Abstract
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.
Highlights
Despite efforts, at different levels, to achieve safety in coal mines, such as the innovative use of new technology and the implementation of safety-related regulations, the occurrence of accidents and incidents is a concern
China), we adopt six direct and indirect worker states related to unsafe behaviour, e.g., Inadequate Safety Awareness (ISA), Poor Vigilance Awareness (PVA), Insufficient Experience (IE), Insufficient Competencies (IC), Poor Situation Awareness (PSA), and Alcoholic Intoxication (AI), since most of them are proven as being the main individual factors that affect unsafe behaviors in [23]
Unsafe behaviours are commonly identified as important causal factors in coal mine accidents
Summary
At different levels, to achieve safety in coal mines, such as the innovative use of new technology and the implementation of safety-related regulations, the occurrence of accidents and incidents is a concern. Liu [16] established a human factor analysis and classification system for China’s mines (HFACS-CM) based on the statistical results of 362 major coal mine accidents in China, and investigated the poor safety practices of coal miners and their related influencing factors. Most of these works mainly use statistical methods to examine the relationship between human errors in an accident. We will use the phrase “behaviour network” to refer to our networks for the worker state and unsafe behaviour
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