Smart Structures and Systems

  • 제23권5호
  • /
  • Pages.507-520
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  • 2019
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

CNN-based damage identification method of tied-arch bridge using spatial-spectral information

  • Duan, Yuanfeng (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Chen, Qianyi (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Zhang, Hongmei (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Yun, Chung Bang (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Wu, Sikai (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Zhu, Qi (College of Civil Engineering and Architecture, Zhejiang University)
  • 투고 : 2019.01.30
  • 심사 : 2019.04.15
  • 발행 : 2019.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

키워드

과제정보

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

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