Detection of multiple cracks in beams using particle swarm optimization and artificial neural network

Abstract

This paper presents a new procedure for identification of multiple cracks in beam. Natural frequency is frequently used as a parameter for detection of cracks in the structures. The process of crack identification in presented procedure is consists of four stages. In first stage, three natural frequencies of a cantilever beam for different locations and depths of cracks were obtained using Finite Element Method (FEM). Assumed beam of this study include two cracks. In second stage, four Multi Layer Feed Forward (MLFF) neural networks were created. In third stage, Particle Swarm Optimization (PSO) method was used to training the neural networks. The inputs of neural networks were first three natural frequencies. The outputs of first and second neural networks were corresponding locations of first and second cracks, and the outputs of third and forth neural networks were corresponding depths of first and second cracks, respectively. In forth stage, some of natural frequencies of beam with distinct crack conditions as inputs applied to trained neural networks. Finally the calculated results showed that cracks characteristics were in good agreements with actual data.