The resilient modulus (MR) is an important parameter for the base course layer in the pavement design process. Use of recycled asphalt pavement (RAP) for this layer must take into consideration the effect of various factors that may occur in the field on the MR. Previous numerical models used for the granular base layer could be used for the RAP. The study reported here examined the suitability of RAP procedures under the effect of different factors (e.g., water content, dry density, freeze-thaw cycles). Various percentages of the RAP (50%, 75%, and 100% by weight) were employed in this research. All the models included in the study took into account the effect of state of stresses directly, but they also considered the other factors mentioned and the effect of their interactions with the MR indirectly. The intent of the study was twofold: (a) to determine the adequacy of the models employed in the use of the RAP procedure in the base course layer and (b) to determine which model best described RAP behavior under the effects of the tested factors. On the basis of a review of the literature, nine prediction models were chosen to investigate the granular base course layers so as to predict MR for RAP. A pilot analysis was made of these models to compare the measured and predicted values of MR under the tested factors. Three models showed a good prediction for the MR. These three models were reassessed in a sensitivity analysis on regression parameters to choose the best-fit model for the RAP applications.
E. Noureldin and M. Abdelrahman, "Modeling of the Resilient Modulus for Recycled Asphalt Pavement Applications in Base Course Layers," Transportation Research Record, no. 2371, pp. 121 - 132, National Research Council (U.S.), Jan 2013.
The definitive version is available at https://doi.org/10.3141/2371-14
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Best-Fit Models; Freeze-Thaw Cycles; Pavement Design; Prediction Model; Recycled Asphalt Pavement; Regression Parameters; Resilient Modulus; State Of Stress
International Standard Serial Number (ISSN)
Article - Journal
© 2013 National Research Council (U.S.), All rights reserved.
01 Jan 2013