 109.7 Vulcan 340 246
73 75.2 1980 38.7 A1 762 25 43.4 57.3 100.7 MENCK 1500 617
74 74.0 1980 B2 762 43.4 56.4 99.8 MENCK 1500 715
75 74.8 1980 A3 762 43.4 57.0 100.4 MENCK 1500 837
76 55.0 1991 29.1 A1 914.4 25 35.1 50.3 85.4 Vulcan 530 154
77 64.2 1991 A2 914.4 35.1 58.7 93.8 Vulcan 530 121
78 64.7 1991 B1 914.4 35.1 59.1 94.2 Vulcan 530 154
79 64.7 1991 B2 914.4 35.1 59.1 94.2 Vulcan 530 161
80 78.9 1993 21.6 A1 762 25 27.6 60.1 87.7 Menck3000 104
81 78.3 1993 B2 762 27.6 59.7 87.3 Menck3000 120
82 79.0 1993 A3 762 27.6 60.2 87.8 Menck3000 124
83 72.4 1995 20.7 A1 914.4 25 28.7 66.2 94.9 70M 39
84 80.1 1995 A2 914.4 28.7 73.2 101.9 70M 49
85 72.4 1995 B1 914.4 28.7 66.2 94.9 70M 33
86 80.1 1995 B2 914.4 28.7 73.2 101.9 70M 39
87 67.8 1995 12.5 A1 914.4 25 20.5 62.0 82.5 Vulcan-560 33
88 76.6 1995 A2 914.4 20.5 70.0 90.5 Vulcan-560 46
89 67.8 1995 B1 914.4 20.5 62.0 82.5 Vulcan-560 26
90 76.6 1995 B2 914.4 20.5 70.0 90.5 Vulcan-560 39
91 36.7 1997 29.9 A1 1219 25 36.3 44.8 81.1 Menck 3000 80
92 36.3 1997 A2 1219 36.3 44.3 80.6 Vulcan 560 75
93 36.3 1997 B1 1219 36.3 44.3 80.6 Menck 3000 84
94 36.3 1997 B2 1219 36.3 44.3 80.6 Vulcan 560 66
95 49.9 1998 40.8 A1 1066.8 25 46.6 53.3 99.9 MENCK 3000 121
96 47.3 1998 A2 1066.8 46.6 50.5 97.1 Vulcan 560 98
97 49.9 1998 B1 1066.8 46.6 53.3 99.9 Menck 3000 105
98 47.3 1998 B2 1066.8 46.6 50.5 97.1 Menck 3000 160
99 53.3 2000 25.2 A1 1219.2 32 33.2 65.0 98.2 Vulcan 560 124
100 65.6 2000 A2 1219.2 33.2 80.0 113.2 Menck 3900 312
101 53.3 2000 B1 1219.2 33.2 65.0 98.2 Vulcan 560 156
102 64.4 2000 B2 1219.2 33.2 78.5 111.7 Menck 3900 1216
103 76.6 2000 12.8 A1 914 25 22.0 70.0 92.0 Vulcan 530 152
104 76.6 2000 A2 914 22.0 70.0 92.0 Vulcan 530 140
105 76.6 2000 B1 914 22.0 70.0 92.0 Vulcan 530 168
106 76.6 2000 B2 914 22.0 70.0 92.0 Vulcan 530 144
107 61.3 1981 A1 914.4 25 28.9 56.1 85.0 Delmag D55 302
108 61.3 1981 A2 914.4 28.9 56.1 85.0 Delmag D55 276
109 62.3 1981 B1 914.4 28.9 57.0 85.9 Delmag D55 420
110 62.3 1981 B2 914.4 28.9 57.0 85.9 Delmag D55 525
111 60.4 1981 A1 914 25 35.1 55.2 90.2 Menck 3000 135
112 60.4 1981 A2 914 35.1 55.2 90.2 Menck 3000 161
113 60.9 1981 B1 914 35.1 55.6 90.7 Menck 3000 92
Penetration Pile Wall above mudline Penetration Total Hammer Blow
S.N. Ratio Year W.D. Pile Dia. Thick L1 L2 L1+L2 Type Countmm mm m m bpm
114 47.0 1981 B2 914 35.1 43.0 78.0 Menck 3000 272
115 45.7 2004 27.24 A1 1219 32 34.4 55.8 90.2 MHU600 80
116 46.0 2004 A2 1219 34.4 56.1 90.5 MHU600 100
117 45.9 2004 B1 1219 34.4 56.0 90.4 MHU600 80
118 45.9 2004 B2 1219 34.4 56.0 90.4 MHU600 80
119 109.7 1992 35 A1 762 16 40.0 83.6 123.6 Vulcan 530 384
120 109.8 1992 B2 762 40.0 83.6 123.6 Vulcan 530 1529
121 109.7 1992 A3 762 40.0 83.6 123.6 Vulcan 530 377
122 67.3 1981 36.1 A1 914 25 41.4 61.6 103.0 Menck MRBS 1500 197
123 67.5 1981 A2 914 41.4 61.7 103.1 Menck MRBS 1500 253
124 67.3 1981 B1 914 41.4 61.6 103.0 Menck MRBS 1500 151
125 67.3 1981 B2 914 41.4 61.6 103.0 Menck MRBS 1500 190
126 41.4 1999 35.55 A1 1219 25 41.3 50.5 91.8 Vulcan 530 32
127 41.4 1999 A2 1219 41.3 50.5 91.8 Vulcan 530 140
128 43.3 1999 B1 1219 41.3 52.8 94.1 Vulcan 530 96
129 41.4 1999 B2 1219 41.3 50.5 91.8 Vulcan 530 28
130 77.2 1978 25.9 A1 762 25 30.6 58.8 89.4 Vulcan 020 184
131 77.2 1978 B2 762 30.6 58.8 89.4 Vulcan 020 217
132 76.0 1978 A3 762 30.6 57.9 88.5 Vulcan 020 217
133 68.0 1980 22.9 A1 762 25 27.6 51.8 79.4 Vulcan 040 1047
134 80.0 1980 B2 762 27.6 61.0 88.6 Vulcan 040 66
135 66.2 1980 A3 762 27.6 50.4 78.0 Vulcan 040 991
136 86.5 1981 30.1 A1 762 25 34.8 65.9 100.7 Vulcan 040 138
137 108.6 1981 B2 762 34.8 82.8 117.6 Vulcan 040 125
138 86.4 1981 A3 762 34.8 65.8 100.6 Vulcan 040 141
Appendix C
SOIL PROFILE DATABASE AND ENGINEERING PARAMETERS
16
16
22
40
40
162
34
14
762 PILEABOVE
MUDPENETRA
TION HAMMER PS2A
A1 40 43Vulcan 140-C
A2 40 43 do
B3 40 43 doB4 40 43 do
B2 40 43 do
A4 40 43 doA3 40 43 do
B1 40 43 do
43MUDLINE
MEAN SEA LEVEL
45
38
38
38
50
PS2C
25
169
541
1532
PILEABOVE
MUDPENETRA
TION HAMMER PS2CA1 41 83 Vulcan 560A2 41 83 do
B1 41 83 doB2 41 83 do
A3 41 83 do
B3 41 83 do
4183
23
914
MUDLINE
MEAN SEA LEVEL
PS 2
32
4121
62
PILEABOVE
MUDPENETRA
TION HAMMER PS2D
A1 41 77Menck 3000
A2 41 87Menck 4600
B1 41 85Menck 4600
B2 41 85 do
A4 41 77Menck 3000
A3 41 86Menck 4600
B3 41 85 doB4 41 85 do
32
87
77
23
10
0.686 shoe -45mm
4120
914
MUDLINE
MEAN SEA LEVEL
32
&
45 SHOE
5025
452
3
PILEABOVE
MUDPENETRA
TION HAMMER PS2E
A1 41 63MRBS 3000
A2 41 63 doB1 41 63 doB2 41 63 do
76
762
MUDLINE
MEAN SEA LEVEL
32
4121
23
414
762 PILEABOVE
MUDPENETRA
TION HAMMER
A1 41 66Menck 3000
A2 41 66 doB1 41 71 doB2 41 66 do
66
71
32
45 shoe
572mm
45 shoe
572mm
762
762
MUDLINE
MEAN SEA LEVEL
16
5327
462
4
PILEABOVE
MUDPENETRA
TION HAMMERA1 41 61 Vulcan 040B2 41 61 doA3 41 61 do
79
762
MUDLINE
MEAN SEA LEVEL
25
25
38
32
2228
1016
23
5019
3
762 PILEABOVE
MUDPENETRA
TION HAMMERA1 40 84 Vulcan 530B2 40 84 DOA3 40 84 DO
4910
3
762
MUDLINE
MEAN SEA LEVEL
40
25
31.2
305
PILE ABOVE MUD PENETRATION HAMMERA1 42 51 Vulcan 530A2 42 51 DOB1 42 51 DOB2 42 51 DO
54.6
66.3
1219
MUDLINE
MEAN SEA LEVEL
50
40
38
32
38
38
25 38
1118
315
326
6
1219 PILEABOVE
MUDPENETRA
TION HAMMERA1 42 73 MHUT500TA2 42 73 DOB1 42 73 DOB2 42 73 DO
4985
2
1219
MUDLINE
MEAN SEA LEVEL
25 & 38 SHOE
6.1
4. 443
.14. 1
54.7
25
914 PILEABOVE
MUDPENETRA
TION HAMMER
A1 41 62Menck 3000
A2 41 62 DOB1 41 62 DOB2 41 62 DO
82.7
914
MUDLINE
MEAN SEA LEVEL
32
47
2238
1910
28
82
3
1219
MUDLINE
MEAN SEA LEVEL
1219 PILEABOVE
MUDPENETRA
TION HAMMERA1 25 80 4600/150A2 25 80 DOA3 25 86 DOA4 25 80 DOB1 25 98 DOB2 25 98 DOB3 25 98 DOB4 25 79 DO
2914
720
2545
92
99
112
1219
1219
1219
915 mm shoe
45mm 915 mm shoe
45mm
MUDLINE
MEAN SEA LEVEL
2713
719
2323
85
762 PILEABOVE
MUDPENETRA
TION HAMMERA1 25 80 3000/150A2 25 74 DOB1 25 78 DOB2 25 77 DO
21
78
16
79
MUDLINE
MEAN SEA LEVEL
2713
719
2420
762 PILEABOVE
MUDPENETRA
TION HAMMERA1 25 77 3000/150A2 25 70 DOB1 25 60 DOB2 25 60 DO
84
76
65
137
1924
13
137
1924
2
MUDLINE
MEAN SEA LEVEL
25
u
25
314
205
2536
7
914 PILEABOVE
MUDPENETRA
TION HAMMERA1 35 64 Vulcan 530A2 35 64 DOB1 35 64 DOB2 35 64 DO
3971
MUDLINE
MEAN SEA LEVEL
25
45
38
25 24
1521
2618
43
4
762 PILEABOVE
MUDPENETRA
TION HAMMERA1 23 66 Vulcan 040B2 23 76 DOA3 23 67 DO
2891
4
25
45
38
25
83
1512
105
107
2126
4
MUDLINE
MEAN SEA LEVEL
38
25
32
25
1028
39
34 1
194
PILEABOVE
MUDPENETRA
TION HAMMERA1 31 59 Vulcan 020B2 31 59 doA3 31 59 do
76
762
MUDLINE
MEAN SEA LEVEL
38
25
32
25 16
305
314
2618
4
762 PILEABOVE
MUDPENETRA
TION HAMMERA1 37 61 Vulcan 340B2 37 61 DOA3 37 61 DO
4676
33MUDLINE
MEAN SEA LEVEL
38
25
32
25
611
33
525
436
42
4
762 PILEABOVE
MUDPENETRA
TION HAMMERA1 26 84 Vulcan 340B2 26 84 DOA3 26 84 DO
3410
9
MUDLINE
MEAN SEA LEVEL
25
&
35 SHOE
15.3
35.4
6.2
54.5
11
762 PILEABOVE
MUDPENETRA
TION HAMMER
A1 43 57Menck 1500
B2 43 57 doA3 43 57 do
72.2
762
MUDLINE
MEAN SEA LEVEL
25
38
4031
2215
141
4
762 PILEABOVE
MUDPENETRA
TION HAMMERA1 28 52 Vulcan 040B2 28 61 doA3 28 52 do
75
87
25
38
MUDLINE
MEAN SEA LEVEL
38
25
35
25 24
194
33
425
244
95
PILEABOVE
MUDPENETRA
TION HAMMERA1 35 66 Vulcan 040B2 35 83 DOA3 35 66 DO
4380
38
25
35
25
423
1245
414
14
4310
1
34
5MUDLINE
MEAN SEA LEVEL
25
32
38
25 5
264
97
2024
762 PILEABOVE
MUDPENETRA
TION HAMMER
A1 28 60Menck 3000
B2 28 60 DOA3 28 60 DO
3268
MUDLINE
MEAN SEA LEVEL
25
32
25
107
1110
2635
174
88
PILEABOVE
MUDPENETRA
TION HAMMERA1 29 66 70MA2 29 73 doB1 29 66 doB2 29 73 do
7925
32
25
152
1926
97
824
MUDLINE
MEAN SEA LEVEL
25
25
32
32
176
147
137
2512
78
914 PILEABOVE
MUDPENETRA
TION HAMMERA1 21 62 Vulcan 560A2 21 70 doB1 21 62 doB2 21 70 do
69
23
25
25
32
32
815
3
MUDLINE
MEAN SEA LEVEL
38
25
0
453
43
1425
32
PILEABOVE
MUDPENETRA
TION HAMMER
A1 36 44Menck 3000
A2 36 44 Vulcan 560
B1 36 44Menck 3000
B2 36 44 Vulcan 560
55
MUDLINE
MEAN SEA LEVEL
38
32.9
13.6
57.6
511
PILEABOVE
MUDPENETRA
TION HAMMER
A1 47 53Menck 3000
A2 47 50 Vulcan 560
B1 47 53Menck 3000
B2 47 50 Vulcan 560
55.3
62.4
MUDLINE
MEAN SEA LEVEL
PILEABOVE
MUDPENETRATION HAMMER
A1 33 65 Vulcan 560
A2 33 80Menck 3900
B1 33 65 Vulcan 560
B2 33 80Menck 3900
71
88
38
32
32
40
50
44
45
32 11
.15
15.4
6.6
9.9
11.1
123
75
25
38
32
32
40
50
44
45
32
25 14
.413
.28.
410
15.4
6.6
9.9
27.6
43
55
244 shoe
36.2
515
44 shoe
MUDLINE
MEAN SEA LEVEL
38
32
25
25
32
38
50
44
32 12
.29.
725
.67.
37.
37.
96.
718
.115
.5
914 PILEABOVE
MUDPENETRA
TION HAMMERA1 22 70 Vulcan 530A2 22 70 DOB1 22 70 DOB2 22 70 DO
26.8
85.2
MUDLINE
MEAN SEA LEVEL
5038
25
32
38
44
32
25
38
32 55
247
1212
1614
45
41
44
2 . 0
PILEABOVE
MUDPENETRA
TION HAMMERA1 34 56 MHU 600A2 34 56 doB1 34 56 doB2 34 56 do
7546
MUDLINE
MEAN SEA LEVEL
914 PILEABOVE
MUDPENETRA
TION HAMMER
A1 29 56Delmag
D55A2 29 56 doB1 29 57 doB2 29 57 do
25
&
38 SHOE
38
359
3815
43
69
MUDLINE
MEAN SEA LEVEL
34
33
1313
134
PILEABOVE
MUDPENETRA
TION HAMMER
A1 35 55Menck 3000
A2 35 56 DOB1 35 56 DOB2 35 43 DO
66
25
38
41
33
43
1321
3
25
38
MUDLINE
MEAN SEA LEVEL
32
&
45 SHOE
4121
372
3
PILEABOVE
MUDPENETRA
TION HAMMER PS2E
A1 41 63MRBS 3000
A2 41 63 doB1 41 63 doB2 41 63 do
63
762
MUDLINE
MEAN SEA LEVEL
Appendix D
PREDICTED STATIC CAPACITY OF PILES IN THE DATABASE
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 32.0 mm Ko non-cohesive 1.0 Steel cross section area 0.119 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.048 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
Nq =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.00 0.17 0.14 0.7 3 2 3 2 1.00 3 2 3 2
1.00 0 8 8 0.00 0.00 1.1 1.4 320 320 335 38 2 43 Cored 0.20 0.89 335 38 2 43 12% 0.001.00 0 8 8 0.00 0.00 1.0 1.0 64 64 67 2 67 2 0.00
6.50 0.13 0.13 4.7 117 111 119 113 1.00 117 111 119 1137.50 0 67 67 0.00 0.00 8.4 8.4 532 532 557 63 113 296 Cored 557 63 113 296 79% 0.027.50 75 67 44 1.69 0.44 32.9 32.9 675 675 707 113 707 113 0.00
1.70 0.00 0.00 33.8 220 208 339 322 0.67 147 139 266 2529.20 75 83 55 1.36 0.46 34.7 34.7 675 675 707 81 322 742 Cored 707 81 252 599 87% 0.039.20 0 83 83 0.00 0.00 18.1 11.6 1984 1984 2078 322 2078 252 0.00
2.60 0.22 0.14 13.2 132 125 471 446 1.00 132 125 398 37711.80 0 106 106 0.00 0.00 23.2 14.8 2545 2545 2667 304 446 1221 Cored 2667 304 377 1078 72% 0.0611.80 75 106 71 1.06 0.49 37.0 37.0 675 675 707 446 707 377 0.00
2.10 0.00 0.00 38.1 306 290 777 736 0.67 204 193 602 57013.90 75 123 82 0.92 0.52 39.2 39.2 675 675 707 81 707 1565 Plugged 707 81 570 1252 94% 0.0713.90 0 123 123 0.00 0.00 21.0 17.2 4914 4914 5149 736 5149 570 0.00
1.10 0.17 0.14 17.9 75 71 852 808 1.00 75 71 677 64215.00 0 133 133 0.00 0.00 22.7 18.6 5310 5100 5343 609 808 2269 Cored 5343 609 642 1927 68% 0.1115.00 75 133 89 0.85 0.54 40.7 40.7 675 675 707 707 707 642 0.00
3.00 0.00 0.00 42.7 491 465 1343 1273 0.67 327 310 1004 95118.00 75 160 107 0.70 0.60 44.7 44.7 675 675 707 81 707 2131 Plugged 707 81 707 1792 96% 0.1018.00 0 160 160 0.00 0.00 27.3 22.0 1278 1278 1339 1273 1339 951 0.00
1.10 0.17 0.14 22.0 93 88 1436 1360 1.00 93 88 1097 103919.10 0 170 170 0.00 0.00 29.0 22.0 1357 1357 1422 162 1360 2958 Cored 1422 162 1039 2298 93% 0.1319.10 0 170 170 0.00 0.00 29.0 22.0 6786 5100 5343 1360 5343 1039 0.00
2.40 0.17 0.14 22.0 202 192 1638 1552 1.00 202 192 1299 123121.50 0 191 191 0.00 0.00 32.7 22.0 7650 5100 5343 609 1552 3799 Cored 5343 609 1231 3139 81% 0.1821.50 0 191 191 0.00 0.00 32.7 22.0 4590 4590 4809 1552 4809 1231 0.00
3.00 0.17 0.14 22.0 253 239 1891 1792 1.00 253 239 1552 147024.50 0 218 218 0.00 0.00 37.3 22.0 5238 5100 5343 609 1792 4291 Cored 5343 609 1470 3631 83% 0.2124.50 130 218 146 0.89 0.53 68.8 68.8 1170 1170 1226 1226 1226 1226 0.00
14.00 0.00 0.00 78.0 4182 3962 6073 5754 0.67 2788 2642 4340 411238.50 130 351 234 0.56 0.67 87.2 87.2 1170 1170 1226 140 1226 7438 Plugged 1226 140 1226 5705 98% 0.3338.50 0 351 351 0.00 0.00 77.0 22.0 8430 5100 5343 5343 5343 5343 0.00
8.50 0.22 0.14 22.0 716 679 6789 6433 1.00 716 679 5056 479147.00 0 419 419 0.00 0.00 91.9 22.0 10062 5100 5343 609 5343 12741 Plugged 5343 609 4791 10455 94% 0.6147.00 0 419 419 0.00 0.00 71.7 22.0 16770 5100 5343 5343 5343 4791 0.00
1.10 0.17 0.14 22.0 93 88 6882 6520 1.00 93 88 5149 487848.10 0 429 429 0.00 0.00 73.4 22.0 17166 5100 5343 609 5343 12834 Plugged 5343 609 4878 10636 94% 0.6248.10 0 429 429 0.00 0.00 73.4 22.0 3433 3433 3597 3597 3597 3597 0.00
41.90 0.17 0.14 22.0 3530 3345 10412 9865 1.00 3530 3345 8679 822390.00 0 827 827 0.00 0.00 141.5 22.0 6618 5100 5343 609 5343 16364 Plugged 5343 609 5343 14631 96% 0.8590.00 300 827 551 0.54 0.68 203.4 203.4 2700 2700 2829 2829 2829 2829 0.00
10.20 0.00 0.00 209.2 8170 7741 18582 17606 0.67 5447 5161 14126 13384100.20 300 924 616 0.49 0.72 215.0 215.0 2700 2700 2829 322 2829 21733 Plugged 2829 322 2829 17277 98% 1.00
5100 100000 100000 0
22 5100 20
hard CLAY C 0 9.5 0 0 22
0.47 8 22 5100dense carbonate S 25 9.5
5100 22 5100 20
22 5100 25
CALCARENITE, S 25 9.0 0.47 40 22
25
20
medium dense t S 30 8.0 0.47 24 22 5100
24
40
8
0
0
510022
510022
100000
CALCULATIONS SRDq
kPa
fkPa
5100
5100
20
15
100000
5100
22
100000
0.47
25510022
0 10000022 5100
220.47
100000
20
0
5100
5100
5100
20
20
0.47
0
0
0.47
0
0.47
0.47
25
0
8.0
9.0
9.0
9.0
9.0
9.0
9.5
25
0
25
25
9.5
9.0
8.0
0
30
25
20
0
9.0
C
S
C
S
0
8
40
0
8
24
40
S
C
S
S
stiff calcareous s
dense carbonate
CALCARENITE,
medium dense c
dense carbonate
firm to stiff carbo
CALCARENITE,
stiff carbonate C
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
stiff carbonate cl
INPUT DATA
CALCARENITE,
loose to medium
C
S
S
0.47
22
22
22
100000
22
100000
22
8
22 5100
5100
22 5100
22 5100
510022
SOIL IDENTIFICATION: 3M05
K.tan��limit
K.tan�
22
10000022 5100
22 5100
5100
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.094 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.073 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Qp Qu Sunc Q f Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
re
Nq =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssu
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t c
q= u
nit b
earin
g pr
essu
re c
onsd
er
Shaf
t out
er fr
ictio
n fo
r eac
h la
ye
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r =
Shaf
t out
er fr
ictio
n fo
r eac
h la
y e
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 00.20 0.17 0.14 0.1 0 0 0 0 1.00 0 0 0 0
0.20 0 2 2 0.00 0.00 0.2 0.3 19 19 21 2 0 2 Cored 0.20 0.18 21 2 0 2 10% 0.000.20 0 2 2 0.00 0.00 0.3 0.2 19 19 21 0 21 0 0.00
0.70 0.17 0.14 0.6 2 2 2 2 1.00 2 2 2 20.90 0 7 7 0.00 0.00 1.2 1.0 86 86 93 8 2 12 Cored 93 8 2 12 30% 0.000.90 0 7 7 0.00 0.00 1.2 1.0 86 86 93 2 93 2 0.00
6.10 0.17 0.14 4.6 108 104 110 106 1.00 108 104 110 1067.00 0 59 59 0.00 0.00 10.1 8.3 709 709 761 66 106 282 Cored 761 66 106 282 76% 0.027.00 0 59 59 0.00 0.00 10.1 8.3 709 709 761 106 761 106 0.00
3.00 0.17 0.14 9.9 114 110 224 215 1.00 114 110 224 21510.00 0 83 83 0.00 0.00 14.2 11.6 997 997 1070 93 215 533 Cored 1070 93 215 533 82% 0.0410.00 0 83 83 0.00 0.00 14.2 11.6 997 997 1070 215 1070 215 0.00
1.50 0.17 0.14 12.5 72 69 296 284 1.00 72 69 296 28411.50 0 96 96 0.00 0.00 16.4 13.4 1150 1150 1234 108 284 688 Cored 1234 108 284 688 84% 0.0511.50 0 96 96 0.00 0.00 16.4 13.4 1150 1150 1234 284 1234 284 0.00
3.50 0.17 0.14 15.5 208 199 504 483 1.00 208 199 504 48315.00 0 126 126 0.00 0.00 21.5 17.6 1507 1507 1617 141 483 1129 Cored 1617 141 483 1129 87% 0.0915.00 0 126 126 0.00 0.00 27.5 17.6 1507 1507 1617 483 1617 483 0.00
6.40 0.22 0.14 19.8 485 465 989 948 1.00 485 465 989 94821.40 0 177 177 0.00 0.00 38.7 22.0 2121 2121 2276 199 948 2136 Cored 2276 199 948 2136 91% 0.1721.40 150 177 118 1.27 0.47 70.6 70.6 1350 1350 1449 948 1449 948 0.00
15.60 0.00 0.00 79.3 4736 4542 5725 5490 0.67 3157 3028 4146 397637.00 150 309 206 0.73 0.59 87.9 87.9 1350 1350 1449 127 1449 7301 Plugged 1449 127 1449 5722 98% 0.4437.00 0 309 309 0.00 0.00 52.9 22.0 3712 3712 3984 3984 3984 3976 0.00
1.00 0.17 0.14 22.0 84 81 5809 5571 1.00 84 81 4231 405738.00 0 319 319 0.00 0.00 54.6 22.0 3827 3827 4108 359 4108 10276 Plugged 4108 359 4057 8647 96% 0.6738.00 150 319 213 0.71 0.60 89.3 89.3 1350 1350 1449 1449 1449 1449 0.00
10.50 0.00 0.00 95.9 3855 3697 9664 9268 0.67 2570 2465 6801 652248.50 150 420 280 0.54 0.68 102.4 102.4 1350 1350 1449 127 1449 11240 Plugged 1449 127 1449 8376 98% 0.6548.50 0 420 420 0.00 0.00 71.8 22.0 5037 5037 5406 5406 5406 5406 0.00
2.00 0.17 0.14 22.0 169 162 9833 9429 1.00 169 162 6969 668350.50 0 437 437 0.00 0.00 74.7 22.0 5241 5100 5474 478 5474 15785 Plugged 5474 478 5474 12921 96% 1.0050.50 0 437 437 0.00 0.00 74.7 22.0 5241 5100 5474 5474 5474 5474 0.00
4.50 0.17 0.14 22.0 379 364 10212 9793 1.00 379 364 7348 704755.00 0 473 473 0.00 0.00 80.9 22.0 5673 5100 5474 478 5474 16164 Plugged 5474 478 5474 13300 96% 1.0355.00 0 473 473 0.00 0.00 80.9 22.0 5673 5100 5474 5474 5474 5474 0.00
10.00 0.17 0.14 22.0 843 808 11054 10601 1.00 843 808 8191 785565.00 0 558 558 0.00 0.00 95.4 22.0 6693 5100 5474 478 5474 17006 Plugged 5474 478 5474 14143 97% 1.0965.00 0 558 558 0.00 0.00 122.2 22.0 11155 5100 5474 5474 5474 5474 0.00
2.50 0.22 0.14 22.0 211 202 11265 10803 1.00 211 202 8401 805767.50 0 583 583 0.00 0.00 127.7 22.0 11655 5100 5474 478 5474 17217 Plugged 5474 478 5474 14353 97% 1.1167.50 150 583 389 0.39 0.80 120.7 120.7 1350 1350 1449 1449 1449 1449 0.00
6.00 0.00 0.00 123.4 2836 2720 14101 13523 0.67 1891 1813 10292 987073.50 150 637 425 0.35 0.84 126.2 126.2 1350 1350 1449 127 1449 15677 Plugged 1449 127 1449 11868 99% 0.9273.50 0 637 637 0.00 0.00 108.9 22.0 7641 5100 5474 5474 5474 5474 0.00
1.00 0.17 0.14 22.0 84 81 14185 13604 1.00 84 81 10376 995174.50 0 645 645 0.00 0.00 110.3 22.0 7737 5100 5474 478 5474 20137 Plugged 5474 478 5474 16328 97% 1.2674.50 150 645 430 0.35 0.85 127.0 127.0 1350 1350 1449 1449 1449 1449 0.00
11.50 0.00 0.00 131.6 5796 5558 19981 19162 0.67 3864 3705 14240 1365686.00 150 743 495 0.30 0.91 136.2 136.2 1350 1350 1449 127 1449 21557 Plugged 1449 127 1449 15816 99% 1.22
100000 100000 0
5100 20
silt C 0 8.5 0 0 22 5100
12 22 5100 22S 25 8.0 0.47
5100 100000 100000 0
22 5100 25
silt C 0 9.0 0 0 22
5100 20
gypsum S 30 10.0 0.47 20 22 5100
20
silt S 25 8.5 0.47 12 22 5100 22
22 5100 22 5100calc S 25 8.0 0.47 12
calc
CALCULATIONS
5100
5100
5000
5100 20
0.47
0
20
qkPa
20
25
20
20
20
20
12
5100
5100
0.47
12
12
12
120.47 22
510022
5100
0
20
100000
5100
100000
22
22
0
0.47
0.47
0.47
0.47
0
0.4725
0
25
8.5
8.5
8.0
8.5
9.6
9.6
8.5
25
25
25
0
8.025
25
25
25 8.5
8.0
8.0
S
S
S
S
22 5100
22 5100
C
S
S
C
sand
clay
sand
clay
calc
sand
silt
calc
22
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
SRD
SOIL IDENTIFICATION: 3N09
K.tan��limit
K.tan�
100000
sand
INPUT DATA
sand
calc
S
S
S
0.47
22
5100
22
12
fkPa
12
12
12
0
22
22
22 5100
5100
22 5100
22 5100
22
100000
0 22 5100
22 5000
22 5100
22 5100
22
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
de
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 03.05 0.22 0.14 2.3 17 16 17 16 1.00 17 16 17 16
3.05 0 21 21 0.00 0.00 3.0 4.7 897 897 357 52 16 85 Cored 0.20 2.36 357 52 16 85 39% 0.013.05 45 21 14 3.16 0.37 16.9 16.9 405 405 161 16 161 16 0.00
3.35 0.00 0.00 18.9 151 141 168 157 0.67 101 94 118 1106.40 45 50 33 1.35 0.46 20.9 20.9 405 405 161 23 157 349 Cored 161 23 110 252 91% 0.046.40 0 50 50 0.00 0.00 13.5 0.0 2093 0 0 0 0 0 0.00
1.22 0.27 0.14 0.0 0 0 168 157 1.00 0 0 118 1107.62 0 61 61 0.00 0.00 16.7 0.0 2579 0 0 0 0 168 Plugged 0 0 0 118 100% 0.027.62 60 61 41 1.47 0.45 27.3 27.3 540 540 215 157 215 110 0.00
1.52 0.00 0.00 27.9 102 95 270 252 0.67 68 63 186 1749.14 60 74 50 1.21 0.48 28.6 28.6 540 540 215 31 215 516 Plugged 215 31 174 390 92% 0.069.14 0 74 74 0.00 0.00 16.3 10.4 1784 1784 710 252 710 174 0.00
3.05 0.22 0.14 12.1 88 83 358 335 1.00 88 83 274 25612.19 0 99 99 0.00 0.00 21.6 13.8 2370 2370 943 137 335 831 Cored 943 137 256 667 79% 0.1012.19 0 99 99 0.00 0.00 26.8 13.8 2370 2370 943 335 943 256 0.00
3.66 0.27 0.14 16.3 142 133 501 468 1.00 142 133 417 38915.85 0 134 134 0.00 0.00 36.2 18.7 3204 3204 1276 185 468 1154 Cored 1276 185 389 991 81% 0.1415.85 0 134 134 0.00 0.00 29.3 18.7 1335 1335 532 468 532 389 0.00
0.91 0.22 0.14 19.1 42 39 543 507 1.00 42 39 458 42816.76 0 140 140 0.00 0.00 30.7 19.6 1399 1399 557 81 507 1131 Cored 557 81 428 967 92% 0.1416.76 0 140 140 0.00 0.00 30.7 19.6 3357 3357 1337 507 1337 428 0.00
4.58 0.22 0.14 20.8 228 213 771 720 1.00 228 213 686 64121.34 0 179 179 0.00 0.00 39.2 22.0 4291 4291 1709 248 720 1739 Cored 1709 248 641 1576 84% 0.2321.34 225 179 119 1.89 0.43 96.0 96.0 2025 2025 806 720 806 641 0.00
15.23 0.00 0.00 103.0 3755 3508 4525 4228 0.67 2503 2339 3189 298036.57 225 308 206 1.09 0.49 110.0 110.0 2025 2025 806 117 806 5449 Plugged 806 117 806 4113 97% 0.6036.57 0 308 308 0.00 0.00 83.6 22.0 7398 5100 2031 2031 2031 2031 0.00
7.43 0.27 0.14 22.0 391 366 4916 4594 1.00 391 366 3581 334644.00 0 379 379 0.00 0.00 102.8 22.0 9092 5100 2031 295 2031 7242 Plugged 2031 295 2031 5906 95% 0.8644.00 0 379 379 0.00 0.00 64.8 22.0 9092 5100 2031 2031 2031 2031 0.00
18.50 0.17 0.14 22.0 974 910 5891 5504 1.00 974 910 4555 425662.50 0 518 518 0.00 0.00 88.5 22.0 12422 5100 2031 295 2031 8216 Plugged 2031 295 2031 6881 96% 1.0062.50 0 518 518 0.00 0.00 140.5 22.0 12422 5100 2031 2031 2031 2031 0.00
6.50 0.27 0.14 22.0 342 320 6233 5824 1.00 342 320 4897 457669.00 0 579 579 0.00 0.00 157.2 22.0 13904 5100 2031 295 2031 8559 Plugged 2031 295 2031 7223 96% 1.0569.00 0 579 579 0.00 0.00 157.2 81.1 13904 10000 3982 3982 3982 3982 0.00
6.00 0.27 0.14 85.1 1222 1142 7455 6966 1.00 1222 1142 6120 571875.00 0 636 636 0.00 0.00 172.7 89.1 15272 10000 3982 579 3982 12016 Plugged 3982 579 3982 10680 95% 1.55
10000 100 10000 30
22 5100 30
dense carbonate SAND S 35 9.5 0.47 24 100
0.47 24 22 5100dense carbonate SAND S 35 9.5
CALCULATIONS SRD
25
0
qkPa
30
25
30
25
0
5100
5100
100000
25
0
0
0
5100
5100
0.47
0100000100000
5100
30
20
5100
100000
51000.47
0.47
0.47
0.47
0.47
0.47
00
35
25
8.0
9.5
7.0
8.5
8.5
9.5
7.5
30
35
25
30
8.50
30
0
35 9.5
8.5
7.0
0
42
42
420.47 0
S
S
S
C
0 0
0 0
S
S
C
S
very weak carbonate SILTSTONE wit
dense cemented carbonate SAND wit
stiff carbonate CLAY with traces of gy
very weak fine to coarse carbonate SA
firm to stiff carbonate CLAY
dense carbonate SAND
dense carbonate SAND
clayey carbonate SILT interbedded w
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
dense carbonate SILTY SAND slightly
INPUT DATA
loose carbonate SAND slightly cemen
firm carbonate CLAY with shell fragm
S
C
S
0
22
100000
24
fkPa
24
24
10
24
100000
22
22
22
22 5100
22 5100
22
22
22
22
22 5100
5100
22 5100
0 0
SOIL IDENTIFICATION: 3SC-K05
K.tan��limit
K.tan�
22 5100
22 5100
22 5100
24
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
de
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
g
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th t h
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th t h
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 07.50 0.22 0.14 7.1 127 119 127 119 1.00 127 119 127 119
7.50 0 65 65 0.00 0.00 9.1 14.2 2591 2591 1032 150 119 396 Cored 0.20 7.17 1032 150 119 396 62% 0.037.50 75 65 43 1.73 0.44 32.6 32.6 674 674 268 119 268 119 0.00
2.32 0.00 0.00 33.9 188 175 315 295 0.67 125 117 253 2369.81 75 87 58 1.30 0.47 35.1 35.1 674 674 268 39 268 623 Plugged 268 39 236 528 93% 0.059.81 0 87 87 0.00 0.00 23.5 12.1 3464 3464 1379 295 1379 236 0.00
4.63 0.27 0.14 15.2 168 157 484 452 1.00 168 157 421 39314.45 0 130 130 0.00 0.00 35.3 18.2 5210 5100 2031 295 452 1231 Cored 2031 295 393 1110 73% 0.0914.45 0 130 130 0.00 0.00 35.3 18.2 3126 3126 1245 452 1245 393 0.00
7.96 0.27 0.14 20.1 383 358 867 810 1.00 383 358 804 75122.40 0 205 205 0.00 0.00 55.7 22.0 4924 4924 1961 285 810 1962 Cored 1961 285 751 1840 85% 0.1622.40 0 205 205 0.00 0.00 55.7 22.0 4924 4924 1961 810 1961 751 0.00
4.08 0.27 0.14 22.0 215 201 1082 1011 1.00 215 201 1019 95226.49 0 244 244 0.00 0.00 66.1 22.0 5848 5100 2031 295 1011 2388 Cored 2031 295 952 2267 87% 0.1926.49 0 244 244 0.00 0.00 66.1 22.0 2924 2924 1164 1011 1164 952 0.00
8.02 0.27 0.14 22.0 422 394 1504 1405 1.00 422 394 1441 134734.50 0 325 325 0.00 0.00 88.3 22.0 3905 3905 1555 226 1405 3135 Cored 1555 226 1347 3014 93% 0.2634.50 0 325 325 0.00 0.00 71.3 22.0 3905 3905 1555 1405 1555 1347 0.00
7.01 0.22 0.14 22.0 369 345 1873 1750 1.00 369 345 1811 169241.51 0 386 386 0.00 0.00 84.6 22.0 4632 4632 1844 268 1750 3892 Cored 1844 268 1692 3770 93% 0.3241.51 300 386 257 1.17 0.48 144.4 144.4 2700 2700 1075 1075 1075 1075 0.00
27.98 0.00 0.00 162.3 10870 10157 12744 11907 0.67 7247 6771 9058 846369.49 300 650 433 0.69 0.60 180.2 180.2 2700 2700 1075 156 1075 13975 Plugged 1075 156 1075 10289 98% 0.8869.49 0 650 650 0.00 0.00 176.3 22.0 15590 5100 2031 2031 2031 2031 0.00
6.49 0.27 0.14 22.0 342 319 13086 12227 1.00 342 319 9399 878375.99 0 711 711 0.00 0.00 192.9 22.0 17058 5100 2031 295 2031 15411 Plugged 2031 295 2031 11725 97% 1.00
K.tan��limit
100000 100000
K.tan�
22 5000
INPUT DATA
SANDSTONE S 25220.47 5000
5100
22 5100
22 5100
5100
22 5100
22
35 9.4 5100
0
22
22
22
12
24 220.47
0.47
22
CORAL & SAND
SAND WITH SO
SANDSTONE
SILT WITH SOM
CLAY WITH SO
SAND S
0
35
0
35
510022
5100S
C
CALCULATIONS
MUD
fkPa
9.4
8.630
0
40
0
40
9.4
9.4
22SILTSTONE 510024
35
C
0.47 22
S
S
S
S
22 5100
10000022 5100
35
30
0
10.2
8.6
9.4
9.4 24
5100
5100
0
0.47
100000
5100
12
0.47
0.47
SRD
0
30
qkPa
30
25
30
30
30
SOIL IDENTIFICATION: 3SA
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
de
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
g
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th t h
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th t h
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 35 0 0 0.00 0.00 0.0 0.0 315 315 0 0 012.19 0.00 0.00 13.1 459 434 459 434 0.67 306 290 459 290
12.19 35 118 79 0.44 0.75 26.2 26.2 315 315 184 22 184 666 Plugged 0.20 8.73 184 22 184 666 97% 0.0512.19 0 118 118 0.00 0.00 41.9 16.6 4730 4730 2773 434 2773 290 0.00
8.53 0.35 0.14 19.3 472 447 932 881 1.00 472 447 932 73620.73 0 224 224 0.00 0.00 79.4 22.0 8964 5100 2990 356 881 2169 Cored 2990 356 736 2024 82% 0.1520.73 165 224 149 1.10 0.49 80.5 80.5 1485 1485 871 871 871 736 0.00
4.57 0.00 0.00 83.4 1095 1035 2026 1916 0.67 730 690 1662 142625.30 165 271 180 0.91 0.52 86.3 86.3 1485 1485 871 104 871 3001 Plugged 871 104 871 2636 96% 0.1925.30 0 271 271 0.00 0.00 95.9 22.0 10829 5100 2990 1916 2990 1426 0.00
3.35 0.35 0.14 22.0 212 200 2238 2116 1.00 212 200 1873 162628.65 0 312 312 0.00 0.00 110.6 22.0 12492 5100 2990 356 2116 4710 Cored 2990 356 1626 3856 91% 0.2828.65 165 312 208 0.79 0.56 92.7 92.7 1485 1485 871 871 871 871 0.00
31.39 0.00 0.00 112.3 10122 9568 12360 11684 0.67 6748 6379 8621 800560.05 165 633 422 0.39 0.80 131.9 131.9 1485 1485 871 104 871 13334 Plugged 871 104 871 9595 99% 0.7060.05 0 633 633 0.00 0.00 178.6 22.0 15181 5100 2990 2990 2990 2990 0.00
11.58 0.28 0.14 22.0 732 692 13092 12375 1.00 732 692 9353 869671.63 0 734 734 0.00 0.00 207.4 22.0 17624 5100 2990 356 2990 16438 Plugged 2990 356 2990 12699 97% 0.9371.63 0 734 734 0.00 0.00 160.9 22.0 7343 5100 2990 2990 2990 2990 0.00
16.15 0.22 0.14 22.0 1020 965 14112 13340 1.00 1020 965 10373 966187.78 0 912 912 0.00 0.00 199.8 22.0 9119 5100 2990 356 2990 17458 Plugged 2990 356 2990 13720 97% 1.00
K.tan��limit
K.tan�
22 5000 100000
22 5100
100000
5100
22
22
22 5100
22 5100
22
fkPa
0
24
10
40
8
40
0CLAY
INPUT DATA
SILT MUDDY
SANDSTONE
C
S
C
0.47
100000
22
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
SANDSTONE
CLAY
SAND
SILT
0 100000
S
S
C
S
22 5100
22 510012.442
0
42
0 10.2
12.4
9.7
0
36
40
10.2
8.8
11.0
0
0.47
0.47
0
37
0.47
5100
5100
0
37510022
100000
100000
SOIL IDENTIFICATION: 2SA
31
25
0
0
5100
CALCULATIONS SRDq
kPa
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t c
q= u
nit b
earin
g pr
essu
re c
onsd
eri
Shaf
t out
er fr
ictio
n fo
r eac
h la
ye
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
ye
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 02.59 0.19 0.14 1.7 11 10 11 10 1.00 11 10 11 10
2.59 0 18 18 0.00 0.00 2.6 3.4 0 0 0 0 0 11 Plugged 0.20 2.03 0 0 0 11 100% 0.002.59 40 18 12 3.23 0.37 14.7 14.7 356 356 142 10 142 10 0.00
2.01 0.00 0.00 16.2 78 73 88 83 0.67 52 48 62 584.60 40 37 25 1.59 0.45 17.6 17.6 356 356 142 21 83 192 Cored 142 21 58 141 85% 0.024.60 50 37 25 2.00 0.42 20.9 20.9 448 448 178 83 178 58 0.00
1.58 0.00 0.00 21.7 82 77 171 160 0.67 55 51 117 1106.19 50 50 33 1.50 0.45 22.5 22.5 448 448 178 26 160 356 Cored 178 26 110 253 90% 0.046.19 0 50 50 0.00 0.00 11.5 7.0 0 0 0 0 0 0 0.00
2.62 0.23 0.14 8.7 55 51 225 211 1.00 55 51 172 1618.81 0 74 74 0.00 0.00 17.2 10.4 0 0 0 0 0 225 Plugged 0 0 0 172 100% 0.038.81 60 74 50 1.21 0.48 28.6 28.6 539 539 214 211 214 161 0.00
2.68 0.00 0.00 29.9 192 179 417 390 0.67 128 119 300 28011.49 60 98 65 0.92 0.52 31.2 31.2 539 539 214 31 214 663 Plugged 214 31 214 545 94% 0.0911.49 0 98 98 0.00 0.00 22.5 13.7 0 0 0 0 0 0 0.00
9.51 0.23 0.14 17.8 406 379 823 769 1.00 406 379 706 65921.00 0 187 187 0.00 0.00 43.2 22.0 0 0 0 0 0 823 Plugged 0 0 0 706 100% 0.1221.00 0 187 187 0.00 0.00 34.9 22.0 0 0 0 0 0 0 0.00
1.65 0.19 0.14 22.0 87 81 910 850 1.00 87 81 792 74022.65 0 199 199 0.00 0.00 37.1 22.0 0 0 0 0 0 910 Plugged 0 0 0 792 100% 0.1322.65 150 199 133 1.13 0.48 72.7 72.7 1349 1349 537 537 537 537 0.00
7.71 0.00 0.00 76.6 1414 1321 2323 2171 0.67 942 881 1735 162130.36 150 259 173 0.87 0.54 80.5 80.5 1349 1349 537 78 537 2938 Plugged 537 78 537 2350 97% 0.3930.36 300 259 173 1.74 0.44 130.7 130.7 2700 2700 1075 1075 1075 1075 0.00
11.70 0.00 0.00 136.3 3819 3568 6142 5739 0.67 2546 2379 4281 400042.06 300 360 240 1.25 0.47 141.9 141.9 2700 2700 1075 156 1075 7374 Plugged 1075 156 1075 5512 97% 0.9242.06 0 360 360 0.00 0.00 83.2 22.0 0 0 0 0 0 0 0.00
10.06 0.23 0.14 22.0 530 495 6672 6234 1.00 530 495 4810 449552.12 0 447 447 0.00 0.00 103.3 22.0 0 0 0 0 0 6672 Plugged 0 0 0 4810 100% 0.8052.12 0 447 447 0.00 0.00 65.1 22.0 0 0 0 0 0 0 0.00
22.89 0.15 0.14 22.0 1206 1126 7878 7361 1.00 1206 1126 6016 562175.01 0 663 663 0.00 0.00 96.5 22.0 0 0 0 0 0 7878 Plugged 0 0 0 6016 100% 1.00
CALCULATIONS SRD
0
0
qkPa
30
25
0
0
100000
100000
5000
100000
25
0
0.4
5100
5100
0.4
30510022
5100
30
20
100000
100000
51000.4
0.4
0
0.4
0.4
0
00
35
25
8.6
9.4
7.1
7.9
8.6
8.6
9.4
0
35
25
0
9.435
30
0
0 7.9
9.4
7.1
0 100000
S
S
C
S
22 5100
22 5100
S
S
C
C
SILTSTONE/
CLAY
CLAY
SANDSTONE
SAND/SAND
CLAY
SANDSTONE
SILT/SAND
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
CLAY
INPUT DATA
SAND/SAND
CLAY
C
C
S
0
22
100000
fkPa
100000
22
22
100000
22 5100
22 5100
22
100000
22
22
22 5100
5100
22 5100
22 5100
SOIL IDENTIFICATION: 3SC
K.tan��limit
K.tan�
22 5100
22 5000
22 5100
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.094 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.073 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
g
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 03.00 0.13 0.13 1.5 17 17 17 17 1.00 17 17 17 17
3.00 0 24 24 0.00 0.00 3.0 3.0 1008 900 966 84 17 118 Cored 0.20 2.66 966 84 17 118 29% 0.013.00 0 24 24 0.00 0.00 4.1 3.4 1008 1008 1082 17 1082 17 0.00
4.20 0.17 0.14 6.0 97 93 114 109 1.00 97 93 114 1097.20 0 62 62 0.00 0.00 10.6 8.7 2596 1900 2039 178 109 401 Cored 2039 178 109 401 56% 0.027.20 70 62 41 1.70 0.44 30.7 30.7 630 630 676 109 676 109 0.00
6.80 0.00 0.00 34.3 892 856 1006 965 0.67 595 571 709 68014.00 70 123 82 0.85 0.54 37.9 37.9 630 630 676 59 676 1742 Plugged 676 59 676 1444 96% 0.0614.00 0 123 123 0.00 0.00 21.0 17.2 2952 1900 2039 965 2039 680 0.00
9.00 0.17 0.14 18.6 641 615 1648 1580 1.00 641 615 1350 129523.00 0 204 204 0.00 0.00 34.9 20.0 4896 1900 2039 178 1580 3406 Cored 2039 178 1295 2823 94% 0.1323.00 200 204 136 1.47 0.45 90.8 90.8 1800 1800 1932 1580 1932 1295 0.00
37.00 0.00 0.00 114.6 16233 15568 17881 17148 0.67 10822 10378 12173 1167360.00 200 574 383 0.52 0.69 138.3 138.3 1800 1800 1932 169 1932 19982 Plugged 1932 169 1932 14273 99% 0.6460.00 500 574 383 1.31 0.47 233.8 233.8 4500 4500 4830 4830 4830 4830 0.00
4.50 0.00 0.00 236.2 4070 3903 21951 21051 0.67 2713 2602 14886 1427564.50 500 621 414 1.21 0.48 238.5 238.5 4500 4500 4830 422 4830 27203 Plugged 4830 422 4830 20138 98% 0.9164.50 0 621 621 0.00 0.00 136.2 67.0 0 0 0 0 0 0 0.00
8.00 0.22 0.14 67.0 2053 1968 24004 23019 1.00 2053 1968 16939 1624472.50 0 701 701 0.00 0.00 153.7 67.0 0 0 0 0 0 24004 Plugged 0 0 0 16939 100% 0.7672.50 0 701 701 0.00 0.00 120.0 50.0 16830 1800 1932 1932 1932 1932 0.00
12.50 0.17 0.14 50.0 2394 2295 26397 25315 1.00 2394 2295 19332 1853985.00 0 826 826 0.00 0.00 141.3 50.0 19830 1800 1932 169 1932 28498 Plugged 1932 169 1932 21433 99% 0.9685.00 0 826 826 0.00 0.00 181.1 50.0 0 0 0 0 0 0 0.00
15.10 0.22 0.14 50.0 2891 2773 29289 28087 1.00 2891 2773 22223 21312100.10 0 977 977 0.00 0.00 214.2 50.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 0 0.00
0.00 0.00 0.00 0.0 0 0 29289 28087 0.67 0 0 22223 21312100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 0 0.00
0.00 0.00 0.00 0.0 0 0 29289 28087 0.67 0 0 22223 21312100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00
CALCULATIONS SRD
20
25
qkPa
0
25
0
0
100000
100000
900
1900
15
20
0.47
0
2900
100000
0.47
20190020
100000
0
0
1800
3000
1000000
0
0
0
0.47
0.47
0.4730
0
0
10.0
10.5
10.0
10.0
10.0
0.0
0.0
0
0
25
25
9.025
20
25
0 9.0
9.0
8.0
24
42
42
420 100000
S
C
C
S
0 0
20 1900
C
C
S
Sdense carbonate
dense silty carb
very weak to we
stiff to hard silty
very hard clayey
loose to very de
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
firm to stiff claye
INPUT DATA
verysoft carbona
very weak to we
C
S
S
0.47
10
20
24
fkPa
0
24
0
24
50
100000
100000
50
0 0
0 0
67
100000
100000
67
0 0
2900
50 1800
50 3000
SOIL IDENTIFICATION: 2G08
K.tan��limit
K.tan�
0 0
10 900
20 1900
24
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
� =
pile
fric
tion
angl
e
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r ea
ch la
ye
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r ea
ch la
ye
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 00.25 0.17 0.14 0.2 0 0 0 0 1.00 0 0 0 0
0.25 0 2 2 0.00 0.00 0.3 0.3 84 84 49 6 0 6 Cored 0.20 0.22 49 6 0 6 4% 0.000.25 0 2 2 0.00 0.00 0.4 0.3 84 84 49 0 49 0 0.00
1.95 0.22 0.14 1.4 8 7 8 7 1.00 8 7 8 72.20 0 18 18 0.00 0.00 3.9 2.5 739 739 433 52 7 67 Cored 433 52 7 67 23% 0.002.20 0 18 18 0.00 0.00 3.0 2.5 422 422 248 7 248 7 0.00
8.80 0.17 0.14 8.0 202 191 210 199 1.00 202 191 210 19911.00 0 97 97 0.00 0.00 16.6 13.6 2323 2323 1362 162 199 571 Cored 1362 162 199 571 72% 0.0411.00 0 97 97 0.00 0.00 12.2 12.2 774 774 454 199 454 199 0.00
4.00 0.13 0.13 14.5 166 157 376 356 1.00 166 157 376 35615.00 0 133 133 0.00 0.00 16.7 16.7 1062 1062 623 74 356 806 Cored 623 74 356 806 91% 0.0615.00 30 133 89 0.34 0.86 25.8 25.8 270 270 158 158 158 158 0.00
3.50 0.00 0.00 27.2 274 259 650 614 0.67 182 172 559 52818.50 30 164 110 0.27 0.96 28.7 28.7 270 270 158 19 158 827 Plugged 158 19 158 736 97% 0.0518.50 0 164 164 0.00 0.00 28.1 22.0 3943 3943 2312 614 2312 528 0.00
3.50 0.17 0.14 22.0 221 209 871 823 1.00 221 209 780 73722.00 0 191 191 0.00 0.00 32.6 22.0 4573 4573 2681 319 823 2013 Cored 2681 319 737 1836 83% 0.1322.00 70 191 127 0.55 0.67 47.1 47.1 630 630 369 369 369 369 0.00
11.40 0.00 0.00 52.8 1729 1634 2600 2457 0.67 1153 1090 1932 182733.40 70 293 195 0.36 0.84 58.5 58.5 630 630 369 44 369 3013 Plugged 369 44 369 2346 98% 0.1733.40 0 293 293 0.00 0.00 50.1 22.0 7036 5100 2990 2457 2990 1827 0.00
2.30 0.17 0.14 22.0 145 137 2745 2595 1.00 145 137 2078 196435.70 0 314 314 0.00 0.00 53.7 22.0 7532 5100 2990 356 2595 5696 Cored 2990 356 1964 4397 92% 0.3235.70 0 314 314 0.00 0.00 53.7 22.0 12554 5100 2990 2595 2990 1964 0.00
10.30 0.17 0.14 22.0 651 615 3396 3210 1.00 651 615 2728 257946.00 0 407 407 0.00 0.00 69.5 22.0 16262 5100 2990 356 2990 6742 Plugged 2990 356 2579 5663 94% 0.4146.00 150 407 271 0.55 0.67 100.8 100.8 1350 1350 792 792 792 792 0.00
18.00 0.00 0.00 110.5 5711 5398 9106 8608 0.67 3807 3599 6535 617864.00 150 578 385 0.39 0.80 120.2 120.2 1350 1350 792 94 792 9992 Plugged 792 94 792 7421 99% 0.5464.00 350 578 385 0.91 0.52 183.5 183.5 3150 3150 1847 1847 1847 1847 0.00
14.00 0.00 0.00 194.1 7802 7375 16908 15983 0.67 5201 4917 11736 1109478.00 350 718 478 0.73 0.58 204.6 204.6 3150 3150 1847 220 1847 18975 Plugged 1847 220 1847 13803 98% 1.0078.00 0 718 718 0.00 0.00 157.3 22.0 17221 5100 2990 2990 2990 2990 0.00
4.50 0.22 0.14 22.0 284 269 17192 16252 1.00 284 269 12021 1136382.50 0 758 758 0.00 0.00 166.1 22.0 18193 5100 2990 356 2990 20538 Plugged 2990 356 2990 15367 98% 1.1182.50 0 758 758 0.00 0.00 129.7 22.0 6064 5100 2990 2990 2990 2990 0.00
17.90 0.17 0.14 22.0 1131 1069 18323 17321 1.00 1131 1069 13151 12432100.40 0 937 937 0.00 0.00 160.3 22.0 7496 5100 2990 356 2990 21669 Plugged 2990 356 2990 16498 98% 1.20
CALCULATIONS SRD
20
20
qkPa
20
25
20
0
5100
100000
5000
5100
20
25
0.47
40
100000
5100
0.47
15510022
100000
0
0
5100
5100
1000000
0
0
0.47
0
0.47
0.4725
0
0
9.0
7.5
9.0
9.0
9.0
9.5
10.0
0
25
0
25
9.020
25
30
25 9.0
8.0
8.0
8
42
42
240.47 22
C
S
C
S
22 5100
22 5100
C
C
S
S
CLAY, hard silty
SILT, dense car
CALCARENITE,
CLAY, stiff to ve
SILT, loose to m
SILT, soft carbo
SAND, medium
SILT, firm carbo
SAND, loose to
INPUT DATA
CORAL, Weak t
GRAVEL, Loose
S
S
S
0.47
22
22
0
fkPa
0
24
0
24
22
100000
100000
22
100000
22
22 5100
22 5100
22
22 5100
5100
22 5100
22 5100
K.tan��limit
K.tan�
22 5100
22 5000
22 5100
0
100000
5100SAND, dense ca S 30 9.0
22
0.47 24 22
25 10.0 0.47 8
SOIL IDENTIFICATION: 3G09
5100 22 5100 20
22 5100 25
SILT, dense car S
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
Nq =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.80 0.13 0.13 1.0 5 5 5 5 1.00 5 5 5 5
1.80 0 15 15 0.00 0.00 1.9 1.9 122 122 72 9 5 18 Cored 0.20 1.69 72 9 5 18 53% 0.001.80 0 15 15 0.00 0.00 2.6 2.1 184 184 108 5 108 5 0.00
4.20 0.17 0.14 4.8 58 55 63 59 1.00 58 55 63 596.00 0 53 53 0.00 0.00 9.1 7.4 637 637 374 44 59 167 Cored 374 44 59 167 73% 0.016.00 20 53 35 0.56 0.67 13.3 13.3 180 180 106 59 106 59 0.00
6.00 0.00 0.00 16.1 277 262 340 321 0.67 185 175 248 23412.00 20 107 71 0.28 0.94 18.9 18.9 180 180 106 13 106 458 Plugged 106 13 106 366 97% 0.0212.00 0 107 107 0.00 0.00 23.5 15.0 1285 1285 754 321 754 234 0.00
3.00 0.22 0.14 16.7 144 136 484 457 1.00 144 136 391 37015.00 0 131 131 0.00 0.00 28.7 18.4 1573 1573 922 110 457 1051 Cored 922 110 370 871 87% 0.0515.00 0 131 131 0.00 0.00 22.4 18.4 2622 2622 1537 457 1537 370 0.00
5.50 0.17 0.14 21.8 345 326 828 783 1.00 345 326 736 69620.50 0 181 181 0.00 0.00 30.9 25.3 3612 2900 1700 202 783 1814 Cored 1700 202 696 1634 88% 0.1020.50 150 181 120 1.25 0.47 71.0 71.0 1350 1350 792 783 792 696 0.00
13.50 0.00 0.00 78.9 3060 2893 3889 3676 0.67 2040 1929 2776 262434.00 150 302 201 0.74 0.58 86.9 86.9 1350 1350 792 94 792 4774 Plugged 792 94 792 3662 97% 0.2234.00 200 302 201 0.99 0.50 100.3 100.3 1800 1800 1055 1055 1055 1055 0.00
3.00 0.00 0.00 102.7 884 836 4773 4512 0.67 590 557 3366 318237.00 200 331 220 0.91 0.52 105.0 105.0 1800 1800 1055 126 1055 5954 Plugged 1055 126 1055 4547 97% 0.2837.00 90 331 220 0.41 0.78 70.4 70.4 810 810 475 475 475 475 0.00
12.00 0.00 0.00 76.3 2630 2486 7403 6998 0.67 1753 1657 5119 483949.00 90 451 300 0.30 0.91 82.2 82.2 810 810 475 57 475 7934 Plugged 475 57 475 5650 99% 0.3549.00 150 451 300 0.50 0.71 106.1 106.1 1350 1350 792 792 792 792 0.00
14.50 0.00 0.00 123.1 5125 4845 12528 11842 0.67 3417 3230 8535 806863.50 200 588 392 0.51 0.70 140.0 140.0 1800 1800 1055 126 1055 13709 Plugged 1055 126 1055 9716 99% 0.6063.50 0 588 588 0.00 0.00 159.7 82.4 23534 9600 5628 5628 5628 5628 0.00
6.00 0.27 0.14 86.4 1488 1406 14015 13249 1.00 1488 1406 10023 947569.50 0 645 645 0.00 0.00 175.1 90.3 25814 9600 5628 670 5628 20314 Plugged 5628 670 5628 16322 96% 1.0069.50 0 645 645 0.00 0.00 110.4 48.0 5163 1900 1114 1114 1114 1114 0.00
18.50 0.17 0.14 48.0 2550 2410 16565 15659 1.00 2550 2410 12573 1188588.00 0 821 821 0.00 0.00 140.5 48.0 6569 1900 1114 133 1114 17812 Plugged 1114 133 1114 13820 99% 0.8588.00 0 821 821 0.00 0.00 103.4 67.0 9853 2900 1700 1700 1700 1700 0.00
12.30 0.13 0.13 67.0 2366 2237 18932 17896 1.00 2366 2237 14939 14122100.30 0 938 938 0.00 0.00 118.1 67.0 11255 2900 1700 202 1700 20834 Plugged 1700 202 1700 16842 99% 1.03
1567 2900 67 2900medium dense t S 20 9.5 0.47 12
CALCULATIONS SRD
0
0
qkPa
0
25
0
20
100000
2900
1900
2900
15
20
0.47
0
100000
100000100000
100000
100000
81 2900
0
0.47
2529006712
8
12
00 100000
1900
30
20
100000
100000
96000.47
0.47
0.47
0
0
0
00
35
25
9.0
9.0
9.5
10.0
9.5
9.5
9.5
25
0
0
0
8.030
20
25
0 9.0
9.0
8.5
C
C
S
S
0 0
67 2900
S
S
C
C
dense carbonate
stiff carbonate s
very stiff to hard
dense siliceous f
dense silty carbo
silty fine to coars
stiff to very stiff c
very stiff to hard
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
very soft to soft c
INPUT DATA
very loose carbo
loose to dense c
C
S
S
0.47
48
67
8
fkPa
20
0
0
0
100000
96
48
81
0
0
48 1900
0
0 0
0 0
SOIL IDENTIFICATION: 2G07
K.tan��limit
K.tan�
96 9600
48 1900
67 2900
40
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t c
q= u
nit b
earin
g pr
essu
re c
onsd
eri
Shaf
t out
er fr
ictio
n fo
r eac
h la
y e
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
ye
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.00 0.22 0.14 1.3 3 3 3 3 1.00 3 3 3 3
1.00 0 12 12 0.00 0.00 1.7 2.6 480 480 191 28 3 34 Cored 0.20 1.33 191 28 3 34 18% 0.001.00 0 12 12 0.00 0.00 1.5 1.5 288 288 115 3 115 3 0.00
8.00 0.13 0.13 6.0 116 108 119 111 1.00 116 108 119 1119.00 0 84 84 0.00 0.00 10.6 10.6 2016 2016 803 117 111 347 Cored 803 117 111 347 66% 0.049.00 0 84 84 0.00 0.00 14.4 11.8 1680 1680 669 111 669 111 0.00
12.00 0.17 0.14 16.9 485 453 604 564 1.00 485 453 604 56421.00 0 180 180 0.00 0.00 30.8 22.0 3600 3600 1433 208 564 1376 Cored 1433 208 564 1376 85% 0.1421.00 175 180 120 1.46 0.45 79.6 79.6 1575 1575 627 564 627 564 0.00
28.00 0.00 0.00 95.9 6431 6009 7034 6573 0.67 4287 4006 4891 457049.00 175 432 288 0.61 0.64 112.2 112.2 1575 1575 627 91 627 7753 Plugged 627 91 627 5609 98% 0.5849.00 0 432 432 0.00 0.00 94.7 22.0 17280 5100 2031 2031 2031 2031 0.00
1.00 0.22 0.14 22.0 53 49 7087 6622 1.00 53 49 4944 461950.00 0 442 442 0.00 0.00 96.9 22.0 17680 5100 2031 295 2031 9413 Plugged 2031 295 2031 7269 96% 0.7550.00 300 442 295 1.02 0.50 149.3 149.3 2700 2700 1075 1075 1075 1075 0.00
4.50 0.00 0.00 152.7 1645 1537 8732 8159 0.67 1097 1025 6040 564454.50 300 487 325 0.92 0.52 156.0 156.0 2700 2700 1075 156 1075 9963 Plugged 1075 156 1075 7271 98% 0.7554.50 0 487 487 0.00 0.00 106.7 22.0 11688 5100 2031 2031 2031 2031 0.00
6.50 0.22 0.14 22.0 342 320 9074 8479 1.00 342 320 6382 596461.00 0 552 552 0.00 0.00 121.0 22.0 13248 5100 2031 295 2031 11400 Plugged 2031 295 2031 8708 97% 0.8961.00 0 552 552 0.00 0.00 94.4 22.0 4416 4416 1758 1758 1758 1758 0.00
18.00 0.17 0.14 22.0 948 886 10022 9365 1.00 948 886 7330 684979.00 0 732 732 0.00 0.00 125.2 22.0 5856 5100 2031 295 2031 12348 Plugged 2031 295 2031 9656 97% 0.9979.00 0 732 732 0.00 0.00 160.4 22.0 17568 5100 2031 2031 2031 2031 0.00
1.80 0.22 0.14 22.0 95 89 10117 9453 1.00 95 89 7425 693880.80 0 750 750 0.00 0.00 164.4 22.0 18000 5100 2031 295 2031 12443 Plugged 2031 295 2031 9751 97% 1.00
0
0.47
SRD
20
25
qkPa
0
25
20
25
10.0 24
5100
100000
0.47
0.47
5100
5100
24
00
30
25
10.0
10.0
10.0
30
S
0.47 22
S
C
S
C
22 5100
Very Stiff Calcareous CLAY 510000 9.0 0
8.0
0.47
Silty Carbonate SAND
fkPa
9.0
12.030
150.47
22
22
0.47 40 22 5100
20
25S
S
22
very Weak CALCARENITE
Crystalline GYPSUM
Hard Silty Calcareous CLAY
Medium Dense Silica SAND
Dense carbonate SILT
Dense Carbonate SAND S
30 10.0 5100
8
22
100000
22
0
2222 5100
22 5100
22 5100
5100
22 5100
22
22
K.tan�
22 5000
INPUT DATA
CAPROCK S 255000
CALCULATIONS
40
100000100000
5100
K.tan��limit
510024
20
SOIL IDENTIFICATION: 3D07
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 32.0 mm Ko non-cohesive 1.0 Steel cross section area 0.119 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.048 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 012.30 0.17 0.14 9.9 466 441 466 441 1.00 466 441 466 441
12.30 0 116 116 0.00 0.00 16.2 19.8 1387 1387 1454 166 441 1073 Cored 0.20 12.80 1454 166 441 1073 85% 0.0512.30 0 116 116 0.00 0.00 25.3 16.2 2312 2312 2423 441 2423 441 0.00
0.90 0.22 0.14 16.8 58 55 524 496 1.00 58 55 524 49613.20 0 125 125 0.00 0.00 27.4 17.5 2500 2500 2619 298 496 1319 Cored 2619 298 496 1319 77% 0.0613.20 0 125 125 0.00 0.00 21.4 17.5 1500 1500 1571 496 1571 496 0.00
7.60 0.17 0.14 19.7 575 545 1099 1041 1.00 575 545 1099 104120.80 0 204 204 0.00 0.00 34.9 22.0 2448 2448 2565 292 1041 2432 Cored 2565 292 1041 2432 88% 0.1120.80 118 204 136 0.87 0.54 63.3 63.3 1062 1062 1113 1041 1113 1041 0.00
1.40 0.00 0.00 65.0 348 330 1447 1371 0.67 232 220 1331 126122.20 123 216 144 0.85 0.54 66.6 66.6 1107 1107 1160 132 1160 2739 Plugged 1160 132 1160 2623 95% 0.1222.20 0 216 216 0.00 0.00 37.0 22.0 2598 2598 2722 1371 2722 1261 0.00
2.20 0.17 0.14 22.0 185 176 1632 1547 1.00 185 176 1516 143724.40 0 239 239 0.00 0.00 40.9 22.0 2872 2872 3009 343 1547 3522 Cored 3009 343 1437 3296 90% 0.1524.40 131 239 160 0.82 0.55 72.3 72.3 1179 1179 1235 1235 1235 1235 0.00
24.00 0.00 0.00 99.5 9150 8669 10782 10216 0.67 6100 5779 7616 721648.40 213 453 302 0.71 0.60 126.8 126.8 1917 1917 2009 229 2009 13019 Plugged 2009 229 2009 9853 98% 0.4648.40 213 453 302 0.71 0.60 126.8 126.8 1917 1917 2009 2009 2009 2009 0.00
17.60 0.00 0.00 137.0 9231 8746 20013 18962 0.67 6154 5831 13770 1304766.00 213 610 406 0.52 0.69 147.1 147.1 1917 1917 2009 229 2009 22250 Plugged 2009 229 2009 16007 99% 0.7566.00 0 610 610 0.00 0.00 133.6 22.0 12192 5100 5343 5343 5343 5343 0.00
5.50 0.22 0.14 22.0 463 439 20476 19401 1.00 463 439 14233 1348671.50 0 666 666 0.00 0.00 146.0 22.0 13325 5100 5343 609 5343 26429 Plugged 5343 609 5343 20186 97% 0.9571.50 0 666 666 0.00 0.00 114.0 22.0 7995 5100 5343 5343 5343 5343 0.00
13.00 0.17 0.14 22.0 1095 1038 21572 20439 1.00 1095 1038 15329 1452484.50 0 804 804 0.00 0.00 137.5 22.0 9649 5100 5343 609 5343 27524 Plugged 5343 609 5343 21281 97% 1.0084.50 0 804 804 0.00 0.00 176.2 22.0 16081 5100 5343 5343 5343 5343 0.00
9.20 0.22 0.14 22.0 775 734 22347 21174 1.00 775 734 16104 1525893.70 0 877 877 0.00 0.00 192.1 22.0 17535 5100 5343 609 5343 28299 Plugged 5343 609 5343 22056 97% 1.0493.70 140 877 584 0.24 1.00 140.0 140.0 1260 1260 1320 1320 1320 1320 0.00
0.80 0.00 0.00 140.0 429 406 22776 21580 0.67 286 271 16390 1552994.50 140 884 589 0.24 1.00 140.0 140.0 1260 1260 1320 150 1320 24246 Plugged 1320 150 1320 17860 99% 0.84
08.90
510022207.9 0.4730
CALCULATIONS SRD
25
20
qkPa
0
25
20
20
5100
5100
5000
5100
20
25
0
12
100000
100000
0.47
0
0
0.47
0.47
100000
0.47
01000001000000
12
20
120.47 22
100000
25
0
5100
5100
5100
0
0
25
10.4
8.9
8.9
10.3
10.6
25
0
0
30
8.90
25
30
25 10.4
10.4
9.4
C
C
S
C
S
S
S
CARBONATE CLAY
CARBONATE SAND
CARBONATE SILT
CARBONATE SILTSTONE
C
CARBONATE CLAY
CARBONATE SANDSTONE
CALCAREOUS CLAY
CALCAREOUS CLAY
CARBONATE SANDSTONE
INPUT DATA
CARBONATE SIL
CARBONATE SANDSTONE
S
S
S
0.47
22
22
fkPa
12
0
K.tan�
22 5000
22 5100
22
20
22
22
10000022 5100
22 5100
100000
22
22 5100
22 5100
22 5100
22 5100
K.tan��limit
22 5100
22 5100
SOIL IDENTIFICATION: 32A
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.49 0.23 0.14 1.8 6 6 6 6 1.00 6 6 6 6
1.49 0 16 16 0.00 0.00 2.2 3.6 652 652 260 38 6 50 Cored 0.20 1.72 260 38 6 50 25% 0.011.49 0 16 16 0.00 0.00 3.6 2.2 652 652 260 6 260 6 0.00
1.31 0.23 0.14 3.1 10 9 16 15 1.00 10 9 16 152.80 0 29 29 0.00 0.00 6.7 4.1 1225 1225 488 71 15 102 Cored 488 71 15 102 31% 0.012.80 0 29 29 0.00 0.00 6.7 4.1 1225 1225 488 15 488 15 0.00
1.01 0.23 0.14 4.8 12 11 28 26 1.00 12 11 28 263.81 0 40 40 0.00 0.00 9.1 5.5 1664 1664 663 96 26 150 Cored 663 96 26 150 36% 0.023.81 0 40 40 0.00 0.00 7.9 5.5 951 951 379 26 379 26 0.00
8.50 0.20 0.14 10.8 220 205 247 231 1.00 220 205 247 23112.31 0 114 114 0.00 0.00 22.8 16.0 2747 2747 1094 159 231 637 Cored 1094 159 231 637 75% 0.0712.31 96 114 76 1.25 0.47 45.2 45.2 862 862 343 231 343 231 0.00
5.18 0.00 0.00 64.3 797 745 1044 976 0.67 531 496 779 72817.50 192 164 109 1.75 0.43 83.3 83.3 1724 1724 686 100 686 1830 Plugged 686 100 686 1565 94% 0.1617.50 0 164 164 0.00 0.00 32.8 22.0 3941 3941 1569 976 1569 728 0.00
6.89 0.20 0.14 22.0 363 339 1407 1315 1.00 363 339 1141 106724.38 0 230 230 0.00 0.00 46.0 22.0 5528 5100 2031 295 1315 3017 Cored 2031 295 1067 2503 88% 0.2624.38 144 230 154 0.94 0.52 74.3 74.3 1293 1293 515 515 515 515 0.00
3.05 0.00 0.00 76.6 559 522 1966 1837 0.67 373 348 1514 141527.43 144 260 173 0.83 0.55 78.9 78.9 1296 1296 516 75 516 2557 Plugged 516 75 516 2105 96% 0.2227.43 0 260 260 0.00 0.00 51.8 22.0 6230 5100 2031 1837 2031 1415 0.00
7.32 0.20 0.14 22.0 385 360 2351 2197 1.00 385 360 1899 177534.75 0 336 336 0.00 0.00 67.0 22.0 8056 5100 2031 295 2031 4677 Plugged 2031 295 1775 3969 93% 0.4134.75 192 336 224 0.86 0.54 103.5 103.5 1724 1724 686 686 686 686 0.00
23.26 0.00 0.00 126.4 7038 6577 9390 8774 0.67 4692 4384 6592 615958.00 239 559 373 0.64 0.62 149.3 149.3 2155 2155 858 125 858 10372 Plugged 858 125 858 7574 98% 0.7858.00 0 559 559 0.00 0.00 111.5 22.0 13414 5100 2031 2031 2031 2031 0.00
4.00 0.20 0.14 22.0 210 197 9600 8970 1.00 210 197 6802 635662.00 0 597 597 0.00 0.00 119.2 22.0 14335 5100 2031 295 2031 11926 Plugged 2031 295 2031 9128 97% 0.9462.00 0 597 597 0.00 0.00 119.2 22.0 14335 5100 2031 2031 2031 2031 0.00
11.00 0.20 0.14 22.0 579 541 10179 9511 1.00 579 541 7381 689773.00 0 685 685 0.00 0.00 136.7 22.0 16447 5100 2031 295 2031 12505 Plugged 2031 295 2031 9707 97% 1.00
K.tan��limit
K.tan�
22 5100
22 5000
22 5100
24
100000
22 5100
5100
22 5100
22 5100
22
100000
22
22 5100
22 5100
22
24
fkPa
0
24
0
24
100000
22
22
LIMESTONE
INPUT DATA
LIMESTONE
CARBONATE SAND CEMENTED
S
S
S
0.47
22
22
CARBONATE SILTY SAND
CARBONATE CLAY
CARBONATE SILTY SAND
SILTY CARBONATE CLAY
CARBONATE SILT
CARBONATE SAND
SILTY CALCAREOUS CLAY
CEMENTED CARBONATE SAND S
S
C
S
0.47 22
C
S
C
S
22 5100
22 510024
42
42
42
8.828
31
31
31 10.4
10.4
10.4
0
28
0
28
0
28
28
9.6
9.6
9.6
10.4
9.6
9.6
8.0
0.47
0.47
0
0.47
0
0.47
0
5100
23
23
5100
100000
5100
26
26
0.47
0
100000
5100
0.47
23510022
5100
100000
5000
5100
CALCULATIONS SRD
23
0
qkPa
23
25
26
0
SOIL IDENTIFICATION: 3
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
de
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
g
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 09.12 0.22 0.14 9.3 243 230 243 230 1.00 243 230 243 230
9.12 0 85 85 0.00 0.00 11.9 18.6 2036 2036 1193 142 230 616 Cored 0.20 9.39 1193 142 230 616 77% 0.059.12 72 85 57 1.27 0.47 33.9 33.9 648 648 380 230 380 230 0.00
4.00 0.00 0.00 55.4 636 602 880 832 0.67 424 401 668 63113.12 190 123 82 2.33 0.40 76.9 76.9 1710 1710 1003 119 832 1831 Cored 1003 119 631 1418 92% 0.1013.12 0 123 123 0.00 0.00 33.3 17.2 2941 2941 1724 832 1724 631 0.00
8.00 0.27 0.14 19.6 450 425 1330 1257 1.00 450 425 1117 105621.12 0 198 198 0.00 0.00 53.7 22.0 4751 4751 2786 332 1257 2918 Cored 2786 332 1056 2505 87% 0.1821.12 140 198 132 1.06 0.49 69.0 69.0 1260 1260 739 739 739 739 0.00
5.04 0.00 0.00 72.3 1047 990 2376 2246 0.67 698 660 1815 171626.16 140 246 164 0.86 0.54 75.7 75.7 1260 1260 739 88 739 3203 Plugged 739 88 739 2642 97% 0.1926.16 0 246 246 0.00 0.00 66.6 22.0 5892 5100 2990 2246 2990 1716 0.00
6.48 0.27 0.14 22.0 409 387 2786 2633 1.00 409 387 2225 210332.64 0 312 312 0.00 0.00 84.6 22.0 7480 5100 2990 356 2633 5775 Cored 2990 356 2103 4684 92% 0.3432.64 120 312 208 0.58 0.66 79.0 79.0 1080 1080 633 633 633 633 0.00
10.36 0.00 0.00 96.4 2868 2711 5653 5344 0.67 1912 1807 4136 391043.00 190 409 273 0.70 0.60 113.9 113.9 1710 1710 1003 119 1003 6775 Plugged 1003 119 1003 5258 98% 0.3943.00 120 409 273 0.44 0.75 90.5 90.5 1080 1080 633 633 633 633 0.00
18.50 0.00 0.00 121.6 6462 6109 12116 11453 0.67 4308 4072 8445 798361.50 240 584 389 0.62 0.64 152.8 152.8 2160 2160 1266 151 1266 13533 Plugged 1266 151 1266 9862 98% 0.7261.50 0 584 584 0.00 0.00 158.4 22.0 14012 5100 2990 2990 2990 2990 0.00
4.05 0.27 0.14 22.0 256 242 12371 11695 1.00 256 242 8700 822465.55 0 622 622 0.00 0.00 168.8 22.0 14928 5100 2990 356 2990 15718 Plugged 2990 356 2990 12047 97% 0.8865.55 0 622 622 0.00 0.00 78.3 22.0 9952 5100 2990 2990 2990 2990 0.00
25.46 0.13 0.13 22.0 1608 1520 13980 13215 1.00 1608 1520 10309 974591.01 0 822 822 0.00 0.00 103.5 22.0 13150 5100 2990 356 2990 17326 Plugged 2990 356 2990 13655 97% 1.00
K.tan��limit
INPUT DATA
1000008 510022
0.47 22 5000
SAND
SAND
CLAY
S
C
S
0
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
SOIL IDENTIFICATION: 3J07
CLAY
SAND
CLAY C
S
C
240.47 22
CLAY
S
SSAND
SILTY SAND
C
22 5100
9.40
30
0
35 9.4
9.4
9.3
20
10.2
9.4
9.4
9.4
7.9
35
0
0
35
0
0.47
0.47
100000
16
8
22 5100
22 5100
24
SRDq
kPa
5000 25
f
22
kPaK.tan�
0
CALCULATIONS
5100
305100
100000
100000 00
2224
8
10000022 5100
220.47
0
5100
8
25
022 5100 100000
30
100000
100000
24
22 15
305100
510022 5100
22
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 32.0 mm Ko non-cohesive 1.0 Steel cross section area 0.119 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.048 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
Nq =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.00 0.17 0.14 0.7 3 2 3 2 1.00 3 2 3 2
1.00 0 8 8 0.00 0.00 1.1 1.4 320 320 335 38 2 43 Cored 0.20 0.89 335 38 2 43 12% 0.001.00 0 8 8 0.00 0.00 1.0 1.0 64 64 67 2 67 2 0.00
6.50 0.13 0.13 4.7 117 111 119 113 1.00 117 111 119 1137.50 0 67 67 0.00 0.00 8.4 8.4 532 532 557 63 113 296 Cored 557 63 113 296 79% 0.027.50 75 67 44 1.69 0.44 32.9 32.9 675 675 707 113 707 113 0.00
1.70 0.00 0.00 33.8 220 208 339 322 0.67 147 139 266 2529.20 75 83 55 1.36 0.46 34.7 34.7 675 675 707 81 322 742 Cored 707 81 252 599 87% 0.039.20 0 83 83 0.00 0.00 18.1 11.6 1984 1984 2078 322 2078 252 0.00
2.60 0.22 0.14 13.2 132 125 471 446 1.00 132 125 398 37711.80 0 106 106 0.00 0.00 23.2 14.8 2545 2545 2667 304 446 1221 Cored 2667 304 377 1078 72% 0.0611.80 75 106 71 1.06 0.49 37.0 37.0 675 675 707 446 707 377 0.00
2.10 0.00 0.00 38.1 306 290 777 736 0.67 204 193 602 57013.90 75 123 82 0.92 0.52 39.2 39.2 675 675 707 81 707 1565 Plugged 707 81 570 1252 94% 0.0713.90 0 123 123 0.00 0.00 21.0 17.2 4914 4914 5149 736 5149 570 0.00
1.10 0.17 0.14 17.9 75 71 852 808 1.00 75 71 677 64215.00 0 133 133 0.00 0.00 22.7 18.6 5310 5100 5343 609 808 2269 Cored 5343 609 642 1927 68% 0.1115.00 75 133 89 0.85 0.54 40.7 40.7 675 675 707 707 707 642 0.00
3.00 0.00 0.00 42.7 491 465 1343 1273 0.67 327 310 1004 95118.00 75 160 107 0.70 0.60 44.7 44.7 675 675 707 81 707 2131 Plugged 707 81 707 1792 96% 0.1018.00 0 160 160 0.00 0.00 27.3 22.0 1278 1278 1339 1273 1339 951 0.00
1.10 0.17 0.14 22.0 93 88 1436 1360 1.00 93 88 1097 103919.10 0 170 170 0.00 0.00 29.0 22.0 1357 1357 1422 162 1360 2958 Cored 1422 162 1039 2298 93% 0.1319.10 0 170 170 0.00 0.00 29.0 22.0 6786 5100 5343 1360 5343 1039 0.00
2.40 0.17 0.14 22.0 202 192 1638 1552 1.00 202 192 1299 123121.50 0 191 191 0.00 0.00 32.7 22.0 7650 5100 5343 609 1552 3799 Cored 5343 609 1231 3139 81% 0.1821.50 0 191 191 0.00 0.00 32.7 22.0 4590 4590 4809 1552 4809 1231 0.00
3.00 0.17 0.14 22.0 253 239 1891 1792 1.00 253 239 1552 147024.50 0 218 218 0.00 0.00 37.3 22.0 5238 5100 5343 609 1792 4291 Cored 5343 609 1470 3631 83% 0.2124.50 130 218 146 0.89 0.53 68.8 68.8 1170 1170 1226 1226 1226 1226 0.00
14.00 0.00 0.00 78.0 4182 3962 6073 5754 0.67 2788 2642 4340 411238.50 130 351 234 0.56 0.67 87.2 87.2 1170 1170 1226 140 1226 7438 Plugged 1226 140 1226 5705 98% 0.3338.50 0 351 351 0.00 0.00 77.0 22.0 8430 5100 5343 5343 5343 5343 0.00
8.50 0.22 0.14 22.0 716 679 6789 6433 1.00 716 679 5056 479147.00 0 419 419 0.00 0.00 91.9 22.0 10062 5100 5343 609 5343 12741 Plugged 5343 609 4791 10455 94% 0.6147.00 0 419 419 0.00 0.00 71.7 22.0 16770 5100 5343 5343 5343 4791 0.00
1.10 0.17 0.14 22.0 93 88 6882 6520 1.00 93 88 5149 487848.10 0 429 429 0.00 0.00 73.4 22.0 17166 5100 5343 609 5343 12834 Plugged 5343 609 4878 10636 94% 0.6248.10 0 429 429 0.00 0.00 73.4 22.0 3433 3433 3597 3597 3597 3597 0.00
41.90 0.17 0.14 22.0 3530 3345 10412 9865 1.00 3530 3345 8679 822390.00 0 827 827 0.00 0.00 141.5 22.0 6618 5100 5343 609 5343 16364 Plugged 5343 609 5343 14631 96% 0.8590.00 300 827 551 0.54 0.68 203.4 203.4 2700 2700 2829 2829 2829 2829 0.00
10.20 0.00 0.00 209.2 8170 7741 18582 17606 0.67 5447 5161 14126 13384100.20 300 924 616 0.49 0.72 215.0 215.0 2700 2700 2829 322 2829 21733 Plugged 2829 322 2829 17277 98% 1.00
SOIL IDENTIFICATION: 3M05
K.tan��limit
K.tan�
22
10000022 5100
22 5100
5100
8
22 5100
5100
22 5100
22 5100
510022 22
100000
22
100000
22
stiff carbonate cl
INPUT DATA
CALCARENITE,
loose to medium
C
S
S
0.47
22
22
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
dense carbonate
firm to stiff carbo
CALCARENITE,
stiff carbonate C
stiff calcareous s
dense carbonate
CALCARENITE,
medium dense c S
C
S
S
0
8
40
0
8
24
40
C
S
C
S
9.0
8.0
0
30
25
20
0
9.0
0
25
25
9.5
25
0
8.0
9.0
9.0
9.0
9.0
9.0
9.5
25
0.47
0
0
0.47
0
0.47
0.47
100000
20
0
5100
5100
5100
20
20
0.47
25510022
0 10000022 5100
220.47
100000
5100
22
100000
5100
5100
20
15
CALCULATIONS SRDq
kPa
fkPa
24
40
8
0
0
510022
510022
100000
25
20
medium dense t S 30 8.0 0.47 24 22 5100 22 5100 25
CALCARENITE, S 25 9.0 0.47 40 22 5100 22 5100 20
dense carbonate S 25 9.5 0.47 8 22 5100 22 5100 20
hard CLAY C 0 9.5 0 0 22 5100 100000 100000 0
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.094 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.073 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Qp Qu Sunc Q f Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
re
Nq =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssu
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t c
q= u
nit b
earin
g pr
essu
re c
onsd
er
Shaf
t out
er fr
ictio
n fo
r eac
h la
ye
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r =
Shaf
t out
er fr
ictio
n fo
r eac
h la
y e
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 00.20 0.17 0.14 0.1 0 0 0 0 1.00 0 0 0 0
0.20 0 2 2 0.00 0.00 0.2 0.3 19 19 21 2 0 2 Cored 0.20 0.18 21 2 0 2 10% 0.000.20 0 2 2 0.00 0.00 0.3 0.2 19 19 21 0 21 0 0.00
0.70 0.17 0.14 0.6 2 2 2 2 1.00 2 2 2 20.90 0 7 7 0.00 0.00 1.2 1.0 86 86 93 8 2 12 Cored 93 8 2 12 30% 0.000.90 0 7 7 0.00 0.00 1.2 1.0 86 86 93 2 93 2 0.00
6.10 0.17 0.14 4.6 108 104 110 106 1.00 108 104 110 1067.00 0 59 59 0.00 0.00 10.1 8.3 709 709 761 66 106 282 Cored 761 66 106 282 76% 0.027.00 0 59 59 0.00 0.00 10.1 8.3 709 709 761 106 761 106 0.00
3.00 0.17 0.14 9.9 114 110 224 215 1.00 114 110 224 21510.00 0 83 83 0.00 0.00 14.2 11.6 997 997 1070 93 215 533 Cored 1070 93 215 533 82% 0.0410.00 0 83 83 0.00 0.00 14.2 11.6 997 997 1070 215 1070 215 0.00
1.50 0.17 0.14 12.5 72 69 296 284 1.00 72 69 296 28411.50 0 96 96 0.00 0.00 16.4 13.4 1150 1150 1234 108 284 688 Cored 1234 108 284 688 84% 0.0511.50 0 96 96 0.00 0.00 16.4 13.4 1150 1150 1234 284 1234 284 0.00
3.50 0.17 0.14 15.5 208 199 504 483 1.00 208 199 504 48315.00 0 126 126 0.00 0.00 21.5 17.6 1507 1507 1617 141 483 1129 Cored 1617 141 483 1129 87% 0.0915.00 0 126 126 0.00 0.00 27.5 17.6 1507 1507 1617 483 1617 483 0.00
6.40 0.22 0.14 19.8 485 465 989 948 1.00 485 465 989 94821.40 0 177 177 0.00 0.00 38.7 22.0 2121 2121 2276 199 948 2136 Cored 2276 199 948 2136 91% 0.1721.40 150 177 118 1.27 0.47 70.6 70.6 1350 1350 1449 948 1449 948 0.00
15.60 0.00 0.00 79.3 4736 4542 5725 5490 0.67 3157 3028 4146 397637.00 150 309 206 0.73 0.59 87.9 87.9 1350 1350 1449 127 1449 7301 Plugged 1449 127 1449 5722 98% 0.4437.00 0 309 309 0.00 0.00 52.9 22.0 3712 3712 3984 3984 3984 3976 0.00
1.00 0.17 0.14 22.0 84 81 5809 5571 1.00 84 81 4231 405738.00 0 319 319 0.00 0.00 54.6 22.0 3827 3827 4108 359 4108 10276 Plugged 4108 359 4057 8647 96% 0.6738.00 150 319 213 0.71 0.60 89.3 89.3 1350 1350 1449 1449 1449 1449 0.00
10.50 0.00 0.00 95.9 3855 3697 9664 9268 0.67 2570 2465 6801 652248.50 150 420 280 0.54 0.68 102.4 102.4 1350 1350 1449 127 1449 11240 Plugged 1449 127 1449 8376 98% 0.6548.50 0 420 420 0.00 0.00 71.8 22.0 5037 5037 5406 5406 5406 5406 0.00
2.00 0.17 0.14 22.0 169 162 9833 9429 1.00 169 162 6969 668350.50 0 437 437 0.00 0.00 74.7 22.0 5241 5100 5474 478 5474 15785 Plugged 5474 478 5474 12921 96% 1.0050.50 0 437 437 0.00 0.00 74.7 22.0 5241 5100 5474 5474 5474 5474 0.00
4.50 0.17 0.14 22.0 379 364 10212 9793 1.00 379 364 7348 704755.00 0 473 473 0.00 0.00 80.9 22.0 5673 5100 5474 478 5474 16164 Plugged 5474 478 5474 13300 96% 1.0355.00 0 473 473 0.00 0.00 80.9 22.0 5673 5100 5474 5474 5474 5474 0.00
10.00 0.17 0.14 22.0 843 808 11054 10601 1.00 843 808 8191 785565.00 0 558 558 0.00 0.00 95.4 22.0 6693 5100 5474 478 5474 17006 Plugged 5474 478 5474 14143 97% 1.0965.00 0 558 558 0.00 0.00 122.2 22.0 11155 5100 5474 5474 5474 5474 0.00
2.50 0.22 0.14 22.0 211 202 11265 10803 1.00 211 202 8401 805767.50 0 583 583 0.00 0.00 127.7 22.0 11655 5100 5474 478 5474 17217 Plugged 5474 478 5474 14353 97% 1.1167.50 150 583 389 0.39 0.80 120.7 120.7 1350 1350 1449 1449 1449 1449 0.00
6.00 0.00 0.00 123.4 2836 2720 14101 13523 0.67 1891 1813 10292 987073.50 150 637 425 0.35 0.84 126.2 126.2 1350 1350 1449 127 1449 15677 Plugged 1449 127 1449 11868 99% 0.9273.50 0 637 637 0.00 0.00 108.9 22.0 7641 5100 5474 5474 5474 5474 0.00
1.00 0.17 0.14 22.0 84 81 14185 13604 1.00 84 81 10376 995174.50 0 645 645 0.00 0.00 110.3 22.0 7737 5100 5474 478 5474 20137 Plugged 5474 478 5474 16328 97% 1.2674.50 150 645 430 0.35 0.85 127.0 127.0 1350 1350 1449 1449 1449 1449 0.00
11.50 0.00 0.00 131.6 5796 5558 19981 19162 0.67 3864 3705 14240 1365686.00 150 743 495 0.30 0.91 136.2 136.2 1350 1350 1449 127 1449 21557 Plugged 1449 127 1449 15816 99% 1.22
0 22 5100
22 5000
22 5100
22 5100
22
22
22 5100
5100
22 5100
22 5100
22
100000
5100
22
12
fkPa
12
12
12
0
22
100000
sand
INPUT DATA
sand
calc
S
S
S
0.47
22
22
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
SRD
SOIL IDENTIFICATION: 3N09
K.tan��limit
K.tan�
calc
sand
silt
calc
sand
clay
sand
clay C
S
S
C
22 5100
22 5100
S
S
S
S 8.025
25
25
25 8.5
8.0
8.0
25
25
25
0
25
0
25
8.5
8.5
8.0
8.5
9.6
9.6
8.5
0
0.47
0.47
0.47
0.47
0
0.47
510022
5100
0
20
100000
5100
100000
22
22
12
5100
5100
0.47
12
12
12
120.47 22
0
20
qkPa
20
25
20
20
20
20
0.47 12
calc
CALCULATIONS
5100
5100
5000
5100 20
0.47
calc S 25 8.0 22 5100 22 5100 20
silt S 25 8.5 0.47 12 22 5100 22 5100 20
gypsum S 30 10.0 0.47 20 22 5100 22 5100 25
silt C 0 9.0 0 0 22 5100 100000 100000 0
S 25 8.0 0.47 12 22 5100 22
0 0 22 5100silt C 0 8.5 100000 100000 0
5100 20
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
de
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 03.05 0.22 0.14 2.3 17 16 17 16 1.00 17 16 17 16
3.05 0 21 21 0.00 0.00 3.0 4.7 897 897 357 52 16 85 Cored 0.20 2.36 357 52 16 85 39% 0.013.05 45 21 14 3.16 0.37 16.9 16.9 405 405 161 16 161 16 0.00
3.35 0.00 0.00 18.9 151 141 168 157 0.67 101 94 118 1106.40 45 50 33 1.35 0.46 20.9 20.9 405 405 161 23 157 349 Cored 161 23 110 252 91% 0.046.40 0 50 50 0.00 0.00 13.5 0.0 2093 0 0 0 0 0 0.00
1.22 0.27 0.14 0.0 0 0 168 157 1.00 0 0 118 1107.62 0 61 61 0.00 0.00 16.7 0.0 2579 0 0 0 0 168 Plugged 0 0 0 118 100% 0.027.62 60 61 41 1.47 0.45 27.3 27.3 540 540 215 157 215 110 0.00
1.52 0.00 0.00 27.9 102 95 270 252 0.67 68 63 186 1749.14 60 74 50 1.21 0.48 28.6 28.6 540 540 215 31 215 516 Plugged 215 31 174 390 92% 0.069.14 0 74 74 0.00 0.00 16.3 10.4 1784 1784 710 252 710 174 0.00
3.05 0.22 0.14 12.1 88 83 358 335 1.00 88 83 274 25612.19 0 99 99 0.00 0.00 21.6 13.8 2370 2370 943 137 335 831 Cored 943 137 256 667 79% 0.1012.19 0 99 99 0.00 0.00 26.8 13.8 2370 2370 943 335 943 256 0.00
3.66 0.27 0.14 16.3 142 133 501 468 1.00 142 133 417 38915.85 0 134 134 0.00 0.00 36.2 18.7 3204 3204 1276 185 468 1154 Cored 1276 185 389 991 81% 0.1415.85 0 134 134 0.00 0.00 29.3 18.7 1335 1335 532 468 532 389 0.00
0.91 0.22 0.14 19.1 42 39 543 507 1.00 42 39 458 42816.76 0 140 140 0.00 0.00 30.7 19.6 1399 1399 557 81 507 1131 Cored 557 81 428 967 92% 0.1416.76 0 140 140 0.00 0.00 30.7 19.6 3357 3357 1337 507 1337 428 0.00
4.58 0.22 0.14 20.8 228 213 771 720 1.00 228 213 686 64121.34 0 179 179 0.00 0.00 39.2 22.0 4291 4291 1709 248 720 1739 Cored 1709 248 641 1576 84% 0.2321.34 225 179 119 1.89 0.43 96.0 96.0 2025 2025 806 720 806 641 0.00
15.23 0.00 0.00 103.0 3755 3508 4525 4228 0.67 2503 2339 3189 298036.57 225 308 206 1.09 0.49 110.0 110.0 2025 2025 806 117 806 5449 Plugged 806 117 806 4113 97% 0.6036.57 0 308 308 0.00 0.00 83.6 22.0 7398 5100 2031 2031 2031 2031 0.00
7.43 0.27 0.14 22.0 391 366 4916 4594 1.00 391 366 3581 334644.00 0 379 379 0.00 0.00 102.8 22.0 9092 5100 2031 295 2031 7242 Plugged 2031 295 2031 5906 95% 0.8644.00 0 379 379 0.00 0.00 64.8 22.0 9092 5100 2031 2031 2031 2031 0.00
18.50 0.17 0.14 22.0 974 910 5891 5504 1.00 974 910 4555 425662.50 0 518 518 0.00 0.00 88.5 22.0 12422 5100 2031 295 2031 8216 Plugged 2031 295 2031 6881 96% 1.0062.50 0 518 518 0.00 0.00 140.5 22.0 12422 5100 2031 2031 2031 2031 0.00
6.50 0.27 0.14 22.0 342 320 6233 5824 1.00 342 320 4897 457669.00 0 579 579 0.00 0.00 157.2 22.0 13904 5100 2031 295 2031 8559 Plugged 2031 295 2031 7223 96% 1.0569.00 0 579 579 0.00 0.00 157.2 81.1 13904 10000 3982 3982 3982 3982 0.00
6.00 0.27 0.14 85.1 1222 1142 7455 6966 1.00 1222 1142 6120 571875.00 0 636 636 0.00 0.00 172.7 89.1 15272 10000 3982 579 3982 12016 Plugged 3982 579 3982 10680 95% 1.55
SOIL IDENTIFICATION: 3SC-K05
K.tan��limit
K.tan�
22 5100
22 5100
22 5100
24
22 5100
5100
22 5100
0 0
22
22 5100
22 5100
22
22
22
22
24
fkPa
24
24
10
24
100000
22
22
dense carbonate SILTY SAND slightly
INPUT DATA
loose carbonate SAND slightly cemen
firm carbonate CLAY with shell fragm
S
C
S
0
22
100000
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
firm to stiff carbonate CLAY
dense carbonate SAND
dense carbonate SAND
clayey carbonate SILT interbedded w
very weak carbonate SILTSTONE wit
dense cemented carbonate SAND wit
stiff carbonate CLAY with traces of gy
very weak fine to coarse carbonate SA S
S
C
S
0.47 0
S
S
S
C
0 0
0 00
42
42
42
8.50
30
0
35 9.5
8.5
7.0
30
35
25
30
0
35
25
8.0
9.5
7.0
8.5
8.5
9.5
7.5
0.47
0.47
0.47
0.47
0.47
0.47
0
5100
30
20
5100
100000
5100
25
0
0
0
5100
5100
0.47
0100000100000
0
5100
5100
100000
CALCULATIONS SRD
25
0
qkPa
30
25
30
25
dense carbonate SAND S 35 9.5 0.47 24 22 5100 22 5100 30
dense carbonate SAND S 35 9.5 0.47 24 100 10000 100 10000 30
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
de
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
g
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th t h
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th t h
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 07.50 0.22 0.14 7.1 127 119 127 119 1.00 127 119 127 119
7.50 0 65 65 0.00 0.00 9.1 14.2 2591 2591 1032 150 119 396 Cored 0.20 7.17 1032 150 119 396 62% 0.037.50 75 65 43 1.73 0.44 32.6 32.6 674 674 268 119 268 119 0.00
2.32 0.00 0.00 33.9 188 175 315 295 0.67 125 117 253 2369.81 75 87 58 1.30 0.47 35.1 35.1 674 674 268 39 268 623 Plugged 268 39 236 528 93% 0.059.81 0 87 87 0.00 0.00 23.5 12.1 3464 3464 1379 295 1379 236 0.00
4.63 0.27 0.14 15.2 168 157 484 452 1.00 168 157 421 39314.45 0 130 130 0.00 0.00 35.3 18.2 5210 5100 2031 295 452 1231 Cored 2031 295 393 1110 73% 0.0914.45 0 130 130 0.00 0.00 35.3 18.2 3126 3126 1245 452 1245 393 0.00
7.96 0.27 0.14 20.1 383 358 867 810 1.00 383 358 804 75122.40 0 205 205 0.00 0.00 55.7 22.0 4924 4924 1961 285 810 1962 Cored 1961 285 751 1840 85% 0.1622.40 0 205 205 0.00 0.00 55.7 22.0 4924 4924 1961 810 1961 751 0.00
4.08 0.27 0.14 22.0 215 201 1082 1011 1.00 215 201 1019 95226.49 0 244 244 0.00 0.00 66.1 22.0 5848 5100 2031 295 1011 2388 Cored 2031 295 952 2267 87% 0.1926.49 0 244 244 0.00 0.00 66.1 22.0 2924 2924 1164 1011 1164 952 0.00
8.02 0.27 0.14 22.0 422 394 1504 1405 1.00 422 394 1441 134734.50 0 325 325 0.00 0.00 88.3 22.0 3905 3905 1555 226 1405 3135 Cored 1555 226 1347 3014 93% 0.2634.50 0 325 325 0.00 0.00 71.3 22.0 3905 3905 1555 1405 1555 1347 0.00
7.01 0.22 0.14 22.0 369 345 1873 1750 1.00 369 345 1811 169241.51 0 386 386 0.00 0.00 84.6 22.0 4632 4632 1844 268 1750 3892 Cored 1844 268 1692 3770 93% 0.3241.51 300 386 257 1.17 0.48 144.4 144.4 2700 2700 1075 1075 1075 1075 0.00
27.98 0.00 0.00 162.3 10870 10157 12744 11907 0.67 7247 6771 9058 846369.49 300 650 433 0.69 0.60 180.2 180.2 2700 2700 1075 156 1075 13975 Plugged 1075 156 1075 10289 98% 0.8869.49 0 650 650 0.00 0.00 176.3 22.0 15590 5100 2031 2031 2031 2031 0.00
6.49 0.27 0.14 22.0 342 319 13086 12227 1.00 342 319 9399 878375.99 0 711 711 0.00 0.00 192.9 22.0 17058 5100 2031 295 2031 15411 Plugged 2031 295 2031 11725 97% 1.00
SOIL IDENTIFICATION: 3SA
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
SRD
0
30
qkPa
30
25
30
30
30
24
5100
5100
0
0.47
100000
5100
12
0.47
0.47
10000022 5100
35
30
0
10.2
8.6
9.4
SILTSTONE 510024
35
C
S
S
S
S
9.4
C
CALCULATIONS
MUD
fkPa
9.4
8.630
0
40
0
35
0
35
510022
5100
0
40
9.4
22
CORAL & SAND
SAND WITH SO
SANDSTONE
SILT WITH SOM
CLAY WITH SO
SAND S
S 9.4
35 9.4 5100
0
22
22
22
12
24 22
22 5100
22 5100
5100
22 5100
22
22
22
220.47 5000
51000.47
0.47
0.47 225100
K.tan��limit
100000 100000
K.tan�
22 5000
INPUT DATA
SANDSTONE S 25
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
de
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
g
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th t h
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th t h
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 35 0 0 0.00 0.00 0.0 0.0 315 315 0 0 012.19 0.00 0.00 13.1 459 434 459 434 0.67 306 290 459 290
12.19 35 118 79 0.44 0.75 26.2 26.2 315 315 184 22 184 666 Plugged 0.20 8.73 184 22 184 666 97% 0.0512.19 0 118 118 0.00 0.00 41.9 16.6 4730 4730 2773 434 2773 290 0.00
8.53 0.35 0.14 19.3 472 447 932 881 1.00 472 447 932 73620.73 0 224 224 0.00 0.00 79.4 22.0 8964 5100 2990 356 881 2169 Cored 2990 356 736 2024 82% 0.1520.73 165 224 149 1.10 0.49 80.5 80.5 1485 1485 871 871 871 736 0.00
4.57 0.00 0.00 83.4 1095 1035 2026 1916 0.67 730 690 1662 142625.30 165 271 180 0.91 0.52 86.3 86.3 1485 1485 871 104 871 3001 Plugged 871 104 871 2636 96% 0.1925.30 0 271 271 0.00 0.00 95.9 22.0 10829 5100 2990 1916 2990 1426 0.00
3.35 0.35 0.14 22.0 212 200 2238 2116 1.00 212 200 1873 162628.65 0 312 312 0.00 0.00 110.6 22.0 12492 5100 2990 356 2116 4710 Cored 2990 356 1626 3856 91% 0.2828.65 165 312 208 0.79 0.56 92.7 92.7 1485 1485 871 871 871 871 0.00
31.39 0.00 0.00 112.3 10122 9568 12360 11684 0.67 6748 6379 8621 800560.05 165 633 422 0.39 0.80 131.9 131.9 1485 1485 871 104 871 13334 Plugged 871 104 871 9595 99% 0.7060.05 0 633 633 0.00 0.00 178.6 22.0 15181 5100 2990 2990 2990 2990 0.00
11.58 0.28 0.14 22.0 732 692 13092 12375 1.00 732 692 9353 869671.63 0 734 734 0.00 0.00 207.4 22.0 17624 5100 2990 356 2990 16438 Plugged 2990 356 2990 12699 97% 0.9371.63 0 734 734 0.00 0.00 160.9 22.0 7343 5100 2990 2990 2990 2990 0.00
16.15 0.22 0.14 22.0 1020 965 14112 13340 1.00 1020 965 10373 966187.78 0 912 912 0.00 0.00 199.8 22.0 9119 5100 2990 356 2990 17458 Plugged 2990 356 2990 13720 97% 1.00
SOIL IDENTIFICATION: 2SA
31
25
0
0
5100
CALCULATIONS SRDq
kPa
510022
100000
1000000
0.47
0.47
0
37
0.47
5100
5100
0
37
0
36
40
10.2
8.8
11.0
12.442
0
42
0 10.2
12.4
9.7
0 100000
S
S
C
S
22 5100
22 5100SANDSTONE
CLAY
SAND
SILT
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
CLAY
INPUT DATA
SILT MUDDY
SANDSTONE
C
S
C
0.47
100000
22
22
fkPa
0
24
10
40
8
40
0
22 5100
100000
5100
22
22
22 5100
22 5100
K.tan��limit
K.tan�
22 5000 100000
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t c
q= u
nit b
earin
g pr
essu
re c
onsd
eri
Shaf
t out
er fr
ictio
n fo
r eac
h la
ye
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
ye
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 02.59 0.19 0.14 1.7 11 10 11 10 1.00 11 10 11 10
2.59 0 18 18 0.00 0.00 2.6 3.4 0 0 0 0 0 11 Plugged 0.20 2.03 0 0 0 11 100% 0.002.59 40 18 12 3.23 0.37 14.7 14.7 356 356 142 10 142 10 0.00
2.01 0.00 0.00 16.2 78 73 88 83 0.67 52 48 62 584.60 40 37 25 1.59 0.45 17.6 17.6 356 356 142 21 83 192 Cored 142 21 58 141 85% 0.024.60 50 37 25 2.00 0.42 20.9 20.9 448 448 178 83 178 58 0.00
1.58 0.00 0.00 21.7 82 77 171 160 0.67 55 51 117 1106.19 50 50 33 1.50 0.45 22.5 22.5 448 448 178 26 160 356 Cored 178 26 110 253 90% 0.046.19 0 50 50 0.00 0.00 11.5 7.0 0 0 0 0 0 0 0.00
2.62 0.23 0.14 8.7 55 51 225 211 1.00 55 51 172 1618.81 0 74 74 0.00 0.00 17.2 10.4 0 0 0 0 0 225 Plugged 0 0 0 172 100% 0.038.81 60 74 50 1.21 0.48 28.6 28.6 539 539 214 211 214 161 0.00
2.68 0.00 0.00 29.9 192 179 417 390 0.67 128 119 300 28011.49 60 98 65 0.92 0.52 31.2 31.2 539 539 214 31 214 663 Plugged 214 31 214 545 94% 0.0911.49 0 98 98 0.00 0.00 22.5 13.7 0 0 0 0 0 0 0.00
9.51 0.23 0.14 17.8 406 379 823 769 1.00 406 379 706 65921.00 0 187 187 0.00 0.00 43.2 22.0 0 0 0 0 0 823 Plugged 0 0 0 706 100% 0.1221.00 0 187 187 0.00 0.00 34.9 22.0 0 0 0 0 0 0 0.00
1.65 0.19 0.14 22.0 87 81 910 850 1.00 87 81 792 74022.65 0 199 199 0.00 0.00 37.1 22.0 0 0 0 0 0 910 Plugged 0 0 0 792 100% 0.1322.65 150 199 133 1.13 0.48 72.7 72.7 1349 1349 537 537 537 537 0.00
7.71 0.00 0.00 76.6 1414 1321 2323 2171 0.67 942 881 1735 162130.36 150 259 173 0.87 0.54 80.5 80.5 1349 1349 537 78 537 2938 Plugged 537 78 537 2350 97% 0.3930.36 300 259 173 1.74 0.44 130.7 130.7 2700 2700 1075 1075 1075 1075 0.00
11.70 0.00 0.00 136.3 3819 3568 6142 5739 0.67 2546 2379 4281 400042.06 300 360 240 1.25 0.47 141.9 141.9 2700 2700 1075 156 1075 7374 Plugged 1075 156 1075 5512 97% 0.9242.06 0 360 360 0.00 0.00 83.2 22.0 0 0 0 0 0 0 0.00
10.06 0.23 0.14 22.0 530 495 6672 6234 1.00 530 495 4810 449552.12 0 447 447 0.00 0.00 103.3 22.0 0 0 0 0 0 6672 Plugged 0 0 0 4810 100% 0.8052.12 0 447 447 0.00 0.00 65.1 22.0 0 0 0 0 0 0 0.00
22.89 0.15 0.14 22.0 1206 1126 7878 7361 1.00 1206 1126 6016 562175.01 0 663 663 0.00 0.00 96.5 22.0 0 0 0 0 0 7878 Plugged 0 0 0 6016 100% 1.00
SOIL IDENTIFICATION: 3SC
K.tan��limit
K.tan�
22 5100
22 5000
22 5100
22 5100
5100
22 5100
22 5100
100000
22 5100
22 5100
22
100000
22
22
fkPa
100000
22
22
CLAY
INPUT DATA
SAND/SAND
CLAY
C
C
S
0
22
100000
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
SAND/SAND
CLAY
SANDSTONE
SILT/SAND
SILTSTONE/
CLAY
CLAY
SANDSTONE S
S
C
C
0 100000
S
S
C
S
22 5100
22 51009.435
30
0
0 7.9
9.4
7.1
0
35
25
0
0
35
25
8.6
9.4
7.1
7.9
8.6
8.6
9.4
0.4
0.4
0
0.4
0.4
0
0
5100
30
20
100000
100000
5100
25
0
0.4
5100
5100
0.4
30510022
100000
100000
5000
100000
CALCULATIONS SRD
0
0
qkPa
30
25
0
0
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.094 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.073 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
g
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r ea
ch la
yer
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 03.00 0.13 0.13 1.5 17 17 17 17 1.00 17 17 17 17
3.00 0 24 24 0.00 0.00 3.0 3.0 1008 900 966 84 17 118 Cored 0.20 2.66 966 84 17 118 29% 0.013.00 0 24 24 0.00 0.00 4.1 3.4 1008 1008 1082 17 1082 17 0.00
4.20 0.17 0.14 6.0 97 93 114 109 1.00 97 93 114 1097.20 0 62 62 0.00 0.00 10.6 8.7 2596 1900 2039 178 109 401 Cored 2039 178 109 401 56% 0.027.20 70 62 41 1.70 0.44 30.7 30.7 630 630 676 109 676 109 0.00
6.80 0.00 0.00 34.3 892 856 1006 965 0.67 595 571 709 68014.00 70 123 82 0.85 0.54 37.9 37.9 630 630 676 59 676 1742 Plugged 676 59 676 1444 96% 0.0614.00 0 123 123 0.00 0.00 21.0 17.2 2952 1900 2039 965 2039 680 0.00
9.00 0.17 0.14 18.6 641 615 1648 1580 1.00 641 615 1350 129523.00 0 204 204 0.00 0.00 34.9 20.0 4896 1900 2039 178 1580 3406 Cored 2039 178 1295 2823 94% 0.1323.00 200 204 136 1.47 0.45 90.8 90.8 1800 1800 1932 1580 1932 1295 0.00
37.00 0.00 0.00 114.6 16233 15568 17881 17148 0.67 10822 10378 12173 1167360.00 200 574 383 0.52 0.69 138.3 138.3 1800 1800 1932 169 1932 19982 Plugged 1932 169 1932 14273 99% 0.6460.00 500 574 383 1.31 0.47 233.8 233.8 4500 4500 4830 4830 4830 4830 0.00
4.50 0.00 0.00 236.2 4070 3903 21951 21051 0.67 2713 2602 14886 1427564.50 500 621 414 1.21 0.48 238.5 238.5 4500 4500 4830 422 4830 27203 Plugged 4830 422 4830 20138 98% 0.9164.50 0 621 621 0.00 0.00 136.2 67.0 0 0 0 0 0 0 0.00
8.00 0.22 0.14 67.0 2053 1968 24004 23019 1.00 2053 1968 16939 1624472.50 0 701 701 0.00 0.00 153.7 67.0 0 0 0 0 0 24004 Plugged 0 0 0 16939 100% 0.7672.50 0 701 701 0.00 0.00 120.0 50.0 16830 1800 1932 1932 1932 1932 0.00
12.50 0.17 0.14 50.0 2394 2295 26397 25315 1.00 2394 2295 19332 1853985.00 0 826 826 0.00 0.00 141.3 50.0 19830 1800 1932 169 1932 28498 Plugged 1932 169 1932 21433 99% 0.9685.00 0 826 826 0.00 0.00 181.1 50.0 0 0 0 0 0 0 0.00
15.10 0.22 0.14 50.0 2891 2773 29289 28087 1.00 2891 2773 22223 21312100.10 0 977 977 0.00 0.00 214.2 50.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 0 0.00
0.00 0.00 0.00 0.0 0 0 29289 28087 0.67 0 0 22223 21312100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 0 0.00
0.00 0.00 0.00 0.0 0 0 29289 28087 0.67 0 0 22223 21312100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00
SOIL IDENTIFICATION: 2G08
K.tan��limit
K.tan�
0 0
10 900
20 1900
24
0 0
2900
50 1800
50 3000
50
0 0
0 0
67
100000
100000
67
24
fkPa
0
24
0
24
50
100000
100000
firm to stiff claye
INPUT DATA
verysoft carbona
very weak to we
C
S
S
0.47
10
20
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
very weak to we
stiff to hard silty
very hard clayey
loose to very de
dense carbonate
dense silty carb
C
C
S
S
0 100000
S
C
C
S
0 0
20 190024
42
42
42
9.025
20
25
0 9.0
9.0
8.0
0
0
25
25
30
0
0
10.0
10.5
10.0
10.0
10.0
0.0
0.0
0
0
0
0
0.47
0.47
0.47
100000
0
0
1800
3000
100000
15
20
0.47
0
2900
100000
0.47
20190020
100000
100000
900
1900
CALCULATIONS SRD
20
25
qkPa
0
25
0
0
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soil
Des
crip
tion
(San
d S,
Cla
y C
)
� =
pile
fric
tion
angl
e
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
N q =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
f lim =
lim
it un
it sk
in fr
ictio
n
q lim =
lim
it un
it be
arin
g pr
essu
re
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r ea
ch la
ye
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
Plas
ticity
Inde
x
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adju
stm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r ea
ch la
ye
Shaf
t inn
er fr
ictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fr
ictio
n
End
Bea
ring
of t
he s
oil b
enea
th
End
Bea
ring
on
annu
lus
Min
imum
Qsi
or
Ap
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SRD
for G
RLW
EAP
INPU
T
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 00.25 0.17 0.14 0.2 0 0 0 0 1.00 0 0 0 0
0.25 0 2 2 0.00 0.00 0.3 0.3 84 84 49 6 0 6 Cored 0.20 0.22 49 6 0 6 4% 0.000.25 0 2 2 0.00 0.00 0.4 0.3 84 84 49 0 49 0 0.00
1.95 0.22 0.14 1.4 8 7 8 7 1.00 8 7 8 72.20 0 18 18 0.00 0.00 3.9 2.5 739 739 433 52 7 67 Cored 433 52 7 67 23% 0.002.20 0 18 18 0.00 0.00 3.0 2.5 422 422 248 7 248 7 0.00
8.80 0.17 0.14 8.0 202 191 210 199 1.00 202 191 210 19911.00 0 97 97 0.00 0.00 16.6 13.6 2323 2323 1362 162 199 571 Cored 1362 162 199 571 72% 0.0411.00 0 97 97 0.00 0.00 12.2 12.2 774 774 454 199 454 199 0.00
4.00 0.13 0.13 14.5 166 157 376 356 1.00 166 157 376 35615.00 0 133 133 0.00 0.00 16.7 16.7 1062 1062 623 74 356 806 Cored 623 74 356 806 91% 0.0615.00 30 133 89 0.34 0.86 25.8 25.8 270 270 158 158 158 158 0.00
3.50 0.00 0.00 27.2 274 259 650 614 0.67 182 172 559 52818.50 30 164 110 0.27 0.96 28.7 28.7 270 270 158 19 158 827 Plugged 158 19 158 736 97% 0.0518.50 0 164 164 0.00 0.00 28.1 22.0 3943 3943 2312 614 2312 528 0.00
3.50 0.17 0.14 22.0 221 209 871 823 1.00 221 209 780 73722.00 0 191 191 0.00 0.00 32.6 22.0 4573 4573 2681 319 823 2013 Cored 2681 319 737 1836 83% 0.1322.00 70 191 127 0.55 0.67 47.1 47.1 630 630 369 369 369 369 0.00
11.40 0.00 0.00 52.8 1729 1634 2600 2457 0.67 1153 1090 1932 182733.40 70 293 195 0.36 0.84 58.5 58.5 630 630 369 44 369 3013 Plugged 369 44 369 2346 98% 0.1733.40 0 293 293 0.00 0.00 50.1 22.0 7036 5100 2990 2457 2990 1827 0.00
2.30 0.17 0.14 22.0 145 137 2745 2595 1.00 145 137 2078 196435.70 0 314 314 0.00 0.00 53.7 22.0 7532 5100 2990 356 2595 5696 Cored 2990 356 1964 4397 92% 0.3235.70 0 314 314 0.00 0.00 53.7 22.0 12554 5100 2990 2595 2990 1964 0.00
10.30 0.17 0.14 22.0 651 615 3396 3210 1.00 651 615 2728 257946.00 0 407 407 0.00 0.00 69.5 22.0 16262 5100 2990 356 2990 6742 Plugged 2990 356 2579 5663 94% 0.4146.00 150 407 271 0.55 0.67 100.8 100.8 1350 1350 792 792 792 792 0.00
18.00 0.00 0.00 110.5 5711 5398 9106 8608 0.67 3807 3599 6535 617864.00 150 578 385 0.39 0.80 120.2 120.2 1350 1350 792 94 792 9992 Plugged 792 94 792 7421 99% 0.5464.00 350 578 385 0.91 0.52 183.5 183.5 3150 3150 1847 1847 1847 1847 0.00
14.00 0.00 0.00 194.1 7802 7375 16908 15983 0.67 5201 4917 11736 1109478.00 350 718 478 0.73 0.58 204.6 204.6 3150 3150 1847 220 1847 18975 Plugged 1847 220 1847 13803 98% 1.0078.00 0 718 718 0.00 0.00 157.3 22.0 17221 5100 2990 2990 2990 2990 0.00
4.50 0.22 0.14 22.0 284 269 17192 16252 1.00 284 269 12021 1136382.50 0 758 758 0.00 0.00 166.1 22.0 18193 5100 2990 356 2990 20538 Plugged 2990 356 2990 15367 98% 1.1182.50 0 758 758 0.00 0.00 129.7 22.0 6064 5100 2990 2990 2990 2990 0.00
17.90 0.17 0.14 22.0 1131 1069 18323 17321 1.00 1131 1069 13151 12432100.40 0 937 937 0.00 0.00 160.3 22.0 7496 5100 2990 356 2990 21669 Plugged 2990 356 2990 16498 98% 1.20
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
SOIL IDENTIFICATION: 3G09
5100 22 5100 20
22 5100 25
SILT, dense car S 25 10.0 0.47 8 22
0.47 24 22 5100SAND, dense ca S 30 9.0
K.tan��limit
K.tan�
22 5100
22 5000
22 5100
0
100000
22 5100
5100
22 5100
22 5100
22
100000
22
22 5100
22 5100
22
0
fkPa
0
24
0
24
22
100000
100000
SAND, loose to
INPUT DATA
CORAL, Weak t
GRAVEL, Loose
S
S
S
0.47
22
22
SILT, loose to m
SILT, soft carbo
SAND, medium
SILT, firm carbo
CLAY, hard silty
SILT, dense car
CALCARENITE,
CLAY, stiff to ve C
C
S
S
0.47 22
C
S
C
S
22 5100
22 51008
42
42
24
9.020
25
30
25 9.0
8.0
8.0
0
25
0
25
25
0
0
9.0
7.5
9.0
9.0
9.0
9.5
10.0
0
0
0
0.47
0
0.47
0.47
100000
0
0
5100
5100
100000
20
25
0.47
40
100000
5100
0.47
15510022
5100
100000
5000
5100
CALCULATIONS SRD
20
20
qkPa
20
25
20
0
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res s
Nq =
bea
ring
capa
city
fact
or
c or
Su =
und
rain
ed s
hear
stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
c
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
.
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu =
Ulti
mat
e Pi
le C
apac
ity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.80 0.13 0.13 1.0 5 5 5 5 1.00 5 5 5 5
1.80 0 15 15 0.00 0.00 1.9 1.9 122 122 72 9 5 18 Cored 0.20 1.69 72 9 5 18 53% 0.001.80 0 15 15 0.00 0.00 2.6 2.1 184 184 108 5 108 5 0.00
4.20 0.17 0.14 4.8 58 55 63 59 1.00 58 55 63 596.00 0 53 53 0.00 0.00 9.1 7.4 637 637 374 44 59 167 Cored 374 44 59 167 73% 0.016.00 20 53 35 0.56 0.67 13.3 13.3 180 180 106 59 106 59 0.00
6.00 0.00 0.00 16.1 277 262 340 321 0.67 185 175 248 23412.00 20 107 71 0.28 0.94 18.9 18.9 180 180 106 13 106 458 Plugged 106 13 106 366 97% 0.0212.00 0 107 107 0.00 0.00 23.5 15.0 1285 1285 754 321 754 234 0.00
3.00 0.22 0.14 16.7 144 136 484 457 1.00 144 136 391 37015.00 0 131 131 0.00 0.00 28.7 18.4 1573 1573 922 110 457 1051 Cored 922 110 370 871 87% 0.0515.00 0 131 131 0.00 0.00 22.4 18.4 2622 2622 1537 457 1537 370 0.00
5.50 0.17 0.14 21.8 345 326 828 783 1.00 345 326 736 69620.50 0 181 181 0.00 0.00 30.9 25.3 3612 2900 1700 202 783 1814 Cored 1700 202 696 1634 88% 0.1020.50 150 181 120 1.25 0.47 71.0 71.0 1350 1350 792 783 792 696 0.00
13.50 0.00 0.00 78.9 3060 2893 3889 3676 0.67 2040 1929 2776 262434.00 150 302 201 0.74 0.58 86.9 86.9 1350 1350 792 94 792 4774 Plugged 792 94 792 3662 97% 0.2234.00 200 302 201 0.99 0.50 100.3 100.3 1800 1800 1055 1055 1055 1055 0.00
3.00 0.00 0.00 102.7 884 836 4773 4512 0.67 590 557 3366 318237.00 200 331 220 0.91 0.52 105.0 105.0 1800 1800 1055 126 1055 5954 Plugged 1055 126 1055 4547 97% 0.2837.00 90 331 220 0.41 0.78 70.4 70.4 810 810 475 475 475 475 0.00
12.00 0.00 0.00 76.3 2630 2486 7403 6998 0.67 1753 1657 5119 483949.00 90 451 300 0.30 0.91 82.2 82.2 810 810 475 57 475 7934 Plugged 475 57 475 5650 99% 0.3549.00 150 451 300 0.50 0.71 106.1 106.1 1350 1350 792 792 792 792 0.00
14.50 0.00 0.00 123.1 5125 4845 12528 11842 0.67 3417 3230 8535 806863.50 200 588 392 0.51 0.70 140.0 140.0 1800 1800 1055 126 1055 13709 Plugged 1055 126 1055 9716 99% 0.6063.50 0 588 588 0.00 0.00 159.7 82.4 23534 9600 5628 5628 5628 5628 0.00
6.00 0.27 0.14 86.4 1488 1406 14015 13249 1.00 1488 1406 10023 947569.50 0 645 645 0.00 0.00 175.1 90.3 25814 9600 5628 670 5628 20314 Plugged 5628 670 5628 16322 96% 1.0069.50 0 645 645 0.00 0.00 110.4 48.0 5163 1900 1114 1114 1114 1114 0.00
18.50 0.17 0.14 48.0 2550 2410 16565 15659 1.00 2550 2410 12573 1188588.00 0 821 821 0.00 0.00 140.5 48.0 6569 1900 1114 133 1114 17812 Plugged 1114 133 1114 13820 99% 0.8588.00 0 821 821 0.00 0.00 103.4 67.0 9853 2900 1700 1700 1700 1700 0.00
12.30 0.13 0.13 67.0 2366 2237 18932 17896 1.00 2366 2237 14939 14122100.30 0 938 938 0.00 0.00 118.1 67.0 11255 2900 1700 202 1700 20834 Plugged 1700 202 1700 16842 99% 1.03
SOIL IDENTIFICATION: 2G07
K.tan��limit
K.tan�
96 9600
48 1900
67 2900
40
81
0
0
48 1900
0
0 0
0 0
8
fkPa
20
0
0
0
100000
96
48
very soft to soft c
INPUT DATA
very loose carbo
loose to dense c
C
S
S
0.47
48
67
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
dense silty carbo
silty fine to coars
stiff to very stiff c
very stiff to hard
dense carbonate
stiff carbonate s
very stiff to hard
dense siliceous f S
S
C
C
0 0
67 2900
C
C
S
S 8.030
20
25
0 9.0
9.0
8.5
25
0
0
0
0
35
25
9.0
9.0
9.5
10.0
9.5
9.5
9.5
0.47
0.47
0.47
0
0
0
0
1900
30
20
100000
100000
9600
0.47
2529006712
8
12
00 100000
0.47
0
100000
100000100000
100000
100000
81 2900
0
1900
2900
15
20
0
20
100000
2900
0.47 12
CALCULATIONS SRD
0
0
qkPa
0
25
medium dense t S 20 9.5 1567 2900 67 2900
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t c
q= u
nit b
earin
g pr
essu
re c
onsd
eri
Shaf
t out
er fr
ictio
n fo
r eac
h la
y e
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
ye
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.00 0.22 0.14 1.3 3 3 3 3 1.00 3 3 3 3
1.00 0 12 12 0.00 0.00 1.7 2.6 480 480 191 28 3 34 Cored 0.20 1.33 191 28 3 34 18% 0.001.00 0 12 12 0.00 0.00 1.5 1.5 288 288 115 3 115 3 0.00
8.00 0.13 0.13 6.0 116 108 119 111 1.00 116 108 119 1119.00 0 84 84 0.00 0.00 10.6 10.6 2016 2016 803 117 111 347 Cored 803 117 111 347 66% 0.049.00 0 84 84 0.00 0.00 14.4 11.8 1680 1680 669 111 669 111 0.00
12.00 0.17 0.14 16.9 485 453 604 564 1.00 485 453 604 56421.00 0 180 180 0.00 0.00 30.8 22.0 3600 3600 1433 208 564 1376 Cored 1433 208 564 1376 85% 0.1421.00 175 180 120 1.46 0.45 79.6 79.6 1575 1575 627 564 627 564 0.00
28.00 0.00 0.00 95.9 6431 6009 7034 6573 0.67 4287 4006 4891 457049.00 175 432 288 0.61 0.64 112.2 112.2 1575 1575 627 91 627 7753 Plugged 627 91 627 5609 98% 0.5849.00 0 432 432 0.00 0.00 94.7 22.0 17280 5100 2031 2031 2031 2031 0.00
1.00 0.22 0.14 22.0 53 49 7087 6622 1.00 53 49 4944 461950.00 0 442 442 0.00 0.00 96.9 22.0 17680 5100 2031 295 2031 9413 Plugged 2031 295 2031 7269 96% 0.7550.00 300 442 295 1.02 0.50 149.3 149.3 2700 2700 1075 1075 1075 1075 0.00
4.50 0.00 0.00 152.7 1645 1537 8732 8159 0.67 1097 1025 6040 564454.50 300 487 325 0.92 0.52 156.0 156.0 2700 2700 1075 156 1075 9963 Plugged 1075 156 1075 7271 98% 0.7554.50 0 487 487 0.00 0.00 106.7 22.0 11688 5100 2031 2031 2031 2031 0.00
6.50 0.22 0.14 22.0 342 320 9074 8479 1.00 342 320 6382 596461.00 0 552 552 0.00 0.00 121.0 22.0 13248 5100 2031 295 2031 11400 Plugged 2031 295 2031 8708 97% 0.8961.00 0 552 552 0.00 0.00 94.4 22.0 4416 4416 1758 1758 1758 1758 0.00
18.00 0.17 0.14 22.0 948 886 10022 9365 1.00 948 886 7330 684979.00 0 732 732 0.00 0.00 125.2 22.0 5856 5100 2031 295 2031 12348 Plugged 2031 295 2031 9656 97% 0.9979.00 0 732 732 0.00 0.00 160.4 22.0 17568 5100 2031 2031 2031 2031 0.00
1.80 0.22 0.14 22.0 95 89 10117 9453 1.00 95 89 7425 693880.80 0 750 750 0.00 0.00 164.4 22.0 18000 5100 2031 295 2031 12443 Plugged 2031 295 2031 9751 97% 1.00
SOIL IDENTIFICATION: 3D07
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
K.tan��limit
510024
20
CALCULATIONS
40
100000100000
5100
22 5000
INPUT DATA
CAPROCK S 5000
22 5100
22 5100
5100
22 5100
22
22
5100
8
22
100000
22
0
2222 5100
22
very Weak CALCARENITE
Crystalline GYPSUM
Hard Silty Calcareous CLAY
Medium Dense Silica SAND
Dense carbonate SILT
Dense Carbonate SAND S
30 10.0
20
25S
S
8.0
0.47
Silty Carbonate SAND
fkPa
9.0
12.030
150.47
Very Stiff Calcareous CLAY 510000 9.0 0
30
S
0.47 22
S
C
S
C
22 5100
0
30
25
10.0
10.0
10.0
10.0 24
5100
100000
0.47
0.47
5100
5100
24
0
25
qkPa
0
25
20
25
K.tan�
25
0
0.47
SRD
20
22
22
0.47 40 22 5100
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 32.0 mm Ko non-cohesive 1.0 Steel cross section area 0.119 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.048 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 012.30 0.17 0.14 9.9 466 441 466 441 1.00 466 441 466 441
12.30 0 116 116 0.00 0.00 16.2 19.8 1387 1387 1454 166 441 1073 Cored 0.20 12.80 1454 166 441 1073 85% 0.0512.30 0 116 116 0.00 0.00 25.3 16.2 2312 2312 2423 441 2423 441 0.00
0.90 0.22 0.14 16.8 58 55 524 496 1.00 58 55 524 49613.20 0 125 125 0.00 0.00 27.4 17.5 2500 2500 2619 298 496 1319 Cored 2619 298 496 1319 77% 0.0613.20 0 125 125 0.00 0.00 21.4 17.5 1500 1500 1571 496 1571 496 0.00
7.60 0.17 0.14 19.7 575 545 1099 1041 1.00 575 545 1099 104120.80 0 204 204 0.00 0.00 34.9 22.0 2448 2448 2565 292 1041 2432 Cored 2565 292 1041 2432 88% 0.1120.80 118 204 136 0.87 0.54 63.3 63.3 1062 1062 1113 1041 1113 1041 0.00
1.40 0.00 0.00 65.0 348 330 1447 1371 0.67 232 220 1331 126122.20 123 216 144 0.85 0.54 66.6 66.6 1107 1107 1160 132 1160 2739 Plugged 1160 132 1160 2623 95% 0.1222.20 0 216 216 0.00 0.00 37.0 22.0 2598 2598 2722 1371 2722 1261 0.00
2.20 0.17 0.14 22.0 185 176 1632 1547 1.00 185 176 1516 143724.40 0 239 239 0.00 0.00 40.9 22.0 2872 2872 3009 343 1547 3522 Cored 3009 343 1437 3296 90% 0.1524.40 131 239 160 0.82 0.55 72.3 72.3 1179 1179 1235 1235 1235 1235 0.00
24.00 0.00 0.00 99.5 9150 8669 10782 10216 0.67 6100 5779 7616 721648.40 213 453 302 0.71 0.60 126.8 126.8 1917 1917 2009 229 2009 13019 Plugged 2009 229 2009 9853 98% 0.4648.40 213 453 302 0.71 0.60 126.8 126.8 1917 1917 2009 2009 2009 2009 0.00
17.60 0.00 0.00 137.0 9231 8746 20013 18962 0.67 6154 5831 13770 1304766.00 213 610 406 0.52 0.69 147.1 147.1 1917 1917 2009 229 2009 22250 Plugged 2009 229 2009 16007 99% 0.7566.00 0 610 610 0.00 0.00 133.6 22.0 12192 5100 5343 5343 5343 5343 0.00
5.50 0.22 0.14 22.0 463 439 20476 19401 1.00 463 439 14233 1348671.50 0 666 666 0.00 0.00 146.0 22.0 13325 5100 5343 609 5343 26429 Plugged 5343 609 5343 20186 97% 0.9571.50 0 666 666 0.00 0.00 114.0 22.0 7995 5100 5343 5343 5343 5343 0.00
13.00 0.17 0.14 22.0 1095 1038 21572 20439 1.00 1095 1038 15329 1452484.50 0 804 804 0.00 0.00 137.5 22.0 9649 5100 5343 609 5343 27524 Plugged 5343 609 5343 21281 97% 1.0084.50 0 804 804 0.00 0.00 176.2 22.0 16081 5100 5343 5343 5343 5343 0.00
9.20 0.22 0.14 22.0 775 734 22347 21174 1.00 775 734 16104 1525893.70 0 877 877 0.00 0.00 192.1 22.0 17535 5100 5343 609 5343 28299 Plugged 5343 609 5343 22056 97% 1.0493.70 140 877 584 0.24 1.00 140.0 140.0 1260 1260 1320 1320 1320 1320 0.00
0.80 0.00 0.00 140.0 429 406 22776 21580 0.67 286 271 16390 1552994.50 140 884 589 0.24 1.00 140.0 140.0 1260 1260 1320 150 1320 24246 Plugged 1320 150 1320 17860 99% 0.84
SOIL IDENTIFICATION: 32A
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
K.tan��limit
22 5100
22 5100
100000
22
22 5100
22 5100
22 5100
22 510020
22
22
10000022 5100
22 5100
fkPa
12
0
K.tan�
22 5000
22 5100
22
CARBONATE SANDSTONE
INPUT DATA
CARBONATE SIL
CARBONATE SANDSTONE
S
S
S
0.47
22
22
CARBONATE CLAY
CARBONATE SANDSTONE
CALCAREOUS CLAY
CALCAREOUS CLAY
S
S
S
CARBONATE CLAY
CARBONATE SAND
CARBONATE SILT
CARBONATE SILTSTONE
C
C
C
S
C 8.90
25
30
25 10.4
10.4
9.4
25
10.4
8.9
8.9
10.3
10.6
25
0
0
30
0.47 22
100000
25
0
5100
5100
5100
0
0
0.47
100000
0.47
01000001000000
12
20
12
20
25
0
12
100000
100000
0.47
0
0
0.47
5100
5100
5000
5100
CALCULATIONS SRD
25
20
qkPa
0
25
20
20
08.90
510022207.9 0.4730
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
line
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(San
d S
, Cla
y C
)
f = p
ile fr
ictio
n an
gle
� =
subm
erge
d U
nit W
eigh
t
K =
coe
ffici
ent o
f lat
eral
ear
th p
res
Nq
= be
arin
g ca
paci
ty fa
ctor
c or
Su
= un
drai
ned
shea
r stre
ngth
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
f lim =
lim
it un
it sk
in fr
ictio
n
q lim
= li
mit
unit
bear
ing
pres
sure
� =
soil-
pile
fric
tion
angl
e
p o =
effe
ctiv
e ov
erbu
rden
pre
ssur
e
�=c
/po'
� =
dim
ensi
onle
ss fa
ctor
f = u
nit s
kin
frict
ion
with
out c
onsi
d e
f = u
nit s
kin
frict
ion
incl
udin
g fli
m
q= u
nit b
earin
g pr
essu
re w
ithou
t co
q= u
nit b
earin
g pr
essu
re c
onsd
erin
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
Pla
stic
ity In
dex
Und
rain
ed s
hear
stre
ngth
of s
ame
OC
R
Fp =
Fric
tion
Adj
ustm
ent f
acto
r = 0
Shaf
t out
er fr
ictio
n fo
r eac
h la
yer
Sha
ft in
ner f
rictio
n fo
r eac
h la
yer
Acc
umul
ated
out
er fr
ictio
n
Acc
umul
ated
inne
r fric
tion
End
Bea
ring
of th
e so
il be
neat
h t
End
Bea
ring
on a
nnul
us
Min
imum
Qsi
or A
p
Qu
= U
ltim
ate
Pile
Cap
acity
%ge
Sha
ft R
esis
tanc
e
SR
D fo
r GR
LWE
AP
INP
UT
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.49 0.23 0.14 1.8 6 6 6 6 1.00 6 6 6 6
1.49 0 16 16 0.00 0.00 2.2 3.6 652 652 260 38 6 50 Cored 0.20 1.72 260 38 6 50 25% 0.011.49 0 16 16 0.00 0.00 3.6 2.2 652 652 260 6 260 6 0.00
1.31 0.23 0.14 3.1 10 9 16 15 1.00 10 9 16 152.80 0 29 29 0.00 0.00 6.7 4.1 1225 1225 488 71 15 102 Cored 488 71 15 102 31% 0.012.80 0 29 29 0.00 0.00 6.7 4.1 1225 1225 488 15 488 15 0.00
1.01 0.23 0.14 4.8 12 11 28 26 1.00 12 11 28 263.81 0 40 40 0.00 0.00 9.1 5.5 1664 1664 663 96 26 150 Cored 663 96 26 150 36% 0.023.81 0 40 40 0.00 0.00 7.9 5.5 951 951 379 26 379 26 0.00
8.50 0.20 0.14 10.8 220 205 247 231 1.00 220 205 247 23112.31 0 114 114 0.00 0.00 22.8 16.0 2747 2747 1094 159 231 637 Cored 1094 159 231 637 75% 0.0712.31 96 114 76 1.25 0.47 45.2 45.2 862 862 343 231 343 231 0.00
5.18 0.00 0.00 64.3 797 745 1044 976 0.67 531 496 779 72817.50 192 164 109 1.75 0.43 83.3 83.3 1724 1724 686 100 686 1830 Plugged 686 100 686 1565 94% 0.1617.50 0 164 164 0.00 0.00 32.8 22.0 3941 3941 1569 976 1569 728 0.00
6.89 0.20 0.14 22.0 363 339 1407 1315 1.00 363 339 1141 106724.38 0 230 230 0.00 0.00 46.0 22.0 5528 5100 2031 295 1315 3017 Cored 2031 295 1067 2503 88% 0.2624.38 144 230 154 0.94 0.52 74.3 74.3 1293 1293 515 515 515 515 0.00
3.05 0.00 0.00 76.6 559 522 1966 1837 0.67 373 348 1514 141527.43 144 260 173 0.83 0.55 78.9 78.9 1296 1296 516 75 516 2557 Plugged 516 75 516 2105 96% 0.2227.43 0 260 260 0.00 0.00 51.8 22.0 6230 5100 2031 1837 2031 1415 0.00
7.32 0.20 0.14 22.0 385 360 2351 2197 1.00 385 360 1899 177534.75 0 336 336 0.00 0.00 67.0 22.0 8056 5100 2031 295 2031 4677 Plugged 2031 295 1775 3969 93% 0.4134.75 192 336 224 0.86 0.54 103.5 103.5 1724 1724 686 686 686 686 0.00
23.26 0.00 0.00 126.4 7038 6577 9390 8774 0.67 4692 4384 6592 615958.00 239 559 373 0.64 0.62 149.3 149.3 2155 2155 858 125 858 10372 Plugged 858 125 858 7574 98% 0.7858.00 0 559 559 0.00 0.00 111.5 22.0 13414 5100 2031 2031 2031 2031 0.00
4.00 0.20 0.14 22.0 210 197 9600 8970 1.00 210 197 6802 635662.00 0 597 597 0.00 0.00 119.2 22.0 14335 5100 2031 295 2031 11926 Plugged 2031 295 2031 9128 97% 0.9462.00 0 597 597 0.00 0.00 119.2 22.0 14335 5100 2031 2031 2031 2031 0.00
11.00 0.20 0.14 22.0 579 541 10179 9511 1.00 579 541 7381 689773.00 0 685 685 0.00 0.00 136.7 22.0 16447 5100 2031 295 2031 12505 Plugged 2031 295 2031 9707 97% 1.00
SOIL IDENTIFICATION: 3
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF
SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
CALCULATIONS SRD
23
0
qkPa
23
25
26
0
5100
100000
5000
5100
26
26
0.47
0
100000
5100
0.47
23510022
5100
23
23
5100
100000
51000.47
0.47
0
0.47
0
0.47
00
28
28
9.6
9.6
9.6
10.4
9.6
9.6
8.0
0
28
0
28
8.828
31
31
31 10.4
10.4
10.4
24
42
42
420.47 22
C
S
C
S
22 5100
22 5100
S
S
C
S
CARBONATE SILT
CARBONATE SAND
SILTY CALCAREOUS CLAY
CEMENTED CARBONATE SAND
CARBONATE SILTY SAND
CARBONATE CLAY
CARBONATE SILTY SAND
SILTY CARBONATE CLAY
LIMESTONE
INPUT DATA
LIMESTONE
CARBONATE SAND CEMENTED
S
S
S
0.47
22
22
24
fkPa
0
24
0
24
100000
22
22
22
100000
22
22 5100
22 5100
22
22 5100
5100
22 5100
22 5100
K.tan��limit
K.tan�
22 5100
22 5000
22 5100
24
100000
THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULFSOIL & FOUNDATION ENGINEERING - PILE CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul
SOIL IDENTIFICATION: 3J07
INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section are 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m
mm
Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u
m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN
Dep
th b
elow
mud
l
Dep
th o
f Lay
er
Soi
l Des
crip
tion
(S
f = p
ile fr
ictio
n an
g
� =
subm
erge
d U
n
K =
coe
ffici
ent o
f l
Nq =
bea
ring
capa
c or
Su =
und
rain
ed
f lim =
lim
it un
it sk
in
q lim
= li
mit
unit
bea
f lim =
lim
it un
it sk
in
q lim
= li
mit
unit
bea
Laye
r Num
ber
� =
soil-
pile
fric
tion
p o =
effe
ctiv
e ov
er
�=c
/po'
� =
dim
ensi
onle
ss
f = u
nit s
kin
frict
ion
f = u
nit s
kin
frict
ion
q= u
nit b
earin
g pr
e
q= u
nit b
earin
g pr
e
Shaf
t out
er fr
ictio
n
Sha
ft in
ner f
rictio
n
Acc
umul
ated
out
e
Acc
umul
ated
inne
End
Bea
ring
of th
e
End
Bea
ring
on a
n
Min
imum
Qsi
or A
Qu =
Ulti
mat
e Pi
le
Laye
r Num
ber
Pla
stic
ity In
dex
Und
rain
ed s
hear
s
OC
R
Fp =
Fric
tion
Adju
s
Shaf
t out
er fr
ictio
n
Sha
ft in
ner f
rictio
n
Acc
umul
ated
out
e
Acc
umul
ated
inne
End
Bea
ring
of th
e
End
Bea
ring
on a
n
Min
imum
Qsi
or A
Qu =
Ulti
mat
e Pi
le
%ge
Sha
ft R
esis
ta
SR
D fo
r GR
LWE
A
0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 09.12 I 0.22 0.14 9.3 243 230 243 230 I 1.00 243 230 243 230
9.12 0 85 85 0.00 0.00 11.9 18.6 2036 2036 1193 142 230 616 Cored 0.20 9.39 1193 142 230 616 77% 0.059.12 72 85 57 1.27 0.47 33.9 33.9 648 648 380 230 380 230 0.00
4.00 II 0.00 0.00 55.4 636 602 880 832 II 0.67 424 401 668 63113.12 190 123 82 2.33 0.40 76.9 76.9 1710 1710 1003 119 832 1831 Cored 1003 119 631 1418 92% 0.1013.12 0 123 123 0.00 0.00 33.3 17.2 2941 2941 1724 832 1724 631 0.00
8.00 III 0.27 0.14 19.6 450 425 1330 1257 III 1.00 450 425 1117 105621.12 0 198 198 0.00 0.00 53.7 22.0 4751 4751 2786 332 1257 2918 Cored 2786 332 1056 2505 87% 0.1821.12 140 198 132 1.06 0.49 69.0 69.0 1260 1260 739 739 739 739 0.00
5.04 IV 0.00 0.00 72.3 1047 990 2376 2246 IV 0.67 698 660 1815 171626.16 140 246 164 0.86 0.54 75.7 75.7 1260 1260 739 88 739 3203 Plugged 739 88 739 2642 97% 0.1926.16 0 246 246 0.00 0.00 66.6 22.0 5892 5100 2990 2246 2990 1716 0.00
6.48 V 0.27 0.14 22.0 409 387 2786 2633 V 1.00 409 387 2225 210332.64 0 312 312 0.00 0.00 84.6 22.0 7480 5100 2990 356 2633 5775 Cored 2990 356 2103 4684 92% 0.3432.64 120 312 208 0.58 0.66 79.0 79.0 1080 1080 633 633 633 633 0.00
10.36 VI 0.00 0.00 96.4 2868 2711 5653 5344 VI 0.67 1912 1807 4136 391043.00 190 409 273 0.70 0.60 113.9 113.9 1710 1710 1003 119 1003 6775 Plugged 1003 119 1003 5258 98% 0.3943.00 120 409 273 0.44 0.75 90.5 90.5 1080 1080 633 633 633 633 0.00
18.50 VII 0.00 0.00 121.6 6462 6109 12116 11453 VII 0.67 4308 4072 8445 798361.50 240 584 389 0.62 0.64 152.8 152.8 2160 2160 1266 151 1266 13533 Plugged 1266 151 1266 9862 98% 0.7261.50 0 584 584 0.00 0.00 158.4 22.0 14012 5100 2990 2990 2990 2990 0.00
4.05 VIII 0.27 0.14 22.0 256 242 12371 11695 VIII 1.00 256 242 8700 822465.55 0 622 622 0.00 0.00 168.8 22.0 14928 5100 2990 356 2990 15718 Plugged 2990 356 2990 12047 97% 0.8865.55 0 622 622 0.00 0.00 78.3 22.0 9952 5100 2990 2990 2990 2990 0.00
25.46 IX 0.13 0.13 22.0 1608 1520 13980 13215 IX 1.00 1608 1520 10309 974591.01 0 822 822 0.00 0.00 103.5 22.0 13150 5100 2990 356 2990 17326 Plugged 2990 356 2990 13655 97% 1.00
25
0
24
22 15
305100
5100
22 5100 100000
30
0
2224
8
10000022 5100
22
0
CALCULATIONS
5100
305100
100000
100000 0
SRDq
kPa
5000 25
fINPUT DATA
kPa
0.47
0
0
0.47
0.47
100000
16
8 22 5100
22 510020
10.2
9.4
9.4
9.4
7.9
35
0
0
35
9.40
30
0
35 9.4
9.4
9.3
0.47 2222 5100
C
C
S
C
S
SSAND
SILTY SA
CLAY
SAND
CLAY
CLAY
0 1000008 510022
SAND
SAND
CLAY
S
C
S
K.tan�
22 50000.47 24 22
100000
100000
22
K.tan��limit
22 5100
24
5100
8
Appendix E
“ACTUAL” CAPACITY OF PILES USING BACK ANALYSIS PROCEDURE
Pile above mudline Penetration Total Hammer Blowcount Bias FactorS.N. Dia. L1 L2 L1+L2 Type
mm m m kN BpF NO SETUP With SETUP API
1 762 39.6 42.7 82.3 Vulcan 140-C 6096 125 2198 4396 0.7212 762 39.6 42.7 82.3 Vulcan 140-C 6096 136 1158 2316 0.3803 762 39.6 42.7 82.3 Vulcan 140-C 6096 136 1158 2316 0.3804 762 39.6 42.7 82.3 Vulcan 140-C 6096 147 2051 4102 0.6735 762 39.6 44.2 83.8 Vulcan 140-C 6487 106 1158 2316 0.3576 762 39.6 42.4 82.0 Vulcan 140-C 6018 143 2051 4102 0.6827 762 39.6 42.7 82.3 Vulcan 140-C 6096 107 1158 2316 0.3808 762 39.6 42.7 82.3 Vulcan 140-C 6096 145 2051 4102 0.6739 914 41.0 82.9 123.9 Vulcan 560 19420 53 6873 13745 0.70810 914 41.0 82.6 123.6 Vulcan 560 19401 44 4725 9449 0.48711 914 41.0 82.3 123.2 Vulcan 560 19382 82 6873 13745 0.70912 914 41.0 82.9 123.9 Vulcan 560 19420 89 6873 13745 0.70813 914 41.0 82.3 123.2 Vulcan 560 19382 45 4725 9449 0.48814 914 41.0 83.2 124.2 Vulcan 560 19438 67 6873 13745 0.70715 914.4 41.1 76.9 118.0 Menck 3000 19044 89 8021 16041 0.84216 914.4 41.1 87.7 128.8 Menck 4600 19732 292 10015 20030 1.01517 914.4 41.1 85.7 126.8 Menck 4600 19596 295 10015 20030 1.02218 914.4 41.1 76.4 117.5 Menck 3000 19009 60 6003 12005 0.63219 914.4 41.1 84.7 125.8 Menck 4600 19533 177 10015 20030 1.02520 914.4 41.1 85.4 126.5 Menck 4600 19577 201 10015 20030 1.02321 914.4 41.1 84.9 126.0 Menck 4600 19546 300 10015 20030 1.02522 914.4 41.1 84.8 125.9 Menck 4600 19540 292 10015 20030 1.02523 762 41.1 62.5 103.6 MRBS 3000/150 12173 26 4770 9541 0.78424 762 41.1 62.6 103.7 MRBS 3000/150 12207 23 2592 5183 0.42525 762 41.1 62.6 103.7 MRBS 3000/150 12207 30 4770 9541 0.78226 762 41.1 62.6 103.7 MRBS 3000/150 12207 20 2592 5183 0.42527 762 41.1 65.5 106.6 Menck 3000 13120 62 6978 13955 1.06428 762 41.1 65.6 106.7 Menck 3000 13244 64 6978 13955 1.05429 762 41.1 70.8 111.9 Menck 3000 15089 40 6430 12859 0.85230 762 41.1 65.7 106.8 Menck 3000 13270 55 6978 13955 1.05231 762 40.7 61.0 101.7 Vulcan 040 11663 111 3583 7167 0.61432 762 40.7 61.7 102.4 Vulcan 040 11900 89 3583 7167 0.60233 762 40.7 61.4 102.1 Vulcan 040 11798 169 3583 7167 0.60734 1219 41.5 73.0 114.5 MHU500T 26555 45 14681 29361 1.10635 1219 41.5 72.2 113.7 MHU500T 26488 64 15997 31995 1.20836 1219 41.5 73.2 114.7 MHU500T 26572 46 14785 29569 1.11337 1219 41.5 72.2 113.7 MHU500T 26488 65 16045 32090 1.21138 1219 24.1 51.8 75.9 Menck3000 14265 21 7210 14419 1.01139 1219 24.1 51.8 75.9 Menck3000 14265 21 7210 14419 1.01140 1219 24.1 51.8 75.9 Menck3000 14265 23 7579 15158 1.06341 1219 24.1 51.8 75.9 Menck3000 14265 18 6372 12744 0.89342 1219 24.1 51.8 75.9 Menck3000 14265 21 7210 14419 1.01143 1219 24.1 51.8 75.9 Menck3000 14265 23 7579 15158 1.06344 1219.2 25.3 79.9 105.2 4600/150 22818 26 11023 22047 0.96645 1219.2 25.3 79.2 104.5 4600/150 22759 34 12472 24944 1.09646 1219.2 25.3 86.0 111.3 4600/150 23332 37 13348 26696 1.14447 1219.2 25.3 79.6 104.9 4600/150 22793 29 11636 23273 1.02148 1219.2 25.3 98.8 124.1 4600/150 24410 287 17319 34637 1.41949 1219.2 25.3 98.1 123.4 4600/150 24351 160 16582 33164 1.36250 1219.2 25.3 98.8 124.1 4600/150 24410 264 17295 34589 1.41751 1219.2 25.3 78.9 104.2 4600/150 22734 31 12045 24090 1.06052 762 25.3 76.8 102.1 3000/150 12705 26 9353 18705 1.47253 762 25.3 69.5 94.8 3000/150 12321 28 9394 18788 1.52554 762 25.3 60.0 85.3 3000/150 11821 18 6710 13420 1.13555 762 25.3 60.0 85.3 3000/150 11821 18 6710 13420 1.13556 762 25.3 80.2 105.5 3000/150 12884 24 9003 18006 1.39857 762 25.3 73.8 99.1 3000/150 12547 27 9635 19270 1.53658 762 25.3 78.0 103.3 3000/150 12769 25 9187 18374 1.43959 762 25.3 71.3 96.6 3000/150 12416 26 9434 18868 1.52060 914.4 35.2 63.5 98.7 Vulcan 530 14956 60 7822 15643 1.046
API Capacity GRLWEAP Results
Pile above mudline Penetration Total Hammer Blowcount Bias FactorS.N. Dia. L1 L2 L1+L2 Type
mm m m kN BpF NO SETUP With SETUP API
API Capacity GRLWEAP Results
61 914.4 35.2 63.5 98.7 Vulcan 530 14596 45 7601 15202 1.04262 914.4 35.2 63.5 98.7 Vulcan 530 14596 45 7601 15202 1.04263 914.4 35.2 63.5 98.7 Vulcan 530 14596 41 7546 15092 1.03464 762 23.3 66.1 89.4 Vulcan 040 12142 242 6333 12665 1.04365 762 23.3 76.4 99.7 Vulcan 040 12684 10 1703 3406 0.26866 762 23.3 68.9 92.2 Vulcan 040 12289 249 6351 12703 1.03467 762 36.9 61.0 97.9 Vulcan 340 8137 98 5192 10385 1.27668 762 36.9 61.0 97.9 Vulcan 340 8137 132 5518 11036 1.35669 762 36.9 61.0 97.9 Vulcan 340 8137 59 4819 9637 1.18470 762 25.6 84.7 110.3 Vulcan 340 14197 80 4061 8122 0.57271 762 25.6 83.5 109.1 Vulcan 340 13937 48 3606 7212 0.51772 762 25.6 84.1 109.7 Vulcan 340 14067 75 3990 7980 0.56773 762 43.4 57.3 100.7 MENCK 1500 11205 188 6873 13746 1.22774 762 43.4 56.4 99.8 MENCK 1500 11158 218 7240 14480 1.29875 762 43.4 57.0 100.4 MENCK 1500 11189 255 7432 14864 1.32876 914.4 35.1 50.3 85.4 Vulcan 530 7888 47 3944 7888 1.00077 914.4 35.1 58.7 93.8 Vulcan 530 8419 37 4210 8419 1.00078 914.4 35.1 59.1 94.2 Vulcan 530 8444 47 4222 8444 1.00079 914.4 35.1 59.1 94.2 Vulcan 530 8444 49 4222 8444 1.00080 762 27.6 60.1 87.7 Menck3000 11821 32 6442 12884 1.09081 762 27.6 59.7 87.3 Menck3000 11805 37 6918 13837 1.17282 762 27.6 60.2 87.8 Menck3000 11831 38 7038 14075 1.19083 914.4 28.7 66.2 94.9 70M 15127 12 6040 12080 0.79984 914.4 28.7 73.2 101.9 70M 15556 15 7093 14185 0.91285 914.4 28.7 66.2 94.9 70M 15127 10 5338 10676 0.70686 914.4 28.7 73.2 101.9 70M 15556 12 6040 12080 0.77787 914.4 20.5 62.0 82.5 Vulcan-560 18003 10 3362 6725 0.37488 914.4 20.5 70.0 90.5 Vulcan-560 18593 14 4857 9714 0.52289 914.4 20.5 62.0 82.5 Vulcan-560 18003 8 2452 4904 0.27290 914.4 20.5 70.0 90.5 Vulcan-560 18593 12 4267 8535 0.45991 1219 36.3 44.8 81.1 Menck 3000 13817 24 5941 11881 0.86092 1219 36.3 44.3 80.6 Vulcan 560 13817 23 5502 11005 0.79693 1219 36.3 44.3 80.6 Menck 3000 13817 26 6044 12088 0.87594 1219 36.3 44.3 80.6 Vulcan 560 13817 20 5061 10122 0.73395 1066.8 46.6 53.3 99.9 MENCK 3000 14016 37 8361 16722 1.19396 1066.8 46.6 50.5 97.1 Vulcan 560 12589 30 7323 14647 1.16397 1066.8 46.6 53.3 99.9 Menck 3000 14016 32 7885 15769 1.12598 1066.8 46.6 50.5 97.1 Menck 3000 12589 49 8568 17135 1.36199 1219.2 33.2 65.0 98.2 Vulcan 560 13811 38 9869 12829 0.929100 1219.2 33.2 80.0 113.2 Menck 3900 15521 95 14409 18731 1.207101 1219.2 33.2 65.0 98.2 Vulcan 560 13811 48 10633 13823 1.001102 1219.2 33.2 78.5 111.7 Menck 3900 15395 371 16038 20850 1.354103 914 22.0 70.0 92.0 Vulcan 530 14415 46 5586 11171 0.775104 914 22.0 70.0 92.0 Vulcan 530 14415 43 5299 10597 0.735105 914 22.0 70.0 92.0 Vulcan 530 14415 51 5968 11936 0.828106 914 22.0 70.0 92.0 Vulcan 530 14415 44 5394 10789 0.748107 914.4 28.9 56.1 85.0 Delmag D55 10429 92 4271 8543 0.819108 914.4 28.9 56.1 85.0 Delmag D55 10426 84 4083 8166 0.783109 914.4 28.9 57.0 85.9 Delmag D55 10854 128 4537 9074 0.836110 914.4 28.9 57.0 85.9 Delmag D55 10854 160 4741 9482 0.874111 914 35.1 55.2 90.2 Menck 3000 9951 41 7438 14876 1.495112 914 35.1 55.2 90.2 Menck 3000 9951 49 7718 15435 1.551113 914 35.1 55.6 90.7 Menck 3000 9976 28 6637 13274 1.331114 914 35.1 43.0 78.0 Menck 3000 9180 83 10081 20163 2.196115 1219 34.4 55.8 90.2 MHU600 16231 24 7759 15518 0.956116 1219 34.4 56.1 90.5 MHU600 16257 30 8621 17242 1.061117 1219 34.4 56.0 90.4 MHU600 16248 24 7759 15518 0.955118 1219 34.4 56.0 90.4 MHU600 16248 24 7759 15518 0.955119 762 40.0 83.6 123.6 Vulcan 530 13489 117 6745 13489 1.000120 762 40.0 83.6 123.6 Vulcan 530 16852 466 8426 16852 1.000121 762 40.0 83.6 123.6 Vulcan 530 13489 115 6745 13489 1.000
Pile above mudline Penetration Total Hammer Blowcount Bias FactorS.N. Dia. L1 L2 L1+L2 Type
mm m m kN BpF NO SETUP With SETUP API
API Capacity GRLWEAP Results
122 914 41.4 61.6 103.0 Menck MRBS 1500 15811 60 4997 9995 0.632123 914 41.4 61.7 103.1 Menck MRBS 1500 15811 77 4997 9995 0.632124 914 41.4 61.6 103.0 Menck MRBS 1500 15811 46 4997 9995 0.632125 914 41.4 61.6 103.0 Menck MRBS 1500 15811 58 4997 9995 0.632126 1219 41.3 50.5 91.8 Vulcan 530 15184 10 517 1033 0.068127 1219 41.3 50.5 91.8 Vulcan 530 15184 43 5875 11751 0.774128 1219 41.3 52.8 94.1 Vulcan 530 16287 29 4533 9066 0.557129 1219 41.3 50.5 91.8 Vulcan 530 15184 9 1880 3760 0.248130 762 30.6 58.8 89.4 Vulcan 020 9519 56 2978 5955 0.626131 762 30.6 58.8 89.4 Vulcan 020 9519 66 3063 6126 0.644132 762 30.6 57.9 88.5 Vulcan 020 9160 66 3063 6126 0.669133 762 27.6 51.8 79.4 Vulcan 040 8551 319 5867 11733 1.372134 762 27.6 61.0 88.6 Vulcan 040 9025 20 2793 5586 0.619135 762 27.6 50.4 78.0 Vulcan 040 8551 302 5806 11612 1.358136 762 34.8 65.9 100.7 Vulcan 040 8396 42 4433 8866 1.056137 762 34.8 82.8 117.6 Vulcan 040 13776 38 3884 7767 0.564138 762 34.8 65.8 100.6 Vulcan 040 8396 43 4485 8971 1.068
Appendix F
COMMON STATISTICAL DISTRIBUTIONS USED IN THIS RESEARCH
From the databases collated in this research, the histograms and basic statistics of the
results are obtained. Then, several competing probability distributions were fitted to
the data by the method of moments. Finally, the observed data and models were
plotted and quantitative tests on the goodness-of-fit were conducted. This section
presents a number of commonly used statistical distributions and defines their
parameters.
F.1 Normal Distribution The normal probability distribution was investigated because historically it has been
favored by researchers more than any other distribution. The versatility of the
normal distribution arises from its very convenient property that if two normally
distributed variables are added, their sum also has a normal distribution. Corotis and
Doshi (1977) suggested that the normal model is often adopted with little or no
physical justification due to its ability to serve as a good approximation to many
other distributions. Frequently, it has been used simply because an observed
histogram is roughly bell-shaped and approximately symmetric.
In some situations this may be justified on the basis that it is the limiting form of
several other distributions (Lewis, 1996). For example, if a random variable x can be
expressed as a sum of the random variables, xi, i =1, 2,…., N where no one of them is
dominant, then x can be described as a normal distribution, even though xi is
described by non-normal distributions that may not even be the same for different
values of i (Lewis, 1996). More importantly, if the general characteristics, rather
than the details of the shape, are of interest, the normal distribution may serve as a
widely tabulated, if rough, approximation to empirical data.
While the normal distribution may often provide a reasonable approximation, it may
not be appropriate to represent situations such as the case when the data exhibit
significant skewness. Moreover, if the interest is in the “tails” of the distribution, the
use of a normal distribution is likely to lead to large errors. Due to the importance of
the tail regions in structural reliability, it was appropriate to test alternative
distributions.
F.2 Lognormal Distribution The lognormal distribution was investigated in this research to model skewness in
the observed data. A variable that is described by the lognormal distribution will
have logarithms that follow a normal distribution. While the lognormal distribution
is not quite as convenient a mathematical form as the normal, the simple relationship
between the two retains numerical tractability.
The lognormal distribution defines a variable that is limited to positive values and
exhibits a parameter-determined positive skewness. It may be derived as the limiting
distribution of the product of a large number of relatively independent variables, and
as such it exhibits regeneration under multiplication. These characteristics support
the investigation of the lognormal distribution.
A variable Y has a lognormal distribution if �YZ ln� has a normal distribution with
mean Z� and standard deviation Z .
For a variable Y which is lognormally distributed with �YZ ln� , the probability
density function (PDF) for Z is defined as:
� ���
���
� ���
2
2
ln21exp
21
Z
Z
Zy
yy
yf
��
Equation F- 1
Where z� = mean value of the normal variable Z
2z = variance of the distribution in Z
The median of the variable Y is
)exp(~ZY �� Equation F- 2
The mean of the lognormally distributed variable is:
���
��� ��� 22
21exp)
21exp(~
ZZZYY � Equation F- 3
The variance of the lognormally distributed variable Y is:
� �� �1exp2exp 22 ���� ZzzVARIANCE � Equation F- 4
The coefficient of variation of the lognormally distributed variable Y is:
� ZZYCOV �� 1exp 2 Equation F- 5
F.3 Weibull Distribution A third distribution commonly encountered is the Weibull distribution. For a random
variable X that has a Weibull distribution (2 parameters), the CDF with respect to X
is given by:
����
�
���
����
������
mxXF!
exp1 Equation F- 6
The distribution is put in a form for probability plotting by first solving for �F�11
and then taking the logarithm twice, which leads to (Lewis, 1996):
����
��
��
�XF
y1
1lnln Equation F- 7
and
�Xx ln� Equation F- 8
The mean X of the Weibull distributed variable is:
���
��� �"��
mX 11! Equation F- 9
The standard deviation of the variable X is:
21121 ���
��� �"��
��
��� �"��
mmX ! Equation F- 10
where m is the shape parameter and! is the scale parameter.
� � 53 12601
3601
121ln
212ln
21ln
zzzzzzz �����
��
��� ���" � Equation F- 11
The shape parameter is equal to the slope of the line fitting the data and the scale
parameter is estimated in terms of the slope and intercept as follows: (Lewis, 1996):
�dataSLOPEam �� Equation F- 12
� � ��
�
����
����
��
�����
dataSLOPEdataINTERCEPT
ab expexp! Equation F- 13
Where a = Slope of the line fitting the data
b = Intercept of the line fitting the data
m = Shape parameter
! = Scale parameter
F.4 Extreme Value (Gumbell Distribution) A fourth distribution type is the Gumbell distribution. For extreme value
distributions, or more precisely asymptotic extreme value distributions, the CDF is
given by:
�#$%
&'(
��
���
����
��� �
���!
uxXF expexp Equation F- 14
The mean is given by:
!�� ��� u Equation F- 15
and
22
2
6!� � Equation F- 16
where 5772157.0��
� !!ux
XFy ����
�
���
��
11lnln Equation F- 17
The distribution parameters may be estimated in terms of the slope and intercept to
be (Lewis, 1996):
�dataSLOPEa11
����! Equation F- 18
� �dataSLOPE
dataINTERCEPTabu ���� Equation F- 19
Appendix G
STRUCTURAL RELIABILITY ANALYSIS (SRA)
The traditional formulation of SRA is element-based in that it starts from a single failure
mode of a structural element. Consideration of randomness (including time-dependent
randomness) is confined to load variables (including environmental variables), geometric
variables, and material properties and in the mathematical models (e.g., models relating
loads to load-effects). Uncertainties in the failure criteria itself are sometimes
considered. However, those arising from gross human errors are not.
The SRA results obtained are often referred to as notional reliability estimates.
Depending on the degree of approximation and on the format of application, reliability
analysis methods can be categorized as Level 1, Level 2 and Level 3. In addition, the term
Level 4 signifies methods that incorporate economic as well as social data (Madsen et al.,
1986). However, the boundaries separating the different “levels” are not distinct, the
terminology has become archaic.
Level 1 is a semi-probabilistic design process in which partial factors are defined and
applied to characteristic values of loads and resistance. A level 1 structural design is
commonly called limit state design. Level 1 is used to incorporate the results of
reliability methods to engineering practice, although the reliability aspects are not
transparent to the designer.
Level 2 is known as First Order Reliability Method (FORM). The measure of reliability
is based on the reliability index. In level 2 methods, design variables can have any form
of probability distributions. Typically, Level 2 methods use two values to describe each
uncertain variable, i.e. mean and variance. FORM has been widely adopted in the
structural community for RBD (e.g., Allen, 1975; Ellingwood et al., 1980; ACI, 1983;
AISC, 1996) and it appears in many of the recently proposed RBD codes for foundations
(e.g., Barker et al., 1991; Berger and Goble, 1992; Becker et al., 1993).
The Second-order methods (SORM) improve the accuracy of first-order probability
estimates. The presence of significant differences between the results of the two methods
may suggest the use of Monte Carlo simulations to confirm the probability of failure
estimate. For most practical reliability applications, there is usually little difference
between FORM and SORM estimates. Moses (1990) concluded that FORM and SORM
provided comparable results for structural problems and Lacasse (1996) confirmed the
similarity in results between FORM and SORM for Geotechnical problems.
Level 3 has multi-dimensional joint probability distributions which enables the
determination of the “exact” probability of failure for a structure or structural component.
Level 3 makes use of a full probabilistic description of the joint occurrence of the various
quantities which affect the response of the structure and takes account of the true nature
of the failure domain. System effects and time-variance may be incorporated. Level 3
methods include numerical integration methods such as Monte Carlo simulation. The
application of Monte Carlo simulation employs commercial software such as @RISK
software.
The Monte-Carlo simulation (MCS) offers a direct method for estimating the failure
probability. In essence, the technique involves sampling a set of values of the basic
variables at random from the probability density function and evaluating the failure
function for the values to see if failure occurs. By generating a large number of samples
or trials, the probability density function is simulated and the ratio of the number of trials
leading to failure to the total number of trials tends to the exact probability of failure.
The drawback with crude Monte Carlo simulation is the computational effort involved.
To produce a reasonably accurate estimate of the failure probability at least 100/Pf trials
are required. For probability of failure around 10-4, this requires that at least one million
trials be generated. It can be made more efficient with Latin Hypercube Sampling (LHS)
which is a Monte-Carlo simulation optimized by “organized” sampling. It reduces the
number of simulations required for a reliable distribution of the response. If used
intelligently, Monte Carlo methods are a readily understood and easily applied tool and
can be used to produce ‘exact’ answers to problems that cannot be accurately modeled
using FORM or SORM such as load combinations and time-varying problems.
Level 4 includes any of the above together with economic data for prediction of
maximum benefit or minimum cost. DNV (1992) defines level 4 as a method that
compares a structural prospect with a reference prospect according to the principles of
engineering economic analysis under uncertainty. Such decision analysis considers costs
and benefits of construction, maintenance, repair and consequences of failure.
Appendix H
APPROACHES FOR PREDICTING AXIAL PILE CAPACITY
H.1 EMPIRICAL APPROACH – STATIC METHOD
The empirical approach was derived from industry review of static loading tests carried
out on onshore piles and forms the basis of the pile design method in API RP2A-LRFD
(1993). Evaluation of the ultimate capacity of piled foundation using API RP2A-LRFD
(1993) formulation typically uses soil parameters obtained from laboratory tests
performed on “undisturbed” samples. For an open-ended pipe pile, the capacity is
generally taken as the sum of shaft resistance and end bearing values computed from unit
skin friction and unit toe resistance applied to the pile size and length as per the following
equation:
wpiissult AqAfAfQ �)�)� Equation H- 1
Where, ultQ = Ultimate static capacity
sf = Unit outside shaft friction
sA = Outside shaft area of the pile
if = Unit inside shaft friction
iA = Inside shaft area of pile
pq = Unit end bearing capacity
wA = Cross-sectional area of steel wall at toe of pile
The API RP2A method assumes that the shaft resistance and end bearing are
simultaneously fully activated. Basically, this approach computes “long-term” static pile
capacity since it utilizes soil parameters that represent natural ground conditions
unaffected by the pile driving process.
In Equation 1, the skin (shaft) friction of the piled foundation is integrated over the
respective pile lengths. The effects of local fluctuations of the skin friction from point to
point along each pile average out over the length of the pile. The axial capacity of piles
is thus calculated from average skin friction properties only without considering any local
variability.
The values assigned to the various parameters shown in Equation 1 depend on the soil
type, behavior of soil plug and interpretation of nominal values. API RP2A-LRFD (1993)
defines two types of soils, granular and cohesive soils.
For granular soils, the unit skin friction, sf , is a function of the effective overburden
pressure, * the coefficient of lateral earth pressure, K, and the angle of skin friction, �,
and is expressed as:
limtan fKf s +� � Equation H- 2
For a constant K, the above equation suggests that the unit skin friction increases linearly
with depth. However, many investigators (e.g. Vesic, 1967; Tavenas, 1971) observed
that the unit skin friction reaches a limiting value and thereafter remains essentially
constant with further increase in depth. Hence, usually a limiting value of unit skin
friction, flim, is specified. API RP2A-LRFD (1993) specifies limits for the engineering
parameters for cohesionless siliceous soils as shown in Table H- 1.
The observation that sf does not increase indefinitely with depth but reaches a limiting
value was explained by many investigators in terms of the ‘arching effect’ (e.g. Vesic,
1969; Reese and Cox, 1976). This effect curtails the increase of lateral earth pressure
with depth.
Further, for granular soils, the unit end bearing pq is expressed in terms of the overburden
pressure and a bearing capacity factor Nq as follows
qp Nq � Equation H- 3
�,fNq � Equation H- 4
Where , = Frictional angle of shearing resistance
Arching has also been cited as one of the factors causing the end bearing to attain limiting
value at large depths. In addition, Ranganatham and Kaniraj (1978) observed that sand
grains crush under the pile tip, even in non-carbonate sands. Vesic (1977) postulated that
Nq is a function of the rigidity of the sand. At high stress, crushing causes a decrease in
the rigidity index which leads to a reduction in Nq and hence pq .
For cohesive soils, the unit shaft friction f and the unit end bearing uq are expressed as
follows:
ucf .�� Equation H- 5
uu cq .9� Equation H- 6
Where � = A dimensionless factor derived as outlined below
uc = Undrained shear strength of the soil at the point in question
0.1+���
���
uc Equation H- 7
21
.5.0 ���
����
� uc Equation H- 8
0.1-���
���
uc Equation H- 9
21
.5.0 ���
����
� uc Equation H- 10
Under certain conditions, the accumulated internal skin friction may exceed the ultimate
static capacity of the soil below the toe of the pile. The pile then behaves as if it is close-
ended or plugged. Its static capacity is then given by:
wpssult AqAfQ �)� Equation H- 11
Static pile capacity calculation must determine whether an open pile section will exhibit
plugged or unplugged behavior. O’Neill and Raines (1991) and Paikowsky and Whitman
(1990) suggested that plugging of an open pile in medium dense to dense sands generally
begins at a pile penetration to pile diameter ratio of 20, but can be as high as 35 for piles
in soft to stiff clays.
When an open pile section is driven it may behave as low displacement piles and “cookie
cut” through the soil, but may also act as a displacement pile if a soil plug forms near the
pile toe. The behavior of the soil plug is complex and is different under dynamic and
static loading. Stevens (1988) reported that plugging of pipe piles in clay does not occur
during driving if pile accelerations (along the plug zone) are greater than 22g. Holloway
and Beddard (1995) reported that hammer blow size (impact force and energy) influences
the dynamic response of the soil plug. With a large hammer blow, the plug “slipped’
under the dynamic event whereas under a lesser hammer blow the pile encountered low
resistance typical of a plugged condition.
The key soil parameters required for the application of the static method are the
submerged unit weight, the friction angle of granular soil and the undrained shear
strength of cohesive soil. In engineering practice, these values are usually provided in
geotechnical reports. The application of the LRFD method requires definition of nominal
values in those geotechnical reports. Unfortunately, the importance of defining nominal
soil strengths has frequently been overlooked (CIRIA, 1977; Been and Jefferies, 1993;
Dahlberg and Ronald, 1993).
Existing procedures for the selection of nominal soil strengths are neither well defined
nor followed uniformly by all engineers (Whitman, 1984). Goble (1999) observed a
similar trend and found that different calculation methods are preferred in different
localities or even by different geotechnical engineers in the same locality. The manner in
which the resistance factor is incorporated with the nominal values in the prediction
equation is also highly varied (Kulhawy 1984, 1996).
Despite the confusion in the consistent selection of soil parameters, the definition of
nominal soil strengths for the reliability-based design (RBD) format should be consistent
with those that are used in traditional foundation engineering practice. Terzaghi and Peck
(1948) recommended the use of an average value of the measured strength within a
significant depth from each boring, and then using the smallest average for design. The
Norwegian Petroleum Directorate (NPD) prescribes the use of a “conservatively assessed
mean value” for the nominal shear strength (Dahlberg and Ronald, 1993). The
probability-based Swedish Building Code (SC-89) also defines the nominal value of soil
strength as the mean of the measurements (Bengtson et al., 1993). Moses and Larrabee
(1988) suggested that nominal values of the soil parameters be close to the mean in their
calibration of the API RP2A-LRFD (1993). Criswell and Vanderbilt (1987) used the
mean value for the RBD of transmission structures.
A number of Authors, including DiGioia and Rojas (1990), European Committee for
Standardization (1993) and Been and Jefferies (1993), advocated an alternative definition
for the nominal value that is based on the concept of the exclusion limit. However, the
use of a small exclusion limit may not be appropriate for foundation design for various
reasons. First, the amount of data required for the reliable determination of a 5 to 10%
exclusion limit is typically much larger than the number of measurements taken during
project execution (Bengtsson, et al., 1993; Been and Jefferies, 1993; Lo and Li, 1993).
For example, Bengtsson et al. (1993) estimated that approximately 200 measurements
were required to establish the 5% exclusion limit on soil strength, while the determination
of the mean only required about 20 measurements. Another shortcoming of the exclusion
limit was the requirement for probability computations which are currently not performed
in engineering practice. The main purpose of probability-based codes is to relieve
practicing engineers from unfamiliar probability calculations so that focus can be placed
on the geotechnical aspects of the problem. Use of the exclusion limit introduces
unnecessary complications and partially undermines the objective of calibrating
resistance factors. In addition, the exclusion limit concept is less intuitive than that for
the mean value. Hence, it would be reasonable to say that most foundation engineers feel
more comfortable using the mean value. The use of a mean value provided a physical
feel from past experience of working with realistic soil strength parameters (Olson, et al.,
1989; Bengtsson, et al., 1993).
Regardless of the choice, it is important to emphasize that the definition of nominal
values cannot be left to the judgment of the engineer, because the load and resistance
factors are not independent of the nominal values.
In this research, nominal parameters of soil strength were defined at the mean for reasons
of simplicity and compatibility with foundation design practice. The use of the mean
value in this research is also consistent with the approach used by Moses and Larrabee
(1983) in the calibration of API RP2A-LRFD (1993).
H.2 EMPIRICAL APPROACH – DYNAMIC METHOD
McVay et al. (2000) presented several dynamic methods to predict pile capacity
including ENR Formula (ENR, 1965), Gates Formula (Gates, 1957), FDOT Formula
(FDOT, 1991), Paikowsky’s method (Paikowsky, 1994) and the Japanese method (1996).
The use of each method is associated with a specific factor of safety, ranging from 1.0
(for the FDOT Formula) to 6.0 (for the ENR Formula).
Pile capacities determined from dynamic formulae have shown poor correlations and
wide scatter when statistically compared with static loading test results. According to
Hannigan et al. (1997), the simple dynamic formula is fundamentally incorrect due to the
modeling of each component within the pile driving process (driving system, soil and
pile). Dynamic formulae poorly represent the driving system and the energy losses of its
components. The implicit assumption of rigid pile in the dynamic formula neglects pile
axial stiffness and length effects on driveability, and further assumes that soil resistance
is constant and instantaneous at the impact force. Consequently, the dynamic empirical
method was not used in this research.
H.3 ENGINEERING MECHANICS APPROACH – WAVE EQUATION ANALYSIS (WEA)
Taking advantage of wave propagation theories developed in the 1950s, Smith (1960)
developed a discrete numerical solution with realistic hammer, pile and soil models,
which became known as the “Wave Equation Method”. The wave equation method
solves the one-dimensional partial differential equation for an idealized hammer-pile-soil
system.
The one-dimensional WEA offered the only analytical tool which incorporates so many
important pile-driving variables into a rational framework to arrive at a realistic estimate
of the pile capacity using back-analysis procedure. The one-dimensional WEA of pile
driving is based on the discrete element idealization of the hammer-pile-soil system. A
schematic of the wave equation hammer-pile-soil model is shown in Figure H- 1.
As the WEA commences, a calculated or estimated ultimate capacity ultR from user
specified values is distributed among the elastic-plastic springs along the shaft and toe.
Similarly, user specified damping factors are assigned to shaft and toe to represent the
dynamic soil resistance. The analysis then proceeds by calculating a ram velocity using
the input hammer efficiency and stroke. The ram movement causes displacements of
helmet and pile head springs and therefore compressions (or extensions) and related
forces acting at the top and bottom of the segments. Furthermore, the movement of a pile
segment causes soil resistance forces. A summation of all forces acting on a segment
divided by its mass yields the acceleration of the segment. The product of acceleration
and time step summed over time is the segment velocity. The velocity multiplied by the
time step yields a change of segment displacement which then results in new spring
forces. These forces divided by the pile cross sectional area at the corresponding section
equal the stress at that point.
Similar calculations are made for each segment until the accelerations, velocities and
displacements of all segments are calculated using the time step. The analysis then
repeats for the next time step using the updated motion of the segments from the previous
time step. From this process, the accelerations, velocities, displacements, forces and
stresses of each segment are computed over time. Additional time steps are analyzed
until the pile toe begins to rebound.
The permanent set (mm) of the pile toe is calculated by subtracting a weighted average of
the shaft and toe quakes from the maximum pile toe displacement. The inverse of the
permanent set is the driving resistance (blow count) that corresponds to the input ultimate
capacity. By performing wave equation analyses over a wide range of ultimate
capacities, a curve or “bearing graph” can be plotted which relates ultimate capacity to
driving resistance. The wave equation bearing graph is associated with a single driving
system, hammer stroke, pile type, soil profile and a particular pile length. If any of the
above items is changed, the bearing graph will also change.
Table H- 1: API RP2A recommended values of limiting skin friction and end bearing of piled foundations in cohesionless siliceous soils
Limiting Skin
Friction
Limiting Unit
End Bearing
Density Soil Description
kPa MPa
Very Loose Sand
Loose Sand-Silt
Medium Silt
47.8 1.9
Medium Sand
Dense Sand-Silt 81.3 4.8
Dense Sand
Very Dense Sand-Silt 95.7 9.6
Dense Gravel
Very Dense Sand 114.8 12.0
Figure H- 1: Typical mathematical model of wave equation analysis (GRLWEAP Manual, 1995). The hammer, helmet and pile are modeled by a series of segments each consisting of a concentrated mass and a weightless spring
Appendix I
APIPILE MANUAL
Parameter Description
Outer Diameter, OD Outside diameter that was used in the computations
Inner Diameter, ID Equal the outside diameter minus 2 x wall thickness.
The spreadsheet computes skin friction on both outside
and inside area for pipe piles. In clay layers, the
remoulded shear strength was used to compute the inside
skin friction.
Total Length, L Maximum pile penetration that is expected for the pile.
The spreadsheet computes side resistance and end
bearing at every depth interval until reaching the total
length.
Pile Material Only applicable to steel piles.
Modulus of Elasticity, E Elastic modulus of the pile. The elastic modulus of the
pile is used for load versus settlement analysis.
Soil Layers This column allows the different types of soil and
material properties to be specified for the computations.
Layer This is a sequential number that is provided to each soil
layer.
Soil Type Two types of soils may be specified for internal
generation of soil response under axial loading in the
spreadsheet, namely sand or clay. These values
correspond to the vertical coordinate at the bottom of
each soil layer starting from the top layer. The origin of
coordinates for these values is located at the ground
surface. As a minimum requirement, the bottom of the
last soil layer must be two pile diameters deeper than the
depth of the modeled pile.
Parameter Description
Layer Data This allows the definition of soil properties for each
soil stratum. The spreadsheet requires different input
parameters for each type of soil. A detailed description
of the parameters needed for sand and clay layers are
described below.
Maximum Side Friction In this column entry the maximum permissible value of
skin-friction transfer for a given stratum is specified. If
a value for the maximum-permissible and skin friction
is entered, the spreadsheet compares the internally-
computed value with the maximum provided and uses
the smaller of the two for the final computation. If no
restriction on the computed value is required, a very
large figure can be entered to suppress this option so
that the internally-computed values are used. The
limits on side friction transfers are identified in this
thesis for carbonate soils.
Maximum End Bearing In this column entry the maximum permissible value of
transfer in end bearing for a given stratum is specified.
If values for the maximum-permissible and end bearing
are specified, the spreadsheet compares the internally-
computed value with the maximum provided and use
the smaller of these two for the final computation. If
restriction on the computed values is required, a large
amount can be entered to suppress this option and
always use the internally-computed values.
Sand Layer Data This column is used to describe properties of the sand
layers. The user inputs soil properties for the top and
bottom of each soil layer. The spreadsheet interpolates
the data linearly between those two depths.
Parameter Description
Unit Weight This column entry corresponds to values of effective unit
weight of the soil. Values for the top and bottom of the
sand layer are entered in standard units of force per unit
volume (kN/m3).
Friction Angle This column entry corresponds to values of the internal
angle of friction (also known as angle) for the top and
bottom of the sand layer. The values of internal angle of
friction are entered in standard units of degrees.
Lateral Earth Pressure, K This column reflects values for the lateral earth pressure
coefficient for the top and bottom of the sand layer. The
earth pressure coefficient, Ko, is used to calculate the
skin friction in granular soils. A Ko of 0.8 is
recommended for open-ended pipe piles that are driven
unplugged for loadings in both tension and compression.
A K of 1.0 is recommended for full-displacement piles.
Bearing Capacity Factor,
Nq
This factor is used to calculate the end-bearing capacity
of piles in granular soils.
Clay Layer Data This column is used to describe the properties of clay
layers. The required properties for clay layers are
explained below.
Location The user inputs soil properties for the top and bottom of
each soil layer. Different parameters for the top and the
bottom of each layer may be entered.
Unit Weight This column entry corresponds to values of total unit
weight of the soil. Values for the top and bottom of the
sand layer were entered in standard units of force per
unit volume (kN/m3).
Parameter Description
Undrained Shear
Strength
This column represents the input values for the
unconfined-undrained shear strength, cu, at the top and
bottom of the clay layer. These values were taken as one
half of the compression strength of samples obtained
from unconsolidated-unconfined triaxial tests.
Remoulded Shear
Strength
When a pipe pile is driven into clay soils, the clay inside
the pipe forms a plug. The plugged clay may be
remoulded during the driving process. This column
represents the values for the shear strength used for
computing the side friction from the remoulded soil plug
inside a steel pipe.
Blow counts (Optional) This entry corresponds to the number of blow counts
obtained at the top and bottom of the sand layer while
performing a Standard Penetration Test (SPT). An
optional input blow counts obtained from SPT tests in
cases when the vsalues of internal friction angle are not
readily available. When a value different than zero for
the friction angle is specified, the values for blow count
are not used in the computations and may be entered as
zero. Gibbs and Holtz (1957) investigated the
relationship between the number of blow counts and soil
internal friction angle for various overburden pressures
as shown in Table I-1, which can be used to convert
between the values of blow counts from SPT to
equivalent values of internal friction angle.
Table I-1: Relationship between number of blow counts N and soil internal friction angle � (Gibbs and Holtz, 1957)
N Overburden stress, lb/in2
(SPT) 0 20 40 Blows/ft Value of �, degrees
0 0 2 32 4 34 6 36 30
10 38 32 31 15 42 34 32 20 45 36 34 25 37 35 30 39 36 35 40 36 40 41 37 45 42 38 50 44 39 55 45 39 60 40 65 41 70 42 75 42 80 43 85 43 90 44
Appendix J
SACS INPUT FILES IN ASCII FORMAT
LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYSIS – 1 YEAR INPUT DATA OPTIONS EN SDUCJT 2 1 C PT PTPT PTPT LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1 MEMBER 107 207 LG1 MEMBER 201 301 LG2 MEMBER 203 303 LG2 MEMBER 205 305 LG2 MEMBER 207 307 LG2 MEMBER 301 401 LG3 MEMBER 303 403 LG3 MEMBER 305 405 LG3 MEMBER 307 407 LG3 MEMBER 401 501 LG4
MEMBER 403 503 LG4 MEMBER 405 505 LG4 MEMBER 407 507 LG4 MEMBER 501 601 LG5 MEMBER 503 603 LG5 MEMBER 505 605 LG5 MEMBER 507 607 LG5 MEMBER 601 701 LG6 MEMBER 603 703 LG6 MEMBER 605 705 LG6 MEMBER 607 707 LG6 MEMBER 701 801 LG7 MEMBER 703 803 LG7 MEMBER 705 805 LG7 MEMBER 707 807 LG7 MEMBER 102 202 PL1 MEMBER 104 204 PL1 MEMBER 106 206 PL1 MEMBER 108 208 PL1 MEMBER 202 302 PL2 MEMBER 204 304 PL2 MEMBER 206 306 PL2 MEMBER 208 308 PL2 MEMBER 302 402 PL3 MEMBER 304 404 PL3 MEMBER 306 406 PL3 MEMBER 308 408 PL3 MEMBER1 402 501 PL4 MEMBER OFFSETS MEMBER1 404 503 PL4 MEMBER OFFSETS MEMBER1 406 505 PL4 MEMBER OFFSETS MEMBER1 408 507 PL4 MEMBER OFFSETS MEMBER1 209 212 T01 MEMBER OFFSETS 10.00-13.32 -11.56 10.00 MEMBER1 210 209 T01 MEMBER OFFSETS -11.56-10.00 10.00 13.32 MEMBER1 211 210 T01 MEMBER OFFSETS -10.00 13.32 11.55-10.00 MEMBER1 212 211 T01 MEMBER OFFSETS 11.55 10.00 -10.00-13.32 MEMBER1 301 303 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 301 305 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 301 309 T01 MEMBER OFFSETS 15.40 14.39 MEMBER1 303 307 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 303 309 T01 MEMBER OFFSETS -15.54 14.51 MEMBER1 303 401 T01 MEMBER OFFSETS -24.38 4.09 32.75 21.00 -3.53-28.21 MEMBER1 305 307 T01
MEMBER OFFSETS 21.00 -21.10 MEMBER1 305 309 T01 MEMBER OFFSETS 15.40-14.39 MEMBER1 307 309 T01 MEMBER OFFSETS -15.54-14.51 MEMBER1 307 403 T01 MEMBER OFFSETS -4.75-27.10 47.52 3.04 17.36-30.43 MEMBER1 405 301 T01 MEMBER OFFSETS -17.36-30.43 27.10 47.52 MEMBER1 407 305 T01 MEMBER OFFSETS -18.27 3.54-28.35 21.00 -4.07 32.59 MEMBER1 201 209 T02 MEMBER OFFSETS 21.16 MEMBER1 201 212 T02 MEMBER OFFSETS 21.00 MEMBER1 201 303 T02 MEMBER OFFSETS 21.00 4.44 35.50-18.05 -3.81-30.51 MEMBER1 203 211 T02 MEMBER OFFSETS 21.16 MEMBER1 203 307 T02 MEMBER OFFSETS -3.83 25.95 38.29 2.64-17.87-26.37 MEMBER1 205 210 T02 MEMBER OFFSETS 21.00 MEMBER1 209 205 T02 MEMBER OFFSETS -21.16 MEMBER1 210 207 T02 MEMBER OFFSETS -21.10 MEMBER1 211 207 T02 MEMBER OFFSETS -21.16 MEMBER1 212 203 T02 MEMBER OFFSETS -21.10 MEMBER1 301 205 T02 MEMBER OFFSETS 17.87-26.37 -25.95 38.30 MEMBER1 305 207 T02 MEMBER OFFSETS 21.00 3.80-30.37-24.67 -4.46 35.67 MEMBER1 401 403 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 401 405 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 401 409 T03 MEMBER OFFSETS 17.32 11.97 MEMBER1 403 407 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 403 409 T03 MEMBER OFFSETS -17.48 12.08 MEMBER1 405 407 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 405 409 T03 MEMBER OFFSETS 17.32-11.97 MEMBER1 407 409 T03 MEMBER OFFSETS -17.48-12.08 MEMBER1 101 109 T04 MEMBER OFFSETS 12.67 16.88 MEMBER1 103 109 T04 MEMBER OFFSETS -12.76 17.01 MEMBER1 105 109 T04
MEMBER OFFSETS 12.67-16.88 MEMBER1 107 109 T04 MEMBER OFFSETS -12.76-17.01 MEMBER1 101 103 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 101 105 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 101 205 T05 MEMBER OFFSETS 25.80 37.10 -17.56-28.84 MEMBER1 103 107 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 103 201 T05 MEMBER OFFSETS -24.99 4.86 38.90 21.00 -4.11-32.88 MEMBER1 105 107 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 105 207 T05 MEMBER OFFSETS 21.00 -4.73 37.83-17.91 3.99-31.92 MEMBER1 107 203 T05 MEMBER OFFSETS -3.70-25.78 36.98 2.87 17.57-28.74 MEMBER1 201 202 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 203 204 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 205 206 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 207 208 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 301 302 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 303 304 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 305 306 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 307 308 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 401 402 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 403 404 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 405 406 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 407 408 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 701 714 W01 MEMBER 705 717 W01 MEMBER 714 715 W01 MEMBER 715 703 W01 MEMBER 717 718 W01 MEMBER 718 707 W01 MEMBER 801 834 W01 MEMBER 803 836 W01 MEMBER 805 837 W01 MEMBER 807 839 W01 MEMBER 834 835 W01 MEMBER 835 803 W01 MEMBER 837 838 W01
MEMBER 838 807 W01 MEMBER 701 705 W02 MEMBER 703 707 W02 MEMBER 705 720 W02 MEMBER 707 723 W02 MEMBER 709 701 W02 MEMBER 710 714 W02 MEMBER 711 715 W02 MEMBER 712 703 W02 MEMBER 714 717 W02 MEMBER 715 718 W02 MEMBER 717 721 W02 MEMBER 718 722 W02 MEMBER 801 805 W02 MEMBER 803 807 W02 MEMBER 805 840 W02 MEMBER 807 843 W02 MEMBER 829 801 W02 MEMBER 830 834 W02 MEMBER 831 835 W02 MEMBER 832 803 W02 MEMBER 833 836 W02 MEMBER 834 837 W02 MEMBER 835 838 W02 MEMBER 836 839 W02 MEMBER 837 841 W02 MEMBER 838 842 W02 MEMBER 839 844 W02 PGRUPPGRUP P01 0.3750I29.000 0.25036.000 490.000 PLATE PLATE AAAC 801 834 805 837P01 0 PLATE AAAD 834 835 837 838P01 0 JOINTJOINT 101 -24. -50. -261. -3.000 JOINT 102 -24. -50. -261. -3.000 PILEHD JOINT 103 51. -50. -261. 4.800 -3.000 JOINT 104 51. -50. -261. 4.800 -3.000 PILEHD JOINT 105 -24. 50. -261. 3.000 JOINT 106 -24. 50. -261. 3.000 PILEHD JOINT 107 51. 50. -261. 4.800 3.000 JOINT 108 51. 50. -261. 4.800 3.000 PILEHD JOINT 109 13. 0. -261. 8.400 JOINT 201 -24. -38. -164. -1.500 JOINT 202 -24. -38. -164. -1.500 JOINT 203 41. -38. -164. 8.400 -1.500 JOINT 204 41. -38. -164. 8.400 -1.500 JOINT 205 -24. 38. -164. 1.500 JOINT 206 -24. 38. -164. 1.500 JOINT 207 41. 38. -164. 8.400 1.500 JOINT 208 41. 38. -164. 8.400 1.500 JOINT 209 -24. 0. -164. JOINT 210 8. 38. -164. 10.296 1.500 JOINT 211 41. 0. -164. 8.400 JOINT 212 8. -38. -164. 10.296 -1.500 JOINT 301 -24. -26. -69. -3.000
JOINT 302 -24. -26. -69. -3.000 JOINT 303 32. -26. -69. 2.400 -3.000 JOINT 304 32. -26. -69. 2.400 -3.000 JOINT 305 -24. 26. -69. 3.000 JOINT 306 -24. 26. -69. 3.000 JOINT 307 32. 26. -69. 2.400 3.000 JOINT 308 32. 26. -69. 2.400 3.000 JOINT 309 4. 0. -69. 1.200 JOINT 401 -24. -16. 6. -9.756 6.000 JOINT 402 -24. -16. 6. -9.756 6.000 JOINT 403 24. -16. 6. 7.800 -9.756 6.000 JOINT 404 24. -16. 6. 7.800 -9.756 6.000 JOINT 405 -24. 16. 6. 9.756 6.000 JOINT 406 -24. 16. 6. 9.756 6.000 JOINT 407 24. 16. 6. 7.800 9.756 6.000 JOINT 408 24. 16. 6. 7.800 9.756 6.000 JOINT 409 0. 0. 6. 3.900 6.000 JOINT 501 -24. -16. 10. -4.500 JOINT 503 24. -16. 10. 3.600 -4.500 JOINT 505 -24. 16. 10. 4.500 JOINT 507 24. 16. 10. 3.600 4.500 JOINT 601 -24. -16. 13. JOINT 603 24. -16. 13. JOINT 605 -24. 16. 13. JOINT 607 24. 16. 13. JOINT 701 -24. -16. 50. JOINT 703 24. -16. 50. JOINT 705 -24. 16. 50. JOINT 707 24. 16. 50. JOINT 709 -24. -26. 50. -2.964 JOINT 710 -8. -26. 50. -2.424 -2.964 JOINT 711 8. -26. 50. 2.424 -2.964 JOINT 712 24. -26. 50. -2.964 JOINT 714 -8. -16. 50. -2.424 JOINT 715 8. -16. 50. 2.424 JOINT 717 -8. 16. 50. -2.424 JOINT 718 8. 16. 50. 2.424 JOINT 720 -24. 26. 50. 2.964 JOINT 721 -8. 26. 50. -2.424 2.964 JOINT 722 8. 26. 50. 2.424 2.964 JOINT 723 24. 26. 50. 2.964 JOINT 801 -24. -16. 75. JOINT 803 24. -16. 75. JOINT 805 -24. 16. 75. JOINT 807 24. 16. 75. JOINT 829 -24. -26. 75. -2.964 JOINT 830 -8. -26. 75. -2.424 -2.964 JOINT 831 8. -26. 75. 2.424 -2.964 JOINT 832 24. -26. 75. -2.964 JOINT 833 41. -26. 75. 0.132 -2.964 JOINT 834 -8. -16. 75. -2.424 JOINT 835 8. -16. 75. 2.424 JOINT 836 41. -16. 75. 0.132 JOINT 837 -8. 16. 75. -2.424 JOINT 838 8. 16. 75. 2.424 JOINT 839 41. 16. 75. 0.132
JOINT 840 -24. 26. 75. 2.964 JOINT 841 -8. 26. 75. -2.424 2.964 JOINT 842 8. 26. 75. 2.424 2.964 JOINT 843 24. 26. 75. 2.964 JOINT 844 41. 26. 75. 0.132 2.964 CDMCDM AP LOADLOADCN 31 CURRCURR 0.000 0.000 0.000 0.800 US NL FPS AWP CURR 30.000 2.410 0.000 CURR 60.000 2.650 0.000 CURR 80.000 2.770 0.000 CURR 100.000 2.860 0.000 CURR 120.000 2.950 0.000 CURR 150.000 3.030 0.000 CURR 180.000 3.110 0.000 CURR 200.000 3.150 0.000 CURR 261.000 3.280 0.000 WAVE WAVE0.95STOK 23.29261.00 8.50 0.00 D 0.00 5.00 72MS10 1 0 LOADCN 32 CURRCURR 0.000 0.000 45.000 0.850 US NL FPS AWP CURR 30.000 2.410 45.000 CURR 60.000 2.650 45.000 CURR 80.000 2.770 45.000 CURR 100.000 2.860 45.000 CURR 120.000 2.950 45.000 CURR 150.000 3.030 45.000 CURR 180.000 3.110 45.000 CURR 200.000 3.150 45.000 CURR 261.000 3.280 45.000 WAVE WAVE0.95STOK 23.29261.00 8.50 0.00 D 0.00 5.00 72MM10 1 0 LOADCN 33 CURRCURR 0.000 0.000 90.000 0.800 US NL FPS AWP CURR 30.000 2.410 90.000 CURR 60.000 2.650 90.000 CURR 80.000 2.770 90.000 CURR 100.000 2.860 90.000 CURR 120.000 2.950 90.000 CURR 150.000 3.030 90.000 CURR 180.000 3.110 90.000 CURR 200.000 3.150 90.000 CURR 261.000 3.280 90.000 WAVE WAVE0.95STOK 23.29261.00 8.50 90.00 D 0.00 5.00 72MS10 1 0 LOADCN 34 CURRCURR 0.000 0.000 135.000 0.850 US NL FPS AWP CURR 30.000 2.410 135.000 CURR 60.000 2.650 135.000 CURR 80.000 2.770 135.000
CURR 100.000 2.860 135.000 CURR 120.000 2.950 135.000 CURR 150.000 3.030 135.000 CURR 180.000 3.110 135.000 CURR 200.000 3.150 135.000 CURR 261.000 3.280 135.000 WAVE WAVE0.95STOK 23.29261.00 8.50 135.00 D 0.00 5.00 72MM10 1 0 LOADCN 35 CURRCURR 0.000 0.000 180.000 0.800 US NL FPS AWP CURR 30.000 2.410 180.000 CURR 60.000 2.650 180.000 CURR 80.000 2.770 180.000 CURR 100.000 2.860 180.000 CURR 120.000 2.950 180.000 CURR 150.000 3.030 180.000 CURR 180.000 3.110 180.000 CURR 200.000 3.150 180.000 CURR 261.000 3.280 180.000 WAVE WAVE0.95STOK 23.29261.00 8.50 180.00 D 0.00 5.00 72MS10 1 0 LOADCN 36 CURRCURR 0.000 0.000 225.000 0.850 US NL FPS AWP CURR 30.000 2.410 225.000 CURR 60.000 2.650 225.000 CURR 80.000 2.770 225.000 CURR 100.000 2.860 225.000 CURR 120.000 2.950 225.000 CURR 150.000 3.030 225.000 CURR 180.000 3.110 225.000 CURR 200.000 3.150 225.000 CURR 261.000 3.280 225.000 WAVE WAVE0.95STOK 23.29261.00 8.50 225.00 D 0.00 5.00 72MM10 1 0 LOADCN 37 CURRCURR 0.000 0.000 270.000 0.800 US CN FPS AWP CURR 30.000 2.410 270.000 CURR 60.000 2.650 270.000 CURR 80.000 2.770 270.000 CURR 100.000 2.860 270.000 CURR 120.000 2.950 270.000 CURR 150.000 3.030 270.000 CURR 180.000 3.110 270.000 CURR 200.000 3.150 270.000 CURR 261.000 3.280 270.000 WAVE WAVE0.95STOK 23.29261.00 8.50 270.00 D 0.00 5.00 72MS10 1 0 LOADCN 38 CURRCURR 0.000 0.000 315.000 0.850 US NL FPS AWP CURR 30.000 2.410 315.000 CURR 60.000 2.650 315.000 CURR 80.000 2.770 315.000
CURR 100.000 2.860 315.000 CURR 120.000 2.950 315.000 CURR 150.000 3.030 315.000 CURR 180.000 3.110 315.000 CURR 200.000 3.150 315.000 CURR 261.000 3.280 315.000 WAVE WAVE0.95STOK 23.29261.00 8.50 315.00 D 0.00 5.00 72MM10 1 0 LOADCNAREA ***LDS1** 24.000 -26.247 50.000 24.000 26.247 50.000 -24.000 ***LDS2** -26.247 50.000 -24.000 26.247 50.000 -10.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES10PSFL LOAD Z 701 705 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 703 707 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 705 720 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 707 723 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 709 701 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 710 714 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 711 715 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 712 703 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 714 717 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 715 718 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 717 721 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 718 722 -0.1610 -0.1610 GLOB UNIF 10PSFL ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.000 ***LDS2** -26.247 75.000 -24.000 26.247 75.000 -15.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES15PSFU LOAD Z 801 805 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 803 807 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 805 840 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 807 843 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 829 801 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 830 834 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 831 835 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 832 803 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 833 836 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 834 837 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 835 838 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 836 839 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 837 841 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 838 842 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 839 844 -0.1280 -0.1280 GLOB UNIF 15PSFU LOADCNEQPT ***LDS1** 16.000 6.000 75.000 16.000 6.000 75.000 ***LDS2** -250.000 20.000 10.000 ***LDS3** 10.000 1 2 2 0 0EQPT -1EQUPSKIDSKID1 X LOAD Z 835 838 17.4040-65.579 GLOB CONC SKID1 LOAD Z 835 838 27.4040-65.579 GLOB CONC SKID1 LOAD Z 803 807 17.4040-59.421 GLOB CONC SKID1 LOAD Z 803 807 27.4040-59.421 GLOB CONC SKID1 ***LDS1** -16.000 -16.000 75.000 -16.000 -16.000 75.000 ***LDS2** -150.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID2 X LOAD Z 829 801 6.49700-35.653 GLOB CONC SKID2 LOAD Z 830 834 6.49700-39.347 GLOB CONC SKID2 LOAD Z 834 837 4.15400-39.347 GLOB CONC SKID2
LOAD Z 801 805 4.15400-35.653 GLOB CONC SKID2 ***LDS1** -16.000 50.000 -16.000 50.000 ***LDS2** -100.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID3 X LOAD Z 701 705 12.6540-23.769 GLOB CONC SKID3 LOAD Z 701 705 20.1540-23.769 GLOB CONC SKID3 LOAD Z 714 717 12.6540-26.231 GLOB CONC SKID3 LOAD Z 714 717 20.1540-26.231 GLOB CONC SKID3 ***LDS1** 32.000 19.000 75.000 32.000 19.000 75.000 ***LDS2** -35.000 20.000 7.000 ***LDS3** 7.000 1 2 3 0 0EQPT -1EQUPSKIDSKID4 X LOAD Z 803 807 31.5000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 3.00000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 6.50000-6.1800 GLOB CONC SKID4 LOAD Z 836 839 31.5000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 3.00000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 6.50000-5.4866 GLOB CONC SKID4 LOADCNLIVE ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.606 ***LDS2** 26.247 75.000 -24.606 -26.247 75.000 -100.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES100PSFU Z LOAD Z 829 801 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 830 834 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 831 835 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 832 803 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 833 836 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 834 837 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 835 838 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 836 839 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 837 841 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 838 842 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 839 844 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 801 805 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 803 807 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 805 840 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 807 843 -1.6400 -1.6400 GLOB UNIF 100PSFU ***LDS1** 24.606 -26.247 50.000 24.606 26.247 50.000 -24.606 ***LDS2** 26.247 50.000 -24.606 -26.247 50.000 -50.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES50PSFL Z LOAD Z 701 705 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 703 707 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 705 720 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 707 723 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 709 701 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 710 714 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 711 715 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 712 703 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 714 717 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 715 718 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 717 721 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 718 722 -0.8200 -0.8200 GLOB UNIF 50PSFL LOADCNMISC LOAD Z 712 703 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 703 707 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 707 723 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 833 836 -0.1900 -0.1900 GLOB UNIF WALK2
LOAD Z 836 839 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 839 844 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD 807 -20.000 GLOB JOIN CRANE ***LDS1** -8.000 20.000 50.000 -8.000 20.000 50.000 ***LDS2** -10.000 34.000 0.100 ***LDS3** 0.100 1 2 2 0 0MISC -1EQUPSKIDFIREWALLX LOAD Z 705 720 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 705 720 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 4.05000-1.6667 GLOB CONC FIREWALL LCOMBLCOMB 1001 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 31 1.000 LCOMB 1002 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 32 1.000 LCOMB 1003 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 33 1.000 LCOMB 1004 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 34 1.000 LCOMB 1005 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 35 1.000 LCOMB 1006 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 36 1.000 LCOMB 1007 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 37 1.000 LCOMB 1008 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 38 1.000 END
LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYSIS 5 year INPUT DATA OPTIONS EN SDUCJT 2 1 C PT PTPT PTPT LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1
MEMBER 107 207 LG1 MEMBER 201 301 LG2 MEMBER 203 303 LG2 MEMBER 205 305 LG2 MEMBER 207 307 LG2 MEMBER 301 401 LG3 MEMBER 303 403 LG3 MEMBER 305 405 LG3 MEMBER 307 407 LG3 MEMBER 401 501 LG4 MEMBER 403 503 LG4 MEMBER 405 505 LG4 MEMBER 407 507 LG4 MEMBER 501 601 LG5 MEMBER 503 603 LG5 MEMBER 505 605 LG5 MEMBER 507 607 LG5 MEMBER 601 701 LG6 MEMBER 603 703 LG6 MEMBER 605 705 LG6 MEMBER 607 707 LG6 MEMBER 701 801 LG7 MEMBER 703 803 LG7 MEMBER 705 805 LG7 MEMBER 707 807 LG7 MEMBER 102 202 PL1 MEMBER 104 204 PL1 MEMBER 106 206 PL1 MEMBER 108 208 PL1 MEMBER 202 302 PL2 MEMBER 204 304 PL2 MEMBER 206 306 PL2 MEMBER 208 308 PL2 MEMBER 302 402 PL3 MEMBER 304 404 PL3 MEMBER 306 406 PL3 MEMBER 308 408 PL3 MEMBER1 402 501 PL4 MEMBER OFFSETS MEMBER1 404 503 PL4 MEMBER OFFSETS MEMBER1 406 505 PL4 MEMBER OFFSETS MEMBER1 408 507 PL4 MEMBER OFFSETS MEMBER1 209 212 T01
MEMBER OFFSETS 10.00-13.32 -11.56 10.00 MEMBER1 210 209 T01 MEMBER OFFSETS -11.56-10.00 10.00 13.32 MEMBER1 211 210 T01 MEMBER OFFSETS -10.00 13.32 11.55-10.00 MEMBER1 212 211 T01 MEMBER OFFSETS 11.55 10.00 -10.00-13.32 MEMBER1 301 303 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 301 305 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 301 309 T01 MEMBER OFFSETS 15.40 14.39 MEMBER1 303 307 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 303 309 T01 MEMBER OFFSETS -15.54 14.51 MEMBER1 303 401 T01 MEMBER OFFSETS -24.38 4.09 32.75 21.00 -3.53-28.21 MEMBER1 305 307 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 305 309 T01 MEMBER OFFSETS 15.40-14.39 MEMBER1 307 309 T01 MEMBER OFFSETS -15.54-14.51 MEMBER1 307 403 T01 MEMBER OFFSETS -4.75-27.10 47.52 3.04 17.36-30.43 MEMBER1 405 301 T01 MEMBER OFFSETS -17.36-30.43 27.10 47.52 MEMBER1 407 305 T01 MEMBER OFFSETS -18.27 3.54-28.35 21.00 -4.07 32.59 MEMBER1 201 209 T02 MEMBER OFFSETS 21.16 MEMBER1 201 212 T02 MEMBER OFFSETS 21.00 MEMBER1 201 303 T02 MEMBER OFFSETS 21.00 4.44 35.50-18.05 -3.81-30.51 MEMBER1 203 211 T02 MEMBER OFFSETS 21.16 MEMBER1 203 307 T02 MEMBER OFFSETS -3.83 25.95 38.29 2.64-17.87-26.37 MEMBER1 205 210 T02 MEMBER OFFSETS 21.00 MEMBER1 209 205 T02 MEMBER OFFSETS -21.16 MEMBER1 210 207 T02
MEMBER OFFSETS -21.10 MEMBER1 211 207 T02 MEMBER OFFSETS -21.16 MEMBER1 212 203 T02 MEMBER OFFSETS -21.10 MEMBER1 301 205 T02 MEMBER OFFSETS 17.87-26.37 -25.95 38.30 MEMBER1 305 207 T02 MEMBER OFFSETS 21.00 3.80-30.37-24.67 -4.46 35.67 MEMBER1 401 403 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 401 405 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 401 409 T03 MEMBER OFFSETS 17.32 11.97 MEMBER1 403 407 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 403 409 T03 MEMBER OFFSETS -17.48 12.08 MEMBER1 405 407 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 405 409 T03 MEMBER OFFSETS 17.32-11.97 MEMBER1 407 409 T03 MEMBER OFFSETS -17.48-12.08 MEMBER1 101 109 T04 MEMBER OFFSETS 12.67 16.88 MEMBER1 103 109 T04 MEMBER OFFSETS -12.76 17.01 MEMBER1 105 109 T04 MEMBER OFFSETS 12.67-16.88 MEMBER1 107 109 T04 MEMBER OFFSETS -12.76-17.01 MEMBER1 101 103 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 101 105 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 101 205 T05 MEMBER OFFSETS 25.80 37.10 -17.56-28.84 MEMBER1 103 107 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 103 201 T05 MEMBER OFFSETS -24.99 4.86 38.90 21.00 -4.11-32.88 MEMBER1 105 107 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 105 207 T05
MEMBER OFFSETS 21.00 -4.73 37.83-17.91 3.99-31.92 MEMBER1 107 203 T05 MEMBER OFFSETS -3.70-25.78 36.98 2.87 17.57-28.74 MEMBER1 201 202 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 203 204 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 205 206 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 207 208 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 301 302 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 303 304 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 305 306 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 307 308 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 401 402 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 403 404 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 405 406 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 407 408 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 701 714 W01 MEMBER 705 717 W01 MEMBER 714 715 W01 MEMBER 715 703 W01 MEMBER 717 718 W01 MEMBER 718 707 W01 MEMBER 801 834 W01 MEMBER 803 836 W01 MEMBER 805 837 W01 MEMBER 807 839 W01 MEMBER 834 835 W01 MEMBER 835 803 W01 MEMBER 837 838 W01 MEMBER 838 807 W01 MEMBER 701 705 W02 MEMBER 703 707 W02 MEMBER 705 720 W02 MEMBER 707 723 W02 MEMBER 709 701 W02
MEMBER 710 714 W02 MEMBER 711 715 W02 MEMBER 712 703 W02 MEMBER 714 717 W02 MEMBER 715 718 W02 MEMBER 717 721 W02 MEMBER 718 722 W02 MEMBER 801 805 W02 MEMBER 803 807 W02 MEMBER 805 840 W02 MEMBER 807 843 W02 MEMBER 829 801 W02 MEMBER 830 834 W02 MEMBER 831 835 W02 MEMBER 832 803 W02 MEMBER 833 836 W02 MEMBER 834 837 W02 MEMBER 835 838 W02 MEMBER 836 839 W02 MEMBER 837 841 W02 MEMBER 838 842 W02 MEMBER 839 844 W02 PGRUPPGRUP P01 0.3750I29.000 0.25036.000 490.000 PLATEPLATE AAAC 801 834 805 837P01 0 PLATE AAAD 834 835 837 838P01 0 JOINTJOINT 101 -24. -50. -261. -3.000 JOINT 102 -24. -50. -261. -3.000 PILEHD JOINT 103 51. -50. -261. 4.800 -3.000 JOINT 104 51. -50. -261. 4.800 -3.000 PILEHD JOINT 105 -24. 50. -261. 3.000 JOINT 106 -24. 50. -261. 3.000 PILEHD JOINT 107 51. 50. -261. 4.800 3.000 JOINT 108 51. 50. -261. 4.800 3.000 PILEHD JOINT 109 13. 0. -261. 8.400 JOINT 201 -24. -38. -164. -1.500 JOINT 202 -24. -38. -164. -1.500 JOINT 203 41. -38. -164. 8.400 -1.500 JOINT 204 41. -38. -164. 8.400 -1.500 JOINT 205 -24. 38. -164. 1.500 JOINT 206 -24. 38. -164. 1.500 JOINT 207 41. 38. -164. 8.400 1.500 JOINT 208 41. 38. -164. 8.400 1.500 JOINT 209 -24. 0. -164.
JOINT 210 8. 38. -164. 10.296 1.500 JOINT 211 41. 0. -164. 8.400 JOINT 212 8. -38. -164. 10.296 -1.500 JOINT 301 -24. -26. -69. -3.000 JOINT 302 -24. -26. -69. -3.000 JOINT 303 32. -26. -69. 2.400 -3.000 JOINT 304 32. -26. -69. 2.400 -3.000 JOINT 305 -24. 26. -69. 3.000 JOINT 306 -24. 26. -69. 3.000 JOINT 307 32. 26. -69. 2.400 3.000 JOINT 308 32. 26. -69. 2.400 3.000 JOINT 309 4. 0. -69. 1.200 JOINT 401 -24. -16. 6. -9.756 6.000 JOINT 402 -24. -16. 6. -9.756 6.000 JOINT 403 24. -16. 6. 7.800 -9.756 6.000 JOINT 404 24. -16. 6. 7.800 -9.756 6.000 JOINT 405 -24. 16. 6. 9.756 6.000 JOINT 406 -24. 16. 6. 9.756 6.000 JOINT 407 24. 16. 6. 7.800 9.756 6.000 JOINT 408 24. 16. 6. 7.800 9.756 6.000 JOINT 409 0. 0. 6. 3.900 6.000 JOINT 501 -24. -16. 10. -4.500 JOINT 503 24. -16. 10. 3.600 -4.500 JOINT 505 -24. 16. 10. 4.500 JOINT 507 24. 16. 10. 3.600 4.500 JOINT 601 -24. -16. 13. JOINT 603 24. -16. 13. JOINT 605 -24. 16. 13. JOINT 607 24. 16. 13. JOINT 701 -24. -16. 50. JOINT 703 24. -16. 50. JOINT 705 -24. 16. 50. JOINT 707 24. 16. 50. JOINT 709 -24. -26. 50. -2.964 JOINT 710 -8. -26. 50. -2.424 -2.964 JOINT 711 8. -26. 50. 2.424 -2.964 JOINT 712 24. -26. 50. -2.964 JOINT 714 -8. -16. 50. -2.424 JOINT 715 8. -16. 50. 2.424 JOINT 717 -8. 16. 50. -2.424 JOINT 718 8. 16. 50. 2.424 JOINT 720 -24. 26. 50. 2.964 JOINT 721 -8. 26. 50. -2.424 2.964 JOINT 722 8. 26. 50. 2.424 2.964 JOINT 723 24. 26. 50. 2.964 JOINT 801 -24. -16. 75.
JOINT 803 24. -16. 75. JOINT 805 -24. 16. 75. JOINT 807 24. 16. 75. JOINT 829 -24. -26. 75. -2.964 JOINT 830 -8. -26. 75. -2.424 -2.964 JOINT 831 8. -26. 75. 2.424 -2.964 JOINT 832 24. -26. 75. -2.964 JOINT 833 41. -26. 75. 0.132 -2.964 JOINT 834 -8. -16. 75. -2.424 JOINT 835 8. -16. 75. 2.424 JOINT 836 41. -16. 75. 0.132 JOINT 837 -8. 16. 75. -2.424 JOINT 838 8. 16. 75. 2.424 JOINT 839 41. 16. 75. 0.132 JOINT 840 -24. 26. 75. 2.964 JOINT 841 -8. 26. 75. -2.424 2.964 JOINT 842 8. 26. 75. 2.424 2.964 JOINT 843 24. 26. 75. 2.964 JOINT 844 41. 26. 75. 0.132 2.964 CDMCDM AP LOADLOADCN 31 CURRCURR 0.000 0.000 0.000 0.800 US NL FPS AWP CURR 30.000 2.410 0.000 CURR 60.000 2.650 0.000 CURR 80.000 2.770 0.000 CURR 100.000 2.860 0.000 CURR 120.000 2.950 0.000 CURR 150.000 3.030 0.000 CURR 180.000 3.110 0.000 CURR 200.000 3.150 0.000 CURR 261.000 3.280 0.000 WAVE WAVE0.95STOK 26.24261.00 9.10 0.00 D 0.00 5.00 72MS10 1 0 LOADCN 32 CURRCURR 0.000 0.000 45.000 0.850 US NL FPS AWP CURR 30.000 2.410 45.000 CURR 60.000 2.650 45.000 CURR 80.000 2.770 45.000 CURR 100.000 2.860 45.000 CURR 120.000 2.950 45.000 CURR 150.000 3.030 45.000 CURR 180.000 3.110 45.000
CURR 200.000 3.150 45.000 CURR 261.000 3.280 45.000 WAVE WAVE0.95STOK 26.24261.00 9.10 0.00 D 0.00 5.00 72MM10 1 0 LOADCN 33 CURRCURR 0.000 0.000 90.000 0.800 US NL FPS AWP CURR 30.000 2.410 90.000 CURR 60.000 2.650 90.000 CURR 80.000 2.770 90.000 CURR 100.000 2.860 90.000 CURR 120.000 2.950 90.000 CURR 150.000 3.030 90.000 CURR 180.000 3.110 90.000 CURR 200.000 3.150 90.000 CURR 261.000 3.280 90.000 WAVE WAVE0.95STOK 26.24261.00 9.10 90.00 D 0.00 5.00 72MS10 1 0 LOADCN 34 CURRCURR 0.000 0.000 135.000 0.850 US NL FPS AWP CURR 30.000 2.410 135.000 CURR 60.000 2.650 135.000 CURR 80.000 2.770 135.000 CURR 100.000 2.860 135.000 CURR 120.000 2.950 135.000 CURR 150.000 3.030 135.000 CURR 180.000 3.110 135.000 CURR 200.000 3.150 135.000 CURR 261.000 3.280 135.000 WAVE WAVE0.95STOK 26.24261.00 9.10 135.00 D 0.00 5.00 72MM10 1 0 LOADCN 35 CURRCURR 0.000 0.000 180.000 0.800 US NL FPS AWP CURR 30.000 2.410 180.000 CURR 60.000 2.650 180.000 CURR 80.000 2.770 180.000 CURR 100.000 2.860 180.000 CURR 120.000 2.950 180.000 CURR 150.000 3.030 180.000 CURR 180.000 3.110 180.000 CURR 200.000 3.150 180.000 CURR 261.000 3.280 180.000 WAVE WAVE0.95STOK 26.24261.00 9.10 180.00 D 0.00 5.00 72MS10 1 0
LOADCN 36 CURRCURR 0.000 0.000 225.000 0.850 US NL FPS AWP CURR 30.000 2.410 225.000 CURR 60.000 2.650 225.000 CURR 80.000 2.770 225.000 CURR 100.000 2.860 225.000 CURR 120.000 2.950 225.000 CURR 150.000 3.030 225.000 CURR 180.000 3.110 225.000 CURR 200.000 3.150 225.000 CURR 261.000 3.280 225.000 WAVE WAVE0.95STOK 26.24261.00 9.10 225.00 D 0.00 5.00 72MM10 1 0 LOADCN 37 CURRCURR 0.000 0.000 270.000 0.800 US CN FPS AWP CURR 30.000 2.410 270.000 CURR 60.000 2.650 270.000 CURR 80.000 2.770 270.000 CURR 100.000 2.860 270.000 CURR 120.000 2.950 270.000 CURR 150.000 3.030 270.000 CURR 180.000 3.110 270.000 CURR 200.000 3.150 270.000 CURR 261.000 3.280 270.000 WAVE WAVE0.95STOK 26.24261.00 9.10 270.00 D 0.00 5.00 72MS10 1 0 LOADCN 38 CURRCURR 0.000 0.000 315.000 0.850 US NL FPS AWP CURR 30.000 2.410 315.000 CURR 60.000 2.650 315.000 CURR 80.000 2.770 315.000 CURR 100.000 2.860 315.000 CURR 120.000 2.950 315.000 CURR 150.000 3.030 315.000 CURR 180.000 3.110 315.000 CURR 200.000 3.150 315.000 CURR 261.000 3.280 315.000 WAVE WAVE0.95STOK 26.24261.00 9.10 315.00 D 0.00 5.00 72MM10 1 0 LOADCNAREA***LDS1** 24.000 -26.247 50.000 24.000 26.247 50.000 -24.000 ***LDS2** -26.247 50.000 -24.000 26.247 50.000 -10.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES10PSFL
LOAD Z 701 705 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 703 707 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 705 720 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 707 723 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 709 701 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 710 714 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 711 715 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 712 703 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 714 717 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 715 718 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 717 721 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 718 722 -0.1610 -0.1610 GLOB UNIF 10PSFL ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.000 ***LDS2** -26.247 75.000 -24.000 26.247 75.000 -15.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES15PSFU LOAD Z 801 805 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 803 807 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 805 840 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 807 843 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 829 801 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 830 834 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 831 835 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 832 803 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 833 836 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 834 837 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 835 838 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 836 839 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 837 841 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 838 842 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 839 844 -0.1280 -0.1280 GLOB UNIF 15PSFU LOADCNEQPT ***LDS1** 16.000 6.000 75.000 16.000 6.000 75.000 ***LDS2** -250.000 20.000 10.000 ***LDS3** 10.000 1 2 2 0 0EQPT -1EQUPSKIDSKID1 X LOAD Z 835 838 17.4040-65.579 GLOB CONC SKID1 LOAD Z 835 838 27.4040-65.579 GLOB CONC SKID1 LOAD Z 803 807 17.4040-59.421 GLOB CONC SKID1 LOAD Z 803 807 27.4040-59.421 GLOB CONC SKID1 ***LDS1** -16.000 -16.000 75.000 -16.000 -16.000 75.000 ***LDS2** -150.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID2 X LOAD Z 829 801 6.49700-35.653 GLOB CONC SKID2 LOAD Z 830 834 6.49700-39.347 GLOB CONC SKID2 LOAD Z 834 837 4.15400-39.347 GLOB CONC SKID2 LOAD Z 801 805 4.15400-35.653 GLOB CONC SKID2 ***LDS1** -16.000 50.000 -16.000 50.000
***LDS2** -100.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID3 X LOAD Z 701 705 12.6540-23.769 GLOB CONC SKID3 LOAD Z 701 705 20.1540-23.769 GLOB CONC SKID3 LOAD Z 714 717 12.6540-26.231 GLOB CONC SKID3 LOAD Z 714 717 20.1540-26.231 GLOB CONC SKID3 ***LDS1** 32.000 19.000 75.000 32.000 19.000 75.000 ***LDS2** -35.000 20.000 7.000 ***LDS3** 7.000 1 2 3 0 0EQPT -1EQUPSKIDSKID4 X LOAD Z 803 807 31.5000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 3.00000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 6.50000-6.1800 GLOB CONC SKID4 LOAD Z 836 839 31.5000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 3.00000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 6.50000-5.4866 GLOB CONC SKID4 LOADCNLIVE ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.606 ***LDS2** 26.247 75.000 -24.606 -26.247 75.000 -100.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES100PSFU Z LOAD Z 829 801 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 830 834 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 831 835 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 832 803 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 833 836 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 834 837 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 835 838 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 836 839 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 837 841 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 838 842 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 839 844 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 801 805 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 803 807 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 805 840 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 807 843 -1.6400 -1.6400 GLOB UNIF 100PSFU ***LDS1** 24.606 -26.247 50.000 24.606 26.247 50.000 -24.606 ***LDS2** 26.247 50.000 -24.606 -26.247 50.000 -50.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES50PSFL Z LOAD Z 701 705 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 703 707 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 705 720 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 707 723 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 709 701 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 710 714 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 711 715 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 712 703 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 714 717 -0.8200 -0.8200 GLOB UNIF 50PSFL
LOAD Z 715 718 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 717 721 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 718 722 -0.8200 -0.8200 GLOB UNIF 50PSFL LOADCNMISC LOAD Z 712 703 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 703 707 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 707 723 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 833 836 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 836 839 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 839 844 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD 807 -20.000 GLOB JOIN CRANE ***LDS1** -8.000 20.000 50.000 -8.000 20.000 50.000 ***LDS2** -10.000 34.000 0.100 ***LDS3** 0.100 1 2 2 0 0MISC -1EQUPSKIDFIREWALLX LOAD Z 705 720 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 705 720 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 4.05000-1.6667 GLOB CONC FIREWALL LCOMBLCOMB 1001 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 31 1.000 LCOMB 1002 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 32 1.000 LCOMB 1003 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 33 1.000 LCOMB 1004 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 34 1.000 LCOMB 1005 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 35 1.000 LCOMB 1006 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 36 1.000 LCOMB 1007 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 37 1.000 LCOMB 1008 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 38 1.000 END
LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYSIS OPTIONS EN SDUCJT 2 1 C PT PTPT PTPT LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1
MEMBER 107 207 LG1 MEMBER 201 301 LG2 MEMBER 203 303 LG2 MEMBER 205 305 LG2 MEMBER 207 307 LG2 MEMBER 301 401 LG3 MEMBER 303 403 LG3 MEMBER 305 405 LG3 MEMBER 307 407 LG3 MEMBER 401 501 LG4 MEMBER 403 503 LG4 MEMBER 405 505 LG4 MEMBER 407 507 LG4 MEMBER 501 601 LG5 MEMBER 503 603 LG5 MEMBER 505 605 LG5 MEMBER 507 607 LG5 MEMBER 601 701 LG6 MEMBER 603 703 LG6 MEMBER 605 705 LG6 MEMBER 607 707 LG6 MEMBER 701 801 LG7 MEMBER 703 803 LG7 MEMBER 705 805 LG7 MEMBER 707 807 LG7 MEMBER 102 202 PL1 MEMBER 104 204 PL1 MEMBER 106 206 PL1 MEMBER 108 208 PL1 MEMBER 202 302 PL2 MEMBER 204 304 PL2 MEMBER 206 306 PL2 MEMBER 208 308 PL2 MEMBER 302 402 PL3 MEMBER 304 404 PL3 MEMBER 306 406 PL3 MEMBER 308 408 PL3 MEMBER1 402 501 PL4 MEMBER OFFSETS MEMBER1 404 503 PL4 MEMBER OFFSETS MEMBER1 406 505 PL4 MEMBER OFFSETS MEMBER1 408 507 PL4 MEMBER OFFSETS MEMBER1 209 212 T01
MEMBER OFFSETS 10.00-13.32 -11.56 10.00 MEMBER1 210 209 T01 MEMBER OFFSETS -11.56-10.00 10.00 13.32 MEMBER1 211 210 T01 MEMBER OFFSETS -10.00 13.32 11.55-10.00 MEMBER1 212 211 T01 MEMBER OFFSETS 11.55 10.00 -10.00-13.32 MEMBER1 301 303 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 301 305 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 301 309 T01 MEMBER OFFSETS 15.40 14.39 MEMBER1 303 307 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 303 309 T01 MEMBER OFFSETS -15.54 14.51 MEMBER1 303 401 T01 MEMBER OFFSETS -24.38 4.09 32.75 21.00 -3.53-28.21 MEMBER1 305 307 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 305 309 T01 MEMBER OFFSETS 15.40-14.39 MEMBER1 307 309 T01 MEMBER OFFSETS -15.54-14.51 MEMBER1 307 403 T01 MEMBER OFFSETS -4.75-27.10 47.52 3.04 17.36-30.43 MEMBER1 405 301 T01 MEMBER OFFSETS -17.36-30.43 27.10 47.52 MEMBER1 407 305 T01 MEMBER OFFSETS -18.27 3.54-28.35 21.00 -4.07 32.59 MEMBER1 201 209 T02 MEMBER OFFSETS 21.16 MEMBER1 201 212 T02 MEMBER OFFSETS 21.00 MEMBER1 201 303 T02 MEMBER OFFSETS 21.00 4.44 35.50-18.05 -3.81-30.51 MEMBER1 203 211 T02 MEMBER OFFSETS 21.16 MEMBER1 203 307 T02 MEMBER OFFSETS -3.83 25.95 38.29 2.64-17.87-26.37 MEMBER1 205 210 T02 MEMBER OFFSETS 21.00 MEMBER1 209 205 T02 MEMBER OFFSETS -21.16 MEMBER1 210 207 T02
MEMBER OFFSETS -21.10 MEMBER1 211 207 T02 MEMBER OFFSETS -21.16 MEMBER1 212 203 T02 MEMBER OFFSETS -21.10 MEMBER1 301 205 T02 MEMBER OFFSETS 17.87-26.37 -25.95 38.30 MEMBER1 305 207 T02 MEMBER OFFSETS 21.00 3.80-30.37-24.67 -4.46 35.67 MEMBER1 401 403 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 401 405 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 401 409 T03 MEMBER OFFSETS 17.32 11.97 MEMBER1 403 407 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 403 409 T03 MEMBER OFFSETS -17.48 12.08 MEMBER1 405 407 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 405 409 T03 MEMBER OFFSETS 17.32-11.97 MEMBER1 407 409 T03 MEMBER OFFSETS -17.48-12.08 MEMBER1 101 109 T04 MEMBER OFFSETS 12.67 16.88 MEMBER1 103 109 T04 MEMBER OFFSETS -12.76 17.01 MEMBER1 105 109 T04 MEMBER OFFSETS 12.67-16.88 MEMBER1 107 109 T04 MEMBER OFFSETS -12.76-17.01 MEMBER1 101 103 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 101 105 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 101 205 T05 MEMBER OFFSETS 25.80 37.10 -17.56-28.84 MEMBER1 103 107 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 103 201 T05 MEMBER OFFSETS -24.99 4.86 38.90 21.00 -4.11-32.88 MEMBER1 105 107 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 105 207 T05
MEMBER OFFSETS 21.00 -4.73 37.83-17.91 3.99-31.92 MEMBER1 107 203 T05 MEMBER OFFSETS -3.70-25.78 36.98 2.87 17.57-28.74 MEMBER1 201 202 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 203 204 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 205 206 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 207 208 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 301 302 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 303 304 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 305 306 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 307 308 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 401 402 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 403 404 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 405 406 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 407 408 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 701 714 W01 MEMBER 705 717 W01 MEMBER 714 715 W01 MEMBER 715 703 W01 MEMBER 717 718 W01 MEMBER 718 707 W01 MEMBER 801 834 W01 MEMBER 803 836 W01 MEMBER 805 837 W01 MEMBER 807 839 W01 MEMBER 834 835 W01 MEMBER 835 803 W01 MEMBER 837 838 W01 MEMBER 838 807 W01 MEMBER 701 705 W02 MEMBER 703 707 W02 MEMBER 705 720 W02 MEMBER 707 723 W02 MEMBER 709 701 W02
MEMBER 710 714 W02 MEMBER 711 715 W02 MEMBER 712 703 W02 MEMBER 714 717 W02 MEMBER 715 718 W02 MEMBER 717 721 W02 MEMBER 718 722 W02 MEMBER 801 805 W02 MEMBER 803 807 W02 MEMBER 805 840 W02 MEMBER 807 843 W02 MEMBER 829 801 W02 MEMBER 830 834 W02 MEMBER 831 835 W02 MEMBER 832 803 W02 MEMBER 833 836 W02 MEMBER 834 837 W02 MEMBER 835 838 W02 MEMBER 836 839 W02 MEMBER 837 841 W02 MEMBER 838 842 W02 MEMBER 839 844 W02 PGRUPPGRUP P01 0.3750I29.000 0.25036.000 490.000 PLATEPLATE AAAC 801 834 805 837P01 0 PLATE AAAD 834 835 837 838P01 0 JOINTJOINT 101 -24. -50. -261. -3.000 JOINT 102 -24. -50. -261. -3.000 PILEHD JOINT 103 51. -50. -261. 4.800 -3.000 JOINT 104 51. -50. -261. 4.800 -3.000 PILEHD JOINT 105 -24. 50. -261. 3.000 JOINT 106 -24. 50. -261. 3.000 PILEHD JOINT 107 51. 50. -261. 4.800 3.000 JOINT 108 51. 50. -261. 4.800 3.000 PILEHD JOINT 109 13. 0. -261. 8.400 JOINT 201 -24. -38. -164. -1.500 JOINT 202 -24. -38. -164. -1.500 JOINT 203 41. -38. -164. 8.400 -1.500 JOINT 204 41. -38. -164. 8.400 -1.500 JOINT 205 -24. 38. -164. 1.500 JOINT 206 -24. 38. -164. 1.500 JOINT 207 41. 38. -164. 8.400 1.500 JOINT 208 41. 38. -164. 8.400 1.500 JOINT 209 -24. 0. -164.
JOINT 210 8. 38. -164. 10.296 1.500 JOINT 211 41. 0. -164. 8.400 JOINT 212 8. -38. -164. 10.296 -1.500 JOINT 301 -24. -26. -69. -3.000 JOINT 302 -24. -26. -69. -3.000 JOINT 303 32. -26. -69. 2.400 -3.000 JOINT 304 32. -26. -69. 2.400 -3.000 JOINT 305 -24. 26. -69. 3.000 JOINT 306 -24. 26. -69. 3.000 JOINT 307 32. 26. -69. 2.400 3.000 JOINT 308 32. 26. -69. 2.400 3.000 JOINT 309 4. 0. -69. 1.200 JOINT 401 -24. -16. 6. -9.756 6.000 JOINT 402 -24. -16. 6. -9.756 6.000 JOINT 403 24. -16. 6. 7.800 -9.756 6.000 JOINT 404 24. -16. 6. 7.800 -9.756 6.000 JOINT 405 -24. 16. 6. 9.756 6.000 JOINT 406 -24. 16. 6. 9.756 6.000 JOINT 407 24. 16. 6. 7.800 9.756 6.000 JOINT 408 24. 16. 6. 7.800 9.756 6.000 JOINT 409 0. 0. 6. 3.900 6.000 JOINT 501 -24. -16. 10. -4.500 JOINT 503 24. -16. 10. 3.600 -4.500 JOINT 505 -24. 16. 10. 4.500 JOINT 507 24. 16. 10. 3.600 4.500 JOINT 601 -24. -16. 13. JOINT 603 24. -16. 13. JOINT 605 -24. 16. 13. JOINT 607 24. 16. 13. JOINT 701 -24. -16. 50. JOINT 703 24. -16. 50. JOINT 705 -24. 16. 50. JOINT 707 24. 16. 50. JOINT 709 -24. -26. 50. -2.964 JOINT 710 -8. -26. 50. -2.424 -2.964 JOINT 711 8. -26. 50. 2.424 -2.964 JOINT 712 24. -26. 50. -2.964 JOINT 714 -8. -16. 50. -2.424 JOINT 715 8. -16. 50. 2.424 JOINT 717 -8. 16. 50. -2.424 JOINT 718 8. 16. 50. 2.424 JOINT 720 -24. 26. 50. 2.964 JOINT 721 -8. 26. 50. -2.424 2.964 JOINT 722 8. 26. 50. 2.424 2.964 JOINT 723 24. 26. 50. 2.964 JOINT 801 -24. -16. 75.
JOINT 803 24. -16. 75. JOINT 805 -24. 16. 75. JOINT 807 24. 16. 75. JOINT 829 -24. -26. 75. -2.964 JOINT 830 -8. -26. 75. -2.424 -2.964 JOINT 831 8. -26. 75. 2.424 -2.964 JOINT 832 24. -26. 75. -2.964 JOINT 833 41. -26. 75. 0.132 -2.964 JOINT 834 -8. -16. 75. -2.424 JOINT 835 8. -16. 75. 2.424 JOINT 836 41. -16. 75. 0.132 JOINT 837 -8. 16. 75. -2.424 JOINT 838 8. 16. 75. 2.424 JOINT 839 41. 16. 75. 0.132 JOINT 840 -24. 26. 75. 2.964 JOINT 841 -8. 26. 75. -2.424 2.964 JOINT 842 8. 26. 75. 2.424 2.964 JOINT 843 24. 26. 75. 2.964 JOINT 844 41. 26. 75. 0.132 2.964 CDMCDM AP LOADLOADCN 31 CURRCURR 0.000 0.000 0.000 0.800 US NL FPS AWP CURR 30.000 2.410 0.000 CURR 60.000 2.650 0.000 CURR 80.000 2.770 0.000 CURR 100.000 2.860 0.000 CURR 120.000 2.950 0.000 CURR 150.000 3.030 0.000 CURR 180.000 3.110 0.000 CURR 200.000 3.150 0.000 CURR 261.000 3.280 0.000 WAVE WAVE0.95STOK 28.54261.00 9.50 0.00 D 0.00 5.00 72MS10 1 0 LOADCN 32 CURRCURR 0.000 0.000 45.000 0.850 US NL FPS AWP CURR 30.000 2.410 45.000 CURR 60.000 2.650 45.000 CURR 80.000 2.770 45.000 CURR 100.000 2.860 45.000 CURR 120.000 2.950 45.000 CURR 150.000 3.030 45.000 CURR 180.000 3.110 45.000
CURR 200.000 3.150 45.000 CURR 261.000 3.280 45.000 WAVE WAVE0.95STOK 28.54261.00 9.50 0.00 D 0.00 5.00 72MM10 1 0 LOADCN 33 CURRCURR 0.000 0.000 90.000 0.800 US NL FPS AWP CURR 30.000 2.410 90.000 CURR 60.000 2.650 90.000 CURR 80.000 2.770 90.000 CURR 100.000 2.860 90.000 CURR 120.000 2.950 90.000 CURR 150.000 3.030 90.000 CURR 180.000 3.110 90.000 CURR 200.000 3.150 90.000 CURR 261.000 3.280 90.000 WAVE WAVE0.95STOK 28.54261.00 9.50 90.00 D 0.00 5.00 72MS10 1 0 LOADCN 34 CURRCURR 0.000 0.000 135.000 0.850 US NL FPS AWP CURR 30.000 2.410 135.000 CURR 60.000 2.650 135.000 CURR 80.000 2.770 135.000 CURR 100.000 2.860 135.000 CURR 120.000 2.950 135.000 CURR 150.000 3.030 135.000 CURR 180.000 3.110 135.000 CURR 200.000 3.150 135.000 CURR 261.000 3.280 135.000 WAVE WAVE0.95STOK 28.54261.00 9.50 135.00 D 0.00 5.00 72MM10 1 0 LOADCN 35 CURRCURR 0.000 0.000 180.000 0.800 US NL FPS AWP CURR 30.000 2.410 180.000 CURR 60.000 2.650 180.000 CURR 80.000 2.770 180.000 CURR 100.000 2.860 180.000 CURR 120.000 2.950 180.000 CURR 150.000 3.030 180.000 CURR 180.000 3.110 180.000 CURR 200.000 3.150 180.000 CURR 261.000 3.280 180.000 WAVE WAVE0.95STOK 28.54261.00 9.50 180.00 D 0.00 5.00 72MS10 1 0
LOADCN 36 CURRCURR 0.000 0.000 225.000 0.850 US NL FPS AWP CURR 30.000 2.410 225.000 CURR 60.000 2.650 225.000 CURR 80.000 2.770 225.000 CURR 100.000 2.860 225.000 CURR 120.000 2.950 225.000 CURR 150.000 3.030 225.000 CURR 180.000 3.110 225.000 CURR 200.000 3.150 225.000 CURR 261.000 3.280 225.000 WAVE WAVE0.95STOK 28.54261.00 9.50 225.00 D 0.00 5.00 72MM10 1 0 LOADCN 37 CURRCURR 0.000 0.000 270.000 0.800 US CN FPS AWP CURR 30.000 2.410 270.000 CURR 60.000 2.650 270.000 CURR 80.000 2.770 270.000 CURR 100.000 2.860 270.000 CURR 120.000 2.950 270.000 CURR 150.000 3.030 270.000 CURR 180.000 3.110 270.000 CURR 200.000 3.150 270.000 CURR 261.000 3.280 270.000 WAVE WAVE0.95STOK 28.54261.00 9.50 270.00 D 0.00 5.00 72MS10 1 0 LOADCN 38 CURRCURR 0.000 0.000 315.000 0.850 US NL FPS AWP CURR 30.000 2.410 315.000 CURR 60.000 2.650 315.000 CURR 80.000 2.770 315.000 CURR 100.000 2.860 315.000 CURR 120.000 2.950 315.000 CURR 150.000 3.030 315.000 CURR 180.000 3.110 315.000 CURR 200.000 3.150 315.000 CURR 261.000 3.280 315.000 WAVE WAVE0.95STOK 28.54261.00 9.50 315.00 D 0.00 5.00 72MM10 1 0 LOADCNAREA***LDS1** 24.000 -26.247 50.000 24.000 26.247 50.000 -24.000 ***LDS2** -26.247 50.000 -24.000 26.247 50.000 -10.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES10PSFL
LOAD Z 701 705 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 703 707 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 705 720 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 707 723 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 709 701 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 710 714 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 711 715 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 712 703 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 714 717 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 715 718 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 717 721 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 718 722 -0.1610 -0.1610 GLOB UNIF 10PSFL ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.000 ***LDS2** -26.247 75.000 -24.000 26.247 75.000 -15.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES15PSFU LOAD Z 801 805 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 803 807 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 805 840 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 807 843 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 829 801 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 830 834 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 831 835 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 832 803 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 833 836 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 834 837 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 835 838 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 836 839 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 837 841 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 838 842 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 839 844 -0.1280 -0.1280 GLOB UNIF 15PSFU LOADCNEQPT ***LDS1** 16.000 6.000 75.000 16.000 6.000 75.000 ***LDS2** -250.000 20.000 10.000 ***LDS3** 10.000 1 2 2 0 0EQPT -1EQUPSKIDSKID1 X LOAD Z 835 838 17.4040-65.579 GLOB CONC SKID1 LOAD Z 835 838 27.4040-65.579 GLOB CONC SKID1 LOAD Z 803 807 17.4040-59.421 GLOB CONC SKID1 LOAD Z 803 807 27.4040-59.421 GLOB CONC SKID1 ***LDS1** -16.000 -16.000 75.000 -16.000 -16.000 75.000 ***LDS2** -150.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID2 X LOAD Z 829 801 6.49700-35.653 GLOB CONC SKID2 LOAD Z 830 834 6.49700-39.347 GLOB CONC SKID2 LOAD Z 834 837 4.15400-39.347 GLOB CONC SKID2 LOAD Z 801 805 4.15400-35.653 GLOB CONC SKID2 ***LDS1** -16.000 50.000 -16.000 50.000
***LDS2** -100.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID3 X LOAD Z 701 705 12.6540-23.769 GLOB CONC SKID3 LOAD Z 701 705 20.1540-23.769 GLOB CONC SKID3 LOAD Z 714 717 12.6540-26.231 GLOB CONC SKID3 LOAD Z 714 717 20.1540-26.231 GLOB CONC SKID3 ***LDS1** 32.000 19.000 75.000 32.000 19.000 75.000 ***LDS2** -35.000 20.000 7.000 ***LDS3** 7.000 1 2 3 0 0EQPT -1EQUPSKIDSKID4 X LOAD Z 803 807 31.5000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 3.00000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 6.50000-6.1800 GLOB CONC SKID4 LOAD Z 836 839 31.5000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 3.00000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 6.50000-5.4866 GLOB CONC SKID4 LOADCNLIVE ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.606 ***LDS2** 26.247 75.000 -24.606 -26.247 75.000 -100.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES100PSFU Z LOAD Z 829 801 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 830 834 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 831 835 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 832 803 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 833 836 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 834 837 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 835 838 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 836 839 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 837 841 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 838 842 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 839 844 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 801 805 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 803 807 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 805 840 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 807 843 -1.6400 -1.6400 GLOB UNIF 100PSFU ***LDS1** 24.606 -26.247 50.000 24.606 26.247 50.000 -24.606 ***LDS2** 26.247 50.000 -24.606 -26.247 50.000 -50.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES50PSFL Z LOAD Z 701 705 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 703 707 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 705 720 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 707 723 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 709 701 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 710 714 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 711 715 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 712 703 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 714 717 -0.8200 -0.8200 GLOB UNIF 50PSFL
LOAD Z 715 718 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 717 721 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 718 722 -0.8200 -0.8200 GLOB UNIF 50PSFL LOADCNMISC LOAD Z 712 703 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 703 707 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 707 723 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 833 836 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 836 839 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 839 844 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD 807 -20.000 GLOB JOIN CRANE ***LDS1** -8.000 20.000 50.000 -8.000 20.000 50.000 ***LDS2** -10.000 34.000 0.100 ***LDS3** 0.100 1 2 2 0 0MISC -1EQUPSKIDFIREWALLX LOAD Z 705 720 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 705 720 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 4.05000-1.6667 GLOB CONC FIREWALL LCOMBLCOMB 1001 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 31 1.000 LCOMB 1002 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 32 1.000 LCOMB 1003 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 33 1.000 LCOMB 1004 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 34 1.000 LCOMB 1005 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 35 1.000 LCOMB 1006 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 36 1.000 LCOMB 1007 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 37 1.000 LCOMB 1008 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 38 1.000 END
LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYSIS INPUT DATA 50 YEARS OPTIONS EN SDUCJT 2 1 C PT PTPT PTPT LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1
MEMBER 107 207 LG1 MEMBER 201 301 LG2 MEMBER 203 303 LG2 MEMBER 205 305 LG2 MEMBER 207 307 LG2 MEMBER 301 401 LG3 MEMBER 303 403 LG3 MEMBER 305 405 LG3 MEMBER 307 407 LG3 MEMBER 401 501 LG4 MEMBER 403 503 LG4 MEMBER 405 505 LG4 MEMBER 407 507 LG4 MEMBER 501 601 LG5 MEMBER 503 603 LG5 MEMBER 505 605 LG5 MEMBER 507 607 LG5 MEMBER 601 701 LG6 MEMBER 603 703 LG6 MEMBER 605 705 LG6 MEMBER 607 707 LG6 MEMBER 701 801 LG7 MEMBER 703 803 LG7 MEMBER 705 805 LG7 MEMBER 707 807 LG7 MEMBER 102 202 PL1 MEMBER 104 204 PL1 MEMBER 106 206 PL1 MEMBER 108 208 PL1 MEMBER 202 302 PL2 MEMBER 204 304 PL2 MEMBER 206 306 PL2 MEMBER 208 308 PL2 MEMBER 302 402 PL3 MEMBER 304 404 PL3 MEMBER 306 406 PL3 MEMBER 308 408 PL3 MEMBER1 402 501 PL4 MEMBER OFFSETS MEMBER1 404 503 PL4 MEMBER OFFSETS MEMBER1 406 505 PL4 MEMBER OFFSETS MEMBER1 408 507 PL4 MEMBER OFFSETS MEMBER1 209 212 T01
MEMBER OFFSETS 10.00-13.32 -11.56 10.00 MEMBER1 210 209 T01 MEMBER OFFSETS -11.56-10.00 10.00 13.32 MEMBER1 211 210 T01 MEMBER OFFSETS -10.00 13.32 11.55-10.00 MEMBER1 212 211 T01 MEMBER OFFSETS 11.55 10.00 -10.00-13.32 MEMBER1 301 303 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 301 305 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 301 309 T01 MEMBER OFFSETS 15.40 14.39 MEMBER1 303 307 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 303 309 T01 MEMBER OFFSETS -15.54 14.51 MEMBER1 303 401 T01 MEMBER OFFSETS -24.38 4.09 32.75 21.00 -3.53-28.21 MEMBER1 305 307 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 305 309 T01 MEMBER OFFSETS 15.40-14.39 MEMBER1 307 309 T01 MEMBER OFFSETS -15.54-14.51 MEMBER1 307 403 T01 MEMBER OFFSETS -4.75-27.10 47.52 3.04 17.36-30.43 MEMBER1 405 301 T01 MEMBER OFFSETS -17.36-30.43 27.10 47.52 MEMBER1 407 305 T01 MEMBER OFFSETS -18.27 3.54-28.35 21.00 -4.07 32.59 MEMBER1 201 209 T02 MEMBER OFFSETS 21.16 MEMBER1 201 212 T02 MEMBER OFFSETS 21.00 MEMBER1 201 303 T02 MEMBER OFFSETS 21.00 4.44 35.50-18.05 -3.81-30.51 MEMBER1 203 211 T02 MEMBER OFFSETS 21.16 MEMBER1 203 307 T02 MEMBER OFFSETS -3.83 25.95 38.29 2.64-17.87-26.37 MEMBER1 205 210 T02 MEMBER OFFSETS 21.00 MEMBER1 209 205 T02 MEMBER OFFSETS -21.16 MEMBER1 210 207 T02
MEMBER OFFSETS -21.10 MEMBER1 211 207 T02 MEMBER OFFSETS -21.16 MEMBER1 212 203 T02 MEMBER OFFSETS -21.10 MEMBER1 301 205 T02 MEMBER OFFSETS 17.87-26.37 -25.95 38.30 MEMBER1 305 207 T02 MEMBER OFFSETS 21.00 3.80-30.37-24.67 -4.46 35.67 MEMBER1 401 403 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 401 405 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 401 409 T03 MEMBER OFFSETS 17.32 11.97 MEMBER1 403 407 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 403 409 T03 MEMBER OFFSETS -17.48 12.08 MEMBER1 405 407 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 405 409 T03 MEMBER OFFSETS 17.32-11.97 MEMBER1 407 409 T03 MEMBER OFFSETS -17.48-12.08 MEMBER1 101 109 T04 MEMBER OFFSETS 12.67 16.88 MEMBER1 103 109 T04 MEMBER OFFSETS -12.76 17.01 MEMBER1 105 109 T04 MEMBER OFFSETS 12.67-16.88 MEMBER1 107 109 T04 MEMBER OFFSETS -12.76-17.01 MEMBER1 101 103 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 101 105 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 101 205 T05 MEMBER OFFSETS 25.80 37.10 -17.56-28.84 MEMBER1 103 107 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 103 201 T05 MEMBER OFFSETS -24.99 4.86 38.90 21.00 -4.11-32.88 MEMBER1 105 107 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 105 207 T05
MEMBER OFFSETS 21.00 -4.73 37.83-17.91 3.99-31.92 MEMBER1 107 203 T05 MEMBER OFFSETS -3.70-25.78 36.98 2.87 17.57-28.74 MEMBER1 201 202 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 203 204 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 205 206 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 207 208 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 301 302 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 303 304 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 305 306 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 307 308 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 401 402 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 403 404 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 405 406 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 407 408 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 701 714 W01 MEMBER 705 717 W01 MEMBER 714 715 W01 MEMBER 715 703 W01 MEMBER 717 718 W01 MEMBER 718 707 W01 MEMBER 801 834 W01 MEMBER 803 836 W01 MEMBER 805 837 W01 MEMBER 807 839 W01 MEMBER 834 835 W01 MEMBER 835 803 W01 MEMBER 837 838 W01 MEMBER 838 807 W01 MEMBER 701 705 W02 MEMBER 703 707 W02 MEMBER 705 720 W02 MEMBER 707 723 W02 MEMBER 709 701 W02
MEMBER 710 714 W02 MEMBER 711 715 W02 MEMBER 712 703 W02 MEMBER 714 717 W02 MEMBER 715 718 W02 MEMBER 717 721 W02 MEMBER 718 722 W02 MEMBER 801 805 W02 MEMBER 803 807 W02 MEMBER 805 840 W02 MEMBER 807 843 W02 MEMBER 829 801 W02 MEMBER 830 834 W02 MEMBER 831 835 W02 MEMBER 832 803 W02 MEMBER 833 836 W02 MEMBER 834 837 W02 MEMBER 835 838 W02 MEMBER 836 839 W02 MEMBER 837 841 W02 MEMBER 838 842 W02 MEMBER 839 844 W02 PGRUPPGRUP P01 0.3750I29.000 0.25036.000 490.000 PLATEPLATE AAAC 801 834 805 837P01 0 PLATE AAAD 834 835 837 838P01 0 JOINTJOINT 101 -24. -50. -261. -3.000 JOINT 102 -24. -50. -261. -3.000 PILEHD JOINT 103 51. -50. -261. 4.800 -3.000 JOINT 104 51. -50. -261. 4.800 -3.000 PILEHD JOINT 105 -24. 50. -261. 3.000 JOINT 106 -24. 50. -261. 3.000 PILEHD JOINT 107 51. 50. -261. 4.800 3.000 JOINT 108 51. 50. -261. 4.800 3.000 PILEHD JOINT 109 13. 0. -261. 8.400 JOINT 201 -24. -38. -164. -1.500 JOINT 202 -24. -38. -164. -1.500 JOINT 203 41. -38. -164. 8.400 -1.500 JOINT 204 41. -38. -164. 8.400 -1.500 JOINT 205 -24. 38. -164. 1.500 JOINT 206 -24. 38. -164. 1.500 JOINT 207 41. 38. -164. 8.400 1.500 JOINT 208 41. 38. -164. 8.400 1.500 JOINT 209 -24. 0. -164.
JOINT 210 8. 38. -164. 10.296 1.500 JOINT 211 41. 0. -164. 8.400 JOINT 212 8. -38. -164. 10.296 -1.500 JOINT 301 -24. -26. -69. -3.000 JOINT 302 -24. -26. -69. -3.000 JOINT 303 32. -26. -69. 2.400 -3.000 JOINT 304 32. -26. -69. 2.400 -3.000 JOINT 305 -24. 26. -69. 3.000 JOINT 306 -24. 26. -69. 3.000 JOINT 307 32. 26. -69. 2.400 3.000 JOINT 308 32. 26. -69. 2.400 3.000 JOINT 309 4. 0. -69. 1.200 JOINT 401 -24. -16. 6. -9.756 6.000 JOINT 402 -24. -16. 6. -9.756 6.000 JOINT 403 24. -16. 6. 7.800 -9.756 6.000 JOINT 404 24. -16. 6. 7.800 -9.756 6.000 JOINT 405 -24. 16. 6. 9.756 6.000 JOINT 406 -24. 16. 6. 9.756 6.000 JOINT 407 24. 16. 6. 7.800 9.756 6.000 JOINT 408 24. 16. 6. 7.800 9.756 6.000 JOINT 409 0. 0. 6. 3.900 6.000 JOINT 501 -24. -16. 10. -4.500 JOINT 503 24. -16. 10. 3.600 -4.500 JOINT 505 -24. 16. 10. 4.500 JOINT 507 24. 16. 10. 3.600 4.500 JOINT 601 -24. -16. 13. JOINT 603 24. -16. 13. JOINT 605 -24. 16. 13. JOINT 607 24. 16. 13. JOINT 701 -24. -16. 50. JOINT 703 24. -16. 50. JOINT 705 -24. 16. 50. JOINT 707 24. 16. 50. JOINT 709 -24. -26. 50. -2.964 JOINT 710 -8. -26. 50. -2.424 -2.964 JOINT 711 8. -26. 50. 2.424 -2.964 JOINT 712 24. -26. 50. -2.964 JOINT 714 -8. -16. 50. -2.424 JOINT 715 8. -16. 50. 2.424 JOINT 717 -8. 16. 50. -2.424 JOINT 718 8. 16. 50. 2.424 JOINT 720 -24. 26. 50. 2.964 JOINT 721 -8. 26. 50. -2.424 2.964 JOINT 722 8. 26. 50. 2.424 2.964 JOINT 723 24. 26. 50. 2.964 JOINT 801 -24. -16. 75.
JOINT 803 24. -16. 75. JOINT 805 -24. 16. 75. JOINT 807 24. 16. 75. JOINT 829 -24. -26. 75. -2.964 JOINT 830 -8. -26. 75. -2.424 -2.964 JOINT 831 8. -26. 75. 2.424 -2.964 JOINT 832 24. -26. 75. -2.964 JOINT 833 41. -26. 75. 0.132 -2.964 JOINT 834 -8. -16. 75. -2.424 JOINT 835 8. -16. 75. 2.424 JOINT 836 41. -16. 75. 0.132 JOINT 837 -8. 16. 75. -2.424 JOINT 838 8. 16. 75. 2.424 JOINT 839 41. 16. 75. 0.132 JOINT 840 -24. 26. 75. 2.964 JOINT 841 -8. 26. 75. -2.424 2.964 JOINT 842 8. 26. 75. 2.424 2.964 JOINT 843 24. 26. 75. 2.964 JOINT 844 41. 26. 75. 0.132 2.964 CDMCDM AP LOADLOADCN 31 CURRCURR 0.000 0.000 0.000 0.800 US NL FPS AWP CURR 30.000 2.410 0.000 CURR 60.000 2.650 0.000 CURR 80.000 2.770 0.000 CURR 100.000 2.860 0.000 CURR 120.000 2.950 0.000 CURR 150.000 3.030 0.000 CURR 180.000 3.110 0.000 CURR 200.000 3.150 0.000 CURR 261.000 3.280 0.000 WAVE WAVE0.95STOK 31.49261.00 10.00 0.00 D 0.00 5.00 72MS10 1 0 LOADCN 32 CURRCURR 0.000 0.000 45.000 0.850 US NL FPS AWP CURR 30.000 2.410 45.000 CURR 60.000 2.650 45.000 CURR 80.000 2.770 45.000 CURR 100.000 2.860 45.000 CURR 120.000 2.950 45.000 CURR 150.000 3.030 45.000 CURR 180.000 3.110 45.000
CURR 200.000 3.150 45.000 CURR 261.000 3.280 45.000 WAVE WAVE0.95STOK 31.49261.00 10.00 0.00 D 0.00 5.00 72MM10 1 0 LOADCN 33 CURRCURR 0.000 0.000 90.000 0.800 US NL FPS AWP CURR 30.000 2.410 90.000 CURR 60.000 2.650 90.000 CURR 80.000 2.770 90.000 CURR 100.000 2.860 90.000 CURR 120.000 2.950 90.000 CURR 150.000 3.030 90.000 CURR 180.000 3.110 90.000 CURR 200.000 3.150 90.000 CURR 261.000 3.280 90.000 WAVE WAVE0.95STOK 31.49261.00 10.00 90.00 D 0.00 5.00 72MS10 1 0 LOADCN 34 CURRCURR 0.000 0.000 135.000 0.850 US NL FPS AWP CURR 30.000 2.410 135.000 CURR 60.000 2.650 135.000 CURR 80.000 2.770 135.000 CURR 100.000 2.860 135.000 CURR 120.000 2.950 135.000 CURR 150.000 3.030 135.000 CURR 180.000 3.110 135.000 CURR 200.000 3.150 135.000 CURR 261.000 3.280 135.000 WAVE WAVE0.95STOK 31.49261.00 10.00 135.00 D 0.00 5.00 72MM10 1 0 LOADCN 35 CURRCURR 0.000 0.000 180.000 0.800 US NL FPS AWP CURR 30.000 2.410 180.000 CURR 60.000 2.650 180.000 CURR 80.000 2.770 180.000 CURR 100.000 2.860 180.000 CURR 120.000 2.950 180.000 CURR 150.000 3.030 180.000 CURR 180.000 3.110 180.000 CURR 200.000 3.150 180.000 CURR 261.000 3.280 180.000 WAVE WAVE0.95STOK 31.49261.00 10.00 180.00 D 0.00 5.00 72MS10 1 0
LOADCN 36 CURRCURR 0.000 0.000 225.000 0.850 US NL FPS AWP CURR 30.000 2.410 225.000 CURR 60.000 2.650 225.000 CURR 80.000 2.770 225.000 CURR 100.000 2.860 225.000 CURR 120.000 2.950 225.000 CURR 150.000 3.030 225.000 CURR 180.000 3.110 225.000 CURR 200.000 3.150 225.000 CURR 261.000 3.280 225.000 WAVE WAVE0.95STOK 31.49261.00 10.00 225.00 D 0.00 5.00 72MM10 1 0 LOADCN 37 CURRCURR 0.000 0.000 270.000 0.800 US CN FPS AWP CURR 30.000 2.410 270.000 CURR 60.000 2.650 270.000 CURR 80.000 2.770 270.000 CURR 100.000 2.860 270.000 CURR 120.000 2.950 270.000 CURR 150.000 3.030 270.000 CURR 180.000 3.110 270.000 CURR 200.000 3.150 270.000 CURR 261.000 3.280 270.000 WAVE WAVE0.95STOK 31.49261.00 10.00 270.00 D 0.00 5.00 72MS10 1 0 LOADCN 38 CURRCURR 0.000 0.000 315.000 0.850 US NL FPS AWP CURR 30.000 2.410 315.000 CURR 60.000 2.650 315.000 CURR 80.000 2.770 315.000 CURR 100.000 2.860 315.000 CURR 120.000 2.950 315.000 CURR 150.000 3.030 315.000 CURR 180.000 3.110 315.000 CURR 200.000 3.150 315.000 CURR 261.000 3.280 315.000 WAVE WAVE0.95STOK 31.49261.00 10.00 315.00 D 0.00 5.00 72MM10 1 0 LOADCNAREA***LDS1** 24.000 -26.247 50.000 24.000 26.247 50.000 -24.000 ***LDS2** -26.247 50.000 -24.000 26.247 50.000 -10.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES10PSFL
LOAD Z 701 705 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 703 707 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 705 720 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 707 723 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 709 701 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 710 714 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 711 715 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 712 703 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 714 717 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 715 718 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 717 721 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 718 722 -0.1610 -0.1610 GLOB UNIF 10PSFL ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.000 ***LDS2** -26.247 75.000 -24.000 26.247 75.000 -15.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES15PSFU LOAD Z 801 805 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 803 807 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 805 840 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 807 843 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 829 801 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 830 834 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 831 835 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 832 803 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 833 836 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 834 837 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 835 838 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 836 839 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 837 841 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 838 842 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 839 844 -0.1280 -0.1280 GLOB UNIF 15PSFU LOADCNEQPT ***LDS1** 16.000 6.000 75.000 16.000 6.000 75.000 ***LDS2** -250.000 20.000 10.000 ***LDS3** 10.000 1 2 2 0 0EQPT -1EQUPSKIDSKID1 X LOAD Z 835 838 17.4040-65.579 GLOB CONC SKID1 LOAD Z 835 838 27.4040-65.579 GLOB CONC SKID1 LOAD Z 803 807 17.4040-59.421 GLOB CONC SKID1 LOAD Z 803 807 27.4040-59.421 GLOB CONC SKID1 ***LDS1** -16.000 -16.000 75.000 -16.000 -16.000 75.000 ***LDS2** -150.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID2 X LOAD Z 829 801 6.49700-35.653 GLOB CONC SKID2 LOAD Z 830 834 6.49700-39.347 GLOB CONC SKID2 LOAD Z 834 837 4.15400-39.347 GLOB CONC SKID2 LOAD Z 801 805 4.15400-35.653 GLOB CONC SKID2 ***LDS1** -16.000 50.000 -16.000 50.000
***LDS2** -100.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID3 X LOAD Z 701 705 12.6540-23.769 GLOB CONC SKID3 LOAD Z 701 705 20.1540-23.769 GLOB CONC SKID3 LOAD Z 714 717 12.6540-26.231 GLOB CONC SKID3 LOAD Z 714 717 20.1540-26.231 GLOB CONC SKID3 ***LDS1** 32.000 19.000 75.000 32.000 19.000 75.000 ***LDS2** -35.000 20.000 7.000 ***LDS3** 7.000 1 2 3 0 0EQPT -1EQUPSKIDSKID4 X LOAD Z 803 807 31.5000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 3.00000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 6.50000-6.1800 GLOB CONC SKID4 LOAD Z 836 839 31.5000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 3.00000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 6.50000-5.4866 GLOB CONC SKID4 LOADCNLIVE ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.606 ***LDS2** 26.247 75.000 -24.606 -26.247 75.000 -100.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES100PSFU Z LOAD Z 829 801 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 830 834 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 831 835 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 832 803 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 833 836 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 834 837 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 835 838 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 836 839 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 837 841 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 838 842 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 839 844 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 801 805 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 803 807 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 805 840 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 807 843 -1.6400 -1.6400 GLOB UNIF 100PSFU ***LDS1** 24.606 -26.247 50.000 24.606 26.247 50.000 -24.606 ***LDS2** 26.247 50.000 -24.606 -26.247 50.000 -50.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES50PSFL Z LOAD Z 701 705 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 703 707 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 705 720 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 707 723 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 709 701 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 710 714 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 711 715 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 712 703 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 714 717 -0.8200 -0.8200 GLOB UNIF 50PSFL
LOAD Z 715 718 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 717 721 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 718 722 -0.8200 -0.8200 GLOB UNIF 50PSFL LOADCNMISC LOAD Z 712 703 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 703 707 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 707 723 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 833 836 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 836 839 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 839 844 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD 807 -20.000 GLOB JOIN CRANE ***LDS1** -8.000 20.000 50.000 -8.000 20.000 50.000 ***LDS2** -10.000 34.000 0.100 ***LDS3** 0.100 1 2 2 0 0MISC -1EQUPSKIDFIREWALLX LOAD Z 705 720 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 705 720 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 4.05000-1.6667 GLOB CONC FIREWALL LCOMBLCOMB 1001 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 31 1.000 LCOMB 1002 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 32 1.000 LCOMB 1003 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 33 1.000 LCOMB 1004 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 34 1.000 LCOMB 1005 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 35 1.000 LCOMB 1006 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 36 1.000 LCOMB 1007 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 37 1.000 LCOMB 1008 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 38 1.000 END
LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYIS INPUT DATA 100 YEAR LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 OPTIONS EN SDUCJT 2 1 PTPTPTPTPTPTPTPTPTPT PT SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1
MEMBER 107 207 LG1 MEMBER 201 301 LG2 MEMBER 203 303 LG2 MEMBER 205 305 LG2 MEMBER 207 307 LG2 MEMBER 301 401 LG3 MEMBER 303 403 LG3 MEMBER 305 405 LG3 MEMBER 307 407 LG3 MEMBER 401 501 LG4 MEMBER 403 503 LG4 MEMBER 405 505 LG4 MEMBER 407 507 LG4 MEMBER 501 601 LG5 MEMBER 503 603 LG5 MEMBER 505 605 LG5 MEMBER 507 607 LG5 MEMBER 601 701 LG6 MEMBER 603 703 LG6 MEMBER 605 705 LG6 MEMBER 607 707 LG6 MEMBER 701 801 LG7 MEMBER 703 803 LG7 MEMBER 705 805 LG7 MEMBER 707 807 LG7 MEMBER 102 202 PL1 MEMBER 104 204 PL1 MEMBER 106 206 PL1 MEMBER 108 208 PL1 MEMBER 202 302 PL2 MEMBER 204 304 PL2 MEMBER 206 306 PL2 MEMBER 208 308 PL2 MEMBER 302 402 PL3 MEMBER 304 404 PL3 MEMBER 306 406 PL3 MEMBER 308 408 PL3 MEMBER1 402 501 PL4 MEMBER OFFSETS MEMBER1 404 503 PL4 MEMBER OFFSETS MEMBER1 406 505 PL4 MEMBER OFFSETS MEMBER1 408 507 PL4 MEMBER OFFSETS MEMBER1 209 212 T01
MEMBER OFFSETS 10.00-13.32 -11.56 10.00 MEMBER1 210 209 T01 MEMBER OFFSETS -11.56-10.00 10.00 13.32 MEMBER1 211 210 T01 MEMBER OFFSETS -10.00 13.32 11.55-10.00 MEMBER1 212 211 T01 MEMBER OFFSETS 11.55 10.00 -10.00-13.32 MEMBER1 301 303 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 301 305 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 301 309 T01 MEMBER OFFSETS 15.40 14.39 MEMBER1 303 307 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 303 309 T01 MEMBER OFFSETS -15.54 14.51 MEMBER1 303 401 T01 MEMBER OFFSETS -24.38 4.09 32.75 21.00 -3.53-28.21 MEMBER1 305 307 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 305 309 T01 MEMBER OFFSETS 15.40-14.39 MEMBER1 307 309 T01 MEMBER OFFSETS -15.54-14.51 MEMBER1 307 403 T01 MEMBER OFFSETS -4.75-27.10 47.52 3.04 17.36-30.43 MEMBER1 405 301 T01 MEMBER OFFSETS -17.36-30.43 27.10 47.52 MEMBER1 407 305 T01 MEMBER OFFSETS -18.27 3.54-28.35 21.00 -4.07 32.59 MEMBER1 201 209 T02 MEMBER OFFSETS 21.16 MEMBER1 201 212 T02 MEMBER OFFSETS 21.00 MEMBER1 201 303 T02 MEMBER OFFSETS 21.00 4.44 35.50-18.05 -3.81-30.51 MEMBER1 203 211 T02 MEMBER OFFSETS 21.16 MEMBER1 203 307 T02 MEMBER OFFSETS -3.83 25.95 38.29 2.64-17.87-26.37 MEMBER1 205 210 T02 MEMBER OFFSETS 21.00 MEMBER1 209 205 T02 MEMBER OFFSETS -21.16 MEMBER1 210 207 T02
MEMBER OFFSETS -21.10 MEMBER1 211 207 T02 MEMBER OFFSETS -21.16 MEMBER1 212 203 T02 MEMBER OFFSETS -21.10 MEMBER1 301 205 T02 MEMBER OFFSETS 17.87-26.37 -25.95 38.30 MEMBER1 305 207 T02 MEMBER OFFSETS 21.00 3.80-30.37-24.67 -4.46 35.67 MEMBER1 401 403 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 401 405 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 401 409 T03 MEMBER OFFSETS 17.32 11.97 MEMBER1 403 407 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 403 409 T03 MEMBER OFFSETS -17.48 12.08 MEMBER1 405 407 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 405 409 T03 MEMBER OFFSETS 17.32-11.97 MEMBER1 407 409 T03 MEMBER OFFSETS -17.48-12.08 MEMBER1 101 109 T04 MEMBER OFFSETS 12.67 16.88 MEMBER1 103 109 T04 MEMBER OFFSETS -12.76 17.01 MEMBER1 105 109 T04 MEMBER OFFSETS 12.67-16.88 MEMBER1 107 109 T04 MEMBER OFFSETS -12.76-17.01 MEMBER1 101 103 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 101 105 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 101 205 T05 MEMBER OFFSETS 25.80 37.10 -17.56-28.84 MEMBER1 103 107 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 103 201 T05 MEMBER OFFSETS -24.99 4.86 38.90 21.00 -4.11-32.88 MEMBER1 105 107 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 105 207 T05
MEMBER OFFSETS 21.00 -4.73 37.83-17.91 3.99-31.92 MEMBER1 107 203 T05 MEMBER OFFSETS -3.70-25.78 36.98 2.87 17.57-28.74 MEMBER1 201 202 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 203 204 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 205 206 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 207 208 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 301 302 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 303 304 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 305 306 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 307 308 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 401 402 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 403 404 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 405 406 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 407 408 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 701 714 W01 MEMBER 705 717 W01 MEMBER 714 715 W01 MEMBER 715 703 W01 MEMBER 717 718 W01 MEMBER 718 707 W01 MEMBER 801 834 W01 MEMBER 803 836 W01 MEMBER 805 837 W01 MEMBER 807 839 W01 MEMBER 834 835 W01 MEMBER 835 803 W01 MEMBER 837 838 W01 MEMBER 838 807 W01 MEMBER 701 705 W02 MEMBER 703 707 W02 MEMBER 705 720 W02 MEMBER 707 723 W02 MEMBER 709 701 W02
MEMBER 710 714 W02 MEMBER 711 715 W02 MEMBER 712 703 W02 MEMBER 714 717 W02 MEMBER 715 718 W02 MEMBER 717 721 W02 MEMBER 718 722 W02 MEMBER 801 805 W02 MEMBER 803 807 W02 MEMBER 805 840 W02 MEMBER 807 843 W02 MEMBER 829 801 W02 MEMBER 830 834 W02 MEMBER 831 835 W02 MEMBER 832 803 W02 MEMBER 833 836 W02 MEMBER 834 837 W02 MEMBER 835 838 W02 MEMBER 836 839 W02 MEMBER 837 841 W02 MEMBER 838 842 W02 MEMBER 839 844 W02 PGRUPPGRUP P01 0.3750I29.000 0.25036.000 490.000 PLATEPLATE AAAC 801 834 805 837P01 0 PLATE AAAD 834 835 837 838P01 0 JOINTJOINT 101 -24. -50. -261. -3.000 JOINT 102 -24. -50. -261. -3.000 PILEHD JOINT 103 51. -50. -261. 4.800 -3.000 JOINT 104 51. -50. -261. 4.800 -3.000 PILEHD JOINT 105 -24. 50. -261. 3.000 JOINT 106 -24. 50. -261. 3.000 PILEHD JOINT 107 51. 50. -261. 4.800 3.000 JOINT 108 51. 50. -261. 4.800 3.000 PILEHD JOINT 109 13. 0. -261. 8.400 JOINT 201 -24. -38. -164. -1.500 JOINT 202 -24. -38. -164. -1.500 JOINT 203 41. -38. -164. 8.400 -1.500 JOINT 204 41. -38. -164. 8.400 -1.500 JOINT 205 -24. 38. -164. 1.500 JOINT 206 -24. 38. -164. 1.500 JOINT 207 41. 38. -164. 8.400 1.500 JOINT 208 41. 38. -164. 8.400 1.500 JOINT 209 -24. 0. -164.
JOINT 210 8. 38. -164. 10.296 1.500 JOINT 211 41. 0. -164. 8.400 JOINT 212 8. -38. -164. 10.296 -1.500 JOINT 301 -24. -26. -69. -3.000 JOINT 302 -24. -26. -69. -3.000 JOINT 303 32. -26. -69. 2.400 -3.000 JOINT 304 32. -26. -69. 2.400 -3.000 JOINT 305 -24. 26. -69. 3.000 JOINT 306 -24. 26. -69. 3.000 JOINT 307 32. 26. -69. 2.400 3.000 JOINT 308 32. 26. -69. 2.400 3.000 JOINT 309 4. 0. -69. 1.200 JOINT 401 -24. -16. 6. -9.756 6.000 JOINT 402 -24. -16. 6. -9.756 6.000 JOINT 403 24. -16. 6. 7.800 -9.756 6.000 JOINT 404 24. -16. 6. 7.800 -9.756 6.000 JOINT 405 -24. 16. 6. 9.756 6.000 JOINT 406 -24. 16. 6. 9.756 6.000 JOINT 407 24. 16. 6. 7.800 9.756 6.000 JOINT 408 24. 16. 6. 7.800 9.756 6.000 JOINT 409 0. 0. 6. 3.900 6.000 JOINT 501 -24. -16. 10. -4.500 JOINT 503 24. -16. 10. 3.600 -4.500 JOINT 505 -24. 16. 10. 4.500 JOINT 507 24. 16. 10. 3.600 4.500 JOINT 601 -24. -16. 13. JOINT 603 24. -16. 13. JOINT 605 -24. 16. 13. JOINT 607 24. 16. 13. JOINT 701 -24. -16. 50. JOINT 703 24. -16. 50. JOINT 705 -24. 16. 50. JOINT 707 24. 16. 50. JOINT 709 -24. -26. 50. -2.964 JOINT 710 -8. -26. 50. -2.424 -2.964 JOINT 711 8. -26. 50. 2.424 -2.964 JOINT 712 24. -26. 50. -2.964 JOINT 714 -8. -16. 50. -2.424 JOINT 715 8. -16. 50. 2.424 JOINT 717 -8. 16. 50. -2.424 JOINT 718 8. 16. 50. 2.424 JOINT 720 -24. 26. 50. 2.964 JOINT 721 -8. 26. 50. -2.424 2.964 JOINT 722 8. 26. 50. 2.424 2.964 JOINT 723 24. 26. 50. 2.964 JOINT 801 -24. -16. 75.
JOINT 803 24. -16. 75. JOINT 805 -24. 16. 75. JOINT 807 24. 16. 75. JOINT 829 -24. -26. 75. -2.964 JOINT 830 -8. -26. 75. -2.424 -2.964 JOINT 831 8. -26. 75. 2.424 -2.964 JOINT 832 24. -26. 75. -2.964 JOINT 833 41. -26. 75. 0.132 -2.964 JOINT 834 -8. -16. 75. -2.424 JOINT 835 8. -16. 75. 2.424 JOINT 836 41. -16. 75. 0.132 JOINT 837 -8. 16. 75. -2.424 JOINT 838 8. 16. 75. 2.424 JOINT 839 41. 16. 75. 0.132 JOINT 840 -24. 26. 75. 2.964 JOINT 841 -8. 26. 75. -2.424 2.964 JOINT 842 8. 26. 75. 2.424 2.964 JOINT 843 24. 26. 75. 2.964 JOINT 844 41. 26. 75. 0.132 2.964 CDMCDM AP LOADLOADCN 31 CURRCURR 0.000 0.000 0.000 0.800 US NL FPS AWP CURR 30.000 2.410 0.000 CURR 60.000 2.650 0.000 CURR 80.000 2.770 0.000 CURR 100.000 2.860 0.000 CURR 120.000 2.950 0.000 CURR 150.000 3.030 0.000 CURR 180.000 3.110 0.000 CURR 200.000 3.150 0.000 CURR 261.000 3.280 0.000 WAVE WAVE0.95STOK 32.15261.00 10.20 0.00 D 0.00 5.00 72MS10 1 0 LOADCN 32 CURRCURR 0.000 0.000 45.000 0.850 US NL FPS AWP CURR 30.000 2.410 45.000 CURR 60.000 2.650 45.000 CURR 80.000 2.770 45.000 CURR 100.000 2.860 45.000 CURR 120.000 2.950 45.000 CURR 150.000 3.030 45.000 CURR 180.000 3.110 45.000
CURR 200.000 3.150 45.000 CURR 261.000 3.280 45.000 WAVE WAVE0.95STOK 32.15261.00 10.20 0.00 D 0.00 5.00 72MM10 1 0 LOADCN 33 CURRCURR 0.000 0.000 90.000 0.800 US NL FPS AWP CURR 30.000 2.410 90.000 CURR 60.000 2.650 90.000 CURR 80.000 2.770 90.000 CURR 100.000 2.860 90.000 CURR 120.000 2.950 90.000 CURR 150.000 3.030 90.000 CURR 180.000 3.110 90.000 CURR 200.000 3.150 90.000 CURR 261.000 3.280 90.000 WAVE WAVE0.95STOK 32.15261.00 10.20 90.00 D 0.00 5.00 72MS10 1 0 LOADCN 34 CURRCURR 0.000 0.000 135.000 0.850 US NL FPS AWP CURR 30.000 2.410 135.000 CURR 60.000 2.650 135.000 CURR 80.000 2.770 135.000 CURR 100.000 2.860 135.000 CURR 120.000 2.950 135.000 CURR 150.000 3.030 135.000 CURR 180.000 3.110 135.000 CURR 200.000 3.150 135.000 CURR 261.000 3.280 135.000 WAVE WAVE0.95STOK 32.15261.00 10.20 135.00 D 0.00 5.00 72MM10 1 0 LOADCN 35 CURRCURR 0.000 0.000 180.000 0.800 US NL FPS AWP CURR 30.000 2.410 180.000 CURR 60.000 2.650 180.000 CURR 80.000 2.770 180.000 CURR 100.000 2.860 180.000 CURR 120.000 2.950 180.000 CURR 150.000 3.030 180.000 CURR 180.000 3.110 180.000 CURR 200.000 3.150 180.000 CURR 261.000 3.280 180.000 WAVE WAVE0.95STOK 32.15261.00 10.20 180.00 D 0.00 5.00 72MS10 1 0
LOADCN 36 CURRCURR 0.000 0.000 225.000 0.850 US NL FPS AWP CURR 30.000 2.410 225.000 CURR 60.000 2.650 225.000 CURR 80.000 2.770 225.000 CURR 100.000 2.860 225.000 CURR 120.000 2.950 225.000 CURR 150.000 3.030 225.000 CURR 180.000 3.110 225.000 CURR 200.000 3.150 225.000 CURR 261.000 3.280 225.000 WAVE WAVE0.95STOK 32.15261.00 10.20 225.00 D 0.00 5.00 72MM10 1 0 LOADCN 37 CURRCURR 0.000 0.000 270.000 0.800 US CN FPS AWP CURR 30.000 2.410 270.000 CURR 60.000 2.650 270.000 CURR 80.000 2.770 270.000 CURR 100.000 2.860 270.000 CURR 120.000 2.950 270.000 CURR 150.000 3.030 270.000 CURR 180.000 3.110 270.000 CURR 200.000 3.150 270.000 CURR 261.000 3.280 270.000 WAVE WAVE0.95STOK 32.15261.00 10.20 270.00 D 0.00 5.00 72MS10 1 0 LOADCN 38 CURRCURR 0.000 0.000 315.000 0.850 US NL FPS AWP CURR 30.000 2.410 315.000 CURR 60.000 2.650 315.000 CURR 80.000 2.770 315.000 CURR 100.000 2.860 315.000 CURR 120.000 2.950 315.000 CURR 150.000 3.030 315.000 CURR 180.000 3.110 315.000 CURR 200.000 3.150 315.000 CURR 261.000 3.280 315.000 WAVE WAVE0.95STOK 32.15261.00 10.20 315.00 D 0.00 5.00 72MM10 1 0 LOADCNAREA***LDS1** 24.000 -26.247 50.000 24.000 26.247 50.000 -24.000 ***LDS2** -26.247 50.000 -24.000 26.247 50.000 -10.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES10PSFL
LOAD Z 701 705 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 703 707 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 705 720 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 707 723 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 709 701 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 710 714 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 711 715 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 712 703 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 714 717 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 715 718 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 717 721 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 718 722 -0.1610 -0.1610 GLOB UNIF 10PSFL ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.000 ***LDS2** -26.247 75.000 -24.000 26.247 75.000 -15.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES15PSFU LOAD Z 801 805 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 803 807 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 805 840 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 807 843 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 829 801 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 830 834 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 831 835 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 832 803 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 833 836 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 834 837 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 835 838 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 836 839 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 837 841 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 838 842 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 839 844 -0.1280 -0.1280 GLOB UNIF 15PSFU LOADCNEQPT ***LDS1** 16.000 6.000 75.000 16.000 6.000 75.000 ***LDS2** -250.000 20.000 10.000 ***LDS3** 10.000 1 2 2 0 0EQPT -1EQUPSKIDSKID1 X LOAD Z 835 838 17.4040-65.579 GLOB CONC SKID1 LOAD Z 835 838 27.4040-65.579 GLOB CONC SKID1 LOAD Z 803 807 17.4040-59.421 GLOB CONC SKID1 LOAD Z 803 807 27.4040-59.421 GLOB CONC SKID1 ***LDS1** -16.000 -16.000 75.000 -16.000 -16.000 75.000 ***LDS2** -150.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID2 X LOAD Z 829 801 6.49700-35.653 GLOB CONC SKID2 LOAD Z 830 834 6.49700-39.347 GLOB CONC SKID2 LOAD Z 834 837 4.15400-39.347 GLOB CONC SKID2 LOAD Z 801 805 4.15400-35.653 GLOB CONC SKID2 ***LDS1** -16.000 50.000 -16.000 50.000
***LDS2** -100.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID3 X LOAD Z 701 705 12.6540-23.769 GLOB CONC SKID3 LOAD Z 701 705 20.1540-23.769 GLOB CONC SKID3 LOAD Z 714 717 12.6540-26.231 GLOB CONC SKID3 LOAD Z 714 717 20.1540-26.231 GLOB CONC SKID3 ***LDS1** 32.000 19.000 75.000 32.000 19.000 75.000 ***LDS2** -35.000 20.000 7.000 ***LDS3** 7.000 1 2 3 0 0EQPT -1EQUPSKIDSKID4 X LOAD Z 803 807 31.5000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 3.00000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 6.50000-6.1800 GLOB CONC SKID4 LOAD Z 836 839 31.5000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 3.00000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 6.50000-5.4866 GLOB CONC SKID4 LOADCNLIVE ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.606 ***LDS2** 26.247 75.000 -24.606 -26.247 75.000 -100.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES100PSFU Z LOAD Z 829 801 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 830 834 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 831 835 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 832 803 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 833 836 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 834 837 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 835 838 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 836 839 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 837 841 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 838 842 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 839 844 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 801 805 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 803 807 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 805 840 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 807 843 -1.6400 -1.6400 GLOB UNIF 100PSFU ***LDS1** 24.606 -26.247 50.000 24.606 26.247 50.000 -24.606 ***LDS2** 26.247 50.000 -24.606 -26.247 50.000 -50.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES50PSFL Z LOAD Z 701 705 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 703 707 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 705 720 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 707 723 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 709 701 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 710 714 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 711 715 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 712 703 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 714 717 -0.8200 -0.8200 GLOB UNIF 50PSFL
LOAD Z 715 718 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 717 721 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 718 722 -0.8200 -0.8200 GLOB UNIF 50PSFL LOADCNMISC LOAD Z 712 703 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 703 707 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 707 723 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 833 836 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 836 839 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 839 844 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD 807 -20.000 GLOB JOIN CRANE ***LDS1** -8.000 20.000 50.000 -8.000 20.000 50.000 ***LDS2** -10.000 34.000 0.100 ***LDS3** 0.100 1 2 2 0 0MISC -1EQUPSKIDFIREWALLX LOAD Z 705 720 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 705 720 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 4.05000-1.6667 GLOB CONC FIREWALL LCOMBLCOMB 1001 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 31 1.000 LCOMB 1002 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 32 1.000 LCOMB 1003 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 33 1.000 LCOMB 1004 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 34 1.000 LCOMB 1005 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 35 1.000 LCOMB 1006 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 36 1.000 LCOMB 1007 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 37 1.000 LCOMB 1008 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 38 1.000 END