Human factors analysis of major coal mine accidents in China based on the HFACS-CM model and AHP method

Liu, Rulin; Cheng, Weimin; Yu, Yanbin; Xu, Qingfeng

doi:10.1016/j.ergon.2018.08.009

Cited by 125 publications

(60 citation statements)

References 24 publications

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“…These research findings indicate that there are strong interrelationships between the preconditions of unsafe behavior, unsafe supervision, and organizational factors, which also proves the systematic characteristics of HFACs. As confirmed by other studies [45], the conclusions of this study confirmed the possible ways to reduce the occurrence of hazardous chemical accidents in terms of organization, supervision, and working environment.…”

Section: Theoretical Implicationsupporting

confidence: 88%

Cause Analysis of Unsafe Behaviors in Hazardous Chemical Accidents: Combined with HFACs and Bayesian Network

Liu

Liu³

2019

IJERPH

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Hazardous chemical accidents (HCAs) seriously endanger public life, property, and health. Human and organizational factors are important causes of many kinds of accidents. In order to systematically explore the influencing factors of unsafe behaviors in HCAs in China, the method of human factors analysis and classification system based on the Bayesian network (BN-HFACs) was introduced. According to the 39 investigation reports of HCAs in China, the origin Bayesian network (BN) was obtained and the failure sensitivity of every node in BN was calculated. The results have shown that hazardous material environment (1.63) and mechanical equipment (0.49) in the level of preconditions of unsafe behavior have the same direction failure effect with operation error, while there is no factor has the same direction failure effect with operation violate. Some factors in organization influence and unsafe supervision, such as organization climate (0.34), operation guidance (0.37), planned operation (0.22), and legal supervision (0.19), are also important reasons for operational errors, while resource management (0.12), hidden investigation (0.18) and legal supervision (0.13) have an impact on operation violates. Moreover, there are still close relationships between other hierarchical elements, such as the operation guidance effect on the hazardous material environment (6.60), and the organizational climate has the most obvious impact on other factors at the level of organizational factors. Based on the above research conclusions, suggestions for individual, enterprise, and government were put forward, respectively, and the limitations of this study were also clarified.

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Section: Theoretical Implicationsupporting

confidence: 88%

Cause Analysis of Unsafe Behaviors in Hazardous Chemical Accidents: Combined with HFACs and Bayesian Network

Liu

Liu³

2019

IJERPH

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“…To achieve favorable levels of accident prevention and control, the statistics of 362 major coal mine accidents in China between 2000 and 2016 were collected and a human factor analysis and classification system for China's mines (HFACS-CM) was established [15]. Based on these statistical methods, 106 coal dust explosion accidents that occurred in China between the years of 1949 and 2007 were investigated, so as to review the overall situation and provide quantitative information on coal dust explosions [16].…”

Section: Introductionmentioning

confidence: 99%

Impact of Gas Control Policy on the Gas Accidents in Coal Mine

Wang

2020

Processes

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Coal mine gas accidents pose a serious threat to the safety of coal mines in China. To prevent such accidents, the Chinese government and relevant agencies have issued a number of related control policies, though the effect of these policies on gas accidents is still lacking. In this study, the relevant data of coal mine gas accidents in China from 2003 to 2018 are sorted and analyzed. The analysis results show that the number of coal mine accidents and deaths has decreased significantly. However, gas accidents account for the highest proportion of deaths and pose the largest threat, compared with other types of accidents. Subsequently, the developmental stages of China’s gas control policies are summarized; they are the independent development stage, the promotion stage and the consolidation stage. A method of calculating the intervention degree of the gas control policies is proposed. The results show that the policy intervention degree is the highest, and the number of accidents is the fastest in the stage of promotion. It is concluded that gas control policies have obvious inhibitory effects on the occurrence of gas accidents, and these policies are fundamental in preventing coal mine gas accidents.

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“…A modified version of HFACS was used to analyse accident cases from across the state of Queensland to identify human factor trends within mining [15]. 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.…”

Section: Introductionmentioning

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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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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Human factors analysis of major coal mine accidents in China based on the HFACS-CM model and AHP method

Cited by 125 publications

References 24 publications

Cause Analysis of Unsafe Behaviors in Hazardous Chemical Accidents: Combined with HFACs and Bayesian Network

Cause Analysis of Unsafe Behaviors in Hazardous Chemical Accidents: Combined with HFACs and Bayesian Network

Impact of Gas Control Policy on the Gas Accidents in Coal Mine

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

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