Analysis of intervention strategies for coal miners' unsafe behaviors based on analytic network process and system dynamics

Yu, Kai; Cao, Qing; Xie, Changzhen; Qu, Nannan; Zhou, Liangchen

doi:10.1016/j.ssci.2019.05.002

Cited by 86 publications

(52 citation statements)

References 23 publications

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“…Furthermore, in this work, insufficient experience is the most significant contributory factor to unsafe behaviour. Meanwhile, in [23], safety awareness is ranked as the most influencing factor of unsafe behaviors of coal miners, followed by experience. Finally, poor fitness for duty is the principal state that causes unsafe behaviours.…”

Section: Discussionmentioning

confidence: 99%

“…Simultaneously, the states of the worker, Adverse Mental States (AMS), Adverse Physiological States (APS), Physical/Mental Limitations (PML), and Fitness for Duty (FD), can be furtherly reflected by other more-concrete states. First, based on 163 accident investigation reports from the Fenxi Coal Mine Safety Bureau in Shanxi (the largest coal-producing province in 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]. Actually, the above six worker states are directly refined from accident investigation reports, and some example are listed in Table 1.…”

Section: Structure Of the Behaviour Networkmentioning

confidence: 99%

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

2019

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Structure Of the Behaviour Networkmentioning

confidence: 99%

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

2019

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“…When applied to the safety-related outcomes at work, COR theory can also provide some insights as to why job insecurity can induce unsafe behaviours in the context of the current research. For coal miners in China, the perceived threat of job insecurity becomes more imminent and intense (Lam, Liang, Ashford, & Lee, 2015), since their low levels of education and lack of transferable skills make it difficult for them to find alternative jobs in other industries (Yu, Cao, Xie, Qu & Zhou, 2019). Therefore, coal miners are especially anxious about maintaining their employment (Masia & Pienaar, 2011).…”

Section: The Relationship Between Job Insecurity and Unsafe Behaviourmentioning

confidence: 99%

“…A third limitation relates to the generalisability of our research findings. Many contextual factors may influence unsafe behaviours in coal miners, such as physical environment, leadership in safety matters, safety management, and group factors (Yu, Cao, Xie, Qu, & Zhou, 2019). Future research should systematically investigate the effects of the contextual variables in our model to determine whether our findings are applicable to other contexts.…”

Section: Limitationsmentioning

confidence: 99%

Job Insecurity and Unsafe Behaviour: Exploring Curvilinear and Moderated Relationships

Guo¹,

Shang

Wang³

et al. 2020

Proceedings

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This study examines a curvilinear relationship between job insecurity and unsafe behaviour among coal miners. Drawing from conservation of resources theory, we develop and investigate an explanation for coal miners' reactions to job insecurity based on their allocation of psychological resources at different levels of job insecurity. By using three-wave longitudinal data from 209 coal miners in northern China, we find support for the U-shaped relationship between job insecurity and unsafe behaviour. That is, coal miners' unsafe behaviour gradually decreases when they perceive a level of insecurity in their jobs, but beyond a certain point, their unsafe behaviour increases. Furthermore, we find that psychological detachment, but not self-control, moderates the curvilinear relationship, such that coal miners with higher psychological detachment exhibit less unsafe behaviours. We also discuss the theoretical implications of this study on job insecurity and unsafe behaviours, and the practical implications for organisations in the coal mining industry.

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“…The prediction of coalmine water inrush must be rapid, accurate, and effective. Hence, it is important to improve the operating speed of the prediction model, while ensuring the prediction accuracy [21][22][23]. Bhattacharjee et al [24] developed a hybrid prediction model based on extreme learning machine (ELM) and the principal component analysis (PCA), and proved that the hybrid model is more accurate and faster than the least squares (LS) SVM and traditional BP model.…”

Section: Introductionmentioning

confidence: 99%

A Coalmine Water Inrush Prediction Model Based on Artificial Intelligence

Wang¹,

Wei²,

Yao³

2020

IJSSE

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The prevention of water inrush is of great significance to the work safety in coalmines. However, the existing prediction models for coalmine water inrush cannot achieve desirable speed, accuracy, or generalization ability, owing to the complexity and diversity of causes of this accident. Therefore, this paper develops an artificial intelligence (AI)based coalmine water inrush safety prediction model, making coalmine water inrush prediction more accurate, real-time, and robust. Firstly, the causes of coalmine water inrush were combed, and used to build a reasonable evaluation index system. Next, the extreme learning machine (ELM) was optimized with particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm, and developed into a coalmine water inrush safety prediction model. The dimensionality reduction in subset classification was introduced in great details. Finally, the effectiveness of our model was proved through experiments. The research results provide the basis for the application of combinatory optimized learning machines in hazard prediction of other fields.

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Analysis of intervention strategies for coal miners' unsafe behaviors based on analytic network process and system dynamics

Cited by 86 publications

References 23 publications

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

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

Job Insecurity and Unsafe Behaviour: Exploring Curvilinear and Moderated Relationships

A Coalmine Water Inrush Prediction Model Based on Artificial Intelligence

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