An information entropy-based risk (IER) index of mining safety using clustering and statistical methods

The U.S. mining industry has made progress in reducing accidents and injuries, but interpreting safety data remains difficult due to changes in workforce size and productivity. The Mine Safety and Health Administration (MSHA) uses tools like the pattern of violation (POV) and significant & substantial (S&S) calculator to monitor safety, though these have limitations. To address this, we developed an information entropybased risk (IER) index that combines various safety metrics, including citations, penalties and injuries. Using data from 2011 to 2020, the IER index was validated with statistical methods and clustering algorithms to ensure it accurately reflects risk levels. The analysis showed clear differences in risk across mining sites, proving the index’s usefulness. The IER index was then applied to a coal mine to demonstrate its effectiveness. This new tool can help mining companies better assess their safety risks and take action to improve workplace safety.