This study presents a novel water mist fire fighting system that integrates water mist sprays and water mist curtains, designed to achieve simultaneous fire suppression, thermal insulation, and smoke control. Three full-scale experiments were conducted under various fire scenarios, and the changes in fire behavior and heat release rate were examined to evaluate the effectiveness of the water mist system in extinguishing fires. Additionally, the spatiotemporal changes in ceiling temperature were monitored to assess the cooling and protective effects of the water mist. The thermal insulation capability of the system was also investigated by detecting the temperature distribution inside the tunnel. Moreover, the smoke conditions upstream and downstream of the tunnel were analyzed to evaluate the smoke-blocking performance of the water mist system. The findings demonstrate that the water mist fire fighting system is highly efficient in attenuating the fire and restricting its progression. Within the water mist spray section, the average ceiling temperature decreased exponentially during both the initial and steady burning phases across all tested fire scenarios. Nonetheless, the smoke-carrying capacity of the water mist spray is limited. Fortunately, the dispersed smoke was diluted by water mist, markedly enhancing visibility and mitigating the impact of smoke on tunnel illumination.

To investigate public perceptions regarding tunnel fire disasters and optimize the tunnel fire disaster prevention framework, this study takes the emerging social media platform Douyin as a case study, conducting an in-depth analysis of 2133 short videos related to tunnel fires on the platform. A computational communication method was used for analysis, Latent Dirichlet Allocation was used to cluster the discussion topics of these tunnel fire short videos, and a spatiotemporal evolution analysis of the number of videos posted, user comments, and emotional inclinations across different topics was performed. The findings reveal that there is a noticeable divergence in public opinion regarding emergency decision making in tunnel fires, related to the complexity of tunnel fire incidents, ethical dilemmas in tunnel fire escape scenarios, and insufficient knowledge popularization of fire safety practices. The study elucidates the public’s actual needs during tunnel fire incidents, and a dynamic disaster prevention framework for tunnel fires based on social media and artificial intelligence is proposed on this basis to enhance emergency response capabilities. Utilizing short videos on social media, the study constructs a critical target dataset under real tunnel fire scenarios. It proposes a computer vision-based model for identifying critical targets in tunnel fires. This model can accurately and in real-time identify key targets such as fires, smoke, vehicles, emergency exits, and people in real tunnel fire environments, achieving an average detection precision of 77.3%. This research bridges the cognitive differences between the general public and professionally knowledgeable tunnel engineers regarding tunnel fire evacuation, guiding tunnel fire emergency responses and personnel evacuation.