Exploring the future trends of critical infrastructure network restoration through machine-learning techniques

Critical-infrastructure networks, such as power grids, water networks and transportation systems, are essential for societal functionality. Disruptions from natural disasters, cyber-attacks or operational failures necessitate efficient recovery strategies. This report explores the potential of machine learning within the broader field of artificial intelligence to enhance infrastructure-recovery processes, while critically assessing whether these technologies can overcome existing challenges. Furthermore, it investigates the integration of machine-learning algorithms with topological network-based approaches to optimise critical infrastructure recovery. By evaluating these algorithms, we aim to address the challenges in predicting recovery times, resource allocation and restoration priorities.This study also explores traditional recovery methods, emphasising topological approaches, and proposes a step-by-step decision-support framework for critical-infrastructure-network restoration using machine learning. Challenges, recommendations and future directions are discussed toguide the development of machine-learning-based, robust, data-driven recovery frameworks. (auth)