Finite Elements in Analysis and Design
Backcalculation of flexible pavement moduli from falling weight deflectometer data using artificial neural networks
Optimization of the deflection basin by genetic algorithm and neural network approach
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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In current practice, the evaluation of the performance of existing road pavements has become a priority issue for many highway engineers. To make appropriate rehabilitation and management decisions the engineer must rely on an efficient method for determining the structural conditions of pavements. Nondestructive testing (NDT) reveals the stress-strain properties of pavement layers at relatively low strain levels. Since the theoretical approaches used to determine the stress-strain relationships in pavement layers calculate the deflections for given mechanical properties, it is necessary to make an inversion using a backcalculation tool. Several methods have been developed to backcalculate the mechanical properties of flexible pavement; these methods vary in analysis type, material model, and optimization algorithm. This study is designed to explain these methods and to compare and contrast them in terms of modeling precision, computational expense, and calculation details. Consequently, innovations and advances in backcalculating flexible pavements are considered in this paper.