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
Copyright Owner
Taylor & Francis
Copyright Date
2011
Language
en
File Format
application/pdf
Recommended Citation
Ceylan, Halil and Gopalakrishnan, Kasthurirangan, "Computationally efficient surrogate response models for mechanistic-empirical pavement analysis and design" (2011). Civil, Construction and Environmental Engineering Publications. 55.
https://lib.dr.iastate.edu/ccee_pubs/55
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.