Abstract: Tunnel fires are characterized by their sudden occurrence, rapid smoke propagation, and low visibility, posing severe threats to transportation infrastructure and the safety of tunnel users. To address these challenges, this study proposes a real-time temperature field reconstruction model for tunnel fires based on a Transposed Convolutional Neural Network (TCNN). Furthermore, a “hierarchical perception fire scene strategy” is introduced, which enables AI models trained on local tunnel segments to be deployed in full-scale tunnel environments, thereby overcoming the limitations of traditional temperature measurement methods in terms of spatial coverage, real-time capability, and cost-effectiveness. The TCNN model efficiently captures the spatial features of the fire temperature field through transposed convolution layers, allowing precise inversion of the temperature distribution in tunnel fires. The model is developed, trained, and evaluated using a modular Fire Dynamics Simulator (FDS) simulation database. On this basis, a deployment framework is designed that integrates Kafka message queues and WebSocket protocols to achieve asynchronous transmission of inference results and real-time front-end visualization. A dual-thread control mechanism is also implemented to enable intelligent state switching and system control. The proposed method provides a viable technical pathway and engineering reference for the design and implementation of intelligent fire protection systems in tunnel environments.

Key words: tunnel fire, TCNN, temperature field inversion, deep learning, deployment strategy