Prediction of Deformation of Circular Plates Subjected to Impulsive Loading Using GMDH-type Neural Network

Abstract

In this paper, experimental responses of the clamped mild steel, copper, and aluminium circular plates are presented subjected to blast loading. The GMDH-type neural networks (Group Method of Data Handling) are then used for the modelling of the mid-point deflection thickness ratio of the circular plates using those experimental results. The aim of such modelling is to show how the mid-point deflection varies with the variations of the important parameters. Further, it is shown that the use of dimensionless input variables, rather than the actual physical parameters, in such GMDH-type network modelling leads to simpler polynomial expressions which can be used for modelling and prediction purposes. It is also demonstrated that Singular Value Decomposition (SVD) can be effectively used to find the vector of coefficients of quadratic sub-expressions embodied in such GMDH-type networks. Such application of SVD will highly improve the performance of GMDH-type networks to model of nonlinear dynamic behavior of circular plates.