Revolutionizing Firefighting Training with Digital Twins: Real-Time Fire Spread Simulation and Escape Route Optimization

The advancement of digital twin (DT) systems had revolutionized various industrial and safety applications, offering virtual replicas of physical processes for improved monitoring and training. Fire scenarios are highly dynamic, with conditions changing rapidly due to factors like wind direction, material flammability, and structural integrity. This study explored the application of a smart fire scene DT system (SFSDTS) for firefighting training and safety management. The novelty and contributions of this study was the proposed SFSDTS integrated machine learning models to predict fire spreading paths and estimate escape routes in real-time, providing an immersive and interactive training environment for firefighters. Various ML models, including random forest (RF), XGBoost, decision tree (DR), logistic regression (LR), and Knearest neighbors (KNN), were utilized for predicting fire spreading. The performance of these models was evaluated using metrics such as accuracy, precision, recall, and F1 score, with XGBoost and RF models demonstrating superior performance. The proposed SFSDTS also employed the A* algorithm for optimized escape route estimation based on dynamic fire conditions. User experience was assessed through a standardized questionnaire, user experience questionnaire (UEQ), revealing positive ratings for the proposed SFSDTS’s efficiency, stimulation, and perspicuity. Compared to commercial products, the proposed SFSDTS offered improved accessibility and real-time simulation functionalities. The study highlighted the potential of proposed SFSDTS in transforming firefighting training and safety management.