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<p>Surface mining operations face significant collision risks due to equipment interactions, necessitating robust risk management frameworks to enhance safety. This study evaluates the integration of the EMESRT (Earth Moving Equipment Safety Round Table) vehicle interaction framework with GPS-based Collision Warning Systems (GPS-CWS) to mitigate these risks and improve safety outcomes. A data-driven approach was employed, analyzing 13.9 million raw warnings acquired from operational data. After data cleaning, 12.4 million reliable entries were identified, with 4.8 million alerts classified by potential accident severity. Statistical analysis, including the Mann–Kendall test, was used to assess trends and the effectiveness of GPS-CWS implementation. The analysis revealed that 41% of warnings fell into potential accident categories: Near Miss (86.26%), Property Damage Incidents (PDAs) (8.41%), and Lost Time Injuries (LTIs) or Fatalities (5.33%). Post-GPS-CWS implementation, a significant decline in potential LTIs, fatalities, and PDAs was observed, with 62.6% of alerts occurring in high-speed zones. The Mann–Kendall test confirmed a sustained reduction in potential LTI or fatality warnings starting October 2022, demonstrating the system's effectiveness. The integration of GPS-CWS with targeted safety interventions has significantly improved safety outcomes, reducing collision-related incidents in surface mining operations. Continuous monitoring and adaptive safety practices remain critical for sustaining these improvements. The findings suggest potential for broader application in similar operational contexts. Future research should focus on advanced technologies to further enhance risk prediction and safety measures in mining environments.</p>

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From Alerts to Action: Data-Driven Evaluation of Collision Risks in Open-Pit Mining Operations

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Gabriel Alencar Silva Almeida Dantas,
  • Arthur Franca,
  • Giorgio de Tomi

摘要

Surface mining operations face significant collision risks due to equipment interactions, necessitating robust risk management frameworks to enhance safety. This study evaluates the integration of the EMESRT (Earth Moving Equipment Safety Round Table) vehicle interaction framework with GPS-based Collision Warning Systems (GPS-CWS) to mitigate these risks and improve safety outcomes. A data-driven approach was employed, analyzing 13.9 million raw warnings acquired from operational data. After data cleaning, 12.4 million reliable entries were identified, with 4.8 million alerts classified by potential accident severity. Statistical analysis, including the Mann–Kendall test, was used to assess trends and the effectiveness of GPS-CWS implementation. The analysis revealed that 41% of warnings fell into potential accident categories: Near Miss (86.26%), Property Damage Incidents (PDAs) (8.41%), and Lost Time Injuries (LTIs) or Fatalities (5.33%). Post-GPS-CWS implementation, a significant decline in potential LTIs, fatalities, and PDAs was observed, with 62.6% of alerts occurring in high-speed zones. The Mann–Kendall test confirmed a sustained reduction in potential LTI or fatality warnings starting October 2022, demonstrating the system's effectiveness. The integration of GPS-CWS with targeted safety interventions has significantly improved safety outcomes, reducing collision-related incidents in surface mining operations. Continuous monitoring and adaptive safety practices remain critical for sustaining these improvements. The findings suggest potential for broader application in similar operational contexts. Future research should focus on advanced technologies to further enhance risk prediction and safety measures in mining environments.