Smart Structures and Systems

  • 제31권6호
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  • Pages.585-599
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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

Investigation on vibration behavior of a high-speed railway bridge based on monitoring data

  • Qingxin Zhu (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Hao Wang (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Billie F. Spencer Jr (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign)
  • 투고 : 2021.07.30
  • 심사 : 2023.06.12
  • 발행 : 2023.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Field monitoring techniques offer an attractive approach for understanding bridge behavior under in-service loads. However, the investigations on bridge behavior under high-speed train load using field monitoring data are limited. The focus of this study is to explore the structural behavior of an in-service long-span steel truss arch bridge based on field monitoring data. First, the natural frequencies of the structure, as well as the train driving frequencies, are extracted. Then, the train-induced bearing displacement and structural strain are explored to identify the effects of train loads and bearings. Subsequently, a sensitivity analysis is performed for the impact factor of strain responses with respect to the train speed, train weight, and temperature to identify the fundamental issues affecting these responses. Additionally, a similar sensitivity analysis is conducted for the peak acceleration. The results indicate that the friction force in bearings provides residual deformations when two consecutive trains are in opposite directions. In addition, the impact factor and peak acceleration are primarily affected by train speed, particularly near train speeds that result in the resonance of the bridge response. The results can provide additional insight into the behavior of the long-span steel truss bridges under in-service high-speed train loads.

키워드

과제정보

The authors would like to gratefully acknowledge the supports from the National Natural Science Foundation of China (Grant No. 51978155), the National Ten Thousand Talent Program for Young Top-notch Talents (Grant No. W03070080), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant No. KYCX19_0095), the CSC Scholarship (Grant No. CSC201906090075), and the Fundamental Research Funds for the Central Universities (Grant No. 2242022k30030).

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