IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i24p16934-d1005667.html
   My bibliography  Save this article

An Integrated Quantitative Risk Assessment Method for Underground Engineering Fires

Author

Listed:
  • Qi Yuan

    (School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Hongqinq Zhu

    (School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Xiaolei Zhang

    (School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    China Academy of Safety Science and Technology, Beijing 100012, China)

  • Baozhen Zhang

    (School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Xingkai Zhang

    (School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    China Academy of Safety Science and Technology, Beijing 100012, China)

Abstract

Fires are one of the main disasters in underground engineering. In order to comprehensively describe and evaluate the risk of underground engineering fires, this study proposes a UEF risk assessment method based on EPB-FBN. Firstly, based on the EPB model, the static and dynamic information of the fire, such as the cause, occurrence, hazard, product, consequence, and emergency rescue, was analyzed. An EPB model of underground engineering fires was established, and the EPB model was transformed into a BN structure through the conversion rules. Secondly, a fuzzy number was used to describe the state of UEF variable nodes, and a fuzzy conditional probability table was established to describe the uncertain logical relationship between UEF nodes. In order to make full use of the expert knowledge and empirical data, the probability was divided into intervals, and a triangulated fuzzy number was used to represent the linguistic variables judged by experts. The α-weighted valuation method was used for de-fuzzification, and the exact conditional probability table parameters were obtained. Through fuzzy Bayesian inference, the key risk factors can be identified, the sensitivity value of key factors can be calculated, and the maximum risk chain can be found in the case of known evidence. Finally, the method was applied to the deductive analysis of three scenarios. The results show that the model can provide realistic analysis ideas for fire safety evaluation and emergency management of underground engineering. The proposed EPB risk assessment model provides a new perspective for the analysis of UEF accidents and contributes to the ongoing development of UEF research.

Suggested Citation

  • Qi Yuan & Hongqinq Zhu & Xiaolei Zhang & Baozhen Zhang & Xingkai Zhang, 2022. "An Integrated Quantitative Risk Assessment Method for Underground Engineering Fires," IJERPH, MDPI, vol. 19(24), pages 1-26, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16934-:d:1005667
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/24/16934/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/24/16934/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiansong Wu & Zhuqiang Hu & Jinyue Chen & Zheng Li, 2018. "Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network," Sustainability, MDPI, vol. 10(10), pages 1-21, October.
    2. Matellini, D.B. & Wall, A.D. & Jenkinson, I.D. & Wang, J. & Pritchard, R., 2013. "Modelling dwelling fire development and occupancy escape using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 75-91.
    3. Khakzad, Nima & Landucci, Gabriele & Reniers, Genserik, 2017. "Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 232-247.
    4. Weiyi Ju & Jie Wu & Qingchun Kang & Juncheng Jiang & Zhixiang Xing, 2022. "Fire Risk Assessment of Subway Stations Based on Combination Weighting of Game Theory and TOPSIS Method," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
    5. Hanea, D.M. & Jagtman, H.M. & Ale, B.J.M., 2012. "Analysis of the Schiphol Cell Complex fire using a Bayesian belief net based model," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 115-124.
    6. Zhang, Limao & Wu, Xianguo & Skibniewski, Miroslaw J. & Zhong, Jingbing & Lu, Yujie, 2014. "Bayesian-network-based safety risk analysis in construction projects," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 29-39.
    7. Thompson, J.A. & Maidment, G.G. & Missenden, J.F., 2006. "Modelling low-energy cooling strategies for underground railways," Applied Energy, Elsevier, vol. 83(10), pages 1152-1162, October.
    8. Tianpei Tang & Senlai Zhu & Yuntao Guo & Xizhao Zhou & Yang Cao, 2019. "Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method," IJERPH, MDPI, vol. 16(7), pages 1-17, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiansong Wu & Weipeng Fang & Xing Tong & Shuaiqi Yuan & Weiqi Guo, 2019. "Bayesian analysis of school bus accidents: a case study of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(3), pages 463-483, February.
    2. Weiyi Ju & Jie Wu & Qingchun Kang & Juncheng Jiang & Zhixiang Xing, 2022. "Fire Risk Assessment of Subway Stations Based on Combination Weighting of Game Theory and TOPSIS Method," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
    3. Jiansong Wu & Zhuqiang Hu & Jinyue Chen & Zheng Li, 2018. "Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network," Sustainability, MDPI, vol. 10(10), pages 1-21, October.
    4. Xiao Zhang & Xiaofeng Hu & Yiping Bai & Jiansong Wu, 2020. "Risk Assessment of Gas Leakage from School Laboratories Based on the Bayesian Network," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    5. Chen, Chao & Yang, Ming & Reniers, Genserik, 2021. "A dynamic stochastic methodology for quantifying HAZMAT storage resilience," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Wang, Wenhao & Wang, Yanhui & Wang, Guangxing & Li, Man & Jia, Limin, 2023. "Identification of the critical accident causative factors in the urban rail transit system by complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    7. Khakzad, Nima, 2021. "Optimal firefighting to prevent domino effects: Methodologies based on dynamic influence diagram and mathematical programming," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    8. Ahn, Jonghoon & Cho, Soolyeon & Chung, Dae Hun, 2016. "Development of a statistical analysis model to benchmark the energy use intensity of subway stations," Applied Energy, Elsevier, vol. 179(C), pages 488-496.
    9. Singh, Kritika & Maiti, J, 2020. "A novel data mining approach for analysis of accident paths and performance assessment of risk control systems," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    10. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
    11. Khakzad, Nima, 2023. "A methodology based on Dijkstra's algorithm and mathematical programming for optimal evacuation in process plants in the event of major tank fires," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    12. Kai Guo & Wei Wang & Shixiang Tian & Juntao Yang & Zebiao Jiang & Zhangyin Dai, 2022. "Research on Optimization Technology of Cross-Regional Synergistic Deployment of Fire Stations Based on Fire Risk," Sustainability, MDPI, vol. 14(23), pages 1-14, November.
    13. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    14. Guo, Qingjun & Amin, Shohel & Hao, Qianwen & Haas, Olivier, 2020. "Resilience assessment of safety system at subway construction sites applying analytic network process and extension cloud models," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    15. Özkan Uğurlu & Serdar Yıldız & Sean Loughney & Jin Wang & Shota Kuntchulia & Irakli Sharabidze, 2020. "Analyzing Collision, Grounding, and Sinking Accidents Occurring in the Black Sea Utilizing HFACS and Bayesian Networks," Risk Analysis, John Wiley & Sons, vol. 40(12), pages 2610-2638, December.
    16. Zhongzhen Yang & Liquan Guo & Zaili Yang, 2019. "Emergency logistics for wildfire suppression based on forecasted disaster evolution," Annals of Operations Research, Springer, vol. 283(1), pages 917-937, December.
    17. Ünsal-Altuncan, Izel & Vanhoucke, Mario, 2024. "A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks," European Journal of Operational Research, Elsevier, vol. 315(2), pages 511-527.
    18. Kaptan, Mehmet & Uğurlu, Özkan & Wang, Jin, 2021. "The effect of nonconformities encountered in the use of technology on the occurrence of collision, contact and grounding accidents," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    19. Zhou, Ying & Li, Chenshuang & Zhou, Cheng & Luo, Hanbin, 2018. "Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 152-167.
    20. Rachel Aldred & Susana García-Herrero & Esther Anaya & Sixto Herrera & Miguel Ángel Mariscal, 2019. "Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment," IJERPH, MDPI, vol. 17(1), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16934-:d:1005667. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.