Intelligent Infrastructure and Predictive Resilience: Using AI and Data Analytics to Manage Critical Infrastructure Systems

Melissa Valentine

Intelligent Infrastructure and Predictive Resilience: Using AI and Data Analytics to Manage Critical Infrastructure Systems

Author(s)	Melissa Valentine
Country	United States
Abstract	Critical infrastructure systems such as transportation networks, energy grids, water-supply systems, telecommunications, healthcare facilities, and urban utilities form the foundation of modern societies. Their increasing interdependence, ageing assets, climate-related hazards, rapid urbanisation, and exposure to cyber and physical threats have created an urgent need for more resilient infrastructure-management approaches. Artificial Intelligence (AI), machine learning, Internet of Things (IoT) technologies, digital twins, and advanced data analytics provide new opportunities to move infrastructure management from reactive maintenance towards predictive and proactive resilience. This study examines the role of AI and data analytics in developing intelligent infrastructure and predictive resilience. A qualitative and descriptive research methodology based on secondary literature, technical reports, policy documents, and interdisciplinary research is adopted. The study evaluates AI applications in predictive maintenance, infrastructure condition monitoring, disaster-risk assessment, traffic management, energy-system resilience, water-network management, and cybersecurity. Particular attention is given to the integration of real-time sensor data, historical infrastructure records, environmental information, and digital twins for predicting failures and supporting rapid decision-making. The analysis indicates that intelligent infrastructure can improve asset reliability, reduce downtime, optimise resource allocation, and strengthen preparedness for disruptive events. However, challenges involving data quality, cybersecurity, interoperability, algorithmic transparency, financial investment, skills shortages, and institutional coordination remain significant. The study concludes that predictive resilience requires a combination of AI technologies, reliable data infrastructure, human expertise, robust governance, and continuous monitoring.
Keywords	Intelligent Infrastructure, Artificial Intelligence, Predictive Resilience, Data Analytics, Critical Infrastructure, Predictive Maintenance, Digital Twins, IoT, Infrastructure Management, Risk Management.
Field	Engineering
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-10-31

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American Journal of Advanced Multidisciplinary Research and Innovation

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Intelligent Infrastructure and Predictive Resilience: Using AI and Data Analytics to Manage Critical Infrastructure Systems

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