Artificial neural network comparison for a SHM procedure applied to composite structures.

Abstract

In this paper different architectures of Artificial Neural Networks (ANNs) for structural damage detection are studied. The main objective is to create an ANN able to detect and localize damage without any prior knowledge on its characteristics so as to serve as a realtime data processor for SHM systems. Two different architectures are studied: the standard feed-forward Multi Layer Perceptron (MLP) and the Radial Basis Function (RBF) ANNs. The training data are given, in terms of a Damage Index ℑD, properly defined using the piezoelectric sensor signal output to obtain suitable information on the damage position and dimensions. The electromechanical response of the assembled structure has been computed by means of a Dual Reciprocity Boundary Element code developed in the framework of piezoelectricity. On this basis, the neural networks are then used to recognize the location of the damage and its characteristics and the numerical results highlight the main differences on the the performances of the two different ANNs analyzed.