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A novel PSO-based algorithm for structural damage detection using Bayesian multi-sample objective function

STRUCTURAL ENGINEERING AND MECHANICS 2017年第6期63卷 825-835页

作者： Chen, Ze-peng Yu, Ling Jinan Univ Sch Mech & Construct Engn Guangzhou 510632 Guangdong Peoples R China Jinan Univ MOE Key Lab Disaster Forecast & Control Engn Guangzhou 510632 Guangdong Peoples R China China Three Gorges Univ Coll Civil Engn & Architecture Yichang 443002 Peoples R China

Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.

关键词： structural damage detection PSO-INM multi-sample objective function benchmark model

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