Structural Engineering and Mechanics

  • 제83권5호
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  • Pages.593-607
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  • 2022
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A two-stage structural damage detection method using dynamic responses based on Kalman filter and particle swarm optimization

  • Beygzadeh, Sahar (Department of Civil Engineering, Shahid Bahonar University of Kerman) ;
  • Torkzadeh, Peyman (Department of Civil Engineering, Shahid Bahonar University of Kerman) ;
  • Salajegheh, Eysa (Department of Civil Engineering, Shahid Bahonar University of Kerman)
  • 투고 : 2022.02.10
  • 심사 : 2022.05.16
  • 발행 : 2022.09.10
⟨ 이전 논문 다음 논문 ⟩

초록

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

키워드

참고문헌

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