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Mining is often the subject of a study of safety and health at work as one part of industry that carries the greatest risk of injury. The most of these analyses are directed on studying the method of injuries of workers and procedures that can prevent injury. Financial expenditures resulting from injuries of workers in Bogovina coalmine which occurred in 2016 are analyzed in this paper. Injury costs were processed based on the available data: compensations of the insurance companies to the injured workers, gross salaries of injured workers and similar. Within these data, the 'cost of one work day lost' (COWDL) is defined. Comparison of the COWDL, based on qualification structure of the injured worker and the severity of the injuries, was performed. Also, an impact analysis of expenditures related to indemnity related to injuries at work to mine operational costs were carried out.

The risk of an injury at work, occupational disease or damage to the health of employees due to danger is, lately, the common subject of numerous studies. The appropriate identification of the risks is the starting point for development the safety and health of the employees in a company. In order to adequately anticipate the risk based on the statistical data on incidents, e.g. injuries at work, it is necessary to identify the critical points in the enterprise. It is impossible to start in motion a risk-management plan without the serious statistics with the elaborately processed data on an injury. Most companies, conditioned by a strict legal framework in the field of safety and health at work, keep their records on injuries. However, with development in this domain, the need for a uniform database for all injuries that occur on the territory of the Republic of Serbia is created. This paper proposes the form, use and further development of the unified database for injuries at work. The base is modeled after the existing form of the report on injuries at work, and its possibilities are shown through the example of injuries that have occurred in the coal mine Bogovina in 2016.

Modern mining is characterized by highly mechanized production, large - scale projects with huge amounts of mined ore and waste, large areas influenced by mining works, large tailings and waste deposits, deep underground mines, constant development of existing and introduction of new technologies, huge influence ofglobal financial and market trends to economy of mines and obligations related to environmental protection. On the other hand, development of IT sector enabled application of advanced systems for management of spatial data and their integration into common spatial information system - GIS. Main goal of introducing GIS into RTB Bor Group is to improve the processes of mineral resource management and create spatial information system of the copper mines with a geobase, with information from spatial plan documentation, as well as visualization of all graphic and alphanumeric data from geo-database, using GIS portals and Web GIS. Application of GIS system would enable simpler and more comprehensive approach to information in the areas of geological explorations, mining, mineral processing and metallurgy, all included in RTB Bor Group.

Objectives. This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mineral extraction involving people, machinery and limited workplaces. Methods. A universal model for predicting occupational accidents takes into account influential factors such as organizational aspects, personal and collective protective equipment, on-the-job training and leadership factors. The selected networks achieved a prediction accuracy of >90%. Results. The study successfully identifies potential risks and critical worker groups leading to injuries. The sensitivity analysis provides insights for targeted safety measures and improved organizational practices. Conclusion. This data-driven approach makes a valuable contribution to safety in the mining industry. Implementation of the predictive model can reduce injuries and machine damage, and improve worker well-being. [ABSTRACT FROM AUTHOR]