IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v266y2026ipbs0951832025009986.html

Risk assessment of coal mine gas explosion based on cloud model and Bayesian network

Author

Listed:
  • He, Shan
  • Shi, Shiliang
  • Lin, Zhijun
  • Lu, Yi
  • Li, He
  • You, Bo

Abstract

Aiming at quantifying the risk of coal mine gas explosions and addressing the deficiency in handling uncertainty in risk assessments, a gas explosion risk assessment method based on the Bayesian network and the cloud model was proposed in this study. Firstly, the main risk factors affecting gas explosions were determined, and the topology model of gas explosion risk was constructed. Meanwhile, the distribution ranges of prior and conditional probabilities of risk factors affecting gas explosions, as well as the probability values of basic events were determined in light of the cloud model theory. Furthermore, the probability of gas explosions was calculated by Bayesian forward causal reasoning, while the causation and mechanism of gas explosions was analyzed by Bayesian backward diagnostic reasoning. These reasoning technologies enabled the swift identification of the most probable risk factors. Finally, key risk factors affecting gas explosions were identified by means of Bayesian sensitivity analysis. The case study reveals that the risk probability of gas explosions in a coal mine in Anhui Province is 5.9%. However, the risk level of gas explosions rises substantially when the underground production conditions change, especially when multiple risk factors occur simultaneously.

Suggested Citation

  • He, Shan & Shi, Shiliang & Lin, Zhijun & Lu, Yi & Li, He & You, Bo, 2026. "Risk assessment of coal mine gas explosion based on cloud model and Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
    • Handle: RePEc:eee:reensy:v:266:y:2026:i:pb:s0951832025009986
    DOI: 10.1016/j.ress.2025.111798
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832025009986
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2025.111798?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    2. Huang, Congzhi & He, Jiaxuan & Zheng, Wei & Ke, Zhiwu, 2025. "A health monitoring method based on multivariate-time series adaptive gated recurrent unit transfer learning model for coal mill system," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    3. Refaul Ferdous & Faisal Khan & Rehan Sadiq & Paul Amyotte & Brian Veitch, 2011. "Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations," Risk Analysis, John Wiley & Sons, vol. 31(1), pages 86-107, January.
    4. Mousavi, Milad & Shen, Xuesong & Zhang, Zhigang & Barati, Khalegh & Li, Binghao, 2025. "IoT-Bayes fusion: Advancing real-time environmental safety risk monitoring in underground mining and construction," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    5. Nguyen, Hoang & Bui, Xuan-Nam & Topal, Erkan, 2023. "Reliability and availability artificial intelligence models for predicting blast-induced ground vibration intensity in open-pit mines to ensure the safety of the surroundings," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Liu, Chengfei & Wang, Enyuan & Li, Zhonghui & Zang, Zesheng & Li, Baolin & Yin, Shan & Zhang, Chaolin & Liu, Yubing & Wang, Jinxin, 2025. "Research on multi-factor adaptive integrated early warning method for coal mine disaster risks based on multi-task learning," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    7. 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).
    8. Liu, Binglong & Li, Zhonghui & Zang, Zesheng & Yin, Shan, 2025. "Research on coal and gas outburst security situations based on expert knowledge and graph convolutional models," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    9. Li, Yuanzhen & She, Yunlei & Shi, Ying & Ding, Rijia, 2025. "Modeling and analysis of open-pit coal mine accident causation based on directed weighted network," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    10. You, Qidong & Guo, Jianbin & Zeng, Shengkui & Che, Haiyang, 2024. "A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Chris J Needham & James R Bradford & Andrew J Bulpitt & David R Westhead, 2007. "A Primer on Learning in Bayesian Networks for Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-8, August.
    12. Lin, Haifei & Li, Wenjing & Li, Shugang & Wang, Lin & Ge, Jiaqi & Tian, Yu & Zhou, Jie, 2024. "Coal mine gas emission prediction based on multifactor time series method," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    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. Li, Feng & Duan, Baoyan & Zhang, Yue & Liang, Dongdong, 2026. "Post-risk assessment model for gas explosion accidents based on the coupling effect of disaster-causing factors," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
    2. Liu, Chengfei & Wang, Enyuan & Li, Zhonghui & Zang, Zesheng & Li, Baolin & Yin, Shan & Zhang, Chaolin & Liu, Yubing & Wang, Jinxin, 2025. "Research on multi-factor adaptive integrated early warning method for coal mine disaster risks based on multi-task learning," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    3. Liu, Binglong & Li, Zhonghui & Zang, Zesheng & Yin, Shan, 2025. "Research on coal and gas outburst security situations based on expert knowledge and graph convolutional models," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    4. Yinnan, He & Bangjun, Wang & Linyu, Cui, 2026. "Configuration analysis of coal mine accident causation: uncovering pathways, substitutions, and provincial heterogeneity in China," Reliability Engineering and System Safety, Elsevier, vol. 269(C).
    5. Li, Yuanzhen & She, Yunlei & Shi, Ying & Ding, Rijia, 2025. "Modeling and analysis of open-pit coal mine accident causation based on directed weighted network," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    6. Ge, Jiaqi & Lin, Haifei & Li, Shugang & Zhou, Jie & Li, Wenjing, 2026. "Research on multi-task leakage identification methods for gas drainage pipeline," Reliability Engineering and System Safety, Elsevier, vol. 267(PB).
    7. Xiao, Xiao & Song, Meiqi & Liu, Xiaojing, 2025. "A reliable and adaptive prediction framework for nuclear power plant system through an improved Transformer model and Bayesian uncertainty analysis," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    8. Guo, Jian & Ma, Kaijiang, 2024. "Risk analysis for hazardous chemical vehicle-bridge transportation system: A dynamic Bayesian network model incorporating vehicle dynamics," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    9. Martin Folch-Calvo & Francisco Brocal-Fernández & Cristina González-Gaya & Miguel A. Sebastián, 2020. "Analysis and Characterization of Risk Methodologies Applied to Industrial Parks," Sustainability, MDPI, vol. 12(18), pages 1-35, September.
    10. Kai Pan & Hui Liu & Xiaoqing Gou & Rui Huang & Dong Ye & Haining Wang & Adam Glowacz & Jie Kong, 2022. "Towards a Systematic Description of Fault Tree Analysis Studies Using Informetric Mapping," Sustainability, MDPI, vol. 14(18), pages 1-28, September.
    11. Rahman, Md Samsur & Khan, Faisal & Shaikh, Arifusalam & Ahmed, Salim & Imtiaz, Syed, 2020. "A conditional dependence-based marine logistics support risk model," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    12. Wu, Menglong & Zhang, Xiaobing & Shan, WeiWei & Liu, Yang & Jiang, Yanfeng, 2026. "Coupled disaster mechanisms and theoretical framework in multi-hazard scenarios: insights from non-coal mines," Reliability Engineering and System Safety, Elsevier, vol. 268(C).
    13. Zhang, Haoyuan & Marsh, D. William R, 2021. "Managing infrastructure asset: Bayesian networks for inspection and maintenance decisions reasoning and planning," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    14. Chai, Naijie & Zhou, Wenliang & Hu, Xinlei, 2022. "Safety evaluation of urban rail transit operation considering uncertainty and risk preference: A case study in China," Transport Policy, Elsevier, vol. 125(C), pages 267-288.
    15. Guo, Jianbin & Ma, Shuo & Zeng, Shengkui & Che, Haiyang & Pan, Xing, 2024. "A risk evaluation method for human-machine interaction in emergencies based on multiple mental models-driven situation assessment," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    16. Park, Jae-Hyun, 2017. "Time-dependent reliability of wireless networks with dependent failures," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 47-61.
    17. Pengxia Zhao & Tie Li & Biao Wang & Ming Li & Yu Wang & Xiahui Guo & Yue Yu, 2022. "The Scenario Construction and Evolution Method of Casualties in Liquid Ammonia Leakage Based on Bayesian Network," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    18. Wang, Lei & Liu, Qing & Dong, Shiyu & Guedes Soares, C., 2022. "Selection of countermeasure portfolio for shipping safety with consideration of investment risk aversion," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    19. Li, Mei & Liu, Zixian & Li, Xiaopeng & Liu, Yiliu, 2019. "Dynamic risk assessment in healthcare based on Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 327-334.
    20. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:reensy:v:266:y:2026:i:pb:s0951832025009986. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.