A Data-Driven Danger Zone Estimation Method Based on Bayesian Inference for Utility Tunnel Fires and Experimental Verification
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 1
Abstract
The ongoing challenge is to find an effective and precise fire detection method for safety concerns of utility tunnels to predict the fire danger zone and take measures for firefighting and intervention promptly. A data-driven danger zone estimation method was established based on Bayesian inference. The probability distribution of the fire danger zone can be obtained by this method. In particular, the governing equation is a simplified physical model, in which only crucial parameters of the fire states are referred to, including the fire source location, the maximum temperature, and the attenuation coefficient. This task shows superiority because it can avoid additional workload to provide the forward database for Bayesian inference. A prototype experiment was conducted in the largest utility tunnel experimental platform in China to verify the validity of the proposed method. Results demonstrated that the fire danger zone could be estimated with high accuracy merely based on several sensor data, which are not limited to specific scenes. The temperature distribution of the whole tunnel could also be predicted according to the fire parameters. Moreover, analysis of the measurement noise and disturbance conditions shows the robustness of the proposed method. Low costs, low consumption, and universality make it have broad engineering application prospects.
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Data Availability Statement
Some or all data, models, or code 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 the National Program on Key R&D Project of China (Grant No. 2020YFB2103502), Program of Chang Jiang Scholars of Ministry of Education, and National Natural Science Foundation of China (Grant No. 52008104), to which the authors are most grateful.
References
Bai, Y., R. Zhou, and J. Wu. 2020. “Hazard identification and analysis of urban utility tunnels in China.” Tunnelling Underground Space Technol. 106 (Dec): 103584. https://doi.org/10.1016/j.tust.2020.103584.
CS (Chinese Standard). 2015. Technical code for urban utility tunnel engineering. [In Chinese.] GB50838-2015. Beijing: National Standard of the People's Republic of China.
CS (Chinese Standard). 2017. Technical standard for supervision and alarm system engineering of urban utility tunnel. [In Chinese.] GB/T 51274-2017. Beijing: National Standard of the People's Republic of China.
CS (Chinese Standard). 2020. Specifications for operation service of urban utility tunnel. [In Chinese.] GB/T 38550-2020. Beijing: National Standard of the People's Republic of China.
Dai, J., Z.-D. Xu, P.-P. Gai, and Z.-W. Hu. 2021. “Optimal design of tuned mass damper inerter with a Maxwell element for mitigating the vortex-induced vibration in bridges.” Mech. Syst. Sig. Process. 148 (Feb): 107180. https://doi.org/10.1016/j.ymssp.2020.107180.
Delichatsios, M. A. 1981. “The flow of fire gases under a beamed ceiling.” Combust. Flame 43: 1–10. https://doi.org/10.1016/0010-2180(81)90002-X.
Ge, T., Z.-D. Xu, Y.-Q. Guo, X.-H. Huang, and Z.-F. He. 2022. “Experimental investigation and multiscale modeling of VE damper considering chain network and ambient temperature influence.” J. Eng. Mech. 148 (1): 04021124. https://doi.org/10.1061/(ASCE)EM.1943-7889.0002012.
Gong, L., L. Jiang, S. Li, N. Shen, Y. Zhang, and J. Sun. 2016. “Theoretical and experimental study on longitudinal smoke temperature distribution in tunnel fires.” Int. J. Therm. Sci. 102 (Apr): 319–328. https://doi.org/10.1016/j.ijthermalsci.2015.12.006.
Guo, S. D., R. Yang, H. Zhang, and X. Zhang. 2010. “New inverse model for detecting fire-source location and intensity.” J. Thermophys. Heat Transfer 24 (4): 745–755. https://doi.org/10.2514/1.46513.
Halim, S. Z., N. Quddus, and H. Pasman. 2021. “Time-trend analysis of offshore fire incidents using nonhomogeneous poisson process through bayesian inference.” Process Saf. Environ. Prot. 147 (Mar): 421–429. https://doi.org/10.1016/j.psep.2020.09.049.
Han, K., R. Zuo, P. Ni, Z. Xue, D. Xu, J. Wang, and D. Zhang. 2020. “Application of a genetic algorithm to groundwater pollution source identification.” J. Hydrol. 589 (Oct): 125343. https://doi.org/10.1016/j.jhydrol.2020.125343.
Hu, L. H. 2006. Studies on thermal physics of smoke movement in tunnel fires. Ph.D. thesis, Dept. of Safety Science and Engineering, Univ. of Science and Technology of China.
Huang, P., S. Ye, L. Qin, Y. Huang, J. Yang, L. Yu, and D. Wu. 2022. “Experimental study on the maximum excess ceiling gas temperature generated by horizontal cable tray fires in urban utility tunnels.” Int. J. Therm. Sci. 172 (Part B): 107341. https://doi.org/10.1016/j.ijthermalsci.2021.107341.
Jahn, W., G. Rein, and J. L. Torero. 2011. “Forecasting fire growth using an inverse zone modelling approach.” Fire Saf. J. 46 (3): 81–88. https://doi.org/10.1016/j.firesaf.2010.10.001.
Kaipio, J., and E. Somersalo. 2005. Statistical and computational inverse problems. New York: Springer-Verlag.
Li, Y. Z., and H. Ingason. 2018. “Overview of research on fire safety in underground road and railway tunnels.” Tunnelling Underground Space Technol. 81 (Nov): 568–589. https://doi.org/10.1016/j.tust.2018.08.013.
Lin, C.-C., G. Zhao, and L. L. Wang. 2015. “Using real-time sensing data for predicting future state of building fires.” In Proc., 2015 IEEE Int. Conf. on Automation Science and Engineering, 1313–1318. New York: IEEE.
Liu, H. N., G. Q. Zhu, R. L. Pan, M. M. Yu, and Z. H. Liang. 2019. “Experimental investigation of fire temperature distribution and ceiling temperature prediction in closed utility tunnel.” Case Stud. Thermal Eng. 14 (Sep): 100493. https://doi.org/10.1016/j.csite.2019.100493.
Liu, X., B. Sun, Z.-D. Xu, and X. Liu. 2021. “An adaptive particle swarm optimization algorithm for fire source identification of the utility tunnel fire.” Fire Saf. J. 126 (Dec): 103486. https://doi.org/10.1016/j.firesaf.2021.103486.
Liu, X., B. Sun, Z.-D. Xu, X. Liu, and D. Xu. 2022a. “Identification of multiple fire sources in the utility tunnel based on a constrained particle swarm optimization algorithm.” Fire Technol. 58 (5): 2825–2845. https://doi.org/10.1007/s10694-022-01284-5.
Liu, X., B. Sun, Z.-D. Xu, X. Liu, and D. Xu. 2022b. “An intelligent fire detection algorithm and sensor optimization strategy for utility tunnel fires.” J. Pipeline Syst. Eng. Pract. 13 (2): 04022009. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000642.
Lu, H., H. Peng, Z.-D. Xu, J. C. Matthews, N. Wang, and T. Iseley. 2022. “A feature selection–based intelligent framework for predicting maximum depth of corroded pipeline defects.” J. Perform. Constr. Facil. 36 (5): 04022044. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001753.
Lu, H., Z.-D. Xu, T. Iseley, and J. C. Matthews. 2021. “Novel data-driven framework for predicting residual strength of corroded pipelines.” J. Pipeline Syst. Eng. 12 (4): 04021045. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000587.
Ma, D., and Z. Zhang. 2016. “Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere.” J. Hazard. Mater. 311 (Jul): 237–245. https://doi.org/10.1016/j.jhazmat.2016.03.022.
Meng, B., L. Li, W. Zhong, Z. Tan, and Q. Du. 2022. “Improving anti-progressive collapse capacity of welded connection based on energy dissipation cover-plates.” J. Constr. Steel Res. 188: 107051. https://doi.org/10.1016/j.jcsr.2021.107051.
Muduli, L., P. K. Jana, and D. P. Mishra. 2018. “Wireless sensor network based fire monitoring in underground coal mines: A fuzzy logic approach.” Process Saf. Environ. Prot. 113 (Jan): 435–447. https://doi.org/10.1016/j.psep.2017.11.003.
Muhammad, K., J. Ahmad, Z. Lv, P. Bellavista, P. Yang, and S. W. Baik. 2019. “Efficient deep CNN-based fire detection and localization in video surveillance applications.” IEEE Trans. Syst. Man Cybern. Syst. 49 (7): 1419–1434. https://doi.org/10.1109/TSMC.2018.2830099.
Overholt, K. J., and O. A. Ezekoye. 2015. “Quantitative testing of fire scenario hypotheses: A bayesian Iinference approach.” Fire Technol. 51 (2): 335–367. https://doi.org/10.1007/s10694-013-0384-z.
Pan, R., G. Zhu, Z. Liang, G. Zhang, H. Liu, and X. Zhou. 2020. “Experimental study on the fire shape and maximum temperature beneath ceiling centerline in utility tunnel under the effect of curved sidewall.” Tunnelling Underground Space Technol. 99 (May): 103304. https://doi.org/10.1016/j.tust.2020.103304.
Pan, R., G. Zhu, G. Xu, and X. Liu. 2021. “Experimental analysis on burning rate and temperature profile produced by pool fire in a curved tunnel as a function of fire location.” Process Saf. Environ. Prot. 152 (Aug): 549–567. https://doi.org/10.1016/j.psep.2021.06.039.
Rojas Alva, W. U., G. Jomaas, and A. S. Dederichs. 2017. “The influence of vehicular obstacles on longitudinal ventilation control in tunnel fires.” Fire Saf. J. 87 (Jan): 25–36. https://doi.org/10.1016/j.firesaf.2016.11.001.
Shen, N., L. Chen, and R. Chen. 2022. “Displacement detection based on bayesian inference from GNSS kinematic positioning for deformation monitoring.” Mech. Syst. Sig. Process. 167 (Part B): 108570. https://doi.org/10.1016/j.ymssp.2021.108570.
Sun, B., Z. Hu, X. Liu, Z.-D. Xu, and D. Xu. 2022a. “A physical model-free ant colony optimization network algorithm and full scale experimental investigation on ceiling temperature distribution in the utility tunnel fire.” Int. J. Therm. Sci. 174 (Apr): 107436. https://doi.org/10.1016/j.ijthermalsci.2021.107436.
Sun, B., X. Liu, and Z.-D. Xu. 2022b. “A multiscale bridging material parameter and damage inversion algorithm from macroscale to mesoscale based on ant colony optimization.” J. Eng. Mech. 148 (2): 04021150. https://doi.org/10.1061/(ASCE)EM.1943-7889.0002067.
Sun, B., X. Liu, Z.-D. Xu, and D. Xu. 2022c. “An improved updatable backpropagation neural network for temperature prognosis in tunnel fires.” J. Perform. Constr. Facil. 36 (2): 04022012. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001718.
Sun, B., X. Liu, Z.-D. Xu, and D. Xu. 2022d. “Temperature data-driven fire source estimation algorithm of the underground pipe gallery.” Int. J. Therm. Sci. 171 (Jan): 107247. https://doi.org/10.1016/j.ijthermalsci.2021.107247.
Sun, M., Y. Q. Tang, S. Yang, M. W. Sigrist, J. Li, and F. Z. Dong. 2017. “Fiber optic distributed temperature sensing for fire source localization.” Meas. Sci. Technol. 28: 085102. https://doi.org/10.1088/1361-6501/aa7436.
Tung, T. X., and J.-M. Kim. 2011. “An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems.” Fire Saf. J. 46 (5): 276–282. https://doi.org/10.1016/j.firesaf.2011.03.003.
Wang, X., L. Li, J. L. Beck, and Y. Xia. 2021. “Sparse bayesian factor analysis for structural damage detection under unknown environmental conditions.” Mech. Syst. Sig. Process. 154 (Jun): 107563. https://doi.org/10.1016/j.ymssp.2020.107563.
Wu, H., D. Wu, and J. Zhao. 2019. “An intelligent fire detection approach through cameras based on computer vision methods.” Process Saf. Environ. Prot. 127 (Jul): 245–256. https://doi.org/10.1016/j.psep.2019.05.016.
Wu, N., R. Yang, H. Zhang, and L. F. Qiao. 2013. “Decentralized inverse model for estimating building fire source location and intensity.” J. Thermophys. Heat Transfer 27 (3): 563–575. https://doi.org/10.2514/1.T3976.
Wu, X. Q., Y. Park, A. Li, X. Y. Huang, F. Xiao, and A. Usmani. 2020. “Smart detection of fire source in tunnel based on the numerical database and artificial intelligence.” Fire Technol. 57 (2): 657–682. https://doi.org/10.1007/s10694-020-00985-z.
Xu, Z.-D., Y.-F. Guo, S.-A. Wang, and X.-H. Huang. 2013. “Optimization analysis on parameters of multi-dimensional earthquake isolation and mitigation device based on genetic algorithm.” Nonlinear Dyn. 72 (4): 757–765. https://doi.org/10.1007/s11071-013-0751-9.
Xu, Z.-D., X.-H. Huang, F.-H. Xu, and J. Yuan. 2019. “Parameters optimization of vibration isolation and mitigation system for precision platforms using non-dominated sorting genetic algorithm.” Mech. Syst. Sig. Process. 128 (Aug): 191–201. https://doi.org/10.1016/j.ymssp.2019.03.031.
Xu, Z.-D., Y. Yang, and A.-N. Miao. 2021. “Dynamic analysis and parameter optimization of pipelines with multidimensional vibration isolation and mitigation device.” J. Pipeline Syst. Eng. 12 (1): 04020058. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000504.
Yan, Q., Y. Zhang, and Q. Sun. 2020. “Characteristic study on gas blast loadings in an urban utility tunnel.” J. Perform. Constr. Facil. 34 (4): 04020076. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001477.
Ye, K., X. D. Zhou, Y. Zheng, H. Liu, X. Tang, B. Cao, Y. B. Huang, Y. Q. Chen, and L. Z. Yang. 2019. “Estimating the longitudinal maximum gas temperature attenuation of ceiling jet flows generated by strong fire plumes in an urban utility tunnel.” Int. J. Therm. Sci. 142 (Aug): 434–448. https://doi.org/10.1016/j.ijthermalsci.2019.04.023.
Zhang, Q., J. Xu, L. Xu, and H. Guo. 2016. “Deep convolutional neural networks for forest fire detection.” In Proc., 2016 Int. Forum on Management, Education and Information Technology Application, 568–575. Paris: Atlantis Press.
Zhang, Z., Z. He, Z.-D. Xu, and L.-W. Chen. 2021. “Calculating moisture emissivity of timber members with different surface treatment.” Constr. Build. Mater. 269 (Feb): 121253. https://doi.org/10.1016/j.conbuildmat.2020.121253.
Zheng, H. Y., X. Wang, and S. B. Wei. 2018. Fire source localization method based on genetic algorithm. Wales, UK: EasyChair.
Zhu, C., Z.-D. Xu, H. Lu, and Y. Lu. 2022. “Evaluation of cross-sectional deformation in pipes using reflection of fundamental guided-waves.” J. Eng. Mech. 148 (5): 04022016. https://doi.org/10.1061/(ASCE)EM.1943-7889.0002095.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 37 • Issue 1 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jul 12, 2022
Accepted: Oct 3, 2022
Published online: Dec 13, 2022
Published in print: Feb 1, 2023
Discussion open until: May 13, 2023
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Design (by type)
- Detection methods
- Disaster risk management
- Disasters and hazards
- Engineering fundamentals
- Fire resistance
- Fires
- Geotechnical engineering
- Infrastructure
- Lifeline systems
- Man-made disasters
- Mathematics
- Methodology (by type)
- Parameters (statistics)
- Research methods (by type)
- Statistical analysis (by type)
- Statistics
- Structural design
- Tunnels
- Utilities
- Verification
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