Keywords
Fuzzy Bayesian network
High-speed railway
Runge-Kutta
Risk assessment
Submitted : 2025-12-10
Accepted : 2025-12-25
Published : 2026-01-09
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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