Analysis, Observation, and Estimation of Worker Safety in Indian Mines

Abstract

The mining business is well-known across the globe for its high-risk, high-hazard work environment. The growth of output levels due to technological advancements in ore extraction processes has raised safety concerns in this business.  Thus far this year, science on the subject of safeguarding has shown us that a percentage of events in dangerous situations are preventable. Industries are caused by human error, which may be reduced to a significant amount by controlling it. The current study examines human elements such as hazardous behaviors, as well as the conditions that contribute to harmful behavior, dangerous leadership, and organizational ramifications.  To assess the likelihood of fatalities in manganese mines in India, a customized human factor analysis and classification system (HFACS) was used, and a worrisome bizarre coincidences correlation fuzzy reasoning approach (FRA)-based edifice was invented, based on the interpretation of factors such as age, workforce awareness, the shift of jobs, and so on. The outcomes of the analysis revealed that skill-based mistakes are the most hazardous and demand rapid treatment. The developed FRA-based effective predictive system provides a risk score linked with the observed accident-prone situation, which can be utilized to develop an effective mitigation plan.