Looking to the future: the next-generation hot mix asphalt dynamic modulus prediction models

Abstract

This paper describes an innovative approach related to the development of a new hot mix asphalt (HMA) dynamic modulus (|E *|) prediction model by employing the artificial neural networks (ANNs) methodology. Many studies have been conducted over the last 50 years related to the development of HMA |E *| prediction models based on the regression analysis of laboratory measurements. The current study is an attempt to replace the regression analysis with the ANNs that have proved useful for solving certain types of problems that are too complex, poorly understood or resource intensive to tackle using more traditional numerical and statistical methods. The ANN |E *| prediction models were developed using the latest comprehensive |E *| database that is available to the researchers (from the NCHRP Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the 1999 version of the Witczak |E *| prediction model, which is included in the mechanistic–empirical pavement design guide (MEPDG) and the new revised version as well. The sensitivity of input variables to the ANN model predictions were also examined and discussed. The ANN |E *| models show significantly higher prediction accuracy compared with the existing regression models and could easily be incorporated into the MEPDG. This approach may lead to more accurate characterisation of the HMA dynamic modulus resulting in better performance prediction, thereby reducing the risk of premature pavement failure.