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Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model


  • Zhaobo Chen

    (Key Research Bases for Humanities and Social Sciences in Shanxi: Research Center for Innovation and Development of Equipment Manufacturing Industry, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Gangzhu Qiao

    (Division of Big Data and Visual Computing, North University of China, Taiyuan 030051, China)

  • Jianchao Zeng

    (Division of Big Data and Visual Computing, North University of China, Taiyuan 030051, China)


Unsafe behaviours, such as violations of rules and procedures, are commonly identified as important causal factors in coal mine accidents. Meanwhile, a recurring conclusion of accident investigations is that worker states, such as mental fatigue, illness, physiological fatigue, etc., are important contributory factors to unsafe behaviour. In this article, we seek to provide a quantitative analysis on the relationship between the worker state and unsafe behaviours in coal mine accidents, based on a case study drawn from Chinese practice. Using Bayesian networks (BN), a graphical structure of the network was designed with the help of three experts from a coal mine safety bureau. In particular, we propose a verbal versus numerical fuzzy probability assessment method to elicit the conditional probability of the Bayesian network. The junction tree algorithm is further employed to accomplish this analysis. According to the BN established by expert knowledge, the results show that when the worker is in a poor state, the most vulnerable unsafe behaviour is violation, followed by decision-making error. Furthermore, insufficient experience may be the most significant contributory factor to unsafe behaviour, and poor fitness for duty may be the principal state that causes unsafe behaviours.

Suggested Citation

  • Zhaobo Chen & Gangzhu Qiao & Jianchao Zeng, 2019. "Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:5021-:d:267100

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    References listed on IDEAS

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    Cited by:

    1. Li Yang & Xue Wang & Junqi Zhu & Liyan Sun & Zhiyuan Qin, 2022. "Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD," IJERPH, MDPI, vol. 19(17), pages 1-29, August.
    2. Inmaculada Silla & Francisco J. Gracia & José M. Peiró, 2020. "Upward Voice: Participative Decision Making, Trust in Leadership and Safety Climate Matter," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    3. Ismail, Siti Noraishah & Ramli, Azizan & Aziz, Hanida Abdul, 2021. "Influencing factors on safety culture in mining industry: A systematic literature review approach," Resources Policy, Elsevier, vol. 74(C).
    4. Donghun Lee & Hyungju Kim & Kwiyeon Koo & Sooyeon Kwon, 2024. "Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)," Sustainability, MDPI, vol. 16(9), pages 1-26, April.
    5. Xinping Wang & Cheng Zhang & Jun Deng & Chang Su & Zhenzhe Gao, 2022. "Analysis of Factors Influencing Miners’ Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model," IJERPH, MDPI, vol. 19(12), pages 1-30, June.
    6. Xiaowei Li & Tiezhong Liu & Yongkui Liu, 2019. "Cause Analysis of Unsafe Behaviors in Hazardous Chemical Accidents: Combined with HFACs and Bayesian Network," IJERPH, MDPI, vol. 17(1), pages 1-15, December.
    7. Lei Chen & Hongxia Li & Shuicheng Tian, 2022. "Application of AHP and DEMATEL for Identifying Factors Influencing Coal Mine Practitioners’ Unsafe State," Sustainability, MDPI, vol. 14(21), pages 1-18, November.

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