Document Type

Article

Publication Date

2011

Journal or Book Title

Structure and Infrastructure Engineering

Volume

7

Issue

4

First Page

297

Last Page

304

DOI

10.1080/15732470802550077

Abstract

This paper proposes the use of neural network- (NN-) based pavement structural analysis tools as surrogates for the flexible pavement response analysis in the new mechanistic empirical pavement design guide (MEPDG) developed for the American State Highway and Transportation Officials (AASHTO). Some of the recent successful applications of NN-based structural analysis models for predicting critical flexible pavement responses and nonlinear pavement layer moduli from falling weight deflectometer (FWD) deflection basins are highlighted. Because NNs excel at mapping in higher-order spaces, such models can go beyond the existing univariate relationships between pavement structural responses and performance (such as the subgrade strain criteria for considering flexible pavement rutting). The NN-based rapid prediction models could easily be incorporated into the newer versions of the MEPDG, which will continue to be updated. This can lead to better performance prediction and also reduce the risk of premature pavement failure.

Research Focus Area

Transportation Engineering

Comments

This is an accepted manuscript of an article published by Taylor & Francis in Structure and Infrastructure Engineering on December 24, 2008, available online: http:// www.tandf.com/10.1080/15732470802550077.

Copyright Owner

Taylor & Francis

Language

en

File Format

application/pdf

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