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Structural Engineering and Mechanics Volume 63, Number 6, September25 2017 , pages 825-835 DOI: https://doi.org/10.12989/sem.2017.63.6.825 |
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A novel PSO-based algorithm for structural damage detection using Bayesian multi-sample objective function |
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Ze-peng Chen and Ling Yu
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| Abstract | ||
| 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 lowcomputing 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. | ||
| Key Words | ||
| structural damage detection; PSO-INM; multi-sample objective function; benchmark model | ||
| Address | ||
| Ze-peng Chen: School of Mechanics and Construction Engineering, Jinan University, Guangzhou 510632, China; MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou 510632, China Ling Yu: School of Mechanics and Construction Engineering, Jinan University, Guangzhou 510632, China; MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou 510632, China; College of Civil Engineering and Architecture, China Three Gorges University, Yichang 443002, China | ||