An Improved Updatable Backpropagation Neural Network for Temperature Prognosis in Tunnel Fires
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 2
Abstract
Because it is impossible to predict the temperature in advance, specific fire scenes (fire type, fire location, tunnel geometry, etc.) are unknown in traditional tunnel fire research. To address this difficulty, this work developed a novel algorithm to achieve temperature prognosis in tunnel fires that includes an updatable backpropagation (BP) neural network and a smoothing procedure. The data-driven algorithm is not limited to a specific fire scene, which makes it easy to fit real complex tunnel fire disasters. In addition, a full-scale fire test was conducted and utilized to verify the algorithm. Two innovations, including the updatable BP neural network and the smoothing procedure, made the predicted results match well with the experimental results. We can achieve a real-time precise temperature prediction 20 s in advance at a high accuracy of about 85.6%. If there is no sudden external factor intervention, the accuracy is about 99.4%. The algorithm provides an effective numerical tool for early fire warning and firefighting decision making that can address the temperature prognosis of tunnel fires.
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Data Availability Statement
All data generated or used during the study appear in the published article.
Acknowledgments
The work described in this paper was financially supported by the National Program on Key Research and Development (R&D) Project of China (Grant No. 2020YFB2103503), the National Natural Science Foundation of China (Grant No. 52008104), and the Program of Chang Jiang Scholars of the Ministry of Education. The authors are very grateful to the reviewers for carefully reading the paper and for their comments and suggestions, which have improved the paper.
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Journal of Performance of Constructed Facilities
Volume 36 • Issue 2 • April 2022
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© 2022 American Society of Civil Engineers.
History
Received: Oct 4, 2021
Accepted: Dec 14, 2021
Published online: Feb 15, 2022
Published in print: Apr 1, 2022
Discussion open until: Jul 15, 2022
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