AI-Driven Digital Twin and Reinforcement Learning for Intelligent Network and Power Systems: Advancing Efficient and Resilient Smart Urban Critical Infrastructure

Abstract

This study has examined the development of an artificial intelligence-driven digital twin and reinforcement learning framework for advancing efficient and resilient smart urban critical infrastructure, with particular attention to the coupled operation of intelligent network and power systems in selected United States metropolitan utility and municipal contexts. The main problem has been the growing interdependence of urban power grids, communication networks, water systems, and transportation control layers, in which a disturbance in one network can cascade into others, while conventional control approaches rely on siloed supervisory systems, static offline models, rule-based automation, and reactive restoration that cannot anticipate, simulate, or coordinate responses across coupled infrastructures in real time. The purpose of the study has been to evaluate whether digital twin fidelity, real-time data and sensing integration, reinforcement learning decision effectiveness, cyber-physical security and interoperability, and operational scalability and compute adequacy significantly improve cross-infrastructure resilience and operational performance. A quantitative, cross-sectional, case-based research design has been used, and data have been collected from professionals engaged in power systems operation, network engineering, smart city control, digital twin development, industrial control security, and municipal infrastructure analytics. Out of 274 distributed questionnaires, 233 valid responses have been retained, producing an 85.0% valid response rate. The analysis plan has included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, multiple regression modeling, hypothesis testing, reinforcement learning prototype evaluation in a digital twin sandbox, and Python-driven analytical workflow validation. The findings have shown strong agreement across the constructs, with digital twin fidelity recording the highest mean score of 4.24, followed by real-time data and sensing integration at 4.18, reinforcement learning decision effectiveness at 4.09, cross-infrastructure resilience and operational performance at 4.05, operational scalability and compute adequacy at 3.99, and cyber-physical security and interoperability at 3.91. Reliability has been confirmed through Cronbach’s alpha values ranging from .83 to .91, with overall reliability of .93. Correlation results have shown significant positive relationships between resilience and real-time data integration (r = .70), digital twin fidelity (r = .67), reinforcement learning effectiveness (r = .65), operational scalability (r = .59), and cyber-physical security (r = .57), all at p < .001. The regression model has been significant, F(5, 227) = 55.14, p < .001, explaining 54.8% of variance. The study implies that coupling high-fidelity digital twins with reinforcement learning control can improve anticipation, autonomous coordination, restoration speed, and cross-network resilience of smart urban critical infrastructure.