DOI QR코드

DOI QR Code

Damage detection of railway bridges using operational vibration data: theory and experimental verifications

  • Azim, Md Riasat (Department of Civil & Environmental Engineering, University of Alberta) ;
  • Zhang, Haiyang (Department of Civil & Environmental Engineering, University of Alberta) ;
  • Gul, Mustafa (Department of Civil & Environmental Engineering, University of Alberta)
  • 투고 : 2020.04.02
  • 심사 : 2020.06.02
  • 발행 : 2020.06.25

초록

This paper presents the results of an experimental investigation on a vibration-based damage identification framework for a steel girder type and a truss bridge based on acceleration responses to operational loading. The method relies on sensor clustering-based time-series analysis of the operational acceleration response of the bridge to the passage of a moving vehicle. The results are presented in terms of Damage Features from each sensor, which are obtained by comparing the actual acceleration response from the sensors to the predicted response from the time-series model. The damage in the bridge is detected by observing the change in damage features of the bridge as structural changes occur in the bridge. The relative severity of the damage can also be quantitatively assessed by observing the magnitude of the changes in the damage features. The experimental results show the potential usefulness of the proposed method for future applications on condition assessment of real-life bridge infrastructures.

키워드

과제정보

The study is funded by IC-IMPACTS (the India-Canada Centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability), established through the Networks of Centres of Excellence of Canada.

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피인용 문헌

  1. A Data-Driven Damage Assessment Tool for Truss-Type Railroad Bridges Using Train Induced Strain Time-History Response vol.22, pp.2, 2020, https://doi.org/10.1080/13287982.2021.1908710
  2. Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response vol.4, pp.2, 2021, https://doi.org/10.3390/vibration4020028
  3. Data-driven damage identification technique for steel truss railroad bridges utilizing principal component analysis of strain response vol.17, pp.8, 2020, https://doi.org/10.1080/15732479.2020.1785512