Thesis title: Smart Tunnel in Industry 5.0: Improving Road Tunnel Resilience by Dynamic Risk Analysis
This doctoral research investigates SCADRA (Supervisory Control Acquisition and Dynamic Risk Analysis) system, an innovative safety-management platform designed for real-time risk assessment in road tunnels. The study illustrates the operating principles of SCADRA and examines its application in major Italian motorway tunnels, including Valico della Cisa (A15), Rimazano (A12), Melarancio Sud (A1), and Caltanissetta (S.S. 640) tunnels. The objective is to evaluate its effectiveness, identify potential enhancements, and define future development directions.
SCADRA integrates static design data with real-time operational and environmental information to compute quantitative safety indicators, such as the Expected Value of Damage (EVD). By performing Dynamic Risk Analysis at predefined intervals - and immediately following unexpected variations in tunnel conditions - the system enables proactive safety management. It supports decision-making by suggesting risk-mitigation measures, and when conditions permit, allows energy-optimisation strategies without compromising user safety.
The analysis of field results demonstrates that SCADRA reliably tracks variations in tunnel-risk profiles, responding effectively to changes in traffic volumes, system availability, and environmental conditions. The system has proven capable of maintaining alignment with regulatory thresholds, offering tunnel operators an advanced, data-driven tool aligned with European and Italian safety frameworks.
The research contributes to the advancement of next-generation tunnel-safety technologies and positions SCADRA as a strategic enabler of the Industry 5.0 paradigm, characterised by human-centric, sustainable and resilient infrastructure management.
Future developments include system strengthening through machine-learning algorithms, expanded multi-sensor integration, and extension to railway and metro tunnels.