An Integrated Method to Classify Ground-Fall Accidents and to Estimate Ground-Fall Trends in U.S. Mines Using Machine Learning Algorithms - SME Annual Meeting 2024

Ground falls in U.S. underground coal mines can lead to significant consequences, including loss of life, injuries, damaged equipment, and production stoppage. Improving the safety of the workplace is of utmost importance for mine workers and the U.S. economy. The Mine Safety and Health Administration (MSHA) accident/injury/illness dataset provides short narratives for reported incidents, including ground-falls. The main objective of this study is to develop a framework that includes: 1) utilizing machine learning algorithms to categorize ground-fall incidents from narratives based on the main cause of the occurrence and 2) demonstrating an example of a user-friendly visualization to display injury/fatality trends from narratives in U.S. coal mines between 1983 and 2021. The developed framework was tested on a subset of the data and achieved an average F1-score of 96% in categorizing the incidents. The outcome will help identify areas requiring additional research and innovative solutions to reduce severe occupational hazards.