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Smart Detection of Fire Source in Tunnel Based on the Numerical Database and Artificial Intelligence

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Abstract

The fire event in a tunnel creates a rapid spread of heat and smoke flows in a long and confined space, which not only endangers human life but also challenges the fire-evacuation and firefighting strategies. A quick and accurate identification for the location and size of the original fire source is of great scientific and practical value in guiding fire rescue and fighting the tunnel fire. Nevertheless, it is a big challenge to acquire fire-source information in an actual tunnel fire event. In this study, the framework of artificial intelligence (AI) and big data is applied to predict the fire source in a numerical model of the tunnel. A big tunnel fire database of numerical simulations, with varying fire locations, fire sizes, and ventilation conditions, is constructed. Temporally varied temperatures measured by multiple sensor devices are used to train a long-short term memory recurrent neural network. Results demonstrate that the location and size of the tunnel fire and the ventilation wind speed can be predicted by the trained model with an accuracy of 90%. Sensitivity analysis is also carried out to optimize the database configuration and spatial–temporal arrangement of sensors in order to achieve a fast and reliable fire prediction. This work addresses the possibility of AI-based detection and prediction of fire source and hazard, thus, providing scientifically based guidance for smart-firefighting technologies and paving the way for future emergency-response tactics in a smart city.

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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

MAE:

Mean absolute error

MSE:

Mean squared error

RMSE:

Root mean squared error 

RNN:

Recurrent neural network

SVM:

Support vector machine

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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, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Xiqiang Wu, Younggi Park, Ao Li, Xinyan Huang, Fu Xiao & Asif Usmani

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

    Xinyan Huang

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Appendix

Appendix

Figure 12 compares the temperature measured at the middle of the tunnel in 2 and 3D modellings when the fire location, HRR and wind equal to 64 m, 50 MW and 1 m/s, respectively. Roughly they coincide with each other though the temperature curve of 2D modelling shows a slightly larger fluctuation. Considering that the main goal of this paper is to demonstrate the use of AI method in the prediction of fire source in tunnel rather than precisely modelling the fire behavior, it is rational to utilize 2-D modelling in this study.

Figure 12
figure 12

Comparison of temperatures measured at the middle of the tunnel between 2 and 3D modeling

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Figure 13 shows that the temperature measured at left exit, middle and right exit of the tunnel, indicating that temperature varies periodically after around 30 s. The steady stage is assumed to be reached when the temperature measured at various locations of the tunnel varies periodically (Table 1).

Figure 13
figure 13

Temperature measured at various locations when the fire location, HRR and wind equal to 64 m, 50 MW and 1 m/s, respectively

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Table 1 Modeled Tunnel Fire Cases for Sensor Allocation Optimization
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Wu, X., Park, Y., Li, A. et al. Smart Detection of Fire Source in Tunnel Based on the Numerical Database and Artificial Intelligence. Fire Technol 57, 657–682 (2021). https://doi.org/10.1007/s10694-020-00985-z

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