Date of Award


Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Mining Engineering (MS)

Administrative Home Department

Department of Geological and Mining Engineering and Sciences

Advisor 1

Snehamoy Chatterjee

Committee Member 1

Nathan Manser

Committee Member 2

Aref Majdara


Over the last decade, the mining industry has seen a significant reduction in the number of fatalities in the United States. However, the annual employee hours have also decreased during the same period. Therefore, it is crucial to evaluate the historical mining data and identify the potential risks of a mine through the mine risk index. The risk indicators also describe the severity of accidents and injuries at mine sites. The variables such as citation, order, significant & substantial citations, lost time, and no lost time injuries and penalties are considered for the determination of the risk index. However, using multiple risk indicators to understand the safety standard of a mine could be a complicated process. The mining industry historically uses arithmetic averaging that considers equal weights for each indicator to calculate the mining risk index. This research proposed a new approach to calculate weight values for different risk indicators for calculating the mining risk index. The weights were calculated through the information entropy approach to understand the degree of dispersion. The influence of these variables on the risk analysis was evaluated to comprehend the risk index. The risk index is validated through hierarchical clustering algorithms such as BIRCH clustering. MANOVA and post-hoc tests were performed to validate the clustering performance. The statistical differences amongst the means of risk index from different clusters were tested through box plots and ANOVA test. The investigation was conducted during the 2011-2020 period utilizing the open-source mine safety and health administration (MSHA) databases. Results from the statistical analysis show that the risk indicators significantly differ from one cluster to the others for all periods. The results also show that the top 25 high-risk mines constitute around 64.8% of coal mines for all periods. The risk index results from the clustering and statistical analysis can help the mining industry to determine the risk index, thereby focusing on ensuring workplace safety.