An investigation of the relationship between human an organizational factors in occupational accidents using Bayesian network approach: A case study in mining accidents

Background and aims: Human errors are major causes of the accident that occurring in the industries. However, attributing incidents to human error, regardless of the nature of human error, cannot be useful in preventing accidents. Identifying organizational and supervisory factors that affecting human errors, as well as determining the interactions between these factors, can be used in the management of appropriate control strategies to reduce the accidents. The Human Factors Analysis and Classification System framework (HFACS) is one of the most important and comprehensive qualitative tools to identify human and organizational contributing factors involved in an accident. Until now, several studies have tried to integrate the HFACS with a quantitative analysis tool in order to determine the interactions between human and organizational factors to reduce accidents. There are many types of quantitative tools that researchers usually used for this purpose. Fuzzy analytical hierarchy process, analytical network process, and artificial neural network are the most used analytical quantitative tools in this regard. Powerful graphical probability-based modeling approaches have been less well considered for quantitative analysis of the interaction and relationship between different variables. Bayesian network (BN) is one of the most important quantitative tools in this regard. BN is a probabilistic graphical model that uses for various types of inference such as diagnostic and predictive. Belief updating or sensitivity analysis is one of the exclusive feature of BN that researchers using this feature can examine the sensitivity of one “target variable” to changes in other variables. In the modeling, sensitivity analysis is used to rank the influence of input variables on the predicting of output variables. This study aimed to integrate the HFACS framework and BN to identify different factors that influence unsafe acts and determine the relationships and interactions among identified those factors to provide appropriate intervention strategies for preventing accidents in the future. Methods: In this study, the accidents occurred in one of the largest mines in Iran that occurred during a period of 5 years (2011-2015) were collected, and then accidents with serious consequences such as fatalities, disabling injuries, or considerable property damage were screened. In the next step, all contributing factors in each accident were identified using an accident analysis te