A Bayesian Network Analysis for Occupational Accidents of Mining Sector

Abstract

The mining sector is one of the most important raw material sources and wealth sources for countries. On the other hand, many work accidents occur during its activities due to the adverse working conditions. Research is being conducted to reduce the security risk factor, which is one of the most critical obstacles to the social sustainability of the mining industry. In this study, under-recorded mine accidents and injuries are handled, rather than the accidents of the roof falling and the explosions which are frequently considered in the literature. In this scope of the study, accidents and incidents that occur during the specified processes (support, face, loading and transportation activities) of an underground chrome mine are investigated. Expert judgments have been used since no past accidents are allowing statistical inferences. BN has been used to find out the issues about the safety risk by addressing the causal relationships between the events. OHS education, OHS inspection, employee attention and, rock and ground structure of the working area have been deduced as the root causes of the accidents which occur mostly during the labor-intensive processes. By using the updating ability of the BN, comprehensive sensitivity analysis has been performed with the new information related to root causes. According to different scenarios associated with the various states of the root causes, the results and the future suggestions are presented.