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Indian Journal of Engineering and Materials Sciences (IJEMS) >
IJEMS Vol.12 [2005] >
IJEMS Vol.12(1) [February 2005] >
| Title: | Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness |
| Authors: | Saltan, Mehmet
Terzi, Serdal |
| Issue Date: | Feb-2005 |
| Publisher: | CSIR |
| IPC Code: | E01C 9/10 |
| Abstract: | Pavement deflection data are often used to evaluate a
pavement’s structural condition non-destructively. It is essential not only to
evaluate the structural integrity of an existing pavement but also to have
accurate information on pavement surface condition in order to establish a
reasonable pavement rehabilitation design system. Pavement layers are
characterized by their elastic moduli estimated from surface deflections
through backcalculation. Backcalculating the pavement layer moduli is a
well-accepted procedure for the evaluation of the structural capacity of
pavements. The ultimate aim of the backcalculation process from non-destructive
testing (NDT) results is to estimate the pavement material properties. Using
backcalculation analysis, flexible pavement layer thicknesses together with in-situ material properties can be
backcalculated from the measured field data through appropriate analysis
techniques. In this study, artificial neural networks (ANN) and gene expression
programming (GEP) are used in backcalculating the pavement layer thickness from
deflections measured on the surface of the flexible pavements. Experimental
deflection data groups from NDT are used to show the capability of the ANN and
GEP approaches in backcalculating the pavement layer thickness and compared
each other. These approaches can be easily and realistically performed to solve
the optimization problems which do not have a formulation or function about the
solution. |
| Page(s): | 42-50 |
| ISSN: | 0975-1017 (Online); 0971-4588 (Print) |
| Source: | IJEMS Vol.12(1) [February 2005]
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