Smart Structures and Systems

  • 제31권5호
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  • Pages.501-516
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  • 2023
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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

Real-time prediction of dynamic irregularity and acceleration of HSR bridges using modified LSGAN and in-service train

  • Huile Li (Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, School of Civil Engineering, Southeast University) ;
  • Tianyu Wang (School of Urban Construction and Safety Engineering, Shanghai Institute of Technology) ;
  • Huan Yan (School of Civil Engineering, Southeast University)
  • 투고 : 2022.11.01
  • 심사 : 2023.03.21
  • 발행 : 2023.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

Dynamic irregularity and acceleration of bridges subjected to high-speed trains provide crucial information for comprehensive evaluation of the health state of under-track structures. This paper proposes a novel approach for real-time estimation of vertical track dynamic irregularity and bridge acceleration using deep generative adversarial network (GAN) and vibration data from in-service train. The vehicle-body and bogie acceleration responses are correlated with the two target variables by modeling train-bridge interaction (TBI) through least squares generative adversarial network (LSGAN). To realize supervised learning required in the present task, the conventional LSGAN is modified by implementing new loss function and linear activation function. The proposed approach can offer pointwise and accurate estimates of track dynamic irregularity and bridge acceleration, allowing frequent inspection of high-speed railway (HSR) bridges in an economical way. Thanks to its applicability in scenarios of high noise level and critical resonance condition, the proposed approach has a promising prospect in engineering applications.

키워드

과제정보

The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (Grant No. 51708112), and National Key Research and Development Program of China (Grant No. 2020YFC1511905).

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