<![CDATA[National critical infrastructure, including energy, transportation, and communication systems, plays a vital role in sustaining modern society, yet failures within these systems can trigger severe economic, environmental, and security consequences. Conventional maintenance approaches often lack the capability to anticipate failures in complex and large-scale infrastructures. Recent advancements in Digital Twin technology and Artificial Intelligence (AI) provide innovative opportunities to enhance predictive maintenance and infrastructure resilience. This study aims to integrate Digital Twins with systemic AI to optimize predictive maintenance strategies for national critical infrastructure by leveraging real-time data and intelligent prediction mechanisms. The research employs a combined framework in which sensor-generated data from infrastructure components are continuously synchronized with Digital Twin models and analyzed using machine learning algorithms to monitor system conditions, simulate operational behavior, and predict potential failures. The proposed framework was implemented in a case study of a national energy grid to evaluate its effectiveness. The results indicate that the integrated system significantly improved predictive maintenance performance, achieving a 30% reduction in unplanned downtime and a 25% decrease in maintenance costs through accurate failure prediction and timely intervention. These findings demonstrate that the integration of Digital Twins and systemic AI offers a robust, scalable, and efficient solution for enhancing reliability, resilience, and sustainability in the management of national critical infrastructure.]]>
