Structural Engineering and Mechanics

  • 제54권2호
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  • Pages.379-392
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  • 2015
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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 by principle component analysis of long-gauge dynamic strains

  • Xia, Q. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University) ;
  • Tian, Y.D. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University) ;
  • Zhu, X.W. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University) ;
  • Xu, D.W. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University) ;
  • Zhang, J. (Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University)
  • 투고 : 2014.12.16
  • 심사 : 2015.02.27
  • 발행 : 2015.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

A number of acceleration-based damage detection methods have been developed but they have not been widely applied in engineering practices because the acceleration response is insensitive to minor damage of civil structures. In this article, a damage detection approach using the long-gauge strain sensing technology and the principle component analysis technology is proposed. The Long gauge FBG sensor has its special merit for damage detection by measuring the averaged strain over a long-gauge length, and it can be connected each other to make a distributed sensor network for monitoring the large-scale civil infrastructure. A new damage index is defined by performing the principle component analyses of the long-gauge strains measured from the intact and damaged structures respectively. Advantages of the long gauge sensing and the principle component analysis technologies guarantee the effectiveness for structural damage localization. Examples of a simple supported beam and a steel stringer bridge have been investigated to illustrate the successful applications of the proposed method for structural damage detection.

키워드

참고문헌

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피인용 문헌

  1. Covariance of dynamic strain responses for structural damage detection vol.95, 2017, https://doi.org/10.1016/j.ymssp.2017.03.020
  2. Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network pp.1868-5145, 2018, https://doi.org/10.1007/s12652-018-0997-7
  3. Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition vol.26, pp.2, 2018, https://doi.org/10.1002/stc.2288