A real-time forecast of tunnel fire based on numerical database and artificial intelligence  

作　　者：Xiqiang Wu Xiaoning Zhang Xinyan Huang Fu Xiao Asif Usmani 

机构地区：[1]Department of Building Services Engineering,Hong Kong Polytechnic University,Hong Kong,China [2]Research Institute for Sustainable Urban Development,Hong Kong Polytechnic University,Hong Kong,China

出　　处：《Building Simulation》2022年第4期511-524,共14页建筑模拟（英文）

基　　金：This work is funded by the Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N);the PolyU Emerging Frontier Area(EFA)Scheme of RISUD(P0013879).

摘　　要：The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters.To alleviate the potential casualties,fast while reasonable decisions should be made for rescuing,based on the timely prediction of fire development in tunnels.This paper targets to achieve a real-time prediction(within 1 s)of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence(Al)methods.A CFD database of 100 simulated tunnel fire scenarios under various fire location,fire size,and ventilation condition is established.The proposed Al model combines a Long Short-term Memory(LSTM)model and a Transpose Convolution Neural Network(TCNN).The real-time ceiling temperature profile and thousands of temperature-field images are used as the training input and output.Results show that the predicted temperature field 60 s in advance achieves a high accuracy of around 97%.Also,the Al model can quickly identify the critical temperature field for safe evacuation(i.e.,a critical event)and guide emergency responses and firefighting activities.This study demonstrates the promising prospects of Al-based fire forecasts and smart firefighting in tunnel spaces.

关 键 词：tunnel fires smart firefighting critical event CFD deep learning LSTM/TCNN 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TU18[自动化与计算机技术—控制科学与工程]