Structural Health Monitoring Procedure for Composite Structures through the use of Artifcial Neural Networks

In this paper different architectures of Artifcial Neural Networks (ANNs) for structural damage detection are studied. The main objective is to investigate an ANN able to detect and localize damage without any prior knowledge on its characteristics so as to serve as a real-time data processor for Structural Health Monitoring (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 =D, properly defined using a 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 Multidomain 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 performances of the two different ANNs analyzed.