Structural Engineering and Mechanics

  • 제91권2호
  • /
  • Pages.227-238
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  • 2024
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Double 𝑙1 regularization for moving force identification using response spectrum-based weighted dictionary

  • Yuandong Lei (MOE Key laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University) ;
  • Bohao Xu (MOE Key laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University) ;
  • Ling Yu (MOE Key laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University)
  • 투고 : 2024.03.13
  • 심사 : 2024.07.06
  • 발행 : 2024.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

Sparse regularization methods have proven effective in addressing the ill-posed equations encountered in moving force identification (MFI). However, the complexity of vehicle loads is often ignored in existing studies aiming at enhancing MFI accuracy. To tackle this issue, a double 𝑙1 regularization method is proposed for MFI based on a response spectrum-based weighted dictionary in this study. Firstly, the relationship between vehicle-induced responses and moving vehicle loads (MVL) is established. The structural responses are then expanded in the frequency domain to obtain the prior knowledge related to MVL and to further construct a response spectrum-based weighted dictionary for MFI with a higher accuracy. Secondly, with the utilization of this weighted dictionary, a double 𝑙1 regularization framework is presented for identifying the static and dynamic components of MVL by the alternating direction method of multipliers (ADMM) method successively. To assess the performance of the proposed method, two different types of MVL, such as composed of trigonometric functions and driven from a 1/4 bridge-vehicle model, are adopted to conduct numerical simulations. Furthermore, a series of MFI experimental verifications are carried out in laboratory. The results shows that the proposed method's higher accuracy and strong robustness to noises compared with other traditional regularization methods.

키워드

과제정보

This work was jointly sponsored by the National Natural Science Foundation of China under grant numbers 52178290 and 51678278.

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