A DE-Based Algorithm for Structural Damage Detection

Abstract

The development of a methodology for the accurate and reliable assessment of structural damages, as one crucial step in the structural health monitoring (SHM) field, is very important to ensure the safety, integrity and stability of structures. An improved adaptive differential evolution (IADE) algorithm is proposed for structural damage detection (SDD) based on DE algorithm and FE model-updating techniques. An objective function is defined as minimizing the discrepancies between the experimental and analytical modal parameters (namely, natural frequencies and mode shapes). It is set as a nonlinear least-squares problem with bound constraints. Unlike the commonly used line-search methods, the IADE approach, a heuristic method for the direct search of the optimal point of the given objective function, is employed to make the optimization process more robust and reliable. Some numerical simulations for single and multiple damage cases of a 25-bar space truss frame structure have been conducted for evaluation on the reliability and robustness of the proposed method. The illustrated results show that the IADE algorithm is very effective for SDD. It can not only locate the structural damages but also quantify the severity of damages. Regardless of slight damage or multiple damages, the identification accuracy is very high and noise immunity is better, which shows that the IADE algorithm is feasible and effective for SDD.