DOI QR코드

DOI QR Code

Real-time condition assessment of railway tunnel deformation using an FBG-based monitoring system

  • Zhou, Lu (Hong Kong Branch of Chinese National Rail Transit Electrification and Automation Engineering Technology Research Center) ;
  • Zhang, Chao (Hong Kong Branch of Chinese National Rail Transit Electrification and Automation Engineering Technology Research Center) ;
  • Ni, Yi-Qing (Hong Kong Branch of Chinese National Rail Transit Electrification and Automation Engineering Technology Research Center) ;
  • Wang, Chung-Yue (Department of Civil Engineering, National Central University)
  • 투고 : 2017.12.29
  • 심사 : 2018.03.31
  • 발행 : 2018.05.25

초록

A tunnel deformation monitoring system is developed with the use of fiber Bragg grating (FBG) sensing technique, aiming at providing continuous monitoring of railway tunnel deformation in the long term, and early warning for the rail service maintainers and authorities to avoid catastrophic consequences when significant deformation occurs. Specifically, a set of FBG bending gauges with the ability of angle measurement and temperature compensation is designed and manufactured for the purpose of online monitoring of tunnel deformation. An overall profile of lateral tunnel displacement along the longitudinal direction can be obtained by implementing an array of the FBG bending gauges interconnected by rigid rods, in conjunction with a proper algorithm. The devised system is verified in laboratory experiments with a test setup enabling to imitate various patterns of tunnel deformation before the implementation of this system in an in-service high-speed railway (HSR) tunnel.

키워드

과제정보

연구 과제 주관 기관 : Ministry of Science and Technology of China, Hong Kong Branch of Chinese National Rail Transit Electrification and Automation Engineering Technology Research Center

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피인용 문헌

  1. Damage Detection and Evaluation for an In-Service Shield Tunnel Based on the Monitored Increment of Neutral Axis Depth Using Long-Gauge Fiber Bragg Grating Sensors vol.19, pp.8, 2018, https://doi.org/10.3390/s19081840
  2. In situ automatic monitoring and working state assessment of inclined shafts in coal mines vol.174, pp.4, 2018, https://doi.org/10.1680/jgeen.19.00294