Article,
Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness
Indian Journal of Engineering and Materials Sciences, 12 (1): 42--50 (February 2005)
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 characterised by
their elastic moduli estimated from surface deflections
through back calculation. Backcalculating the pavement
layer moduli is a well-accepted procedure for the
evaluation of the structural capacity of pavements. The
ultimate aim of the back calculation 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 back calculated
from the measured field data through appropriate
analysis techniques. In this study, artificial neural
networks (ANN) and gene expression programming (GEP)
are used in back calculating 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 back calculating the pavement
layer thickness and compared each other. These
approaches can be easily and realistically performed to
solve the optimisation problems which do not have a
formulation or function about the solution.
