Advanced approaches to hot-mix asphalt dynamic modulus prediction

TLDR

Dynamic modulus is a key input in the new Mechanistic–empirical pavement design guide, yet existing regression‑based prediction models lack sufficient accuracy. The study develops a simplified hot‑mix asphalt dynamic modulus prediction model using artificial neural network methodology. The ANN model was trained on the 7,400 data points from 346 HMA mixtures in the NCHRP 547 database, benchmarked against the Hirsch model, and its input‑variable sensitivities were examined. The ANN predictions achieved significantly higher accuracy than the Hirsch model predictions.

Abstract

The dynamic modulus (|E*|) is one of the primary hot-mix asphalt (HMA) material property inputs at all three hierarchical levels in the new Mechanistic–empirical pavement design guide (MEPDG). The existing |E*| prediction models were developed mainly from regression analysis of an |E*| database obtained from laboratory testing over many years and, in general, lack the necessary accuracy for making reliable predictions. This paper describes the development of a simplified HMA |E*| prediction model employing artificial neural network (ANN) methodology. The intelligent |E*| prediction models were developed using the latest comprehensive |E*| database that is available to researchers (from National Cooperative Highway Research Program Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the Hirsch |E*| prediction model, which has a logical structure and a relatively simple prediction model in terms of the number of input parameters needed with respect to the existing |E*| models. The ANN-based |E*| predictions showed significantly higher accuracy compared with the Hirsch model predictions. The sensitivity of input variables to the ANN model predictions were also examined and discussed.