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| Research and validation of the intelligent disaster prevention system for the Jinan Yellow River Tunnel |
| Duan Yuexin1, Chen Xin2*, Shen Yi2, Zhang Yi3,4, Yan Zhiguo2
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1. Jinan City Construction Group Co., Ltd., Jinan 250014, Shandong, China;
2. Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China;
3. China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, Hubei, China;
4. National-Local Joint Engineering Research Center of Underwater Tunnelling Technology, Wuhan 430063, Hubei, China |
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Abstract An intelligent disaster prevention system for complex tunnel scenarios was developed to improve environmental perception, fire monitoring, situation reconstruction, and emergency evacuation in long underwater river-crossing tunnels. The system consisted of four functional modules: environmental-parameter perception, fire monitoring and early warning, fire situation reconstruction, and dynamic evacuation. Environmental monitoring and ventilation regulation were conducted using a fuzzy-control-based method. A GA-BP(genetic algorithm-backpropagation)neural network was employed for fire early warning through multi-source data fusion, while a long short-term memory(LSTM)network was used to predict the fire source location and heat release rate(HRR). A multi-agent model was introduced to generate dynamic evacuation strategies, with smoke propagation, visibility, toxic gases, and occupant movement characteristics taken into consideration.The proposed system was validated using the Jinan Yellow River Tunnel as the engineering background. The results showed that the fire warning model reached the warning criterion approximately 20 s earlier than the conventional temperature-difference threshold method. The LSTM model achieved coefficients of determination(R2)of 95.9% and 93.7% for HRR prediction under complete data and partial high-temperature sensor failure conditions, respectively. The total evacuation time for 16 typical evacuation scenarios ranged from 654 to 780 s. The effectiveness of the proposed system in continuous and coordinated environmental perception, fire warning, situation reconstruction, and dynamic evacuation decision-making was demonstrated through the validation.The proposed framework was considered to provide an engineering reference for intelligent disaster prevention and emergency management in complex tunnels.
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Published: 12 October 2026
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