Prediction and Early Warning of Extreme Winds for High-Speed Railway Bridge Construction Using Machine-Learning Methods

Yishun Xie, Xiangyu Chang, Jianxiao Mao (), Youhao Ni and Hao Wang ()
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Yishun Xie: Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
Xiangyu Chang: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
Jianxiao Mao: Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
Youhao Ni: Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
Hao Wang: Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China

Sustainability, 2023, vol. 15, issue 24, 1-19

Abstract: Measuring the impact of extreme winds is important in high-speed railway bridge construction to avoid the risk of engineering accidents. This study presents an early-warning framework for high-speed railway (HSR) bridge construction under extreme wind conditions, using a long-span continuous beam bridge constructed in a typhoon-prone area as a case study. Specifically, on-site wind environment measurements during a typhoon are utilized, and parameters such as fluctuating wind turbulence intensity, gust factor, turbulence integral scale, and power spectral density are employed to characterize the wind environment. Multi-step prediction of gust wind speed during the bridge construction is performed using the hybrid Back Propagation-Genetic Algorithm (BP-GA). Hierarchical warnings and control actions are proposed and implemented based on the prediction results. The analysis results of wind parameters revealed a discrepancy between the measured and specified typhoon turbulence intensities, with the windward turbulence intensity being lower than specified. The longitudinal average gust factor exceeded the transverse value. The prediction curve based on the BP-GA algorithm closely resembles the actual curve, meeting accuracy requirements. Notably, the one-step prediction provided the most accurate results. Based on the predicted wind speed trend for the upcoming half-hour, appropriate hierarchical warnings and control measures were conducted.

Keywords: high-speed railway bridge; construction; extreme winds; prediction; early warning; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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