Structural Engineering and Mechanics

  • 제63권6호
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  • Pages.825-835
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  • 2017
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

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

  • Chen, Ze-peng (School of Mechanics and Construction Engineering, Jinan University) ;
  • Yu, Ling (School of Mechanics and Construction Engineering, Jinan University)
  • 투고 : 2016.10.26
  • 심사 : 2017.05.12
  • 발행 : 2017.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China

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  3. Directed particle swarm optimization with Gaussian-process-based function forecasting vol.295, pp.1, 2017, https://doi.org/10.1016/j.ejor.2021.02.053