An Intelligent Fire Detection Algorithm and Sensor Optimization Strategy for Utility Tunnel Fires
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 13, Issue 2
Abstract
With the rapid development of utility tunnels, fire safety after construction is increasingly important, especially in the cable compartment. An intelligent fire detection method based on the particle swarm optimization algorithm was proposed for fire state estimation of the utility tunnel, including the fire source location, the maximum temperature value, and the temperature attenuation coefficient. Additionally, a corresponding sensor optimization strategy was also established. The dispersion coefficient of the fire source location was defined as the judgment criteria of sensor optimization. The validity of the proposed algorithm and the sensor optimization strategy were demonstrated in the application of a full-scale experimental example. The maximum errors of the identified fire source location and the maximum temperature value after sensor optimization were 34.7164 m and 8.5403°C, respectively. The total number of temperature sensors was reduced by more than 50%. The proposed intelligent fire detection algorithm can provide precise guidance for fire protection and extinguishing plan. Particularly, the sensor optimization strategy can economize the cost of temperature sensors.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The works described in this paper are financially supported by National Program on Key R&D Project of China (Grant No. 2020YFB2103502), Program of Chang Jiang Scholars of Ministry of Education, the National Science Fund for Distinguished Young Scholars of China (Grant No. 51625803), and National Natural Science Foundation of China (Grant No. 52008104), to which the authors are most grateful.
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Journal of Pipeline Systems Engineering and Practice
Volume 13 • Issue 2 • May 2022
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© 2022 American Society of Civil Engineers.
History
Received: Jun 2, 2021
Accepted: Dec 28, 2021
Published online: Feb 26, 2022
Published in print: May 1, 2022
Discussion open until: Jul 26, 2022
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