Dynamically Self-Adaptive Fuzzy PSO Technique for Smart Diagnosis of Transverse Crack

Abstract

An artificial intelligence (AI)-based methodology is proposed for the damage diagnosis of beam members of engineering structures under complex loading conditions. The current proposed method has been developed using particle swarm optimization (PSO) technique. A single transverse open-edge crack has been developed on a beam structure, modeled by a local flexibility matrix that is calculated analytically to determine natural frequencies and mode shapes. The modified PSO employs the strategy of nonlinearly decreasing inertia weight, which varies from a large value to a small value. Furthermore, a fuzzy adaptive PSO (APSO) has been used that incorporates the dynamically varying inertia weight based on the variance of the population fitness. Numerical and experimental studies on the cracked beam structure were also conducted to ensure the integrity of the above algorithms. The results show that both the size and location of the crack can be predicted efficiently through the proposed APSO.