Automated Haulage Trucks: Impact on Workplace Safety and Efficiency in Surface Mining Systems
Abstract
The mining industry continues to face significant safety challenges, particularly with powered haulage equipment (PHE). PHE incidents account for a substantial percentage of mining-related fatalities, often resulting from vehicle collisions, equipment rollovers, operator errors, and blind-spot hazards. Despite the industry’s efforts to improve safety protocols, fatal accidents involving haulage trucks remain persistent. The mining industry has increasingly adopted automation to enhance operational efficiency and improve safety, particularly in surface mines where haulage truck accidents remain a critical concern. Automation has significantly reduced human exposure to hazardous tasks by removing operators from dangerous environments, thereby mitigating risks associated with human error and fatigue-related accidents. However, achieving zero fatalities in mining operations remains an ongoing challenge, necessitating a deeper evaluation of current technologies and safety interventions. This paper explores the review and integration of advanced safety technologies, such as real-time monitoring, machine learning-based predictive models, and enhanced automation frameworks to improve hazard detection and response time. A structured methodology is employed to review automated systems, accident data analysis, and an assessment of automation technologies in active mining operations. Specific findings highlight the impact of automation on reducing accident rates, the effectiveness of various intervention strategies, and challenges in full-scale implementation. The novelty of this paper lies in its roadmap to achieving zero fatalities through a review of structured integration of automation and predictive safety interventions. It outlines the broader benefits of Automated Haulage Systems, including productivity gains and operational cost reductions, contributing to the ongoing discourse on mining safety by providing a data-driven framework for the successful implementation of automated haulage trucks, ensuring a safer and more efficient mining environment.