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Structural Engineering and Mechanics Volume 91, Number 2, July25 2024 , pages 227-238 DOI: https://doi.org/10.12989/sem.2024.91.2.227 |
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Double l1 regularization for moving force identification using response spectrum-based weighted dictionary |
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Yuandong Lei, Bohao Xu and Ling Yu
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| Abstract | ||
| 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. | ||
| Key Words | ||
| inverse analysis; moving force identification; response spectrum; sparse regularization; vehicle-bridge interaction; weighted dictionary | ||
| Address | ||
| Yuandong Lei, Bohao Xu and Ling Yu: MOE Key laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University, Guangzhou 510632, P.R. China | ||