Disasters occur frequently around the world, affecting structures and people, thus resulting in massive disruption of services. This is particularly the case with the increasing frequency and intensity of disasters, leading to the necessity of more proactive steps to improve the critical systems’ resiliency. Predictive maintenance (PM), which uses data to predict when equipment might fail, provides a solution in this sense as it enables organizations to plan for repairs that will prevent major failures. This chapter aims to discuss how PM can be incorporated into disaster management plans to mitigate the impact of natural disasters and enhance the durability of structures. This chapter provides an overview of applications of information methods mentioned previously, including descriptive, diagnostic, predictive, and prescriptive analytics. It also reveals the issues of data quality, data accessibility, and multidisciplinary data fusion from weather forecasts, seismic data, and Internet of Things (IoT) sensor data. Besides, it also describes how PM can improve risk evaluation and assessment solutions, early warning systems, infrastructure health, and disaster management solutions. The chapter outlines how predictive maintenance redefines disaster planning and management based on real-life case studies. The relevance of data integration and availability can be a barrier, but PM is a strong positive lever for enhancing the protection of critical infrastructure in disaster-sensitive areas.
Keywords: Climate change, Climatological disasters, Data analytics, Data integration, Disaster management, Geophysical disasters, Hydrological disasters, Meteorological disasters, Predictive maintenance.
