the national academies keck center, washington dc march 10, 2011 1

NCHRP 08-71Methodology for Estimating

Life Expectancies of Highway Assets

Workshop for the Project Panel and Invited Participants

The National Academies Keck Center, Washington DC

March 10, 2011 1

Session 1

How to Use the Guidebook

2

Who should use the Guide

Roles in asset management

Elected OfficialsGovernor • legislature • county commissioners • city council

Appointed OversightTransportation commission • MPO board

Funding BodiesFHWA • FTA

The Public

Interest GroupsHighway users • homeowners associations • business groups • constituency groups

Senior managementExecutives • Districts • Modal units • Engineering disciplines • Planning • Design • Maintenance

Engineering staffProject engineers and managers • pavement surveys • materials/research • bridge design/rating • bridge inspection

Maintenance staffMaintenance engineers/managers • Facility managers • Maintenance crew leaders • Emergency response

Planning and supportBudget/finance • Program management • Strategic planning • Public information • Information technology

Asset management leadershipAsset management director • Bridge management engineer •Pavement management engineer

Outside stakeholders Internal participants

3

Who should use the Guide

Senior management – top-down vision

Oversight bodies – make service life tangible

Asset managers – decision outcome measure Practitioners – Learn how to compute and present

life expectancy Engineers and planners – Learn how to use life

expectancy in design and planning System designers – How to build life expectancy

into software and tools Researchers – Improve state of the practice

4

Evaluate and refineAssess quality, sensitivityImprove model realism

Develop applicationsPrepare user groupPrototype applicationsPilot test and evaluate toolsRefine and roll outDocument tools, procedures

Develop foundation toolsPrototype lifespan calculationsEvaluate prototype resultsRefine computationsImplement foundation toolsDocument methods and tools

Establish the frameworkDefine performance measuresConceptualize the analysisDetermine data requirementsMock up tools and reportsGain buy-in, build expectations

Plan for implementationDocument business processesPlan the change strategyList desired reports and toolsDefine work plan, resourcesSet quality metrics, milestones

Define the scopeSet goals and objectivesIdentify desired applicationsIdentify network of interestIdentify asset typesAssess gaps and readiness

12

3

45

6

Planning

Development

How to use this guide

How to plan life expectancy models

How to designlife expectancy models

How to computelife expectancy models

How to apply life expectancy models

How to improvelife expectancy models

Prolong implementationMeasure, promote successAdd to management systems

7

How to perpetuatelife expectancy models

Structure of the Guide

5

Potential goals and objectives

6

Justify maintenance funding

Plan timing and scope of actions

Plan staffing and equipment

Set inventory levels

Evaluate new materials, methods

Reduce workzone frequency

Improve consistency of reports

Optimize cash flow

Build credibility

Potential applications

Life expectancy if no maintenance Life expectancy under a proposed maint policy Life extension effects of preservation actions Compare preservation alternatives

7

Optimal replacement interval Optimal preventive maintenance interval Optimal expenditure on periodic maint Scope and timing to maximize life extension

Potential applications

Compare design alternatives using life cycle cost

Price point where a new material is attractive

Coordinate replacement of multiple assets

Plan corridor work zones and traffic control

8

Multi-objective prioritization Funding allocation and effect of

budget cuts Select treatment application

policies Establish research priorities

Scope of the effort

Start small, build incrementally

Expansion to agency-wide and to partner agencies

Statewide limited rolloutPilot test or

experimental application

Prototypeor proof-of-

concept

9

Assess gaps and readiness

Asset management maturity scale

Maturity Level

Generalized Description

Initial No effective support from strategy, processes, or tools. There can be lack of motivation to improve.

Awakening Recognition of a need, and basic data collection. There is often reliance on heroic effort of individuals.

Structured Shared understanding, motivation, and coordination. Development of processes and tools.

Proficient Expectations and accountability drawn from asset management strategy, processes, and tools.

Best Practice Asset management strategies, processes, and tools are routinely evaluated and improved.

change

10

Self-assessment topicsPart A. Policy Guidance. How does policy guidance benefit from improved asset management practice?

Policy guidance benefitting from good asset management practice Strong framework for performance-based resource allocation Proactive role in policy formulation

Part B. Planning and Programming Do Resource allocation decisions reflect good practice in asset management?

Consideration of alternatives in planning and programming Performance-based planning and a clear linkage among policy, planning and programming Performance-based programming processes

Part C. Program Delivery Do program delivery processes reflect industry good practices?

Consideration of alternative project delivery mechanisms Effective program management Cost tracking and estimating

Part D. Information and Analysis Do information resources effectively support asset management policies and decisions?

Effective and efficient data collection Information integration and access Use of decision-support tools System monitoring and feedback 11

How to use self-assessment

Get ducks in a row Policies in place Procedures defined Ability to deliver planned actions Availability of data

Decide how far to reach in next 2-3 years

Visualize agency capabilities at the end

Create implementation plan How to get from here to there

12

Questions?Discussion

13

Session 2

Plan for Implementation

14

Change management

Asset management tools, such as life expectancy analysis, are built in order to improve the way your agency does business.

Organizational change can be beneficial, and can be scary.

You need a vision and a strategy in order to be successful.

15

What to expect

Credible long-term view of asset performance

Accountability (benefits and fears)Tangible levels of serviceUnderstanding of deterioration and growthOptimal preservation Improved competitiveness for fundingConstructive political relationships

Be ready to follow through to win these benefits

16

Document relevant business processes

Why?Ensure the tools are relevantUnderstand how they will be

usedBuild the right tools for the jobSelect appropriate

methodsHelp others understandGain buy-in

Identify assets needing work

Develop work packages as

projects

Prioritize and schedule

Assess data quality

Monitor performance

Set minimum tolerable performance

Develop deterioration models

Develop lifeexpectancy models

Select rehabilitation actions

Design rehabilitation actions

Prioritize for further development

Developcost models

Develop effectiveness models

Develop corridor plans

Evaluate market conditions

Find economiesof scale

Evaluate equity

Evaluatefiscal uncertainty

Negotiate with funding bodies

Plan for delivery

Develop budget constraints

Develop performance targets

STIP

Designs

Lettings

Needs

Corridorplans

Inspectreports

AnnualReports

Projectplans

Gather data:Inventory • GeodataCondition • Traffic

Risk • Safety

17

Change strategy

Convince staff of the need and benefit of the change and the tools

Create a change leadership coalition

Develop a vision of the end result Communicate the vision regularly Take actions consistent with the

vision Make sure staff are involved and

empowered Show short-term successes Keep the focus on the change effort Anchor new approaches into the culture

18

Planning technical implementation

1. Data acquisition and management

2. Plan foundation analysis methods

3. List/describe applications and reports

4. Write a work plan5. Set quality metrics

and milestones19

Databases used in life expectancy

Geo-referencingTraffic countsCrashesAsset inventoryAsset conditionAsset vulnerabilityClimateSoils

NOAA Climate Divisions

20

Select foundation tools

Considerations: Purpose of the tools Types of assets to be addressed Performance measures Define end-of-life Define intervention possibilities Account for uncertainty

Analysis level:• Network level – Life expectancy of families of

assets based on general characteristics• Project level – Life expectancy of a single asset

based on age, condition, and asset characteristics21

Describe applications and reports

Considerations:Subject matter

FilteringAggregationSortingGraphics

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Pro

bab

ility

Age

Cumulative

This year

Average

22

Example work planTask 1. Define scope of the analysis.

Task 2. Develop implementation plan.

Task 3. Define performance metrics and analysis concepts, including data requirements and mock-ups.

Task 4. Develop foundation tools and models.

Task 5. Build applications, possibly through a series of prototypes.

Task 6. Ensure long-term support. Evaluate usage of the product and make improvements.

23


24

Session 3

Establishing the Framework

25

Life expectancy estimation based on replacement intervals

Value

Age

Straight-line depreciation

Value

Age

Interval replacement

Prematurefailure

26

Life expectancy estimation based on asset condition/performance

Performance, condition, or

value

Age

Deterioration model

End-of-lifethreshold


value

Age

Decision-sensitive


27

Life expectancy as a measure of investment benefit

Performance or condition

Age

Replacement: Extended life = 10 years, Cost = $100,000


You are here(end of 10 year life)

Repair: Extended life = 4 years, Cost = $50,000

28

Defining end-of-life

Life expectancy depends on how you define the end-of-life.

Agencies may often have a degree of control over life expectancy.

Lifespan can often bemanaged to maximizeagency objectives orminimize life cyclecosts.

29



value

Age

Sudden failure


value

Age

Obsolescence due to raised standard

Standard

30



value

Age

End-of-life defined by age

Performanceunknown, notmeasured, ordoesn't matter

Remaining capacity, stock,

or value

Age

End-of-life based on utilization

Consumption or utilization rate

31


Probability of failure

Age

Median time to failOptimal

replacement interval

Probabilisticend-of-life

Pavementcondition

Age

End-of-life from terminal criteria


Cracking

Roughness

First to fail

32

Coordinating lifespans of asset components

Bridge condition

Age


Substructure rehab adds 10 more years, allows full utilization of the third deck

Normal substructure life expectancy 50 years

Normal deck life expectancy 20 years

33

Planning component life based on functional life

Bridge condition,

performance

Age


Traffic forecast calls for unacceptable level of service after 30 years

Plan for two deck rehab projects to extend deck life until ready for replacement

34

Life extension

Condition

Age


Current conditionRemaining service life

Life extension

35

Role of uncertainty in program planning


Age

Median time to fail (life expectancy) = 12 years

20% will have failed by 10 years Program period

ends at 10 years

36

Forecasting life expectancyTechniques are related to

deterioration modeling, but usually simpler.

Select a method based on the kind of data available, the needs of the application, and the importance of uncertainty

37

Types of models


value

Age

ContinuousDeterministic


value

Age

DiscreteDeterministic


value

Age

ContinuousProbabilistic


value

Age

DiscreteProbabilistic

38

Data collection

Visual inspection (100% sample)

10% sample of road segments

Automated data collection39

Example report/app mockups

Digital dashboards

40


Using Excel for report mock-ups

41


Using Excel for

application developme

nt

42


43

Session 4

Developing Foundation Tools

44

Presentation Outline

What to Model Influence of Framework

Model Selection Selection Criteria Data Availability Nature of Prediction and Outcome

Estimation Techniques Regression Survival Models Markov Chains

What to Model

Model

Selection

Estimation

Techniques

Conclusion

45 45

Defining End-of-Life

End-of-Life can be taken as the time until

▪ Functional Obsolescence▪ Changes in standards▪ Changes in functional requirements

▪ Structural Deficiency▪ Deterioration▪ Extreme events

If modeled separately – Min. life assumed

If combined – Direct prediction of life

46

What to Model

Model

Selection

Estimation

Techniques

Conclusion

46

Interval-based

Two general approaches Interval-based

▪ Predict time until end-of-life event occurs

▪ Directly predict life based on historical replacement intervals

47

Reconstruction, Y Construction, X

Service Life

Year TX Year

Year TY

What to Model

Model

Selection

Estimation

Techniques

Conclusion

47

Condition-based

Two general approaches Condition-based

▪ Predict condition or measure of performance as a function of time

▪ Predict asset value as a function of time

48


value

Age

Deterioration model



value

Age

Decision-sensitive


What to Model

Model

Selection

Estimation

Techniques

Conclusion

48

Model Selection Criteria

General Criteria Transparent

▪ Staff Knowledge▪ Able to Replicate and Revise

Applicable▪ Data Availability▪ Widespread Use of Results

Focused▪ Prioritize on Predicting Life▪ Not necessarily Deterioration-

based 49

What to Model

Model

Selection

Estimation

Techniques

Conclusion

49

Data Availability

Model Selection depends on Data Availability

▪ Historical Service Life▪ Dominating end-of-life condition preferred

▪ Condition Data by Age▪ Archived Data Preferred

50

What to Model

Model

Selection

Estimation

Techniques

Conclusion

50

Continuous vs. Discrete

Model Selection depends on Nature of Dependent Variable

▪ Continuous Variable▪ Time until rationale event occurs▪ Performance Measures (e.g., IRI, Rutting, NBI Sufficiency Rating)

▪ Discrete Variable▪ Performance Measures (e.g. NBI element Condition Rating, PSI)

51

What to Model

Model

Selection

Estimation

Techniques

Conclusion

51

Deterministic vs. Probabilistic

Model Selection depends on Nature of End Result

▪ Deterministic

▪ Probabilistic

52


value

Age

ContinuousPerformance,

condition, or value

Age

Discrete


value

Age

ContinuousPerformance,

condition, or value

Age

Discrete

What to Model

Model

Selection

Estimation

Techniques

Conclusion

52

Interpreting Probability

Probabilistic estimates can be represented by Density functions

53

What to Model

Model

Selection

Estimation

Techniques

Conclusion 0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

30 35 40 45 50 55 60

Prob

abili

ty

Service Life in years

Probability Density Function

Median

Confidence Interval

53

Interpreting Probability

Probabilistic estimates can be represented by Survival or Cumulative functions

▪ Survival Prob. = 1 - Cum. Prob.

54

What to Model

Model

Selection

Estimation

Techniques

Conclusion0.00.10.20.30.40.50.60.70.80.91.0

30 35 40 45 50 55 60

Prob

abili

ty o

f Pas

sing

Service Life in years

Survival Function

Median

Confidence Interval

54

Techniques

Basic Techniques Deterministic

▪ Regression (Continuous Data) Probabilistic

▪ Simple Average (Continuous Data)▪ Survival Models (Continuous Data)▪ Markov Chains (Discrete Data)

Alternatively, may be forced to rely on published life expectancy values or expert opinion

55

What to Model

Model

Selection

Estimation

Techniques

Conclusion

55

Simple Average

Data requirements Requires historical replacement data Does not require explanatory factors

Method Fits distributions to groups of assets

based on average and standard deviation of data

56

What to Model

Model

Selection

Estimation

Techniques

Conclusion

Average age at replacementa is culvert age, N is number of culverts

Population standard deviation(use if list is w hole population)

Sample standard deviation(use if list is a random sample)s is an estimate of σ

N

iia

Na

1

1

N

ii aa

N 1

21

N

ii aa

Ns

1

2

1

1

56

Simple Average

Example Demonstration

57

What to Model

Model

Selection

Estimation

Techniques

Conclusion

57

Ordinary Regression

Data requirements Requires

▪ Historical replacement data or Continuous performance/condition data & age

▪ Set of independent, explanatory factors Method

Predicts dependent variable as a function of explanatory factors▪ E.g., predict life as a function of traffic

volume, maintenance history, material type, climate conditions, etc.

58

What to Model

Model

Selection

Estimation

Techniques

ConclusionLife Prediction

nn XbXbXbt 2211

58

Ordinary Regression


59

What to Model

Model

Selection

Estimation

Techniques

Conclusion

59

Cox Regression

Data requirements Historical replacement data or

Time until end-of-life criteria reached

Set of independent, explanatory variables

Method Predicts survival curve (% assets

passing beyond point in time) as a function of explanatory variables

No assumption of statistical distribution

Median life = 50% survival probability60

What to Model

Model

Selection

Estimation

Techniques

Conclusion

Probability of Passing

nng XbXbXbgy 22111 exp/0.1exp

60

Cox Regression


61

What to Model

Model

Selection

Estimation

Techniques

Conclusion

61

Quick-and-Simple Weibull

Data requirements Historical replacement data or

Time until end-of-life criteria reached Method

Predicts survival curve (% assets passing beyond point in time)

Probabilities governed by Weibull distribution (or Markov/Exponential model if shape parameter = 1)

Median life = 50% survival probability

62

What to Model

Model

Selection

Estimation

Techniques

Conclusion


/0.1exp1 gy g

62


63

Quick-and-Simple Weibull

What to Model

Model

Selection

Estimation

Techniques

Conclusion

63

Weibull Regression

Improves upon Quick-and-Simple Weibull technique by adjusting predictions to a set of independent, life expectancy factors


64

What to Model

Model

Selection

Estimation

Techniques

Conclusion


nng XbXbXbgy 22111 exp/0.1exp

64

Intro to Markov Chains

Common technique for predicting the probability of being in any discrete condition state at any point in time

65

What to Model

Model

Selection

Estimation

Techniques

Conclusion

Good Fair Poor

Pii ≡ Probability of staying in same condition state i after unit time

Pij ≡ Probability of transitioning from state i to a worse condition state j after unit time

65

Intro to Markov Chains

Probabilities represented in matrix form

66

What to Model

Model

Selection

Estimation

Techniques

Conclusion

Markov transition probability matrixStateToday Good Fair Poor

Good 95.3 4.6 0.1Fair 0 93.2 3.9Poor 0 0 100.0

State probability in one year

Good Fair Poor

PGG=95.3

PGF=4.6

PGP=0.1

PFF=93.2 PPP=100.0

PFP=3.9

66

Quick-and-Dirty Markov Chain

Data Requirements Pairs of inspection Data with Discrete

Condition Rating

Method Estimate transition probability between 2

states: ‘failed’ and ‘not failed’ Compares % assets in each condition state

from one year to the next Median Life taken as

67

What to Model

Model

Selection

Estimation

Techniques

Conclusion

67


68

Quick-and-Dirty Markov Chain

What to Model

Model

Selection

Estimation

Techniques

Conclusion

68

Markov Chain

Similar to Quick-and-Dirty but now analyzes multiple (>2) states

Data Requirements Transition probabilities by way of expert

opinion, observed frequency, optimization, one-step process, etc.

Method Probabilistic estimate of condition states by

age Median Life = 50% assets in threshold state

69

What to Model

Model

Selection

Estimation

Techniques

Conclusion

Probability of state k next year: for all k

j is the condition state this year and x is the fraction in state jp is the transition probability from j to k

j

jkjk pxy

69


70

Markov Chain

What to Model

Model

Selection

Estimation

Techniques

Conclusion

70

One-Step Process

Data Requirements Pairs of inspection Data with Discrete

Condition Rating

Method Predicts transition probabilities by comparing

% assets in a condition state at the end of the year to that at the beginning of the year

Assumes condition state never drops more than one step per year

Life prediction same as previous example

71

What to Model

Model

Selection

Estimation

Techniques

Conclusion

71


72

One-Step Process

What to Model

Model

Selection

Estimation

Techniques

Conclusion

72

Equivalent Age Markov

Data Requirements Transition probabilities by way of expert

opinion, observed frequency, optimization, one-step process, etc.

Method Predict age as a function of condition Calculate condition index weighted by time

spent in condition state or lower state Approach converts a Markov model into a

Weibull model

73

What to Model

Model

Selection

Estimation

Techniques

Conclusion)log(

)5.0log(

jjj p

t g is equivalent ageCI is condition index

CI

glnlog

^10

73


74

Equivalent Age Markov

What to Model

Model

Selection

Estimation

Techniques

Conclusion

74

Conclusion

▪ End-of-Life Definition(s) Needed

▪ Interval- or Condition-based Approaches

▪ Selected Models should be transparent, applicable, and focused

▪ Selection influenced by nature of dependent variable and estimate

▪ Basic modeling techniques include▪ Regression▪ Survival Models▪ Markov Chains

What to Model

Model

Selection

Estimation

Techniques

Conclusion

75


76

Session 5

How to apply the life expectancy models

77

Presentation Outline• Life Expectancy Estimates from

Deterioration Model • Additional Building Blocks for Life

Expectancy Application • Example Applications• User Groups• Conclusion

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

78

APPLYING THE MODELS

Deterioration Model• Life expectancy estimates -- easily derived from deterioration

models• Additional tools are developed on top of life expectancy

estimate to help management decision making process

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

79

APPLYING THE MODELS

Additional Building Blocks for Life Expectancy Application

• Techniques of life expectancy analysis open the door for many useful applications to support TAM decision making, but few more building blocks are required:– Equivalent age– Life extension benefits of actions– Remaining service life– Life cycle cost models

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

80

APPLYING THE MODELS

Equivalent age

• Deterioration models often use age of an asset to forecast its condition

• However, many applications require finding out ‘equivalent age’ from known condition of an asset

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

81

APPLYING THE MODELS

Life Extension Benefits of Actions

• Effect of repair & rehabilitation actions is expressed as an improvement in condition

• Once the improved condition is forecast, we can find equivalent age, before and after the action

• The difference in age is one way of expressing the benefit of the action

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

82

Condition

Age

Change in equivalent age = Life extension benefit

Life extension action improves condition

APPLYING THE MODELS

Remaining Service Life

• Computed by subtracting actual age of an asset from its life expectancy (provided no repair was done)

• If an asset has been repaired, it is more accurate to use a condition-based approach (i.e., taking advantage of deterioration and equivalent age models)

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

83

Condition

Age


Current condition

Remaining life

Unknown past work

• Current condition of the asset can be converted to its equivalent age, which is

then subtracted from life expectancy to estimate remaining service life

APPLYING THE MODELS

Life Cycle Cost Models

• Life cycle cost models, combined with life expectancy and deterioration models, may be used in numerous useful applications to support TAM decision making

• Few concepts associated with life cycle cost models– Time value of money– Benefit/cost ratio– Comparing alternatives using Net Present Value (NPV)– Comparing alternatives using Equivalent Uniform Annual

Cost (EUAC)– Comparing alternatives using Present Worth at Perpetuity– Comparing alternatives using Internal Rate of Return

(IRR)

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

84

APPLYING THE MODELS

Example Applications

Many useful asset management applications can be created using the building blocks discussed

– Routine preventive maintenance– Optimal replacement interval– Comparing and optimizing design alternatives– Comparing and optimizing life extension

alternatives– Pricing design and preservation alternatives– Synchronizing replacements– Effect of funding constraints– Value of life expectancy information

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

85

APPLYING THE MODELS

Routine Preventive Maintenance

• An example of comparing a preventive maintenance scenario against do-nothing scenario

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

86

Cost per lane-mile by strategyYear Routine Preventive

MaintenanceDo-Nothing

1...4 $400...8 $400...12 $400...16 $400...20 $400 $30,000...24 $30,000

APPLYING THE MODELS

Routine Preventive Maintenance (contd.)

• Let us assume, interest rate = 4%• The EUAC of the two alternatives can be compared as follows:

= $9,083/lane-mile

= $596/lane-mile

= $768/lane-mile

• In this example, the agency could reduce annual costs by $172 per lane-mile if routine preventive maintenance is completed

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

87

APPLYING THE MODELS

Optimal Replacement Interval

• Assets may have a number of service life alternatives, depending on different strategies for maintenance and life extension

• Optimal service life would be the life cycle activity profile that can be sustained at minimum life cycle cost

• Here is an example of comparing several alternative profiles

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

88

Option 1 Option 2 Option 3 Option 4Replacement Cost 600 600 600 600Rehabilitation Cost 200 200 200 200

Annual Maintenance Cost 5 5 5 5Estimated service life (N) 50 60 70 80Rehabilitation years 25 25 25 20

40 45 45 4055 60

Interest rate 0.05 0.05 0.05 0.05

Compounded Life Cycle Cost $7884 $12727 $21146 $35411

Present Worth at Perpetuity $753 $720 $719 $729

APPLYING THE MODELS

Optimal Replacement Interval (contd.)

• Plot suggests that options 2& 3 are preferred and the optimal interval for replacement is between 60-70 years

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

89

40 45 50 55 60 65 70 75 80 85700

710

720

730

740

750

760

Option 1

Option 2 Option 3

Option 4

Replacement cycle (year)

Pre

sen

t w

ort

h a

t P

erp

etu

ity

($10

00)

APPLYING THE MODELS

Comparing/optimizing Design Alternatives

• Comparing two products or methods that have different costs, different life expectancies, and different life extension possibilities

• Here is an example, deciding on whether to apply coating to a pipe culvert– a non-coated culvert, expected to survive 50 years with a

construction cost of $1000, and a coated culvert, expected to survive 56 years with a construction cost of $1200

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

90Therefore, the coated design option is preferred

APPLYING THE MODELS

Pricing Design and Preservation Alternatives

• Many agencies have active research programs to develop new and improved maintenance materials and techniques

• But, how cheap does it need to be before it’s worth using?

• The methods of life expectancy analysis can often play a part in this evaluation

– Example: To assess feasibility of switching from traditional carbon steel reinforcement bars to solid stainless steel reinforcement bars

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

91

APPLYING THE MODELS

Pricing Design and Preservation Alternatives (contd.)

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

92

APPLYING THE MODELS

Source: Cope et al. (2011)

ILLUSTRATION: Material for bridge deck reinforcement

At what price ratio is stainless steel (SS) more cost-effective than traditional steel (TS)?

Answer: depends on service life of each alternative

FHWA Laboratory and field simulations: SS – 100 years (no deck replacement)TS – 70 years (1 deck replacement, 2 deck rehabs)

0.6

0.7

0.8

0.9

1

1.1

0 2 4 6 8 10

Rat

io o

f E

UA

C

for

Sta

inle

ss S

teel

to

T

rad

itio

nal

ste

el

Ratio of Stainless Steel Price to Traditional Steel Price

Current Ratio

ThresholdRatio

Stainless Steel is MORE cost-effective

Stainless Steel is LESS cost-effective

Effect of Funding Constraints

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

93

• Decision support tools based on life expectancy and life cycle cost can help an agency to do more with less

– Example: an agency calculated utility of a set of projects

with respect to life expectancy, deterioration, life cycle

cost, and estimated project cost. Let budget be $2.75M

Activity Utility Cost

Bridge A replacement 100 $2400k

Bridge B rehabilitation 75 $250k

Box Culvert A replacement 55 $100k

Pipe Culvert A replacement 35 $5k

Bridge C deck patching 32 $20k

APPLYING THE MODELS

Effect of Funding Constraints (contd.)

• Optimization techniques can be applied to select a set of projects (Solver option in Excel may be used)

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

94

Optimal solution: Total utility 242 at a cost $2.675M; remaining $75k to be carried over

APPLYING THE MODELS

Activity Utility Cost

Bridge A replacement 100 $2400k

Bridge B rehabilitation 75 $250k

Box Culvert A replacement 55 $100k

Pipe Culvert A replacement 35 $5k

Bridge C deck patching 32 $20k

Role of User Groups

• One of the best ways to create involvement and buy-in is to form a user group for the applications that are to be developed

• A user group should consist of people who will be hands-on users of the applications, as well as people who may receive and act on the information

Deterioration Model

Building Blocks

Example

Role of User Groups

Conclusion

95

Steering/leadership committee

President

Subcommittee

Subcommittee

Subcommittee

Outside stakeholders

Senior management

Asset management leadership

APPLYING THE MODELS

Role of User Groups (contd.)

• The user group’s tasks include planning, development, & production of different applications

• Often the user group will be large and may expand over time to include all hands-on users and many indirect users of the applications

• Once the group reaches sufficient size, it should create sub-groups to whom it delegates many of the tasks above

Deterioration Model

Building Blocks

Example

Role of User Groups

Conclusion

96

APPLYING THE MODELS

Conclusion

• An agency may launch a big system development effort to implement various applications of lifecycle estimations

• Alternatively, it can select relatively small subset of applications at first (often just one), and develop working prototype– The prototype addresses core functions, from data

collection to analysis & reports– Should gradually expand to cover more applications and

to add more features – Should identify data gaps, procedures and standards that

are required, in the context of a working application

Deterioration Model

Building Blocks

Example

Role of Users

Conclusion

97

APPLYING THE MODELS

Conclusion

– It gives users more day-to-day control and involves them more deeply in the creation of the tools they will use, thus helps avoiding “not invented here” syndrome Deterioration

Model

Building Blocks

Example

Role of Users

Conclusion

98

APPLYING THE MODELS


99

Session 6

Accounting for Uncertainty

100

Presentation Outline

Rationale for Incorporating Uncertainty

Causes of UncertaintySensitivity AnalysisRisk Analysis

Uncertain Inputs Uncertain Outputs

Rationale

Causes

Sensitivity

Risk

Conclusion

101101

Rationale

Life expectancy estimates affect business processes

but asset life is inherently uncertain…

102

Rationale

Causes

Sensitivity

Risk

Conclusion

Human Resources

Data collectionPreservation

Planning

ProjectDevelopment

Programming

Budgeting

PreservationPolicy

NetworkPlanning

CorridorDevelopment

Design

Maintenance

Research

InformationTechnology

Finance

LifeExpectancy

Analysis

102

Causes of Uncertainty

Uncertainty result of random

103

Rationale

Causes

Sensitivity

Risk

Conclusion

Random Process Example

Structural Response Actual strength unknown due to material imperfections

Loadings Uncertainty surrounding future traffic levels and % trucks

Site Conditions Uncertain soil properties. future climate conditions, or random extreme weather events

Human Influence Unknown construction and/or inspection rating quality

Externalities Unforeseen development of new technologies or standards

103

Quantifying Uncertainty

Methods to quantify uncertainty

- Both can be used to produce ranges of life estimates

- Risk analysis additionally describes the likelihood of life estimates

104

Rationale

Causes

Sensitivity

Risk

Conclusion

Characteristic Sensitivity Analysis

Risk Analysis

Nature of Outcome

Deterministic Probabilistic

Assesses how Outcome varies due to...

Unit Changes Random Changes

104

Sensitivity Analysis

Benefits Identify most influential factors

Guide design selections Assess potential life extensions

Plan for mitigation

105

Rationale

Causes

Sensitivity

Risk

Conclusion

105

Sensitivity by Model Selection

Analysis varies by model selection For models without explanatory

variables, can assess how life prediction varies for different groupings of assets

106

Rationale

Causes

Sensitivity

Risk

Conclusion

Markov Chains

Quick & Simple Weibull

Simple Average

106

Sensitivity by Model Selection

Analysis varies by model selection For models with explanatory variables,

can assess how life prediction changes when vary factors over a range of values

107

Rationale

Causes

Sensitivity

Risk

Conclusion

Cox Regression

Weibull Regression

Ordinary Regression

107

Sensitivity by Model Type

Analysis varies by model type

For Ordinary Regression▪ Unit Δ in factor = β Δ in life prediction

For Cox Regression models▪ Unit Δ in factor = exp(β) % Δ in Hazard

Ratio

For Weibull Regression models▪ Unit Δ in factor = exp(β) % Δ in Average

Life

where β represents the parameter estimate 108

Rationale

Causes

Sensitivity

Risk

Conclusion

108

Showing Sensitivity

Tornado Diagram Representation

109

Rationale

Causes

Sensitivity

Risk

Conclusion

Factor 1

Factor 2

Factor n

Δ in Life Predictions

.

.

.

.

.

.

.

.

Increase in Factor leads to a Decrease in Life

Increase in Factor leads to an Increase in Life

Increasing Influence on Life

109

Example Sensitivity Analysis


110

Rationale

Causes

Sensitivity

Risk

Conclusion

110

Probabilistic Techniques

To mitigate uncertainty, probabilistic techniques emphasized Describe likelihood of life expectancy

and related business processes Ranges of life produced by level of

confidence (μ point estimate)

111

Rationale

Causes

Sensitivity

Risk

Conclusion


Age

Median time to fail (life expectancy) = 12 years

20% will have failed by 10 years

Program period ends at 10 years

111

Risk Analysis

Risk Identification

Describe Likelihood and Consequence of Risk

Risk Assessment

Quantify Likelihood and Consequence

Risk Management

Decide on Mitigation Strategy

Risk Monitoring

Monitor Effectiveness of Strategy

112

Rationale

Causes

Sensitivity

Risk

Conclusion

112

Risk Assessment

Risk Assessment Process

<Van Dorp, 2009 – GWU>

113

Rationale

Causes

Sensitivity

Risk

Conclusion

X

Y

Z O

Step 1: Quantify uncertainty surrounding life expectancy factors (e.g., climate conditions, traffic loading) using probability distributions

113

Risk Assessment



114

Rationale

Causes

Sensitivity

Risk

Conclusion

X

Y

Z O

Step 2: Randomly sample input distributions and calculate life using the calibrated model

114

Risk Assessment



115

Rationale

Causes

Sensitivity

Risk

Conclusion

X

Y

Z O

Step 3: Assess the distribution of life estimates 115

Example Risk Analysis

Suppose an agency is interested in the risk of potential climate change on business processes

Propagating

Uncertainty116

Rationale

Causes

Sensitivity

Risk

Conclusion

Climate ServiceLife

Annual Costs

Budget Needs

Project Utility

116


Assume 30 year old bridge asset with following characteristics: Normal annual temperature (°F) = 49 Normal annual precipitation (in.) = 43 Part of NHS system Non-Corrosive Soil Steel, girder bridge 50 feet long Wearing Surface Applied $50k Replacement Cost 4% Interest Rate

117

Rationale

Causes

Sensitivity

Risk

Conclusion

117


Assess how life changes due to uncertain climate

<ICF International, 2009 in CCSP, 2009>

118

Rationale

Causes

Sensitivity

Risk

Conclusion

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 2 4 6

Prob

abili

ty

Δ in Temperature (°F)

Δ in Temperature Forecasts

Low Emissions

Moderately High Emissions

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

-15 -10 -5 0 5 10 15 20

Prob

abili

ty

Δ in Precipitation (in.)

% Δ in Precipitation Forecasts

Low Emissions


118


After 2,500 Simulations

119

Rationale

Causes

Sensitivity

Risk

Conclusion

0.00.10.20.30.40.50.60.70.80.91.0

0 20 40 60 80 100

Prob

abili

ty

Median Life (years)

Uncertain Survival for Low Emissions

Confidence Interval

Expected

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Prob

abili

ty

Median Life (years)

Uncertain Survival for Moderately High Emissions

Confidence Interval

Expected

119


Median Life Current: 50 years 90%

CI Low Emissions: 50 yrs [46,53] Mod. High Emissions: 49 yrs [45,54]

120

Rationale

Causes

Sensitivity

Risk

Conclusion

0.000.020.040.060.080.100.120.140.160.180.20

40 45 50 55 60

Prob

abili

ty

Median Life (years)

Uncertain Median Life

Low Emissions


120


Uncertain Life Uncertain EUAC Current: $2,328 90%

CI Low: $2,328

[$2,286,$2,394] Mod. High: $2,343

[$2,273,$2,394]

121

Rationale

Causes

Sensitivity

Risk

Conclusion

0.000.020.040.060.080.100.120.140.160.180.20

$2,200 $2,300 $2,400 $2,500

Prob

abili

ty

EUAC ($)

Uncertain Median EUAC

Low Emissions


121


Probability Future Average Life or EUAC < Current Average Life or EUAC

Low Emissions: 48.8% chance

Mod. High Emissions: 51.6% chance

122

Rationale

Causes

Sensitivity

Risk

Conclusion

122


Suppose assessing needs for 10 year planning horizon If assumed 50 year life then would not set

aside funds for 30 year old bridge If consider risk of ‘failure’ then would

expect to needP(‘Failure’ within planning

horizon)*Cost[1-S(30+10)] * Replacement Cost= $16,712 for Low Emissions= $16,917 for Moderately High

Emissions123

Rationale

Causes

Sensitivity

Risk

Conclusion

123


Risk of programming the wrong project

Assume ranking based decision on ΔURSL

124

Rationale

Causes

Sensitivity

Risk

Conclusion

𝑈= 1.1659∗ሾ1−𝐸𝑋𝑃ሺ−0.0195∗𝑅𝑆𝐿2ሻሿ

00.10.20.30.40.50.60.70.80.9

1

0 2 4 6 8 10

Util

ity

Remaining Service Life in Years

124


Suppose ranking replacement projects for 10 year planning horizon based solely on RSL If assumed 50 year life then would

estimate no utility for replacing 30 year old bridge

Considering risk for either emission scenario…

125

Rationale

Causes

Sensitivity

Risk

Conclusion

Expected ΔU P(Max Benefit)

P(Benefit)

+25 18.0% 31.7%

125


Full Demonstration of Example

Programmed into Spreadsheet

126

Rationale

Causes

Sensitivity

Risk

Conclusion

126

Conclusion

▪ Need to incorporate uncertainty

▪ Causes of uncertainty▪ Methods for assessing uncertainty

▪ Sensitivity Analysis▪ Risk Analysis

▪ Recommended to move towards probabilistic planning and management framework127

Rationale

Causes

Sensitivity

Risk

Conclusion

127


128

Session 7 Ensuring Implementation

129

Measuring success

To how many of these can you answer “yes”?

LONG TERM VIEW Does the agency now feel confident in publishing life

expectancy estimates, and using them to evaluate and anchor budgetary requests?

Do senior managers have confidence that they know how much it will cost in the long term to sustain the desired level of service?

Do outside stakeholders agree with management estimates of the long-term cost of sustaining the desired level of service?

Do senior managers and stakeholders know what level of service can be sustained under current or proposed future funding levels? 130

Measuring success


TRANSPARENCY

Is there a public comparison of forecast vs actual life expectancies?

Are actions taken in response to life expectancy estimates and findings, and do stakeholders know what these actions are?

Are comparisons routinely and publicly made of the agency’s performance against peer agencies, and against itself over time?

131

Measuring success


LEVELS OF SERVICE

Can the agency accurately measure, track, and publish the level of service it is currently providing?

Are life extension and replacement decisions accurately timed to avoid interruptions in service while minimizing costs?

Is the agency reducing the annual number of traffic disruptions due to planned and unplanned maintenance, repair, and replacement activity?

132

Measuring success


EFFICIENCY

Is the agency improving in its quantitative performance, in relation to the cost of providing the desired levels of service?

Can the agency show, from its actual data, that its more refined timing of life extension and replacement actions is saving money, relative to earlier practice?

Does the agency routinely compute, and effectively communicate, the life cycle costs of its services? Are these costs showing a clear trend of improvement?

133

Measuring success


AGENCY COMPETITIVENESS

Is the agency using its asset management information as a competitive weapon to secure adequate funding?

Are legislators confident that the agency is doing everything it can to control costs?

Is the agency able to maintain adequate funding levels over time, in the face of competing uses of the money?

134

Measuring success


CONSTRUCTIVE RELATIONSHIPS

Is the agency working actively with outside stakeholders on strategies to maintain and enhance the level of service provided to the public?

Do outside stakeholders understand how their own interests are served by maintaining the agency’s level of service objectives?

Do legislators and funding bodies rely on the agency’s models of the relationship between level of service and funding?

135


136

the national academies keck center, washington dc march 10, 2011 1

Documents

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life expectancy models

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