Peru. The dataset included several thousand CAS alerts corresponding to equipment interactions across different operational zones, including Haul Truck (HT)–Light Vehicle (LV) interactions. According to the system design, collision risk severity is estimated on a continuous scale from 0 to 100%. For reporting purposes, the severity output was grouped into three aggregated levels: Low, Medium, and High. Alerts classified within the High category were considered priority events requiring operational attention. The results are summarized in the histogram shown in Figure 4. Across the evaluated dataset, approximately 2–3% of interactions were categorized within the High severity level according to the contextual risk interpretation produced by the expert system, while the remaining interactions were distributed across the Medium and Low levels. Figure 4 – Severity distribution of CAS alerts after fuzzy expert system prioritization. This distribution illustrates how the expert system translates CAS interaction alerts into contextual risk indicators, enabling operations to highlight interactions that may require closer review according to site-specific risk interpretation. This behavior aligns with the operational role of CAS, which continuously monitors equipment proximity under dynamic mining conditions. The prioritized alerts were integrated into a safety monitoring dashboard, enabling near real-time visualization and review by dispatch and safety personnel and supporting more structured safety analysis and operational decision-making. To evaluate the consistency of the prioritization approach, a CAS safety specialist reviewed the operational context of each interaction and performed a binary classification indicating whether the event represented a high-priority safety situation (positive class) or a routine interaction requiring no further attention (negative class). The specialist’s assessment was used as the reference, while the output of the fuzzy expert system represented the evaluated result. Based on this comparison, a normalized confusion matrix was constructed, as shown in Table 1. In this matrix, high-severity interactions identified by the expert system were considered positive, while the remaining interactions were considered negative. Table 1 – Normalized confusion matrix (expert system vs. CAS specialist) Positive Expert system Negative Expert system Positive CAS specialist 97.2% 2.8% Negative CAS specialist 9.5% 90.5%
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