Crack identification using model reduction based on proper orthogonal decomposition coupled with radial basis functions

Abstract

This work deals with the crack identification using model reduction based on the proper orthogonal decomposition method. The proposed inverse problem consists of the estimation of the crack length and its position in a plate using boundary displacements as input data. Genetic algorithm and particle swarm optimization were applied for the minimization of the error function expressed as the difference between the boundary displacements of the actual crack and those of the estimated crack. It was found that the proposed approach is able to accurately estimate crack size and detect its location. The stability of the identification algorithm was tested against measurement uncertainty by introducing a white Gaussian noise in the input data. The approach showed high stability for noise levels lower than 5Â %. The efficiency of the approach using small number of sensor points was also demonstrated.