Analysis of the Mechanistic-Empirical Pavement Design Guide Performance Predictions: Influence of Asphalt Material Input Properties

Report No: 11-R3

Published in 2010

About the report:

The Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures (MEPDG) is an improved methodology for pavement design and the evaluation of paving materials.  The Virginia Department of Transportation (VDOT) is expecting to transition to using the MEPDG methodology in the near future.  The purpose of this research was to support this implementation effort. 

 A catalog of mixture properties from 11 asphalt mixtures (3 surface mixtures, 4 intermediate mixtures, and 4 base mixtures) was compiled along with the associated asphalt binder properties to provide input values.  The predicted fatigue and rutting distresses were used to evaluate the sensitivity of the MEPDG software to differences in the mixture properties and to assess the future needs for implementation of the MEPDG.  Two pavement sections were modeled: one on a primary roadway and one on an interstate roadway.  The MEPDG was used with the default calibration factors.  Pavement distress data were compiled for the interstate and primary route corresponding to the modeled sections and were compared to the MEPDG-predicted distresses.

 Predicted distress quantities for fatigue cracking and rutting were compared to the calculated distress model predictive errors to determine if there were significant differences between material property input levels.  There were differences between all rutting and fatigue predictions using Level 1, 2, and 3 asphalt material inputs, although not statistically significant.  Various combinations of Level 3 inputs showed expected trends in rutting predictions when increased binder grades were used, but the differences were not statistically significant when the calibration model error was considered.  Pavement condition data indicated that fatigue distress predictions were approximately comparable to the pavement condition data for the interstate pavement structure, but fatigue was over-predicted for the primary route structure.  Fatigue model predictive errors were greater than the distress predictions for all predictions.

 Based on the findings of this study, further refinement or calibration of the predictive models is necessary before the benefits associated with their use can be realized.  A local calibration process should be performed to provide calibration and verification of the predictive models so that they may accurately predict the conditions of Virginia roadways.  Until then, implementation using Level 3 inputs is recommended.  If the models are modified, additional evaluation will be necessary to determine if the other recommendations of this study are impacted.  Further studies should be performed using Level 1 and Level 2 input properties of additional asphalt mixtures to validate the trends seen in the Level 3 input predictions and isolate the effects of binder grade changes on the predicted distresses.  Further, additional asphalt mixture and binder properties should be collected to populate fully a catalog for VDOT’s future implementation use. 

The implementation of these recommendations and use of the MEPDG are expected to provide VDOT with a more efficient and effective means for pavement design and analysis.  The use of optimal pavement designs will provide economic benefits in terms of initial construction and lifetime maintenance costs.

Disclaimer Statement:The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.

Last updated: November 17, 2023

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