Smart Structures and Systems

  • 제27권2호
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  • Pages.257-266
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  • 2021
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Damage detection of bridge structures under unknown seismic excitations using support vector machine based on transmissibility function and wavelet packet energy

  • Liu, Lijun (School of Architecture and Civil Engineering, Xiamen University) ;
  • Mi, Jianan (School of Architecture and Civil Engineering, Xiamen University) ;
  • Zhang, Yixiao (School of Architecture and Civil Engineering, Xiamen University) ;
  • Lei, Ying (School of Architecture and Civil Engineering, Xiamen University)
  • 투고 : 2020.08.16
  • 심사 : 2020.12.10
  • 발행 : 2021.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

Since it may be hard to obtain the exact external load in practice, damage identification of bridge structures using only structural responses under unknown seismic excitations is an important but challenging task. Since structural responses are determined by both structural properties and seismic excitation, it is necessary to remove the effects of external excitation and only retain the structural information for structural damage identification. In this paper, a data-driven approach using structural responses only is proposed for structural damage alarming and localization of bridge structures. The transmissibility functions (TF) of structural responses are used to eliminate the influence of unknown seismic excitations. Moreover, the inverse Fourier transform of TFs and wavelet packet transform are used to reduce the influence of frequency bands and to extract the damage-sensitive feature, respectively. Based on Support vector machines (SVM), structural responses under ambient excitations are used for training SVM. Then, structural responses under unknown seismic excitations are also processed accordingly and used for damage alarming and localization by the trained SMV. The numerical simulation examples of beam-type bridge and a cable-stayed bridge under unknown seismic excitations are studied to illustrate the performance of the proposed approach.

키워드

과제정보

The research in this paper is supported by the National Key R&D Program of China via Grant No. 2017YFC1500603. The authors thank Associate Prof. Ye Xia at Tongji University for providing the benchmark model of Haiwen bridge.

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