Optimization of High-Speed Railway Bridge Disaster Warning Systems Using Fuzzy Bayesian Networks and Embedded Runge-Kutta Pairs for Real-Time Risk Assessment

Yufei Zhang, Hongyu Liang, Yuebing Liu, Yuxin Fan, Qiuyan Chen, Wenjie Fu, Genxi Wang, Yucheng Shen

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¹ Hope College, Southwest Jiaotong University, Chengdu 610400, Sichuan, China,

² School of Education, Fuzhou University of International Studies and Trade, Fuzhou 350200, Fujian, China,

JWA 2025 , 9(6), 63–72; https://doi.org/10.26689/jwa.v9i6.13334

© 2025 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

Abstract

High-speed railway (HSR) bridges face multi-hazard risks from wind, earthquakes, and fires, necessitating optimized warning systems for safety and efficiency. This study proposes a framework integrating fuzzy Bayesian networks (FBNs) for probabilistic risk modeling with embedded Runge-Kutta pairs for dynamic simulations, enabling real-time assessments. FBNs handle uncertainties across 22 indicators from standards like GB 50352-2019, categorizing capabilities in prevention, extinguishing, evacuation, rescue, and management. Runge-Kutta (orders 6/5) solves ODEs for transient responses, approximating finite element outputs in surrogates like LSTM-RNNs. Alarm optimization uses objective functions balancing busyness and alarm frequency, tested on Chinese HSR lines. Results show wind alarm reductions up to 47.6% with minimal downtime increases, fire risks graded “good” (54.3%) with management as key improvement, and collision missed alarms < 6% at < 1ms. The system reduces missed warnings by 6% and delivers alerts in milliseconds, advancing HSR resilience and minimizing disruptions.

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