Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Estimation of Key PCC, Base, Subbase, 

and Pavement Engineering Properties

from Routine Tests and Physical 

Characteristics

PUBLICATION NO. FHWA-HRT-12-030 AUGUST 2012
FOREWORD

Material characterization is a basic aspect of pavement engineering and is critical for analysis, 

performance prediction, design, construction, quality control/quality assurance, pavement 

management, and rehabilitation. Advanced tools like the American Association of State 

Highway and Transportation Officials Mechanistic-Empirical Pavement Design Guide, Interim 

Edition: A Manual of Practice (MEPDG) can be used to estimate the influence of several 

fundamental engineering material parameters on the long-term performance of a pavement.

(1)

Consequently, there is a need for more information about material properties, which are

addressed only to a limited extent with currently available resources for performing laboratory 

and field testing. Reliable correlations between material parameters and index properties offer a 

cost-effective alternative, and the derived material property values are equivalent to the level 2 

inputs in the MEPDG. This study initially verified the adequacy of the Long-Term Pavement 

Performance (LTPP) data and also made a preliminary assessment of the feasibility of 

developing the correlation models. In the next phase of the study, prediction models were 

developed to help practicing engineers estimate proper MEPDG inputs. This report describes 

the basis for selecting material parameters that need predictive models, provides a review of 

current LTPP program data, and proposes several statistically derived models to predict material 

properties. The models developed under this effort have been incorporated into a simple software 

program compatible with current versions of Microsoft Windows® operating system.

Jorge E. Pagán-Ortiz

Director, Office of Infrastructure

Research and Development

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document. This report does not constitute a standard, 

specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. FHWA 

uses standards and policies are used to ensure and maximize the quality, objectivity, utility, and 

integrity of its information. It also periodically reviews quality issues and adjusts its programs 

and processes to ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-12-030

2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle

Estimation of Key PCC, Base, Subbase, and Pavement Engineering 

Properties from Routine Tests and Physical Characteristics

5. Report Date 

August 2012

6. Performing Organization Code

7. Author(s) 

C. Rao, L. Titus-Glover, B. Bhattacharya, M.I. Darter, M. Stanley, and 

H. L. Von Quintus

8. Performing Organization Report No.

9. Performing Organization Name and Address

Applied Research Associates, Inc.

100 Trade Centre Drive, Suite 200 

Champaign, IL 61820

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-02-C-00138

12. Sponsoring Agency Name and Address

Office of Infrastructure Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Interim Report

14. Sponsoring Agency Code

15. Supplementary Notes

The Contracting Officer’s Technical Representative (COTR) was Larry Wiser, HRDI LTPP data analysis contract.

16. Abstract

Material characterization is a critical component of modern day pavement analysis, design, construction, quality 

control/quality assurance, management, and rehabilitation. At each stage during the life of a project, the influence of 

several fundamental engineering material parameters on the long-term performance of the pavement can be predicted 

using advanced tools like the American Association of State Highway and Transportation Officials MechanisticEmpirical Pavement Design Guide, Interim Edition: A Manual of Practice (MEPDG). Consequently, there is a need for 

more information about material properties, which are addressed only to a limited extent with currently available 

resources for performing laboratory and field testing. Reliable correlations between material parameters and index 

properties offer a cost-effective alternative and are equivalent to the level 2 MEPDG inputs. The Long-Term Pavement 

Performance database provides data suitable for developing predictive models for Portland cement concrete (PCC) 

materials, stabilized materials, and unbound materials, as well as other design-related inputs for the MEPDG. This report 

describes the procedure for developing the following models:

• PCC materials: Compressive strength, flexural strength, elastic modulus, tensile strength, and coefficient of 

thermal expansion.

• Stabilized materials: Elastic modulus of lean concrete base.

• Unbound materials: Resilient modulus of fine-grained and coarse-grained materials.

• Rigid pavement design features: Pavement curl/wrap effective temperature difference for jointed plain concrete 

pavement and continuously reinforced concrete pavement designs.

17. Key Words

Pavements, LTPP, Material properties, MEPDG, 

Prediction model, Index properties

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

19. Security Classification (of this report)

Unclassified

20. Security Classification (of this page)

Unclassified

21. No. of Pages

214

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXI MATE CONV ERSIONS T O SI UNIT S

Symbol When You Know Multiply By To Find Symbol 

LENGT H

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

ARE A 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS 

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERAT URE (exa ct degree s) o

F Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATI ON 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXI MATE CONV ERSIONS F ROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGT H

mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

ARE A 

mm2 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERAT URE (exa ct degree s) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATI ON 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March2003)
iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 1

BACKGROUND ..................................................................................................................... 1

MODEL DEVELOPMENT................................................................................................... 1

LIST OF MODELS................................................................................................................. 3

CHAPTER 1. INTRODUCTION................................................................................................ 5

BACKGROUND ..................................................................................................................... 5

DATA NEEDS......................................................................................................................... 5

Design ................................................................................................................................. 5

Construction........................................................................................................................ 6

Pavement Management....................................................................................................... 8

OBJECTIVES AND SCOPE ................................................................................................. 8

SCOPE OF THE REPORT AND ORGANIZATION......................................................... 9

CHAPTER 2. MATERIAL PROPERTIES FOR PREDICTIVE EQUATIONS................. 11

INPUTS FOR MEPDG......................................................................................................... 11

Hierarchical Inputs for MEPDG....................................................................................... 11

Input Categories for the MEPDG ..................................................................................... 12

Correlations Developed/Adapted for the MEPDG ........................................................... 14

SELECTION OF MATERIAL PARAMETERS............................................................... 15

CHAPTER 3. LITERATURE REVIEW.................................................................................. 21

CORRELATION BETWEEN PCC MIX MECHANICAL PROPERTIES AND 

PCC MIX CONSTITUENT PROPORTIONS AND OTHER PCC PROPERTIES...... 21

Compressive Strength ....................................................................................................... 21

Flexural Strength and Splitting Tensile Strength.............................................................. 28

Elastic Modulus ................................................................................................................ 31

CTE................................................................................................................................... 34

Drying Shrinkage .............................................................................................................. 35

PREDICTION OF CHEMICALLY STABILIZED MATERIAL PROPERTIES ........ 36

PREDICTION OF UNBOUND MATERIAL AND SOIL RESILIENT MODULUS.... 37

Overview........................................................................................................................... 37

Resilient Modulus Prediction Models............................................................................... 38

Regression Constant Estimates Based on Soil Properties ................................................ 46

SELECTION OF INDEPENDENT VARIABLES FOR THE PREDICTION OF 

MATERIAL ENGINEERING PROPERTIES.................................................................. 56

CHAPTER 4. DATA ASSEMBLY AND MODELING CONSIDERATIONS..................... 61

DATA SELECTION FOR CURRENT STUDY................................................................ 61

DATA REVIEW.................................................................................................................... 65

Key Findings From Data Review ..................................................................................... 65
iv

CHAPTER 5. MODEL DEVELOPMENT............................................................................... 69

STATISTICAL ANALYSES METHODS ADOPTED ..................................................... 69

Formulating Data for Statistical Models........................................................................... 69

Statistical Criteria for Used for Model Development....................................................... 73

Other Modeling Considerations........................................................................................ 76

Procedures for Model Development ................................................................................. 78

PCC MODELS...................................................................................................................... 79

Data Used in PCC Models................................................................................................ 79

PCC Compressive Strength Models.................................................................................. 81

PCC Flexural Strength Models....................................................................................... 110

PCC Elastic Modulus Models......................................................................................... 119

PCC Tensile Strength Models......................................................................................... 129

PCC CTE Models ........................................................................................................... 131

RIGID PAVEMENT DESIGN FEATURES MODELS.................................................. 140

deltaT—JPCP Design ..................................................................................................... 140

deltaT—CRCP Design.................................................................................................... 149

Erosion for CRCP Design............................................................................................... 154

EI for JPCP Design ......................................................................................................... 154

STABILIZED MATERIALS MODELS .......................................................................... 155

LCB Elastic Modulus Model .......................................................................................... 155

UNBOUND MATERIALS MODELS............................................................................... 156

Data Assembly for Resilient Modulus Model Development.......................................... 157

Resilient Modulus Model Development ......................................................................... 168

PRACTICAL GUIDE AND SOFTWARE PROGRAM................................................. 175

CHAPTER 6. SUMMARY AND FUTURE WORK ............................................................. 177

SUMMARY ......................................................................................................................... 177

PCC MATERIALS ............................................................................................................. 179

PCC Compressive Strength Models................................................................................ 179

PCC Flexural Strength Models....................................................................................... 180

PCC Elastic Modulus Models......................................................................................... 181

PCC Indirect Tensile Strength Models........................................................................... 182

PCC CTE Models ........................................................................................................... 182

RIGID PAVEMENT DESIGN FEATURES MODELS.................................................. 183

deltaT—JPCP Design ..................................................................................................... 183

deltaT—CRCP Design.................................................................................................... 184

Erosion for CRCP Design............................................................................................... 184

EI for JPCP Design ......................................................................................................... 184

STABILIZED MATERIALS MODELS .......................................................................... 184

LCB Elastic Modulus Model .......................................................................................... 184

UNBOUND MATERIALS MODELS............................................................................... 185

Resilient Modulus of Unbound Materials....................................................................... 185

FUTURE WORK................................................................................................................ 185

REFERENCES.......................................................................................................................... 187
v

LIST OF FIGURES

Figure 1. Illustration. MEPDG performance prediction during the design and construction 

stage ................................................................................................................................................ 7

Figure 2. Illustration. Summary of factors affecting PCC strength .............................................. 24

Figure 3. Equation. fc according to Abrams.................................................................................. 27

Figure 4. Equation. fc according to Neville ................................................................................... 27

Figure 5. Equation. fc according to Colak..................................................................................... 27

Figure 6. Equation. fc according to ACI 318................................................................................. 28

Figure 7. Equation. fc according to Wang et al ............................................................................. 28

Figure 8. Equation. MR................................................................................................................. 29

Figure 9. Equation. MR according to Neville ............................................................................... 30

Figure 10. Equation. MR according to Sozen et al ....................................................................... 30

Figure 11. Equation. MOR according to NCHRP......................................................................... 30

Figure 12. Equation. f'sp ................................................................................................................ 30

Figure 13. Equation. fctk,min according to CEB-FIP Model Code 1990 ......................................... 30

Figure 14. Equation. fctk,max according to CEB-FIP Model Code 1990......................................... 30

Figure 15. Equation. fct,mean according to CEB-FIP Model Code 1990......................................... 30

Figure 16. Equation. f'sp according to Mehta and Monteiro.......................................................... 31

Figure 17. Equation. f'sp according to NCHRP ............................................................................. 31

Figure 18. Equation. Ec where constants are defined by various researchers............................... 33

Figure 19. Equation. Ec according to ACI 318.............................................................................. 33

Figure 20. Equation. Ec for normal strength concrete according to Tomosawa et al ................... 33

Figure 21. Equation. Ec according to CEB-FIP Model Code 1990 .............................................. 33

Figure 22. Equation. MR according to Jensen via Irick et al ........................................................ 33

Figure 23. Equation. MR according to Hognestad via Irick et al.................................................. 34

Figure 24. Equation. Ec according to Turkish Standard 500......................................................... 34

Figure 25. Equation. Ec according to Noguchi et al...................................................................... 34

Figure 26. Equation. Ect/Ec,28 according to NCHRP..................................................................... 34

Figure 27. Equation. according to Neekhra .............................................................................. 35

Figure 28. Equation. according to NCHRP......................................................................... 35

Figure 29. Equation. E for lean concrete or cement treated aggregate ......................................... 36

Figure 30. Equation. E for soil cement ......................................................................................... 36

Figure 31. Equation. E for lime-cement-flyash ............................................................................ 37

Figure 32. Equation. Mr for lime-stabilized soils.......................................................................... 37

Figure 33. Equation. Mr according to NCHRP Project 1-28A...................................................... 38

Figure 34. Equation. Mr according to Jones and Witczak............................................................. 39

Figure 35. Equation. Mr according to Thompson and LaGrow .................................................... 39

Figure 36. Equation. Mr according to Thompson and Robnett ..................................................... 39

Figure 37. Equation. Mr according to Carmichael and Stuart for fine subgrade soils 

containing clay and silts................................................................................................................ 39

Figure 38. Equation. Mr according to Carmichael and Stuart for coarse granular soils and 

aggregate bases ............................................................................................................................. 40

Figure 39. Equation. Mr according to Elliot et al. with deviator stress of 4 psi............................ 40

Figure 40. Equation. Mr according to Elliot et al. with deviator stress of 8 psi............................ 40

Figure 41. Equation. Mr according to Rahim for fine-grained soils.............................................. 40

αc

αPCC
vi

Figure 42. Equation. Mr according to Rahim for coarse-grained soils.......................................... 40

Figure 43. Equation. Mr according to Farrar and Turner .............................................................. 40

Figure 44. Equation. Mr according to Hudson et al....................................................................... 40

Figure 45. Equation. Mr according to Li and Selig ....................................................................... 40

Figure 46. Equation. Mr according to Gupta et al ......................................................................... 41

Figure 47. Equation. Mr according to Berg et al ........................................................................... 41

Figure 48. Equation. Mr according to Pezo and Hudson .............................................................. 41

Figure 49. Equation. Correlation between Mr and CBR according to Heukelom and Klomp...... 42

Figure 50. Equation. Correlation between Mr and CBR according to Powell et al ...................... 42

Figure 51. Equation. Correlation between Mr and CBR according to George ............................. 42

Figure 52. Equation. Correlation between Mr and CBR according to the Asphalt Institute......... 42

Figure 53. Equation. Correlation between Mr and R-value according to AASHTO .................... 42

Figure 54. Equation. Correlation between Mr and R-value according to Buu.............................. 42

Figure 55. Equation. Correlation between Mr and R-value according to Yeh and Su.................. 43

Figure 56. Equation. Correlation between Mr and R-value according to MEPDG ...................... 43

Figure 57. Equation. Correlation between Mr and unconfined compressive strength 

according to Thompson and Robnett ............................................................................................ 43

Figure 58. Equation. Correlation between Mr and shear strength according to Lee et al............. 43

Figure 59. Equation. Correlation between Mr and DCP test method according to Hassan .......... 43

Figure 60. Equation. Correlation between Mr and DCP test method according to Chen et al...... 43

Figure 61. Equation. Correlation between Mr and DCP test method for coarse-grained sandy 

soils according to George and Uddin............................................................................................ 43

Figure 62. Equation. Correlation between Mr and DCP test method for fine-grained clays

according to George and Uddin .................................................................................................... 43

Figure 63. Equation. Correlation between Mr and DCP test method according to 

Chen et al ...................................................................................................................................... 44

Figure 64. Equation. Correlation between Mr and DCP test method DCPI only according 

to Mohammad et al ....................................................................................................................... 44

Figure 65. Equation. Correlation between Mr and DCP test method DCPI and soil 

properties according to Mohammad et al...................................................................................... 44

Figure 66. Equation. Correlation between Mr and CPT test method in situ conditions 

according to Mohammad et al....................................................................................................... 44

Figure 67. Equation. Correlation between Mr and CPT test method traffic loading 

conditions according to Mohammad et al..................................................................................... 45

Figure 68. Equation. Mr of Mississippi materials for fine-grained soils....................................... 46

Figure 69. Equation. k1 of Mississippi materials for fine-grained soils........................................ 46

Figure 70. Equation. k2 of Mississippi materials for fine-grained soils........................................ 47

Figure 71. Equation. Mr of Mississippi materials for coarse-grained soils................................... 47

Figure 72. Equation. k1 of Mississippi materials for coarse-grained soils.................................... 47

Figure 73. Equation. k2 of Mississippi materials for coarse-grained soils.................................... 47

Figure 74. Equation. Mr of Georgia materials .............................................................................. 47

Figure 75. Equation. k1 of Georgia materials................................................................................ 48

Figure 76. Equation. k2 of Georgia materials................................................................................ 48

Figure 77. Equation. k3 of Georgia materials................................................................................ 48

Figure 78. Equation. Mr of Louisiana materials............................................................................ 48

Figure 79. Equation. k1 of Louisiana materials............................................................................. 49
vii

Figure 80. Equation. k2 of Louisiana materials............................................................................. 49

Figure 81. Equation. k3 of Louisiana materials............................................................................. 49

Figure 82. Equation. Mr of LTPP materials.................................................................................. 49

Figure 83. Equation. k1 of LTPP materials—crushed stone ......................................................... 50

Figure 84. Equation. k2 of LTPP materials—crushed stone ......................................................... 50

Figure 85. Equation. k3 of LTPP materials—crushed stone ......................................................... 50

Figure 86. Equation. k1 of LTPP materials—crushed gravel........................................................ 51

Figure 87. Equation. k2 of LTPP materials—crushed gravel........................................................ 51

Figure 88. Equation. k3 of LTPP materials—crushed gravel........................................................ 51

Figure 89. Equation. k1 of LTPP materials—uncrushed gravel.................................................... 51

Figure 90. Equation. k2 of LTPP materials—uncrushed gravel.................................................... 51

Figure 91. Equation. k3 of LTPP materials—uncrushed gravel.................................................... 51

Figure 92. Equation. k1 of LTPP materials—sand........................................................................ 51

Figure 93. Equation. k2 of LTPP materials—sand........................................................................ 51

Figure 94. Equation. k3 of LTPP materials—sand........................................................................ 52

Figure 95. Equation. k1 of LTPP materials—coarse-grained soil-aggregate mixture................... 52

Figure 96. Equation. k2 of LTPP materials—coarse-grained soil-aggregate mixture................... 52

Figure 97. Equation. k3 of LTPP materials—coarse-grained soil-aggregate mixture................... 52

Figure 98. Equation. k1 of LTPP materials—fine-grained soil-aggregate mixture....................... 52

Figure 99. Equation. k2 of LTPP materials—fine-grained soil-aggregate mixture....................... 52

Figure 100. Equation. k3 of LTPP materials—fine-grained soil-aggregate mixture..................... 52

Figure 101. Equation. k1 of LTPP materials—fine-grained soil................................................... 53

Figure 102. Equation. k2 of LTPP materials—fine-grained soil................................................... 53

Figure 103. Equation. k3 of LTPP materials—fine-grained soil................................................... 53

Figure 104. Equation. k1 of LTPP materials—coarse-grained gravel soils .................................. 53

Figure 105. Equation. k2 of LTPP materials—coarse-grained gravel soils .................................. 53

Figure 106. Equation. k3 of LTPP materials—coarse-grained gravel soils .................................. 53

Figure 107. Equation. k1 of LTPP materials—coarse-grained sand soils..................................... 53

Figure 108. Equation. k2 of LTPP materials—coarse-grained sand soils..................................... 53

Figure 109. Equation. k3 of LTPP materials—coarse-grained sand soils..................................... 53

Figure 110. Equation. k1 of LTPP materials—fine-grained silt soils ........................................... 54

Figure 111. Equation. k2 of LTPP materials—fine-grained silt soils ........................................... 54

Figure 112. Equation. k3 of LTPP materials—fine-grained silt soils ........................................... 54

Figure 113. Equation. k1 of LTPP materials—fine-grained clay soils.......................................... 54

Figure 114. Equation. k2 of LTPP materials—fine-grained clay soils.......................................... 54

Figure 115. Equation. k3 of LTPP materials—fine-grained clay soils.......................................... 54

Figure 116. Equation. k1 of Wisconsin materials—fine-grained soils.......................................... 55

Figure 117. Equation. k2 of Wisconsin materials—fine-grained soils.......................................... 55

Figure 118. Equation. k3 of Wisconsin materials—fine-grained soils.......................................... 55

Figure 119. Equation. k1 of Wisconsin materials—non-plastic coarse-grained soils................... 55

Figure 120. Equation. k2 of Wisconsin materials—non-plastic coarse-grained soils................... 55

Figure 121. Equation. k3 of Wisconsin materials—non-plastic coarse-grained soils................... 55

Figure 122. Equation. k1 of Wisconsin materials—plastic coarse-grained soils .......................... 55

Figure 123. Equation. k2 of Wisconsin materials—plastic coarse-grained soils .......................... 56

Figure 124. Equation. k3 of Wisconsin materials—plastic coarse-grained soils .......................... 56

Figure 125. Equation. Cp............................................................................................................... 74
viii

Figure 126. Equation. MSEr.......................................................................................................... 74

Figure 127. Equation. VIF ............................................................................................................ 74

Figure 128. Equation. fc linear model form with no transformation............................................. 78

Figure 129. Equation. Ec,t nonlinear model form with no transformation .................................... 78

Figure 130. Equation. k1 linear model form with transformation................................................. 78

Figure 131. Equation. k1 nonlinear model form with transformation........................................... 78

Figure 132. Equation. fc model form............................................................................................. 82

Figure 133. Graph. Predicted versus measured for 28-day cylinder compressive 

strength model............................................................................................................................... 90

Figure 134. Graph. Residual error plot for 28-day cylinder compressive strength model ........... 91

Figure 135. Equation. Prediction model 1 for fc,28d....................................................................... 91

Figure 136. Graph. 28-day compressive strength model sensitivity to w/c ratio ......................... 92

Figure 137. Graph. 28-day compressive strength model sensitivity to CMC............................... 92

Figure 138. Equation. Prediction model 2 for fc,t.......................................................................... 93

Figure 139. Graph. Predicted versus measured for short-term cylinder compressive 

strength model............................................................................................................................... 94

Figure 140. Graph. Residual errors for short-term cylinder compressive strength model ........... 94

Figure 141. Graph. Short-term cylinder compressive strength sensitivity to CMC ..................... 95

Figure 142. Graph. Short-term cylinder compressive strength sensitivity to w/c ratio ................ 95

Figure 143. Graph. Short-term cylinder compressive strength sensitivity to age......................... 95

Figure 144. Graph. Comparison of core and cylinder strengths for SPS sections........................ 96

Figure 145. Equation. Prediction model 3 for fc,t.......................................................................... 97

Figure 146. Graph. Predicted versus measured for short-term core compressive 

strength model............................................................................................................................... 99

Figure 147. Graph. Residual errors for short-term core compressive strength model.................. 99

Figure 148. Graph. Short-term core compressive strength sensitivity to CMC............................ 99

Figure 149. Graph. Short-term core compressive strength sensitivity to unit weight ................ 100

Figure 150. Graph. Short-term core compressive strength sensitivity to MAS.......................... 100

Figure 151. Graph. Short-term core compressive strength sensitivity to w/c ratio .................... 100

Figure 152. Graph. Short-term core compressive strength sensitivity to fine aggregate FM..... 101

Figure 153. Graph. Short-term core compressive strength sensitivity to age............................. 101

Figure 154. Equation. Prediction model 4 for fc,t........................................................................ 101

Figure 155. Graph. Predicted versus measured for all ages core compressive strength 

model........................................................................................................................................... 103

Figure 156. Graph. Residual errors for all ages core compressive strength model .................... 103

Figure 157. Graph. All ages core compressive strength sensitivity to w/c ratio ........................ 103

Figure 158. Graph. All ages core compressive strength sensitivity to CMC.............................. 104

Figure 159. Graph. All ages core compressive strength sensitivity to unit weight .................... 104

Figure 160. Graph. All ages core compressive strength sensitivity to age ................................. 104

Figure 161. Equation. Prediction model 5 for fc,LT...................................................................... 105

Figure 162. Graph. Predicted versus measured for long-term core compressive 

strength model............................................................................................................................. 106

Figure 163. Graph. Residual errors for long-term core compressive strength model................. 106

Figure 164. Graph. Model compressive strength prediction for varying CMC.......................... 107

Figure 165. Graph. Model compressive strength prediction for varying w/c ratio..................... 107

Figure 166. Graph. Model compressive strength prediction for varying unit weights............... 108
ix

Figure 167. Graph. Strength gain in the short-term predicted by three models.......................... 108

Figure 168. Graph. Long-term strength gain predicted by the models....................................... 109

Figure 169. Equation. Mr ............................................................................................................ 110

Figure 170. Graph. Predicted versus measured for validating 0.5 power flexural 

strength model............................................................................................................................. 111

Figure 171. Graph. Predicted versus measured for validating 0.667 power flexural 

strength model............................................................................................................................. 111

Figure 172. Equation. Prediction model 6 for MR...................................................................... 112

Figure 173. Graph. Predicted versus measured values for flexural strength model 

based on compressive strength.................................................................................................... 113

Figure 174. Graph. Residuals errors for flexural strength model based on compressive 

strength........................................................................................................................................ 113

Figure 175. Graph. Comparison of flexural strength models based on compressive strength ... 114

Figure 176. Equation. Prediction model 7 for MRt ..................................................................... 114

Figure 177. Graph. Predicted versus measured values for flexural strength model based 

on age, unit weight, and w/c ratio............................................................................................... 116

Figure 178. Graph. Residuals errors for flexural strength model based on age, unit weight, 

and w/c ratio................................................................................................................................ 116

Figure 179. Equation. Prediction model 8 for MRt ..................................................................... 116

Figure 180. Graph. Predicted versus measured values for flexural strength model based 

on age, unit weight, and CMC.................................................................................................... 118

Figure 181. Graph. Residuals errors for flexural strength model based on age, unit weight, 

and CMC..................................................................................................................................... 118

Figure 182. Graph. Sensitivity of flexural strength predictions to CMC.................................... 118

Figure 183. Graph. Sensitivity of flexural strength predictions to w/c ratio .............................. 119

Figure 184. Graph. Sensitivity of flexural strength predictions to unit weight .......................... 119

Figure 185. Graph. Sensitivity of flexural strength predictions to age....................................... 119

Figure 186. Equation. Ec as a function of square root of compressive strength ......................... 120

Figure 187. Equation. Model form for E as a function of compressive strength with slope 

and intercept................................................................................................................................ 120

Figure 188. Equation. Ec ............................................................................................................. 121

Figure 189. Equation. E as function of unit weight and compressive strength .......................... 121

Figure 190. Equation. Prediction model 9 for Ec ........................................................................ 122

Figure 191. Equation. E .............................................................................................................. 124

Figure 192. Graph. Predicted versus measured for elastic modulus model based on 

aggregate type ............................................................................................................................. 124

Figure 193. Graph. Residual errors for elastic modulus model based on aggregate type........... 125

Figure 194. Equation. Prediction model 10 for Ec,t .................................................................... 125

Figure 195. Graph. Predicted versus measured for elastic modulus model based on age 

and compressive strength............................................................................................................ 126

Figure 196. Graph. Residual errors for elastic modulus model based on age and 

compressive strength................................................................................................................... 127

Figure 197. Equation. Prediction model 11 for Ec,t .................................................................... 127

Figure 198. Graph. Predicted versus measured for elastic modulus model based on age 

and 28-day compressive strength................................................................................................ 128
x

Figure 199. Graph. Residual errors for elastic modulus model based on age and 28-day 

compressive strength................................................................................................................... 129

Figure 200. Equation. Prediction model 12 for ft........................................................................ 129

Figure 201. Graph. Predicted versus measured for tensile strength model ................................ 130

Figure 202. Graph. Residuals error plot for tensile strength model............................................ 131

Figure 203. Graph. Sensitivity of tensile strength prediction model to change compressive 

strength........................................................................................................................................ 131

Figure 204. Graph. Comparison of average values from other sources and recommended 

CTE values based on aggregate type from LTPP data ............................................................... 134

Figure 205. Equation. CTEPCC .................................................................................................... 136

Figure 206. Equation. CTEPCC as a function of volumetric proportion ...................................... 136

Figure 207. Equation. CTEPCC as a function of coarse aggregate volumetric proportio ............ 136

Figure 208. Equation. Prediction model 14 for CTEPCC ............................................................. 136

Figure 209. Graph. Predicted versus measured for CTE model based on mix volumetrics....... 138

Figure 210. Graph. Residual errors for CTE model based on mix volumetrics......................... 138

Figure 211. Graph. Comparison of CTE model prediction with average values reported in 

literature for each aggregate rock type........................................................................................ 139

Figure 212. Graph. CTE model prediction versus average values reported in literature for 

each aggregate rock type............................................................................................................. 139

Figure 213. Graph. Sensitivity of the CTE model to coarse aggregate content.......................... 139

Figure 214. Equation. Prediction model 15 for deltaT/inch ....................................................... 142

Figure 215. Graph. Predicted versus measured for JPCP deltaT gradient model....................... 144

Figure 216. Graph. Residual errors for JPCP deltaT gradient model ......................................... 144

Figure 217. Graph. Predicted versus measured deltaT based on the JPCP deltaT gradient 

model........................................................................................................................................... 145

Figure 218. Graph. Sensitivity of predicted deltaT to temperature range during month 

of construction ............................................................................................................................ 145

Figure 219. Graph. Sensitivity of predicted deltaT to slab width............................................... 146

Figure 220. Graph. Sensitivity of predicted deltaT to slab thickness......................................... 146

Figure 221. Graph. Sensitivity of predicted deltaT to PCC slab unit weight ............................. 146

Figure 222. Graph. Sensitivity of predicted deltaT to PCC w/c ratio......................................... 147

Figure 223. Graph. Sensitivity of predicted deltaT to latitude of the project location ............... 147

Figure 224. Graph. Predicted deltaT for different locations in the United States ...................... 147

Figure 225. Equation. Prediction model 16 for deltaT/inch ....................................................... 150

Figure 226. Graph. Predicted versus measured for CRCP deltaT model ................................... 151

Figure 227. Graph. Residual errors for CRCP deltaT model...................................................... 152

Figure 228. Graph. Effect of maximum temperature on CRCP deltaT prediction model.......... 152

Figure 229. Graph. Effect of temperature range on CRCP deltaT prediction model ................. 153

Figure 230. Graph. Effect of slab thickness on CRCP deltaT prediction model........................ 153

Figure 231. Graph. Effect of geographic location on CRCP deltaT prediction.......................... 153

Figure 232. Equation. Prediction model 17 for ELCB .................................................................. 155

Figure 233. Graph. Predicted versus measured for the LCB elastic modulus model ................. 156

Figure 234. Graph. Residual errors for the LCB elastic modulus model ................................... 156

Figure 235. Graph. Mean percent clay fraction for unbound material types included in

the model development database ................................................................................................ 158
xi

Figure 236. Graph. Mean percent coarse sand fraction for unbound material types 

included in the model development database ............................................................................. 158

Figure 237. Graph. Mean percent fine sand fraction for unbound material types included 

in the model development database ............................................................................................ 159

Figure 238. Graph. Mean percent silt fraction for unbound material types included in the 

model development database ...................................................................................................... 159

Figure 239. Graph. Mean hydraulic conductivity for unbound material types included 

in the model development database ............................................................................................ 160

Figure 240. Graph. Plot of bulk stress versus lab tested resilient modulus for section 

45_1025 (base layer)................................................................................................................... 160

Figure 241. Graph. Percent passing No. 4 sieve for unbound material types included 

in the model development database ............................................................................................ 161

Figure 242. Graph. Percent passing No. 40 sieve for unbound material types included 

in the model development database ............................................................................................ 161

Figure 243. Graph. Percent passing No. 200 sieve for unbound material types included in 

the model development database ................................................................................................ 162

Figure 244. Graph. Liquid limit for unbound material types included in the model 

development database ................................................................................................................. 162

Figure 245. Graph. Plasticity index for unbound material types included in the model 

development database ................................................................................................................. 163

Figure 246. Graph. Maximum dry density for unbound material types included in the 

model development database ...................................................................................................... 163

Figure 247. Graph. Optimum moisture content for unbound material types included in the 

model development database ...................................................................................................... 164

Figure 248. Equation. Mr ............................................................................................................ 165

Figure 249. Graph. Resilient modulus parameter k1 for unbound material types included in 

the model development database ................................................................................................ 166

Figure 250. Graph. Resilient modulus parameter k2 for unbound material types included in 

the model development database ................................................................................................ 166

Figure 251. Graph. Resilient modulus parameter k3 for unbound material types included in

the model development database ................................................................................................ 167

Figure 252. Graph. Plot of measured versus calculated resilient modulus (using k1, k2, and 

k3 computed from constitutive model)........................................................................................ 168

Figure 253. Equation. Prediction model 18 for k1 ...................................................................... 169

Figure 254. Equation. Prediction model 19 for k2 ...................................................................... 170

Figure 255. Equation. Prediction model 20 for k3 ...................................................................... 170

Figure 256. Graph. Measured versus predicted resilient modulus (using k1, k2, and k3 from 

figure 70 through figure 72)........................................................................................................ 171

Figure 257. Graph. Predicted and measured resilient modulus versus bulk stress for fineand coarse-grained soils.............................................................................................................. 171

Figure 258. Graph. Effect of material type (AASHTO soil class) on predicted resilient 

modulus....................................................................................................................................... 172

Figure 259. Graph. Effect of percent passing 0.5-inch sieve on predicted resilient modulus.... 173

Figure 260. Graph. Effect of liquid limit on predicted resilient modulus................................... 173

Figure 261. Graph. Effect of optimum moisture content on predicted resilient modulus.......... 174

Figure 262. Graph. Effect of No. 80 sieve on predicted resilient modulus ................................ 174
xii

Figure 263. Graph. Effect of gravel content on predicted resilient modulus.............................. 175

Figure 264. Graph. Effect of effective size on predicted resilient modulus............................... 175

Figure 265. Equation. Prediction model 1 for fc,28d..................................................................... 179

Figure 266. Equation. Prediction model 2 for fc,t........................................................................ 179

Figure 267. Equation. Prediction model 3 for fc,t........................................................................ 179

Figure 268. Equation. Prediction model 4 for fc,t........................................................................ 179

Figure 269. Equation. Prediction model 5 for fc,LT...................................................................... 180

Figure 270. Equation. Prediction model 6 for MR...................................................................... 180

Figure 271. Equation. Prediction model 7 for MRt ..................................................................... 180

Figure 272. Equation. Prediction model 8 for MRt ..................................................................... 180

Figure 273. Equation. Prediction model 9 for Ec ........................................................................ 181

Figure 274. Equation. Prediction model 10 for Ec,t .................................................................... 181

Figure 275. Equation. Prediction model 11 for Ec,t .................................................................... 182

Figure 276. Equation. Prediction model 12 for ft........................................................................ 182

Figure 277. Equation. Prediction model 14 for CTEPCC ............................................................. 183

Figure 278. Equation. Prediction model 15 for deltaT/inch ....................................................... 183

Figure 279. Equation. Prediction model 16 for deltaT/inch ....................................................... 184

Figure 280. Equation. Prediction model 17 for ELCB .................................................................. 184

Figure 281. Equation. Mr ............................................................................................................ 185

Figure 282. Equation. Prediction model 18 for k1 ...................................................................... 185

Figure 283. Equation. Prediction model 19 for k2 ...................................................................... 185

Figure 284. Equation. Prediction model 20 for k3 ...................................................................... 185
xiii

LIST OF TABLES

Table 1. Major material types for the MEPDG............................................................................. 13

Table 2. PCC material inputs beyond strength considered by the MEPDG for JPCP and 

CRCP ............................................................................................................................................ 14

Table 3. PCC material properties and rigid pavement design features considered for 

generating predictive models........................................................................................................ 16

Table 4. Chemically stabilized materials properties considered for generating predictive 

models........................................................................................................................................... 18

Table 5. Unbound material properties considered for generating predictive models................... 19

Table 6. Potential or likely independent variables to derive prediction models for PCC 

material properties or design features for rigid pavements........................................................... 57

Table 7. Independent variables to derive prediction models for stabilized materials................... 58

Table 8. Independent variables to derive prediction models for unbound materials.................... 59

Table 9. LTPP sections selected to review data for each category............................................... 63

Table 10. LTPP data tables queried to obtain data for review and future analyses...................... 64

Table 11. Summary of data availability for material property predictive models........................ 67

Table 12. Summary of MEPDG calibration sections to develop predictive models for rigid 

pavement design features.............................................................................................................. 68

Table 13. Model types used to derive predictive relationships for PCC material properties or 

design features for rigid pavements.............................................................................................. 72

Table 14. Model types used to derive predictive relationships for stabilized materials............... 72

Table 15. Model types used to derive predictive relationships for unbound materials ................ 73

Table 16. Cp selection method for 28-day cylinder compressive strength model ........................ 83

Table 17. Regression statistics for the four-variable model suggested by Cp analysis with 

subset of data that were available for all parameters evaluated.................................................... 87

Table 18. Regression statistics for the four-variable model suggested by Cp analysis with 

complete dataset available for the parameters selected ................................................................ 87

Table 19. Regression statistics for the three-variable model suggested by Cp analysis ............... 88

Table 20. Regression statistics for the two-variable model suggested by Cp analysis ................. 88

Table 21. Regression statistics for selected prediction model for 28-day PCC cylinder 

strength.......................................................................................................................................... 90

Table 22. Range of data used for 28-day PCC cylinder strength.................................................. 90

Table 23. Regression statistics for short-term cylinder strength model........................................ 93

Table 24. Range of data used for short-term cylinder strength model.......................................... 93

Table 25. Paired t-test results for comparison of core and cylinder strengths in SPS data .......... 97

Table 26. Regression statistics for short-term core strength model.............................................. 98

Table 27. Range of data used for short-term core strength model................................................ 98

Table 28. Regression statistics for all ages core strength model ................................................ 102

Table 29. Range of data used for all ages core strength model .................................................. 102

Table 30. Regression statistics for long-term core strength model............................................. 105

Table 31. Range of data used for long-term core strength model............................................... 105

Table 32. Power models developed for flexural strength prediction using LTPP data for 

validation..................................................................................................................................... 111

Table 33. Regression statistics for flexural strength model based on compressive strength...... 112

Table 34. Range of data used for flexural strength model based on compressive strength........ 112
xiv

Table 35. Regression statistics for flexural strength model based on age, unit weight, 

and w/c ratio................................................................................................................................ 115

Table 36. Range of data used for flexural strength model based on age, unit weight, 

and w/c ratio................................................................................................................................ 115

Table 37. Regression statistics for flexural strength model based on age, unit weight, 

and CMC..................................................................................................................................... 117

Table 38. Range of data used for flexural strength model based on age, unit weight, 

and CMC..................................................................................................................................... 117

Table 39. Regression statistics for elastic modulus model based on aggregate type.................. 123

Table 40. Range of data used for elastic modulus model based on aggregate type.................... 124

Table 41. Regression statistics for elastic modulus model based on age and compressive 

strength........................................................................................................................................ 126

Table 42. Range of data used for elastic modulus model based on age and compressive 

strength........................................................................................................................................ 126

Table 43. Regression statistics for elastic modulus model based on age and 28-day 

compressive strength................................................................................................................... 128

Table 44. Range of data used for elastic modulus model based on age and 28-day 

compressive strength................................................................................................................... 128

Table 45. Model statistics for tensile strength prediction model................................................ 130

Table 46. Range of data used for tensile strength prediction model........................................... 130

Table 47. Prediction model 13 PCC CTE based on aggregate type (x 10-6 inch/inch/°F).......... 134

Table 48. Statistical analysis results for CTE model based on mix volumetrics........................ 137

Table 49. Range of data used for CTE model based on mix volumetrics .................................. 137

Table 50. Summary of field measured distress and predicted distress for section 1_3028 ........ 141

Table 51. Error calculations for section 1_3028......................................................................... 141

Table 52. Determining optimal deltaT........................................................................................ 142

Table 53. Regression statistics for JPCP deltaT model .............................................................. 143

Table 54. Range of data used for JPCP deltaT model ................................................................ 143

Table 55. Regression statistics for CRCP deltaT model............................................................. 150

Table 56. Range of data used for CRCP deltaT model............................................................... 151

Table 57. Model 13. CTE based on aggregate type.................................................................... 182
xv

LIST OF ABBREVIATIONS AND SYMBOLS

AASHTO American Association of State Highway and Transportation Officials

AC Asphalt concrete

ACI American Concrete Institute

ANOVA Analysis of variance

CBR California bearing ratio

CEB-FIP Committee Euro-International du Beton

CMC Cementitious materials content

CRCP Continuously reinforced concrete pavement

CTE Coefficient of thermal expansion

DCP Dynamic cone penetrometer

EI Erodibility Index

EICM Enhanced integrated climatic model

FEA Finite element analysis

FHWA Federal Highway Administration

FM Fineness modulus

FWD Falling weight deflectometer

GLM Generalized linear model

GPS General Pavement Studies

HMA Hot mix asphalt

HRIS Highway Research Information Service

IRI International Roughness Index

ITZ Interfacial zone

JPCP Jointed plain concrete pavement

JRCP Jointed reinforced concrete pavement

LCB Lean concrete base

LEA Layered elastic analysis

LTPP Long-term pavement performance

MAS Maximum aggregate size

M-E Mechanistic-empirical

MEPDG Mechanistic-Empirical Pavement Design Guide

MPT Maximum paste thickness
xvi

MR Modulus of rupture

NCHRP National Cooperative Highway Research Program

PCA Portland Cement Association

PCC Portland cement concrete

PRS Performance-related specification

QA Quality assurance

QC Quality control

RMSE Root mean square error

SEE Standard error of estimate

SCM Supplemental cementitious material

SHA State highway agency

SHRP Strategic Highway Research Program

SPS Specific Pavement Studies

TFHRC Turner-Fairbank Highway Research Center

UCS Unified Classification System

VIF Variance inflation factor
1

EXECUTIVE SUMMARY

BACKGROUND

The goal of this study was to develop predictive models to estimate material properties and 

pavement engineering properties for use in routine practice. The study focused on rigid pavement 

and relevant material types, primarily Portland cement concrete (PCC) materials, stabilized 

materials, and unbound materials, including subgrade soils. As such, the objectives of this study 

were as follows:

• Identify a set of material engineering properties for which predictive relationships would 

be useful in pavement design, construction quality control/quality assurance (QC/QA), 

and pavement management applications.

• Establish and/or validate relationships between the identified engineering properties and 

routine test results, index properties, and other readily available information. 

• Develop a practical guide accompanied by user friendly software incorporating 

the recommendations.

In recent years, pavement engineering practices have emphasized the importance of proper 

material characterization to optimize pavement performance. Procedures like the MechanisticEmpirical Pavement Design Guide (MEPDG) use various material property inputs for pavement 

performance prediction.

(2) The greater need for estimating material properties is being addressed 

only to a limited extent with the currently available resources. Reliable correlations between 

material parameters and index properties offer a cost-effective alternative and are equivalent to 

the level 2 MEPDG inputs. These models can also support agencies in improving QC/QA 

specifications and pavement management functions.

MODEL DEVELOPMENT

The Long-Term Pavement Performance (LTPP) study database, which contains material 

property test results and material index properties, provided the necessary data to develop the 

models in this study. The most recent version of the LTPP database that was available at the time 

of the study, Standard Data Release 23.0, was used.

(3) Material properties and pavement 

engineering properties for which develop predictive models were developed were selected based 

on the following:

• Material inputs requirements for the MEPDG design procedure and the sensitivity of the 

specific parameter for performance prediction.

• Typical agency needs for determining material properties during QA.

• Typical agency needs for determining material properties during routine pavement 

management functions.

• Data availability in the LTPP database.
2

Predictive models were developed for PCC compressive strength, PCC flexural strength, PCC 

elastic modulus, PCC tensile strength, lean concrete base (LCB) modulus, and unbound materials 

resilient modulus. In addition, rigid pavement design feature input properties were developed 

using the MEPDG calibration data. These include the jointed plain concrete pavement (JPCP) 

and continuously reinforced concrete pavement (CRCP) deltaT parameters, where deltaT is 

defined as the equivalent temperature differential that corresponds to the effective permanent 

curl-warp locked into the pavement. For all PCC material properties, multiple models were 

developed for use in different project situations and also provided users with prediction model 

alternatives depending on the extent of mix design information available.

In developing the models, a uniform set of statistical criteria were used to select independent 

parameters to define a relationship as well as to mathematically formulate prediction functions. 

The analyses examined several statistical parameters in choosing the optimal model and in 

determining the predictive ability of the model. In general, the optimal set of independent 

variables (through the Mallows coefficient, Cp), the interaction effects (through the variance 

inflation factor (VIF)), the significance of the variable (through the p-value), and the goodness of 

fit (through the R2 value) were verified. Additionally, the study validated or refined existing 

models and developed new relationships. In the analyses, the following general observations 

were made:

• PCC compressive strength could be correlated to several index properties. It was found to 

increase with decreasing water/cement (w/c) ratio and increasing cementitious materials 

content (CMC), curing time, and unit weight while decreasing maximum aggregate size

(MAS) for a given level of w/c ratio and fineness modulus (FM) of the sand.

• PCC flexural strength could be correlated to the compressive strength using a power 

model. These relationships were validated and refined using the LTPP data. It also could 

be correlated to the w/c ratio, unit weight, CMC, and curing time. The correlation was 

improved significantly in the new models with the additional parameters. The flexural 

strength increased proportionally with all parameters listed except w/c ratio, with which it 

had an inverse relationship.

• PCC elastic modulus could be correlated to the compressive strength and unit weight 

using a power model, as has been done in past studies. These relationships were validated 

and verified with the data used in this study. Predictions could be made based on

aggregate type, unit weight, compressive strength, and age with improved correlation. 

The elastic modulus increased with an increase in magnitude of all parameters listed.

• PCC tensile strength was found to correlate well with the compressive strength using a 

power relationship.

• The coefficient of thermal expansion (CTE) of PCC was most sensitive to the coarse 

aggregate type and the volumetrics of the mix design.

• JPCP deltaT negative gradient increased with an increase in temperature range at the 

project location for the month of construction and slab width and increased with a 

decrease in PCC thickness, unit weight, w/c ratio, and latitude of the project location.
3

• CRCP deltaT negative gradient increased with an increase in maximum temperature at 

the project location for the month of construction and maximum temperature range and 

decreased with the use of chert, granite, limestone, and quartzite.

• The modulus of LCB correlated well with its 28-day compressive strength based on a 

power model.

• The prediction of resilient modulus was possible using parameters k1, k2, and k3 of the 

constitutive model as follows:

o The parameter k1 increased with decreasing percent passing the 1/2-inch sieve, 

increasing liquid limit, and decreasing optimum moisture content.

o The parameter k2 increased with decreasing percent passing the No. 80 sieve, 

decreasing liquid limit and percent gravel, and increasing maximum particle size 

of the smallest 10 percent of the soil sample.

o The parameter k3 was dependent on the soil classification (coarse-grained versus

fine-grained materials).

LIST OF MODELS

The following models have been developed under this study.

PCC compressive strength models include the following:

• Compressive strength model 1—28-day cylinder strength model.

• Compressive strength model 2—Short-term cylinder strength model.

• Compressive strength model 3—Short-term core strength model.

• Compressive strength model 4—All ages core strength model.

• Compressive strength model 5—Long-term core strength model.

PCC flexural strength models include the following:

• Flexural strength model 1—Flexural strength based on compressive strength.

• Flexural strength model 2—Flexural strength based on age, unit weight, and w/c ratio.

• Flexural strength model 3—Flexural strength based on age, unit weight, and CMC.
4

PCC elastic modulus models include the following:

• Elastic modulus model 1—Model based on aggregate type.

• Elastic modulus model 2—Model based on age and compressive strength.

• Elastic modulus model 3—Model based on age and 28-day compressive strength.

PCC indirect tensile strength model is as follows:

• PCC indirect tensile strength model—Model based on compressive strength.

PCC CTE models include the following:

• CTE model 1—CTE based on aggregate type (level 3 equation for MEPDG).

• CTE model 2—CTE based on mix volumetrics (level 2 equation for MEPDG).

The JPCP design deltaT model is as follows:

• JPCP deltaT model—JPCP deltaT gradient based on temperature range, slab width, slab 

thickness, PCC unit weight, w/c ratio, and latitude.

The CRCP design deltaT model is as follows:

• CRCP deltaT model—CRCP deltaT gradient based on maximum temperature, maximum 

temperature range, and aggregate type.

The lean concrete base elastic modulus is as follows:

• Elastic modulus model—Elastic modulus based on 28-day compressive strength.

The unbound materials resilient modulus is as follows:

• Resilient modulus model—Resilient modulus using constitutive model based on 

gradation, Atterberg limits, optimum moisture content, and soil classification.
5

CHAPTER 1. INTRODUCTION

BACKGROUND

Material characterization is vital to pavement analyses and has received increasing focus as 

it forms a critical component in recent improvements to engineering practices. This pertains 

to all aspects of pavement engineering—analysis, design, construction, QC/QA, pavement 

management, and rehabilitation. At each stage during the life of a project, the influence of 

several fundamental engineering material parameters on the long-term performance of the 

pavement has been recognized. There is a greater emphasis for optimizing the performance of 

concrete pavements, which involves a detailed understanding of the variables that affect 

pavement behavior and the properties of concrete that correspond to the desired performance.

Consequently, there is a need for more information about material properties so that they can be 

characterized accurately for predicting performance or for verifying their quality during the 

construction phase. With limited resources for performing laboratory and field tests to determine 

material properties, the need for a secondary means to obtain these material property values

(i.e., through correlations or predictive models based on data from routine or less expensive 

tests) is obvious. Additionally, the American Association of State Highway and Transportation 

Officials (AASHTO) MEPDG offers users the option of using inputs obtained through 

correlations.

(2,4,1) The MEPDG defines level 2 inputs as those obtained from correlations between 

the primary inputs (level 1 measured) and other parameters that are material-specific or are 

measured through simpler tests. The LTPP database provides an excellent source of information 

to develop these correlations using material properties of field sections.

(5)

The current report addresses critical data needs for design, construction, and pavement 

management operations under the LTPP Data Analysis Technical Support contract. This project 

focuses on developing predictive models to estimate PCC and unbound material properties using 

LTPP data.

DATA NEEDS

Material property data needs in the context of this study are grouped into the following 

three categories:

• Inputs during the design stage.

• QC/QA during construction.

• Scheduling maintenance and rehabilitation in a pavement management program.

Design

In both empirical and mechanistic-empirical (M-E) design systems, material property inputs are 

essential to characterize pavement behavior and to predict pavement responses, such as the 

magnitudes of stress, strain, and displacement, when subjected to applied traffic loads and 

environmental conditions. Furthermore, major pavement distresses are associated directly with 
6

the material properties of a component (or layer) of the pavement structure. For example, in 

JPCP, transverse cracking is influenced by PCC flexural strength. Faulting can be related to the 

erodibility of the underlying base/subbase material. Punchout development in CRCP can be 

related to PCC tensile strength.

The MEPDG, developed under National Cooperative Highway Research Program (NCHRP)

Project 1-37A and subsequently improved under NCHRP 1-40D, allows users to model the 

effects of project-specific climate, traffic loads, materials, design features, and construction 

practices mechanistically to predict pavement performance based on distress models calibrated 

with LTPP field sections.(2,4) The MEPDG is considered a significant improvement over current 

pavement design procedures, and in November 2007, it received the status of an AASHTO 

interim standard.

(1) The publication User Manual and Local Calibration Guide for the 

Mechanistic-Empirical Pavement Design Guide and Software developed under NCHRP 1-40B 

provides guidance to State highway agencies (SHAs) that are considering implementing the 

MEPDG.(6) It is expected that SHAs will adopt locally calibrated distress models that are 

representative of their specific materials and design conditions.

The need for a variety of material inputs is being recognized as agencies evaluate the MEPDG 

and streamline efforts for implementation. They continue to face challenges in estimating 

material parameter inputs and understanding their impact on pavement performance. For 

example, agencies do not have measured test data or access to databases and the necessary 

engineering expertise to develop correlations for their needs. Furthermore, due to a lack of 

familiarity with several input categories, they have come to rely on default values to characterize 

their typical materials. These default parameters are often a gross approximation of the true 

value, which may lead to erroneous distress and International Roughness Index (IRI) predictions. 

As another example, the permanent curl/warp gradient in the national calibration was set at 

-10 °F through the slab, as it was not possible to obtain an accurate value for this parameter, 

which depends on construction-related conditions. Analysis of selected LTPP data made it 

possible to derive an improved way to estimate this important input for design.

This study provides much needed procedures to obtain several inputs and provide correlations 

to determine the whole range of material properties based on routine test results and physical 

characteristics. These correlations will supplement the User Manual and Local Calibration 

Guide for the Mechanistic-Empirical Pavement Design Guide and Software to support MEPDG 

implementation efforts.(6)

Construction

Pavement construction practices are being enhanced continually for faster and more efficient 

processes. In addition, new materials and material types are being introduced. For example, 

cement compositions and cement types have changed considerably over the years, resulting in 

PCC properties and durability characteristics that are different from the past.

The focus of QC/QA procedures is now on identifying more reliable and faster QC/QA tests and 

determining material properties that are related directly to performance. The MEPDG enables 

performance prediction of the as-built pavement in addition to that of the as-designed pavement, 

as long as deviations from design assumptions (material properties or construction practices such 
7

as curing or temperature during construction) are identified during the construction process 

(see figure 1). For example, although the density of an unbound material is a good indicator of 

construction quality, the more fundamental resilient modulus is an indicator of performance and 

is a key input to the MEPDG. The ability to predict resilient modulus from index properties 

measured during construction will make the QA process address both construction quality and 

pavement performance issues. Note that in figure 1, material properties measured during 

construction can be used to predict performance in the field and might be different from the 

design/target performance.

Also, performance-related specifications (PRSs) for concrete pavements have been developed in 

recent years. Irick et al., under a Federal Highway Administration (FHWA) study to demonstrate 

PRS system for rigid pavement construction, considered three key performance indicators: PCC 

strength, PCC slab thickness, and initial serviceability.(7) Several relationships for the prediction 

of PCC properties were evaluated under this study. Irick et al. provide a comprehensive literature 

summary of models to predict concrete strength parameters. PRSs have also been implemented 

on several projects that required many correlations between pavement properties and 

performance.(8,9)

Figure 1. Illustration. MEPDG performance prediction during the design and construction

stage.

This study will bridge the gaps in current knowledge regarding the estimation of more 

fundamental material parameters that influence performance based on index properties or other 

commonly measured properties during construction.
8

Pavement Management

One of the key needs in managing pavements is an estimation of remaining life. Several SHAs 

use this parameter to program rehabilitation treatments. Various models (including the MEPDG 

models) are useful in that they can be utilized to predict the remaining life until critical levels 

of each distress and IRI are reached. Also, agencies are now considering the integration of 

construction quality databases with pavement management databases to track the effect of design 

and construction quality on long-term performance.(10) Such efforts lend themselves to more 

accurate performance predictions, whereby the performance of the as-constructed pavement can 

be used for scheduling maintenance and rehabilitation programs (see figure 1). However, many 

model inputs are needed related to the existing pavement, including inputs to characterize 

materials accurately. Index properties from construction QA data can be used to predict 

fundamental material properties that are related to performance.

In summary, the MEPDG provides a tool to specify material characteristics during the design 

and construction processes to achieve desired performance. The same models used in the 

MEPDG for design and construction analyses can be used in the future management of the 

pavement to estimate its remaining structural and functional life. For example, the inputs for a 

10-year-old pavement could be measured from the existing pavement and estimated from the 

MEPDG models to project future slab cracking. The curve can be adjusted to match today’s 

actual performance to improve the prediction. The slab cracking curve can be projected into the 

future to determine when it reaches a critical value to estimate its remaining life. The same could 

be done with joint faulting and IRI.

Therefore, the design, construction, and pavement management stages share a common need for 

determining a variety of material properties based on correlations from index properties and/or 

properties determined from more routine test procedures. This practice has been used in past 

AASHTO pavement design procedures and likely will increase in the future due to the more 

complex fundamental inputs required for the MEPDG procedure.

OBJECTIVES AND SCOPE

This study utilized LTPP data to develop correlations for SHAs to characterize PCC, subbase, 

and subgrade materials as necessary for design, QC/QA, and pavement management. It is 

expected that the findings from this study will assist an agency’s materials selection procedures, 

materials specification, pavement design, and pavement management practices.

To accomplish this overall goal, this research sought to address the following three objectives:

1. Identify a set of material engineering properties for which predictive relationships 

would be useful in pavement design, construction QC/QA, and pavement 

management applications. 

2. Establish and/or validate relationships between these engineering properties and routine 

test results, index properties, and/or other readily available information. 
9

3. Develop a practical guide accompanied by user-friendly software for applying the 

results of the aforementioned tasks in pavement design, construction QC/QA, and 

pavement management.

This project provides the necessary guidance for agencies to use more accurate values for 

material properties in design, construction, and pavement management. The correlations 

developed as part of this study are based on actual data from LTPP sections and, therefore, are

more reliable than default or typical values currently being used. The full potential of the 

MEPDG to predict performance accurately can be realized by providing more accurate input 

values to the procedure. This also supports improvements in material specifications for use in 

pavement construction, particularly for PRS. Eventually, these models can be implemented into 

the PaveSpec PRS software and used for construction.(11) In particular, the following major 

benefits will be obtained from this study:

• Support in local calibration of MEPDG models.

• Support in future use of MEPDG.

• Improved designs.

• Improved QC/QA and specifications.

• Improved pavement management.

Most of the data used in the development of prediction models to estimate material properties 

were obtained from the LTPP Standard Data Release 23.0.

(3)

SCOPE OF THE REPORT AND ORGANIZATION

This report documents the work performed under this project and presents the models developed 

to characterize materials and estimate material inputs. The report consists of five chapters. 

Chapter 2 describes the selection procedure to identify material properties that require predictive 

models. Chapter 3 provides a summary of literature reviewed for this study and concludes with a 

list of index properties (independent variables) used to characterize the material properties 

identified in chapter 2. Chapter 4 explains the data analyses procedures and discusses the 

developed models. Chapter 5 provides a summary of the report and presents the conclusions.

11

CHAPTER 2. MATERIAL PROPERTIES FOR PREDICTIVE EQUATIONS

The primary engineering material properties considered for indepth evaluation in this study were 

those required for pavement analysis and design using the MEPDG. The MEPDG considers the 

effects of a comprehensive set of material properties in the structural design of JPCP and CRCP. 

Its capability to consider strength, modulus, thermal, and other materials properties is the 

foundation for designing for performance under traffic loads and climatic conditions. Also, the 

MEPDG procedure can accommodate various material types and uniquely model each material’s 

response to load, temperature, and moisture and predict their effects on performance. Therefore, 

the research approach was to develop a list of material properties that likely could be estimated 

based on the availability of data in the LTPP database and the needs of the MEPDG performance 

prediction models.

INPUTS FOR MEPDG

Hierarchical Inputs for MEPDG

The MEPDG adopts a hierarchical input level scheme to accommodate the designer’s knowledge 

of the input parameter. Inputs can be provided at three different levels. Level 1 inputs represent 

the greatest knowledge about the input parameter and typically are obtained from a projectspecific data collection or test effort. Level 2 represents a moderate level of knowledge of the 

input parameter and is often calculated from correlations with other site-specific data or a less 

expensive measure. Level 3 represents the least knowledge of the input parameter and is based 

on “best-estimated” or default values. For example, level 1 data for concrete flexural strength 

would involve a flexural beam test, level 2 would be a flexural strength value estimated using a 

compressive strength test and correlation to flexural strength, and level 3 would be a default 

value for concrete strength used by a particular SHA. Most agencies have adequate information 

in their materials and construction quality databases to develop agency-specific default values for 

immediate implementation of the MEPDG (based on current knowledge and surveys conducted 

by Rao et al.).

(10)

During the development of the MEPDG, the need for correlation equations to determine some 

input values was recognized. Many designs must be created years in advance of construction,

and little is known of the exact materials that will be used. However, it is highly desirable to 

have the best estimates of these inputs possible based on available information. The MEPDG 

therefore supports the use of level 2 or 3 data in the absence of level 1 laboratory test data. This 

adaptability is critical to the model types developed in this study and is further discussed in 

chapter 5 of this report.
12

Input Categories for the MEPDG

Table 1 provides a list of major material types considered in the MEPDG. Bolded information

reflects the material types that are relevant in this project. Each material type requires a variety 

of material inputs (not all easily available) during local calibration efforts and after the procedure 

is implemented. As an example, the various PCC material-related inputs considered by the 

MEPDG are presented in table 2 under the following three categories: 

• Material properties required for computing pavement responses. 

• Additional material inputs to the distress/transfer functions.

• Additional material inputs required for climatic modeling.
13

Table 1. Major material types for the MEPDG.(2)

Asphalt Materials:

• Stone matrix asphalt.

• Hot mix asphalt (HMA).

o Dense graded.

o Open graded asphalt.

o Asphalt stabilized base mixes.

o Sand asphalt mixtures.

• Cold mix asphalt.

o Central plant processed.

o In-place recycled.

PCC Materials:

• Intact slabs—PCC. 

o High-strength mixes.

o Lean concrete mixes.

• Fractured slabs.

o Crack/seat.

o Break/seat.

o Rubblized.

Chemically Stabilized Materials:

• Cement stabilized aggregate.

• Soil cement.

• Lime cement fly ash.

• Lime fly ash.

• Lime stabilized soils.

• Open graded cement stabilized 

aggregate.

Non-Stabilized Granular Base/Subbase:

• Granular base/subbase.

• Sandy subbase.

• Cold recycled asphalt (used as 

aggregate).

o Recycled asphalt pavement 

(includes millings).

o Pulverized in-place.

• Cold recycled asphalt pavement (HMA 

plus aggregate base/subbase).

Subgrade Soils: 

• Gravelly Soils (A-1 and A-2).

• Sandy Soils.

o Loose sands (A-3).

o Dense sands (A-3).

o Silty sands (A-2-4 and A-2-5).

o Clayey sands (A-2-6 and A-2-7).

• Silty soils (A-4 and A-5).

o Clayey soils, low plasticity.

o Clays (A-6).

o Dry-hard.

o Moist stiff.

o Wet/sat-soft.

• Clayey soils, high plasticity clays (A-7)

o Dry-hard.

o Moist stiff.

o Wet/sat-soft.

Bedrock:

• Solid, massive, and continuous.

• Highly fractured, weathered.

Note: Bolded information reflects the material types that are relevant in this project.

Table 2 reflects the additional significance of other material engineering properties beyond 

strength properties for PCC materials in the analysis process. As an example, while concrete 

modulus of rupture (MR) was the main material input for the AASHTO 1993 rigid pavement 

design procedure (along with the modulus of elasticity), the MEPDG allows correlations through 

level 2 inputs with compressive strength and requires other volumetric properties such as 

shrinkage, CTE, specific heat, and thermal conductivity for analysis.

(12) In addition, strength 

parameters that are used in the analysis include compressive strength, modulus of elasticity, and 

tensile strength for CRCP. The modulus of elasticity has a much greater effect on performance 

with the MEPDG than with the AASHTO 1993 procedure. In other words, the MEPDG offers a 

framework to optimize mix designs to balance a range of strength, modulus, CTE, shrinkage, and 

other engineering properties for improved performance.(13)
14

Table 2. PCC material inputs beyond strength considered by the MEPDG for JPCP and 

CRCP.

Material Inputs 

Required for 

Critical Response 

Computations

Material Inputs Required for 

Distress/Transfer Functions

Material Inputs 

Required for 

Climatic Modeling

• Static modulus of 

elasticity (E) 

adjusted with time.

• Poisson’s ratio.

• Unit weight.

• CTE.

• MR over time.

• Split tensile strength (CRCP 

only).

• Ultimate shrinkage.*

• Amount of reversible 

shrinkage.

• Time to achieve 50 percent of 

ultimate shrinkage.

• PCC zero-stress temperature.**

• Surface 

shortwave 

absorptivity.

• Thermal 

conductivity.

• Heat capacity.

*Estimated from compressive strength, cement type, curing type, cement content, and w/c ratio.

**Estimated from cement content and mean monthly temperatures at the project location.

Likewise, unbound materials are characterized by material parameters that account for the 

changing stress state in the material with seasonal changes in moisture conditions in a specific 

location. The gradation of the soil, maximum dry density, and optimum moisture content are key 

inputs to the procedure.

The MEPDG also requires the input of construction and field-specific parameters that are critical 

to performance. These construction or site features are not restrictive to a particular material, but 

they are associated with specific material index properties, climatic conditions, and construction 

practices. For example, base erodibility is a function of base material properties such as the 

strength of the base layer, the amount of fines, and site conditions such as the level of 

precipitation and traffic load repetitions.

Correlations Developed/Adapted for the MEPDG

The calibration of the rigid pavement distress models utilized several inputs from levels 2 and 3 

based on the best information available from literature and LTPP databases. The following is a 

partial list of correlations used in the MEPDG models:

• The default concrete strength gain models in the MEPDG were derived from two decades 

of Portland Cement Association (PCA) research and the LTPP Specific Pavement Studies 

(SPS)-2 time series data.

• The ultimate shrinkage calculation model is based on Bazant’s model and was derived as 

a function of compressive strength, cement type, curing type, cement content, and w/c 

ratio.(14) Other shrinkage inputs, such as time to 50 percent of ultimate shrinkage, were 

adopted from the American Concrete Institute (ACI) recommended models.

• CTE defaults by coarse aggregate type were established based on testing and petrography 

performed under the LTPP program.(15)
15

• Correlations between several of the PCC strength and modulus terms are based on work 

done under the LTPP program.(16)

• Erosion of the base layer under a CRCP was calculated using an empirical model that 

considers the climatic conditions and the properties of the base layer to determine the 

extent of erosion and voids under the slab.

• Erodibility Index (EI) for JPCP design is based on the base type and was established 

during the calibration process.

• Resilient modulus at optimum moisture content, which is a key material input for 

unbound layers, was determined through an iterative process by matching the subgrade 

k-value backcalculated from falling weight deflectometer (FWD) test data with the 

subgrade k-value for the same calendar month as determined by the MEPDG analysis.

• Friction coefficients of the slab/base layer in CRCP (mean friction coefficient for each 

type of base course) were determined by matching the predicted and field measured 

transverse crack spacing.

• deltaT in rigid pavement design was set at -10 °F for the calibration of the MEPDG 

distress models. Since it was not possible to determine the actual magnitude of this 

parameter, it was found that a value of -10 °F optimized the transverse cracking 

prediction over the numerous calibration sections. The MEPDG recommends -10 °F 

for this parameter as a default value and is more or less considered a level 3 default.

SELECTION OF MATERIAL PARAMETERS

A preliminary list of material properties was prepared for developing predictive models based on 

inputs required for the MEPDG and their level of significance in performance prediction as well 

as their importance during the design, construction, or pavement management phases. The 

materials are classified broadly as PCC materials, stabilized materials, and unbound materials. 

Note that unbound materials include both coarse-grained and fine-grained soils, which have 

different mechanical behavior in response to applied stress states. The preliminary material 

properties are listed in table 3 through table 5 for PCC, stabilized, and unbound materials. These 

tables list all input variables, identify the conventional source of data for SHAs, and indicate 

whether a predictive model can be developed for the parameter.
Table 3. PCC material properties and rigid pavement design features considered for generating predictive models.

Material 

Property

Constant 

or Time 

Dependent Source

Recommended Test Protocol 

or Data Source

Predictive 

Model

Possibility 

Yes(Y)/

No(N)

Project Stage:

Design (D),

QC/QA (C), or

Pavement 

Management (PM)

Level of 

Significance 

for 

Performance

Rehabilitation of New PCC Slabs

Compressive 

strength

Time 

dependent

Test AASHTO T 22(17) Y D, C, PM High

Elastic modulus Time 

dependent

Test ASTM C 469(18) Y D, PM High

Poisson’s ratio* Constant Test ASTM C 469(18) N D Low

Flexural strength Time 

dependent

Test AASHTO T 97(19) Y D, PM High

Indirect tensile 

strength (CRCP 

only)

Time 

dependent

Test AASHTO T 198(20) Y D High

Unit weight* Constant Test AASHTO T 121(21) N D, C Medium or 

high

Air content* Constant Test AASHTO T 152(22) or 

AASHTO T 196(23)

N C Medium

(affects 

durability)

CTE Constant Test AASHTO TP 60(24) Y D, PM High

Surface 

shortwave 

absorptivity*,**

N/A Estimate MEPDG default(2) N D Low

Thermal 

conductivity*,**

N/A Test ASTM E1952(25) or MEPDG 

default(2)

N D Low

Heat capacity*,** N/A Test ASTM D2766(26) or MEPDG 

default(2)

N D Low

16
PCC zero-stress 

temperature

N/A Estimate MEPDG model(2) Y D, PM Medium

(JPCP); high 

(CRCP)

Ultimate 

shrinkage*

Time 

dependent

Estimate MEPDG predictive model(2) Y D, PM Medium

(JPCP); high 

(CRCP)

deltaT in JPCP 

and CRCP 

design**

Time 

dependent

Estimate MEPDG calibrated with 

default (-10 °F)(2)

Y D, C, PM High

Erosion in CRCP 

design**

Time 

dependent

Estimate MEPDG predictive model(2) Y D High

EI for JPCP 

design**

N/A Estimate MEPDG recommendation for 

base type(2)

Y D Medium

Rehabilitation of Existing PCC Slabs

Compressive 

strength

Time 

dependent

Test AASHTO T 22(17)

(extracted cores)

Y D, C, PM High

Elastic modulus Time 

dependent

Test ASTM C 469(18)

(extracted cores)

Y D, PM High

ASTM D 4694(27)

(NDT deflection testing)

Poisson’s ratio* Constant Test ASTM C 469(18)

(extracted cores)

N D Low

Flexural strength Time 

dependent

Test AASHTO T 97(19)

(extracted beam)

Y D, PM High

N/A = Not applicable.

*Parameter was not selected for model development.

**Considered a design feature input to rigid pavement design process.

17
Table 4. Chemically stabilized materials properties considered for generating predictive models.

Material Type

Material 

Property

Constant

or Time 

Dependent Source

Recommended Test 

Protocol or Data 

Source

Predictive 

Model

Possibility 

Yes (Y)/

No (N)

Project Stage: 

Design (D), 

QC/QA (C), or 

Pavement 

Management 

(PM)

Level of 

Significance 

for 

Performance

Lean concrete 

and cementtreated 

aggregate

Elastic 

modulus

Constant Test ASTM C 469(18) Y D, PM Medium-high

Flexural 

strength (for 

HMA 

pavement)

Constant Test AASHTO T 97(19) Y D, PM Medium-high

Lime-cementfly ash Resilient 

modulus

Time 

dependent

Estimate MEPDG predictive 

model(2)

Y D, PM Medium-high

Soil cement Resilient 

modulus

Time 

dependent

Test AASHTO T 307(28) Y D, PM Medium-high

Lime stabilized 

soil

Resilient 

modulus

Time 

dependent

Estimate MEPDG predictive 

model(2)

Y D, PM Medium-high

All above

material types

Unconfined 

compressive 

strength

Time 

dependent

Test MDTP, AASHTO T

307(28)

Y D, C, PM Medium-high

18
Table 5. Unbound material properties considered for generating predictive models.

Material Property

Constant

or Time 

Dependent Source

Recommended Test 

Protocol or Data Source

Predictive 

Model

Possibility 

Yes (Y)/

No (N)

Project Stage: 

Design (D), 

QC/QA (C), or 

Pavement 

Management (PM)

Level of 

Significance 

for 

Performance

Resilient modulus determined 

using two options:

Time 

dependent

Test AASHTO T 307(28) or 

NCHRP Project 1-28A(29)

Y D, C, PM High

1. Regression coefficients k1, 

k2, and k3 for the constitutive 

model that defines resilient 

modulus as a function of 

stress state

Resilient modulus = f(bulk 

stress, major principal 

stresses, octahedral shear 

stress, normalizing stress) 

2. Determine resilient 

modulus for expected in-place 

stress state from laboratory 

tests

Model coefficients are

different for coarsegrained and fine-grained 

soils

Poisson’s ratio* N/A Estimate No standard test; use 

default values. 

N D Low

Maximum dry density* Constant Test AASHTO T 180(30) N D, C High

Optimum moisture content* Constant Test AASHTO T 180(30) N D High

Specific gravity* Constant Test AASHTO T 100(31) N D Low

Saturated hydraulic 

conductivity*

Constant Test AASHTO T 215(32) N D Medium

Soil water characteristic curve 

parameters*

N/A Test AASHTO T 99, T 100, 

and T 180(33,31,30)

N D High

Rehabilitation of Existing Pavement and Properties of Soil to Be Left In-Place

Modulus (backcalculated) Time 

dependent

Test ASTM D 4694(27) and 

D 5858(34)

N D High

Poisson’s ratio* Time 

dependent

Estimate MEPDG default(2) N D Low

N/A = Not applicable.

*Parameter was not selected for model development.

19
20

In selecting the material properties that require predictive models, the following factors 

were considered:

• The selected parameters are not index properties of the material or they are not part of 

mix design information and hence would require a predictive model to determine its 

value. In other words, these parameters are not independent variables for the material,

and their values depend on the individual properties of constituent materials.

• Routine test procedures are not sufficient to determine the value of these parameters, and 

they require either expensive or time-consuming tests to determine their values. CTE and 

resilient modulus of unbound base materials are examples of such parameters.

• The material parameter exists in the LTPP database, and the required data are available 

for developing the model. For example, PCC shrinkage and modulus of stabilized base 

materials were not part of the database. In fact, they were never intended to be included 

in the LTPP tests when the experiments were designed.

• The engineering property is of significance to pavement performance.

• The material property is not affected significantly by construction factors or by 

parameters that cannot be determined prior to the design stage. For example, the unit 

weight of a material is an important parameter for pavement performance. It contributes 

to the total stresses developed in the PCC slab and is an indicator of the quality of 

consolidation achieved during construction that has a direct impact on the strength and 

durability characteristics. However, unit weight cannot be predicted using index 

properties, as it is also a function of aggregate gradation and packing achieved in 

the field.

• The likelihood of actual value of the material property deviating from the assumed 

defaults and its implications on design or performance is significant. For example, 

Poisson’s ratio of PCC or specific heat of PCC would not qualify as an engineering 

property requiring a predictive relationship because these parameters do not vary 

significantly from the recommended/typical default values. Additionally, within a 

realistic range, their effects on performance are not large.

Accordingly, several variables in table 3 to table 5 were not selected for model development. 

The selected parameters from table 3 to table 5 as well as predictive models to estimate their 

values were the focus of the literature review performed in this study. The literature review 

sought to identify independent variables (index properties) that could be used to determine 

the values of the selected material parameters. A list of index properties that can serve as 

independent variables in the prediction models is provided at the end of chapter 3.
21

CHAPTER 3. LITERATURE REVIEW

This chapter summarizes the literature review performed to investigate the correlations proposed 

by researchers and practitioners to estimate mechanical properties of pavement materials from 

simpler material index or constituent properties. The material types covered in this review are 

PCC and unbound aggregate and soils.

The literature was compiled from the following sources:

• FHWA and U.S. Department of Transportation publications (including those from the 

United States Army Corps of Engineers, United States Air Force, and United States 

Navy), PCA, American Concrete Pavement Association, and SHAs.

• Searches of Internet-based library systems (e.g., the University of Illinois, Transportation 

Research Information Service, and National Technical Information Service).

• Published proceedings of the American Society of Civil Engineers, the Transportation 

Research Board, the Federal Aviation Administration, the International Society for 

Concrete Pavements, and other agencies.

• Textbooks covering the material types of interest.

CORRELATION BETWEEN PCC MIX MECHANICAL PROPERTIES AND PCC MIX 

CONSTITUENT PROPORTIONS AND OTHER PCC PROPERTIES

PCC mix mechanical properties such as compressive strength (f

'

c), flexural strength (also called 

modulus of rupture), tensile strength (ft), elastic modulus (E), CTE, and drying shrinkage were 

among the most commonly studied and discussed in the literature, as they directly impact 

pavement performance. PCC mix constituent properties such as w/c ratio and coarse aggregate 

characteristics appeared to be the more commonly used properties to predict these PCC mix 

mechanical properties.

Other PCC properties, such as temperature at set, unit weight, thermal conductivity, heat 

capacity, and specific heat, which have come into sharper focus since the advent of the MEPDG 

and other pavement modeling programs such as the FHWA’s HIgh PERformance Concrete 

PAVing (HIPERPAV®) software, were not very well documented in the literature.

(35)

The following discussion summarizes the factors affecting some of the commonly cited PCC mix

properties and typical correlations proposed.

Compressive Strength

Compressive strength of concrete is the most frequently used measure of concrete quality in 

design and QA during construction. This parameter, most easily determined through a laboratory 

uniaxial compressive loading test and standardized in ASTM C 39, is considered a fundamental 

strength property.(36) PCC has a high compressive strength, and most structures are designed to 

take advantage of this property. The 28-day compressive strength is a widely accepted strength 
22

index, especially for initial design. PCC strength increases with time and can affect opening 

strength, as well as rehabilitation designs in the long term. The compressive strength is also 

used frequently as an index for other strength types. As will be discussed in chapters 4 and 5, 

emphasis was placed on developing prediction models for compressive strength in this study.

In a majority of materials, including PCC, strength has been found to correlate strongly with its 

intrinsic porosity. Although considered a homogeneous material, concrete contains void spaces 

of various sizes in the matrix and in the interfacial zone (ITZ). Voids in the ITZ are responsible 

for the development of microcracks in the material. The failure modes initiated in the material 

vary with the type of loading applied. The formation of cracks in the matrix under a uniaxial 

compressive load requires greater energy compared to failures resulting from other forms of 

loading. A stable system of cracks exists in the ITZ up to 50 or 60 percent of the failure loads. 

At higher stress levels, cracks initiate and progress through the matrix, combine with those in 

the ITZ, and a failure plane develops 20 to 30 degrees from the direction of load. The failure is

typically more brittle with high-strength concrete.

Concrete strength is influenced by the complex interaction of the characteristics and proportions 

of the individual materials, the consolidation provided (construction factors), the curing 

condition, and the rate/type of loading. Ozyildirim and Carino provide a comprehensive 

summary of the test procedures to determine each strength parameter (i.e., type of loading) and 

the parameters that are most significant.(37) It can be difficult to isolate the effect of a single 

parameter because of the inherent interaction and their confounding effects. The following list 

summarizes the influence of key parameters:

• w/c ratio—Strength varies inversely with w/c ratio, provided strength is evaluated for a 

given degree of hydration. Low w/c ratios produce lower paste porosity and result in 

higher strengths in low- to medium-strength mixes. However, with high-strength mixes, 

a small reduction in the w/c ratio (especially at levels below 0.3) results in a drastic 

increase in the strength essentially due to the improved strength of the ITZ.

• Air content—For a given level of hydration, when air is incorporated into a concrete 

mix, either entrapped or entrained air, the paste porosity and the strength decrease. 

However, the effect of air on concrete strength is influenced by the cement content and 

w/c ratio. At higher w/c ratios and cement contents, the effect is less pronounced than at 

low w/c ratios and cement contents.

• Cement type and content—The effect of cement type is most pronounced with the use 

of type III cement, which contains higher amounts of calcium aluminates and is more 

finely ground. Mixes with type III cements show higher strength, especially during early 

ages (1 to 7 days). Also, mixes with types IV and V cements produce lower strengths at 

early ages but have more or less similar strength characteristics at 28 days. Conventional 

mix design principles also typically use higher cement content to increase strength, and 

this holds true for a certain range of cement contents or CMC while holding other 

parameters constant. However, mix designs can incorporate many other means to 

improve strength. In fact, with the current push for sustainability in design and 

construction, the use of less cement (combined with other mix design practices) is 

recommended to improve strength and durability.
23

• Aggregate type—The coarse aggregate type has a significant effect on strength for high 

strength and for mixes with lightweight aggregate. The aggregate particle being several 

times stronger than the paste, failure in concrete is controlled by the paste strength for 

normal strength concrete. However, with high-strength concrete, as the paste is relatively 

stronger, aggregate type can be a factor affecting strength. Also, the hardness of the 

aggregate influences the deformational characteristics, which are more critical for 

modulus of elasticity and flexural strength. Crushed aggregates provide a stronger bond 

with the aggregate particles, improving the strength of ITZ.

• Aggregate size distribution—For a given cement content in a mix, larger aggregates 

(qualified by MAS from the gradation) tend to reduce the strength of the concrete. Larger 

aggregates have a weaker ITZ and form microcracks that initiate failures at lower load 

levels. PCC tensile strength and flexural strength are more sensitive to changes in 

aggregate size because the ITZ characteristics are critical to the loading pattern used in 

these tests. 

• Mineral and chemical admixtures—Chemical admixtures that are designed to alter the 

rate of strength gain, commonly known as set accelerators or retarders, clearly have a 

significant effect on early strength of the mix. The use of supplementary cementitious 

materials also reduces early age strength but results in similar or higher strength in the 

long term. Mineral admixtures, also referred to as supplemental cementitious materials 

(SCMs), improve the tensile strength of concrete because of reduced porosity in ITZ.

• Unit weight—For a given mix, higher unit weights are indicative of a higher degree of 

consolidation or better construction in the field. High unit weight can also signal low 

entrained air. Since unit weight directly reduces the porosity of the mix, this condition 

results in higher strength. Compressive strengths are more sensitive to unit weight than 

tensile or flexural strengths.

• Curing conditions—Conditions that support better hydration in general increase the 

strength, and strength increases with time. Hence, age is generally defined in quantifying

concrete strength. Higher relative humidity and temperature accelerate the chemical 

processes involved in hydration. The availability of moisture is a necessary condition for 

hydration to proceed regardless of the temperature conditions or the time allowed for 

concrete to gain strength. More recently, the significance of initial curing conditions 

(i.e., accelerated curing, curing temperature, etc.) has been better understood for 

predicting long-term strengths.(38)

• Loading conditions—The rate of loading and the size of the specimen have an effect on 

strength. There is an apparent increase in strength (or modulus of elasticity) at higher 

loading rates. Therefore, standard procedures to determine concrete strength standardize 

the rate of load application.

Irick et al. conducted a pilot PRS study in which they developed a comprehensive summary of 

all PCC material property prediction models. The predictor variables used in that study were 

generally in agreement with the list and discussion above.(7)
24

Figure 2 illustrates the summary of factors and specific properties that affect concrete strength. 

In developing predictive models, every effort was made to evaluate the effect of parameters 

from this list that were available in the LTPP database. Furthermore, unless the data exhibited

the effects discussed here, the parameter would not be recognized as significant in the 

statistical analyses.

Figure 2. Illustration. Summary of factors affecting PCC strength.(39)

Compressive Strength Prediction Models

The porosity of concrete, which includes paste porosity and ITZ porosity, cannot be determined 

realistically. Therefore, no empirical correlations exist to compare concrete porosity and

strength. Other surrogate parameters that affect porosity or strength have been used to model 

compressive strength.

Compressive strength has been related mainly to the w/c ratio. Neville reported about Feret’s 

work in 1896 that correlates mix volumetrics to strength.(40) Abrams’ work proposed a ratio law 

for concrete strength based on a more thorough investigation of other parameters and stated that

the strength of the concrete was solely controlled by the ratio of cement to free water in the 

plastic mixture (i.e., the w/c ratio) as long as the cement type, conventional aggregates, 

placement conditions, curing conditions, and test conditions remained constant. (41)

The empirical coefficients relating PCC compressive strength to w/c ratio depend on the units, 

materials, test methods used, age of testing, and the conditions of validity for Abrams’ law. 

Further, Abrams suggested the following conclusions:

(41)

• Compressive strengths are lower with higher w/c.

• Compressive strengths are higher with lower w/c.

• Compressive strengths are identical with the same w/c.

• The strengths of comparable concrete depend solely on their w/c ratios regardless of 

their compositions.

CONCRETE STRENGTH

LOADING 

PARAMETERS

Stress type

Rate of loading

STRENGTH OF THE COMPONENT 

PHASES

SPECIMEN 

PARAMETERS

Dimensions

Geometry

Moisture state

MATRIX POROSITY

w/c ratio

Admixtures

Degree of hydration

Air content

AGGREGATE 

POROSITY ITZ POROSITY

w/c ratio

Admixtures

Aggregate size and gradation

Consolidation

Degree of hydration

Aggregate-cement chemistry
25

Research suggests that Abrams’ law is valid, provided that the following assumptions hold:

• The strength-developing capabilities of the cements used are identical.

• The quantities and strength influencing effects of the admixtures used are identical.

• The concrete specimens are prepared, cured, and tested under the same conditions.

• The concrete ingredients are distributed evenly.

• Air contents are the same, air voids are distributed uniformly, and none of the voids are 

too large relative to the specimen size.

• Aggregate particles are stronger than the matrix (which suggests that high-strength 

concrete with typically higher paste strength could be more sensitive to aggregate 

properties).

• The bonds between aggregate surfaces and the matrix are strong. The bond can transfer 

the stresses to the aggregate before loading crushes the matrix.

• The strength affecting physical and chemical processes (i.e., drying, aggregate reactivity, 

etc.) are the same and are not overwhelming (hydration).

• The composite nature of the concrete affects the strength of the compared concretes to the 

same extent.

• The contribution of the aggregate skeleton is the same in the various concretes.

In 2006, Colak presented a model for strength prediction based on only w/c ratio.(42) However, 

Abrams’ assumptions are not true, especially in the case of high-strength concrete, for which the 

mechanical properties of the aggregate become important.

(41) However, Alexander and Mindess 

found that no universal or meaningful mathematical relationships were derived to predict 

concrete strength as a function of both paste and aggregate variables.(43) This suggests that mixspecific empirical correlations will possibly need to be developed while considering key cement, 

cement paste, and aggregate attributes of interest to the concrete compressive strength.

The first studies of the effects of aggregates on concrete strength were conducted by Gilkey as 

well as Bloem and Gaynor.

(44,45) Gilkey presented a review of the limitations of the Abrams w/c 

ratio theory. He proposed that Abrams’ law must be extended to include such parameters as the 

ratio of cement to aggregate, stiffness and maximum size of aggregate, and surface texture. 

According to Gilkey, Abrams’ law could be generalized into a series of relationships represented 

by a family of approximately parallel strength curves.

The work of Bloem and Gaynor concentrated mainly on the effect of maximum size of concrete 

aggregate on compressive strength.(45) They found that strength decreased as aggregate size 

increased over the full range of w/c ratio. Their relationships are of limited significance for 

widespread use because the impact on compressive strength of the different types of aggregates 

can be larger than impact of maximum size of aggregate.
26

In the United Kingdom, Kaplan investigated the impact of aggregate surface texture, aggregate 

shape, and modulus of elasticity on compressive strength.(46) Aggregate strength within the limits 

of Kaplan’s tests had no effect on concrete strength, which is where the target strength of the mix 

design is of significance, as discussed previously.

In work performed by Bennett and Khilji in the United Kingdom, an empirical relationship 

between concrete strength and the following variables was developed: w/c ratio, static modulus 

of elasticity of coarse aggregate, coarse aggregate/cement ratio, angularity number, and the 

aggregate impact value.(47) These researchers found that the modulus of elasticity of the coarse 

aggregate had a considerable effect on the strength, exceeded only by w/c ratio.

De Larrard and Bellock developed a semi-empirical method for defining compressive strength 

of concrete.(48) They suggested the concept of maximum paste thickness (MPT), which can be 

interpreted as the mean distance between aggregate particles and is described in terms of three 

important mix variables: the maximum size of aggregate, the aggregate volume concentration, 

and packing density of aggregate. They established that the strength of concrete depends on 

MPT, and they developed the empirical equation between concrete compressive strength and 

matrix strength, which was determined from the paste strength. According to this equation, for 

very high matrix strengths, the composite strength of concrete is controlled by the intrinsic 

strength of the rock. For low matrix strengths (or alternatively very high aggregate strengths), the 

strength of the composite is proportional to the matrix strength, with coefficients depending on 

the bond between the paste and the aggregate.

De Larrard and Bellock’s equations, while based on sound concepts and helpful for theoretical 

strength prediction, do not provide a universally applicable approach to the problem of 

compressive strength prediction. For example, they do not account for the effects of aggregate 

elastic modulus, shape, and texture, as in Kaplan’s investigations. Also, De Larrard and 

Bellock’s results can be applied only to high-strength concrete where it is possible to measure 

paste strength with a reasonable degree of certainty.

The results of a comprehensive study of compressive strength were presented by Namyong 

et al.(49) This study used 1,442 compressive strength test results obtained from specimens with 

a range of w/c ratios and cement/aggregate ratios. Based on the results of correlation analysis, 

Namyong et al. developed empirical equations for predicting compressive strength of 

in situ concrete.

Time effects on compressive strength gain or strength at a given time have been predicted by 

several models. ACI and the Committee Euro-International du Beton (CEB-FIP) provided

models to predict strength at a given time after hydration.(50,51) The ACI model is applicable only 

to moist-cured samples cast with type I cement, and the CEB-FIP model is applicable only for 

specimens cured at 70 °F. FHWA’s HIPERPAV® concrete compressive strength model is based 

on generalized Abrams theory.(52) The HIPERPAV® compressive strength prediction model 

accounts for the influence of w/c ratio, pozzolans, and aggregate contents in addition to concrete 

age. The main limitation of the HIPERPAV® compressive strength model is that it assumes 

all the conditions required to apply Abrams’ rule are satisfied. Also, this model was calibrated 

only for fly ash as the mineral admixture. Wang et al. used historical concrete mix design 
27

and strength test data in Iowa and developed statistical models to predict strength and other 

concrete properties.(53)

Powers’s approach for compressive strength for hardening concrete (time generalization of 

Abrams’ law) has been used wherein the compressive strength was predicted as a function of 

the gel/space ratio, which, in turn, was found to be a function of degree of hydration and the w/c 

ratio.

(54) Tango’s model uses the additional time-dependent parameter, the degree of hydration, 

when considering concrete age.(55) This model was used successfully in the prediction of 

concrete strength in the Brazilian IPT concrete mixture design method. Irick et al. provided a 

comprehensive literature summary of models to predict concrete strength parameters.(7)

The equations in figure 3 through figure 7 summarize a few key models for compressive 

strength, fc. These models are significant from the standpoint of fundamental theories and those 

that are relevant to the context of the current study.

Figure 3. Equation. fc according to Abrams.(41)

Where:

k1, k2 = Empirical constants for a mix.

w/c = water/cement ratio.

f

Figure 4. Equation. fc according to Neville.

(40)

Where:

K = Empirical constant.

c, w, a = Cement, water, and air content volumetric proportions.

Figure 5. Equation. fc according to Colak.

(42)

Where:

= Constants.

f

c

= � k1

k2

w/c�

f

c = K � c

c + w + a

�

2

f

c = 

α

w

c

�β+ λ w

c�

n

α, β, λ, n
28

Figure 6. Equation. fc according to ACI 318.

(49)

Where:

f c(t) = Compressive strength at time, t days.

f c,28 = Compressive strength at 28 days.

t = Time in days.

Figure 7. Equation. fc according to Wang et al.(53)

Where:

A, B, C, D, E, F, G = Empirical constants.

w/b = w/c ratio.

uw = Unit weight.

CMF = Cementitious materials factor.

t = Time after hydration or age in days.

Flexural Strength and Splitting Tensile Strength

The flexural strength or MR and split tensile strength of concrete are the most commonly used 

indices to define the tensile capacity of PCC. Concrete is typically not tested under direct tension 

because the test apparatus and the loading mechanism introduce secondary stresses that are not 

easy to compensate for. Since the factors that affect these two strength estimates share some 

similarities in the nature of failure introduced along with a limited set of dissimilarities, the 

factors affecting their values are quite similar. Like compressive strength, MR is influenced by 

mix design parameters including w/c ratio, cement type, cement content, and aggregate 

properties (aggregate type, maximum size, gradation, and surface texture). In general, the 

material characteristics affect MR in the same manner as compressive strength. However, 

parameters that affect ITZ porosity are more sensitive to flexural strength. Literature published in 

the last decade suggests that the most important factor governing concrete tensile capacity is the 

aggregate matrix bond.(56,57)

The flexural strength of concrete is defined as the maximum tensile strength at rupture at the 

bottom of a simply supported concrete beam during a flexural test with third point loading, as 

standardized in ASTM C 78-02.

(58) Failure in the region between the loads applied at third points, 

or the region of maximum moment, is considered to be acceptable for this test. In other words, 

this test measures the tensile capacity of the material in bending or flexure. MR of a fully 

supported slab is far greater than the flexural strength of a simply supported beam. MR is the 

basis for estimating flexural fatigue in concrete. In the MEPDG, the damage calculated for the 

estimation of transverse cracking is a function of the flexural strength of the concrete. A true 

estimation of MR, therefore, would improve the accuracy of cracking prediction.

Kaplan developed a regression equation for flexural strength after statistical analysis of test 

data.

(46,59) He associated MR with aggregate elastic modulus, void percents, and surface texture 

f

c

(t) = f

c,28 � t

4 + 0.85t

�

f

c = A + B × w⁄b + C × uw + D × CMF + E × log(t) ‒ F ×w⁄b × uw ‒ G × uw × CMF
29

roughness. According to Kaplan, the elastic modulus of the aggregate is the most important 

factor affecting flexural strength.

The split tensile strength, commonly referred to as the indirect tensile strength or simply the 

tensile strength of PCC, is estimated using the ASTM C 496-90 test, which involves subjecting a 

concrete cylinder to compressive loads along two diametrically opposite axial lines.(60) The 

compressive load applied on the side of a cylindrical specimen causes a uniform tensile stress 

along the vertical diameter until the specimen fails. The indirect tensile strength is used in the 

CRCP distress prediction models of the MEPDG. This strength parameter has a direct influence 

on the transverse crack spacing, which, in turn, affects the crack widths, the load transfer 

efficiency of the crack, and eventually damage leading to punchout development. 

Prediction Models and Interrelationship Between Tensile Capacity and Other Strength 

Parameters

Of all the PCC mechanical properties listed, compressive strength is the easiest and, not 

surprisingly, the most commonly tested parameter. Consequently, several researchers have 

developed relationships between this parameter and the three other PCC strength/modulus 

parameters—flexural strength, tensile strength, and elastic modulus. A summary of the flexural 

strength models is presented in figure 8 through figure 11, and a summary of the tensile strength 

correlations to compressive strength is presented in figure 12 through figure 17. 

Various researchers have found that the relationship follows a power curve model. This is mainly 

because as the compressive strength of the concrete increases, the ratio between the tensile 

strength and compressive strength decreases, as does the ratio between the flexural strength and 

compressive strength.(39)

Figure 8. Equation. MR.

Where:

a = 9.0, b = 0.5 (psi).

(61)

a = 7.5, b = 0.5 (psi); a = 0.62, b = 0.5 (MPa).

(50)

a = 8.3, b = 0.5 (psi).

(62)

a = 9.6, b = 0.5 (psi).

(63)

a = 11.7, b = 0.5 (psi) (for high-strength mixes).(64)

a = 12.93, b = 0.4543 (psi).(53)

a = 0.3, b = 0.66 (MPa).

(51)

a = 2.63, b = 0.66 (psi).

(65)

MR = a × f' c

b
30

Figure 9. Equation. MR according to Neville.(40)

Where:

k2 ranges from 3 to 6 psi.

k1 ranges from 0.3 to 0.8 psi.

c f

MR

' 12000 4

3000

+ =

Figure 10. Equation. MR according to Sozen et al.(66)

Figure 11. Equation. MOR according to NCHRP.

(2)

Where:

MOR(t) = The flexural strength at age t years.

MOR28d = The 28-day flexural strength.

b

sp c f ' = a * f '

Figure 12. Equation. f'sp.

Where:

f'sp = Tensile strength.

a = 4.34, b = 0.55 (psi).

(67)

a = 0.53, b = 0.7 (MPa).

(40)

a = 7.11, b = 0.5 (psi) (high strength mixes).(68)

a = 0.89 to 1.7, b = 0.67 to 0.71 (MPa).(See references 69–72.)

a = 1.019, b = 0. 0.7068 (psi).

(53)

a = 1.56, b = 0. 0.67 (MPa).

(51)

2/3

,min 0.95 













 =

cko

ck

ctk f

f f

Figure 13. Equation. fctk,min according to CEB-FIP Model Code 1990.

(51)

2/3

,max 1.85 













 =

cko

ck

ctk f

f f

Figure 14. Equation. fctk,max according to CEB-FIP Model Code 1990.

(51)

2 / 3

, 1.40 













 =

cko

ck

ct mean f

f f

Figure 15. Equation. fct,mean according to CEB-FIP Model Code 1990.

(51)

MR = k2 × �f'

c

�

k1

MOR(t) = �1 + log10 � t

0.0767� ‒ 0.01566log10 � t0.0767�

2

� × MOR28d
31

Where:

fck = Compressive strength, MPa.

fcko = 10 MPa.

f '

sp = a * MR

Figure 16. Equation. f'sp according to Mehta and Monteiro.(39)

Where: 

a is a correlation coefficient and ranges from 0.48 to 0.68.

Figure 17. Equation. f'sp according to NCHRP.

(2)

Where:

f'sp(t) = Tensile strength at age t years.

f'sp, 28d = Tensile strength at 28 days.

= Empirical constants for a mix design.

Tensile strength is typically linear, with MR and estimated tensile strength being between 0.6 and 

0.7 of MR. This is mostly because only a small volume of the material is under tension in the MR

test compared to the entire volume in the tensile strength test. Also, the flexural strength test 

assumes a linear stress strain relationship across the depth of the beam. For very low-strength 

concretes, the tensile strength is nearly half the flexural strength.

Mallela et al. evaluated these models with the 2000 data release of the LTPP materials data.(16) 

The following broad conclusions were drawn:

• Relationships between compressive and flexural strength: The ACI model, described by 

the relationship in figure 8 with coefficients of 7.5 and 0.5 for a and b, respectively, 

produced conservative estimates.

• Tensile/flexural strength relationships were on target.

The MEPDG provides a model form to develop strength gain relationships for agencies 

implementing the procedure.

(2) Based on flexural strength data analyzed in the calibration of the 

MEPDG models, the flexural strength at any age, t (years), can be calculated based on the 28-day 

strength. This generalized relationship is included in figure 7, and it can be modified for specific 

mix designs using test data at different ages. Likewise, the tensile strength gain can be calculated 

using the model form included in figure 17.

Elastic Modulus

Elastic modulus measures material stiffness and is a ratio of the applied stress to measured strain. 

This is determined using the ASTM C 496-90 test procedure, wherein the chord modulus is 

( ) ( ) ( ) sp d t β β t β t f f sp ,28 ' 2

10 log 10 3 log 1 2 ' = + − ×

















β1, β2, β3
32

measured in a concrete cylinder loaded in longitudinal compression at a relatively slow constant 

rate.(60) This test procedure also is used to determine Poisson’s ratio.

The concrete elastic modulus is an important variable in pavement design, as it controls the 

overall slab deflections from traffic loading and slab curling stresses. Historically, in pavement 

applications, this value has not been rigorously estimated; typical values are assumed because it 

is perceived to have little effect. However, newer design methods such as the MEPDG have 

brought the importance of this parameter to the forefront. Generally, PCC elastic modulus 

increases as compressive strength increases, and it was found that in general, the material 

characteristics affect the elastic modulus in the same manner as the compressive strength. 

However, elastic modulus is more sensitive to aggregate characteristics and volumes.

Early works in this area established that concretes having the same compressive strength may

have different moduli of elasticity if different aggregates are used. Additionally, concretes of 

different proportions may have different compressive strength for the same elastic modulus. 

Finally, for equal compressive strength, the elastic modulus increases with increasing 

aggregate/cement ratio.(59,73) 

The properties of aggregates also influence the modulus of elasticity—the higher the modulus of 

elasticity of the aggregate, the higher the modulus of the concrete.(74) The shape of coarse 

aggregate particles and their surface characteristics may also influence the value of the modulus 

of elasticity of concrete.

(40) The aforementioned are very significant findings, suggesting that the 

strength-modulus relationship should be mixture specific.

Mathematical expressions to predict elastic modulus have been developed based on 

concrete being modeled as a two-phase material, a matrix phase and aggregate phase. 

(See references 75–79.)

Elastic Modulus Prediction Models 

Several relationships between elastic modulus and compressive strength are presented in the 

literature. (See references 39, 40, 67, and 80.) All these relations are valid for specific samples of 

data and are affected by the condition of the specimen at the time of testing.

Extensive pavement-specific studies to predict material properties from index properties were

conducted in the 1990s as part of FHWA’s PRS development for concrete pavements.

(8) Mix 

design factors were compared against PCC strength and modulus to evaluate the effects of coarse 

aggregate type, cement content, air content, and w/c ratio. It was found that elastic modulus and 

flexural strength were most sensitive to a change in w/c ratio. The analyses conducted with the 

2000 release of LTPP materials data showed that the compressive strength-static elastic modulus 

relationships produced no consistent trend.(16)

The relationship between modulus of elasticity and strength also depends on the mix 

proportions and on the age of the specimen. At later ages, the modulus increases more 

rapidly than strength.

(40)

In general, the elastic modulus of concrete is influenced by the stiffness of the paste and 

aggregate phases, their volume concentrations, and interface characteristics or the ITZ. The 
33

stiffer the individual phases, the higher the elastic modulus. Concrete elastic modulus also 

increases with increases in the stiffer phase (usually the aggregate). Also of significance is the 

moisture state of the specimen. For a given aggregate, elastic modulus increases with the strength 

of the concrete. The significant influence of aggregates on concrete elastic modulus was 

confirmed in a study of 23 aggregate types conducted by Alexander and Davis.

(81,82) Each 

aggregate type produces a different age-dependent relationship. Likewise, Noguchi et al. 

developed a model for high-strength concrete mixes as a function of aggregate type and 

admixtures used.(83)

The various models developed for the prediction of elastic modulus as well as the MEPDG 

recommended model for the estimation of modulus at any age are presented in figure 18 through

figure 26. In these equations, Ec is the PCC secant elastic modulus in psi (GPa), is the PCC 

unit weight in pounds per cubic foot (kg/m3), and f'c is the PCC compressive strength of a 

standard 5.85- x 11.7-inch cylinder in psi.

b

c c E = af '

Figure 18. Equation. Ec where constants are defined by various researchers.

Where:

a = 57,000, b = 0.5 (psi).

(50)

a = 275,538, b = 0.33(psi).

(51)

a = 77,173, b = 0.46(psi).

(71,72)

a = 80811, b = 0.4659(psi).

(53)

*33* ' 0.5 1.5

c c E = ρ f

Figure 19. Equation. Ec according to ACI 318.(49)

2

1 1.5

5

200 * 2.3

2.1*10 * 



 



  + 

 



 = c

c

f E γ

Figure 20. Equation. Ec for normal strength concrete according to Tomosawa et al.(84)

( )

d

c c f c b

E a  + 

 



 = ' ρ

Figure 21. Equation. Ec according to CEB-FIP Model Code 1990.

(51)

Where:

a = 9.1, b = 2,300, c = 0, d = 0.33.

a = 9.5, b = 2,400, c = 8, d = 0.33.

c f

MR

' 2000 1

6000000

+ =

Figure 22. Equation. MR according to Jensen via Irick et al.(7)

ρ
34

Figure 23. Equation. MR according to Hognestad via Irick et al.(7)

Figure 24. Equation. Ec according to Turkish Standard 500.(85)

(Units in MPa)

or 

(Units in ksi)

Figure 25. Equation. Ec according to Noguchi et al.(83)

Where k1 and k2 are constants, and their values depend on the coarse aggregate type and 

the admixture.

Figure 26. Equation. Ect/Ec,28 according to NCHRP.

(2)

CTE

CTE has received much attention recently since it appears to have a huge impact on the 

pavement design methodology proposed in the MEPDG. Its importance has been recognized 

since Westergaard’s 1927 study from a pavement analysis standpoint.(86) The concrete CTE

parameter directly influences the magnitudes of temperature-related pavement deformations. 

These deformations, in combination with the restraint offered by the base layer and slab weight, 

affect the resulting curling stresses in the slab. CTE is found to be most influenced by the coarse 

aggregate rock type as well as the internal relative humidity of the paste.

A protocol for CTE measurement of concrete, AASHTO TP 60, Standard Test Method for the 

Coefficient of Thermal Expansion of Hydraulic Cement Concrete, was adopted and the method 

is standardized now as a full AASHTO test procedure, AASHTO T 336, which has modified 

calibration requirements consistent with the 2010 LTPP data release.

(24,87) The older 

AASHTO TP 60 should be used with earlier CTE data releases.(24)

Mallela et al. presented results of CTE testing conducted by FHWA as part of the LTPP material 

testing.(15) CTE of the concrete was found to vary widely depending on the predominant 

aggregate type used in the concrete. This was in agreement with the CTE values reported by 

Irick et al. by aggregate type.

(7) The Mississippi Department of Transportation, in its efforts to 

implement the MEPDG, conducted PCC material property tests, including CTE on a range of 

mixes used in Mississippi.(88) In addition to several PCC material test results, it also presented

CTE test results. In general, the effect of aggregate type was significant on reported CTE values.

MR = 1,800,000 + 460 × f'

c

Ec= 39,150�f'

c

�

0.50

+ 2,030,528

Ec = k1 × k2 × 3.35 × 104 × � ρ

2,400�

2

× + �

σb

60�

1

3

Ec = k1 × k2 × 4,860 × � ρ

150�

2

× + �

σb

8.7

�

1

3

Ect

Ec,28 � = �α1+ α2 log10(t)‒ α3 log10(t)2�
35

CTE Prediction Models

CTE of concrete is predicted empirically by CTE of cement paste and aggregate. Aggregate type 

has the greatest influence on the value of thermal expansion due to the high volume content of 

coarse and fine aggregate in concrete.(89,90) Neekhra developed prediction models to estimate 

the CTE of coarse aggregates and PCC based on the CTE of their individual components.(91) 

Individual components are defined as the individual minerals for the estimation of aggregate 

CTE and mortar-aggregate phases for the estimation of PCC CTE. (The estimation of aggregate 

CTE is not of particular relevance to this study, and thus this review discusses only Neekhra’s 

findings regarding the CTE of PCC.) PCC CTE is predicted based on the percent volume 

fraction, CTE, and modulus of elasticity of coarse aggregates and the mortar. In deriving the 

CTE model, Neekhra assumed that the PCC follows the Hirsch composite model, and the 

predictions are based on a 50-50 split in the fraction of material in series and parallel alignments.

CTE measurements were made using the dilatometer, and the model was verified using 

nine different mixes that contained different aggregate types. A level 2 equation based on mix 

volumetrics has been proposed in the MEPDG to predict PCC.(2) The models are provided in 

figure 27 and figure 28.

Figure 27. Equation. according to Neekhra.(91)

Where:

and = CTE of mortar and aggregate.

Vm and Va = Volume fraction of mortar and aggregate.

Em and Ea = Elastic modulus of mortar and aggregate.

X = Relative proportion of material conforming with the upper and lower bound solution 

(assumed as 0.5).

Note that X = 0 implies series arrangement, and X = 1 implies parallel arrangement of constituent 

phases in the Hirsch model.

α PCC = α agg *Vagg +α paste

*Vpaste

Figure 28. Equation. according to NCHRP.

(2)

Where:

= CTE of PCC, aggregate, mortar, and paste.

Vagg and Vpaste = Volume fraction of aggregate and paste.

Drying Shrinkage

Drying shrinkage of hardened concrete is an important factor affecting the performance of PCC 

pavements. The magnitude of drying shrinkage depends on numerous factors, including water 

per unit volume, aggregate type and cement, ambient relative humidity and temperature, and 

curing conditions.

(40) The size and grading of aggregate influences the magnitude of shrinkage. 

αc = X × (αmVm + αaVa) + (1 ‒ X) × �

αmVmEm + αaVaEa

VmEm + VaEa

�

αc

αc αa

αPCC

αPCC, αagg , and αpaste
36

Larger aggregate permits the use of a leaner mix and results in lower shrinkage. Clay minerals in 

the -200 fraction can also increase shrinkage.

Powers demonstrated a significant impact of aggregate volume content on concrete shrinkage.(92) 

For the high values of aggregate content, concrete shrinkage was small in comparison with 

cement paste shrinkage. In general, sandstone aggregates tend to produce higher shrinkage in 

concrete than other aggregates. However, Troxell et al. demonstrated that it is not possible to 

generalize the effects of different aggregate types on concrete shrinkage.(93)

The current ACI equation for ultimate shrinkage is based on a large database of mix designs and 

is a function of cement type, concrete strength, and curing practices.(14,94) This equation has been 

incorporated into the MEPDG and HIPERPAV®.

(2,35)

The LTPP database does not contain shrinkage test results; therefore, within the scope of the 

current project, no prediction models were developed for PCC shrinkage.

(5) Consequently, the 

various shrinkage models are not discussed in detail in this report. However, it is an important 

parameter and influences the performance of CRCP and JPCP. 

PREDICTION OF CHEMICALLY STABILIZED MATERIAL PROPERTIES

Several types of chemically stabilized materials are used under pavements as base courses, 

subbase courses, or treated subgrade. These include lean concrete, cement stabilized or treated 

aggregate, soil cement, lime-cement flyash, and lime-stabilized materials. Typically, the 

compressive strength of these materials is used for construction QA and modulus and flexural 

strength for pavement design. However, compressive strength testing is more common than 

resilient/elastic modulus testing and flexural strength testing. Industry groups and individual 

researchers have published several correlations to estimate chemically stabilized base elastic 

modulus and flexural strength from the compressive strength as shown in figure 29 through 

figure 32. The more common or feasible of these correlations are presented in figure 32. 

Resilient modulus (Mr) can be estimated conservatively as 20 percent of the unconfined 

compressive strength (qu).

(95)

The LTPP database does not contain modulus test results for stabilized materials. Limited data 

were available for modulus tests of LCB layers, which were utilized for model development. 

Therefore, the discussion on stabilized materials in this report is brief.

Figure 29. Equation. E for lean concrete or cement treated aggregate.(96)

Where:

E = Modulus of elasticity, psi.

f'c = Compressive strength, psi tested in accordance with AASHTO T 22.

(17)

Figure 30. Equation. E for soil cement.(96)

E = 57,000 √f'

c

E = 1,200 * qu
37

Where:

E = Modulus of elasticity, psi.

qu = Unconfined compressive strength, psi tested in accordance with ASTM D 1633.

(97)

Figure 31. Equation. E for lime-cement-flyash.(98)

Where:

E = Modulus of elasticity, psi.

qu = Unconfined compressive strength, psi tested in accordance with ASTM C 593.

(99)

= 0.124qu +9.98 M r

Figure 32. Equation. Mr for lime-stabilized soils.(100)

Where:

Mr = Resilient modulus, ksi.

qu = Unconfined compressive strength, psi tested in accordance with ASTM D 5102.

(101)

PREDICTION OF UNBOUND MATERIAL AND SOIL RESILIENT MODULUS

Overview

Resilient modulus of unbound materials and soils is a required input in most pavement design 

procedures. It has a significant effect on the computed pavement responses and, hence, pavement 

performance. Resilient modulus can be tested directly from the laboratory, backcalculated 

using nondestructive test data, or obtained through the use of correlations with other material 

strength and index properties such as the California bearing ratio (CBR), R-value, dynamic 

cone penetrometer (DCP) value, soil Atterberg limit and gradation properties, AASHTO soil 

class, etc.

As a result of extensive research into the characterization of resilient modulus characterization 

conducted over the past four decades, it is now widely recognized that resilient modulus exhibits 

stress-state dependency, material dependency, and moisture and temperature dependency. About 

54 percent of State transportation departments use resilient modulusin routine pavement 

design.(102) Ideally, resilient modulus should be obtained from laboratory measurements;

however, standard test procedures such as AASHTO T 307 and NCHRP 1-28A require 

substantial time and resources and are not used in routine engineering practice, especially 

beyond the design phase of the project.

(28,103)

Of the several approaches put forth to estimate resilient modulus in the laboratory for design 

purposes, the one that has gained considerable traction over time is using a universal constitutive 

model as proposed in NCHRP 1-28A (see figure 33).

(103) The strength of this approach is that 

two of the resilient modulus dependencies, stress-state and material type, can be handled by this 

model form, which is an improvement over previously used discrete models for coarse- and finegrained soils which require knowledge of material behavior prior to applying a function to 

characterize it.

E = 500 + qu
38

The nonlinear elastic coefficients and exponents of the constitutive model are determined by 

using linear or nonlinear regression analyses to fit the model to laboratory generated resilient 

modulus test data.

2 3 k

a

oct

k

a

r 1 P P

M k 













 τ















 θ =

Figure 33. Equation. Mr according to NCHRP Project 1-28A.(103)

Where:

Mr = Resilient modulus, psi.

= Bulk stress ( ).

= Major principal stress.

= Intermediate principal stress = for Mr test on cylindrical specimen.

= Minor principal stress/confining pressure.

oct = Octahedral shear stress.

oct = 2

2 3

2

1 3

2

1 2 ( ) ( ) ( ) 3

1

σ − σ + σ − σ + σ − σ .

Pa = Normalizing stress (atmospheric pressure).

k1, k2, and k3 = Regression constants (obtained by fitting Mr test data to this equation).

Resilient Modulus Prediction Models

There have been numerous attempts to estimate resilient modulus as a function of soil index or 

soil strength properties over time. One of the first studies of the resilient properties of soil with 

the objective of developing correlation equations for predicting resilient modulus from basic soil 

test data was conducted by Carmichael and Stuart.

(104) They used the Highway Research 

Information Service (HRIS) database and developed two regression models, one for fine-grained 

soils and another for coarse-grained soils.

(104) Regression models were developed for individual 

soil types according to the Unified Classification System (UCS). Variables used in the models 

for coarse-grained soils included moisture content and bulk stress. For fine-grained soils, 

plasticity index, confining, and deviatoric stresses were used as predictive variables.

Drumm et al. conducted a resilient modulus study of cohesive soils based on AASHTO soil 

classification.(105) They used deviator stress as the main model parameter. The model coefficients 

were derived as functions of liquid limit of soil, degree of saturation, and unconfined 

compressive strength. 

Two resilient modulus regression models—one each for fine-grained and coarse-grained 

Mississippi soils—were developed by George.(106) Variables used to predict fine-grained soil 

resilient modulus included soil dry density, liquid limit, moisture content, and percentage passing 

the No. 200 sieve. Variables for the coarse-grained soil resilient modulus model included dry 

density, moisture content, and percentage passing the No. 200 sieve.

A potential benefit of estimating the resilient modulus from physical properties is that seasonal 

variations in resilient modulus can be estimated from seasonal changes in a material’s physical 

θ σ1 + σ2 + σ3

σ1

σ2 σ3

σ3

τ

τ
39

properties. A few of the models from phenomenological studies that relate resilient modulus to 

soil properties are presented in figure 34 to figure 48.

Other soil strength parameters have also been used to estimate resilient modulus. Due to its 

historical significance in pavement design, CBR is the most commonly used soil strength 

parameter for correlation. Direct correlations between resilient modulus and CBR are included in 

the MEPDG.

(2) Simple correlation equations were developed to predict resilient modulus from 

standard CBR by several researchers. (See references 107–110.) All these equations are purely 

empirical and do not depend on soil properties and stress state. Figure 49 through figure 58

present a list of correlations between resilient modulus and other strength parameters.

One should exercise caution in using these correlations because they may not produce the 

required input to the MEPDG. The resilient modulus input is required to be at optimum moisture 

content and density of the soil. Most of these correlations do not predict this resilient modulus at 

optimum moisture content.

The resilient modulus also has been correlated to indices determined from other test devices. A 

summary of correlations with in-place test methods is presented in figure 59 to figure 67.

Figure 34. Equation. Mr according to Jones and Witczak.(111) 

Figure 34 was developed for clay type A-7-6. The deviator stress is 6 psi, the confining pressure

is 2 psi, and Mr is measured in ksi.

Figure 35. Equation. Mr according to Thompson and LaGrow.(112) 

Figure 35 was developed for compacted subgrades. The additional moisture susceptibility 

correction factor is 0.7 for clay, silty clay, and silty clay loam; 1.5 for clay loam; and 2.1 

for loam.

M (ksi) ( CLAY ) (PI) ( SILT ) (CLASS ) r = 6.37 + 0.034 % + 0.45 − 0.0038 % − 0.244

Figure 36. Equation. Mr according to Thompson and Robnett.(113) 

Figure 36 was developed for fine-grained cohesive soils. The deviator stress is 6 psi, and there is

no confinement.

( ) ( ) ( ) ( )

( ) ( ) (CH ) (MH )

M ksi PI w S

d

r

0.18 0.32 36.4 17.1

37.4 0.45 0.62 0.14 200

3 − + +

= − − − +

σ σ

Figure 37. Equation. Mr according to Carmichael and Stuart for fine subgrade soils 

containing clay and silts.

(104) 

Figure 37 was developed for fine subgrade soils containing clay and silts. It was developed using 

the HRIS database for individual UCS soil types.

log(M R ) = −0.111w + 0.0217S +1.179

M (ksi) (C) (PI) R = 4.46 + 0.098 + 0.12
40

M (ksi) ( W ) ( ) (SM ) (GR) r = 0.523 − 0.0225 % + 0.544 logθ + 0.173 + 0.197

Figure 38. Equation. Mr according to Carmichael and Stuart for coarse granular soils and 

aggregate bases.

(104) 

Figure 38 was created for coarse granular soils and aggregate bases. It was developed using the 

HRIS database for individual UCS soil types.

( ) ( ) ( ) ( ) r CLAY PI wopt M ksi =11.21+ 0.17 % + 0.20 − 0.73

Figure 39. Equation. Mr according to Elliot et al. with deviator stress of 4 psi.

(114) 

Figure 39 was developed using cohesive subgrades. 

( ) ( ) ( ) ( ) r CLAY PI wopt M ksi = 9.81+ 0.13 % + 0.16 − 0.60

Figure 40. Equation. Mr according to Elliot et al. with deviator stress of 8 psi.(114) 

Figure 40 was developed using cohesive subgrades. 

( )

0.609

200

2.18

1 100

17.29

−





 



 + 















+ = Pw

LL M MPa dr

c

r γ

Figure 41. Equation. Mr according to Rahim for fine-grained soils.

(115) 

Figure 41 was developed using undisturbed field samples.

( )

0.4652

200

0.8998

1 log

324.14

−

































+ =

c u

dr

r c

P

w

M MPa γ

Figure 42. Equation. Mr according to Rahim for coarse-grained soils.

(115) 

Figure 42 was developed using undisturbed field samples.

( ) ( ) ( ) ( ) ( ) ( ) 200 M psi 30280 359 S 325 237 86 PI 107 S r = − − σ d + σ c + +

Figure 43. Equation. Mr according to Farrar and Turner.(116) 

Figure 43 was developed using 13 types of fine-grained soils.

Figure 44. Equation. Mr according to Hudson et al.(117)

Figure 44 was developed using cohesive subgrade soil types A4 through A7-6.

( ( ) ( ) ) M r w w M Ropt

2 = 0.98 − 0.28 ∆ + 0.29 ∆

Figure 45. Equation. Mr according to Li and Selig.(118)

( ) ( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( ) ( ) ( ) c d

r d d

w PL LI S S

M LI S CLASS

σ σ

σ γ

0.0033 0.0012 0.0001 0.0278 0.0017 38.44 log 0.2222 log

log 46.93 0.018 0.033 0.114 0.468 0.0085

2 2 2 2 2

2

∆ − + − − − −

= + + ∆ − + + −
41

Figure 45 was developed for cohesive soils.

Figure 46. Equation. Mr according to Gupta et al.(119) 

Figure 46 was developed for cohesive soils. The valid for bulk stress is 83 kPa, and the

octahedral shear stress is 19.3 kPa.

( ) [ ( )] [ ( )] 30 13.85 0.272 1.518 10 − − M r psi = × f S f σ

Figure 47. Equation. Mr according to Berg et al.(120) 

Figure 47 was developed using fine-grained and coarse-grained soils from the Minnesota Road 

Research Project test site; applicable only to cohesive soils.

M r = F0 × F1 × F2 × F3 × F4 × F5 × F6

Figure 48. Equation. Mr according to Pezo and Hudson.(121) 

Figure 48 was developed for Texas subgrade data. It is valid for silty to clayey subgrades, and 

correction factors are provided.

The definitions of the terms used in figure 34 through figure 48 are as follows:

%CLAY = C = Clay content in percent.

%SILT = Silt content in percent.

%W = w = wc = Moisture content.

d = Deviation from the Standard Proctor maximum dry density in pounds per cubic foot.

w = Deviation from the optimum water content in percent.

dr = Ratio of dry density to maximum dry density.

= = Confining stress.

= Deviator stress.

= Bulk stress.

= Soil suction.

CH = 1 for CH type (clay and high plasticity) soil; 0 otherwise.

CLASS = Soil classification (e.g., soil type A7-6 use 7.6 in expression).

cu = Uniformity coefficient.

f(S) = Normalized saturation by a unit saturation of 1 percent.

f( ) = Normalized octahedral shear stress by a unit stress of 1 psi.

F0 = 9.8 ksi.

F1 = Moisture content correction factor.

F2 = Relative compaction correction factor.

F3 = Soil plasticity correction factor.

F4 = Age correction factor.

F5 = Confining pressure correction factor.

F6 = Deviator stress correction factor.

GR = 1 for gravelly soils (silty gravel, well-graded gravel, clay gravel, and poorly graded 

gravel); 0 otherwise.

Mr (kPa) = -54,105 + 57,898(logψ)

∆γ

∆

γ

σc σ3

σd

θ

ψ

σ
42

LI = Liquidity index in percent.

LL = Liquid limit in percent.

MH = 1 for elastic silt type soil; 0 otherwise.

MRopt = Resilient modulus at optimum water content.

PI = Plasticity index.

PL = Plastic limit.

S = Degree of saturation in percent.

SM = 1 for silty sand type soil; 0 otherwise.

S200 = S200 = P200 = Percent material passing the No. 200 sieve.

wopt = Optimum water content.

M (psi) (CBR) r =1500

Figure 49. Equation. Correlation between Mr and CBR according to Heukelom and 

Klomp.(107) 

Figure 49 shows reasonable estimates for fine-grained soils with CBR less than or equal 

to 10.

( ) ( )

0.64 M r psi = 2554 CBR

Figure 50. Equation. Correlation between Mr and CBR according to Powell et al.(122)

Figure 50 is currently included in the MEPDG.

( )

0.478 Mr = 3116 CBR

Figure 51. Equation. Correlation between Mr and CBR according to George.(106) 

Figure 51 was developed for cohesionless soils and is recommended for medium clay sands.

M (MPa) (CBR) r =10.3

Figure 52. Equation. Correlation between Mr and CBR according to the Asphalt 

Institute.(123)

M (psi) A B(R) r = +

Figure 53. Equation. Correlation between Mr and R-value according to AASHTO.(12)

Where:

A = 772 to 1,155; recommended 1,000. 

B = 369 to 555; recommended 555.

M (ksi) (R) r =1.6 + 0.038

Figure 54. Equation. Correlation between Mr and R-value according to Buu.(124) 

Figure 54 was developed for fine-grained soils. The deviator stress is 6 psi, the confinement 

stress is 2 psi, and the valid for R-value is greater than 20.
43

Figure 55. Equation. Correlation between Mr and R-value according to Yeh and Su.(110)

Figure 56. Equation. Correlation between Mr and R-value according to MEPDG.

(2)

Figure 56 is currently included in the MEPDG. The R-value is between 2.3 and 11.

Figure 57. Equation. Correlation between Mr and unconfined compressive strength 

according to Thompson and Robnett.(113)

Figure 57 was developed assuming a bilinear Mr versus deviator stress behavior.

( ) ( ) r a Su,1% M kPa =

Figure 58. Equation. Correlation between Mr and shear strength according to Lee et al.(125) 

Figure 58 was developed for clayey subgrade soils and is valid for cohesive soils only where:

a = Level of deviator stress.

Su,1% = Undrained shear strength at 1 percent axial strain.

M (psi) ( DCPI) r = 7013− 2040.8 ln

Figure 59. Equation. Correlation between Mr and DCP test method according to

Hassan.(126)

Where DCPI is the DCP index in inches per blow.

( ) ( ) 0.39 338 − M ksi = DCPI r

Figure 60. Equation. Correlation between Mr and DCP test method according to 

Chen et al.(127) 

Figure 60 was developed for subgrade materials using backcalculated FWD modulus values. 

DCPI is in millimeters per blow.

( ) ( ) 0.48 235.3 − M MPa = DCPI r

Figure 61. Equation. Correlation between Mr and DCP test method for coarse-grained 

sandy soils according to George and Uddin.(128)

For figure 61, DCPI is in millimeters per blow.

( ) ( ) 0.49 532.1 − M MPa = DCPI r

Figure 62. Equation. Correlation between Mr and DCP test method for fine-grained clays 

according to George and Uddin.(128)

For figure 62, DCPI is in millimeters per blow.

Mr (psi) = 3,500 + 125(R)

Mr (psi) = 1,150 + 555(R)

Mr (ksi) = 0.86 + 0.31(qu)
44

( ) ( ) 0.67 78.05 − M ksi = DCPI r

Figure 63. Equation. Correlation between Mr and DCP test method according to

Chen et al.(129) 

Figure 63 was developed for both base and subgrade soils materials. DCPI is in millimeters 

per blow.

( ) ( )

1.096

151.8

DCPI

M MPa r =

Figure 64. Equation. Correlation between Mr and DCP test method, DCPI only according 

to Mohammad et al.(130) 

Figure 64 was developed for soil types A-4, A-6, A-7-5, and A-7-6. A field DCP test and 

laboratory test were correlated through statistical analysis. DCPI is in millimeters per blow.

Where:

d = Dry unit weight in kN/m3

.

w = Water content in percent.

( ) 



 



  +



 



 = DCPI w

M MPa d

r

γ 0.0966 1 165.5 1.147

Figure 65. Equation. Correlation between Mr and DCP test method, DCPI, and soil 

properties according to Mohammad et al.(130) 

Figure 65 was developed for soil types A-4, A-6, A-7-5, and A-7-6. Field DCP test and 

laboratory test were correlated through statistical analysis. DCPI is in millimeters per blow. 

Where:

d = Dry unit weight in kN/m3

.

w = Water content in percent.

( ) 















+













 



 



 = +

w

s d

c

c v

r

w

f

q

M

γ

γ

σ σ

31.8 74.8 4.08 1

0.55

Figure 66. Equation. Correlation between Mr and CPT test method, in situ conditions 

according to Mohammad et al.(131) 

Figure 66 was developed for silty clay and heavy clayey cohesive soils.

Where:

qc = Tip resistance (MPa).

fs = Frictional resistance (MPa).

ut = Total pore pressure.

γ

γ
45

( ) 















+













 



 



 = +

w

s d

c

r

w

f

q

M

γ

γ

σ σ

47.0 170.4 1.70 1

1

0.55

3

Figure 67. Equation. Correlation between Mr and CPT test method traffic loading 

conditions according to Mohammad et al.(131) 

Figure 67 was developed for silty clay and heavy clayey cohesive soils.

Where:

= = Confining stress (kPa).

v = Vertical stress (kPa).

w = Water content.

d = Dry unit weight (kN/m3

).

w = Unit weight of water (kN/m3

).

Recently, the resilient modulus has been predicted using a two-step approach. First, models to 

predict parameters k1, k2, and k3 of the constitutive equation are developed. Next, the constitutive 

equation is used to estimate the resilient modulus. Von Quintus and Killingsworth, Dai and 

Zollars, and Santha developed prediction equations by regressing the coefficients of selected 

constitutive equations and relating them to soil physical properties.

(132–134) It was observed that 

the most influential parameters are moisture content, liquid limit, plasticity index, and percent 

passing the No. 200 sieve.

One of the most comprehensive reviews of the resilient modulus test data measured on pavement 

materials and soils was made in the LTPP program.(135) A total of 2,014 resilient modulus 

laboratory tests that passed all the QC checks of the LTPP database (i.e., level E data status,

2000 data release) were used in this review. The study verified that the response characteristics 

correlate to a form of the constitutive model presented in figure 33. The study correlated resilient 

modulus data to the physical properties of the materials and found that the physical properties 

that influenced the resilient modulus varied between the different materials and soils. No one 

physical property was highly correlated to the modulus. For example, the liquid limit, plasticity 

index, and the amount of material passing the smaller sieve sizes were found to be important for 

the lower strength unbound aggregate base/subbase materials, while the moisture content and 

density were important as related to higher strength materials. Furthermore, the amount of 

material passing the larger sieve sizes was important for the unbound aggregate base/subbase 

materials with larger MASs. It also was found that percent clay and test specimen moisture 

content or density were important for all soil groups, while percent silt was important for all soil 

groups except gravel. It was also found that to improve correlations, the sample locations (depth) 

had to be matched while comparing test results for the same test section or site. Based on the 

quality of correlations developed in this study, it was recommended that the results need to be 

verified and confirmed after all resilient modulus tests have been completed. Future model 

refinement was suggested with a more comprehensive dataset.

Appendix CC in the MEPDG presents a detailed discussion of moisture and temperature regime 

influences on resilient modulus.

(2) The values of the resilient moduli at any location and time 

within a given pavement structure are calculated in the MEPDG as a function of the soil moisture 

and freeze-thaw influences. The impact of temporal variations in moisture and temperature on 

σc σ3

σ

γ

γ
46

resilient modulus are considered through the composite environmental adjustment factor, Fenv. 

The Enhanced Integrated Climatic model (EICM) is integral to the MEPDG software (the 

publication by Larson and Dempsey deals with all environmental factors and provides soil 

moisture, suction, and temperature as a function of time, at any location in the unbound layers 

from which Fenv can be determined).

(136) This moisture prediction capability of the EICM was 

improved by Zapata and Houston.(137) The resilient modulus at any time or position within the 

pavement structure is then determined by multiplying the value at optimum with Fenv

( i.e., Mr = FenvMropt ).

Fenv is an adjustment factor, and Mropt is the resilient modulus at optimum conditions (maximum 

dry density and optimum moisture content) at any state of stress. Variations of resilient modulus 

with stress and variations of modulus with environmental factors (i.e., moisture, density, and 

freeze-thaw conditions) are assumed to be independent.

Using the latest testing protocol to measure resilient modulus, AASHTO T 307, Titi et al. 

performed a comprehensive investigation to estimate the resilient modulus of various Wisconsin 

subgrade soils and develop basic models to estimate the resilient modulus from soil 

properties.(28,138) Also, a laboratory testing program was conducted on common subgrade soils to 

characterize their physical properties. The resilient modulus constitutive equation was used to 

determine model factors k1, k2, and k3. Titi et al. compared the predictive capability of these 

models with those developed by Yau and Von Quintus using LTPP data and concluded that the 

two sets of models did not agree.(138,135) Differences in the test procedures, test equipment, 

sample preparation, and other conditions involved with development of the LTPP models and the 

models of this study were cited as potential factors for this lack of correlation. Key prediction 

models to estimate resilient modulus from soil properties are summarized below.

Regression Constant Estimates Based on Soil Properties

Prediction Model—Mississippi Materials

Based on the results of repeated triaxial load tests performed on 12 fine-grained and coarsegrained Mississippi subgrades, stress ratios were used in a bulk stress log-log model to describe 

their behavior. Subsequently, soil properties of the same materials were incorporated in the 

regression constants of the models. Then, the models were verified using a separate subset of 

nine different Mississippi subgrades with good correlation.

(106,139) The resulting equations and 

constants are shown in figure 68 through figure 73.

2

1

1 1

k

c

d

R Pa M k 















+ = +

σ

σ

Figure 68. Equation. Mr of Mississippi materials for fine-grained soils.

( )

0.639

1.996

1 1.12 













 =

c

dr

w

LL k γ

Figure 69. Equation. k1 of Mississippi materials for fine-grained soils.
47

( ) ( )

0.47

1.04 1.46

2 #200

0.27 



 



 = − LL k dr wcr γ

Figure 70. Equation. k2 of Mississippi materials for fine-grained soils.

2

1

1 1

k

d

R Pa M k 















+ = +

σ

θ

Figure 71. Equation. Mr of Mississippi materials for coarse-grained soils.

( ) ( ) ( ) ( ) dr cr u k 0.12 0.90 0.53 w 0.017 #200 0.314 log c 1 = + γ − − +

Figure 72. Equation. k1 of Mississippi materials for coarse-grained soils.

( )

0.124

1.2385

2 log

#200 0.226 













 =

u

dr cr c

k γ w

Figure 73. Equation. k2 of Mississippi materials for coarse-grained soils.

The definitions of the parameters used in figure 68 through figure 73 are as follows:

Pa = Atmospheric pressure.

= Deviator stress = − .

= Major principal stresses.

= Minor principal stresses.

= Confining stress.

= Ratio of dry density to maximum dry density.

LL = Liquid limit in percent.

wc = Moisture content in percent.

wcr = Ratio of moisture content to optimum moisture content.

#200 = Percent material passing the No. 200 sieve.

= Bulk stress = + + .

= Intermediate principal stress.

cu = Uniformity coefficient.

Prediction Model—Georgia Materials

The following equation was developed as a combination of the bulk and deviator stress models 

in an effort to improve the predicted response of resilient modulus test results by including both 

axial and shear effects.

2 3

1

k

a

d

k

a

R a P P

M k P 





























 = θ σ

Figure 74. Equation. Mr of Georgia materials.

σd σ1 σ3

σ1

σ3

σc

γdr

θ σ1 σ2 σ3

σ2
48

Santha estimated values for the regression constants in terms of soil properties using a multiple 

correlation analysis.(134) The resulting constants for the analyzed Georgia granular soils are

shown in figure 75 to figure 77.

( ) ( ) ( ) ( )

( ) ( ) 















+













 − − +

= − + + + +

40

2 2

1

0.003

%

0.006 % 0.025 0.039 0.004

log 3.479 0.07 0.24 3.681 0.011 %

CLAY P

SW CLAY SW

k w w COMP SILT

s

s

c cratio

γ γ

Figure 75. Equation. k1 of Georgia materials.

( ) ( ) ( ) ( )

( ) ( ) ( ) 



 



 + −













 − − + +

= − − + − +

CLAY

SW SH

CLAY

SW SW SH CBR

k w COMP SATU CLAY

s

opt

%

0.31

%

0.0088 0.0069 0.027 0.012 0.003

6.044 0.053 2.076 0.0053 0.0056 %

2

2

γ

Figure 76. Equation. k2 of Georgia materials.

( ) ( ) ( ) ( )

( ) (CBR SH ) SH

SATU

CLAY

SW SH

k w w SILT CLAY

s

c cratio

− × 















+













 − −

= − + − + +

0.00004 0.0026

%

0.026 0.033 0.0009

3.752 0.068 0.309 0.006 % 0.0053 %

2 2

3

γ

Figure 77. Equation. k3 of Georgia materials.

Where:

wc = Moisture content.

wopt = Optimum moisture content.

wcratio = wc /wopt.

COMP = Degree of compaction.

SATU = Percent saturation.

%SILT = Silt content in percent.

%CLAY = Clay content in percent.

SW = Percent swell.

SH = Percent shrinkage.

CBR = California bearing ratio.

= Dry density.

P40 = Material passing No. 40 sieve in percent.

Prediction Model—Louisiana Materials

An equation similar to Uzan’s model using the octahedral normal stress instead of the bulk stress 

was used to study eight types of Louisiana soils.

(140) Figure 79 shows the resilient modulus of 

Louisiana materials.

2 3

1

k

a

oct

k

a

oct

R a P P

M k P 





























 = σ τ

Figure 78. Equation. Mr of Louisiana materials.

γs
49

Linear regression was then applied to estimate the values of the regression constants in terms of 

soil properties. The resulting equations are shown in figure 79 through figure 81.

Figure 79. Equation. k1 of Louisiana materials.

Figure 80. Equation. k2 of Louisiana materials.

Figure 81. Equation. k3 of Louisiana materials.

Where:

oct = Octahedral normal stress ( ) 1 2 3 3

1 = σ +σ +σ .

oct = Octahedral shear stress ( ) ( ) ( )

2

3 1

2

2 3

2

1 2 3

1 = σ −σ + σ −σ + σ −σ .

wc = Moisture content.

wopt = Optimum moisture content.

wcratio = wc /wopt.

LL = Liquid limit.

PL = Plastic limit.

%SILT = Silt content in percent.

%SAND = Sand content in percent.

= Dry density in kN/m3.

= Optimum density in kN/m3

.

Prediction Model—LTPP Materials

Figure 82 has gained considerable acceptance over time and is the constitutive model using the 

same parameters as in figure 33 and proposed in NCHRP Project 1-28A.

(103)

( ) 2 3

1 1

k

a

oct

k

a

r a P P

M psi k P 















+ 













 = θ τ

Figure 82. Equation. Mr of LTPP materials.

logk1= -0.679 + 0.00922(wc) + 0.00559(γs

) + 3054 � γs

γopt

� + 2.57(wcratio) +

0.00676(LL) + 0.0116(PL) + 0.022(%SAND) + 0.0182(%SILT)

logk2= -0.887 + 0.0044(wc) + 0.0093(γs) + 0.264 � γs

γopt

� + 0.305(wcratio) +

0.00877(LL) + 0.00665(PL) + 0.0116(%SAND) + 0.00429(%SILT)

logk3= -0.638 + 0.00252(wc) + 0.00207(γs) + 0.61 � γs

γopt

� + 0.152(wcratio) +

0.00049(LL) + 0.00416(PL) + 0.00311(%SAND) + 0.00143(%SILT)

σ

τ

γs

γopt
50

The strength of this equation is its ability to handle two of the resilient modulus dependencies, 

stress-state and material type. Santha compared a log-log bulk stress model to figure 82 in 

modeling Georgia granular subgrade soils and concluded that the model presented in figure 82

provided a better representation of laboratory measurements of resilient modulus.(134)

One of the most comprehensive validations of figure 82 using LTPP resilient modulus test data 

was performed by Yau and Von Quintus.(135) The authors first identified anomalous data, verified 

if the response characteristics correlated to the constitutive model presented in figure 82, 

subsequently explored the effect of material type and sampling technique, and performed a 

nonlinear regression analysis to establish relationships between the k regression constants in 

figure 82 and various material properties. The resulting regression equations for unbound and 

subgrade materials are presented in figure 83 through figure 115. The definitions of the variables 

used in the figures are as follows:

P3/8 = Percent material passing 3/8-inch sieve.

P4 = Percent material passing No. 4 sieve.

P40 = Percent material passing No. 40 sieve.

P200 = Percent material passing No. 200 sieve.

%SILT = Silt content in percent.

%CLAY = Clay content in percent.

LL = Liquid limit in percent.

PI = Plasticity index.

Wopt = Optimum water content in percent.

Ws = Water content of the test specimen in percent

= Maximum dry unit weight of soil in kg/m3.

= Dry density of the test specimen in kg/m3

.

Unbound Base and Subbase Materials

Figure 83 through figure 85 show equations for crushed stone (LTPP material code 303)

as follows:

( ) ( ) ( ) P LL Wopt opt k 0.736 0.0084 0.0088 0.0371 0.0001γ 1 = + 3 / 8 + − −

Figure 83. Equation. k1 of LTPP materials—crushed stone.

( ) ( ) ( ) 













 = − + − − + × −

40

2

7

2 3 / 8 2.2159 0.0016 0.0008 0.038 0.0006 2.4 10

P

k P LL W opt

opt opt

γ

γ

Figure 84. Equation. k2 of LTPP materials—crushed stone.

Figure 85. Equation. k3 of LTPP materials—crushed stone.

γopt

γs

k3= -1.172 ‒ 0.0082(LL) ‒ 0.0014�wopt� + 0.0005γopt
51

Figure 86 through figure 88 show equations for crushed gravel (LTPP material code 304)

as follows:

Figure 86. Equation. k1 of LTPP materials—crushed gravel.

( ) ( ) ( ) 













 = − − − − + × −

40

2

6

2 4.9555 0.0057 0.0075 0.0470 0.0022 2.8 10

P

k LL PI W opt

s opt

γ

γ

Figure 87. Equation. k2 of LTPP materials—crushed gravel.

s k 3.514 0.0016γ 3 = − +

Figure 88. Equation. k3 of LTPP materials—crushed gravel.

Figure 89 through figure 91 show equations for uncrushed gravel (LTPP material code 302)

as follows:

Figure 89. Equation. k1 of LTPP materials—uncrushed gravel.

( ) 















+













 = − − +

opt

s

opt

s

s W

W k 0.4960 0.0074 P 0.0007 1.6972 0.1199 2 200

γ

γ γ

Figure 90. Equation. k2 of LTPP materials—uncrushed gravel.

Figure 91. Equation. k3 of LTPP materials—uncrushed gravel.

Figure 92 through figure 94 show equations for sand (LTPP material code 306) as follows:

Figure 92. Equation. k1 of LTPP materials—sand.

( ) ( ) ( ) 













 = − − + + × −

40

2

7

2 3 / 8 1.1148 0.0053 0.0095 0.0325 7.2 10

P

k P LL PI opt γ

Figure 93. Equation. k2 of LTPP materials—sand.

k1 = -0.8282 ‒ 0.0065 �P3

8� � + 0.0114(LL) + 0.0004(PI) ‒ 0.0187�Wopt� + 0.0036(Ws) + 

0.0013γs ‒ 2.6 ×10-6 �

γopt

2

P40�

k1 = -1.8961 + 0.0014�γs� ‒ � Ws

Wopt�

k3= -0.5979 + 0.0349�Wopt� + 0.0004γopt ‒ 0.5166 �

Ws

Wopt�

k1= -0.2786 + 0.0097 �P3

8� � + 0.0219(LL) ‒ 0.0737(PI)+ 1.8 × 10-7 �

γopt

2

P40�
52

Figure 94. Equation. k3 of LTPP materials—sand.

Figure 95 through figure 97 show equations for coarse-grained soil-aggregate mixture (LTPP 

material code 308) as follows:

Figure 95. Equation. k1 of LTPP materials—coarse-grained soil-aggregate mixture.

( ) ( ) ( )















 − × 















= − − + −

−

40

2

7

2 200

0.1483 2.7 10

0.7833 0.0060 0.0081 0.0001

W P

W

k P PI

opt

opt

s

opt

γ

γ

Figure 96. Equation. k2 of LTPP materials—coarse-grained soil-aggregate mixture.

Figure 97. Equation. k3 of LTPP materials—coarse-grained soil-aggregate mixture.

Figure 98 through figure 100 show equations for fine-grained soil-aggregate mixture 

(LTPP material code 307) as follows:

Figure 98. Equation. k1 of LTPP materials—fine-grained soil-aggregate mixture.

( ) ( ) 













 = − − +

opt

s k P P

γ

γ 0.4951 0.0141 0.0061 1.3941 2 4 200

Figure 99. Equation. k2 of LTPP materials—fine-grained soil-aggregate mixture.

( ) ( ) 













 = + + −

opt

s k P LL

γ

γ 0.9303 0.0293 0.0036 3.8903 3 3 / 8

Figure 100. Equation. k3 of LTPP materials—fine-grained soil-aggregate mixture.

k3= -0.4508 + 0.0029 �P3

8� � + 0.0185(LL) ‒ 0.0798(PI)

k1 = -0.5856 + 0.0130 �P3

8� � – 0.0174(P4)+ 0.0027 (P200)+ 0.0149(PI)+ 1.6 × 10-6

�γopt� - 0.0426(Ws)+ 1.6456 � γs

γopt

� + 0.3932 � Ws

Wopt� - 8.2 × 10-7 �

γopt

P40

�

k3 = -0.1906 + 0.0026(P200) + 8.1 × 10-7 �

γopt

2

P40�

k1 = -0.7668 + 0.0051(P4) + 0.0128(P200) + 0.0030(LL) ‒ 0.0510�Wopt� + 1.1729 � γs

γopt

�
53

Figure 101 through figure 103 show equations for fine-grained soil (LTPP material code 309)

as follows:

k 0.8409 0.0004(P ) 0.0161(PI) 1 = + 40 +

Figure 101. Equation. k1 of LTPP materials—fine-grained soil.

k 0.6668 0.0007(P ) 0.0139(PI) 2 = − 40 −

Figure 102. Equation. k2 of LTPP materials—fine-grained soil.

Figure 103. Equation. k3 of LTPP materials—fine-grained soil.

Subgrade Soils

Figure 104 through figure 106 show equations for coarse-grained gravel soils as follows:

( ) ( ) ( ) ( ) P CLAY LL Ws k 1.3429 0.0051 0.0124 % 0.0053 0.0231 1 = − 3 / 8 + + −

Figure 104. Equation. k1 of LTPP materials—coarse-grained gravel soils.

( ) ( ) ( ) ( ) ( ) P CLAY LL PI Ws k 0.3311 0.0010 0.0019 % 0.0050 0.0072 0.0093 2 = + 3 / 8 − − − +

Figure 105. Equation. k2 of LTPP materials—coarse-grained gravel soils.

( ) ( ) ( ) ( ) ( ) P CLAY LL PI Ws k 1.5167 0.0302 0.0435 % 0.0626 0.0377 0.2353 3 = − 3 / 8 + + + −

Figure 106. Equation. k3 of LTPP materials—coarse-grained gravel soils.

Figure 107 through figure 109 show equations for coarse-grained sand soils as follows:

Figure 107. Equation. k1 of LTPP materials—coarse-grained sand soils.

( ) ( ) ( ) ( )

( ) ( ) ( ) 















+















+ − × −

= + − × − − −

−

−

opt

s

opt

s

opt s W

W LL W

k P P SILT CLAY

0.0041 0.0014 3.41 10 0.4582 0.1779

0.5670 0.0045 2.98 10 0.0043 % 0.0102 %

5

4

5

2 3 / 8

γ

γ γ

Figure 108. Equation. k2 of LTPP materials—coarse-grained sand soils.

Figure 109. Equation. k3 of LTPP materials—coarse-grained sand soils.

k1 = -0.1667 ‒ 0.0207(PI)

( ) ( ) ( ) ( ) ( ) ( ) P P CLAY LL Wopt s k 3.2868 0.0412 0.0267 0.0137 % 0.0083 0.0379 0.0004 γ 1 = − 3/ 8 + 4 + + − −

k3 = -3.5677 + 0.1142 �P3

8� � ‒ 0.0839(P4) ‒ 0.1249(P200) + 0.1030(%SILT)+ 0.1191

(%CLAY) ‒ 0.0069(LL) ‒ 0.0103�Wopt� ‒ 0.0017�γs

�+ 4.3177 � γs

γopt

� ‒ 1.1095 � Ws

Wopt�
54

Figure 110 through figure 112 show equations for fine-grained silt soils as follows:

( ) ( ) CLAY PI WS k 1.0480 0.0177 % 0.0279 0.370 1 = + + −

Figure 110. Equation. k1 of LTPP materials—fine-grained silt soils.

k 0.5097 0.0286(PI) 2 = −

Figure 111. Equation. k2 of LTPP materials—fine-grained silt soils.

Figure 112. Equation. k3 of LTPP materials—fine-grained silt soils.

Figure 113 through figure 115 show equations for fine-grained clay soils as follows:

( ) ( ) CLAY WS k 1.3577 0.0106 % 0.0437 1 = + −

Figure 113. Equation. k1 of LTPP materials—fine-grained clay soils.

( ) ( ) ( ) ( ) ( ) P P P LL Wopt k2 = 0.5193 − 0.0073 4 + 0.0095 40 − 0.0027 200 − 0.0030 − 0.0049

Figure 114. Equation. k2 of LTPP materials—fine-grained clay soils.

( ) ( ) ( )

( ) ( ) ( ) 













 − − + −

= − + + +

opt

s

opt opt s W

W LL W

k P P SILT

0.0535 0.0672 0.0026 0.0025 0.6055

3 1.4258 0.0288 4 0.0303 40 0.0251 %

γ γ

Figure 115. Equation. k3 of LTPP materials—fine-grained clay soils.

Prediction Model—Wisconsin Materials

Titi et al. evaluated the model presented in figure 82 using repeated triaxial test results performed 

on various Wisconsin subgrade soils and developed correlations between the model’s regression 

constants and soil properties.(138) Good results were achieved when the statistical analysis was 

performed for fine-grained and coarse-grained soils separately. Researchers also used the LTPP 

data to compare their correlations to those developed by Yau and Von Quintus.(135) It was 

discovered that the two sets of models did not agree. Differences in the test procedures, test 

equipment, sample preparation, and other conditions involved with development of the LTPP 

models versus the ones used in the study were cited as potential factors for this lack of 

agreement. The equations developed by Titi et al. are provided in figure 116 through 

figure 124.

(138) The definitions of the terms used in the figures are as follows:

P4 = Percent material passing No. 4 sieve.

P40 = Percent material passing No. 40 sieve.

P200 = Percent material passing No. 200 sieve.

%SAND = Sand content in percent.

%SILT = Silt content in percent.

%CLAY = Clay content in percent.

LL = Liquid limit.

k3 = -0.2218 + 0.0047(%SILT) + 0.0849(PI) ‒ 0.1399(Ws)
55

PL = Plastic limit.

PI = Plasticity index.

LI = Liquidity index.

w = Water content.

wopt = Optimum water content.

= Dry unit weight.

= Maximum dry unit weight.

Figure 116 through figure 118 show equations for fine-grained soils as follows:

( ) ( ) 













 = + + −

opt

d

w

w k 404.166 42.933 PI 52.260 987.353 1 γ

Figure 116. Equation. k1 of Wisconsin materials—fine-grained soils.

( ) 





























 = − +

max

2 0.25113 0.0292 0.5573

γ

γ d

wopt

w k PI

Figure 117. Equation. k2 of Wisconsin materials—fine-grained soils.

Figure 118. Equation. k3 of Wisconsin materials—fine-grained soils.

Figure 119 through figure 121 show equations for non-plastic coarse-grained soils as follows:

( ) ( ) 





























 = + − −

max

1 4 40 809.547 10.568 6.112 578.337

d

d

wopt

w k P P

γ

γ

Figure 119. Equation. k1 of Wisconsin materials—non-plastic coarse-grained soils.

( ) ( ) ( )

( )( ) max

2 40 200

0.001242

0.5661 0.006711 0.02423 0.05849

opt d

opt

w

k P P w w

γ

= + − + − +

Figure 120. Equation. k2 of Wisconsin materials—non-plastic coarse-grained soils.

Figure 121. Equation. k3 of Wisconsin materials—non-plastic coarse-grained soils.

Figure 122 through figure 124 show equations for plastic coarse-grained soils as follows:

( ) ( ) ( ) ( ) 













 = + − − + −

opt

d

w

w k 8642.873 132.643 P 428.067 %SILT 254.685 PI 197.230 381.400 1 200 γ

Figure 122. Equation. k1 of Wisconsin materials—plastic coarse-grained soils.

γd

γdmax

k3 = -0.20772 + 0.23088(PI) + 0.00367�γd� ‒ 5.4238 � w

wopt�

k3 = -0.5079 ‒ 0.041411(P40) + 0.14820(P200) ‒ 0.1726�w ‒ wopt� ‒ 0.01214�wopt��γdmax�
56

( ) ( ) ( ) 















+













 = − + − −

d opt

d

w

w k 2.3250 0.00853 P 0.02579 LL 0.06224 PI 1.73380 0.20911

max

2 200

γ

γ

Figure 123. Equation. k2 of Wisconsin materials—plastic coarse-grained soils.

Figure 124. Equation. k3 of Wisconsin materials—plastic coarse-grained soils.

SELECTION OF INDEPENDENT VARIABLES FOR THE PREDICTION OF 

MATERIAL ENGINEERING PROPERTIES

The information collected from literature was used to identify the independent variables or index 

properties used to predict the material engineering properties identified in chapter 2. The 

independent variables that the researchers considered most likely to be included in deriving 

the prediction models for PCC, stabilized, and unbound materials are listed in table 6 through 

table 8, respectively.

It was envisioned that more than one prediction model might be required or might be derived 

with the data available in the LTPP database. Multiple models are significant for use in different 

projects (e.g., new design versus rehabilitation versus pavement management) or stages of 

pavement life. For example, flexural strength correlations for PCC materials will be derived 

using index properties that can be useful during the design stage if mix design or optimization is 

performed. However, a correlation to compressive strength from a core would be useful for 

predicting the performance of the as-constructed pavement during the QA stage or in pavement 

management applications.

Data selection, analyses, and statistical modeling are discussed in detail in chapters 4 and 5 of 

this report. Predictive models can be based on lab or field test data, such as with the prediction of 

flexural strength based on compressive strength or index properties. Alternatively, correlations 

can be drawn to categorical variables, such as with PCC CTE. CTE can be a function of mix

components and proportioning or a function of aggregate type. The latter option provides SHAs 

with the opportunity to recommend default values for CTE (as is being done for the MEPDG).

MEPDG calibration data were included as inputs to develop prediction models for design feature 

inputs (see chapter 5 for further discussion). These variables include the following:

• deltaT for JPCP designs.

• deltaT for CRCP designs.

• Erosion in CRCP design.

• EI for JPCP design.

k3 = -32.5449 + 0.7691(P200) ‒ 1.1370(%SILT) + 31.5542 � γd

γdmax

� ‒ 0.4128�w ‒ wopt�
57

Table 6. Potential or likely independent variables to derive prediction models for PCC 

material properties or design features for rigid pavements.

Material 

Property

Constant or 

Time 

Dependent

Independent Variables

Primary Model Comments

Secondary 

Model

Rehabilitation of New PCC Slab

Compressive 

strength

Time 

dependent

Aggregate type, cement 

content, air content, w/c, 

unit weight, gradation, 

admixtures, SCMs, and 

age

N/A Prediction for 28-day 

strength and longterm strength in 

separate models; 

strength gain model 

to be updated

Elastic 

modulus

Time 

dependent

Aggregate type, cement 

content, air content, w/c, 

unit weight, admixtures, 

and SCMs

Compressive 

strength/ flexural 

strength

Prediction for 28-day 

value and long-term 

values in separate 

models

Flexural 

strength

Time 

dependent

Aggregate type, cement 

content, air content, w/c, 

unit weight, admixtures, 

and SCMs

Compressive 

strength

Prediction for 28-day 

strength and longterm strength in 

separate models; 

strength gain model 

to be updated

Indirect 

tensile 

strength 

(CRCP only)

Time 

dependent

Compressive 

strength/flexural 

strength

N/A

CTE Constant Coarse and fine 

aggregate type, 

aggregate CTE, coarse 

and fine aggregate 

volume, paste volume, 

and w/c ratio

Aggregate type Default PCC CTE for 

each aggregate type 

and model based on 

mix design

deltaT for 

JPCP and 

CRCP 

design*

Time 

dependent

Base type, construction 

time, PCC index 

properties, and climatic 

variables

N/A Data in MEPDG 

JPCP and CRCP 

calibration to be used

Erosion in 

CRCP 

design**

Time 

dependent

Base type, index 

properties and strength 

of base, and climate 

(precipitation)

N/A Data in MEPDG 

CRCP calibration 

models to be used

EI—JPCP** N/A Base type, base 

properties, and climate 

(precipitation)

N/A Data in MEPDG 

JPCP calibration 

models to be used
58

Rehabilitation of Existing PCC Slab

Compressive 

strength

Time 

dependent

Same as for parameters used in new design

Elastic 

modulus

Time 

dependent

Same as for parameters used in new design

Flexural 

strength

Time 

dependent

Same as for parameters used in new design

N/A = Not applicable.

*Construction dependent.

**Base dependent but listed in PCC properties because it is considered a design feature for JPCP or CRCP design.

Table 7. Independent variables to derive prediction models for stabilized materials.

Material 

Type Material Property

Constant or 

Time 

Dependent

Independent 

Variables

Lean 

concrete and 

cementtreated 

aggregate

Elastic modulus Constant Compressive strength

Flexural strength*

(for HMA pavement 

design)

Constant Compressive strength

Limecement-fly 

ash 

Resilient modulus Time 

dependent

Unconfined 

compressive strength 

or index properties 

(soil type, Atterberg 

limits, and gradation)

Soil cement Resilient modulus Time 

dependent

Unconfined 

compressive strength 

or index properties 

(soil type, Atterberg 

limits, and gradation)

Limestabilized soilResilient modulus Time 

dependent

Unconfined 

compressive strength 

or index properties 

(soil type, Atterberg 

limits, and gradation)

All material 

types listed 

above

Unconfined 

compressive 

strength

Time 

dependent

Soil type, Atterberg 

limits, and gradation

*Construction dependent.
59

Table 8. Independent variables to derive prediction models for unbound materials.

Material Property

Constant or 

Time 

Dependent 

Independent 

Variables Comments

Resilient modulus 

determined using the 

following two options:

• Regression coefficients 

k1, k2, and k3 for the 

generalized constitutive 

model that defines 

resilient modulus as a 

function of stress state 

and regressed from lab 

resilient modulus tests.

• Determine the average 

design resilient 

modulus for the 

expected in-place stress 

state from laboratory 

resilient modulus tests.

Time 

dependent

Soil type, Atterberg 

limits, maximum dry 

density, optimum 

moisture content, 

gradation, and the 

percent passing the 

#200 sieve, P200.

Analyses will verify 

several options and 

combinations of 

grouping data

61

CHAPTER 4. DATA ASSEMBLY AND MODELING CONSIDERATIONS

Data extraction and assembly are key steps to any model development exercise. Depending on 

the specific types of investigation involved or the particular needs of the analyses involved, the 

data extraction and assembly process could control the efficiency of data manipulations that are

performed during analyses. Model development is an iterative process and involves stepwise 

evaluation of the significance of several parameters individually, in combination, and their 

interactive effects. Therefore, assembling data in a versatile manner that is amenable to model 

revisions and multiple evaluations is of paramount importance.

The relationships developed in this study were based primarily on data from the LTPP database. 

The key steps in developing the statistical models were as follows:

1. Identify and assemble all relevant data from the LTPP database.

2. Evaluate the quality of the assembled data by reviewing the assembled data for 

inconsistencies and possible errors, while also identifying missing/suspect data items.

3. Develop methods and procedures for estimating important missing data elements and clean 

data by resolving anomalies and outliers in a consistent manner.

4. Select the appropriate prediction model form and identify variables that emerge as 

significant variables.

5. Evaluate the reasonableness of the model formulated and verify whether the predicted 

correlations are meaningful from an engineering standpoint.

6. Ascertain statistical correctness and suggest tentative prediction models. 

7. Perform sensitivity analysis to validate the tentative models. If validation is not satisfactory, 

revise the model in step 4.

8. Confirm final prediction model(s).

This approach has been used successfully in previous studies and has resulted in practical 

prediction models. Note that steps 4 through 8 are an iterative process; therefore, the data 

extraction and assembly process should allow multiple revisions to the selected variables and 

model forms.

DATA SELECTION FOR CURRENT STUDY

Data in the LTPP program exist for two complementary experiments, General Pavement Studies 

(GPS) and SPS. GPS experiments usually exist in service pavements incorporated into the LTPP 

program, while the SPS experiments are mostly newly constructed or rehabilitated pavements or 

pavements subjected to various maintenance activities at the time they were added to the 

LTPP program. 
62

The GPS and SPS experiments are as follows:

• GPS-1: Asphalt concrete (AC) on granular base.

• GPS-2: AC on bound base.

• GPS-3: Jointed plain concrete.

• GPS-4: Jointed reinforced concrete.

• GPS-5: Continuously reinforced concrete.

• GPS-6A: Existing AC overlay on AC pavements.

• GPS-6B: New AC overlay on AC pavements.

• GPS-7A: Existing AC overlay on PCC pavements.

• GPS-7B: New AC overlay on PCC pavements.

• GPS-9: Unbonded PCC overlays on PCC pavements.

• SPS-1: Strategic study of structural factors for flexible pavements.

• SPS-2: Strategic study of structural factors for rigid pavements.

• SPS-3: Preventive maintenance effectiveness of flexible pavements.

• SPS-4: Preventive maintenance effectiveness of rigid pavements.

• SPS-5: Rehabilitation of AC pavements.

• SPS-6: Rehabilitation of jointed PCC pavements.

• SPS-7: Bonded PCC overlays on concrete pavements.

• SPS-8: Study of environmental effects in the absence of heavy loads.

• SPS-9A: Validation of Strategic Highway Research Program (SHRP) asphalt 

specification and mix design.

Each GPS test site consists of a single 500-ft test section over which all factors remain constant. 

SPS-1 and SPS-2 sites usually consist of a series of adjacent 500-ft test sections with different 

design and material characteristics or maintenance treatments and rehabilitation strategies. The 

test section layouts and the material data formats in the various LTPP data tables are reported 

elsewhere.

(141)
63

The GPS and SPS experimental sections have different data availability. Relative to SPS 

sections, GPS sections are older pavement projects and were not intentionally built for collecting 

data under the LTPP program. SPS sections, in contrast, offer more detailed information on 

materials and construction. Further, the materials used in GPS sections are not necessarily 

representative of current materials, as several elements of the material manufacturing process 

and material specifications have changed. With the likelihood that GPS and SPS section data 

could produce different models, the data extraction and data assembly processes were tailored to 

analyze these data in two separate groups. Data for the various models by material type were 

taken from the GPS and SPS sections as shown in table 9.

Table 9. LTPP sections selected to review data for each category.

Material Category

Selected SPS 

Sections

Selected GPS 

Sections

PCC materials SPS-2, SPS-7, and 

SPS-8

GPS-3, GPS-4, GPS-5, 

GPS-7, and GPS-9

Stabilized materials All All

Unbound materials All All

The following additional points offer reasons for the selection of materials data collected for 

PCC models:

• GPS-1, GPS-2, and GPS-6 projects were not included because they do not include 

PCC layers.

• SPS-1, SPS-3, SPS-5, and SPS-9 projects were not included because they do not include 

PCC layers.

• SPS-4 projects contain older rigid pavements with preventive maintenance, and it was 

found that the data relative to index properties were less accurate than for the newer SPS 

projects. These data were included with the GPS experiment data because the underlying 

pavement data were recovered from the original GPS projects.

• SPS-6 projects consist of underlying PCC layers that have been rehabilitated and 

sometimes cracked or rubblized. The data for the PCC layers are in the original inventory 

database. As with the SPS-4 underlying PCC slab data, these data were included in the 

GPS data tables assembled for this study.

• Data for the overlay only were included from the SPS-7 projects because the underlying 

slab information is relatively less comprehensive.

Table 10 shows the LTPP data tables that were queried to obtain the data necessary for 

developing the prediction models. The table also lists the data elements that were obtained 

from each table.
64

Table 10. LTPP data tables queried to obtain data for review and future analyses.

Data 

Category LTPP Data Table Material or Index Properties

PCC materials

TST_PC01 Compressive strength of cores and 

cylinders and test date

TST_PC02 Tensile strength and test date

TST_PC03 CTE, aggregate type, and test date

TST_PC04 Elastic modulus, Poisson’s ratio, 

unit weight, and test date

TST_PC09 MR and test date

SPS2_PCC_MIXTURE_DATA Mix design, cement type, admixture 

type and quantity, aggregate type, 

and gradation for SPS-2

SPS2_PCC_PLACEMENT_DATA Construction date and curing 

method for SPS-2

RHB_PCCO_AGGR Aggregate type for SPS-7

RHB_PCCO_MIXTURE Mix design, cement type, admixture 

type and quantity, and aggregate 

type for SPS-7

RHB_PCCO_CONSTRUCTION Air temperature at time of 

construction, curing method, and

date for SPS-7

SPS8_PCC_MIXTURE_DATA Mix design, cement type, admixture 

type and quantity, and aggregate 

type for SPS-8

SPS8_PCC_PLACEMENT_DATA Curing method and construction 

date for SPS-8

INV_PCC_MIXTURE Mix design, cement type and 

content, entrained air content, and 

curing method for GPS

INV_AGE Construction date for GPS

Stabilized 

materials

TST_TB02 Compressive strength

Unbound 

materials

TST_SS01_UG01_UG02 Gradation

TST_SS02_UG03 Hydrometer analysis

TST_SS04_UG08 AASHTO soil classification

TST_SS11 TST_UG09 Hydraulic conductivity

TST_UG04_SS03 Atterberg's limits

TST_UG05_SS05 Maximum laboratory dry density

TST_UG05_SS05 Optimum laboratory moisture 

content

TST_UG07_SS07_WKSHT_SUM Average resilient modulus

Climate data

CLW_VWS_HUMIDITY_ANNUAL Local precipitation, humidity, and 

temperature for all sections in the 

MEPDG calibration files

CLW_VWS_HUMIDITY_ANNUAL

CLW_VWS_HUMIDITY_ANNUAL
65

DATA REVIEW

The data review process sought to verify data availability for the predictive models based on the 

dependent and independent variables identified in chapters 2 and 3 of this report. The assembled 

data were reviewed to identify anomalies and missing data elements. The review evaluated 

whether the following occurred:

• All required data were available.

• Available data were reasonable (i.e., trends and values of the data are as expected 

according to literature and engineering knowledge).

• Missing data items and the number of missing datasets significantly impaired the ability 

to develop required models for this study.

• Missing data items could be computed, assumed, or obtained from other readily 

available sources.

To enable such an evaluation, data were assembled for each 500-ft section in the database. As 

explained earlier, the data for each GPS section correspond to the test site. However, for SPS 

sections, it was reasonable to use the index properties of a given material from each site to 

correlate the data to the material properties from multiple test sections that were constructed 

using the same material. The treatment of PCC data from SPS-2 sections are discussed in 

detail in the PCC model development section in chapter 5 of this report.

Also included in developing the reference system were layer number, layer type, and 

construction number.

Key Findings From Data Review

A detailed unpublished report summarizing the results of the data review process was completed 

in phase I of this study and submitted to FHWA. The following conclusions were presented:

• All key data elements required for model development according to the literature 

were available.

• For PCC materials, SPS sections have more extensive data than GPS sections, as 

expected. This necessitates different model considerations and the inclusions of different 

parameters in the models using these two datasets.

• While independent variables exist for most predictive models, there are no dependent 

variable values in the database for parameters that are representative of the construction 

or site conditions. These parameters include PCC zero-stress temperature, EI for JPCP 

design, erosion in CRCP design, base friction coefficient for CRCP design, and deltaT for 

JPCP and CRCP designs. These parameters were never intended for inclusion in the 

LTPP database and are not typically measured in the field during construction. These 

variables are specific to MEPDG inputs and were estimated through correlations during 
66

the development of the MEPDG. It was therefore necessary to use MEPDG calibration 

data to verify or refine these models.

• Most stabilized materials are referred to as treated bases in the LTPP database, and only 

compressive strength data exist for these materials. Index properties were not included 

in the database. Predictive models therefore were not developed for a majority of the 

stabilized base materials. However, the following materials data for LCB layers in SPS-2 

sections are more comprehensive:

o LTPP Standard Data Release 23.0, which was evaluated during phase II of this 

project, contained no elastic modulus data for LCB materials.(3)

o LTPP Standard Data Release 24.0, released after this study was initiated, 

contained elastic modulus data for LCB layers in SPS-2 sections.(142)

• LTPP Standard Data Release 23.0 contains data beyond that utilized in a previous LTPP 

study investigating various models and predictive variables to estimate resilient modulus

of unbound materials.

(3,135)

All data available were of adequate quality and completeness for use in model development. 

However, some missing data and data anomalies were also identified. The specific issues 

encountered and the methods used to overcome them is discussed in chapter 5 for each material 

type and material property. A summary of data availability is presented in table 11 for each 

material type and in table 12 for models to predict rigid pavement design features. 
67

Table 11. Summary of data availability for material property predictive models.

Material Type

Material 

Property

Number of Sections Available for 

Model

GPS SPS* Comment

PCC materials

Compressive 

strength 250

SPS-2 - LS 84

Does not include 

supplementary 

sections for SPS-2

SPS-2 - HS 83

SPS-7 26

SPS-8 10

Total 203

Elastic 

modulus 344

SPS-2 - LS 77

SPS-2 - HS 76

SPS-7 11

SPS-8 8

Total 172

Flexural 

strength 349

SPS-2 - LS 50

SPS-2 - HS 51

SPS-7 15

SPS-8 5

Total 121

Tensile 

strength 95 15

Only for CRCP, 

SPS data for SPS-7

CTE 214

SPS-2 - LS 4

No comments

SPS-2 - HS 7

SPS-7 2

SPS-8 2

Total 15

Ultimate 

shrinkage 258 N/A Only independent 

variables are in 

LTPP database. 

Predictive model 

uses MEPDG 

calibration data. See 

table 12 discussion 

in chapter 5

PCC materials 

(rigid pavement 

design features)

Zero-stress 

temperature 245 N/A

EI for JPCP 

design N/A N/A

Erosion in 

CRCP design N/A N/A

deltaT for JPCP 

and CRCP 

designs N/A N/A

Stabilized 

materials N/A** N/A** 57

Includes only LCB

in SPS-2

Unbound 

materials

Resilient 

modulus 1,416 712 No comments

N/A = Not applicable; LS = Low strength; HS = High strength.

*SPS data have been summarized by experiment type. SPS-2-LS and SPS-2-HS refer to SPS-2 sections that have been 

built with low-strength and high-strength concrete mixes.

**Stabilized material data contain only compressive strength data and elastic modulus data for LCB materials.
68

Table 12. Summary of MEPDG calibration sections to develop predictive models for rigid 

pavement design features.

Calibration 

Sections

Number of 

CRCP Sections

Number of 

JPCP Sections

Non-LTPP sections 33 15

LTPP sections 71 285

Total 104 300
69

CHAPTER 5. MODEL DEVELOPMENT

This chapter discusses the statistical analyses performed to develop the predictive models and the 

sensitivity analyses used to validate the models. All statistical analyses were performed using the 

SAS® software program.

STATISTICAL ANALYSES METHODS ADOPTED

After data assembly was completed, predictive relationships for the parameters identified in

table 6 through table 8 were considered for statistical analyses. The following approaches/

options were considered for developing the various models:

• Refinement of existing models—Refinement of existing ACI PCC compressive 

strength-flexural strength relationship or the MEPDG PCC strength gain model.

• Development of new models—A predictive model to determine CTE based on PCC mix 

constituent properties.

• Development of empirical models specific to MEPDG performance—A predictive 

model to determine deltaT for JPCP design.

Formulating Data for Statistical Models

Data were formulated in three distinct types depending on the nature and extent of data available 

for each parameter and the intended use of the predicted variable. Within each type, different 

model forms can be adopted depending on the relationship the dependent parameter holds with 

the independent variables. The three primary data formulation types adopted are data formulation 

types 1 through 3 and are discussed in the following sections.

Data Formulation Type 1

• Model description—Simple mathematical correlation between a dependent parameter 

and continuous predictors or independent variables.

• Dependent variable type and data source—Continuous variable from the 

LTPP database.

• Independent variable type and data source—Continuous variable from the 

LTPP database.

• Correlation—Direct mathematical correlation between dependent and 

independent variables.

• Example predictive model—Compressive strength of PCC predicted as a function of 

aggregate type, cement content, air content, w/c ratio, unit weight, admixtures, and 

SCMs. Additionally, k1, k2, and k3 coefficients of the resilient modulus universal 
70

constitutive model for unbound materials and soils are predicted as a function of 

aggregate gradation parameters and index properties.

• Model inference space—Applicable to materials representative of the materials in the 

LTPP database and limited to the ranges of material properties (i.e., data points) used in 

the model.

Data Formulation Type 2 

• Model description—Simple correlation between a dependent parameter and categorical 

predictors or independent variables.

• Dependent variable type and data source—Continuous variable from the

LTPP database.

• Independent variable type and data source—Categorical variable from the 

LTPP database.

• Correlation—Average value of the predicted value determined from the database for 

each category of independent variable. A direct correlation between dependent and 

independent variables still exists.

• Example predictive model—CTE predicted as a function of aggregate type.

• Model inference space—Applicable to materials representative of the materials in the 

LTPP database and limited to the ranges of material properties used in the model.

Data Formulation Type 3 

• Model description—Relationship between a dependent parameter and predictor 

variables for dependent variable values established by matching field performance to 

MEPDG predicted performance in LTPP sections used in MEPDG calibration.

• Dependent variable type and data source—Parameter established by trial and error for 

each calibration section so that predicted performance matches field performance for 

each section.

• Independent variable type and data source—Continuous variables from the

LTPP database.

• Correlation—Dependent variable determined from trial and error correlated to 

independent variables from the LTPP database through simple mathematical correlation.

• Example predictive model—The current MEPDG calibration models have used a 

uniform value of -10 °F for deltaT in JPCP and CRCP designs, as this value was found to 

provide the least error term overall in the cracking model. This project will determine the 

deltaT term required to minimize the error in prediction for each calibration section 

individually, which represents the deltaT term that best explains the performance of the 
71

pavement based on MEPDG calibration. The array of deltaT terms correlates to the

independent variables from the LTPP database such as base type, construction time, PCC 

properties (unit weight, compressive strength, etc.), and climatic variables. Another 

example for a parameter that can be predicted using a type 3 model is EI in JPCP 

faulting model prediction. The value resulting in the best faulting prediction can be 

correlated to material (base material), design (dowels/no dowels), and climate 

(precipitation) parameters.

• Model inference space—Specific to MEPDG performance prediction. The MEPDG 

distress model calibration is built into the predictive relationship proposed and therefore

is applicable only to derive MEPDG-specific inputs. However, relative comparisons are

valid. For example, deltaT determined from this relationship for different locations will 

explain the relative potential for developing upward/downward curling at these locations.

Table 13 to table 15 provide summaries of the model types evaluated for developing predictive 

relationships for PCC, stabilized, and unbound materials, respectively.
72

Table 13. Model types used to derive predictive relationships for PCC material properties 

or design features fo

r rigid pavements.

Material Property

Primary Model Secondary Model

Model Variables

Model 

Type

Model 

Variables

Model 

Type

Compressive strength Aggregate type, cement 

content, air content, w/c

ratio, unit weight, 

admixtures, and SCMs

1 N/A N/A

Elastic modulus Aggregate type, cement 

content, air content, w/c

ratio, unit weight, 

admixtures, and SCMs

1 Compressive 

strength/ 

flexural 

strength

1

Flexural strength Aggregate type, cement 

content, air content, w/c

ratio, unit weight, 

admixtures, and SCMs

1 Compressive 

strength

1

Indirect tensile 

strength (CRCP only)

Compressive 

strength/flexural strength

1 N/A N/A

CTE Aggregate type, aggregate 

volume, cement type, paste 

volume, and w/c ratio

1 Aggregate 

type

2

Erosion in CRCP 

design

Base type, index properties 

and strength of base, and 

climate (precipitation)

3 N/A N/A

EI—JPCP Base type, base properties,

and climate (precipitation)

3 N/A N/A

deltaT Base type, construction 

time, PCC index properties, 

and climatic variables

3 N/A N/A

N/A = Not applicable.

Table 14. Model types used to derive predictive relationships for stabilized materials.

Material Type*

Material 

Property

Constant

or Time 

Dependent

Independent 

Variables

Model Type 

Evaluated

Lean concrete and 

cement-treated 

aggregate

Elastic 

modulus

Constant Compressive 

strength

1

*All other material types have been excluded from this table, as the database provides data for 

LCB elastic modulus only.
73

Table 15. Model types used to derive predictive relationships for unbound materials.

Material Property

Independent 

Variables Model Type

Resilient modulus determined using 

the following two options:

• Regression coefficients k1, k2, and 

k3 for the generalized constitutive 

model that defines resilient 

modulus as a function of stress 

state and regressed from lab 

resilient modulus tests

• Determine the average design 

resilient modulus for the expected 

in-place stress state from 

laboratory resilient modulus tests

Soil type, Atterberg 

limits, maximum dry 

density, optimum 

moisture content, 

gradation, and P200

1; after grouping 

data for coarsegrained and finegrained soils

Statistical Criteria for Used for Model Development

The statistical analyses performed in this study examined several statistical parameters in 

choosing the optimal model and in determining the accuracy of the model. The process included 

evaluating various aspects of the model, and the following parameters were generally verified:

• Cp—A statistical term to select the best subset of regressors for a model and an indicator 

of the collinearity of a regression model.

• VIF—A statistical term to evaluate the multicollinearity of the model (i.e., it tracks the 

interaction effects of the regressors identified).

• p-value—A probability calculation to ascertain the significance of the regressor in 

the equation.

• R2

—A statistic that indicates the goodness of fit of a model and describes how closely the 

regression line fits the data points.

Cp

Mallows’ Cp is often used as the criterion for selecting the most appropriate sub-model of 

p regressors (or independent variables) from a full model of k regressors, p < k.

(143) In the current 

study, the potential variables that could likely influence the value of the dependent variable were 

identified from a literature review of specific material parameters. However, it is not clear 

whether the specific dataset being used to develop the models can suitably show the correlation 

expected. In other words, the initial attempt in developing the model could likely include more 

variables or regressors than the model can handle. This can result in forcing variables that are 

highly correlated and whose effects cannot be independently estimated or isolated by the model. 

The Cp term that is used in a step-wise regression process helps avoid an over-fit model by 

identifying the best subset of only the important predictors of the dependent variable.
74

Cp takes into account the mean square error for the two models and the number of variables in 

the reduced model as seen in figure 125.

n p MSE

MSE C n p

f

r

p = ( − ) − + 2

Figure 125. Equation. Cp.

Where:

n = The sample size.

MSEr = The mean square error for the regression for the smaller model of p regressors and is 

expressed as follows:

Figure 126. Equation. MSEr.

MSEf is the mean square error for the regression on the full model of k regressors. Note that for 

p = k, MSEr = MSEf and Cp = p.

Sub-models are ordered in SAS® based on Cp; the smaller the Cp value, the better. While it is a 

reliable measure of the goodness of fit for a model, it is fairly independent of R2 in determining 

the number of predictors in the model. SAS® also lists R2 for each model created with data 

subsets, which greatly enables the selection of a feasible submodel for further evaluation.

However, the variables in the reduced model must all be significantly different from zero and 

cannot be too correlated, which is verified using VIF.

VIF

Generally, VIF can be regarded as the inverse of tolerance. The square root of VIF indicates how 

much larger the standard error is compared with what it would be if that variable is uncorrelated 

with the other independent variables in the equation.

If y is regressed on a set of x variables x1 to xk, VIFs of all x variables should be created in the 

following manner:

For variable xj, VIF is the inverse of (1 − R2

) from the regression of xj on the remainder of the 

x variables. In other words, xj regressed on x1...xj - 1, xj+1...xk, produces a regression with R2

as

Rj

2

. Therefore, figure 127 was created as follows:

( ) 2 1

1 ( )

j

j

R

VIF x − =

Figure 127. Equation. VIF.

VIF is always greater than 1. A VIF value of 10 indicates that 90 percent of xj is not explained by 

the other x variables. A common rule of thumb is that if VIF for any variable is greater than 5, 

( )

2

1

∑=

= −

n

i

r i ri MSE y y
75

multicollinearity exists for that variable and should be excluded from the model. However, in 

cases where the parameter is either known to correlate well or other variables do not provide a 

reasonable model, a cut-off value of 10 is acceptable but less preferred.

R2

R2 is the coefficient of determination and is the square of the sample correlation coefficient 

computed between the outcomes and their predicted values, or, in the case of simple linear 

regression, between the outcome and the values being used for prediction. R2 values vary from 

zero to 1 and are expressed as a percentage. An R2 of x percent indicates x percent of the 

variation in the response variable can be explained by the explanatory variable, and (100 − x) 

percent can be explained by unknown variability. The higher the value of this term, the greater 

the predictive ability of the model. It is the most commonly used statistic to evaluate the quality 

of fit achieved with a model.

From the standpoint of using R2 to select a model, while relationships with higher values are 

desirable, it is not to be treated as the ultimate criterion to establish the model. R2 needs to be 

interpreted with reasonable caution and needs to be combined with the information from the 

other statistical parameters discussed in this section. In fact, it is not the first check to select a 

model; instead, it should serve as the final check to establish the model.

The statistical parameters discussed previously do not individually optimize a model; instead,

these parameters need to be evaluated in combination to derive the most accurate model. 

Furthermore, it is imperative in establishing a model that both statistical and engineering aspects 

be balanced. The accuracy of the model needs to be verified for technical/engineering validity by 

evaluating each variable in the model and confirming that the observed trends are as expected 

(verified in literature) and that the effect of the independent variable on the predicted variable is 

reasonable (verified through sensitivity analyses).

The following list describes the limitations of the Cp, VIF, and R2 parameters and the methods 

used to overcome them:

• Cp, VIF, and R2 do not explain whether the independent variables are a true cause of the 

changes in the dependent variable or if the trends predicted by the model are accurate. 

In other words, they do not identify a random correlation between two variables. 

Engineering knowledge (or knowledge from outside of the modeling exercise) needs 

to be incorporated in accepting or rejecting regressors or a model form.

• Cp, VIF, and R2 do not explain whether the model has a bias because of a variable that 

has been omitted from the list of regressors. As a result, it is important to include, within 

practical considerations, all variables even remotely known to affect the predicted 

parameter in the model for a preliminary analysis to determine if the variables 

are correlated.

• R2 does not indicate whether the variables included in the model are significant. The 

p-value should be limited to the level of confidence desired in the model. Typically, a 
76

confidence level of 95 percent is used, such that the values should remain below 0.05. 

However, in rare cases, this sometimes is limited to 0.1.

• R2 alone does not indicate whether there is collinearity present in the data or whether the 

selected independent variables have an interaction effect. It is necessary to verify using 

Cp and VIF.

• Cp, VIF, and R2 do not offer any suggestions regarding further scope to improve a model 

by using transformed versions of the existing set of independent variables. Again, the use 

of engineering knowledge is necessary to incorporate transformed variables.

Other Modeling Considerations

Interaction Effects of Independent Parameters

Information from the literature points to the influence of independent variables on each material 

property of interest (the dependent variables) in a general sense, without adequately accounting 

for the impact other design and site parameters or independent variables may have on the 

dependent parameters. Therefore, to draw consistent and dependable conclusions on the effect of 

each independent parameter, it would be ideal to compare scenarios that have all other variables 

constant or in common, except for the independent variable under consideration, such as the 

effect of w/c ratio on strength or base type on erosion.

However, in synthesizing information from large databases, as was done in the present study, it 

is essential to adopt statistical tools to assess the relationships between several independent 

variables and the dependent variable. Therefore, where necessary, both linear regressions and the 

generalized linear model (GLM) were utilized to establish a model. GLM can independently 

examine the influence of an independent variable on a dependent variable despite the presence of 

other predictor variables in the data sample. In other words, GLM can isolate the effects of one 

independent variable by normalizing the effect of others, and it predicts whether the effect of 

each independent variable is statistically significant on a dependent variable using the analysis of 

variance (ANOVA) method.

GLM is a generalization of the linear regression model and can accommodate the following:

• Non-linear and linear effects of independent variables.

• Categorical predictor variables as well as continuous predictor variables.

• Dependent variables whose distributions follow several special members of the 

exponential family of distributions (e.g., gamma, Poisson, binomial, etc.), as well as 

normally distributed dependent variable.

Multilevel ANOVA Models

Multilevel ANOVA models are more complex models used in the design of experiments, and in 

the context of the current study, they are more appropriate to use when the dataset contains 

multiple measures or clustered tests. The analyses should account for the fact that the other 
77

regressors in the equation are the same for multiple levels of one of the parameters, which most 

often is the pavement age parameter in the current study. This also is called a hierarchical model.

An example of such a model is one that compares PCC compressive strength for core and 

cylinder measurements. The LTPP database contains compressive strength results for cylinders 

cast during construction and cores taken from the pavement for SPS sections. These cores and 

cylinders have been tested at 14 days, 28 days, and 1 year. The strengths can be compared for 

each section and age. A simple way of doing such a comparison would be to perform a paired 

t-test. However, the number of measurements due to repeated measurements at different ages 

(i.e., 14 days, 28 days, 1 year, 2 years, etc.) should not be allowed to count as a full data point for 

sections with more than one age measurement. Therefore, a multilevel ANOVA model featuring 

State and sections should be used. If the data are balanced so that there are the same number of 

observations for each age and section, the paired t-test and the multilevel ANOVA would show 

the same results in the test whether core and cylinder measurements differ. In this example, the 

dataset is not balanced so the tests are not the same, with the multilevel ANOVA being the more 

appropriate analysis. Likewise, while developing a model to estimate strength at any age, the age 

parameter has to be treated in a hierarchical fashion.

All observations have the same fabrication variables at the State by section code level, and these 

are repeated when sections are tested several times (i.e., at different ages). It is not appropriate 

that the design values for a section tested four times should be allowed to count four times. 

Therefore, a multilevel ANOVA model must be used to guarantee that values from each section 

count only once while the values measured over time are incorporated in the analysis. 

Treatment of Outliers

Generally, a true model representing the dataset used should include all natural data in the 

dataset. In other words, deliberate changes or removal of data artificially alters the inherent 

model. However, in using large datasets, especially when field data are used or when the data are 

from a dataset not originally designed to develop the model, values that lie beyond the scope of a 

field's value range are encountered. Such data, referred to as outliers, cannot be explained by 

other parameters specific to that case or observation. In statistical models, outliers are given 

special consideration and treated in a consistent manner for all points in the model so as to not 

simulate a fabricated dataset.

Outliers are either deleted (treated as missing values) or capped at a minimum or maximum 

value for each variable. In the current study, to the extent possible, outliers were not deleted from 

the datasets. However, certain models necessitated the deletion of select data points. When 

outliers were deleted, the process was based on a consistent criterion. Treatment of outliers is 

discussed separately for each model.

Grouping of Datasets

Any grouping of datasets performed is discussed separately for each model.
78

Procedures for Model Development

As part of model development, various combinations of model forms (i.e., mathematical 

relationships) and transformation of dependent/independent variables were evaluated to 

determine which combination resulted in the best prediction model. The combinations of model 

forms and transformation of dependent/independent variables is presented in figure 128 through 

figure 131.

In general, where past literature agreed on some sort of relationship between dependent and 

independent variables, the relationships were adopted and applied. Where no such agreements 

exist, all the combinations presented in figure 128 through figure 131 were applied.

Figure 128. Equation. fc linear model form with no transformation.

))0.00524

0.03

375.6*( ) *(ln( 1.1

, 28

t E f'c c t = −day

Figure 129. Equation. Ec,t nonlinear model form with no transformation.

Figure 130. Equation. k1 linear model form with transformation.

Figure 131. Equation. k1 nonlinear model form with transformation.

Both linear and nonlinear statistical techniques were utilized for model development and 

calibration of the mathematical equations. The two principal SAS® procedures used for model 

development were REG and NLIN. Other SAS® procedures, such as STEP WISE, RSQUARE, 

and RSREG, were used in selecting the most suitable independent variables for incorporation 

into the tentative model. In general, using the dependent and independent variables (transformed 

or otherwise) and mathematical equations representing the model forms identified above, the 

iterative process in selecting a tentative model was performed as follows:

• Selection of best combination of independent variables—Selection of the best 

combination of independent variables was performed by using the SAS® STEP WISE, 

RSQUARE, RSREG, etc., procedures to determine the best combination of independent 

variables from the general list of possible independent variables identified as part of the 

literature review. Selection of the best combination of independent variables was 

performed based on the p-value of each individual independent variable included in the 

model. In general, an independent variable with a p-value greater than 5 percent was 

deemed not significant and was excluded from the model.

• Selection of submodels—For each model, using Cp and VIF determines the best 

combination of significant independent variables to be included in the tentative model. 

The aim is to limit the total number of independent variables in the models while 

f

c = A0+ A1 × w⁄c + A2 × cementitious content

ln(k1) = 1.12 + 2.4 × �γd� + 3.6 × LL

ln(k1) = 1.12 �γd� 1.996 �

LL

wc

�

0.639
79

minimizing multicollinearity among independent variables, minimizing the error between 

measured and predicted dependent variable, and maximizing R2.

• Maximize R2

—The goal is to select independent variables to maximize R2 without 

compromising model robustness characterized by Cp and VIF.

• Minimize error—The goal is to minimize error in predicted and measured dependent 

variable without compromising model robustness.

PCC MODELS

The prediction models developed for PCC compressive strength, flexural strength, elastic 

modulus, tensile strength, and CTE are discussed in detail in this section. The specific tables 

from which these data were obtained were listed in table 10. The development of the models 

involved an iterative process, and systematic analyses procedures followed. The process is 

described in detail for the first model. The various steps are not repeated in great detail for the 

remaining models, and only results are included.

Data Used in PCC Models

As discussed in chapters 2 and 4, PCC materials data and strength data are available in the LTPP 

database for both GPS and SPS sections. However, the extent of data available is different for the 

two experiment types. For the data used for the study, the PCC data come from PCC layers in 

JPCP, CRCP, and jointed reinforced concrete pavements (JRCPs). All JRCP sections with the 

exception of one test site belonged to GPS test sites. Also, there was a significant difference in 

the extent of data available for PCC index properties between the SPS and GPS sections. SPS 

sections had very detailed mix design information compared to the GPS sections. In addition to 

the materials information available for GPS sections, the SPS sections contained specific details 

about the use of SCMs, admixtures, and the gradation of the coarse and fine aggregates.

The following information should be noted about all of the PCC models with regard to data used, 

data reduction, and assumptions:

• The age at which the test was performed was determined by using the test date and the 

date of placement.

• Each SPS-2 site consists of 12 rigid pavement sections 500 ft long and constructed with 

two PCC mix designs in the surface JPCP layer. Six sections were placed using a highstrength mixture with a target flexural strength of 900 psi, and six sections were placed 

using a low-strength mixture with a target flexural strength of 550 psi. The mix design 

parameters were the same for all six sections in each strength category. The strength tests 

were performed at 14 days, 28 days, and 1 year using both cores from the pavement and 

companion specimens cast during construction. During the data assembly process, data 

for strengths determined from cores versus cast specimens were separated and averaged 

for each site and for each age across all sections that represented a given mix. In other 

words, for a given age, each SPS-2 site provided two data points to the prediction 

models—one for the low-strength concrete and one for the high-strength concrete. In the 
80

data analysis process, codes of 1 and 2 were assigned for the low-strength and highstrength sections, respectively.

• Short-term strength and modulus data were available for SPS sites for 14 days, 28 days, 

1 year, and up to 3 years for some sections. Two SPS sections had 10-year strength data.

• For each age, multiple tests were conducted. The analyses used the average strength for 

each age for all sections (SPS and GPS).

• For correlations between PCC strength parameters, cores were matched with cores and 

cylinders with cylinders, as necessary.

• The total amount of cement and other SCMs (typically fly ash) was summed to obtain the 

amount of CMC in SPS sections because this information was readily available for 

SPS sections. However, the cement content in GPS sections was considered to be the 

total CMC.

• For SPS sections, coarse and fine aggregate gradations were used to compute MAS

and FM.

• Coarse aggregate type was considered a key variable for inclusion in some of the PCC 

models given its impact on CTE and modulus. As this variable is not countable, the 

different coarse aggregate types were considered as categorical variables and assigned 

values of 1 and zero. The aggregate types were basalt, chert, conglomerate, diabase, 

dolomite, gabbro, granite, limestone, quartzite, syenite, diorite, peridotite, and sandstone.

• Admixtures were considered categorical variables and assigned a value of 1 or zero for 

the presence or absence of each admixture type. Admixtures included in the list were air 

entraining agent, fly ash, water reducer, and retarder. Also included were the amounts 

used in the mix design.

• The curing method adopted for each section was considered as categorical variables 

(1 and 0) for none, membrane curing, burlap curing, and insulation.

• Mix design variables included the amounts of cement, water, coarse aggregate, fine 

aggregate, coarse aggregate specific gravity, and fine aggregate specific gravity.

• Cement type in the mix design was considered a categorical variable and included cement 

types 1, 2, and 3. The database contained one section with type 3 cement.

• LTPP data tables were merged as required for each analysis. The common referencing 

elements while merging the tables were STATE_ID, SHRP_ID, averaging code, 

LAYER_NO, and MATERIAL TYPE.
81

Limitations of All PCC Models

A fundamental limitation for any model is that the relationship that exists between the predicted 

parameter and the regressors is only valid for the range of data that has been included in the 

dataset. The statistical modeling procedures, for most part, assume that the variables are 

normally distributed within the dataset. For example, the relationships developed for PCC 

properties (e.g., a compressive strength prediction model) are applicable only for mixes with 

cement types 1 and 2. While one data point with type 3 cement exists in the database (a JRCP 

section), compared to 500 datasets with type 1 and type 2 cements, the strength gain pattern of 

a type 3 cement is outcompeted by the other two cement types in the database. As a result, it 

might not be evident within this dataset that type 3 cements produce higher strengths, especially 

in the early ages.

The model will reflect the intrinsic trends of the dataset used. For example, the data used for 

prediction of the 28-day compressive strength contains target low-strength and high-strength mix 

designs. If the primary means of achieving higher strengths for the States was to increase the 

cement content, the model will show a high correlation between CMC and strength. However, 

there are multiple ways to enhance mix compressive strength, such as the use of lower w/c ratios, 

water reducing agents, higher strength aggregates, curing at higher temperatures and insulation, 

and the use of type 3 cements. This is critical when the prediction models are implemented for 

estimating material properties.

The software program developed under the current study calculates the results for the material 

properties and includes a tool tip that provides the range of values that can be used for each 

variable. The interface also has a section which lists the basic limitations of the model.

PCC Compressive Strength Models

As discussed in chapter 3, compressive strength is the simplest of PCC strength tests and the 

most commonly available strength information for PCC materials. For the same reasons, the 

LTPP database contains extensive compressive strength data for all SPS and GPS sections. Since 

SPS and GPS data contain different levels of PCC materials information, it was considered 

meaningful to attempt to group them in different datasets to evaluate if a different subset of

regressors emerged as significant for model development.

Compressive strength is considered a fundamental strength parameter and is used at different 

stages of a project. Many SHAs specify concrete strength requirements by the concrete’s 

compressive strength, and designers develop pavement layer thicknesses based on compressive 

strength at 28 days. It is an important qualifier for concrete quality and contractor workmanship. 

QA testing programs (both contractor QC and agency QA tests) include compressive strength 

tests on cylinders and cores, and they form the basis for computing strength pay factor in a 

majority of agencies. Also, the compressive strength of the in situ concrete is used to determine 

if the pavement can be opened to traffic. Finally, the compressive strength of a core extracted 

from a pavement ready for rehabilitation is used to estimate the existing structure’s structural 

capacity in overlay design. The age of the concrete is clearly a parameter of significance in all 

these cases (except the 28-day strength, which is used for design). Given the extent of data 
82

available for PCC materials and compressive strength, the project team considered the following 

models, which are discussed in detail in this section:

• 28-day cylinder strength model—Suitable for estimating design strength.

• Short-term (1 year) cylinder strength model—Suitable for estimating opening time.

• Short-term (1 year) core strength model—Suitable for in situ strength and 

opening time.

• All ages core strength model—Suitable for estimating in situ strength at any age.

• Long-term core strength model—Suitable for estimating long-term strength for 

rehabilitation design.

The procedure used to develop the model is explained in detail for the compressive strength 

model in the following section. The development of all other models in this study has entailed a 

similar level of analyses if not more. 

Compressive Strength Model 1: 28-Day Cylinder Strength Model

The data used for this model included SPS sections that had 28-day cylinder test results. The data 

included 42 sets of results after averaging the 28-day cylinder strength for each site and for each 

mix design.

All material properties discussed in the section, Data Used in PCC Models, in chapter 5 of this 

report were evaluated to verify if they co-vary with the predicted variable. The first statistical 

procedure was a Cp analysis wherein various submodels were considered for fit using ANOVA,

and the resulting Cp and R2 values are provided at the end of the SAS® analysis. The results are 

listed by SAS® in order of the resulting Cp value. Also provided in the results are the number of 

variables (regressors) used in each model and a listing of the variables. The Cp analysis results 

are shown in table 16 for the 28-day cylinder strength model.

The Cp analysis summary indicates that 42 observations were read. There were missing data for 

certain PCC mix parameters. For example, the amount of coarse aggregate in the mix design and

the amount of fine aggregate in mix design were missing in 13 cases, while the information on 

admixtures was missing in 21 cases. A summary indicates that only 21 observations had values 

for all variables considered in the model. Using a subset of 21 datasets, the potential prediction 

models created produced the R2 values as listed in the table. The model form used for the 

analysis was as follows:

c n n f = A + A x + A x + A x + A x +...+ A x 0 1 1 2 2 3 3 4 4

Figure 132. Equation. fc model form.
83

Where:

A0 = Model intercept determined through the regression.

A1 through An = Regression coefficients.

x1 through xn = Parameters included in each submodel.

These results do not imply that all models listed in table 16 are feasible models. Cp and R2, as 

explained earlier, do not indicate whether the parameters included in the model, or submodel in 

this case, are significant, exhibit multicollinearity, or physically explain the trend. Each 

submodel suggested by the Cp analysis needs to be further evaluated and verified individually.

Table 16. Cp selection method for 28-day cylinder compressive strength model.

Number of 

Parameters 

in Model Cp R2 Variables in Model

4 1.0058 0.8184 w_c 

cementitious Coarse_Aggregate_Mix_Design

Fine_Aggregate_Mix_Design

4 1.2802 0.8143 cementitious AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

2 1.5747 0.7493 cementitious Coarse_Aggregate_Mix_Design

3 1.807 0.7761 cementitious Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

4 1.8277 0.806 w_c MASm15pct_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

3 1.8478 0.7755 w_c cementitious Coarse_Aggregate_Mix_Design

3 2.0836 0.7719 cementitious AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design

5 2.2057 0.8305 w_c cementitious FM 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

3 2.3622 0.7677 w_c Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

4 2.4771 0.7962 cementitious MASm15pct_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 2.4789 0.8264 cementitious FM AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

4 2.5453 0.7952 w_c FM Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

3 2.5577 0.7647 cementitious MASm15pct_W_c 

Coarse_Aggregate_Mix_Design
84

5 2.7348 0.8225 w_c cementitious MASm15pct_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 2.9597 0.8191 w_c cementitious AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 2.9672 0.819 w_c cementitious AVG_UNIT_WT2 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 2.97 0.819 w_c cementitious AVG_UNIT_WT 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 2.9955 0.8186 cementitious AVG_UNIT_WT2 

AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 3.0046 0.8185 cementitious AVG_UNIT_WT 

AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 3.04 0.8179 w_c MASm15pct_W_c FM 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

4 3.1679 0.7857 cementitious FM Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 3.2171 0.8152 cementitious MASm15pct_W_c 

AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

3 3.4873 0.7507 cementitious FM Coarse_Aggregate_Mix_Design

5 3.4917 0.8111 AVG_UNIT_WT2 MASm15pct_W_c 

AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 3.4928 0.8111 w_c AVG_UNIT_WT2 MASm15pct_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 3.4971 0.811 w_c AVG_UNIT_WT MASm15pct_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

5 3.5058 0.8109 cementitious MASm15pct_W_c FM 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design
85

5 3.5095 0.8108 AVG_UNIT_WT MASm15pct_W_c 

AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

2 3.5215 0.7199 w_c cementitious

5 3.5431 0.8103 w_c MASm15pct_W_c AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

3 3.5548 0.7496 cementitious AVG_UNIT_WT2 

Coarse_Aggregate_Mix_Design

3 3.5574 0.7496 cementitious AVG_UNIT_WT 

Coarse_Aggregate_Mix_Design

4 3.6023 0.7792 FM AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

3 3.6744 0.7478 AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

2 3.711 0.717 cementitious MASm15pct_W_c

4 3.7405 0.7771 w_c cementitious AVG_UNIT_WT2 

Coarse_Aggregate_Mix_Design

4 3.7435 0.777 cementitious AVG_UNIT_WT2 

AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design

4 3.7458 0.777 MASm15pct_W_c AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

4 3.7472 0.777 w_c cementitious AVG_UNIT_WT 

Coarse_Aggregate_Mix_Design

4 3.7504 0.7769 w_c cementitious AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design

4 3.757 0.7768 cementitious AVG_UNIT_WT 

AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design

4 3.7817 0.7765 w_c cementitious FM 

Coarse_Aggregate_Mix_Design

4 3.8069 0.7761 cementitious AVG_UNIT_WT 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

4 3.807 0.7761 cementitious AVG_UNIT_WT2 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

4 3.8079 0.7761 w_c cementitious MASm15pct_W_c 

Coarse_Aggregate_Mix_Design

2 3.9079 0.7141 cementitious AVG_UNIT_WT_W_c
86

4 4.0157 0.7729 cementitious FM AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design

4 4.034 0.7726 w_c AVG_UNIT_WT_W_c 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

6 4.0832 0.8324 w_c cementitious MASm15pct_W_c FM 

Coarse_Aggregate_Mix_Design 

Fine_Aggregate_Mix_Design

Table 17 to table 20 show examples of submodels evaluated in the selection of the optimized 

model for the prediction of 28-day compressive strength of PCC cylinders. This procedure 

typically involves an iterative process and specifically evaluates the following aspects:

• Is it reasonable to assume that the variables selected for the submodel are readily 

available for use at the intended stage of a project?

o For the 28-day compressive strength value to be used by a designer at an initial 

design stage, information regarding the unit weight is perhaps not available. The 

unit weight is dependent on the consolidation achieved in the field. However, it 

could be available from field tests during the QA stage or to determine opening 

time or for long-term strength estimates.

• Do the regressed coefficients for the selected parameters explain the trends expected 

based on literature review and engineering knowledge?

o Verify the absolute value and the sign (plus or minus) of the coefficient. 

• Is the inclusion of the parameter significant to the prediction model (i.e., is the predictive 

ability of the model negatively affected by the inclusion of a given parameter)?

o Verify if the p-value is less than 0.05 for a 95 percent confidence level.

• Does the parameter have an interaction effect with another parameter in the model?

o Verify if VIF is below 5 (or 10 in exceptional cases).

• Is the model showing reasonable sensitivity to each parameter?

o Verify using sensitivity analyses.
87

Table 17. Regression statistics for the four-variable model suggested by Cp analysis with 

subset of data that were available for all parameters evaluated.

Parameter

Degrees of 

Freedom 

(DF) Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept 1 9,907.383 2,732.919 3.63 0.0023 0

w/c 1 -4,893.05 2,532.455 -1.93 0.0712 3.01113

Cementitious content 1 3.30331 1.56188 2.11 0.0505 3.76626

Coarse_Aggregate_

Mix_Design 1 -1.67238 0.61169 -2.73 0.0147 1.38486

Fine_Aggregate_Mix

_Design 1 -1.51914 0.78059 -1.95 0.0694 1.79848

Note: Italicized text indicates that the parameter and statistic do not satisfy the criteria adopted for model 

development.

The model statistics for table 17 are as follows: 

• Root mean square error (RMSE) = 772 psi.

• R2 = 0.8184 percent. 

• N = 21.

Table 18. Regression statistics for the four-variable model suggested by Cp analysis with 

complete dataset available for the parameters selected.

Parameter DF Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept 1 10,789 2,181.11 4.95 <0 .0001 0

w/c 1 -2,050.86 2,200.846 -0.93 0.3607 2.78251

Cementitious content 1 3.57161 1.36819 2.61 0.0153 3.23079

Coarse_Aggregate_

Mix_Design 1 -2.34227 0.51775 -4.52 0.0001 1.25735

Fine_Aggregate_Mix

_Design 1 -2.35301 0.64777 -3.63 0.0013 1.39035

Note: Italicized text indicates that the parameter and statistic do not satisfy the criteria adopted for model 

development.

The model statistics for table 18 are as follows:

• RMSE = 774 psi.

• R2= 0.7688 percent. 

• N = 29.
88

Table 19. Regression statistics for the three-variable model suggested by Cp analysis.

Parameter DF Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept 1 9,381.832 1,569.631 5.98 < 0.0001 0

Cementitious 1 4.57228 0.84557 5.41 < 0.0001 1.24054

Coarse_Aggregate_Mix

_Design 1 -2.50707 0.48533 -5.17 < 0.0001 1.11065

Fine_Aggregate_Mix_

Design 1 -2.23659 0.63393 -3.53 0.0016 1.33863

Note: Italicized text indicates that the parameter and statistic do not satisfy the criteria adopted for model 

development.

The model statistics for table 19 are as follows:

• RMSE = 772 psi.

• R2

= 0.7604 percent.

• N = 29.

Table 20. Regression statistics for the two-variable model suggested by Cp analysis.

Parameter DF Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept 1 4,897.511 1,105.332 4.43 0.0002 0

Cementitious content 1 5.80657 0.92386 6.29 < 0.0001 1.02819

Coarse_Aggregate_Mix

_Design 1 -2.0405 0.56042 -3.64 0.0012 1.02819

Note: Italicized text indicates that the parameter and statistic do not satisfy the criteria adopted for model 

development.

The model statistics for table 20 are as follows:

• RMSE = 927 psi.

• R2= 0.6412 percent.

• N = 29.

In establishing and optimizing a model, each variable selected has to be significant (p < 0.05) 

and not show an interaction effect with other variables (VIF > 5). However, the opposite is not 

true. It is not necessary that a variable with a p-value less than 0.05 and VIF less than 5 be 

included in a model if it is not meaningful from an engineering standpoint or if it does not show 

promise based on a sensitivity analysis.

While this evaluation process can be performed in a systematic manner, it cannot be performed 

in a fully automated manner. Each parameter in each model needs to be assessed manually. 

Table 17 and table 18 show the regression statistics for the four-variable model shown to 

produce the best correlation (R2

) in table 16. Note that the number of data points in the model is
89

different in the two tables (N = 21 and N = 29 in table 17 and table 18, respectively). Table 17

shows the subset of data that was used in the Cp analysis, wherein 21 observations have data in 

all fields evaluated; however, 29 observations have data for the parameters selected for the 

model. R2 in table 17 matches that shown against the four-parameter model in table 16. However, 

the regressed coefficients and R2 in table 18 correspond to the variables selected for this model,

and the contents of table 18 are the proper statistics to report for the model.

The results in table 18 indicate the following:

• The w/c ratio is not significant to the model prediction (p > 0.05).

• A decrease in w/c ratio and an increase in CMC increases strength. Note the coefficients 

for these two variables are negative and positive, respectively.

• An increase in coarse and fine aggregate content reduces compressive strength. While 

this can be true in a certain range of aggregate contents, it is not true for the wide range of 

aggregate contents used in the analysis or that can be evaluated by a potential user of this 

model. This could be random correlation or possibly due to the high correlation between 

strength and CMC or due to a few observations containing mix designs with high 

aggregate content and low strength. Note that mixes with high aggregate contents (in the 

range of over 1,800–2,000 lb/yd3 for paving mixes) tend to increase the water demand, 

reducing the w/c ratio and reducing strength. The dataset used included observations that 

had coarse aggregates above 2,000 lb/yd3 that could have caused the aggregate contents 

to emerge as significant variables.

Removal of the w/c ratio parameter in the three-variable model results in regression statistics 

shown in table 19. Note that the coarse and fine aggregate contents show trends that counter 

engineering knowledge even though the parameters are significant to the model. The best twovariable model, shown in table 20, also shares the same concern. Thus, the iterative process 

needs to evaluate several parameters and balance both statistical and engineering needs. Often, a 

trial and error method has to supplement the pure statistical approach. The model selection is not 

based solely on the best R2 value, either. 

The final model selected for the estimation of 28-day compressive strength is shown in table 21

and includes the w/c ratio and CMC as the regressors. All 42 observations have been included. 

The R2 value is 54.4 percent. Although it is compromised relative to the models discussed above, 

it provides a more meaningful model with a superior predictive ability. RMSE for the model is 

871 psi. Table 22 provides details of the range of data used to develop the model. Figure 133

and figure 134 show the predicted versus measured values and the residuals plot for the 

model, respectively.
90

Table 21. Regression statistics for selected prediction model for 28-day PCC cylinder 

strength.

Variable DF Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept 1 4028.41841 1681.71576 2.4 0.0215 0

w/c ratio 1 -3486.3501 2152.99857 -1.62 0.1134 2.40903

Cementitious content 1 4.02511 1.32664 3.03 0.0043 2.40903

The model statistics for table 21 are as follows:

• RMSE = 871 psi.

• R2 = 0.5444 percent.

• N = 42.

Table 22. Range of data used for 28-day PCC cylinder strength.

Parameter Minimum Maximum Average

w/c ratio 0.27 0.71 0.42

Cementitious content 376 936 664

Compressive strength 3,034 7,611 5,239

Figure 133. Graph. Predicted versus measured for 28-day cylinder compressive strength 

model.

0

2000

4000

6000

8000

0 2000 4000 6000 8000

Predicted Compressive Strength, psi

Measured Compressive Strength, psi

N = 42

R2 = 0.544

RMSE = 871 psi
91

Figure 134. Graph. Residual error plot for 28-day cylinder compressive strength model.

The recommended 28-day compressive strength model is as shown in figure 135.

Figure 135. Equation. Prediction model 1 for fc,28d.

Where:

f’c,28d = 28-day compressive strength, psi.

w/c = Water to cementitious materials ratio.

CMC = Cementitious materials content, lb/yd3

.

Figure 136 and figure 137 show the sensitivity of this model to w/c ratio and CMC. The change 

in compressive strength appears reasonable for both of the parameters for the range of values 

evaluated. They are also consistent with the data in the database. Within practical ranges, a

change in CMC from 500 to 650 lb/ft3 increases the 28-day strength from approximately 4,700 to 

5,300 psi for a w/c ratio of 0.4. Likewise, a decrease in the w/c ratio from 0.5 to 0.35 increases 

the strength from 4,700 to 5,200 psi.

f

c,28d = 4,028.41841 ‒ 3,486.3501 × w⁄c + 4.02511 × CMC
92

Figure 136. Graph. 28-day compressive strength model sensitivity to w/c ratio.

Figure 137. Graph. 28-day compressive strength model sensitivity to CMC.

Compressive Strength Model 2: Short-Term Cylinder Strength Model

Cylinder strength data were available for the SPS sections at pavement ages of 14 days, 28 days, 

and 1 year for a majority of the sections. Although two sections with strength data at 10 years 

were available, data in the model were limited for ages up to 1 year. Therefore, this model 

predicts the strength up to an age of 1 year.

Since this model utilizes only SPS data, a large set of independent variables was available for 

evaluation. Additionally, it is likely that this model will be used after approval of the mix design 

for a project or possibly even after initial construction, during which time more mix design 

parameters will be known for accurate prediction. The model developed includes pavement 

age as an independent parameter. Because the dataset includes multiple measurements or 

repeated readings of the same section, this parameter has been treated with a hierarchical 

modeling approach.

3000

4000

5000

6000

7000

8000

0 0.2 0.4 0.6 0.8

Compressive Strength (psi)

w/c ratio

3000

4000

5000

6000

7000

8000

400 600 800 1000 1200

Compressive Strength (psi)

Cementitious Material Content (lb/yd3)
93

This model was established as shown in figure 138.

6358.60655 3.53012* 34.24312* / * 633.3489*ln( ) , f CMC w c uw t c t = + − +

Figure 138. Equation. Prediction model 2 for fc,t.

Where:

fc,t = Compressive strength at age t years, psi.

CMC = Cementitious materials content, lb/yd3.

w/c = Water to cement ratio.

uw = Unit weight, lb/ft3.

t = Short-term age, years.

The regression statistics for this model are presented in table 23, and details of the range of data 

used to develop the model are presented in table 24.

Table 23. Regression statistics for short-term cylinder strength model.

Variable DF Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept 1 6,358.60655 1,213.09762 5.24 < 0 .0001 0

Cementitious 1 3.53012 0.90968 3.88 0.0002 2.15941

(w/c) × unit weight 1 -34.24312 11.00358 -3.11 0.0026 2.152

Ln(age) 1 633.3489 87.49625 7.24 < 0.0001 1.00604

The model statistics for table 23 are as follows:

• RMSE = 789 psi.

• R2 = 0.666 percent.

• N = 79.

Table 24. Range of data used for short-term cylinder strength model.

Parameter Minimum Maximum Average

w/c ratio 0.27 0.69 0.43

Cementitious content 376 936 660

Unit weight 124 151 143

Pavement age 0.0384 1.0000 0.3081

Compressive strength 2,480 10,032 5,256

The model was developed using 79 data points, and the prediction has an R2 value of 

66.6 percent and an RMSE value of 789 psi. The reason for an improved R2 compared to the 

28-day strength model is not clear from these analyses. Figure 139 and figure 140 show the 

predicted versus measured plot and the residual plot, respectively.
94

Figure 139. Graph. Predicted versus measured for short-term cylinder compressive 

strength model.

Figure 140. Graph. Residual errors for short-term cylinder compressive strength model.

Figure 141 through figure 143 show the sensitivity of this model to CMC, w/c ratio, and age,

respectively. These trends are all reasonable. Figure 141 and figure 142 show the change in 

compressive strength at two ages, 28 days and 1 year, which are almost at the lower and upper 

bounds of ages included in this model. The plot in figure 143 can be considered a strength gain 

curve for typical unit weight and w/c ratios used in mix designs.

0

2000

4000

6000

8000

10000

12000

0 2000 4000 6000 8000 10000 12000

Predicted Compressive Strength, psi

Measured Compressive Strength, psi

N = 79

R2 = 0.666

RMSE = 789 psi
95

Figure 141. Graph. Short-term cylinder compressive strength sensitivity to CMC.

Figure 142. Graph. Short-term cylinder compressive strength sensitivity to w/c ratio.

Figure 143. Graph. Short-term cylinder compressive strength sensitivity to age.

3000

5000

7000

9000

11000

300 500 700 900 1100

Compressive Strength (psi)

Cementitious Material Content (lb/yd3)

PCC COMPRESSSIVE STRENGTH

28 Day Strength

1 Year Strength

w/c - 0.4

Unit Weight - 145 pcf

2000

4000

6000

8000

0 0.2 0.4 0.6 0.8

Compressive Strength (psi)

w/c Ratio

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 Year 

Strength

CMC - 600 lb/yd3

Unit Weight - 145 pcf

3000

5000

7000

9000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Compressive Strength (psi)

Pavement Age (years)

PCC COMPRESSSIVE STRENGTH

Short 

Term 

Strength
96

Compressive Strength Model 3: Short-Term Core Strength Model

The core strength data in the LTPP database were used for this model. While the materials and 

test ages are similar to the short-term cylinder strength model, the compressive strength of the 

cores is representative of the consolidation and quality of construction in the field. An initial 

comparison of core versus cylinder strengths was performed to determine if there was a 

significant difference in two strength values. Data were matched by section and age. Data were 

grouped in several age categories so that strength comparisons could be made at corresponding 

ages. Generally, each category up to 56 days was grouped for ages of ±3 days. For ages close to 

6 months to 1 year, the results were grouped for ages ±30 days. The ages at which strength test 

results were common to both cores and cylinders were 14 days, 21 days, 28 days, 35 days, 

41 days, and 1 year.

The comparison showed that there was no significant difference between strength values 

determined from core or cylinder tests. The paired t-test results shown in table 25 indicate that 

there is no significant difference between the two strengths (P < t-critical). Figure 144, which has

a trend line forced to zero intercept, shows the same results. Note that a trend line with a nonzero intercept produces a higher R2 (0.67), which is consistent with the Pearson correlation value 

of 0.82 presented in table 25.

In the development of this model, parameters similar to the cylinder strength model were 

evaluated. In addition, the effect of curing was considered with greater attention. However, 

curing did not prove to be a significant variable. As this model attempts to predict the strength up 

to 1 year in age, the variable accounting for age was treated in a hierarchical fashion.

Figure 144. Graph. Comparison of core and cylinder strengths for SPS sections.
97

Table 25. Paired t-test results for comparison of core and cylinder strengths in SPS data.

Parameter Core Cylinder

Mean 5345.3 5472.3

Variance 3,307,974.59 3,003,561.77

Observations 312 312

Pearson correlation 0.82

Hypothesized mean 

difference 0

DF 311

t-Stat -2.11

P(T ≤ t) one-tail 0.02

t-critical one-tail 1.65

P(T ≤ t) two-tail 0.04

t-critical two-tail 1.97

This model was established as shown in figure 145.

-199.84664* FM 611.30879 *ln( )

98.92962 5.70412* 28.48527 * uw 2570.13151*MAS* w/c ,

t

f c t CMC

+

= + + +

Figure 145. Equation. Prediction model 3 for fc,t.

Where:

fc,t = Compressive strength at age t years, psi.

CMC = Cementitious materials content, lb/yd3.

uw = Unit weight, lb/ft3

.

MAS = Maximum aggregate size, inch.

w/c = Water to cementitious materials ratio.

FM = Fineness modulus of fine aggregate.

t = Short-term age in years.

The regression statistics for this model are presented in table 26. The model was developed using 

294 points, and the prediction has an R2 value of 67.6 percent and an RMSE value of 1,122 psi. 

Table 27 provides details of the range of data used to develop the model. Figure 146 and 

figure 147 show the predicted versus measured plot and the residual plot, respectively. 

Figure 148 through figure 153 show the sensitivity of this model to CMC, unit weight, MAS, 

w/c ratio, FM, and age, respectively.
98

Table 26. Regression statistics for short-term core strength model.

Variable DF Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept 1 98.92962 1,544.34064 0.06 0.949 0

Cementitious 1 5.70412 0.36589 15.59 < 0.0001 1.23548

Unit weight 1 28.48527 10.59672 2.69 0.0076 1.0182

(MAS) × (w/c ratio) 1 2,570.13151 538.267 -4.77 < 0.0001 1.2201

Fineness modulus (FM) 1 -199.84664 120.68288 -1.66 0.0988 1.01426

Ln(age) 1 611.30879 45.08962 13.56 < 0.0001 1.00026

The model statistics for table 26 are as follows:

• RMSE = 1,122 psi.

• R2 = 0.6761 percent.

• N = 294.

Table 27. Range of data used for short-term core strength model.

Parameter Minimum Maximum Average

w/c ratio 0.27 0.69 0.42

Cementitious content 376 999 670

Unit weight 120 163 144

MAS 0.375 1.000 0.683

FM 2.50 4.37 3.05

Pavement age 0.0380 2.2160 0.4230

Compressive strength 1,990 11,350 5,596
99

Figure 146. Graph. Predicted versus measured for short-term core compressive strength 

model.

Figure 147. Graph. Residual errors for short-term core compressive strength model.

Figure 148. Graph. Short-term core compressive strength sensitivity to CMC.

0

2000

4000

6000

8000

10000

12000

Predicted Compressive Strength, psi

0 2000 4000 6000 8000 10000 12000

Measured Compressive Strength, psi

N = 294

R2 = 0.6761

RMSE = 1122 psi

y = 2E-07x + 0.0005

R2 = 7E-14

-4000

0

4000

0 2000 4000 6000 8000 10000

Residual Compressive Strength, psi

Predicted Compressive Strength, psi

3000

5000

7000

9000

11000

300 500 700 900 1100

Compressive Strength (psi)

Cementitious Material Content (lb/yd3)

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 Year 

Strength

w/c - 0.4

Unit Weight - 145 pcf

MAS - 0.75

FM - 3.0
100

Figure 149. Graph. Short-term core compressive strength sensitivity to unit weight.

Figure 150. Graph. Short-term core compressive strength sensitivity to MAS.

Figure 151. Graph. Short-term core compressive strength sensitivity to w/c ratio.

3000

5000

7000

9000

120 130 140 150 160 170

Compressive Strength (psi)

Unit Weight (pcf)

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 Year 

Strength

CMC - 600 lb/yd3

w/c - 0.4

MAS - 0.75

FM - 3.0

3000

5000

7000

9000

0.2 0.4 0.6 0.8 1 1.2

Compressive Strength (psi)

Maximum Aggregate Size

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 Year 

Strength

CMC - 600 lb/yd3

w/c - 0.4

Unit Weight - 145 pcf

FM - 3.0

3000

5000

7000

9000

0.2 0.4 0.6 0.8

Compressive Strength (psi)

w/c Ratio 

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 Year 

Strength

CMC - 600 lb/yd3

Unit Weight - 145 pcf

MAS - 0.75

FM - 3.0
101

Figure 152. Graph. Short-term core compressive strength sensitivity to fine aggregate FM.

Figure 153. Graph. Short-term core compressive strength sensitivity to age.

Compressive Strength Model 4: All Ages Core Strength Model

The compressive strength model 4 was developed to provide a strength estimate for in situ

strength at all ages of a project and covers both short-term and long-term estimates. This model 

provides the ability to assess the strength development over the entire lifetime of a project and 

make more realistic estimates of distress development. This necessitates the use of both SPS and 

GPS section data. The short-term strength data for this model were primarily from SPS sections,

and the long-term strength data were controlled by GPS sections. Data from SPS and GPS 

sections primarily consisted of strength tests for pavement ages less than 3 years and greater than

5 years, respectively. However, this resulted in the use of a limited set of independent parameters 

for the model. Only information available for GPS sections could be included. For example,

gradation parameters of coarse and fine aggregates were not considered when developing 

this model.

This model can be expressed as follows:

-6022.44 -854.46*w/c 4.8656* 68.5337* 533.15*ln( ) , f CMC uw t c t = + + +

Figure 154. Equation. Prediction model 4 for fc,t.

3000

5000

7000

9000

1 2 3 4 5 6

Compressive Strength (psi)

Fineness Modulus

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 Year 

Strength

CMC - 600 lb/yd3

w/c - 0.4

Unit Weight - 145 pcf

MAS - 0.75

3000

5000

7000

9000

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Compressive Strength (psi)

Pavement Age (years)

PCC COMPRESSSIVE STRENGTH

Short 

Term 

Strength

CMC - 600 lb/yd3

w/c - 0.4

Unit Weight - 145 pcf

MAS - 0.75

FM - 3.0
102

Where:

fc,t = Compressive strength at age t years, psi.

w/c = Water to cement ratio.

CMC = Cementitious materials content, lb/yd3

.

uw = Unit weight, lb/ft3.

t = Short-term age, years.

The regression statistics for this model are presented in table 28. The model was developed using 

580 data points, and the prediction has an R2 value of 55.4 percent and an RMSE value of 

992 psi. Table 29 provides details of the range of data used to develop the model. Figure 155

and figure 156 show the predicted versus measured plot and the residual plot, respectively. 

Figure 157 through figure 160 show the sensitivity of this model to w/c ratio, CMC, unit weight, 

and age, respectively. Again, the sensitivity plots showing the variation in core compressive 

strength with changes in w/c ratio, CMC, and unit weight are presented for 28 days, 1 year, and 

20 years. The rate of strength gain is much higher in the short term (28 days to 1 year) than 

during the next 19 years in the long term. The use of the logarithmic function for the age 

parameter is justified, as these trends mimic actual strength gain in the field or in laboratory cast 

specimens. Figure 160 can be treated as the strength gain relationship representative of a typical 

mix (w/c of 0.4, cement content of 600 lb/yd3

, and unit weight of 145 lb/ft3).

Table 28. Regression statistics for all ages core strength model.

Variable Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept -6,022.44 2,028.37 -2.97 0.0032 0

w/c ratio -854.46 675.86 -1.26 0.2069 2.15941

Cementitious 4.8656 0.5737 8.48 < 0.0001 2.152

Unit weight 68.5337 13.4368 5.1 < 0.0001 1.00604

Ln(age) 533.15 22.3343 23.87 < 0.0001 1.00026

The model statistics for table 28 are as follows:

• RMSE = 992 psi.

• R2 = 0.5538 percent.

• N = 580.

Table 29. Range of data used for all ages core strength model.

Parameter Minimum Maximum Average

w/c ratio 0.00 0.72 0.43

Cementitious content 354 999 615

Unit weight 120 163 145

Pavement age 0.0380 45.3840 6.4320

Compressive strength 1,990 11,750 6,430
103

Figure 155. Graph. Predicted versus measured for all ages core compressive strength 

model.

Figure 156. Graph. Residual errors for all ages core compressive strength model.

Figure 157. Graph. All ages core compressive strength sensitivity to w/c ratio.

0

2000

4000

6000

8000

10000

12000

0 2000 4000 6000 8000 10000 12000

Predicted Compressive Strength, psi

Measured Compressive Strength, psi

N = 580

R2 = 0.5538

RMSE = 992 psi

y = -0.002x + 79.723

R2 = 5E-06

-6000

0

6000

0 2000 4000 6000 8000 10000 12000

Residual Compressive Strength, psi

Predicted Compressive Strength, psi

3000

5000

7000

9000

11000

0.2 0.4 0.6 0.8

Compressive Strength (psi)

w/c Ratio

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 year 

strength

20 Years 

Strength

CMC - 600 lb/yd3

Unit Weight - 145 pcf
104

Figure 158. Graph. All ages core compressive strength sensitivity to CMC.

Figure 159. Graph. All ages core compressive strength sensitivity to unit weight.

Figure 160. Graph. All ages core compressive strength sensitivity to age.

Compressive Strength Model 5: Long-Term Core Strength Model

This model was developed to estimate the long-term strength of cores taken from a pavement. 

Data from only the GPS sections were utilized, and they included sections greater than 5 years in 

age. Strength data at multiple ages were available on some of the sections. A preliminary 

3000

5000

7000

9000

11000

300 500 700 900 1100

Compressive Strength (psi)

Cementitious Material Content (lb/yd3)

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 year 

strength

20 year 

strength

w/c - 0.4

Unit Weight - 145 pcf

3000

5000

7000

9000

11000

120 130 140 150 160

Compressive Strength (psi)

Unit Weight (pcf)

PCC COMPRESSSIVE STRENGTH

28 Day 

Strength

1 year 

strength

20 Years 

Strength

CMC - 600 lb/yd3

w/c - 0.4

3000

5000

7000

9000

11000

0 5 10 15 20

Compressive Strength (psi)

Pavement Age (years)

PCC COMPRESSSIVE STRENGTH

All Ages 

Strength

CMC - 600 lb/yd3

w/c - 0.4

Unit Weight - 145 pcf
105

analysis indicated that pavement age was not a significant factor in the model. In other words, for 

pavements that have been in service for several years, the strength was more a function of its 

material parameters than age. This suggests that strength gain is relatively minimal after 5 years 

or is not noticed in a statistical sense. It then becomes reasonable, or perhaps even necessary,

from a statistical standpoint to average the strengths for each section. 

The model selected for the long-term strength can be expressed as follows:

2 f c,LT = -3467.3508+ 3.63452*CMC + 0.42362*uw

Figure 161. Equation. Prediction model 5 for fc,LT.

Where:

fc, LT = Long-term compressive strength, psi.

CMC = Cementitious materials content, lb/yd3.

uw = Unit weight, lb/ft3

.

The regression statistics for this model are presented in table 30. The model was developed 

using 201 data points, and the prediction has an R2 value of 18 percent and an RMSE value of 

1,179 psi. Table 31 provides details of the range of data used to develop the model. Figure 162

and figure 163 show the predicted versus measured plot and the residual plot, respectively. From 

observing figure 162, it is evident that this model does not have a good predictive ability, and 

while there is no significant bias, the error in prediction is fairly high (see figure 163). This 

model needs to be used with caution, and other means to verify the value would be necessary, 

such as core tests.

Table 30. Regression statistics for long-term core strength model.

Variable DF Estimate

Standard 

Error t-Value Pr > |t| VIF

Intercept 1 -3,467.3508 1,720.49637 -2.02 0.0452 0

Cementitious 1 3.63452 1.38354 2.63 0.0093 1.024

(Unit weight)2 1 0.42362 0.06634 6.39 < 0.0001 1.024

The mode statistics for table 30 are as follows:

• RMSE = 1,179 psi.

• R2 = 0.1803 percent.

• N = 201.

Table 31. Range of data used for long-term core strength model.

Parameter Minimum Maximum Average

Cementitious content 354 781 550

Unit weight 134 156 147

Compressive strength 4,315 11,750 7,655
106

Figure 162. Graph. Predicted versus measured for long-term core compressive strength 

model.

Figure 163. Graph. Residual errors for long-term core compressive strength model.

Relative Comparison of All Compressive Strength Models 

The compressive strength models presented in this section reproduce the trends present in the 

datasets used for each correlation. It is highly recommended that a user estimate the strength 

based on as many models as possible with the information available at the time of analysis. This 

might provide a fair assessment of the ranges of compressive strength likely for the project and at 

different ages.

This section presents a comparison of the various models, and the graphs used for this discussion 

also include raw data plotted with the various relationships. Figure 164 through figure 168 show 

the relationship between compressive strength and CMC, w/c ratio, and unit weight, respectively. 

Figure 167 and figure 168 show the strength gain at short- and long-term ages, respectively. 

Note that relationships have been plotted for typical values for all variables, and the raw data 

used in the models do not necessarily lie on the plots.

4000

6000

8000

10000

12000

4000 6000 8000 10000 12000

Predicted Compressive Strength, psi

Measured Compressive Strength, psi

N = 201

R2 = 0.1803

RMSE = 1179 psi

y = 0.0384x - 291.07

R2 = 0.0003

-4000

0

4000

4000 5000 6000 7000 8000 9000 10000

Residual Compressive Strength, psi

Predicted Compressive Strength, psi
107

Figure 164. Graph. Model compressive strength prediction for varying CMC.

Figure 165. Graph. Model compressive strength prediction for varying w/c ratio.

1000

3000

5000

7000

9000

11000

300 500 700 900 1100

Compressive Strength (psi)

Cementitious Material Content (lb/yd3)

a+b*WC+c*CMC - cyl 28days

a+b*CMC+c*(WC*UW)+d*ln(age) - cyl short term

a+b*WC+c*CMC+d*UW+e*ln(age) - core all ages

a+b*CMC+c*UW+d*(MAS*WC)+e*FM+f*ln(age) - core short term

Raw Data - core

Raw Data - cyl

w/c - 0.4

Unit Weight - 145 pcf

MAS - 0.75

FM - 3.0

1000

3000

5000

7000

9000

11000

0.2 0.3 0.4 0.5 0.6 0.7 0.8

Compressive Strength (psi)

Water Cement Ratio

28 DAY - PCC COMPRESSIVE STRENGTH

a+b*WC+c*CMC - cyl 28days

a+b*CMC+c*(WC*UW)+d*ln(age) - cyl short term

a+b*WC+c*CMC+d*UW+e*ln(age) - core all ages

a+b*CMC+c*UW+d*(MAS*WC)+e*FM+f*ln(age) - core short term

Raw Data - core

Raw Data - cyl

CMC - 600 lb/yd3

Unit Weight - 145 pcf

MAS - 0.75
108

Figure 166. Graph. Model compressive strength prediction for varying unit weights.

Figure 167. Graph. Strength gain in the short-term predicted by three models.

1000

3000

5000

7000

9000

11000

120 125 130 135 140 145 150 155 160

Compressive Strength (psi)

Unit Weight (lb/ft3)

28 DAY - PCC COMPRESSIVE STRENGTH

a+b*WC+c*CMC+d*UW+e*ln(age) - core all ages

a+b*CMC+c*UW+d*(MAS*WC)+e*FM+f*ln(age) - core short term

Raw Data - core

Raw Data - cyl

CMC - 600 lb/yd3

w/c - 0.4

MAS - 0.75

FM - 3.0

4000

5000

6000

7000

8000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Compressive Strength (psi)

Pavement Age (years)

a+b*CMC+c*(WC*UW)+d*ln(age) - cyl short term

a+b*WC+c*CMC+d*UW+e*ln(age) - core all ages

a+b*CMC+c*UW+d*(MAS*WC)+e*FM+f*ln(age) - core short term

CMC - 600 lb/yd3

w/c - 0.4

Unit Weight - 145 pcf

MAS - 0.75

FM - 3.0
109

Figure 168. Graph. Long-term strength gain predicted by the models.

The following observations can be made:

• The predictions are within 700 psi of each other for a given CMC (see figure 164). 

However, for more typical ranges of cement contents, the predictions are within 300 to 

400 psi of each other. The short-term core compressive model has the steepest slope for 

this relationship.

• Figure 165 suggests that the predictions have a range as high as 1,500 psi for a given w/c 

ratio, especially at very high w/c ratios. However, within typical ranges (0.3 to 0.5), the 

models predict within a range of 250 to 800 psi. The range slightly increases at lower w/c 

ratios. The short-term cylinder strength has the highest slope in this case.

• Based on the trend presented in figure 166, for a given level of unit weight, the 

compressive strength predictions are within 200 to 300 psi for typical ranges of unit 

weight (140 to 145 lb/ft3

). The prediction can vary by about 800 psi for very high unit 

weight values. Note that the short-term cylinder compressive strength model has not been 

included in this plot, as the variable appears as a transformed variable in the model, and 

its effect cannot be isolated.

• Short-term strength predictions by all models that are relevant to short-term strengths 

show predictions within 200 to 400 psi of each other. The predictions are closer in value 

at as the age increases from 14 days to 1 year (see figure 167).

• Figure 168 suggests that the long-term strength predicted by the core all ages model 

is close to the strength predicted by the long-term model. This is essentially because 

the data used for strengths at ages greater than 5 years were obtained from the same 

GPS sections.

1000

3000

5000

7000

9000

11000

13000

15000

0 2 4 6 8 10 12 14 16 18 20

Compressive Strength (psi)

Pavement Age (years)

a+b*WC+c*CMC+d*UW+e*ln(age) - core all ages

Raw Data - Core

Series3

CMC - 600 lb/yd3

w/c - 0.4

Unit Weight - 145 pcf

MAS - 0.75

FM - 3.0
110

These observations illustrate the benefit of comparing predictions made by the various models 

available to obtain the range of strength that each project or observation could develop. Any 

other information to substantiate or validate the strength predictions should be utilized whenever 

possible, such as strength values from other projects that have used similar materials and 

mix design.

PCC Flexural Strength Models

The first step with the development of PCC flexural strength model was to assemble the relevant 

data in a manner appropriate for model development, followed by the actual statistical analyses. 

Statistical analyses to develop prediction models for flexural strength involved the validation 

of existing models and model forms as well as the development of new models to predict 

flexural strength.

The validation of existing models was a relatively straightforward exercise that involved fitting 

the data assembled in this study to the most commonly referenced model forms. Flexural 

strength has been correlated to compressive strength in previous models. Furthermore, in the 

development of new models, attempts were made to provide relationships as a function of readily 

available information. This study therefore attempted to develop models based on the 

compressive strength, as well as material properties and age. This provides options on which 

models to use, depending on the parameters and mix design information available.

The following are key points to note about flexural strength data:

• Flexural strength test results are available for SPS sections only.

• Test data were collected at 14 days, 28 days, and 1 year. Compressive strength data 

were also present for these specific ages. The data were matched for these specific 

age categories.

• Flexural strengths for the SPS-2 sections were predominantly at two levels. Strength 

results were in two distinct categories representing the two mix designs targeting low 

(550 psi) and high (900 psi) strengths.

Validation of Existing Models 

Previous attempts have been made to correlate PCC flexural strength to the compressive 

strength, as discussed in chapter 2. These correlations generally have used a power model of the 

following form:

b

r c M = a * f '

Figure 169. Equation. Mr.

Where:

a = 7.5 to 11.7 for b = 0.5.

a = 2 to 2.7 for b = 0.67.
111

The data assembled from the LTPP database was used to develop models with b = 0.5 and 0.67. 

Table 32 shows a summary of the models developed. The regressed constants, a and b, were 

found to be within the range of values reported by the other studies discussed in chapter 2. This 

validation not only provides feasible models, but it also confirms that the data being used in 

this study can reasonably represent the broad range considered in the various studies. The 

correlations are presented in figure 170 and figure 171 for the power models with exponents 

of 0.5 and 0.67, respectively.

Table 32. Power models developed for flexural strength prediction using LTPP 

data for validation.

Model a b R2 N

b

r c M = a * f ' 10.3022 0.5 0.446 185

2.4277 0.67 0.449 185

Figure 170. Graph. Predicted versus measured for validating 0.5 power flexural strength 

model.

Figure 171. Graph. Predicted versus measured for validating 0.667 power flexural strength 

model.

100

300

500

700

900

1100

1300

100 300 500 700 900 1100 1300

Predicted Modulus of Rupture, psi

Measured Modulus of Rupture, psi

N = 185

R2 = 0.4460

RMSE = 84 psi

100

300

500

700

900

1100

1300

100 300 500 700 900 1100 1300

Predicted Modulus of Rupture, psi

Measured Modulus of Rupture, psi

N = 185

R2 = 0.4493

RMSE = 111 psi
112

Flexural Strength Model 1: Flexural Strength Based on Compressive Strength

The flexural strength model 1 provides the best correlation between compressive strength and 

flexural strength with the LTPP data. The model form utilizes the power equation. This model 

will be most useful for cases when the compressive strength of the PCC has been determined 

through a routine cylinder break. 

This model can be expressed as follows:

0.4082 22.7741* '

c MR = f

Figure 172. Equation. Prediction model 6 for MR.

Where:

MR = Flexural strength, psi.

f'c,= Compressive strength determined at the same age, psi.

The regression statistics for this model are presented in table 33. The model was developed using 

185 data points, and the prediction has an R2 value of 45.2 percent and an RMSE value of 69 psi. 

Table 34 provides details of the range of data used to develop the model. The confidence limits 

are both within acceptable ranges for both the regressed coefficients (i.e., limits are positive 

numbers). Figure 173 and figure 174 show the predicted versus measured plot and the residual 

plot, respectively. 

Table 33. Regression statistics for flexural strength model based on compressive strength.

Parameter Estimate

Standard 

Error

Approximate 

95 Percent 

Confidence Limits

a 22.7741 6.6362 9.6807 to 35.8674

b 0.4082 0.0338 0.3416 to 0.4748

The model statistics for table 33 are as follows:

• RMSE = 69 psi.

• R2 = 0.452 percent.

• N = 185.

Table 34. Range of data used for flexural strength model based on compressive strength.

Parameter Minimum Maximum Average

Compressive strength 1,770 10,032 5,431

Flexural strength 467 1,075 754
113

Figure 173. Graph. Predicted versus measured values for flexural strength model based on 

compressive strength.

Figure 174. Graph. Residuals errors for flexural strength model based on compressive 

strength.

Figure 175 shows a comparison of the power models used to validate the data and also to 

develop a new correlation. Note that the three power models (the new equation developed for 

this study as well as the validation models) provide close estimates (within 50 psi) in the 

4,500- to 5,500-psi compressive strength range.

The ACI and PCA models are plotted for comparison. Also plotted in figure 175 are the raw data 

that were used in the model. Clearly, the ACI equation is very conservative for this data. It also 

has been found to give a conservative estimate for several large datasets that have been used in 

flexural strength model prediction. Conversely, the PCA model fits the LTPP data more closely. 

The reasons for this lack of fit of the current data with the previous models may be too many to 

fully explain. The data used in models from prior studies often came from mixes batched under 

controlled laboratory experiments and were typical of paving and structural concrete. The mixes 

used in the current model developed from LTPP data relies on only mixes proportioned for 

typical paving operations. Furthermore, the LTPP data used are from many projects widely 

100

300

500

700

900

1100

1300

100 300 500 700 900 1100 1300

Predicted Modulus of Rupture, psi

Measured Modulus of Rupture, psi

N = 185

R2 = 0.452

RMSE = 69 psi

y = 0.0131x - 10.037

R2 = 0.0001

-500

0

500

100 300 500 700 900 1100 1300

Residual Modulus of Rupture, psi

Predicted Modulus of Rupture, psi
114

dispersed around the United States. This in itself makes the models more robust than any 

previous data used to make similar correlations.

Figure 175. Graph. Comparison of flexural strength models based on compressive strength.

The spread in the raw data about the prediction model in figure 175 indicates that there are 

factors other than compressive strength that influence the flexural strength of PCC. Among the 

various factors influencing flexural strength are the mix design parameters and age of the 

concrete. These variables were considered in the other models developed in this study.

Flexural Strength Model 2: Flexural Strength Based on Age, Unit Weight, and w/c Ratio

Flexural strength model 2 provides a correlation between flexural strength and mix design 

parameters, specifically the unit weight and w/c ratio. Age is also a parameter in this model, 

which helps reduce some of the variability seen in the prediction relative to the predictions 

shown in figure 175. This model will be most useful for cases when the compressive strength 

of the PCC is not determined but mix design information is available. Also, the user has the 

option of predicting the 28-day strength value for design or estimating the strength at traffic 

opening time. 

This model can be expressed as follows:

Figure 176. Equation. Prediction model 7 for MRt.

400

600

800

1000

1200

Modulus of Rupture (psi)

Compressive Strength (psi)

MR = 10.3022* (f'c ^ 0.5)

MR = 2.42 * (f'c^ 0.67)

MR = 22.7741* (f'c^0.4082)

MR = 7.5*(f'c^0.5) - ACI Model

MR = 9.5*(f'c^0.5) - PCA Model

Raw data

MRt = 676.0159 ‒ 1,120.31 × w⁄c + 4.1304 × uw + 35.74627 × ln(t)
115

Where:

MRt = Flexural strength at age t years, psi.

w/c = Water to cement ratio.

uw = Unit weight, lb/ft3

.

t = Pavement age, years.

The regression statistics for this model are presented in table 35. The model was developed using 

62 data points, and the prediction has an R2 value of 61.1 percent and an RMSE value of 69 psi. 

Table 36 provides details of the range of data used to develop the model. Figure 177 and 

figure 178 show the predicted versus measured plot and the residual plot, respectively.

Table 35. Regression statistics for flexural strength model based on age, unit weight, and 

w/c ratio.

Variable DF Estimate

Standard 

Error t-value Pr > t VIF

Intercept 1 676.0159 277.7887 2.43 0.0181 0

w/c 1 -1,120.31 141.3573 -7.93 < 0.0001 1.00591

Unit weight 1 4.1304 1.88934 2.19 0.0329 1.00311

Ln(age) 1 35.74627 8.78516 4.07 0.0001 1.00619

The model statistics for table 35 are as follows:

• RMSE = 91 psi.

• R2 = 0.6111 percent.

• N = 62.

Table 36. Range of data used for flexural strength model based on age, unit weight, and 

w/c ratio.

Parameter Minimum Maximum Average

w/c ratio 0.27 0.58 0.40

Unit weight 124 151 142

Pavement age 0.0384 1.0000 0.3169

Flexural strength 467 978 742
116

Figure 177. Graph. Predicted versus measured values for flexural strength model based on 

age, unit weight, and w/c ratio.

Figure 178. Graph. Residuals errors for flexural strength model based on age, unit weight, 

and w/c ratio.

Flexural Strength Model 3: Flexural Strength Based on Age, Unit Weight, and CMC

The data used in the previous model also provided a good correlation by replacing the w/c ratio 

parameter with CMC. The model is expressed as follows:

Figure 179. Equation. Prediction model 8 for MRt.

Where:

MRt = Flexural strength at age t years, psi.

CMC = Cementitious materials content, lb/yd3

.

uw = Unit weight, lb/ft3.

t = Pavement age, years.

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000 1200 1400

Predicted Modulus of Rupture, psi

Measured Modulus of Rupture, psi

N = 62

R2 = 0.6111

RMSE = 91 psi

y = 1E-05x - 0.0088

R2 = 2E-10

-300

0

300

400 600 800 1000

Residual Modulus of Rupture, psi

Predicted Modulus of Rupture, psi

= 24.15063 + 0.55579×CMC + 2.96376× uw + 35.54463× ln(t) MRt
117

The regression statistics for this model are presented in table 37. The model was developed using 

62 data points, and the prediction has an R2 value of 70.2 percent and an RMSE value of 80 psi. 

Table 38 provides details of the range of data used to develop the model. Figure 180 and 

figure 181 show the predicted versus measured plot and the residual plot, respectively.

Figure 182 to figure 185 present the sensitivity of the mix design-based flexural strength models 

to CMC, w/c ratio, unit weight, and age. Figure 182 and figure 183 show that prediction models 

in figure 176 and figure 179 do not show any sensitivity to CMC and w/c ratio. For typical

values of these parameters, the flexural strength prediction from these two models could show a 

difference of about 200 psi for extreme values of w/c ratios. However, within a typical range of 

0.35 to 0.45, the flexural strength prediction is within 50 psi. Similar trends are evident for the 

w/c ratio parameter. Therefore, if all details about a mix design are available, it is highly 

recommended that both models be used to predict flexural strength so that the user has a fair 

estimate of the MR range. Figure 184 shows that the predictions are close from both models. 

Likewise, figure 185, which is more or less a flexural strength gain model for a typical mix 

design, shows close predictions from both models.

Table 37. Regression statistics for flexural strength model based on age, unit weight, and 

CMC.

Variable DF Estimate

Standard 

Error t-value Pr > t VIF

Intercept 1 24.15063 236.7606 0.1 0.9191 0

CMC 1 0.55579 0.05563 9.99 < 0.0001 1.01522

Unit weight 1 2.96376 1.66087 1.78 0.0796 1.01253

Ln(age) 1 35.54463 7.68504 4.63 < 0.0001 1.00573

The model statistics for table 37 are as follows:

• RMSE = 80 psi.

• R2 = 0.7023 percent.

• N = 62.

Table 38. Range of data used for flexural strength model based on age, unit weight, and 

CMC.

Parameter Minimum Maximum Average

CMC 388 936 668

Unit weight 124 151 142

Pavement age 0.0384 1.0000 0.3169

Flexural strength 467 978 742
118

Figure 180. Graph. Predicted versus measured values for flexural strength model based on 

age, unit weight, and CMC.

Figure 181. Graph. Residuals errors for flexural strength model based on age, unit weight, 

and CMC.

Figure 182. Graph. Sensitivity of flexural strength predictions to CMC.

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000 1200 1400

Predicted Modulus of Rupture, psi

Measured Modulus of Rupture, psi

N = 62

R2 = 0.7023

RMSE = 80 psi

y = 2E-06x - 0.0008

R2 = 6E-12

-300

0

300

400 600 800 1000

Residual Modulus of Rupture, psi

Predicted Modulus of Rupture, psi

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000 1200

Predicted MR, psi

Cementitious Material Content, lb/yd3

MR model as f(w/c)

MR model as f(CMC)

Raw data

w/c = 0.4

unit weight = 145 pcf

age = 28 days
119

Figure 183. Graph. Sensitivity of flexural strength predictions to w/c ratio.

Figure 184. Graph. Sensitivity of flexural strength predictions to unit weight.

Figure 185. Graph. Sensitivity of flexural strength predictions to age.

PCC Elastic Modulus Models

As with flexural strength, the development of PCC elastic modulus models required a detailed 

data assembly appropriate for statistical analyses. These analyses also entailed the validation of 

existing models as well as the development of new models. Model forms of existing models were 

utilized to fit the data assembled in this study for the validation. The development of new models 

was not as straightforward as for the other PCC models, primarily because the physical 

characteristics of a PCC mixture that affect the elastic modulus are not fully captured within 

the data used for building a mathematical relationship.

0

200

400

600

800

1000

1200

1400

0 0.2 0.4 0.6 0.8

Predicted MR, psi

w/c ratio

MR model as f(w/c)

MR model as f(CMC)

Raw data

CMC = 600 lb/yd3

unit weight = 145 pcf

age = 28 days

0

200

400

600

800

1000

1200

1400

100 110 120 130 140 150 160

Predicted MR, psi

Unit Weight, pcf

MR model as f(w/c)

MR model as f(CMC)

Raw data

w/c = 0.4

CMC = 600 lb/yd3

age = 28 days

0

200

400

600

800

1000

1200

1400

0 0.2 0.4 0.6 0.8 1

Predicted MR, psi

Age, years

MR model as f(w/c)

MR model as f(CMC)

w/c = 0.4

CMC = 600 lb/yd3

unit weight = 145 pcf
120

The following are key points to note about flexural strength data:

• Elastic modulus test results were available for SPS and GPS sections.

• Test data were collected at 28 days and 1 year for most of the SPS sections. In addition, 

two SPS-2 sections and two SPS-8 sections had data at 10 and 4.5 years, respectively. 

Compressive strength data also were present for corresponding specific ages. The data 

were matched for these specific age categories.

• SPS-2 sections representing the two mix designs targeting low (550 psi) and high 

(900 psi) strengths showed marginal differences in the elastic modulus results.

• After data reduction, a majority of the observations used in the statistical models 

belonged to GPS sections. Multiple sections at SPS-2 sites were averaged to produce a 

single observation, thereby reducing the number of SPS-2 observations.

Validation of Existing Models 

Existing models correlate elastic modulus to compressive strength and unit weight. Although 

more recent models have attempted to introduce lithological type of the coarse aggregate and 

admixture parameters, within the context of this study, there were not adequate data to validate 

them. Figure 186 to figure 189 summarize the regressed models using LTPP data.

c c E = a * f '

Figure 186. Equation. Ec as a function of square root of compressive strength.

Regressed coefficients for figure 186 are as follows: 

• a = 55,294.

Regression statistics for figure 186 are as follows:

• N = 514.

• R2 = 11.8 percent.

E = a* f'c + b

Figure 187. Equation. Model form for E as a function of compressive strength with slope 

and intercept.

Regressed coefficients for figure 187 are as follows: 

• a = 31,624.6.

• b = 2,013,192.
121

Regression statistics for figure 187 are as follows:

• N = 514.

• R2 = 11.8 percent.

b

c c E = a * f '

Figure 188. Equation. Ec.

Regressed coefficients for figure 188 are as follows: 

• a = 388,082.

• b = 0.2809.

Regression statistics for figure 188 are as follows:

• N = 514.

• R2 = 12.3 percent.

b c E = a *(UW ) *( f'c)

Figure 189. Equation. E as function of unit weight and compressive strength.

Regressed coefficients for figure 189 are as follows: 

• a = 80,849.3.

• b = 0.3648.

• c = 0.2527.

Regression statistics for figure 189 are as follows:

• N = 514.

• R2 = 10.8 percent.

The quality of prediction in the validated models is poor, as indicated by the R2 values in 

figure 186 through figure 189. The predicted versus measured plots for these models have not 

been included in this report, but they show higher predictions for the lower modulus values and 

lower predictions for the higher modulus values. This trend is common with elastic modulus 

models, especially considering that the data used in this study were not generated from 

controlled laboratory experiments. Also, while compressive strength is the most commonly used 

strength parameter and correlations with the compressive strength can be implemented most 

easily, there is an inherent drawback in correlating modulus to compressive strength. Modulus 

does not test the material to its limits, and, as discussed in chapter 2, it is more indicative of the 
122

elastic deformational characteristics of the material. Additionally, it often captures other ITZ 

characteristics and can be a good indicator of concrete durability. Finally, the data contain 

modulus measured at a wide range of ages. The new models developed therefore utilized other 

mix parameters that impact modulus including age.

Elastic Modulus Model 1: Model Based on Aggregate Type

Several mix design parameters were evaluated for the elastic modulus model 1 in addition to the 

compressive strength and unit weight parameters. The model development efforts particularly 

focused on the aggregate type, given the strong influence of the aggregate hardness on the 

measured elastic modulus values.

This model utilizes a subset of the data used in the model validation process and has only 71 data 

points compared to 514 observations in the validation models. This was primarily due to the 

inclusion of aggregate type in the relationship. Coarse aggregate type information is present in 

both the materials tables and the CTE tables of the LTPP database. Several data inconsistencies 

were found in comparing the aggregate types listed in these two tables; therefore, for the 

development of CTE models (discussed later in this chapter), only those cases with the same 

aggregate type in both tables were used. In other words, the two tables were used to validate the 

data against each other. This vastly reduced the dataset used. The dataset included both SPS and 

GPS sections; however, a majority of the data used in this model belonged to GPS sections.

A nonlinear analysis was performed to establish the following equation:

c Dagg E (4.499*(UW ) *( f'c) ) * 2.3481 0.2429 =

Figure 190. Equation. Prediction model 9 for Ec.

Where:

Ec = PCC elastic modulus, psi.

UW = Unit weight, lb/ft3

.

f'c = Compressive strength.

Dagg = Regressed constant depending on aggregate type as follows:

• = 10 for andesite.

• = 0.9286 for basalt.

• = 1.0079 for chert.

• = 0.9215 for diabase.

• = 1.0254 for dolomite.

• = 0.8333 for granite.

• = 1.0 for limestone.
123

• = 0.9511 for quartzite.

• = 1.0 for sandstone.

The development of the model required the use of a model form which accommodates aggregate 

type as categorical variables (assigned values of 1,0). The values for Dagg were initialized to 

1.0 at the start of the analyses and allowed to iteratively determine individual values for each 

aggregate type. The model had 71 observations, an R2 value of 35.8 percent, and an RMSE value

of approximately 500,000 psi. The nonlinear analyses results are presented in table 39.

Table 39. Regression statistics for elastic modulus model based on aggregate type.

Parameter Comment Estimate

Standard 

Error

Approximate 95 Percent 

Confidence Limits

a No comment 4.499 18.6844 -32.8506 41.8485

b No comment 2.3481 0.8998 0.5495 4.1468

c No comment 0.2429 0.1224 -0.00173 0.4875

d

Dagg for 

andesite 1 N/A N/A N/A

e Dagg for basalt 0.9286 0.0956 0.7374 1.1197

f Dagg for chert 1.0079 0.0863 0.8354 1.1803

g

Dagg for 

diabase 0.9215 0.1858 0.5501 1.2928

h

Dagg for 

dolomite 1.0254 0.0624 0.9006 1.1501

i

Dagg for 

granite 0.8333 0.0624 0.7085 0.9581

j

Dagg for 

limestone 1 N/A N/A N/A

k

Dagg for 

quartzite 0.9511 0.1082 0.7349 1.1674

l

Dagg for 

sandstone 1 N/A N/A N/A

N/A = Not applicable.

The model statistics for table 39 are as follows:

• RMSE = 499,856 psi.

• R2 = 0.3582 percent.

• N = 71.
124

The model form described for the statistics presented in this table is as follows:

* * * * * )

( *( ) *( ) ) *( * * * *

h Dolomite i Granite j Limestone k Quartzite l Sandstone

E a UW f'c d Andesite e Basalt f Chert g Diabse b c

+ + + + +

= + + +

Figure 191. Equation. E.

Where:

d through l were iteratively determined through the nonlinear process, and andesite, basalt, chert, 

diabase, dolomite, granite, limestone, quartzite, and sandstone are categorical variables with 

values 0, 1.

Table 39 indicates that the factor that accounts for the aggregate type, Dagg, has a value of 1.0 

for andesite, limestone, and sandstone. Basalt, diabase, granite, and quartzite have lower Dagg

values and therefore lower modulus values than mixes using andesite, limestone, and sandstone 

aggregates. Likewise, chert and dolomite have higher values. Also evident from table 39 is that 

to a very small extent, the statistical optimization has been compromised (note significance of 

parameters a and c are not within limits) in the interest of developing a model with variables 

relevant to elastic modulus predictions. Table 40 provides details of the range of data used 

to develop the model. The R2 value is reasonable and therefore presented as a feasible 

model. Figure 192 and figure 193 show the predicted versus measured plot and the residual 

plot, respectively. 

Table 40. Range of data used for elastic modulus model based on aggregate type.

Parameter Minimum Maximum Average

Compressive strength 1,990 11,310 7,550

Unit weight 137 156 146

Elastic modulus 1,450,000 6,800,000 4,629,646

Figure 192. Graph. Predicted versus measured for elastic modulus model based on 

aggregate type.

y = 0.3647x + 3E+06

R2 = 0.3582

0

2000000

4000000

6000000

8000000

0 2000000 4000000 6000000 8000000

Predicted Elastic Modulus, psi

Measured Elastic Modulus, psi

N = 71

R2 = 0.3582

RMSE = 499856 psi
125

Figure 193. Graph. Residual errors for elastic modulus model based on aggregate type.

Elastic Modulus Model 2: Model Based on Age and Compressive Strength

The data used for validation were further reviewed to identify potential sources of error. Since 

the data covered a wide range of ages, the age parameter was considered for inclusion in the 

model. A model was developed to predict elastic modulus as a function of age and compressive 

strength at the corresponding age. The model was not entirely statistically optimized. The 

regressed constants were adjusted through a trial and error process to provide the best prediction 

(i.e., to match the measured with the predicted values as close as possible along the line of 

equality). The model can be expressed as follows:

1.3 0.2118

, )) 0.03

59.0287 *( ) *(ln( − = t E f'c c t t

Figure 194. Equation. Prediction model 10 for Ec,t.

Where:

Ec,t = Elastic modulus at age t years.

f'ct = Compressive strength at age t years.

t = Age at which modulus is determined, years.

The model used 371 data points and had an R2 value of 26.14 percent. The RMSE value for this 

model is about 900,000 psi. Table 41 shows the results of the nonlinear analysis, and table 42

provides details of the range of data used to develop the model. The measured versus predicted 

plot and the residuals plot for this model are shown in figure 195 and figure 196, respectively. 

y = 0.0178x - 84187

R2 = 0.0002

-2000000

0

2000000

0 2000000 4000000 6000000 8000000

Residual Elastic Modulus, psi

Predicted Elastic Modulus, psi
126

Table 41. Regression statistics for elastic modulus model based on age and compressive 

strength.

Parameter 

Constants Estimate

Standard 

Error

Approximate 

95 Percent 

Confidence Limits

a 59.0287 2.8881 53.3495 64.7079

b -0.2118 0.0284 -0.2677 -0.1559

The model statistics for table 41 are as follows:

• RMSE = 949,404 psi.

• R2 = 0.2614 percent.

• N = 371.

Table 42. Range of data used for elastic modulus model based on age and compressive 

strength.

Parameter Minimum Maximum Average

Compressive strength 1,990 12,360 7,361

Pavement age 0.0384 45.3836 14.0900

Elastic modulus 1,450,000 6,800,000 4,586,545

Figure 195. Graph. Predicted versus measured for elastic modulus model based on age and 

compressive strength.

1000000

3000000

5000000

7000000

9000000

1000000 3000000 5000000 7000000 9000000

Predicted Elastic Modulus, psi

Measured Elastic Modulus, psi

N = 371

R2 = 0.2614

RMSE = 949404 
127

Figure 196. Graph. Residual errors for elastic modulus model based on age and 

compressive strength.

Elastic Modulus Model 3: Model Based on Age and 28-Day Compressive Strength

Since the 28-day compressive strength is usually available for PCC materials, a predictive model 

based on age and the 28-day compressive strength was developed. A relatively smaller dataset

was utilized for this model with only data from SPS sections, as the 28-day compressive strength 

data was a necessary input. Again, this model utilized a nonlinear analysis, and beyond statistical 

optimization, the constants determined for this model were adjusted for closest predictions 

through a trial and error process. The relationship developed for these variables can be expressed 

as follows:

))0.00524

0.03

375.6*( ) *(ln( 1..1

, 28

t E f'c c t = −day

Figure 197. Equation. Prediction model 11 for Ec,t.

Where:

Ec,t = Elastic modulus at age t years.

f'c28-day = 28-day compressive strength.

t = Age at which modulus is determined, years.

The model used 46 data points and had an R2 value of 16.32 percent. The RMSE value for this 

model is about 1,183,400 psi. Table 43 shows the results of the nonlinear analysis, and table 44

provides details of the range of data used to develop the model. The measured versus predicted 

plot and the residuals plot for this model are shown in figure 198 and figure 199, respectively.

This model uses data up to an age of 1 year. It is more appropriate for estimating the short-term 

modulus of a project and for supplementing strength estimates used to determine opening time 

for traffic.

An examination of the statistics proposed for determining elastic modulus suggests that they do 

not possess the predictive ability of the other material parameters presented in this study. The 

y = 0.5885x - 3E+06

R2 = 0.4198

-4000000

0

4000000

1000000 3000000 5000000 7000000 9000000

Residual Elastic Modulus, psi

Predicted Elastic Modulus, psi
128

models are considered fair but not excellent. They provide users with an option of moderate 

estimates when no information about the elastic modulus is available. It is therefore 

recommended that users exercise caution in using the predictive values for analyses.

Table 43. Regression statistics for elastic modulus model based on age and 28-day 

compressive strength.

Parameter 

Constants Estimate

Standard 

Error

Approximate 95 Percent 

Confidence Limits

a 375.6 31.4592 312.5 to 439.3

b 0.00524 0.0714 -0.1388 to -0.1492

The model statistics for table 43 are as follows:

• RMSE = 1,183,400 psi.

• R2 = 0.1632 percent.

• N = 46.

Table 44. Range of data used for elastic modulus model based on age and 28-day 

compressive strength.

Parameter Minimum Maximum Average

28-day compressive strength 3034 7912 5022

Pavement age 0.0384 4.5288 0.9153

Elastic modulus 1,450,000 6,221,000 4,732,101

Figure 198. Graph. Predicted versus measured for elastic modulus model based on age and 

28-day compressive strength.

1000000

3000000

5000000

7000000

9000000

1000000 3000000 5000000 7000000 9000000

Predicted Elastic Modulus, psi

Measured Elastic Modulus, psi

N = 46

R2 = 0.1632

RMSE = 1,183,400 psi
129

Figure 199. Graph. Residual errors for elastic modulus model based on age and 28-day 

compressive strength.

PCC Tensile Strength Models

The data assembly and data reduction were same as that performed for the other PCC models. 

Both SPS and GPS section data were used in the development of this model. PCC tensile 

strength is a critical input to the CRCP models in the MEPDG. It is most likely that compressive 

strength test results could be available for the PCC materials being used in CRCP design/

construction. The intent, therefore, was to correlate PCC tensile strength data to the compressive 

strength. Past studies have correlated the tensile strength to the flexural strength of the mix. 

However, flexural strength test results are available only for the SPS sections, thereby 

drastically reducing the dataset that can be used to generate a tensile strength model based 

on flexural strength. 

PCC Tensile Strength Model Based on Compressive Strength

This model development served as both a validation and development of a new correlation using 

the LTPP database. The model form used was a power equation and can be expressed as follows:

0.4785 ft = 8.9068*( f'c)

Figure 200. Equation. Prediction model 12 for ft.

Where:

ft = Indirect tensile strength of the PCC material.

f'c= Compressive strength of the mix determined at the same age.

The model statistics are presented in table 45. The model was developed using 541 data points, 

with an R2 value of 42.1 percent and an RMSE value of 61 psi. Table 46 provides details of the 

range of data used to develop the model. Figure 201 and figure 202 show the predicted versus

measured plot and the residual errors plot, respectively.

y = 0.7443x - 4E+06

R2 = 0.623

-3000000

0

3000000

1000000 3000000 5000000 7000000 9000000

Residual Elastic Modulus, psi

Predicted Elastic Modulus, psi
130

Table 45. Model statistics for tensile strength prediction model.

Parameter Estimate

Standard 

Error

95 Percent

Confidence Limits

Coefficient 8.9068 2.0204 4.9381 to 12.8756

Power 0.4785 0.0256 0.4282 to 0.5288

The model statistics for table 45 are as follows:

• RMSE = 61 psi.

• R2 = 0.4209 percent.

• N = 541.

Table 46. Range of data used for tensile strength prediction model.

Parameter Minimum Maximum Average

Compressive strength 1,990 12,360 6,763

Tensile strength 316 1,012 600

Figure 201. Graph. Predicted versus measured for tensile strength model.

y = 0.4231x + 346.07

R2 = 0.4209

100

300

500

700

900

1100

1300

100 300 500 700 900 1100 1300

Predicted tensile strength, psi

Measured tensile strength, psi

N = 541

R2 = 0.4209

RMSE = 61 psi
131

Figure 202. Graph. Residuals error plot for tensile strength model.

Figure 203. Graph. Sensitivity of tensile strength prediction model to change compressive 

strength.

Figure 203 shows the sensitivity of the model to compressive strength. The relationship 

developed shows that for typical ranges of compressive strength (i.e., 3,000 to 6,000 psi), the 

PCC tensile strength varies from about 400 to 570 psi, which is a reasonable range for this 

strength parameter.

PCC CTE Models

PCC CTE, which has gained higher importance in recent pavement analysis procedures as a 

material parameter influencing performance, has been included in the TST_PC03 table of the 

LTPP database for both SPS and GPS sections. The TST_PC03 table in the LTPP Standard Data 

Release 23.0 contained 228 CTE test results.

(3) These test data contained multiple CTE 

measurements on a given 500-ft section. Each CTE test result was accompanied by the aggregate 

type and rock type determined by visual examination of the core used for CTE measurements. 

The reasons for collecting this information at the time of CTE tests can be easily perceived. 

Coarse aggregate mineralogy and concrete moisture content have the highest influence of all 

material parameters on PCC CTE values. Testing for CTE was done with the concrete saturated 

y = 0.0054x - 3.2756

R2 = 2E-05

-500

0

500

100 300 500 700 900 1100 1300

Tensile strength, (Pred - Meas), psi

Predicted tensile strength, psi
132

so that this variable was eliminated. Additional data and corrected data have been added to the 

LTPP Standard Data Release 24.0.

(142)

Additionally, aggregate type data were obtained from the materials tables, as explained in 

chapter 4. It is also noted that fine aggregates in PCC are usually silica natural sand. There is no 

indication of fine aggregate type in the LTPP database. Specific tables from which aggregate 

type information were extracted and assembled in the database include SPS2_PCC_MIXTURE_

DATA, RHB_PCCO_AGGR, SPS8_PCC_MIXTURE_DATA, and INV_PCC_MIXTURE for 

SPS-2, SPS-7, SPS-8, and GPS sections. Reviewing and comparing the aggregate type reported 

in the CTE and materials tables revealed the following:

• The aggregate type information was more complete in the CTE table.

• Several discrepancies exist between the reported aggregate types across the two tables

(CTE values, rock type, and aggregate type are being corrected in the next release of the 

LTPP database).

• Multiple cores tested from the same LTPP section reported different aggregate types.

In developing CTE correlations, the following assumptions and data reduction methods

were made:

• It was assumed that the aggregate types in the CTE tables were more accurate, as they 

were recorded from the cores used for CTE measurements.

• Multiple readings from the same section were not averaged in the preliminary analyses,

as they did not necessarily represent the same aggregate type.

• Tentative models examined with the complete dataset produced very poor correlations. It 

was found that deleting suspect datasets from the model data resulted in better correlation 

statistics. Suspect datasets were those that showed multiple aggregate types within the 

same section and those that did not show the same material type in the materials and CTE 

data tables of the LTPP database.

• The data reduction process yielded 91 datasets.

Two model types were developed, either of which could be used depending on the level of 

information available. These models are equivalent to level 3 and level 2 MEPDG inputs. The 

level 3 model provides default CTE values depending on the coarse aggregate rock type used 

in the PCC mix. This is equivalent to the CTE values suggested by Mallela et al. but covers a 

larger database of CTE test results.(15) The level 2 model provides a correlation based on mix 

volumetrics and uses existing model forms. The regressed constants in the model were obtained 

from the database assembled for this study.

Current Issue with CTE Overestimation in LTPP Data

CTE tests of the PCC specimens from LTPP sections were performed by FHWA’s TurnerFairbank Highway Research Center (TFHRC) using the AASHTO TP 60 protocol.

(24) TFHRC 
133

initiated an inter-laboratory study during which an error was discovered with the protocol and 

procedure used to measure concrete CTE.(144) The source of the error was in the assumption of a 

single CTE value for the calibration specimen. Testing performed at independent laboratories 

revealed that a CTE value must be determined for each calibration specimen, and the calibration 

specimen should be tested over the same range of temperature over which the concrete CTE is 

determined—50 to 122 °F. Not meeting these two conditions caused an overestimation of the 

reported CTE by approximately 0.83 inches/inch/°F. Since all of the initial LTPP testing for 

CTE had been done in one laboratory with one calibration specimen, the calibration offset 

can be corrected in the database, and it has been corrected in LTPP Standard Data Release 

24.0.

(3,145,142)

This overestimation of CTE has significant ramifications, especially in light of the fact that the 

TFHRC has tested over 2,100 specimens for the LTPP program and the fact that the LTPP 

database was the primary source for the national calibration of the AASHTO MEPDG rigid 

pavement performance models.

(1) The national calibration coefficients for all JPCP and CRCP 

performance models may be invalid, and the models may need to be recalibrated. As a result, 

local implementation efforts also may be delayed.

The impact of this error in the CTE values on the current study was described in an internal

status report submitted to LTPP. LTPP Standard Data Release 23.0 contained the uncorrected 

CTE values, and therefore, the CTE models developed in this study are not applicable for the 

corrected data.

(3) However, the models demonstrate the ability to develop correlations, and the 

procedures herein may be repeated for the corrected data.

CTE Model 1: CTE Based on Aggregate Type (Level 3 Equation for MEPDG)

CTE test data were averaged for each aggregate type, and this constituted level 3 inputs for 

MEPDG. These averages were determined for the entire set of LTPP data as well as for the 

subset developed by deleting suspect data. A summary of the data is presented in table 47. 

Table 47 also lists the average PCC CTE for each aggregate type as found in the literature. The 

data are all in general agreement, providing a degree of confidence in the level 3 MEPDG input 

recommendations. The average CTE values determined from the data subset are recommended 

by this study.
134

Table 47. Prediction model 13 PCC CTE based on aggregate type (x 10-6 inch/inch/°F).

Aggregate 

Type

Average 

From 

Literature

Average 

From All 

LTPP Data

Average From Data 

Used in Model 

(Recommended)

Basalt 4.85 5.11 4.86

Chert 6.55 6.24 6.90

Diabase 4.85 5.33 5.13

Dolomite 5.75 5.79 5.79

Gabbro 4.85 5.28 5.28*

Granite 4.55 5.62 5.71

Limestone 4.25 5.35 5.25

Quartzite 6.85 6.07 6.18

Andesite 4.85 4.99 5.33

Sandstone 6.05 5.98 6.33

N 228 91

*There were no samples with a Gabbro aggregate type in the data used in the model. 

Hence, the average from the entire dataset is recommended.

Figure 204 shows a plot of recommended CTE values versus average CTE values obtained 

from other sources. While they are in fairly good agreement, the values recommended from this 

study are slightly higher for most cases. This can be explained by the overestimation of CTE 

during testing.

Figure 204. Graph. Comparison of average values from other sources and recommended 

CTE values based on aggregate type from LTPP data.

CTE Model 2: CTE Based on Mix Volumetrics (Level 2 Equation for MEPDG)

A step-wise linear regression analysis that considered all PCC variables, which performed within 

a 5 to 10 percent confidence limit, showed that CTE was most sensitive to aggregate types basalt, 

dolomite, limestone, and quartzite, as well as to coarse aggregate weight, coarse aggregate 
135

specific gravity, and cement content. This demonstrates the influence of mix volumetrics 

and aggregate type on the predicted CTE values. This validated the approach of developing 

a model that uses the CTE of individual components with a weighted average by their 

volumetric proportions.

Volume proportions of each component of the PCC mix were computed using the mix 

proportioning and mix design data, specifically the amount of each component and the specific 

gravity. The specific gravities of the coarse and fine aggregates were included in the LTPP 

database. Air content information was available for only 72 of the 91 cases. Verification for the 

volumetric proportion calculation showed that for most of the sections, the volumetric 

proportions summed up closely to 1.0. The average was 1.007, and all values were between 0.93 

and 1.08. However, two data points with extremely large coarse aggregate contents (greater than

2,700 lb/yd3) resulted in total volume proportions greater than 1.3, which were suspect data. 

With the deletion of these data points, the average value was 0.998.

The iterative procedures during the statistical analyses revealed that the model was handling only 

the volumetric proportions of the coarse aggregate adequately. Therefore, the equation was set 

up to consider the volumetric proportions of the coarse aggregate, VCA, and that of the mortar 

(1 − VCA). Because the individual aggregate CTE values were not available, aggregate CTE 

ranges and means from other sources of literature were used to assign a value to this parameter 

for each aggregate type.

The following ranges were used to determine the minimum, maximum, and mean for each 

aggregate type:

• Andesite: 3 to 4.5 × 10-6 inch/inch/°F.

• Basalt: 3 to 4.5 × 10-6 inch/inch/°F.

• Chert: 6.1 to 7 × 10-6 inch/inch/°F.

• Diabase: 3 to 4.5 × 10-6 inch/inch/°F.

• Dolomite: 3.9 to 5.5 × 10-6 inch/inch/°F.

• Gabbro: 3 to 4.5 × 10-6 inch/inch/°F.

• Granite: 3.2 to 5.3 × 10-6 inch/inch/°F.

• Limestone: 2 to 3.6 × 10-6 inch/inch/°F.

• Quartzite: 6.1 to 7 × 10-6 inch/inch/°F.

• Sandstone: 5.6 to 6.7 × 10-6 inch/inch/°F.
136

The sensitivity of the w/c ratio, a proxy variable to account for the porosity of the paste and 

hence its ability to expand or contract with change in temperature, was evaluated using the 

following model form:

CTEPCC = A*CTECA *VCA + (B*w/ c + C) *Vmortar

Figure 205. Equation. CTEPCC.

Where:

CTEPCC = CTE of the PCC material, inch/inch/°F.

CTECA = CTE of the coarse aggregate, inch/inch/°F.

VCA = Volumetric proportion of the coarse aggregate.

Vmortar = Volumetric proportion of the mortar (1 − VCA).

The regression statistics of this model showed the following:

• The constant A was not significant, indicating that the value of the constant selected to 

represent the CTE of the coarse aggregate, CTECA, was adequate to define the effect of 

the volumetric proportion of the coarse aggregate. 

• The constant B was not significant, indicating that the w/c ratio was not significant to 

the prediction. 

In subsequent iterations, the analysis procedure attempted to optimize the coarse aggregate CTE 

value within the range provided above. The model form was reduced to figure 206 or figure 207.

CTEPCC = CTECA *VCA +C*Vmortar

Figure 206. Equation. CTEPCC as a function of volumetric proportions.

* *(1 ) CA CTEPCC = CTECA VCA +C −V

Figure 207. Equation. CTEPCC as a function of coarse aggregate volumetric proportion.

The model statistics are presented in table 48, and details of the range of data used to develop the 

model are presented in table 49. 

The model was established as follows:

* 6.4514*(1 ) CA CTEPCC = CTECA VCA + −V

Figure 208. Equation. Prediction model 14 for CTEPCC.

Where:

CTECA = Constant determined for each aggregate type as shown in table 48.
137

Table 48. Statistical analysis results for CTE model based on mix volumetrics.

Parameter Comment Estimate

Standard 

Error

95 Percent

Confidence Limits

c No comment 6.4514 0.1889 6.0758 6.827

d CTECA for basalt 3 0 3 3

e CTECA for chert 6.4 0 6.4 6.4

f CTECA for diabase 3.4835 1.2824 0.9337 6.0333

g CTECA for dolomite 5.1184 0.408 4.3071 5.9297

h CTECA for gabbro 3.75 N/A N/A N/A

i CTECA for granite 4.7423 0.4188 3.9096 5.5749

j CTECA for limestone 3.2886 0.3579 2.5771 4.0001

k CTECA for quartzite 6.1 0 6.1 6.1

l CTECA for andesite 3.6243 1.4539 0.7336 6.515

m CTECA for sandstone 4.5 0 4.5 4.5

The model statistics for table 48 are as follows:

• RMSE = 0.35006 psi.

• R2 = 0.4415 percent.

• N = 89.

Table 49. Range of data used for CTE model based on mix volumetrics.

Parameter Minimum Maximum Average

Coarse aggregate content 582 2730 1,811

Coarse aggregate specific gravity 2.42 2.86 2.65

w/c ratio 0 0.71 0.45

Coarse aggregate volume fraction 0.13 0.62 0.41

Mortar volume 0.38 0.87 0.59

The model has an R2 value of 44.1 percent and an RMSE value of 0.35 psi. The predicted 

versus measured plot and the residual error plots are presented in figure 209 and 

figure 210, respectively.
138

Figure 209. Graph. Predicted versus measured for CTE model based on mix volumetrics.

Figure 210. Graph. Residual errors for CTE model based on mix volumetrics.

The constant, C, in the model form, determined as 6.4514, is equivalent to CTE of the mortar. 

(At TFHRC, using the AASHTO TP 60 uncorrected values, a CTE value of 6.2 for mortar 

containing silica sand was determined. Hence, the value of C is in agreement with the test 

result.(24)

) Since the mortar (all components of the mix design except the coarse aggregate, as per 

the definition in this equation) occupies a large volume of the matrix, it was necessary for the 

model to predict higher CTE for increased mortar proportions (or decreasing coarse aggregate 

proportions). In optimizing the model and selecting the representative CTE for each aggregate 

type, it was ensured that the CTE of the aggregate is not above C.

Figure 211 and figure 212 show a comparison of the predicted CTE values with average values 

reported in literature for each aggregate type. The predictions are close, with the model showing 

a slight bias. The over-prediction observed can be a result of the errors in the CTE test procedure 

(over-measured CTEs) or could simply reflect the CTE typical of paving mixes. Figure 213

shows the sensitivity of the model to coarse aggregate content. As expected, CTE decreases as 

the coarse aggregate content increases (or mortar volume decreases). While this is true for most 

cases, it is also observed that for aggregates with high CTE values, such as chert and quartzite, 

CTE of the aggregate approaches CTE of mortar, thereby showing little or no sensitivity to 

coarse aggregate content.

y = 0.4228x + 3.2012

R2 = 0.4415

0

2

4

6

8

0 2 4 6 8

Predicted CTE, x10E-6

Measured CTE, x10E-6

 

y = -0.0443x + 0.233

R2 = 0.0014

-2

0

2

0 1 2 3 4 5 6 7

Residual error, x10E-6

Predicted CTE, x10E-6
139

As with all other models, the user is advised to verify model predictions with other sources of

information. If possible, both CTE models should be evaluated simultaneously to obtain a range.

Figure 211. Graph. Comparison of CTE model prediction with average values reported in 

literature for each aggregate rock type.

Figure 212. Graph. CTE model prediction versus average values reported in literature for 

each aggregate rock type.

Figure 213. Graph. Sensitivity of the CTE model to coarse aggregate content.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

500 1000 1500 2000 2500 3000

PCC CTE, in/in/deg F x 10E-6

Coarse aggregate content in mix lb/yd3

Basalt

Chert

Quartzite

Andesite

Peridotite
140

RIGID PAVEMENT DESIGN FEATURES MODELS

The models developed for the prediction of MEPDG-specific inputs are in the design features 

category. As explained earlier in this chapter, it was never intended for inclusion in the LTPP 

database, nor was the need for inclusion clearly foreseen at the time of designing the LTPP 

database. The data formulation for developing these models is model type 3, wherein the 

dependent variable (e.g., the design feature deltaT for JPCP design) is determined through 

several trial and error runs of the MEPDG and establishing the optimum value that minimizes 

the error prediction.

The MEPDG design files used to generate the dependent variable data were obtained from the 

model calibration performed under NCHRP 1-40D, which produced the MEPDG software 

program version 1.0 in 2007.

(4) However, minor changes and software bug fixes have been 

performed since then, and the official version available at the time of this study was the MEPDG 

software version 1.1. Version 1.1 was used in the generation of the dependent variables for the 

models included in this study.

Therefore, the models developed to predict the design features variables are valid only for use 

with the distress calibration model of version 1.1 of the MEPDG software. At the time this report

was written, the MEPDG is being recalibrated under ongoing project NCHRP 20-07/Task 288. 

This recalibration effort will make the necessary updates to the CTE values used in the rigid 

pavement model calibrations and will handle the various updates and software bug fixes since 

the release of MEPDG version 1.0.

The prediction models presented in this report for the estimation of design feature inputs 

therefore may not be valid once the products of NCHRP 20-07/Task 288 are released. The 

information provided in this section should be considered as a description of a viable method to 

develop design feature input models. This effort has to be repeated after the release of the 

NCHRP 20-07/Task 288 products.

deltaT—JPCP Design

Generating Dependent Variable Data

Both the JPCP transverse fatigue distress model and JPCP mean joint faulting were considered 

for use as the basis for selecting the optimum deltaT with minimized errors. However, JPCP 

transverse fatigue cracking prediction data correlated well with the material, climate, and design 

elements to develop the deltaT prediction model. The procedure used to determine the value of 

the dependent variable in the analysis entailed the following steps.

Step 1: Run MEPDG Calibration Files for a Range of deltaT Values:

The transverse cracking model in the MEPDG was calibrated using 300 design projects at a 

deltaT value of -10 °F. Each of these calibration files were run at deltaT values of -2.5, -5.0, 

-7.5, -10, -12.5, and -15 °F. The number of deltaT levels (six) and the range (-2.5 to -15 °F) were 

selected based on practical considerations of the time required to perform this analysis as well as 

to maintain the bounds of the predicted value within a reasonable range.
141

Step 2: Compile Predicted Cracking Data for All Ages:

Field-measured cracking at different ages was available for all the sections used in the 

calibration models. MEPDG-predicted damage and cracking data were extracted for ages 

corresponding to field data measurements. Table 50 shows a sample for cracking data 

extraction for section 01_3028.

Table 50. Summary of field measured distress and predicted distress for section 1_3028.

Pavement 

Age

(Years)

Measured 

Field 

Cracking 

(Percent)

Predicted Cracking (Percent)

deltaT = 

-15

deltaT = 

-12.5

deltaT = 

-10

deltaT = 

-7.5

deltaT = 

-5

deltaT = 

-2.5

20.31507 0 20.8 4.3 1 0.7 3 12.8

21.84384 0 26.9 5.2 1.2 0.9 3.6 14.7

26.52329 4 48.9 9.3 2.2 1.6 6.8 23.3

28.87123 4 57.7 11.7 2.8 2 8.6 27.6

32.72329 8 70.1 16.8 4.2 3.1 12.7 36

Step 3: Calculate Errors and Determine Optimal deltaT for Each Section:

The predicted cracking for each level of deltaT (as shown in table 50) was compared against 

the field data to compute errors for each age. The sum of squared errors was then computed 

for each age and for each level of deltaT. Table 51 shows an example of error calculation for 

section 01_3028.

Table 51. Error calculations for section 1_3028.

Pavement 

Age

(Years)

Measured 

Field 

Cracking 

(Percent)

Squared Error Calculation

deltaT = 

-15

deltaT = 

-12.5

deltaT = 

-10

deltaT = 

-7.5

deltaT = 

-5

deltaT = 

-2.5

20.31507 0 432.64 18.49 1 0.49 9 163.84

21.84384 0 723.61 27.04 1.44 0.81 12.96 216.09

26.52329 4 2,016.01 28.09 3.24 5.76 7.84 372.49

28.87123 4 2,883.69 59.29 1.44 4 21.16 556.96

32.72329 8 3,856.41 77.44 14.44 24.01 22.09 784

Sum of squared errors 9,912.36 210.35 21.56 35.07 73.05 2,093.38

Note: The bold text in the squared error calculation section indicates the minimum sum of squared error for all ages.

The minimum sum of squared error for all ages, a value of 21.56 as highlighted in table 51, is 

observed for a deltaT of -10 °F in this case. The value -10 °F is therefore the dependent variable 

for this section. The same procedure was repeated for all 301 JPCP sections to develop a list of 

optimum temperatures, or dependent variables, for each calibration file.

The example presented in table 51 used a straightforward process to select the deltaT value. The 

sum of squared errors reached a minimum value for a value of -10 °F. However, there were cases 

where the sum of squared errors did not provide a clear choice for the selection of an optimal 
142

value. As shown in table 52, scenarios A and B are assigned a value of -10 and -12.5 °F,

respectively. Scenario C represents a case where the measured cracking was zero percent for all 

ages, and the predicted cracking also was zero at all values of deltaT. Scenario D represents a 

case where the minimum error was achieved at the bounds of the selected range (i.e., at -15 °F). 

A higher deltaT can result in smaller errors, but the extent of data that could be appropriately 

included in the analyses by evaluating higher deltaT values was minimal. Therefore, all cases 

that resulted in error trends as represented by scenarios C and D were deleted from the 

dataset used for the statistical analyses. The dataset used in the statistical analyses contained 

147 JPCP sections.

Table 52. Determining optimal deltaT.

Scenario

Sum of Squared Errors deltaT at 

Minimum 

Error (°F)

deltaT = 

-15

deltaT = 

-12.5

deltaT = 

-10

deltaT = 

-7.5

deltaT = 

-5

deltaT = 

-2.5

A 19,824.72 420.7 43.12 70.14 146.1 4,186.76 -10

B 5.39 2.6 28.18 405.83 2,655.08 8,924.36 -12.5

C 0 0 0 0 0 0

Cannot be 

determined

D 12,600.71 13,032.5 13,097.05 13,111.56 13,111.56 13,111.56 -15

JPCP deltaT Gradient Model Development

It was observed that the data generated for the dependent variable, deltaT, correlated well with 

the material, design, and climate parameters when transformed from deltaT temperature 

differential to deltaT temperature gradient. This involved dividing the deltaT temperature 

differential by the slab thickness. A step-wise regression analysis and Cp analyses were 

performed to select the variables that are correlated to the dependent variable and to select the 

best combination of variables to develop the model. After an iterative process to optimize the 

model, the equation developed to estimate the deltaT gradient variable is as follows:

Figure 214. Equation. Prediction model 15 for deltaT/inch.

Where:

deltaT/inch = Predicted average gradient through JPCP slab, °F/inch.

TR = Difference between maximum and minimum temperature for the month of

construction, °F.

SW= Slab width, ft.

PCCTHK=JPCP slab thickness, inch.

uw = Unit weight of PCC used in JPCP slab, lb/ft3

.

w/c = w/c ratio.

latitude = Latitude of the project location, degrees.

deltaT

inch � = -5.27805 ‒ 0.00794 × TR ‒ 0.0826 × SW + 0.18632 × PCCTHK

+ 0.01677 × uw + 1.14008 × w⁄c + 0.01784 × latitude
143

The model considers climate (TR, latitude), design (SW, PCCTHK), and material (uw, w/c) 

parameters. The model statistics are presented in table 53. The model was developed with 

147 data points and has an R2 value of 0.4967 percent and an RMSE value of 0.3199 psi. 

Table 54 provides details of the range of data used to develop the model. Figure 215 shows the

predicted versus measured for the proposed JPCP deltaT gradient model, while figure 216 shows 

the residual errors. Note that the measured data here refer to the deltaT gradient determined by 

matching MEPDG prediction to field performance. Figure 217 shows the predicted versus

measured deltaT for the model.

Table 53. Regression statistics for JPCP deltaT model.

Variable DF Estimate

Standard 

Error t-Value Pr > t VIF

Intercept 1 -5.27805 1.06943 -4.94 < 0.0001 0

TR 1 -0.00794 0.00396 -2 0.047 1.86047

SW 1 -0.0826 0.03432 -2.41 0.0174 1.07141

PCCTHK 1 0.18632 0.0195 9.55 < 0.0001 1.0642

uw 1 0.01677 0.00669 2.51 0.0133 1.22792

w/c 1 1.14008 0.2914 3.91 0.0001 1.14857

Latitude 1 0.01784 0.0072 2.48 0.0144 1.85265

The model statistics for table 54 are as follows:

• RMSE = 0.3199 psi.

• R2 = 0.4967 percent.

• N = 147.

Table 54. Range of data used for JPCP deltaT model.

Parameter Minimum Maximum Average

Temperature range 21.2 64.5 47.4

Slab width 12.0 14.0 12.5

PCC thickness 6.4 14.3 9.6

Unit weight 134 156 147

w/c ratio 0.27 0.72 0.46

Latitude 27.93 49.60 39.58
144

Figure 215. Graph. Predicted versus measured for JPCP deltaT gradient model.

Figure 216. Graph. Residual errors for JPCP deltaT gradient model.

-2.5

-2

-1.5

-1

-0.5

0

-2.5 -2 -1.5 -1 -0.5 0

Predicted DeltaT gradient, degF/inch

DeltaT gradient estimated by matching MEPDG and field performance, deg 

F/inch 

N=147

R2 = 49.67

RMSE = 0.31992

y = 3E-06x + 4E-06

R2 = 8E-12

-2.500

-2.000

-1.500

-1.000

-0.500

0.000

0.500

1.000

-2.5 -2 -1.5 -1 -0.5 0

Error in prediction, degF/inch

Predicted deltaT, deg F/inch 
145

Figure 217. Graph. Predicted versus measured deltaT based on the JPCP deltaT gradient 

model.

Figure 218 through figure 224 present the sensitivity analysis performed to examine the impact 

of varying the model parameters on its prediction. The parameters included are temperature 

range, slab width, slab thickness, unit weight, w/c ratio, and latitude. For each sensitivity 

analysis, the variable of interest was varied while holding all other variables constant at their 

typical values. Typical values used in this analysis were 24 °F, 12-ft slab width, 10-inch slab 

thickness, 145 lb/ft3 PCC unit weight, 0.40 w/c ratio, and 40 degrees latitude.

Figure 218. Graph. Sensitivity of predicted deltaT to temperature range during month of 

construction.

-16

-14

-12

-10

-8

-6

-4

-2

0

-16 -14 -12 -10 -8 -6 -4 -2 0

Predicted DeltaT, degF

DeltaT estimated by matching MEPDG and field performance, deg F

Delta-T Model - Temperature Differential Plot
146

Figure 219. Graph. Sensitivity of predicted deltaT to slab width.

Figure 220. Graph. Sensitivity of predicted deltaT to slab thickness.

Figure 221. Graph. Sensitivity of predicted deltaT to PCC slab unit weight.

-15

-10

-5

0

130 135 140 145 150 155 160

Delta T in 10in slab, deg F

PCC unit weight, lb/ft3
147

Figure 222. Graph. Sensitivity of predicted deltaT to PCC w/c ratio.

Figure 223. Graph. Sensitivity of predicted deltaT to latitude of the project location.

Figure 224. Graph. Predicted deltaT for different locations in the United States.

The following list contains brief observations from these sensitivity analyses:

• For the typical values used for each of these variables, the deltaT gradients estimated are

in a reasonable range.

• An increase in local climate temperature range increases the temperature gradient (see 

figure 218). The local climate temperature range is indicative of the level of temperature 

drop the project location can experience. The larger the difference in the temperature 
148

between day and night (assuming paving is performed in the daytime), the larger the 

negative temperature gradient locked into the slab as the slab hardens within a 

24-h period. 

• Wider slabs produce a larger built-in gradient (see figure 219), as has been validated in 

several field studies. The total thermal expansion is larger for a longer/wider slab and,

therefore, the resulting curvature of the slab induces a greater lift-up at the slab corners. 

The data did not show a significant effect of the slab length or joint spacing parameter.

• Thicker slabs reduce the deltaT gradient, as shown in figure 220. This is the expected 

trend, as thicker slabs, due to a greater weight, tend to restrain the corners from curling 

up as the concrete hardens. This figure also shows that for very thin slabs (< 8 inches), 

the effect is reversed. The physical significance of this cannot be fully explained or 

supported with data. It is therefore necessary to evaluate the sensitivity to each parameter 

while selecting a deltaT for each project.

• The larger unit weight of the PCC material used in the JPCP slab also reduces the 

magnitude of built-in gradient, primarily because of the restraint provided by the heavier 

slab during hardening (see figure 221).

• Lower w/c ratios have a higher rate of hydration, and, therefore, the PCC slab remains 

plastic for a shorter duration of time. Strength gain offers the slab the rigidity necessary 

to bear against the base and does not allow the slab corners to curl up. Therefore, lower 

w/c ratios tend to have higher built-in gradients, as seen in figure 222. Furthermore, at 

low w/c ratios, the PCC mix undergoes autogeneous shrinkage, which increases the 

potential for higher gradients in the slab.

• Figure 223 and figure 224 show the effect of latitude on predicted deltaT gradients. The 

United States lies between 30 and 50 degrees latitude in the Northern Hemisphere. The 

full range of latitudes is covered in figure 223. While this plot might appear to show 

deltaT’s high degree of sensitivity to the latitude parameter, for routine predictions using 

this model, the temperature range is a critical input. In other words, a given maximum 

temperature in the southern United States could have a much different temperature range 

relative to a location in the northern United States with the same maximum temperature. 

Therefore, the latitude parameter has to be evaluated combined with the temperature 

range parameter, as shown in figure 224. The predicted deltaT for several locations in the 

United States are presented.

Using the JPCP deltaT Model

This section provides an example for the use of the JPCP deltaT model developed under this 

study. The section used to describe the process is the LTPP SPS-2 section 04_0213 located in 

Maricopa County, AZ, and constructed in July 1993. 
149

The following latitude, design, and material inputs required for the deltaT prediction model can 

be obtained from the MEPDG inputs:

• Latitude: 33.45 degrees north.

• PCC thickness: 8.3 inches.

• Slab width: 14 ft.

• PCC unit weight: 145.3 lb/ft3

.

• PCC w/c ratio: 0.365.

The temperature range input to this model is the difference between the mean monthly maximum 

and minimum temperatures for the month of July from historical climate data records (as climate 

data included in the MEPDG). If the user does not have this information readily available, the 

data to compute the temperature range can be determined from the output file of the MEPDG 

analysis of this section. The output file (e.g., titled “04_0213.xls”) contains a worksheet 

titled “Climate” with key climate data for the specific location (or the virtual climate station 

created). This worksheet includes the monthly climate summary with minimum and maximum 

temperature by month for all years of data used under the headings “Min. Temp. (oF)” and 

“Max. Temp. (oF),” respectively. (Note that this summary also consists of “Average Temp. (oF),”

“Max. Range (oF),” “Precip. (in.),” “Average Wind (mph),” “Average Sun (%),” “Number Wet 

Days,” and “Max. Frost (in.).” However, these data are not of relevance to the deltaT model). 

For the month of July, the average minimum and maximum temperatures are 73 and 111.7 °F,

respectively. The difference between these temperatures is 38.7 °F.

Using these inputs, the deltaT gradient can be calculated as -1.7457138 °F/inch. For the slab 

thickness of 8.3 inches, this is equivalent to a deltaT of -14.5 °F. This value is significantly 

higher than the default of -10 °F/inch. This input can be revised in an MEPDG file and 

reanalyzed to evaluate the predicted transverse cracking performance.

deltaT—CRCP Design

Generating Dependent Variable Data

The procedure followed to obtain the dependent variable—the CRCP deltaT producing the 

closest prediction to field distress—shared several commonalities that followed to generate 

the deltaT for JPCP. Punchout prediction was the basis for selecting the optimum deltaT with 

minimized errors. The CRCP design files used for the calibration of the MEPDG models were 

run at deltaT values of -2.5, -5.0, -7.5, -10, -12.5, and -15 °F. The punchout predictions at 

all these values of deltaT were compared against field measured punchouts. The value 

corresponding to the least sum of squared errors for the prediction at all ages combined was 

selected as the optimum deltaT for that section. This selection of an optimum value for all the 

sections used in the calibration generated the dependent variable dataset.
150

CRCP deltaT Gradient Model Development

As with the JPCP deltaT gradient model, the dependent variable CRCP deltaT data correlated 

well with the material, design, and climate parameters when transformed from deltaT

temperature differential to deltaT temperature gradient. Step-wise regression and Cp analyses 

were performed to select the variables that best correlated to the dependent variable and to select 

the best combination of variables to develop the model. It was observed that coarse aggregate 

type was significant. After an iterative process to optimize the model, the equation developed to 

estimate the deltaT gradient variable is as follows:

+ 0.11299 * PCCTHK

+1.55013*Granite +1.40009 *Limestone + 2.01838*Quartzite

deltaT / inch = 12.93007 - 0.15101* MaxTemp - 0.10241* MaxTempRange + 3.279 *Chert 

Figure 225. Equation. Prediction model 16 for deltaT/inch.

Where:

deltaT/inch = Predicted gradient in CRCP slab, °F/inch.

MaxTemp = Maximum temperature for the month of construction, °F.

MaxTempRange = Maximum temperature range for the month of construction, °F.

PCCTHK= JPCP slab thickness, inch.

Chert =1 if PCC mix coarse aggregate is chert, or 0 if otherwise.

Granite = 1 if PCC mix coarse aggregate is granite, or 0 if otherwise.

Limestone = 1 if PCC mix coarse aggregate is limestone, or 0 if otherwise.

Quartzite = 1 if PCC mix coarse aggregate is quartzite, or 0 if otherwise.

The model considers climate (MaxTemp and MaxTempRange), design parameters (PCCTHK), 

and material (Aggregate type) parameters. The model statistics are presented in table 55. The 

model was developed with 35 data points and has an R2 value of 82.5 percent and an RMSE 

value of 0.27932 psi. Table 56 provides details of the range of data used to develop the model. 

Figure 226 shows the predicted versus measured for the proposed CRCP deltaT gradient model, 

while figure 227 shows the residual errors. Note that the measured data refers to the deltaT

gradient determined by matching MEPDG prediction to field performance.

Table 55. Regression statistics for CRCP deltaT model.

Variable DF Estimate

Standard 

Error t-value Pr > t VIF

Intercept 1 12.93007 1.98459 6.52 < 0.0001 0

MaxTemp 1 -0.15101 0.01793 -8.42 < 0.0001 3.46347

MaxTempRange 1 -0.10241 0.01869 -5.48 < 0.0001 2.00933

Chert 1 3.279 0.30508 10.75 < 0.0001 2.24965

Granite 1 1.55013 0.22656 6.84 < 0.0001 4.96262

Limestone 1 1.40009 0.18956 7.39 < 0.0001 4.00053

Quartzite 1 2.01838 0.39449 5.12 < 0.0001 1.93773

PCCTHK 1 0.11299 0.0705 1.6 0.1207 1.68624
151

The model statistics for table 55 are as follows:

• RMSE = 0.27932 psi.

• R2 = 0.825 percent.

• N = 35.

Table 56. Range of data used for CRCP deltaT model.

Parameter Minimum Maximum Average

Maximum temperature 78.4 99.2 90.3

Temperature range 24.8 40.4 30.4

Chert 0 1 0.06

Granite 0 1 0.31

Limestone 0 1 0.46

Quartzite 0 1 0.03

PCC thickness 5.6 9.5 8.4

Figure 226. Graph. Predicted versus measured for CRCP deltaT model.
152

Figure 227. Graph. Residual errors for CRCP deltaT model.

Figure 228 through figure 231 show the sensitivity of the deltaT differential calculation to the 

parameters maximum temperature of the project location, maximum temperature range, CRCP 

slab thickness, and geographic location, respectively. The trends observed in the model—CRCP 

deltaT increasing with increasing maximum temperature and increasing temperature range—are 

reasonable. While the effect of slab thickness shows a linear relationship with the deltaT

gradient, the magnitude of the coefficient for this variable results causes the deltaT differential 

(CRCP deltaT gradient × thickness) to assume a nonlinear relationship with the deltaT

differential, peaking at about 10 inches. Figure 231 shows the deltaT predictions for projects 

selected from LTPP sites in Texas, Illinois, Virginia, Mississippi, Oregon, and Georgia.

Figure 228. Graph. Effect of maximum temperature on CRCP deltaT prediction model.
153

Figure 229. Graph. Effect of temperature range on CRCP deltaT prediction model.

Figure 230. Graph. Effect of slab thickness on CRCP deltaT prediction model.

Figure 231. Graph. Effect of geographic location on CRCP deltaT prediction.

The sensitivity analyses show reasonable trends but do not demonstrate that the model is 

robust. From an engineering standpoint, it is not clear if the range of predicted values and their 

magnitudes are practical and realistic. The wide range of deltaT will have a significant effect on 

-25

-20

-15

-10

-5

0

20 30 40 50

Delta T in 10in slab, deg F

Temperature range, deg F

-12.1

-12

-11.9

-11.8

-11.7

-11.6

-11.5

-11.4

6 8 10 12 14

Delta T, deg F

Slab thickness, in

-1.3

-31.0

-11.4 -1.5

-25.1

-14.5 -12.4

-40.0

-30.0

-20.0

-10.0

Delta T in 10in slab, deg F

0.0

Location

TX, Cont.=Feb1982, MaxTemp=78.1F,Range=37.3F 

TX, Cont.=July1987, MaxTemp=102.7F,Range=27.4F 

IL, Cont.=May1986, MaxTemp=89.6F,Range=30.2F 

MS, Cont.=Aug1970, MaxTemp=96F,Range=27.3F 

OR, Cont.=Jun1972, MaxTemp=83.2F,Range=42.8F

GA, Cont.=Aug1972, MaxTemp=95.7
154

design thickness. The data used to develop the model show very strong correlations, and it is 

likely that the predictions are valid, at least within a certain range of inputs. The current analyses 

and the data available are not adequate to determine these ranges. It is therefore recommended 

that this model be used with extreme caution.

Using the CRCP deltaT Model

The use of the CRCP deltaT model shares similarities with the JPCP deltaT model. The section 

used to describe the process is the LTPP GPS section in Illinois, 17_5020. This was constructed 

in May 1986. The CRCP thickness is 8.6 inches, and the PCC mix used a limestone aggregate. 

Several of the following inputs can be directly obtained from the MEPDG input file:

• PCCTHK: 8.6 inches.

• Chert: 0.

• Granite: 0.

• Limestone: 1.

• Quartzite: 0.

The maximum temperature and maximum temperature range can be obtained by running the 

design file and deriving this input from the worksheet titled “Climate.” For the month of May, 

the maximum temperature and maximum temperature range for this location are 89.6 and 

39.2 °F, respectively. Using these inputs, the CRCP deltaT gradient can be calculated as 

-1.3214 °F/inch. For the slab thickness of 8.6 inches, this is equivalent to a deltaT of -11.36 °F. 

This value is comparable to the -10 °F default. This input can be revised in an MEPDG file and 

reanalyzed to predict punchout development over time.

Erosion for CRCP Design

The erosion model in the MEPDG was developed during the calibration of the punchout distress 

model. The erosion model is an empirical model that is a function of the base type, the quality of 

the base, precipitation at the project location, and the erosion potential of the subgrade. The 

erosion calculation was an upgrade provided to the CRCP distress model during changes made 

under NCHRP 1-40D.

(4) This model was examined under this study and found to adequately 

consider several parameters known to affect erosion. It also was recognized that within the 

limitations of the analysis procedures of the MEPDG and available LTPP data, the model 

considered all parameters that can possibly be included in the model. No changes are suggested 

for this model. Please note that this model was not developed under the current study but simply 

verified for adequacy.

EI for JPCP Design

EI is a design feature input used in the faulting prediction model of JPCP. The MEPDG 

recommends a rating system for different base types. The CRCP erosion model was used to 

develop a correlation between the calculated erosion values and EI used in the calibration files 
155

of the faulting model. The correlation was poor. However, developing a new basis for the 

calculation of EI for each JPCP section would necessitate the recalibration of the JPCP model, 

which is beyond the scope of this study. No specific recommendations are therefore made for 

the EI model.

STABILIZED MATERIALS MODELS

As stated in chapter 4, the LTPP database does not contain adequate data on modulus values and 

index properties of stabilized materials. It was therefore not within the scope of this project to 

develop predictive models for most of the stabilized materials. Nonetheless, the data could be 

used to develop a single model for predicting the elastic modulus of LCB materials, and that 

model is included in this section.

LCB Elastic Modulus Model

The modulus values of stabilized materials are not contained in the LTPP database. However, the 

database does include the compressive strength test results for LCB materials. For the SPS-2 

sections, compressive strength data are available at 14 days, 28 days, and 1 year. Additionally, 

version 24.0 of the LTPP database software was reviewed.

(142) It was found that this version 

contains the elastic modulus data for SPS-2 sections and that the tests were conducted on 

samples greater than 10 years in age, which can be more or less considered the long-term elastic 

modulus for the material. A predictive model correlating the elastic modulus to the 28-day 

compressive strength can be helpful in using this as a design input. Averaging the data by each 

site resulted in only 11 data points.

The data available for this model were not considered adequate to establish a new model form;

therefore, the most common existing model form (i.e., correlating modulus to the square root of 

the compressive strength) was used. The model was established as follows:

58156 ,28 716886 ' ELCB = f c d +

Figure 232. Equation. Prediction model 17 for ELCB.

Where:

ELCB = Elastic modulus of the LCB layer.

f'c, 28d = 28-day compressive strength of the LCB material.

The predicted versus measured and the residual errors plots for this relationship are presented in 

figure 233 and figure 234, respectively. The model has an R2 value of 41.24 percent, an RMSE

value of 541,600 psi, and uses 11 data points.
156

Figure 233. Graph. Predicted versus measured for the LCB elastic modulus model.

Figure 234. Graph. Residual errors for the LCB elastic modulus model.

UNBOUND MATERIALS MODELS

A key requirement in conducting M-E design or analysis of pavements is to estimate stresses, 

strains, and deflections within unbound base, subbase, embankment, and subgrade layers. The 

critical stresses, strains, and deflections are then used in empirical models to forecast future 

pavement conditions. Mostly, critical stresses, strains, and deflections within all pavement layers 

(including unbound base, subbase, embankment, and subgrade layers) are determined using finite

y = 0.4124x + 2E+06

R2 = 0.4124

0.0E+00

5.0E+05

1.0E+06

1.5E+06

2.0E+06

2.5E+06

3.0E+06

3.5E+06

4.0E+06

0.0E+00 1.0E+06 2.0E+06 3.0E+06 4.0E+06 5.0E+06

Predicted elastic modulus, psi

Measured elastic modulus, psi

N=11

R2 = 0.4124

RMSE = 541619 

y = -8E-06x + 5.9484

R2 = 4E-11

-1.00E+06

-5.00E+05

0.00E+00

5.00E+05

1.00E+06

1.50E+06

0.0E+00 1.0E+06 2.0E+06 3.0E+06 4.0E+06

Error in prediction

Predicted elastic modulus, psi
157

element analysis (FEA) or layered elastic analysis (LEA). A key input required for determining 

critical stresses, strains, and deflections using LEA or FEA techniques is unbound layer material 

resilient modulus.

Resilient modulus is a dynamic response of unbound layer materials to continuous dynamic 

loading of a pavement by vehicles. It is defined as the ratio of the repeated axial deviator stress 

to the recoverable axial strain and is determined in the laboratory by means of a triaxial testing.

Because resilient modulus is sensitive to the stress state the unbound material is subjected to 

(combination of confining stress ( ) and deviator stress ( )), testing typically is done over a 

range of confining and deviator stresses. A mathematical model is then fitted to the resilient 

modulus and confining and deviator stress data for use in estimating resilient modulus for any 

reasonable combination of confining and deviator stresses.

Developing correlations between resilient modulus and basic unbound granular/coarse-grained 

and fine-grained materials for use in pavement analysis and design was one of the objectives of 

this study. As with the other models described in this report, the LTPP database contained 

adequate data to develop a resilient modulus model for unbound materials.

Data Assembly for Resilient Modulus Model Development

The literature review included several models developed for the use in predicting resilient 

modulus using unbound material index properties. From this literature review, a list was 

developed of all unbound material properties that impact resilient modulus and thus could 

potentially be included in a resilient modulus prediction model. 

Unbound material properties of interest, along with resilient modulus data required for model 

development as determined through the literature review, were obtained from the LTPP database

and assembled in a model development database. Also included in this database were actual 

resilient modulus test results and the range of confining and deviator stresses at which resilient 

modulus was determined.

Assembly of Resilient Modulus Model Development Database

Individual LTPP material database tables were merged to develop the resilient modulus model 

development database. Because the LTPP database is a relational database (i.e., it is composed of 

separate but related data tables), data assembly was performed by linking data stored in a simple 

row/column format in tables using unique primary keys to identify LTPP test sections or 

projects. For many of the data tables, the primary keys were the combination of STATE_CODE, 

SHRP_ID, and CONSTRUCTION_NO.

The next step was to determine the AASHTO soil classification for each unbound material of the 

database. The gradation and Atterberg limits were used to determine the soil classification.

Data Review

The assembled data were reviewed thoroughly to identify anomalies and missing data elements,

as well as to assess the reasonableness of the data. While this review was performed for all 

models developed, the review was more in-depth for the resilient modulus data because of the 

σ1 σ3
158

sheer size of the database and because of the potential for large model errors for relatively small 

data discrepancies.

Examples of plots summarizing the assembled data used to assess data reasonableness are

presented in figure 235 through figure 247. The data were assembled and reviewed by each 

AASHTO soil class to assess if the trends in the data were reasonable.

Figure 235. Graph. Mean percent clay fraction for unbound material types included in the 

model development database.

Figure 236. Graph. Mean percent coarse sand fraction for unbound material types 

included in the model development database.

0.0

3.9

8.8

11.8 13.5

2.3

15.0

33.7

23.8

38.9

36.8

0

5

10

15

20

25

30

35

40

45

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Percent Clay Fraction

Unbound Material Type (AASHTO Soil Class)

12.0

23.8

14.7

16.7

10.3

13.6

7.2

9.9

6.9

9.2

4.5

0

5

10

15

20

25

30

35

40

45

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Percent Coarse Sand Fraction

Unbound Material Type (AASHTO Soil Class)
159

Figure 237. Graph. Mean percent fine sand fraction for unbound material types included 

in the model development database.

Figure 238. Graph. Mean percent silt fraction for unbound material types included in the 

model development database.

18.0

28.3

43.3

21.1

12.5

75.3

24.8

17.3 17.5 17.3

11.2

0

10

20

30

40

50

60

70

80

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Percent Fine Sand Fraction

Unbound Material Type (AASHTO Soil Class)

5.9

10.0

15.8

13.6

15.8

4.9

41.9

28.7

42.0

25.2

40.5

0

5

10

15

20

25

30

35

40

45

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4

A-5

A-6

A-7-5A-7-6

Percent Fine Silt Fraction

Unbound Material Type (AASHTO Soil Class)
160

Figure 239. Graph. Mean hydraulic conductivity for unbound material types included in 

the model development database.

Figure 240. Graph. Plot of bulk stress versus lab tested resilient modulus for 

section 45_1025 (base layer).

5.669

0.472

0.118 0.118

1.181

0.472 0.472

0.000 0.236 0.000 0.000

0

1

2

3

4

5

6

7

8

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Hydraulic Conductivity, ft/hr 

Unbound Material Type (AASHTO Soil Class)

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0 20 40 60 80 100

Resilient Modulus, psi

Bulk Stress, psi

45_1025 (Base) Test 2 45_1025 (Base) Test 1
161

Figure 241. Graph. Percent passing No. 4 sieve for unbound material types included in the 

model development database.

Figure 242. Graph. Percent passing No. 40 sieve for unbound material types included in the 

model development database.

50.0

69.3

84.5

67.1

58.5

96.6

90.8 92.9 93.2 95.2 95.2

0

20

40

60

80

100

120

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Percent Passing No. 4 Sieve

Unbound Material Type (AASHTO Soil Class)

21.4

35.2

63.2

42.3 41.0

78.9 79.7 82.1 83.0 81.3

88.3

0

10

20

30

40

50

60

70

80

90

100

A-1-a

A-1-b

A-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Percent Passing No. 40 Sieve

Unbound Material Type (AASHTO Soil Class)
162

Figure 243. Graph. Percent passing No. 200 sieve for unbound material types included in 

the model development database.

Figure 244. Graph. Liquid limit for unbound material types included in the model 

development database.

9.5

13.4

22.8 23.4

29.0

5.3

56.4

65.6 65.6 64.3

77.1

0

10

20

30

40

50

60

70

80

90

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Percent Passing No. 200 Sieve

Unbound Material Type (AASHTO Soil Class)

4.9 5.6

11.7

28.7

46.0

0.0

17.9

49.3

32.6

59.2

49.3

0

10

20

30

40

50

60

70

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Liquid Limit, percent

Unbound Material Type (AASHTO Soil Class)
163

Figure 245. Graph. Plasticity index for unbound material types included in the model 

development database.

Figure 246. Graph. Maximum dry density for unbound material types included in the 

model development database.

0.7 0.9

3.0

14.6

23.5

0.0

4.5

2.2

15.5

25.8

27.5

0

5

10

15

20

25

30

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4

A-5A-6

A-7-5A-7-6

Plasticity Index, percent

Unbound Material Type (AASHTO Soil Class)

138.7

131.5

122.8 123.4

112.7 110.9

118.5

104.0

112.5

95.4

102.1

0

20

40

60

80

100

120

140

160

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Max. Dry Density, Ib/ft3

Unbound Material Type (AASHTO Soil Class)
164

Figure 247. Graph. Optimum moisture content for unbound material types included in the 

model development database.

The following conclusions were made based on the review of assembled data:

• All key data elements required for resilient modulus model development were available.

• Data available were of adequate quality and completeness for use in model development.

• Resilient modulus data were available for a range of confining and deviator stresses. 

Thus, there was no single test result that represented the typical unbound base and 

subgrade soil stress state within a pavement structure. Note that stress state within the 

pavement structure varies considerably according to pavement type, layer types and 

thicknesses, layer modulus, applied truck loading, etc.

The following anomalies were identified when matching material properties data elements 

to Mr data for subgrade soils:

• Sampling of unbound soil/granular materials for performing resilient modulus tests and 

for determining other soil properties may have occurred at different depths of the 

subgrade, depending on how the test sample was obtained (i.e., from test pits, 

coring/boring, auger pits, etc).

• Highly variable subgrade soils at a given site could lead to significant differences in 

materials used for different types of testing.

• Lack of uniformity in test materials types could lead to mismatching of resilient modulus 

and soil properties data.

6.6

8.0

10.1 11.1

15.7

11.4 12.2

18.7

14.9

23.8

19.9

0

5

10

15

20

25

30

A-1-a

A-1-bA-2-4A-2-6

A-2-7

A-3

A-4A-5

A-6

A-7-5A-7-6

Optimum moisture content, percent

Unbound Material Type (AASHTO Soil Class)
165

Resolving Identified Anomalies

Over half the test sections did not have all the data elements (e.g., hydraulic conductivity) 

required to fully characterize the pavement subgrade. However, sufficient data were available for 

key subgrade material properties (e.g., gradation and Atterberg limits). Data elements for which 

little data were available were not used in model development.

A significant anomaly was differences in subgrade soil materials used for different types of 

testing due to sampling location. This anomaly was resolved by matching resilient modulus data 

to other soil test data only when the sample location could be certified as being as close as 

possible (i.e., same depth/strata, same test pit, etc.).

A second anomaly was the lack of a single representative resilient modulus value for a given 

unbound material sample (i.e., series of resilient modulus values corresponding to a combination 

of deviator and confining stresses used during testing process). This situation has been resolved 

in the past by fitting the series of resilient modulus values corresponding to a combination of 

deviator and confining stresses used during testing process with a constitutive equation that 

models resilient modulus behavior for both granular and fine-grained materials.

As defined in chapter 3, the AASHTO Mechanistic-Empirical Pavement Design Guide, Interim 

Edition: A Manual of Practice proposes the constitutive equation for modeling resilient modulus 

behavior when subjected to various stress states the following:

(1)

2 3

1

k

a

oct

k

a

r a P P

M k P 





























 = θ τ

Figure 248. Equation. Mr.

Where:

 = Bulk stress = .

= Principal stress.

= Confining pressure.

Pa = Atmospheric pressure.

= Octahedral normal stress = 1

/3 ( ).

k1, k2, k3 = Regression constants that are a function of soil properties, as defined in figure 75,

through figure 77.

This model can be used for various soil types, and the model attributes (k1, k2, and k3) for a given 

soil type remain the same regardless of stress state. Developing models to predict constitutive 

model attributes for a given set of soil properties is thus an effective approach to modeling 

resilient modulus behavior, rather than developing models individually for each possible 

combination of expected stress states.

θ σ1 + σ2 + σ3

σ1

σ2, σ3

τoct σ1 + 2 σ3
166

Estimating Resilient Modulus Constitutive Model Parameters k1, k2, and k3

Figure 33 presents the constitutive equation that models resilient modulus behavior for both 

granular and fine-grained materials recommended by the AASHTO Mechanistic-Empirical 

Pavement Design Guide, Interim Edition: A Manual of Practice.

(1) The parameters k1, k2, and k3

were calculated for all datasets based on the soil properties. Histograms showing the distribution 

of k1, k2, and k3 values by soil class are shown in figure 249 through figure 251, respectively.

Figure 249. Graph. Resilient modulus parameter k1 for unbound material types included in 

the model development database.

Figure 250. Graph. Resilient modulus parameter k2 for unbound material types included in 

the model development database.

862 858 834

994

1132

792 841 877 897

677

845

0

200

400

600

800

1000

1200

1400

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Resilient Modulus Parameter, k1

Unbound Material Type (AASHTO Soil Class)

0.7

0.6

0.5

0.3

0.4

0.6

0.3

0.2 0.2

0.1

0.2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Resilient Modulus Parameter, k2

Unbound Material Type (AASHTO Soil Class)
167

Figure 251. Graph. Resilient modulus parameter k3 for unbound material types included in 

the model development database.

This model was fitted to all soil samples with resilient modulus available for a range of confining 

and deviator stresses. Model fitting was done individually for each unbound material sample in 

the LTPP database. For each of the soil samples, calculated k1, k2, and k3 and the constitutive 

equation were used to predict resilient modulus at the lab test confining and deviator stresses for 

comparison. The results of the comparison showed a good fit of predicted and measured resilient 

modulus with a high R2 value and low standard error of estimate (SEE), as presented in 

figure 252.

-0.1

-0.3

-0.6

-0.5

-0.3

-0.4

-0.8

-0.5

-0.8

-0.6

-0.8 -0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

A-1-a

A-1-bA-2-4A-2-6A-2-7

A-3A-4A-5A-6

A-7-5A-7-6

Resilient Modulus Parameter, k3

Unbound Material Type (AASHTO Soil Class)
168

Figure 252. Graph. Plot of measured versus calculated resilient modulus (using k1, k2, and 

k3 computed from constitutive model).

Resilient Modulus Model Development

The following five-step procedure using regression analysis was used to develop multiple linear 

regression models relating resilient modulus to unbound material properties: 

1. Determine appropriate model form.

2. Develop inputs for regression analysis.

3. Perform separate regression analyses for each soil class as follows:

• Partition assembled data for use in actual model development and validation of 

tentative models.

• Perform regression analysis and develop tentative models.

• Verify tentative model by validating with “set aside” data and checking various model 

diagnostic statistics to determine the suitability of tentative models developed.

• Select optimal model inputs and establish k1, k2, k3 and material properties relationship.

Predicted Mr = 0.9967*Measured Mr

R2 = 0.987

N = 28051

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

0 10000 20000 30000 40000 50000 60000 70000 80000 90000

Predicted Mr, psi

Measured Mr, psi
169

4. Perform regression with full dataset and determine optimal model coefficients and 

diagnostic statistics.

5. Perform sensitivity analysis and determine final model coefficients.

Details of the procedure are explained in the following sections.

Step 1: Select Appropriate k1, k2, k3 Prediction Model Form

Various forms of mathematical relationships have been used for relating constitutive model 

parameters k1, k2, and k3 to simple material properties. The two most commonly applied, and 

hence most promising, model forms are as follows:

• ki = f (material properties).

• log(ki) = f (material properties).

Where ki is k1, k2, and k3. Because both of these mathematical equations have been used 

successfully, they were both deemed appropriate and were adopted for model fitting in 

this project.

Step 2: Develop Inputs for Regression Analysis

The data assembled contained all inputs required for model development. Details are as 

previously described.

Steps 3 and 4: Perform Regression Analyses

Numerous preliminary multivariable regression runs were performed to produce the “best”

model. The goal was to determine the optimal set of independent variables (material properties) 

that, when included in the model, will maximize adjusted R2 values and minimize errors (SEE). 

Other diagnostic statistics, such as the level of significance of the regression coefficient of each 

independent variable, COLLIN (used to check for multicollinearity) in SAS®, and VIF were 

made to determine the goodness of fit for the model.

Independent variables with regression coefficients significant at the 0.05 significance level 

(determined through performing t-tests) were retained in the models developed. The t-test 

threshold was set at this level so that only variables that impacted k1, k2, and k3 were significantly 

included in the models. The resulting models developed through regression analysis for 

constitutive model parameters k1, k2, and k3 are described below.

Constitutive Model Parameter k1:

Figure 253. Equation. Prediction model 18 for k1.

k1 = 1446.2 - 4.56764*PCTHALF + 4.92*LL - 27.73*OPTMOIST
170

Model statistics for k1 are as follows:

• R2 = 0.16 percent.

• SEE = 237.4.

• N = 1,029.

Constitutive Model Parameter k2:

Figure 254. Equation. Prediction model 19 for k2.

Model statistics for k2 are as follows:

• R2 = 0.67 percent.

• SEE = 0.0934.

• N = 1,032.

Constitutive Model Parameter k3:

Figure 255. Equation. Prediction model 20 for k3.

Where:

PCTHALF = Percent passing 1/2-inch sieve.

LL = Liquid limit, percent.

OPTMOIST = Optimum moisture content, percent.

PCTNO80 = Percent passing No. 80 sieve.

PCTGRVL = Percent gravel fraction (0.078- to 2.36-inch size).

D10 = Maximum particle size of the smallest 10 percent of soil sample.

Model prediction accuracy and reasonableness were evaluated by reviewing the plot of predicted 

and measured resilient modulus for all individual resilient modulus test values used in model 

development as presented in figure 256. Note that the plot presents actual measured resilient 

modulus for each individual sample and stress state and resilient modulus computed using 

predicted k1, k2, and k3 based on actual material properties for each individual sample and the 

resilient modulus constitutive model and stress state. Figure 257 presents a plot of measured and 

predicted resilient modulus versus bulk stress for all fine- and coarse-grained materials included 

in model development database. Note that mean measured k1, k2, and k3 for coarse- and finegrained materials and predicted k1, k2, and k3 using the equations in figure 70 through figure 72

and mean input values for fine- and coarse-grained materials were used for developing this plot. 

A review of the plots presented in figure 256 and figure 257 shows a reasonable prediction of 

resilient modulus.

k2 = 0.45679 - 0.00073376*PCTNO80 - 0.00269*LL + 0.00060555*PCTGRVL + 12.97*D10

k3 = -0.188 (for fine grained soils)

k3 = -0.153 (for coarse grained materials)
171

Figure 256. Graph. Measured versus predicted resilient modulus (using k1, k2, and k3 from 

figure 70 through figure 72).

Figure 257. Graph. Predicted and measured resilient modulus versus bulk stress for 

fine- and coarse-grained soils.

y = 0.9388x

R2 = 0.5661

N = 23056

0

10000

20000

30000

40000

50000

60000

0 10000 20000 30000 40000 50000 60000

Predicted Mr, psi

Measured Mr, psi

0

5000

10000

15000

20000

25000

30000

35000

0 20 40 60 80 100

Mr, psi

Bulk Stress, psi

Predicted Mr (Fine-Grained Soils) Predicted Mr (Coarse-Grained Soils)

Measured Mr (Fine-Grained Soils) Measured Mr (Coarse-Grained Soils)
172

Step 5: Conduct Sensitivity Analysis

The tentative models were further evaluated by conducting a comprehensive sensitivity analysis. 

The goal was to determine if the model behaves as expected based on engineering principles. 

Sensitivity analysis results are presented in figure 258 through figure 264. The results of the 

sensitivity analysis are summarized as follows:

• Soil type had a significant impact on predicted resilient modulus. Coarse-grained 

materials show significantly higher levels of resilient modulus with increasing 

bulk stress.

• Increasing the amount of finer materials resulted in a decrease in resilient modulus.

• Increasing the amount of gravel resulted in increased resilient modulus.

• Increasing effective size increases resilient modulus.

• Increasing optimum moisture content resulted in reduced values of resilient modulus.

• Increasing liquid limit resulted in an increase in resilient modulus.

Overall, the trends observed were deemed reasonable, and the proposed model was established 

as the recommended model for resilient modulus prediction.

Figure 258. Graph. Effect of material type (AASHTO soil class) on predicted resilient 

modulus.

0

5000

10000

15000

20000

25000

30000

35000

40000

0 20 40 60 80 100

Mr, psi

Bulk Stress, psi

A-1-b A-2-4 A-6 A-7-5
173

Figure 259. Graph. Effect of percent passing 0.5-inch sieve on predicted resilient modulus.

Figure 260. Graph. Effect of liquid limit on predicted resilient modulus.

20000

22500

25000

27500

30000

32500

35000

0 20 40 60 80 100

Predicted Mr, psi

Percent Passing 1/2-in Sieve

20000

22500

25000

27500

30000

32500

35000

0 10 20 30 40 50

Predicted Mr, psi

Liquid limit, percent
174

Figure 261. Graph. Effect of optimum moisture content on predicted resilient modulus.

Figure 262. Graph. Effect of No. 80 sieve on predicted resilient modulus.

5000

10000

15000

20000

25000

30000

35000

0 10 20 30 40

Predicted Mr, psi

Optimum moisture content, percent

20000

22500

25000

27500

30000

32500

35000

0 20 40 60 80 100

Predicted Mr, psi

Percent Passing No. 80 Sieve
175

Figure 263. Graph. Effect of gravel content on predicted resilient modulus.

Figure 264. Graph. Effect of effective size on predicted resilient modulus.

PRACTICAL GUIDE AND SOFTWARE PROGRAM

The models developed under this study have been incorporated into a user-friendly software 

program, Correlations, which can be used independently from the MEPDG. The software, 

developed under this study, was developed on the Microsoft.NET platform to be compatible with 

the latest versions of the Microsoft Windows® operating systems. It is programmed in the C# 

language and uses a modern user interface library to provide a familiar look and feel. It features 

multiple windows on the user interface that are initially docked inside the main window. These 

windows can be moved separately from the main window for better viewing of the inputs 

or results.

20000

22500

25000

27500

30000

32500

35000

0 20 40 60 80

Predicted Mr, psi

Percent Gravel Content

20000

22500

25000

27500

30000

32500

35000

0 0.005 0.01 0.015

Predicted Mr, psi

Effective Size (D10), in
176

The program interface features tabs for PCC, design features, stabilized materials, and unbound 

materials. Models that belong to each of these categories are made available through a series of 

radio button selections placed in an accordion control. This placement not only provides the 

ability to make multiple selections, but it also conserves screen space so that the results of the 

calculations can be placed for easy viewing. Once a model is selected, the entry area adds 

controls for the available inputs of the model.

Throughout the software, tooltips are used to provide feedback for each of the areas where data 

can be entered. Calculations occur after all necessary values have been input. Information about 

each of the models is available in an information window initially located at the bottom of the 

screen. This information is context-sensitive to the specific selections that the software user has 

made. Results of each calculation are displayed prominently in the results area window, initially 

placed on the right side of the main window.

The software program may be requested from the LTPP Customer Support Service Center at 

ltppinfo@dot.gov.
177

CHAPTER 6. SUMMARY AND FUTURE WORK

SUMMARY

Material characterization has gained increasing importance in pavement engineering, mainly due 

to the development of analyses and design procedures that are capable of considering material 

properties to predict pavement performance. This becomes crucial not only in the initial design 

phase, but also in QA practices and in pavement management throughout the pavement service 

life. Materials behave differently depending on the material type (PCC materials, unbound finegrained materials, unbound coarse-grained materials, etc.), the type of loading (loading under 

compression, under flexure, under thermal differentials, etc.), and the testing conditions 

(rate of loading, level of loading, etc). Therefore, materials are characterized by different 

properties to capture the behavior of the material under different conditions. Procedures like 

the MEPDG use various material property inputs to model pavement response and to predict 

pavement performance.

Consequently, there is a need for more information about material properties, which is addressed 

only to a limited extent in currently available resources. Reliable correlations between material 

parameters and index properties offer a cost-effective alternative and are equivalent to the level 2 

MEPDG inputs. The LTPP database, which contains material property test results as well as 

material index properties, offers an opportunity to develop such correlations for PCC materials, 

stabilized materials, and unbound materials.

(5) Furthermore, because these data come from realworld materials, workmanship, and construction practices instead of from controlled laboratory 

experiments, correlations developed from LTPP data can be considered suitable for use in 

pavement-related applications.

The MEPDG also requires certain design-related inputs, commonly called design feature 

inputs, which are influenced by material properties as well as climate and construction-related 

parameters. The deltaT values for JPCP and CRCP design are prime examples of design feature 

inputs. These inputs are not directly available from simple test results. In combination with the 

data available from the MEPDG calibration models, the LTPP database offers the potential to 

provide the much-needed guidance to estimate these inputs.

This study involved developing predictive models to estimate material and design parameters. 

The main objectives of this study were as follows:

• Identify a set of material engineering properties for which predictive relationships 

would be useful in pavement design, construction QC/QA, and pavement 

management applications.

• Establish and/or validate relationships between the identified engineering properties and 

routine test results, index properties, and/or other readily available information.

• Develop a practical guide accompanied by user friendly software incorporating 

the recommendations.
178

A thorough review of the literature was performed to identify material properties for which 

predictive models would be required and to identify the index properties that have a significant 

impact on each material property of interest. This was followed by an evaluation of the data 

available in the LTPP database to assess the availability of data essential for developing these 

correlations. Based on the review of the database, the following material categories and material 

properties were selected for developing predictive relationships:

• PCC materials.

o Compressive strength.

o Flexural strength.

o Elastic modulus.

o Tensile strength.

o CTE.

• Rigid pavement design features.

o deltaT in JPCP design.

o deltaT in CRCP design.

o Erosion and EI in CRCP and JPCP design, respectively.

• Stabilized materials.

o Elastic modulus of LCB layers.

• Unbound materials.

o Resilient modulus of base and subgrade layers.

The LTPP database has an extensive record of material test results. Also, test data are available 

for SPS and GPS sections, which have distinctly different levels of detail for material index 

properties and cover different pavement age ranges. Therefore, multiple models were developed 

for each material property if suitable data were available. The data required to develop models 

under the rigid pavement design features category were obtained partly from the LTPP database 

and partly by conducting multiple analysis runs of the LTPP sections used in the calibration of 

the MEPDG distress models. All models were developed under rigorous statistical analysis 

procedures, and a uniform set of criteria was used across all models. The statistical significance 

was discussed in detail throughout the report.

The following is a summary of the models developed under this study, grouped by material type 

and material property.
179

PCC MATERIALS

PCC Compressive Strength Models

Compressive Strength Model 1: 28-Day Cylinder Strength Model

f c,28d = 4028.41841− 3486.3501*w/ c + 4.02511*CMC

Figure 265. Equation. Prediction model 1 for fc,28d.

Where:

fc,28d = 28-day compressive strength, psi.

w/c = Water to cement ratio.

CMC = Cementitious materials content, lb/yd3.

Compressive Strength Model 2: Short-Term Cylinder Strength Model

6358.60655 3.53012* 34.24312* / * 633.3489*ln( ) , f CMC w c uw t c t = + − +

Figure 266. Equation. Prediction model 2 for fc,t.

Where:

fc,t = Compressive strength at age t years, psi.

CMC = Cementitious materials content, lb/yd3.

w/c = Water to cement ratio.

uw = Unit weight, lb/ft3.

t = Short-term age, years.

Compressive Strength Model 3: Short-Term Core Strength Model

Figure 267. Equation. Prediction model 3 for fc,t.

Where:

fc,t = Compressive strength at age t years, psi.

CMC = Cementitious materials content, lb/yd3.

uw = Unit weight, lb/ft3

.

MAS = Maximum aggregate size, inch.

w/c = Water to cement ratio.

FM = Fineness modulus of fine aggregate.

t = Short-term age, years.

Compressive Strength Model 4: All Ages Core Strength Model

Figure 268. Equation. Prediction model 4 for fc,t.

f

c,t = 98.92962 + 5.70412 × CMC + 28.48527 × uw + 2,570.13151 × MAS × w⁄c

‒ 199.84664 × FM + 611.30879 × ln(t)

f

c,t = -6,022.44 ‒ 854.46 × w⁄c + 4.8656 × CMC + 68.5337 × uw + 533.15 × ln(t)
180

Where:

fc,t = Compressive strength at age t years, psi.

w/c = Water to cement ratio.

CMC = Cementitious materials content, lb/yd3

.

uw = Unit weight, lb/ft3.

t= Short-term age, years.

Compressive Strength Model 5: Long-Term Core Strength Model

2 f c,LT = -3467.3508+ 3.63452*CMC + 0.42362*uw

Figure 269. Equation. Prediction model 5 for fc,LT.

Where:

fc, LT = Long-term compressive strength, psi.

CMC = Cementitious materials content, lb/yd3

.

uw = Unit weight, lb/ft3

.

PCC Flexural Strength Models

Flexural Strength Model 1: Flexural Strength Based on Compressive Strength

0.4082 22.7741* '

c MR = f

Figure 270. Equation. Prediction model 6 for MR.

Where:

MR = Flexural strength, psi.

f'c,= Compressive strength determined at the same age, psi.

Flexural Strength Model 2: Flexural Strength Based on Age, Unit Weight, and w/c Ratio

= 676.0159 -1120.31* w/c + 4.1304 * uw + 35.74627 *ln(t) MRt

Figure 271. Equation. Prediction model 7 for MRt.

Where:

MRt = Flexural strength at age t years, psi.

w/c = water to cement ratio.

uw = Unit weight, lb/ft3

.

t = Pavement age, years.

Flexural Strength Model 3: Flexural Strength Based on Age, Unit Weight, and CMC

= 24.15063 + 0.55579*CMC + 2.96376* uw + 35.54463*ln(t) MRt

Figure 272. Equation. Prediction model 8 for MRt.
181

Where:

MRt = Flexural strength at age t years, psi.

CMC = Cementitious materials content, lb/yd3

.

uw = Unit weight, lb/ft3.

t = Pavement age, years.

PCC Elastic Modulus Models

Elastic Modulus Model 1: Model Based on Aggregate Type

c Dagg E (4.499*(UW ) *( f'c) ) * 2.3481 0.2429 =

Figure 273. Equation. Prediction model 9 for Ec.

Where:

Ec = PCC elastic modulus, psi.

uw = Unit weight, lb/ft3

.

f'c = Compressive strength at same age, psi.

Dagg = Regressed constant depending on aggregate type as follows:

• = 1.0 for andesite.

• =0.9286 for basalt.

• = 1.0079 for chert.

• = 0.9215 for diabase.

• = 1.0254 for dolomite.

• = 0.8333 for granite.

• = 1.0 for limestone.

• = 0.9511 for quartzite.

• = 1.0 for sandstone.

Elastic Modulus Model 2: Model Based on Age and Compressive Strength

1.3 0.2118

, )) 0.03

59.0287 *( ) *(ln( − = t E f'c c t t

Figure 274. Equation. Prediction model 10 for Ec,t.
182

Where:

Ec,t = Elastic modulus at age t years

f'ct = Compressive strength at age t years.

t = Age at which modulus is determined, years.

Elastic Modulus Model 3: Model Based on Age and 28-day Compressive Strength

))0.00524

0.03

375.6*( ) *(ln( 1..1

, 28

t E f'c c t = −day

Figure 275. Equation. Prediction model 11 for Ec,t.

Where:

Ec,t = Elastic modulus at age t years.

F'c28-day = 28-day compressive strength.

t = Age at which modulus is determined, years.

PCC Indirect Tensile Strength Models

PCC Indirect Tensile Strength Model: Model Based on Compressive Strength

0.4785 ft = 8.9068*( f'c)

Figure 276. Equation. Prediction model 12 for ft.

Where:

ft = Indirect tensile strength of the PCC material.

f'c= Compressive strength of the mix determined at the same age.

PCC CTE Models

CTE Model 1: CTE Based on Aggregate Type (Level 3 Equation for MEPDG)

Table 57. Model 13. CTE based on aggregate type.

Aggregate 

Type

Average From Data 

Used in Level 2 Model

Basalt 4.86

Chert 6.90

Diabase 5.13

Dolomite 5.79

Gabbro 5.28

Granite 5.71

Limestone 5.25

Quartzite 6.18

Andesite 5.33

Sandstone 6.33
183

CTE Model 2: CTE Based on Mix Volumetrics (Level 2 Equation for MEPDG)

Figure 277. Equation. Prediction model 14 for CTEPCC.

Where:

CTEPCC = CTE of the PCC material, x10-6 inch/inch/°F.

VCA = Volumetric proportion of the coarse aggregate (value between zero and 0.6).

CTECA = Constant determined for each aggregate type as follows:

• CTECA for basalt: 3.

• CTECA for chert: 6.4.

• CTECA for diabase: 3.4835.

• CTECA for dolomite: 5.1184.

• CTECA for gabbro: 3.75.

• CTECA for granite: 4.7423.

• CTECA for limestone: 3.2886.

• CTECA for quartzite: 6.1.

• CTECA for andesite: 3.6243.

• CTECA for Sandstone: 4.5

RIGID PAVEMENT DESIGN FEATURES MODELS

deltaT—JPCP Design

uw w c latitide

deltaT inch TR PCCTHK

+ 0.01677 * + 1.14008* / + 0.01784 *

/ = −5.27805 - 0.00794* - 0.0826 *SW + 0.18632*

Figure 278. Equation. Prediction model 15 for deltaT/inch.

Where:

deltaT/inch = Predicted gradient in JPCP slab, °F/inch.

TR = Difference between maximum and minimum temperature for the month of

construction, °F.

SW = Slab width, ft.

PCCTHK = JPCP slab thickness, inch.

uw= Unit weight of PCC used in JPCP slab, lb/ft3

.

CTEPCC = CTECA × VCA + 6.4514 × (1 ‒VCA)
184

w/c= Water to cement ratio.

latitude = Latitude of the project location, degrees.

deltaT—CRCP Design

+ 0.11299 * PCCTHK

+1.55013*Granite +1.40009 *Limestone + 2.01838*Quartzite

deltaT / inch = 12.93007 - 0.15101* MaxTemp - 0.10241* MaxTempRange + 3.279 *Chert 

Figure 279. Equation. Prediction model 16 for deltaT/inch.

Where:

deltaT/inch = Predicted gradient in CRCP slab, °F/inch.

MaxTemp = Maximum temperature for the month of construction, °F.

MaxTempRange = Maximum temperature range for the month of construction, °F.

PCCTHK = JPCP slab thickness, inch.

Chert =1 if PCC mix coarse aggregate is chert, or 0 if otherwise.

Granite = 1 if PCC mix coarse aggregate is granite, or 0 if otherwise.

Limestone = 1 if PCC mix coarse aggregate is limestone, or 0 if otherwise.

Quartzite = 1 if PCC mix coarse aggregate is quartzite, or 0 if otherwise.

Erosion for CRCP Design

There were no modifications to the existing MEPDG erosion model.

EI for JPCP Design

No model was developed for this parameter. 

STABILIZED MATERIALS MODELS

LCB Elastic Modulus Model

58156 ,28 716886 ' ELCB = f c d +

Figure 280. Equation. Prediction model 17 for ELCB.

Where:

ELCB = Elastic modulus of the LCB layer.

f'c, 28d = 28-day compressive strength of the LCB material.
185

UNBOUND MATERIALS MODELS

Resilient Modulus of Unbound Materials

Resilient modulus will be determined using the following constitutive model:

2 3

1

k

a

oct

k

a

r P P

M k 





























 = θ τ

Figure 281. Equation. Mr.

The constitutive model parameters are defined as follows:

Figure 282. Equation. Prediction model 18 for k1.

Figure 283. Equation. Prediction model 19 for k2.

Figure 284. Equation. Prediction model 20 for k3.

Where:

PCTHALF = Percent passing 1

/2-inch sieve.

LL = Liquid limit, percent.

OPTMOIST = Optimum moisture content, percent.

PCTNO80 = Percent passing No. 80 sieve.

PCTGRVL = Percent gravel fraction (0.078- to 2.36-inch size).

D10 = Maximum particle size of the smallest 10 percent of soil sample.

FUTURE WORK

The models presented in this report, for most part, were developed from LTPP materials tables 

that are comprehensive and have been cleared through rigorous data screening and reviews 

(level E). The CTE values in the database are a relatively recent addition. Over the past year, 

some issues were identified with the accuracy of these data, and FHWA has made other efforts to 

correct the CTE test data. The CTE models developed in this study, therefore, need to be updated 

to reflect the recent changes.

Additionally, the deltaT models for JPCP and CRCP design are based on the calibration in the 

MEPDG version 1.0 software. The MEPDG rigid pavement models are being updated to account 

for changes in CTE values and to address software bugs identified since the release of version 

1.0 in 2006. This version was completed in 2011. Therefore, the deltaT models presented here 

will not be applicable in the new version. These models will require updating. The procedures 

followed to develop these models are valid and can be used in a framework for future revisions.

k1 = 1,446.2 ‒ 4.56764 × PCTHALF + 4.92 × LL ‒ 27.73 × OPTMOIST

k2 = 0.45679 ‒ 0.00073376 × PCTNO80 ‒ 0.00269 × LL + 

0.00060555 × PCTGRVL + 12.97 × D10

k3 = -0.188 (for fine-grained soils)

k3 = -0.153 (for coarse-grained materials)

187

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