Structural Engineering and Mechanics

  • 제63권6호
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  • Pages.789-797
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  • 2017
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

On-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures

  • Lei, Ying (School of Architecture and Civil Engineering, Xiamen University) ;
  • Wang, Longfei (School of Architecture and Civil Engineering, Xiamen University) ;
  • Lu, Lanxin (School of Architecture and Civil Engineering, Xiamen University) ;
  • Xia, Dandan (School of Civil & Architecture Engineering, Xiamen University of Technology)
  • 투고 : 2017.04.05
  • 심사 : 2017.06.16
  • 발행 : 2017.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Recently, some integrated structural identification/damage detection and reliability evaluation of structures with uncertainties have been proposed. However, these techniques are applicable for off-line synthesis of structural identification and reliability evaluation. In this paper, based on the recursive formulation of the extended Kalman filter, an on-line integration of structural identification/damage detection and reliability evaluation of stochastic building structures is investigated. Structural limit state is expanded by the Taylor series in terms of uncertain variables to obtain the probability density function (PDF). Both structural component reliability with only one limit state function and system reliability with multi-limit state functions are studied. Then, it is extended to adopt the recent extended Kalman filter with unknown input (EKF-UI) proposed by the authors for on-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures subject to unknown excitations. Numerical examples are used to demonstrate the proposed method. The evaluated results of structural component reliability and structural system reliability are compared with those by the Monte Carlo simulation to validate the performances of the proposed method.

키워드

과제정보

연구 과제 주관 기관 : Natural Science Foundation of China (NSFC), Shanghai Municipal science and Technology Committee

참고문헌

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