IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v88y2024ics0301420723011364.html
   My bibliography  Save this article

Accident case-driven study on the causal modeling and prevention strategies of coal-mine gas-explosion accidents: A systematic analysis of coal-mine accidents in China

Author

Listed:
  • Yuxin, Wang
  • Gui, Fu
  • Qian, Lyu
  • Jingru, Wu
  • Yali, Wu
  • Meng, Han
  • Yuxuan, Lu
  • Xuecai, Xie

Abstract

Post-accident prevention measures in Chinese coal mining enterprises were based on single accident case training, and the learning of accident information is limited to the violation in the accident or incident. The nature of accidents proves that accident prevention encompasses not only visible violations and hazardous materials, but also deep-rooted organizational factors. How to obtain valuable and learnable safety information from limited accident date remains a key issue. This study investigated the use of a modeled accident-prevention framework for combining accident case data to construct an accident causation network for targeted data mining, which in turn could help obtain accident-prevention measures with more control values. The results of the study showed that (1) A framework for categorizing the factors influencing coal mine accidents, including four layers and 42 indicators, was identified through a literature review and factor extraction from 86 coal mine accident reports. (2) A causation network model of gas-explosion accidents comprising 163 accident causation nodes and 681 connected edges was established, and SP02(failure to implement the stop production instruction), SA01 (poor awareness of system compliance), PB03(fluke psychology) and SH01(habitual violation of procedures and rules) were the most influential accidental causes in the accidental network. (3) An F-Score-based attack strategy for accident-causing networks was proposed, which is superior to generalized attack strategies for reducing accident network connectivity and more conducive to rapidly obtaining accident prevention priorities. (4) A gas explosion accident prevention strategy based on the combination of accident network model and dynamic Bayesian network was constructed, and inference simulation was performed for four cases such as IA01 (unchecked gas concentration), verifying that it has the possibility of predicting accident hazards in the workplace. This study provided accident data-driven prevention interventions to reduce future mining accidents.

Suggested Citation

  • Yuxin, Wang & Gui, Fu & Qian, Lyu & Jingru, Wu & Yali, Wu & Meng, Han & Yuxuan, Lu & Xuecai, Xie, 2024. "Accident case-driven study on the causal modeling and prevention strategies of coal-mine gas-explosion accidents: A systematic analysis of coal-mine accidents in China," Resources Policy, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jrpoli:v:88:y:2024:i:c:s0301420723011364
    DOI: 10.1016/j.resourpol.2023.104425
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420723011364
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2023.104425?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Yuxin & Fu, Gui & Lyu, Qian & Wu, Yali & Jia, Qinsong & Yang, Xiaoyu & Li, Xiao, 2022. "Reform and development of coal mine safety in China: An analysis from government supervision, technical equipment, and miner education," Resources Policy, Elsevier, vol. 77(C).
    2. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    3. Anuoluwapo Ajayi & Lukumon Oyedele & Hakeem Owolabi & Olugbenga Akinade & Muhammad Bilal & Juan Manuel Davila Delgado & Lukman Akanbi, 2020. "Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects," Risk Analysis, John Wiley & Sons, vol. 40(10), pages 2019-2039, October.
    4. Shi, Xunpeng, 2013. "China's small coal mine policy in the 2000s: A case study of trusteeship and consolidation," Resources Policy, Elsevier, vol. 38(4), pages 598-604.
    5. Fa, Ziwei & Li, Xinchun & Qiu, Zunxiang & Liu, Quanlong & Zhai, Zhengyuan, 2021. "From correlation to causality: Path analysis of accident-causing factors in coal mines from the perspective of human, machinery, environment and management," Resources Policy, Elsevier, vol. 73(C).
    6. Mushtaq, Zulqarnain & Wei, Wei & Jamil, Ihsan & Sharif, Maimoona & Chandio, Abbas Ali & Ahmad, Fayyaz, 2022. "Evaluating the factors of coal consumption inefficiency in energy intensive industries of China: An epsilon-based measure model," Resources Policy, Elsevier, vol. 78(C).
    7. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, 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. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Csaba Both & Nima Dehmamy & Rose Yu & Albert-László Barabási, 2023. "Accelerating network layouts using graph neural networks," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. Peng Liu, 2024. "Antinetwork among China A-shares," Papers 2404.00028, arXiv.org.
    4. Charley Presigny & Marie-Constance Corsi & Fabrizio De Vico Fallani, 2024. "Node-layer duality in networked systems," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    5. Li, Gang & Sun, Xiaochen, 2021. "Evolutionary game on a growing multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    6. Lv, Changchun & Yuan, Ziwei & Si, Shubin & Duan, Dongli & Yao, Shirui, 2022. "Cascading failure in networks with dynamical behavior against multi-node removal," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    7. Bier, Vicki & Gutfraind, Alexander, 2019. "Risk analysis beyond vulnerability and resilience – characterizing the defensibility of critical systems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 626-636.
    8. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    9. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    10. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    11. Vinayak, & Raghuvanshi, Adarsh & kshitij, Avinash, 2023. "Signatures of capacity development through research collaborations in artificial intelligence and machine learning," Journal of Informetrics, Elsevier, vol. 17(1).
    12. Chen, Lei & Yue, Dong & Dou, Chunxia, 2019. "Optimization on vulnerability analysis and redundancy protection in interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1216-1226.
    13. Guido Caldarelli & Matthieu Cristelli & Andrea Gabrielli & Luciano Pietronero & Antonio Scala & Andrea Tacchella, 2012. "A Network Analysis of Countries’ Export Flows: Firm Grounds for the Building Blocks of the Economy," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-11, October.
    14. Supriya Tiwari & Pallavi Basu, 2024. "Quasi-randomization tests for network interference," Papers 2403.16673, arXiv.org, revised Oct 2024.
    15. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    16. Anzhi Sheng & Qi Su & Aming Li & Long Wang & Joshua B. Plotkin, 2023. "Constructing temporal networks with bursty activity patterns," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    17. Shang, Lihui & Zhao, Mingming & Ai, Jun & Su, Zhan, 2021. "Opinion evolution in the Sznajd model on interdependent chains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    18. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    19. Doumen, Sjoerd C. & Nguyen, Phuong & Kok, Koen, 2022. "Challenges for large-scale Local Electricity Market implementation reviewed from the stakeholder perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    20. Shogo Mizutaka & Kousuke Yakubo, 2017. "Structural instability of large-scale functional networks," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.

    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:jrpoli:v:88:y:2024:i:c:s0301420723011364. 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: http://www.elsevier.com/locate/inca/30467 .

    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.