An intelligent tunnel firefighting system and small-scale demonstration

Disastrous fire event in the confined tunnel is a fatal hazard, threatening the lives of trapped people and fire-fighters. Considering the rapid development of fire and the complex environment of tunnels, an accurate and timely fire identification system is in urgent need for guiding the evacuation, rescue, and firefighting actions. This study proposes an intelligent system and digital twin composed of four main components to collect, manage, process and visualize the tunnel fire information. As demonstrated in a laboratory-scale tunnel model, the AI model is trained with a large numerical database to successfully identify the fire size and location. The whole system is assessed in terms of accuracy, timeliness and robustness. The AI model attained an overall accuracy of 98% in predicting the tunnel fire scenarios. The total time delay is around 1 s from the on-site measurement of temperature to the final display of the tunnel fire scenario on a remote user interface. Moreover, the system is robust enough to predict fire, even if part of the temperature sensors is failed or destroyed by fire. The proposed intelligent system will be a valuable step for smart firefighting from the concept to practice.

Automated summary

A disastrous fire event in a confined tunnel poses a fatal hazard to trapped individuals and firefighters. To address this issue, an intelligent system and digital twin were proposed to collect, manage, process, and visualize tunnel fire information. The system, which achieved an overall accuracy of 98%, effectively identified fire size and location. Furthermore, it displayed a minimal time delay of approximately 1 second and maintained robustness even with damaged temperature sensors.