Optimization of the deflection basin by genetic algorithm and neural network approach

Terzi S., Saltan M., YILDIRIM T.

ARTIFICAIL NEURAL NETWORKS AND NEURAL INFORMATION PROCESSING - ICAN/ICONIP 2003, vol.2714, pp.662-669, 2003 (SCI-Expanded) identifier identifier


This paper introduces a new concept of integrating artificial neural networks (ANN) and genetic algorithms (GA) in modeling the deflection basins measured on the flexible pavements. Backcalculating pavement layer moduli are well-accepted procedures for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from Nondestructive Testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, in-situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. In order to backcalculate reliable moduli, deflection basin must be realistically modeled. In this work, ANN was used to model the deflection basin characteristics and GA as an optimization tool. Experimental deflection data groups from NDT are used to show the capability of the ANN and GA approach in modeling the deflection bowl. This approach can be easily and realistically performed to solve the optimization problems which do not have a formulation or function about the solution.