Multiparameter Identification of Bridge Cables Using XGBoost Algorithm
Publication: Journal of Bridge Engineering
Volume 28, Issue 5
Abstract
Accurately identifying tension force on cables is of great significance for construction control and the operational status assessment of a bridge during its lifetime. Unlike the conventional vibration methods that encounter problems in the inaccurate identification of short cables and difficulties when identifying multiparameters simultaneously, when solving the vibration differential equation inversely, a novel strategy was proposed that was based on an intelligent algorithm for cable parameter monitoring onsite. The Extreme Gradient Boosting (XGBoost) model was employed to establish the mapping relationship between the natural frequencies of the cable and its tension, bending stiffness, and boundary conditions through data mining. The results revealed that when the measured natural frequencies of a cable were fed into the XGBoost model, the previously mentioned multiparameters could be identified simultaneously with a relative error of <5%. Meanwhile, the proposed intelligent method with the XGBoost algorithm produced a more accurate identification of the cable parameters than the extreme learning machine (ELM) and conventional vibration methods. The current intelligent strategy might provide efficient tools for the simultaneous identification of multiple parameters in cables and, therefore, might facilitate policy decisions for the structural maintenance of cable-supported bridges.
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Acknowledgments
The authors acknowledge the supports from the National Key R&D Program of China (Grant No. 2020YFA0711700), the National Natural Science Foundation of China (Grant Nos. 52122801, 11925206, and 51978609), and Foundation for Distinguished Young Scientists of Zhejiang Province (Grant No. LR20E080003).
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Published In
Journal of Bridge Engineering
Volume 28 • Issue 5 • May 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 24, 2022
Accepted: Dec 28, 2022
Published online: Feb 17, 2023
Published in print: May 1, 2023
Discussion open until: Jul 17, 2023
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Cited by
- Lan-Ying Luo, Yan-Liang Du, Ting-Hua Yi, Song-Han Zhang, Joint Identification of Cable Force and Bending Stiffness Using Vehicle-Induced Cable–Beam Vibration Responses, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-6555, 29, 2, (2024).