Smart Structures and Systems

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Multi-sensor data fusion based assessment on shield tunnel safety

  • Huang, Hongwei (Department of Geotechnical Engineering, Tongji University) ;
  • Xie, Xin (Department of Geotechnical Engineering, Tongji University) ;
  • Zhang, Dongming (Department of Geotechnical Engineering, Tongji University) ;
  • Liu, Zhongqiang (Natural Hazards, Norwegian Geotechnical Institute (NGI)) ;
  • Lacasse, Suzanne (Natural Hazards, Norwegian Geotechnical Institute (NGI))
  • Received : 2019.07.03
  • Accepted : 2019.08.29
  • Published : 2019.12.25
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Abstract

This paper proposes an integrated safety assessment method that can take multiple sources data into consideration based on a data fusion approach. Data cleaning using the Kalman filter method (KF) was conducted first for monitoring data from each sensor. The inclination data from the four tilt sensors of the same monitoring section have been associated to synchronize in time. Secondly, the finite element method (FEM) model was established to physically correlate the external forces with various structural responses of the shield tunnel, including the measured inclination. Response surface method (RSM) was adopted to express the relationship between external forces and the structural responses. Then, the external forces were updated based on the in situ monitoring data from tilt sensors using the extended Kalman filter method (EKF). Finally, mechanics parameters of the tunnel lining were estimated based on the updated data to make an integrated safety assessment. An application example of the proposed method was presented for an urban tunnel during a nearby deep excavation with multiple source monitoring plans. The change of tunnel convergence, bolt stress and segment internal forces can also be calculated based on the real time deformation monitoring of the shield tunnel. The proposed method was verified by predicting the data using the other three sensors in the same section. The correlation among different monitoring data has been discussed before the conclusion was drawn.

Keywords

Acknowledgement

Supported by : Natural Science Foundation Committee, MOST of China

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