Identification of crack-like defects in elastic structural elements on the basis of evolution algorithms

Abstract

A method for the identification of defects in elastic structural elements (SEs) on the basis of artificial neural network (ANN) tools was proposed. The problems of constructing a simulation model of an object, the optimal arrangement of transducers with the use of a genetic algorithm, and the subsequent identification of a defect were solved. The application of different learning architectures and algorithms to feed-forward neural networks (FFNs) was studied and the influence of errors on the estimation accuracy of the parameters of a defect was analyzed.