Optimizing Facilities Management Through Artificial Intelligence and Digital Twin Technology in Mega Facilities

Mega facility management has long been inefficient due to manual, reactive approaches. This study examines the transformative integration of AI and DT technologies into Building Information Modeling (BIM) frameworks using IoT sensors for real-time data collection and predictive analytics. The study uses case studies and simulation models for dynamic data integration and scenario-based analyses. Key findings show a significant reduction in maintenance costs (25%) and energy consumption (20%), as well as increased asset utilization and operational efficiency. With an F1-score of more than 90%, the system shows excellent predictive accuracy for equipment failures and energy forecasting. Practical applications in hospitals and airports demonstrate the developed the ability of the platform to integrate IoT and BIM technologies, shifting facilities management from reactive to proactive. This paper presents a demo platform that integrates the BIM model with DT to improve the predictive maintenance as HVAC systems, equipment, security systems, etc., by recording data from different assets which help streamline asset management, enhance energy efficiency and support decision-making for the buildings’ critical systems.