AIoT-enabled digital twin system for smart tunnel fire safety management
Xiaoning Zhang, Yishuo Jiang, Xiqiang Wu, Zhuojun Nan, Yaqiang Jiang, Jihao Shi, Yuxin Zhang, Xinyan Huang, George Q. Huang
Abstract
High traffic flow in a confined tunnel makes fire safety a critical issue. This paper proposed a digital twin framework for tunnel fire safety management in real-time, driven by dynamic sensor data and AIoT technologies. A deep learning model trained by the Transformer network and simulation dataset is used to predict real-time fire location and size. Then, the AI model is integrated into a 3D digital twin platform developed by the game engine Unity 3D. The performance of the proposed digital twin framework is demonstrated using numerical experiments and large-scale tunnel fire tests. Results show that the established AI model achieved promising accuracy in predicting fire location and power for both numerical and experimental data. The digital twin platform can also visualize the 3D fire scene that supports evacuation, firefighting, and emergency rescue. This research demonstrates the feasibility of using a 3D environment and digital twin in real-time fire safety management. • Propose a digital twin framework for the tunnel fire safety management driven by AIoT. • AI model trained by the Transformer network is used to monitor and predict the real-time tunnel fire scenarios. • Rendered 3D digital twin model is demonstrated by numerical simualtions and full-scale tunnel fire tests.