Author ORCID Identifier

https://orcid.org/0000-0003-2247-0102

Semester

Spring

Date of Graduation

2025

Document Type

Thesis (Campus Access)

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mining Engineering

Committee Chair

Deniz Tuncay

Committee Co-Chair

Vladislav Kecojevic

Committee Member

Vladislav Kecojevic

Committee Member

Deniz Talan

Abstract

The Mine Safety and Health Administration (MSHA) plays a pivotal role in enforcing safety regulations, conducting mine inspections, and providing safety training. In collaboration with industry, labor, and other agencies, MSHA aims to understand accident causes and reduce their frequency. MSHA maintains a publicly accessible accident dataset, encompassing 263,455 accidents since 2000, with detailed information on mine and operator identification, accident characteristics, victim profiles, injury types, and more. This dataset is crucial for analyzing the relationships between various factors and accident occurrences. Furthermore, MSHA compiles extensive reports on accidents, injuries, illnesses, and coal production, serving as a foundation for safety analysis and regulatory actions. This study’s primary objective is to examine the relationships between accident parameters, identify trends in incident severity and frequency, and propose data-driven improvements to MSHA’s reporting framework. By utilizing advanced data processing techniques and exploratory data analysis, the research identifies key variables—such as mine type, worker experience, job function, and age group—that are strongly linked to increased accident risks. The findings highlight that high-risk activities, such as roof bolting and equipment handling, lead to significant injury rates, with younger workers (16-19 years) incurring higher medical costs, while older workers (45-64 years) experience more severe lost-time costs. Additionally, the study reveals that fatalities, especially in high-risk states like West Virginia, remain a significant concern, with fatality rates normalized by employment numbers to provide a more accurate risk assessment. The research also shows that coal mines experience more lost days due to hearing loss and strains, while metal/non-metal mines report more days lost from multiple injuries. This research underscores the value of data-driven approaches in identifying high-risk activities and developing targeted safety interventions. The findings emphasize the necessity for tailored safety protocols, enhanced worker training, ergonomic solutions, and technological innovations to mitigate accident risks. The insights provided aim to refine safety frameworks in the mining industry, reduce accident rates, and bolster operational resilience by advocating for continuous improvements in accident data collection and analysis, ultimately fostering safer working environments.

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