The implementation of autonomous haulage trucks in open-pit mines represents a progressive advancement in the mining industry, but it poses potential safety risks that require thorough assessment. This study proposes an integrated model that combines discrete-event simulation (DES) with a risk matrix to assess collisions associated with three different operational scenarios, including non-autonomous, hybrid, and fully autonomous truck operations. To achieve these objectives, a comprehensive dataset was collected and analyzed using statistical models and natural language processing (NLP) techniques. Multiple scenarios were then developed and simulated to compare the risks of collision and evaluate the impact of eliminating human intervention in hauling operations. A risk matrix was designed to assess the collision likelihood and risk severity of collisions in each scenario, emphasizing the impact on both human safety and project operations. The results revealed an inverse relationship between the number of autonomous trucks and the frequency of collisions, underscoring the potential safety advantages of fully autonomous operations. The collision probabilities show an improvement of approximately 91.7% and 90.7% in the third scenario compared to the first and second scenarios, respectively. Furthermore, high-risk areas were identified at intersections with high traffic. These findings offer valuable insights into enhancing safety protocols and integrating advanced monitoring technologies in open-pit mining operations, particularly those utilizing autonomous haulage truck fleets.

Effective maintenance strategies are critical for ensuring operational reliability, minimizing downtime, and optimizing resource utilization in fleet-based industrial operations. Among these, mining truck fleets represent a particularly high-risk, high-cost context where equipment failures can lead to substantial productivity losses and safety hazards. Despite the operational importance, existing literature lacks a structured framework to guide maintenance strategy selection that considers the practical constraints of data availability, diagnostic capability, and operational variability. To address this gap, this study proposes an evaluation framework that supports the selection and implementation of appropriate maintenance strategies. The framework is developed through a critical literature analysis, which is synthesized using a Frame of References approach. Unlike generic taxonomies, this model classifies maintenance strategies based on decision logic, response timing, data dependency, required infrastructure, and alignment with organizational capabilities. Building upon this structure, a two-level decision-support framework is introduced. The first decision tree assists practitioners in determining the appropriate class of maintenance strategy—corrective, planned, proactive, or predictive—based on operational constraints and system criticality. The second tree refines this selection by mapping available technological resources and data maturity to suitable analytical methods (e.g., rule-based, statistical, or AI-driven). While the framework is demonstrated in the context of mining truck operations, its modular design makes it applicable to other asset-intensive sectors, including logistics, construction, and heavy manufacturing. By bridging analytical insights with real-world constraints, this study offers a practical tool for organizations seeking to develop scalable, reliable, and context-sensitive maintenance strategies.

Satisfying safety issues plays a critical role in mining operations. Although the use of emerging technology became a new trend in preventing powered haulage hazards in the mining industry, these technologies themselves posed new hazards to the problem that are necessary to be identified, assessed, and managed together with common hazards. This study investigates the existing gaps in powered haulage safety to establish a comprehensive framework for conducting risk analysis procedures. To achieve this purpose, a literature search methodology is employed to recognize the most relevant resources and extract the essential information. The most critical hazards in powered haulage operations are identified and classified into main groups. Then, root causes and consequences are designated for these hazards, providing substantial elements for risk analysis, which serves as an effective hazard measurement. Afterward, an overview of popular risk analysis techniques applied in the mining industry is provided to establish a holistic risk analysis framework. Finally, available hazard management strategies are discussed as solutions for mitigating and preventing potential hazards. The study results demonstrated the importance of establishing comprehensive safety protocols, continuously upgrading the advanced technologies, regular training, and continuous risk assessment to mitigate and prevent fatal and non-fatal hazards in mining operations.