Document Type

Article

Publication Date

2008

Journal or Book Title

Canadian Journal of Civil Engineering

Volume

35

Issue

7

First Page

699

Last Page

707

DOI

10.1139/L08-016

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.

Research Focus Area

Transportation Engineering

Comments

This is a manuscript of an article from Canadian Journal of Civil Engineering 35 (2008): 699, doi: 10.1139/L08-016. Posted with permission

Copyright Owner

NRC Canada

Language

en

File Format

application/pdf

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