Safety barriers failures in cold wave-triggered events: a data-driven approach

Natech events, short for natural hazard-triggered technological accidents, add a layer of complexity to disaster risk management. They involve the combination of both natural and anthropogenic hazards. Noticeably, there is an uneven distribution in the extent of research dedicated to natural hazards. Extreme weather events have been overshadowed, despite low temperatures being the third cause of Natech accidents in Europe, ranking only behind lightning and floods. The present study aims to provide a comprehensive analysis of the role of safety barriers within the context of Natech events triggered by cold waves. To attain this, data selected from established databases are subjected to a Layer of Protection Analysis, Unsupervised Machine Learning, and Bayesian network. This rigorous analytical approach facilitates the identification and quantification of the relationships between incident features and safety barrier failures. The Bayesian Network obtained is designed to predict the behavior of safety barriers, providing a dynamic and updatable structure adaptable to future developments and integrable with additional available information. The insights gained from the thorough examination of these incidents provide valuable lessons that are essential components in preventing the recurrence of similar events in the future. Furthermore, such insights provide a robust foundation for the proposition of targeted safety barrier protection programs.