Digital Twin-Based Intelligent Fire Safety Management System for Smart Buildings Integrating IoT-Enabled Mechanical Monitoring and Dynamic Evacuation Optimization

Abstract

With the rapid development of Internet of Things (IoT) technologies and intelligent building systems, traditional fire safety management approaches face challenges in real-time perception, dynamic risk assessment, and adaptive emergency response. This study proposes a digital twin-based intelligent fire safety management system for smart buildings by integrating IoT-enabled mechanical monitoring and dynamic evacuation optimization. A multi-layer digital twin architecture is developed to establish a real-time connection between physical building environments and virtual models, enabling continuous monitoring of thermal conditions, smoke propagation, ventilation performance, and fire protection equipment status. IoT sensor networks are employed to collect real-time environmental and mechanical system data, while data-driven prediction models are applied to identify early fire risks and estimate fire evolution patterns. Furthermore, a dynamic evacuation optimization strategy is developed by considering fire development, occupant distribution, smoke diffusion, and building mechanical conditions. The proposed framework enables adaptive decision-making for emergency evacuation and intelligent control of building safety systems. Simulation-based experiments demonstrate that the digital twin-driven approach can improve fire risk detection accuracy, reduce evacuation time, and enhance the resilience of smart building safety management compared with conventional static evacuation strategies. The proposed system provides an effective solution for next-generation intelligent fire protection by combining digital twin technology, IoT monitoring, mechanical system control, and AI-assisted emergency management.