Structural Engineering and Mechanics

  • 제30권2호
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  • Pages.135-146
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  • 2008
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Structural damage detection in continuum structures using successive zooming genetic algorithm

  • Kwon, Young-Doo (School of Mechanical Engineering, Kyungpook National University) ;
  • Kwon, Hyun-Wook (Ubiquitous Fusion Research Department, Research Institute of Industrial Science & Technology) ;
  • Kim, Whajung (School of Architecture and Civil Engineering, Kyungpook National University) ;
  • Yeo, Sim-Dong (Graduate School, Mechanical Engineering Department, Kyungpook National University)
  • 투고 : 2008.03.21
  • 심사 : 2008.05.02
  • 발행 : 2008.09.30
⟨ 이전 논문 다음 논문 ⟩

초록

This study utilizes the fine-tuning and small-digit characteristics of the successive zooming genetic algorithm (SZGA) to propose a method of structural damage detection in a continuum structure, where the differences in the natural frequencies of a structure obtained by experiment and FEM are compared and minimized using an assumed location and extent of structural damage. The final methodology applied to the structural damage detection is a kind of pseudo-discrete-variable-algorithm that counts the soundness variables as one (perfectly sound) if they are above a certain standard, such as 0.99. This methodology is based on the fact that most well-designed structures exhibit failures at some critical point due to manufacturing error, while the remaining region is free of damage. Thus, damage of 1% (depending on the given standard) or less can be neglected, and the search concentrated on finding more serious failures. It is shown that the proposed method can find out the exact structural damage of the monitored structure and reduce the time and amount of computation.

키워드

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피인용 문헌

  1. Multi-swarm fruit fly optimization algorithm for structural damage identification vol.56, pp.3, 2015, https://doi.org/10.12989/sem.2015.56.3.409
  2. BB-BC optimization algorithm for structural damage detection using measured acceleration responses vol.64, pp.3, 2017, https://doi.org/10.12989/sem.2017.64.3.353