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

  • Research Article
  • Building Thermal, Lighting, and Acoustics Modeling
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Abstract

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 (AI) methods. A CFD database of 100 simulated tunnel fire scenarios under various fire location, fire size, and ventilation condition is established. The proposed AI 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 AI 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 AI-based fire forecasts and smart firefighting in tunnel spaces.

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Abbreviations

AI:

artificial intelligence

ANN:

artificial neural networks

CFD:

computational fluid dynamics

CNN:

convolutional neural network

CVV:

critical ventilation velocity

FDS:

fire dynamics simulator

HRR:

heat release rate (MW)

LSTM:

long short-term memory

ML:

machine learning

RNN:

recurrent neural network

SVM:

support vector machine

TCNN:

transpose convolutional neural network

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Building Services Engineering, Hong Kong Polytechnic University, Hong Kong, China

    Xiqiang Wu, Xiaoning Zhang, Xinyan Huang, Fu Xiao & Asif Usmani

  2. Research Institute for Sustainable Urban Development, Hong Kong Polytechnic University, Hong Kong, China

    Xiqiang Wu & Asif Usmani

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  1. Xiqiang Wu
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  2. Xiaoning Zhang
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  3. Xinyan Huang
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  4. Fu Xiao
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  5. Asif Usmani
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Correspondence to Xinyan Huang.

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Wu, X., Zhang, X., Huang, X. et al. A real-time forecast of tunnel fire based on numerical database and artificial intelligence. Build. Simul. 15, 511–524 (2022). https://doi.org/10.1007/s12273-021-0775-x

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  • DOI: https://doi.org/10.1007/s12273-021-0775-x

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