A robust locating multi-optima approach for damage identification of plate-like structures

• Published in: Applied soft computing Vol. 75; pp. 508 - 522
• Main Authors: Lu, K., Li, Y.Y.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2019
• Subjects: Curvature mode shape; Multi-damage identification; Niche PSO algorithm; Plate-like structures; Robust damage detection; Curvature mode shape; Niche PSO algorithm; Robust damage detection; Plate-like structures; Multi-damage identification
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.11.035

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances. •Fusing particle swarm optimization and niche technique to solve multiple damage identification problems.•Elaborating the subswarm creation, merging and absorbing mechanism of the algorithm.•Verifying its accuracy by comparing the proposed algorithm and evolution algorithms on standard test functions.•Examining particle convergence behavior of different PSO algorithms for noise-free data and noisy data, and exploring robust structural health monitoring problems.•Using curvature mode shapes as the sensitive parameter on objective function optimization.