This study argues that human error has an effect on occupational injury risks in mining companies. It shows through an analysis of existing approaches to occupational risk assessment that it is necessary to develop a quantitative assessment method factoring in individual psychophysiological attributes in order to analyze injury risks posed to miners. The article presents the results of a comprehensive analysis of how workers’ psychophysiological attributes influence their susceptibility to occupational injuries in underground mining conditions. By utilizing statistical data processing methods, such as discriminant and regression analysis, the study develops models to forecast personal injury risks among miners. These quantitative models underlie the proposed method for assessing miners’ susceptibility to injuries. The study outlines an algorithm for the practical application of this method and shows how the method was validated using a training sample. It provides recommendations for managing the human factor, incorporating the results of the proposed method, and emphasizes the importance of implementing a series of protective measures to mitigate the risk of occupational injuries in underground mining operations.
Any industrial agglomeration is a territory, not only with a high concentration of industrial facilities, but also with high population density. This, in turn, is an essential prerequisite to problems associated with the large volume of waste consumption. To solve these problems it is necessary not only to improve the technology for processing different kinds of waste, but also to establish a system by their rapid collection and transport.
