A fuzzy Bayesian network DEMATEL model for predicting safety behavior.

Objectives. Safety behavior significantly affects safety performance in the workplace. This study aimed to develop a Bayesian network (BN) model for managing and improving safety behavior. Methods. This study was carried out in the chemical industries in Iran. The data were gathered by a questionnaire consisting of 13 variables including organization safety priority, systems design, safety communication, safety education, work strategy, human-system interaction, mental workload, environmental distractions, work pressure, fatigue, sleepiness, safety knowledge and locus of control. The BN structure was created using the fuzzy decision-making trial, evaluation laboratory method and expert opinions. Belief updating was used to determine variables with the strongest effect on safety behavior. Results. Locus of control, organization safety priority and safety knowledge were the best predictors of safety behavior. Moreover, it was found that improving organization safety priority and safety knowledge is the best intervention strategy to improve safety behavior significantly. Conclusions. BN is a powerful tool that can model causal relationships among variables. Improving organization safety priority and safety knowledge can lead to the maximum possible level of safety behavior.

Linking mental health to safety behavior in construction workers: The mediating effect of work ability and sleep quality.

BACKGROUND: The severity of occupational accidents and injuries in the construction industry is the greatest across all industries. Few studies have examined the causal relationships among physical and psychological variables affecting the safety behavior of construction workers. OBJECTIVE: This study aimed to investigate the relationships among three factors (metal health, work ability and sleep quality) influencing the safety behavior of construction workers. METHODS: This cross-sectional study was conducted on 204 workers in civil construction projects in Iran. The data were gathered via self-reporting questionnaires. The acquired data were analyzed by the Smart PLS3 software using structural equation modeling with the partial least squares (PLS) regression approach. RESULTS: The R square revealed an acceptable fineness of the structural model. Similarly, as Q square values were higher than 0.15 for all factors, the predictability of the model was approved. The direct effect of mental health on safety behavior was not significant, but work ability and sleep quality had significant direct effects on safety behavior. Moreover, a decrease in mental health significantly reduced construction workers' sleep quality and work ability. CONCLUSION: The results of this study prove that sleep quality and work ability mediate the relationship between mental health and safety behavior. The deterioration of construction workers' mental health can increase the risk of unsafe behavior and occupational injuries. Therefore, appropriate measures need to be taken to maintain and improve the mental health of construction workers.

Explanation and prediction of accidents using the path analysis approach in industrial units: The effect of safety performance and climate.

BACKGROUND: The safety climate in an organization depends on people's understanding of the safety policies and procedures, as well as the value, importance, and priority of safety in the workplace. OBJECTIVE: This study aimed to describe and predict accidents using the path analysis model (PAM) in industrial units though the analysis of the effect of safety performance and climate. METHODS: This cross-sectional study was conducted on 294 workers in industrial units in Hamadan, a province in the western part of Iran. The data on safety performance and climate was collected using a questionnaire. The first part of the questionnaire was a short version inventory (with 25 items on safety climate) that was used to assess five variables of management commitment, supportive environment, training, personal safety prioritization, and perceived work pressure. Moreover, the safety performance was measured using 10 items on safety rules and participation. The PAM was used to describe the effects of safety climate and performance on accidents. RESULTS: The results showed that the safety climate had the strongest negative impact on work pressure and safety compliance toward accident, followed by safety participation, and quality of training. Moreover, the negative influence of safety climate on accident was mainly mediated by two variables: work pressure and safety participation toward accident. The work pressure had the strongest indirect and total influence on accidents. However, none of the variables had a direct effect on accidents. Training was the most important direct cause of promoting personal safety priority. The safety compliance was more effective than safety participation in reducing accidents rates. CONCLUSIONS: Therefore, it seems that perceived work pressure has an indirect effect on accidents which is mediated by other variables, mainly personal safety priority and safety performance.

Dynamic safety assessment of natural gas stations using Bayesian network.

Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.