Resilient infrastructure management systems using real-time analytics and AI-driven disaster preparedness protocols

Abstract

This review explores the convergence of real-time analytics and artificial intelligence (AI) in strengthening resilient infrastructure management systems, particularly for disaster preparedness and response. As climate change and urbanization amplify infrastructure vulnerability, cities and critical systems require intelligent frameworks capable of anticipating, adapting to, and recovering from disruptions. The paper outlines how AI-powered data streams from sensors, digital twins, and geospatial platforms are transforming static infrastructure into self-monitoring, self-correcting networks. It discusses predictive models for hazard forecasting, risk detection, and automated decision-making protocols during emergencies. Emphasis is placed on early warning systems, dynamic resource allocation, and post-event impact analysis, all supported by AI and real-time dashboards. Use cases across transportation, energy, water, and healthcare systems are examined to illustrate the role of integrated AI in building infrastructure resilience. The paper concludes with a call for ethical AI governance, interoperable systems, and cross-sector collaboration to enable sustainable, intelligent infrastructure preparedness.  Keywords: Resilient Infrastructure, Real-Time Analytics, AI-Driven Disaster Preparedness, Risk Forecasting, Critical Infrastructure Management.