Dynamic Modulus Predictive Models for In-Service Asphalt Layers in Hot Climate Areas

Abstract

Dynamic modulus of asphalt mixtures is one of the most important input parameters in design and rehabilitation of asphalt pavements. Dynamic modulus predictive models are alternative methods for laboratory determination of this parameter. These models were developed based on laboratory data. Due to sophisticated and laborious laboratory testing methods, there is a need to develop new models to determine dynamic moduli of in-service asphalt layers. In this study, nine different asphalt pavement sites were selected in two southern provinces of Iran. Falling weight deflectometer (FWD) testing was performed at each site and core samples were taken from the asphalt layers for volumetric analysis. Using the results, dynamic moduli of asphalt layers were determined using six predictive models, including Witczak, modified Witczak, Hirsch, Al-Khateeb, global, and simplified global models. Predicted dynamic moduli were compared with those determined from back-calculation analysis of FWD data. Existing predictive models were calibrated and new models were developed for the prediction of in situ dynamic modulus of asphalt layers in hot climate areas. Performance evaluation and validation of the new models showed high prediction accuracy and low prediction bias with very good correlations between the predicted and back-calculated values (R2=0.96).