Structural Engineering and Mechanics

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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)
  • Received : 2016.10.26
  • Accepted : 2017.05.12
  • Published : 2017.09.25
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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 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.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

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