Data-driven decision support system for the safety management of railway bridge networks

Ensuring the safety of railway bridges across extensive networks requires automated, reliable systems that combine precise monitoring with actionable decision-making. This study introduces a fully automated, model-free, data-driven Decision Support System (DSS) designed to assist railway network operators in managing bridge safety efficiently and proactively. The DSS uses data from diverse sensors to monitor critical damage mechanisms, such as differential settlement, pier tilt, abnormal deck torsion, and flexibility retention ratio in bridge decks. These damage indicators are assessed against mechanics-based and code-based thresholds, ensuring accuracy and robustness in detection. The DSS adopts a probabilistic framework that defines multi-level alarms—low, medium, and high—triggered by pre-established misclassification probabilities. This approach minimizes both false and missed alarms, enhancing the system reliability. A global damage index aggregates local damage indicators, enabling the ranking of monitored bridges by urgency for intervention. This index guides operators in prioritizing inspections, maintenance, or safety measures, optimizing resource allocation across large-scale networks. The methodology was validated through case studies on steel and reinforced concrete bridges, demonstrating its scalability and effectiveness in identifying structural damage, reducing false alarms, and supporting timely decision-making. The proposed DSS represents a significant step toward smart, self-diagnosing railway networks, offering a scalable solution to enhance infrastructure safety and operational efficiency. Future advancements could include integrating predictive algorithms and expanding applicability to diverse environmental conditions and bridge typologies.