Proceedings of the 3rd International Conference on Green Building, Civil Engineering and Smart City (GBCESC 2024)

<Previous Article In Volume
Next Article In Volume>

Research on the Prediction Method of Tunnel Fire Heat Release Rate Based on Informer Network

Authors
Lifan Hu1, *, Xihao Lin1
1College of Civil Engineering, Tongji University, Shanghai, 200092, China
*Corresponding author. Email: 2132560@tongji.edu.cn
Corresponding Author
Lifan Hu
Available Online 19 May 2025.
DOI
10.2991/978-94-6463-728-1_79How to use a DOI?
Keywords
Tunnel fire; Heat release rate prediction; Deep learning; Informer neural network
Abstract

Due to their relatively enclosed characteristics, tunnels facilitate rapid spread of fire and accumulation of harmful smoke gases, posing a significant threat to the safety of human lives and property. The heat release rate (HRR) is a critical parameter that characterizes the scale and severity of a tunnel fire incident. In order to predict the HRR and meet the demand for rapid real-time feedback in tunnel fire scenarios, this study was conducted on three tunnel fire datasets established through numerical simulations and on-site experiments. Then, a deep neural network model, Informer, was applied to train a predictive model for tunnel fire HRR for the first time based on the datasets mentioned above. The results indicate that this method which applies the Informer model demonstrates high accuracy. In short-term predictions, the R2 values on multiple tunnel datasets exceeded 0.85. This study contributes to aiding rescue personnel in promptly obtaining information on the subsequent development trends of a tunnel fire incident.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 3rd International Conference on Green Building, Civil Engineering and Smart City (GBCESC 2024)
Series
Advances in Engineering Research
Publication Date
19 May 2025
ISBN
978-94-6463-728-1
ISSN
2352-5401
DOI
10.2991/978-94-6463-728-1_79How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Lifan Hu
AU  - Xihao Lin
PY  - 2025
DA  - 2025/05/19
TI  - Research on the Prediction Method of Tunnel Fire Heat Release Rate Based on Informer Network
BT  - Proceedings of the 3rd International Conference on Green Building, Civil Engineering and Smart City (GBCESC 2024)
PB  - Atlantis Press
SP  - 856
EP  - 867
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-728-1_79
DO  - 10.2991/978-94-6463-728-1_79
ID  - Hu2025
ER  -