Search

Accident causation models mainly answer the following two questions: (ⅰ) why does an accident occur, and (ⅱ) how does it occur? These models are the most important theoretical basis for safety science, and provide an important method for accident analysis and prevention. To understand accident causation models systematically and comprehensively, this work clarifies the development history of these models over the past 100 years. The work conducted in this study is summarised as follows: (i) The role and origin of accident causation models are introduced. (ii) A new method for classifying accident causation models is proposed. The method divides the accident causal models into linear and nonlinear accident causation models, and the latter are further divided into human-based, statistics-based, energy-based, and system-based accident models. (iii) A review of 29 representative accident causation models proposed in the past 100 years is conducted. The theoretical basis, application flow, and application status of these models are highlighted. (iv) A detailed introduction to the 24Model, an accident causation model with theoretical innovation and more modern safety management, is presented. (v) A comparative analysis of various accident causation models and their development trends are discussed. (vi) This safety also summarises the application status of the accident causal model and prospects for future applications. The research findings of this study are as follows: (i) The newly proposed classification method of accident causation models clarifies the classification of accident causes. (ii) Each type of accident causation model has its own characteristics and application scope. In an accident analysis, an accident model that meets its industry characteristics should be selected. (iii) ‘Organisational factors’ will be replaced by more modern ‘safety management systems’, and people will pay more attention to the role of ‘safety culture’ in accident prevention. Accident causation models will develop in a linear and systematic way. (iv) The current accident causation models consist mainly of qualitative analysis and quantitative analysis, and will develop in the direction of dynamic analysis, accident prediction, and intelligent comprehensive analysis in the future.

Coal is an important energy resource internationally. However, accidents have severely restricted the clean and safe production of coal resources. Among such accidents, coal and gas outburst accidents are a kind of coalmine disaster with high destructive power. Previous research on coal and gas outburst accidents mainly focused on gas factors but ignored the role of human factors. This paper analyses the coal and gas outburst accidents in China from 2008 to 2018 and studies its macroscopic laws. To better understand the causes of coal and gas outbursts, this paper uses the 24modelel to analyse coal and gas accidents and suggest measures for accident prevention from the two aspects ‘gas’ (risk control) and ‘humans’ (behavioural safety). Macroscopic law research found the following: (1) March, May, July, and August are the predominant months for accidents. (2) The second to fourth hours of the working hours and the first hour before the end of work are the peak periods for accidents. (3) Guizhou, Hunan, Henan, Sichuan, Yunnan, and Chongqing are the provinces with the most coal and gas outburst accidents. (4) An overall 75.82 % of accidents occurred in the driving face, and 81.08 % of accidents occurred in coal and gas outburst mines. (5) Blasting, drilling, driving, and coal falling are the main inducing factors. Case analysis of accidents found the following: (1) Human error is the leading cause of accidents. Among the errors, the lack of strict enforcement of outburst prevention measures, illegal command, and the illegal operation of miners are the main unsafe acts. (2) Safety knowledge and awareness of miners is not generally high, and serious habitual violations and unsafe psychologies exist. (3) The gas comprehensive prevention system and supervision system in the coal mine safety management system (SMS) can be easily operated improperly, and the safety training system and emergency management system can be absent. (4) Coalmine enterprises seriously lack safety culture.

Risk prediction of disasters is one of the most effective ways to prevent accidents. To solve the problems in multi-factor complex disaster prediction, this paper proposes a new method for risk prediction and factorial risk analysis. Coal and gas outburst accidents were selected as research objects. First, a new coal and gas outburst prediction model was established that consists of 4 levels and 14 factors. Then, the Improved Fruit Fly Optimization Algorithm (IFOA) and the General Regression Neural Network (GRNN) algorithm were combined to establish the IFOA-GRNN prediction model. After that, the sensitivity analysis method was applied to the analysis of the sensitive factors of coal and gas outbursts. Finally, an apriori algorithm was used to mine the disaster information. The method proposed in this paper was applied to the Pingdingshan No. 8 Min. The application results show that the IFOA-GRNN algorithm proposed in this paper has an accuracy rate of 100% for the prediction of accident risk levels. Compared with the Back Propagation (BP), GRNN and FOA-GRNN algorithms, IFOA-GRNN has the characteristics of a smaller prediction error, higher stability and faster convergence. The sensitivity analysis method can judge the sensitive factors of coal and gas outbursts without knowing the mechanisms of the accident. The a priori algorithm can perform good data mining on the combination of high frequency factors leading to accidents and the relationships between the coal and gas outburst levels and factors. The data mining results are very helpful for the prevention and management of coal and gas outbursts.

Human factors are the primary factors leading to accidents. Therefore, managing human factors is an important way to prevent accidents. This paper aims to introduce a new method to assess and manage human factors. First, the accident causation model was improved based on Reason’s “Swiss-cheese” model, which was then combined with the Human Factor Analysis and Classification System (HFACS) to establish the human factors risk assessment model. The evaluation model includes 5 levels (organization influence, unsafe supervision, preconditions for unsafe acts, unsafe acts, and emergency influence) and 25 human factors. In the risk assessment process, the set pair analysis method was used to calculate the connection number and the partial connection number of each factor, level and whole system. The safety score and risk development interval were calculated by using the connection number, and the risk grade is determined. Thus, the dynamic quantitative evaluation of human risk is realized. By using the partial connection number, the risk development trend of each factor is predicted. Due to the lack of human managed enterprises, the safety status of people is approximately discrete. Therefore, this paper establishes the SPA–Markov chain risk prediction model to predict human risk. The verification results show that the prediction error is less than 2%. This indicates that the prediction model can be applied in practice. To reduce human risk, ABC analysis and the “S-O-R” model were used for human risk management. The application results show that this method has a significant effect on improving human safety factors. Finally, this paper summarizes 12 common unsafe factors and their effective safety “stimulus” measure, researching the accident path. According to the organizational level and individual level of human factors, different kinds of human factors management methods are suggested.

Senior managers in organizations are authorized and obliged to maintain organizational safety. However, to date, little research has considered the relation of senior managers' safety leadership to safety behavior. This study addresses this gap by using path analysis to confirm the validity of a hypothetical model that relates six dimensions of senior managers' safety leadership to two safety behaviors through the safety climate in the petrochemical industry. A questionnaire survey was sent randomly to workers (other than senior managers) in two petrochemical companies in China, and data from 155 usable responses were compiled for the path analysis. Results indicate that in the petrochemical industry, senior managers' safety leadership has a positive impact on safety behavior, and the safety climate plays an intermediary role between them. From the perspective of the dimensions of senior managers' safety leadership and safety behavior, safety concern has the greatest positive effect on safety compliance. Moreover, safety vision has the greatest positive impact on safety participation, whereas safety inspiration and safety awards and punishment have negative effects on safety compliance. Personal character does not directly influence any dimension of safety behavior but indirectly does so by influencing the safety climate. On the basis of these results, measures of improving senior managers' safety leadership in the petrochemical industry are presented to help improve the overall safety performance of the industry. A new view is provided for the petrochemical industry in China to suggest that senior managers’ safety leadership can be treated earnestly.