Structural Engineering and Mechanics

  • 제68권6호
  • /
  • Pages.735-745
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  • 2018
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

An improved Big Bang-Big Crunch algorithm for structural damage detection

  • Yin, Zhiyi (Department of Applied Mechanics and Engineering, School of Aeronautics and Astronautics, Sun Yat-sen University) ;
  • Liu, Jike (Department of Applied Mechanics and Engineering, School of Aeronautics and Astronautics, Sun Yat-sen University) ;
  • Luo, Weili (School of Civil Engineering, Guangzhou University) ;
  • Lu, Zhongrong (Department of Applied Mechanics and Engineering, School of Aeronautics and Astronautics, Sun Yat-sen University)
  • 투고 : 2018.09.07
  • 심사 : 2018.11.10
  • 발행 : 2018.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

The Big Bang-Big Crunch (BB-BC) algorithm is an effective global optimization technique of swarm intelligence with drawbacks of being easily trapped in local optimal results and of converging slowly. To overcome these shortages, an improved BB-BC algorithm (IBB-BC) is proposed in this paper with taking some measures, such as altering the reduced form of exploding radius and generating multiple mass centers. The accuracy and efficiency of IBB-BC is examined by different types of benchmark test functions. The IBB-BC is utilized for damage detection of a simply supported beam and the European Space Agency structure with an objective function established by structural frequency and modal data. Two damage scenarios are considered: damage only existed in stiffness and damage existed in both stiffness and mass. IBB-BC is also validated by an existing experimental study. Results demonstrated that IBB-BC is not trapped into local optimal results and is able to detect structural damages precisely even under measurement noise.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China, Central Universities

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