Contents

Next >>

Seasonal Variations in the Moduli of Unbound Pavement Layers

July 2006
FHWA-HRT-04-079
PDF version (2.59 MB)

FOREWORD

The in situ moduli of unbound pavement materials vary on a seasonal basis as a function of temperature and moisture conditions. Knowledge of these variations is required for accurate prediction of pavement life for pavement design and other pavement management activities. The primary objective of this study is to advance the rational estimation of seasonal variations in backcalculated pavement layer moduli using data collected via the Seasonal Monitoring Program of the Long-Term Pavement Performance (LTPP) Program. Principal components of this endeavor included: evaluation of the moisture predictive capabilities of the Enhanced Integrated Climatic Model (EICM); development of empirical models to predict backcalculated pavement layer moduli as a function of moisture content, stress state, and other explanatory variables; and trial application of the models developed to prediction backcalculated moduli for unbound pavement layers.

This investigation yielded two key findings. First, it provided the impetus for developing EICM Version 2.6 by demonstrating the practical inadequacies of EICM Versions 2.0 and 2.1 when applied to the prediction of in situ moisture content, and then demonstrated that improvement in the moisture predictive capability of the EICM had been achieved in Version 2.6. Second, the research identified fundamental discrepancies between layer moduli backcalculated using linear layered-elastic theory and the laboratory resilient modulus test conditions.

Gary L. Henderson
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. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement.

Technical Report Documentation Page

1. Report No. FHWA-HRT-04-079	2. Government Accession No.		3. Recipient's Catalog No.
4. Title and Subtitle Seasonal Variations in the Moduli of Unbound Pavement Layers		5. Report Date July 2006
		6. Performing Organization Code
7. Author(s) Cheryl Allen Richter			8. Performing Organization Report No.
9. Performing Organization Name and Address Federal Highway Administration 6300 Georgetown Pike McLean, VA 22101			10. Work Unit No. (TRAIS)
			11. Contract or Grant No. DTFH61–00–C–00121
12. Sponsoring Agency's Name and Address Office of Infrastructure Research and Development Federal Highway Administration (FHWA) 6300 Georgetown Pike McLean, VA 22101			13. Type of Report and Period Covered Final Report February 2000 to April 2004
			14. Sponsoring Agency's Code
15. Supplementary Notes Contracting Officer's Technical Representative: Aramis Lopez, HRDI - 13
16. Abstract The in situ moduli of unbound pavement materials vary on a seasonal basis as a function of temperature and moisture conditions. Knowledge of these variations is required for accurate prediction of pavement life for pavement design and other pavement management activities. The primary objective of this study is to advance the rational estimation of seasonal variations in backcalculated pavement layer moduli using data collected via the Seasonal Monitoring Program of the Long-Term Pavement Performance Program. Principal components of this endeavor included: evaluation of the moisture predictive capabilities of the Enhanced Integrated Climatic Model (EICM); development of empirical models to predict backcalculated pavement layer moduli as a function of moisture content, stress state, and other explanatory variables; and trial application of the models developed to prediction backcalculated moduli for unbound pavement layers. This investigation yielded two key findings. First, it provided the impetus for developing EICM Version 2.6 by demonstrating the practical inadequacies of EICM Versions 2.0 and 2.1 when applied to the prediction of in situ moisture content, and then demonstrated that substantial improvement in the moisture predictive capability of the EICM had been achieved in Version 2.6. Second, the research identified fundamental discrepancies between layer moduli backcalculated using linear-layered elastic theory and the laboratory resilient modulus test conditions. Other important findings included (1) variation in moisture content is not always the most important factor associated with seasonal variations in pavement layer moduli, and (2) a model form that fits linear elastic backcalculated moduli reasonably well. The overall accuracy of the modulus predictions achieved in the trial application of the predictive models was not fully acceptable. Several avenues for further research to improve upon these results are identified.
17. Key Words LTPP, Seasonal Monitoring Program, modulus, pavement, Enhanced Integrated Climatic Model, linear layered elastic theory		18. Distribution Statement No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161.
19. Security Classif. (of this report) Unclassified	20. Security Classif. (of this page) Unclassified	21. No. of Pages 283	22. Price

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

SI (Modern Metric) Conversion Factors

TABLE OF CONTENTS

1. Introduction and Research Objectives
   • Introduction
   • Research Objectives
   • Research Approach
2. Literature Review
   • Introduction
   • Soil Modulus: M_r versus E
   • Addressing Seasonal Variations in Pavement Design and Evaluation
   • Factors Influencing the Moduli of Unbound Pavement Materials
     • Stress Conditions
     • Moisture Conditions
     • Density and Soil Structure
     • Material Characteristics
     • Soil State with Respect to Freezing and Thawing
     • Additional Factors Influencing Backcalculated Moduli
   • Relating Resilient Moduli to Material Parameters
   • Seasonal Variations as Observed in Field Investigations
   • Environmental Effects Models Applicable to Pavement Design
   • Related Work With the Integrated Climatic Model
3. Data Acquisition and Assessment
   • Introduction
   • Data Sources
   • Backcalculated Moduli
     • Backcalculation of Pavement Layer Moduli
     • Evaluation of Backcalculated Layer Moduli
     • Variation in Moduli Under Nonfrozen Conditions
   • Moisture Parameters
   • Input to the Integrated Climatic Model
     • Climatic Boundary Conditions
     • Infiltration and Drainage
     • Asphalt Material Properties
     • Material Properties
     • Initial Temperature and Pore Pressure Profile
   • Summary
4. Evaluation of Volumetric Moisture Predictions From the Integrated Climatic Model
   • Introduction
   • Evaluation of eICM Version 2.0
   • Evaluation of eICM Version 2.1
     • Sections Considered
     • Layer Structure
     • Layer Porosity
     • Soil-Water Characteristic Curve Parameters (Gardner Coefficients)
     • Results
   • Application of eICM Version 2.6
     • Layer Structure
     • Layer Porosity
     • Specific Gravity
     • Saturated Permeability
     • Initial Volumetric Water Content
     • Soil-Water Characteristic Curve Model and Parameters (Gardner Coefficients)
     • Results
   • Evaluation of eICM Version 2.6 User Sensitivity
     • Between-User Differences in Model Application
   • Discussion And Conclusions Regarding EICM Moisture Predictions
5. Prediction of Backcalculation Pavement Layer Moduli
   • Introduction
   • Exploration of Relationships Between Backcalculated Moduli and Explanatory Variables
   • Models of the Form E/P_a = k₁θ/P_a^k₂(τ/P_a+1)^k₃
   • Factors Contributing to Differences Between Laboratory and Field-Based Constitutive Model Coefficients
   • Alternative Model Forms
     • Individual Pavement Layer Models
     • Soil Class Models
   • Summary
6. Application of Research Results To Estimate Seasonal Variations in Moduli of Unbound Pavement Layers
   • Introduction
   • Procedure for Prediction of Seasonal Variations in Backcalculated Pavement Layer Moduli
     • Required Input Data
     • Part 1: Derivation of Section- and Layer-Specific Regression Models
     • Part 2: Estimation of Pavement Layer Moduli for Future Date
   • Trial application
     • Results Obtained with Section/Layer-Specific Models Derived From the Full Data Set
     • Results Obtained With Section/Layer-Specific Models Derived From Limited Data Sets
     • Results Obtained with Soil Class Models
   • Summary Observations
7. Conclusions and Recommendations
   • Conclusions
     • The Enhanced Integrated Climatic Model
     • Models for Prediction of Backcalculated Pavement Layer Moduli
     • Variations in Backcalculated Moduli for Unbound Pavement Layers
     • Application of Research Results To Predict Moduli Backcalculated for Unbound Pavement Layers Using Linear Layered-Elastic Theory
   • Recommendations
     • The Enhanced Integrated Climatic Model
     • The State of the Art of Backcalculation of Pavement Layer Moduli
     • Consideration of Stress Dependency in Pavement Modeling
     • LTPP Data Used in This Investigation

Appendix A. Variation in TDR Moisture Data

Appendix B. Input Data for the Enhanced Integrated Climatic Model

Appendix C. Between-user Differences in Application of EICM Version 2.6

Appendix D. Materials Data Used In Development of E Predictive Models

Appendix E. Multiple Regression Results for Individual Pavement Layers

Appendix F. Trial Application Results Based on Section/Layer-Specific Models Derived From All Available Data

Appendix G. Trial Application Results Obtained With One-date Section/Layer-Specific Models

Appendix H. Trial Application Results Obtained With Two-Date Section/Layer-Specific Models

Appendix I. Trial Application Results Obtained With Soil Class Models

References

LIST OF FIGURES

• Figure 1. Chart for estimating effective roadbed soil resilient modulus for flexible pavements designed using the serviceability criteria
• Figure 2. Locations of LTPP seasonal monitoring sections considered in this study
• Figure 3. Seasonal variation in daily average moduli, section 040113 (Arizona)
• Figure 4. Seasonal variations in daily average moduli, section 091803 (Connecticut)
• Figure 5. Seasonal variations in daily average moduli, section 131005 (Georgia)
• Figure 6. Seasonal variations in daily average moduli, section 271018 (Minnesota)
• Figure 7. Seasonal variations in daily average layer moduli, section 481077 (Texas)
• Figure 8. Seasonal variation in moduli, section 501002 (Vermont)
• Figure 9. Frequency histogram for ratio of within-day standard deviation/ maximum TDR Model 1 error
• Figure 10. Predicted and monitored moisture contents for section 091803 (Connecticut), 6/30/94
• Figure 11. Predicted and monitored moisture contents for section 271018 (Minnesota), 8/8/94
• Figure 12. Comparison of monitored and predicted base-layer moisture for EICM Version 2.1
• Figure 13. Comparison of monitored and predicted subgrade moisture for EICM Version 2.1
• Figure 14. Sample EICM Version 2.1 moisture-profile plot, section 091803 (Connecticut)
• Figure 15. Comparison of monitored and predicted base moisture for EICM Version 2.6
• Figure 16. Comparison of monitored and predicted subgrade moisture for EICM Version 2.6
• Figure 17. Variation in monitored and predicted base moisture, section 041024
• Figure 18. Monitored and predicted subgrade moisture content, section 091803 (Connecticut)
• Figure 19. Monitored and predicted subgrade moisture, section 231026 (Maine)
• Figure 20. Monitored and predicted subgrade moisture, section 501002 (Vermont)
• Figure 21. Monitored and predicted subgrade moisture (shallow depths), section 831801 (Manitoba)
• Figure 22. Monitored and predicted subgrade moisture (greater depths), section 831801 (Manitoba)
• Figure 23. Comparison of EICM Version 2.6 moisture predictions for all section.
• Figure 24. Monitored and predicted moisture contents for all sections
• Figure 25. Comparison of moisture predictions, section 081053
• Figure 26. Monitored and predicted base moisture content, section 481077 (Texas)
• Figure 27. Monitored and predicted subgrade moisture content, section 481077 (Texas)
• Figure 28. Comparison of predicted volumetric moisture contents for section 091803 (Connecticut)
• Figure 29. Monitored and predicted moisture content, section 091803 (Connecticut)
• Figure 30. Monitored and predicted base moisture, section 131005 (Georgia)
• Figure 31. Monitored and predicted subgrade moisture, section 131005 (Georgia)
• Figure 32. Vertical load stress versus modulus, section 040113 A-1-a base layer
• Figure 33. Radial load stress versus modulus, section 040113 A-1-a base layer
• Figure 34. Bulk load stress versus modulus, section 040113 A-1-a base layer
• Figure 35. Load-induced octahedral shear stress versus modulus, section 040113 A-1-a base layer
• Figure 36. Comparison of radial and vertical stress components for granular base and subbase layers
• Figure 37. Comparison of radial and vertical stress components for coarse-grained subgrade layers
• Figure 38. Comparison of radial and vertical stress components for fine-grained subgrade layers
• Figure 39. Comparison of bulk and octahedral shear stresses for granular base and subbase layers
• Figure 40. Comparison of bulk and octahedral shear stresses for coarse-grained subgrade layers
• Figure 41. Comparison of bulk and octahedral shear stresses for fine-grained subgrade layers
• Figure 42. Calculation of overburden stress.
• Figure 43. Backcalculated modulus versus estimated temperature compatible modulus for all sections considered in trial applications
• Figure 44. Backcalculated versus section/layer-specific predicted modulus for models derived using all data
• Figure 45. Backcalculated modulus versus estimated temperature compatible modulus for section 271018 (Minnesota)
• Figure 46. Backcalculated modulus versus section/layer-specific one-date predicted modulus
• Figure 47. Backcalculated versus site/layer-specific two-date predicted modulus
• Figure 48. Backcalculated versus soil class predicted moduli—all sections
• Figure 49. Section 040113 (Arizona) E versus E predicted for section-specific models based on data for all available test dates
• Figure 50. Section 040114 (Arizona) E versus E predicted for section-specific models based on data for all available test dates
• Figure 51. Section 091803 (Connecticut) E versus E predicted for section-specific models based on data for all available test dates
• Figure 52. Section 131031 (Georgia) E versus E predicted for section-specific models based on data for all available test dates
• Figure 53. Section 161010 (Idaho) E versus E predicted for section-specific models based on data for all available test dates
• Figure 54. Section 231026 (Maine) E versus E predicted for section-specific models based on data for all available test dates
• Figure 55. Section 271018 (Minnesota) E versus E predicted for section-specific models based on data for all available test dates
• Figure 56. Section 331001 (New Hampshire) E versus E predicted for section-specific models based on data for all available test dates
• Figure 57. Section 351112 (New Mexico) E versus E predicted for section-specific models based on data for all available test dates
• Figure 58. Section 481077 (Texas) E versus Epredicted for section-specific models based on data for all available test dates
• Figure 59. Section 561007 (Wyoming) E versus E predicted for site-specific models based on data for all available test dates
• Figure 60. Section 871622 (Ontario) E versus E predicted for section-specific models based on data for all available test dates
• Figure 61. Section 040113 (Arizona) E versus E predicted for section-specific models based on data for a single test date
• Figure 62. Section 091803 (Connecticut) E versus E predicted for section-specific models based on data for a single test date
• Figure 63. Section 271018 (Minnesota) E versus E predicted for section-specific models based on data for a single test date (A (top) and B (bottom))
• Figure 64. Section 481077 (Texas) E versus E predicted for section-specific models based on data for a single test date
• Figure 65. Section 040113 (Arizona) E versus E predicted for section-specific models based on data for two test dates
• Figure 66. Section 091803 (Connecticut) E versus E predicted for section-specific models based on data for two test dates
• Figure 67. Section 271018 (Minnesota) E versus E predicted for section-specific models based on data for two test dates
• Figure 68. Section 481077 (Texas) E versus E predicted for section-specific models based on data for two test dates
• Figure 69. Section 040113 (Arizona) E versus E predicted for soil class models
• Figure 70. Section 040114 (Arizona) E versus E predicted for soil class models
• Figure 71. Section 091803 (Connecticut) E versus E predicted for soil class models
• Figure 72. Section 131031 (Georgia) E versus E predicted for soil class models
• Figure 73. Section 231026 (Maine) E versus E predicted for soil class models
• Figure 74. Section 481077 (Texas) E versus E predicted for soil class models
• Figure 75. Section 871622 (Ontario) E versus E predicted for soil class models

LIST OF TABLES

• Table 1. Difference between laboratory and backcalculated moduli at equivalent stress states, M_r/E
• Table 2. Subgrade moduli used in the Asphalt Institute DAMA program
• Table 3. Monthly granular base k1 values used in the Asphalt Institute DAMA program
• Table 4. Seasonal variations of unbound material moduli for Washington State
• Table 5. Parameters found to affect laboratory resilient moduli of unbound materials
• Table 6. Dimensionless constitutive model coefficients for Equations 8 (in kPa (psi)) and 10 (dimensionless)
• Table 7. Summary of K1 and K2 statistics by aggregate class
• Table 8. Summary of dimensionless K1-K3 parameters for selected materials modeled using Equation 5
• Table 9. Summary of average elastic coefficients and exponents for LTPP materials modeled using Equation 5 (dimensionless)
• Table 10. Representative (best fit) regression constants for Equation 17 M_r in MPa
• Table 11. Representative regression constants for Equation 18
• Table 12. Regression constants for Equation 21
• Table 13. Resilient modulus prediction equations developed by Santha
• Table 14. Titus Glover and Fernando predictive models for constitutive model coefficients
• Table 15. Predictive equations for Equation 5 constitutive model coefficients as derived by Von Quintus and Killingsworth
• Table 16. Representative modular ratios
• Table 17. Seasonal multipliers for base and subgrade soils in Nevada
• Table 18. Layer thickness ranges for LTPP Seasonal Monitoring Program test sections considered in this investigation
• Table 19. Distribution of soil classification by pavement layer
• Table 20. Modulus ranges used in screening of layer moduli (MPa)
• Table 21. Variation in moduli with applied FWD load by soil class
• Table 22. Variation in moduli with load for individual A-3 layers
• Table 23. Pooled single-point within-day variation in moduli by soil class and layer type
• Table 24. Within-day variation in moduli of A-1-b pavement layers
• Table 25. Extent of variation in backcalculated moduli by soil class
• Table 26. Extent of variation in backcalculated moduli by pavement layer
• Table 27. Volumetric moisture models used in determining moisture content from LTPP TDR data (as reported by Jiang and Tayabji)
• Table 28. Minimum and maximum estimated errors (95 percent confidence interval) for TDR moisture content prediction models (adapted from Jiang and Tayabji)
• Table 29. Assumed crack lengths for distresses quantified in terms of area
• Table 30. LTPP Seasonal Monitoring Program sections used in evaluation of the EICM
• Table 31. Between-user differences in predicted moisture content for EICM Version 2.6
• Table 32. Summary statistics for correlation between E and mean layer moisture
• Table 33. Summary statistics with regard to observed correlation between E and applied FWD load
• Table 34. Relative strength of correlation between log E and bulk and octahedral shear stress parameters (number of layers and soil classes represented)
• Table 35. Sign of observed modulus-stress correlation
• Table 36. Distribution of strongest modulus-stress correlations with respect to stress computation depth
• Table 37. Change in modulus versus change in moisture content by layer type
• Table 38. Combinations of K0 and stress components considered in regression modeling
• Table 39. Regression coefficients for alternative stress computations
• Table 40. Log-transformed models used in regression modeling
• Table 41. Summary of regression coefficients for models 2A and 2B
• Table 42. Summary of goodness-of-fit statistics for Models 1, 2A and 2B
• Table 43. Selected soil class models for prediction of pavement layer moduli using linear layered-elastic theor.
• Table 44. Model forms considered for k1, k2, and k3 in Model 2
• Table 45. Data required for derivation of section/layer-specific models
• Table 46. Data required to predict backcalculated pavement layer moduli
• Table 47. Soil parameters required for application of soil class models to predict backcalculated pavement layer moduli
• Table 48. Modulus ranges
• Table 49. Data sets considered in trial application of modulus prediction
• Table 50. Site-specific, limited data set Model 3 and 3B coefficients and goodness-of-fit statistics
• Table 51. Summary statistics and t-test results for section/layer-specific complete data set model predictions
• Table 52. Summary statistics and t-test results for section/layer-specific one-date model predictions
• Table 53. Summary statistics and t-test results for section/layer-specific two-date model predictions
• Table 54. Summary statistics and t-test results for soil class model predictions
• Table 55. Pooled within-day standard deviation of volumetric moisture from individual TDR probes (percent)
• Table 56. Number of test dates considered in computation of pooled standard deviation for individual TDR moisture probes
• Table 57. Mean number of moisture observations per day considered in computation of within-day standard deviation
• Table 58. Pooled within-day standard deviation divided by maximum Model 1 error
• Table 59. Data sets for which individual within-day standard deviations exceed three times the overall pooled value
• Table 60. Input data and sources for the EICM Versions 2.0 and 2.1 for the Connecticut (091803) and Minnesota (271018) sections
• Table 61. Input data and sources for EICM Version 2.1 for the Maine (231026), New Hampshire (331001), Vermont (501002), and Manitoba (831801) sections
• Table 62. Input data and sources for EICM Version 2.6 for the Arizona (041024), Colorado (081053), Connecticut (091803), and Georgia (131005) sections
• Table 63. Input data and sources for EICM Version 2.6 for the Maine (231026), Minnesota (271018), and New Hampshire (331001) sections
• Table 64. Input data and sources for EICM Version 2.6 for the Texas (481077), Vermont (501002), and Manitoba (831801) sections
• Table 65. Daily rainfall, temperature, and water table depth input for sections 091803 and 131005 (Connecticut and Georgia)
• Table 66. Daily rainfall, temperature, and water table depth input for sections 231026 and 271018 (Maine and Minnesota)
• Table 67. Initial temperature profile data by section used as input to the EICM
• Table 68. Initial moisture profile data by section used in running EICM Version 2.1
• Table 69. Between-user differences in the application of EICM Version 2.6
• Table 70. Material density, relative density, and PI used in soil class models
• Table 71. Material gradation used in soil class models
• Table 72. Source Tables for materials data used in predictive models for backcalculated pavement layer moduli
• Table 73. Multiple regression results for Model 1
• Table 74. Multiple regression results for set 1 (both load and overburden with K0 values of 0.7 and 0.5 for base and subbase/subgrade layers, respectively)
• Table 75. Multiple regression results for set 2 (overburden only in computation of octahedral shear stress, K0 values of 0.7 and 0.5 for base and subbase/subgrade layers, respectively)
• Table 76. Multiple regression results for set 3 (both load and overburden with K0 values of 1.0 for all layers)
• Table 77. Multiple regression results for set 4 (both load and overburden with K0 values of 2.0 and 1.0 for base and subbase/subgrade layers, respectively)
• Table 78. Multiple regression results for set 5 (octahedral shear stress based on computed for load stresses only, K0 values of 2.0 and 1.0 for base and subbase/subgrade layers, respectively)
• Table 79. Multiple regression results for set 6 (bulk and octahedral shear stress computed based on load only)
• Table 80. Multiple regression results for set 7 (bulk stress computed based on load stresses only, octahedral shear stress computed based on both load and overburden with K0 values of 0.7 and 0.5 for base and subbase/subgrade layers, respectively)
• Table 81. Regression results for individual layers using Model 2A
• Table 82. Regression results for individual layers using Model 2B
• Table 83. Goodness-of-fit statistics for Models 1 and 2

Contents

Next >>

Events

• View all Upcoming Pavements Events

More Information

• Pavement Publications

Contacts

Aramis Lopez
Turner Fairbank
202-493-3145
E-mail Aramis

Customer Service
202-493-3035
E-mail Customer

 

This page last modified on 03/22/07