Smart Structures and Systems

  • 제23권3호
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  • Pages.263-278
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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

Identification of moving train loads on railway bridge based on strain monitoring

  • Wang, Hao (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Zhu, Qingxin (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Li, Jian (Department of Civil, Environmental and Architectural Engineering, The University of Kansas) ;
  • Mao, Jianxiao (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Hu, Suoting (Railway Engineering Research Institute, China Academy of Railway Sciences) ;
  • Zhao, Xinxin (Railway Engineering Research Institute, China Academy of Railway Sciences)
  • 투고 : 2018.07.04
  • 심사 : 2019.01.17
  • 발행 : 2019.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

Moving train load parameters, including train speed, axle spacing, gross train weight and axle weights, are identified based on strain-monitoring data. In this paper, according to influence line theory, the classic moving force identification method is enhanced to handle time-varying velocity of the train. First, the moments that the axles move through a set of fixed points are identified from a series of pulses extracted from the second derivative of the structural strain response. Subsequently, the train speed and axle spacing are identified. In addition, based on the fact that the integral area of the structural strain response is a constant under a unit force at a unit speed, the gross train weight can be obtained from the integral area of the measured strain response. Meanwhile, the corrected second derivative peak values, in which the effect of time-varying velocity is eliminated, are selected to distribute the gross train weight. Hence the axle weights could be identified. Afterwards, numerical simulations are employed to verify the proposed method and investigate the effect of the sampling frequency on the identification accuracy. Eventually, the method is verified using the real-time strain data of a continuous steel truss railway bridge. Results show that train speed, axle spacing and gross train weight can be accurately identified in the time domain. However, only the approximate values of the axle weights could be obtained with the updated method. The identified results can provide reliable reference for determining fatigue deterioration and predicting the remaining service life of railway bridges.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China

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