Vehicle–railway bridge collisions represent a persistent safety challenge that can compromise transportation infrastructure, interrupt railway operations, and generate substantial economic losses. Conventional bridge inspection and monitoring practices often rely on periodic inspections or isolated sensing mechanisms, limiting their ability to provide timely collision detection and predictive decision support. Recent developments in structural health monitoring, wireless sensing, intelligent video surveillance, and supervised machine learning have created opportunities for designing integrated monitoring systems capable of identifying collision events while simultaneously assessing infrastructure conditions and supporting proactive maintenance strategies. This paper proposes a Predictive Supervised Learning Architecture for Automated Vehicle–Railway Bridge Collision Detection, Monitoring, and Safety Optimization, which integrates heterogeneous sensing technologies, structural health monitoring, supervised classification algorithms, and predictive risk analysis into a unified decision-support framework. The proposed architecture combines visual monitoring, vibration sensing, synchronized wireless sensor networks, and intelligent data processing to distinguish collision events from normal operational conditions while estimating structural damage severity and recommending appropriate response actions. The framework further incorporates continuous learning mechanisms to improve prediction accuracy using historical infrastructure data and validated collision records. A comprehensive review of existing structural monitoring techniques demonstrates that although significant advances have been achieved in vibration-based damage detection, wireless monitoring, and bridge instrumentation, limited research has focused on integrating supervised learning with real-time railway bridge collision management.

Supervised Machine Learning, Railway Bridge Safety, Collision Detection, Structural Health Monitoring

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