Genetic and Particle Swarm Optimization Algorithms for Damage Detection of Beam-like Structures Using Residual Force Method

Abstract

Vibration-based damage detection methods are widely used to monitor the health of structures. Various methods presented in the literature have different performances. Therefore, the use of a proper method can be useful to find correctly and fast the location and severity of damage in structures. The purpose of this study is to introduce a novel objective function in the optimization problem of damage detection of structures based on the residual force method (RFM) and finite element method (FEM). Then, the proposed objective function is optimized using two algorithms called particle swarm optimization (PSO) and genetic algorithm (GA) to compare their performances in damage detection of a beam-like structure. The effectiveness of two algorithms is evaluated by detecting the location and severity of a damaged beam for several damage scenarios. Results show that the PSO algorithm can predict the location as well as the severity of damage in beam-like structure in both single and multiple damage scenarios better than the GA algorithm based on the accuracy and convergence to the optimum solution.