New Findings on Existing Resilient Modulus Constitutive Models through Performance Comparison on LTPP Data
Journal
Journal of Transportation Engineering Part B: Pavements
Date Issued
June 1, 2024
DOI
10.1061/JPEODX.PVENG-1457
Abstract
Resilient modulus (MR) serves as a fundamental material property utilized for characterizing unbound pavement materials in pavement design. While the repeated load triaxial test (RLT) is the standard method to determine MR, it can be impractical due to the required test facilities and expertise. As a result, various constitutive models have been proposed to predict MR of soil based on the stress state and soil properties. This study evaluates the performance of 10 such constitutive models based on their accuracy in representing the relationship between MR and stress conditions for RLT test data taken from the long-term pavement performance (LTPP) database. The 10 constitutive models consisted of models recommended by various researchers and institutions, including the one proposed by the National Cooperative Highway Research Program (NCHRP) and adopted by the Mechanistic-Empirical Pavement Design Guide (MEPDG). A multivariable nonlinear curve fitting technique was used to fit the data to the models and obtain model coefficients. The performance of these models was then measured by using the coefficient of determination (R2), the root mean square error (RMSE) and the mean absolute error (MAE) values as metrics. The results show that the models proposed by NCHRP, Puppala as well as Ni performed best, with Puppala's model showing slight superiority. On the other hand, the models proposed by Rahim and George, Kim, and Moossazadeh and Witczak produced poor results. The results also indicate that models having the general form of NCHRP and expressed in terms of confining pressure and deviator stress perform better. The findings of this study will provide insights into the reliability of existing models and considerations that should be made for developing better alternative models in the future.