Abstract
Elevated station track system is one of the most vulnerable parts in high-speed railway and prone to various defects during long-term service. The structural mechanical state will be deteriorated with the occurrence of defects, which will finally threaten the operation. Therefore, it is essential to monitor and accurately predict the structural mechanical state of elevated station track system. However, the existing prediction methods cannot achieve an accurate prediction of the structural mechanical state of the elevated station track system. Aiming at the problem, a hybrid model integrating wavelet transform, convolutional neural network, and long-term memory was proposed, which has the best performance compared with state-of-art methods and can be expanded to the state prediction of civil infrastructures. The prediction method can pre-evaluate the structural state, guide timely maintenance, and contribute to the safety of the high-speed railway.
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Acknowledgments
This work was supported by the National Key R&D Program of China (grant number 2016YFB1200402); the Key Program of National Natural Science Foundation of China (grant number U1734206); and the Project of Science and Technology Research and Development Program of China Railway Corporation (grant number 2017G010-A). The authors also wish to thank Mr. Shuai Ma and Mr. Bolun An for their valuable advice on this work.
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Ma, Z., Gao, L. Predicting Mechanical State of High-Speed Railway Elevated Station Track System Using a Hybrid Prediction Model. KSCE J Civ Eng 25, 2474–2486 (2021). https://doi.org/10.1007/s12205-021-1307-z
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DOI: https://doi.org/10.1007/s12205-021-1307-z