performance-based design and testing methods for …€¦ · performance-based design and testing...

Department of Civil, Construction and Environmental Engineering

Performance-based design and testing methods for granular road surface materials (IHRB Project TR-685)

2017 Mid-Continent Transportation Research SymposiumAugust 16, 2017

Cheng Li, Ph.D. Postdoctoral Research [email protected]

Project Principal Investigators:Drs. Jeramy Ashlock, Bora Cetin, and Charles Jahren

mailto:[email protected]


60% of IA road network is unpaved: 68,400 of 114,000 miles

2

6

$14,784 per mile for a 30ft wide road

Reference: Li, C., Ashlock, J. C., White, D. J., & Vennapusa, P. (2015). Low-Cost Rural Surface Alternatives: Demonstration Project., IHRBProject TR-664, Iowa Department of Transportation, Ames, IA, p. 242.


Performance and durability of granular road surface materials are a function of several influence factors

• Gradation (particle size distribution and top size)

• Plasticity (dust and stability)

• Aggregate Quality (degradation and abrasion)

• Aggregate morphology (shape and angularity)

7

Gradation


Particle packing of aggregate materials governs its mechanical performance, especially in wet conditions

9

Reference: Xiao, Y., E. Tutumluer, Y. Qian, and J. A. Siekmeier. Gradation Effects Influencing Mechanical Properties of Aggregate Base-Granular Subbase Materials in Minnesota. Transportation Research Record: Journal of the Transportation Research Board, 2267, 2012, pp. 14-26.

Coarse-graded Optimum Gradation Floating Aggregates


Typical state DOTs’ specifications for gradation and plasticity are not performance related

10

Sieve No. Iowa Class A or B

South Dakota

Illinois CA-6

Minnesota Class 1

Nebraska Rock

Missouri Grade B

1.5 100 1 100 100-90 100 100

3/4 in. 100-95 100 100 1/2 in. 90-70 90-60 3/8 in. 95-65 < 65 No. 4 55-30 78-50 56-30 85-40 60-20 No. 8 40-15 67-37

No. 10 70-25 30-0 25-5 No. 16 40-10 No. 40 35-13 45-10 No. 200 16-6 15-4 12-4 15-8 10-0 Plasticity

Index NA 12-4 9-2 NA NA NA


Hypothesis: the target is too wide and does not specify the most critical parameter: particle packing

11

#10

#40

#100

#200#41/2"

5/8"

SandGravel Silt

Grain Diameter (mm)

0.0010.010.1110100

Per

cent

Pas

sing

(%)

0102030405060708090

100Iowa DOT Class A & B

Existing Aggregate (varied gradations)

Existing AggregateVirgin AggregateVirgin with Existing

1"3/

4"

1/4"

#60

#20

3/8"

Clay

1.5"


Gravel Content (%)

10 20 30 40 50 60 70 80

Soak

ed C

BR (%

)

0

10

20

30

40

50

60

70

80

ExistingExisting+Virgin

100% Existing

100% Virgin

<#40, % vs Soaked CBR, %

Sand Content (%)

10 20 30 40 50 60

Soak

ed C

BR (%

)

0

10

20

30

40

50

60

70

80


100% Virgin

100% Existing

Fines Content (%)

0 5 10 15 20 25 30

Soak

ed C

BR (%

)

0

10

20

30

40

50

60

70

80


100% Existing

100% Virgin

An optimum gradation in terms of shear strength exists for a given well-graded granular material

12


Grain Diameter (mm)

0 5 10 15 20 25 30

Per

cent

Pas

sing

(%)

0102030405060708090

100

Gradations of well-graded granular materials can be described by two parameters using Fuller’s model

13

𝑝𝑝𝑖𝑖 = 𝐷𝐷𝑖𝑖𝐷𝐷𝑚𝑚𝑚𝑚𝑚𝑚

𝑛𝑛× 100

where subscript “𝑖𝑖” represents a particular sieve; 𝑝𝑝𝑖𝑖 = percentage passing the 𝑖𝑖 sieve;𝐷𝐷𝑖𝑖 = opening size of the 𝑖𝑖 sieve; 𝐷𝐷𝑚𝑚𝑚𝑚𝑚𝑚 = the maximum size of aggregate; and 𝑛𝑛 = shape factor of gradation curve.

Existing AggregateFuller's Model

pi = (Di / Dmax) n

R2 = 0.9803Dmax = 23.6 mmn = 0.2794G/S = 0.67


Fuller’s model with two parameters can be used to develop performance-based specifications

14


PSD curves with a wide range of shape factors can meet the current gradation specification band

15

#10

#40

#100

#200#41/2"

SandGravel Fines

Particle Size (mm)

0.010.1110100

Perc

ent

Pass

ing

(%)

0102030405060708090

100

Iowa DOT Spec. Band1"

3/4"

#60

#20

3/8"

n = 0.10

n = 0.15

n = 0.70


The current gradation specifications cannot ensure performance

16

Plasticity


Bentonite treatment significantly reduced dust and provided a much drier surface during thawing

#1A Dirty MacadamA-1-a (USCS: GP-GM)Percent of fines = 9.9%Percent of clay = 1.8%PI = NP

#1B Dirty Macadam+BentoniteA-2-6(0) (USCS: GC)Percent of fines = 14.3%Percent of clay = 5.0%LL=30, PL=12, and PI=18

#2 Dirty Macadam+Chloride

18

March 28, 2015

Reference: Li, C., P. K. R. Vennapusa, J. Ashlock, and D. J. White. (2017) Mechanistic-Based Comparisons for Freeze-Thaw Performance of Stabilized Unpaved Roads. Cold Regions Science and Technology, Vol. 141, 2017, pp. 97-108.


Bentonite was found to be effective to improve the stability and slaking of crushed limestone fines

19


Add 4% bentonite to the surface material passing #40 sieve or control the PI between 7 and 15

20

Moisture Content (%)

0 3 6 9 12 15 18

UC

S (M

Pa)

0.01

0.1

1

10

100

0% Bentonite2% Bentonite4% Bentonite6% Bentonite

Aver

age

Slak

ing

Tim

e (m

in.)

10

100

1000

OMC+2%After Drying

0% Bentonite

2% Bentonite

4% Bentonite

6% Bentonite


Different lab tests for measuring plasticity of soils were evaluated

21

Conventional Atterberg Limit tests

Fall Cone Liquid and Plastic Limit Test

Bar Linear Shrinkage(BLS) Test

Cas

agra

nde

Cup

AS

TM R

olle

r


South Africa DOT’s performance-related unpaved road surface material selection chart

22

(% passing1in . sieve % passing #10 sieve) % passing #4 sieveGrading Coefficient100

− ×=

Shrinkage Product Bar Linear Shrinkage % passing #40 sieve= ×

Reference: Paige-Green, P. The Influence of Geotechnical Properties on the Performance of Gravel Wearing Course Materials. Ph.D. Dissertation, University of Pretoria, South Africa, 1989.


The BLS can be correlated to the PI but is not a sensitive parameter indicating plasticity of soils

23

Bar Linear Shrinkage (%)

0 5 10 15 20 25

Pla

stic

Inde

x

0

5

10

15

20

25

30

35

40

Averagey = 4.84x-5.27; R2 = 0.62n = 36 (All data points)95% Prediction Interval3%

6%

9%

12% Bentonite

Note: Error bars show the maximum or minimum values.


The fall cone test is easier to learn and perform and has better repeatability than the conventional liquid limit test

Fall Cone Liquid Limit (%)

10 15 20 25 30 35 40 45 50

Cas

agra

nde

Cup

Liq

uid

Lim

it (%

)

10

15

20

25

30

35

40

45

50Averagey = 1.17x-5.82; R2 = 0.98n= 45 (All data points)95% Prediction Interval 1:1

Line

Note: Error bars show the maximum or minimum values.

0%

3%

6%

9%12%

Bentonite

24


The repeatability, reproducibility, and overall variability in the tests were statistically evaluated

Two-Way ANOVA-Based repeatability and reproducibility (R&R) Analysis𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖 = 𝜇𝜇 + 𝛼𝛼𝑖𝑖 + 𝛽𝛽𝑖𝑖 + 𝛼𝛼𝛽𝛽𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖

where,𝜇𝜇 = an average measurement of all possible operators and all possible

parts,𝛼𝛼𝑖𝑖 = effects of different parts,𝛽𝛽𝑖𝑖 = effects of different operators,𝛼𝛼𝛽𝛽𝑖𝑖𝑖𝑖 = joint effects peculiar to particular part/operator combinations, and𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 = measurement error.

Reference: Vardeman, S. B., and Jobe, J. M., 1999, Statistical quality assurance methods for engineers, John Wiley, New York.

25


The overall errors caused by the test method itself and operators were statistically evaluated

Test Name No. of Samples

No. of Operators

No. of Repeated Tests

Casagrande Cup LL

5a 3b 3Fall Cone LLASTM Roller PLFall Cone PLBar Linear Shrinkage (BLS)

26

a An unpaved road surface material with 0%, 3%, 6%, 9%, and 12% bentoniteb The three operators were trained at the same time on all the five tests

The fall cone and ASTM roller devices yielded better repeatability and reproducibility than conventional tests

27

ParametersLiquid Limit Plastic Limit

CasagrandeCup

Fall Cone Test

ASTMRoller

Fall Cone Test

Repeatability 0.6% 0.5% 0.4%

NoCorrelation

Reproducibility 1.7% 0.5% 0.6%

Overall R&R 1.8% 0.7% 0.7%

% of overall R&Rdue to reproducibility 89% 50% 73%


Recommended lab tests for determining liquid and plastic limits of soils

Liquid limit Plastic Limit

28

Aggregate Quality and Morphology

Reference: Li, C., Ashlock, J., White, D., Jahren, C., and Cetin, B. (2017). "Gyratory Abrasion with 2D Image Analysis Test Method for Evaluation of Mechanical Degradation and Changes in Morphology and Shear Strength of Compacted Granular Materials." Construction and Building Materials, 152, 547—557, https://doi.org/10.1016/j.conbuildmat.2017.07.013


State DOTs and most researchers use LA abrasion tests to evaluate the quality of granular material

30

From https://www.youtube.com/watch?v=FUGUk-4wEyA

(From ASTM C131)

#12 sieve is used to separate the specimen


Five different materials types from three different sources yielded similar LA abrasion test results

31

Parameters Existing Surface Aggregate

Virgin Surface Aggregate

Road Rock

Class A Stone

Concrete Stone

Abbreviation ESA VSA RR CAS CSSource Granular road Quarry 1 Quarry 1 Quarry 2 Quarry 2

Gravel content (%) 24.0 68.7 65.2 42.9 96.3Sand content (%) 50.0 22.8 19.5 48.9 2.9Fines content (%) 26.0 8.5 15.3 8.2 0.8

Maximum aggregate size (mm) 25.4 38.1 38.1 25.4 25.4USCS symbol SM GP-GC GM SP-SM GP

LA abrasion loss 22.6 24.7 33.9 27.3 26.9

Sieve analysis and LA abrasion test results of the five granular materials


LA abrasion results could be governed by the testing mechanism instead of material properties

Particle Size (mm)

0.010.1110100

Per

cent

Pas

sing

(%)

0102030405060708090

100

ESA Specimen 1 ESA Specimen 2VSA Specimen 1VSA Specimen 2CS CAS RR (Grading A)

38.1

mm

25.4

mm

19.1

mm

12.7

mm

9.53

mm

0.07

5 m

m

SandGravel

0.15

mm

0.25

mm

0.43

mm

0.85

mm

4.75

mm

Fines

Grading B

1.7

mm

Grading A

32

#12


The gyratory compactor can better simulate actual field compaction and traffic loading conditions

33

Specimen

Rigid Mold

PDARam

1.25º

Bottom plate

Top plate

150 mm

170

mm

Resultant load

ey

ex

P3P1

P2

O

120°

x

yLoad cellGyratory Compactor

PDA

(a) (b)

(c)

Reference: Li, C., White, D., and Vennapusa, P. (2015). "Moisture-Density-Strength-Energy Relationships for Gyratory Compacted Geomaterials." Geotechnical Testing Journal, 38(4), 461—473, https://doi.org/10.1520/GTJ20140159


Using a single arbitrary sieve size to quantify the degradation of a material can be misleading

34

Particle Size (mm)

110100

Per

cent

Pas

sing

(%)

0102030405060708090

100Initial PSDFinal PSD

38.1

mm

25.4

mm

19.1

mm

12.7

mm

9.53

mm

4.75

mm

SandGravel

Road RockGravel = 65.2%Sand = 19.5%Fines = 15.3%

Difference in % passingat 4.75mm sieve = 1.8%

Hardin's Total BreakageBt=0.12


Changes in gradation and morphology of specimens can be determined using 2D Image analysis

35

(a) (b) (c)


Influences of aggregate morphology on performance of aggregate materials also need to be quantified

36

Reference: J. Zheng, R.D. Hryciw, (2015) Traditional soil particle sphericity roundness and surface roughness by computational geometry, Géotechnique 65 (6) 494–506.


The test results demonstrate the importance of testing full gradation for evaluating material’s degradation

37

38.1

mm

25.4

mm

19.1

mm

12.7

mm

9.53

mm

4.75

mm

SandGravel

n =

16n

= 11

n =

118

n =

78n

= 49

6n

= 36

2n

= 69

4n

= 51

0

n =

1342

n =

1847

(a)

(b)

Final n = 2808

Per

cent

Pas

sing

or R

etai

ned

0102030405060708090

100

Final % RetainedInitial % Retained

Initial PSDFinal PSD

Concrete StoneGravel = 96.3%Sand = 2.9%Fines = 0.8%

5th

25th

Median75th

95th

10th

90th

Initia No. of Aggregatesn = 2666

Bt=0.12

Particle Size (mm)

110100

Sph

eric

ity

0.10.20.30.40.50.60.70.80.9

Initial SphericityFinal Sphericity

Particle Size (mm)

110100

Sph

eric

ity

0.10.20.30.40.50.60.70.80.9

Initial SphericityFinal Sphericity

38.1

mm

25.4

mm

19.1

mm

12.7

mm

9.53

mm

4.75

mm

SandGravel

n =

1n

= 1

n =

8n

= 7

n =

77n

= 76

n =

139

n =

142

n =

821

n =

817

(a)

(b)

5th

25th

Median

75th

95th

10th

90th

Per

cent

Pas

sing

or R

etai

ned

0102030405060708090

100

Final % RetainedInitial % Retained

Initial PSDFinal PSD

Post-n = 1043

Existing Surface AggregateGravel = 24.0%

Sand = 50.0%Fines = 26.0%

Pre-number of particles n = 1046

Bt = 0.001

LA Abrasion: 26.9 LA Abrasion: 22.6


Using the image analysis can rapidly determine the particle size distribution of aggregate materials

38

Particle Size (mm)

110100

Per

cent

Pas

sing

0

10

20

30

40

50

60

70

80

90

100Sieve AnalysisBounding RectangleBest Fit EllipseFeret Diameter

38.1

mm

25.4

mm

19.1

mm

12.7

mm

9.53

mm

4.75

mm

SandGravel

Size (mm) CAS CS ESA VSA RR25.4 -0.2 -2.9 1.5 -6.4 5.019.1 -3.3 -4.8 4.7 2.1 -2.312.7 3.3 4.0 6.3 14.0 -11.59.53 4.7 4.0 9.4 9.1 -9.2

% passing from Feret Dia. - % passing from Sieve Analysis

Performances of Field Test Sections

A Microsoft Excel-based gradation optimization program was developed to recycle existing materials

40

District Project Date 5/19/2016County Note Designer Cheng Li

500 ft30 ft

Thickness of Existing Aggregate 2.30 in.Target Final Thickness 6.00 in.Compacted Dry Unit Weight 130 pcfMaximum Aggregate Size (Dmax) 1.00 in.PSD Shape Factor (n ) 0.40

Sieve No.

Sieve size (mm)

Target Gradation (%)

Existing Material

Gradation (%)

Target Virgin Material

Gradation (%)

Quarry Material A

Quarry Material B

Quarry Material C

Optimized Virgin Material Gradation (%)

Actual Final Gradation

(%)2 50.8 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

1.5 38.1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.01 25.4 100.0 100.0 100.0 60.0 95.0 100.0 95.9 97.5

3/4 19.00 89.0 97.5 85.9 23.0 84.0 100.0 92.2 94.21/2 12.70 75.8 89.8 71.4 6.3 70.0 83.0 75.2 80.83/8 9.51 67.5 81.8 64.1 6.0 60.0 71.0 64.4 71.1#4 4.76 51.2 66.6 48.8 5.6 37.0 45.0 41.0 50.8#8 2.38 38.8 55.9 36.1 5.1 21.0 27.0 24.8 36.7

#16 1.19 29.4 48.4 25.5 4.6 14.0 20.0 18.4 29.9#30 0.595 22.3 41.9 17.5 4.1 11.0 16.0 14.8 25.2#50 0.297 16.9 35.7 12.0 3.4 8.4 14.0 12.9 21.7

#100 0.149 12.8 29.3 8.6 2.9 6.8 12.0 11.1 18.1#200 0.075 9.7 25.2 5.4 2.3 5.6 10.2 9.4 15.5

Proportion (%) 100 10 0 90 100.0000023Quantity (tons) 300.6 30.5 0.0 270.1

The optimal n value can be selected using the chart developed based on lab CBR test results in the IHRB Project TR-685.

Gradation Optimization for Unpaved Road Surface Materials

Pottawattamie

Average Road Width

TR-685 Test Section 2

Road LengthThis is a trial version of the program

0102030405060708090

100

0.01 0.1 1 10 100

Per

cent

Fin

er (%

)

Sieve Sizes (mm)

Existing GradationTarget Final GradationOptimized Virgin GradationActual Final Gradation

1.5 1

in3/41/23/8#4#8

#16

#30

#50

#100

#200

2 in

RUNINSTALL SOLVER


Field test sections constructed on County Road L66 in Pottawattamie County

41

3 in. Existing Aggregate4 in. Virgin Aggregate 1 +

Existing Aggregate

Subgrade (Average DCP-CBR = 14%)USCS: CL and AASHTO: A-7-6(26)

2 in. 3% Bentonite Treated Mixture

500 ftBentonite Treated

4 in. Virgin Aggregate 1

6 in. Existing Aggregate7 in. Virgin Aggregate 2 +

Existing Aggregate

500 ftOptimal Gradation

1000 ftOff-Spec Gradation

500 ft Existing Gradation


The as-built gradation of Section 2 is very close to the target optimal gradation (Top size=1in., n=0.4)

42

#10

#40

#100

#200#41/2"

5/8"

SandGravel Silt

Particle Size (mm)

0.0010.010.1110100

Per

cent

Pas

sing

(%)

0102030405060708090

100Iowa DOT Specification

Section 1 (n=0.36, Dmax=25.6mm)Section 2 (n=0.39, Dmax=24.2mm)Section 3 (n=0.30, Dmax=24.2mm)Section 4 (n=0.16, Dmax=22.8mm)

Target (n=0.40, Dmax=25.4mm)

1"3/

4"

1/4"

#60

#20

3/8"

Clay

1.5"


Field tests were conducted after construction and one seasonal freeze-thaw period on test sections

• DCP tests for measuring shear strength

• LWD tests for measuring composite stiffness

• Dustometer tests for measuring fugitive dust emissions

43


The trends of the in situ DCP test results generally agree with the predictions of the statistical model.

44

DC

P-C

BRA

GG (%

)

0

100

200

300

400Pr

edic

ted

Soak

ed C

BR (%

)

0

20

40

60

80

100

In Situ CBR (7/25/2016)Predicted Soaked CBR

In Situ CBR (5/5/2017)

S1 S2 S3 S4

BentoniteTreated

Optimal Gradation

Off-Spec Gradation

Existing Gradation


The stiffness of all sections decreased noticeably except for the section with optimal gradation

45

ELW

D (M

Pa)

0

20

40

60

80

100

120ELWD (7/25/2016)ELWD (5/5/2017)

BentoniteTreated

Optimal Gradation

Off-Spec Gradation

Existing Gradation


The bentonite and optimal gradation sections yielded much less dust than the control section

46

7/25/2016 5/5/2017Bentonite Treated 0.9 0.9Optimal Gradation 0.9 1.0Off-Spec Gradation 1.2 1.5Existing Gradation 3.2 4.2

Section NameFugitive Dust Emission (gram per 1000 ft)


Grading Coefficient

Shr

inka

ge P

rodu

ctBentonite (As-constructed)Roller Compacted (As-constructed)

Blended (As-constructed)Control

Good but dusty

Good

Slippery and dusty

Washboards and ravels

Erodible Ravels

15 350

0

100

365

250

Predicted performances of the test sections according to the South Africa performance chart

47

Increasing Coarseness/Gap

Incr

easi

ng P

last

icity

Bentonite-Treated SectionOptimal Gradation Section

Off-Spec Gradation SectionExisting Gradation Section


Our goal is to help to build granular roads with good performance, long service life, and low costs

• A performance-based design method was developed for granular road surface materials

• An MS Excel gradation optimization program was developed to recycle existing surface materials

• Lab test methods for determining plasticity of soils and evaluating quality of aggregate materials were evaluated

48


We welcome your

• Questions

• Suggestions

• Ideas

• Experience

49

Thank you!

performance-based design and testing methods for …€¦ · performance-based design and testing...

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