Performance of Fitness Functions Based on Natural Frequencies in Defect Detection Using the Standard PSO-FEM Approach

Abstract

Structural defect detection based on finite element model (FEM) updating is an optimization problem by minimizing the discrepancy of responses between model and measurement. Researchers have introduced many methods to perform the FEM updating for defect detection of the structures. A popular approach is to adopt the particle swarm optimization (PSO) algorithm. In this process, the fitness function is a critical factor in the success of the PSO-FEM approach. Our objective is to compare the performances of four fitness functions based on natural frequencies using the standard PSO-FEM approach for defect detection. In this paper, the definition of the standard PSO algorithm is first presented. After constructing the finite element benchmark model of the beam structure, four commonly used fitness functions based on natural frequencies are outlined. Their performance in defect detection of beam structures will be evaluated using the standard PSO-FEM approach. Finally, in the numerical simulations, the population diversity, success rate, mean iterations, and CPU time of the four fitness functions for the algorithm are calculated. The simulation results comprehensively evaluate their performances for single defect and multidefect scenario, and the effectiveness and superiority of the fitness function $S_4$ will be demonstrated.