An Effective Approach for Damage Identification in Beam-Like Structures Based on Modal Flexibility Curvature and Particle Swarm Optimization

Journal of Rehabilitation in Civil Engineering

Ali Zare Hosseinzadeh¹  Gholamreza Ghodrati Amiri²  Seyed Ali Seyed Razzaghi²  Siavash Nadjafi³ 

[1] Technology;;Center of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science &Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;

关键词: damage identification;    modal flexibility curvature;    particle swarm optimization (pso);    measurement noise;    beam-like structure;   

DOI  :  10.22075/jrce.2019.553.1081

来源: DOAJ

【 摘 要 】

In this paper, a computationally simple approach for damage localization and quantification in beam-like structures is proposed. This method is in consonance with applying modal flexibility curvature (MFC) and particle swarm optimization (PSO) algorithm. Analytical studies in the literature have revealed that changes in the modal flexibility curvature can be considered as a sensitive and suitable criterion for identifying damage in the beam-like structures. Modal flexibility curvature can be calculated utilizing central difference approximation, based on entries of the modal flexibility matrix. The PSO algorithm, as a powerful optimization tool, is employed in order to minimize the error function which is formulated as an error function between the measured modal flexibility curvatures of the damaged structure and those calculated from the analytical structure. To demonstrate the efficiency of the method, two beam-like structures under different damage scenarios are studied. In addition, the robustness of presented method is investigated only when the first several modal data are available. It is observed that the proposed approach is able to localize and quantify various damage cases only by a few lower vibrational modes and also, it is low-sensitive to measurement noise.

【 授权许可】

Unknown