Step 3. Spatial risk propagation modeling. To incorporate spatial dependencies beyond isolated events, the operation is represented through a spatial dependency model that accounts for interactions between neighboring units. This modeling approach captures proximity effects, operational continuity, and spatial relationships, which are characteristic of mining environments where risk conditions may extend across adjacent areas with similar operational characteristics. Step 4. Identification of operational hotspots. The output is an aggregated risk map highlighting hotspots associated with accumulated collision risk. This systemic perspective enables identification of critical zones from both operational and safety standpoints. Results are presented through a batch-based historical dashboard, allowing selection of analysis windows and exploration of spatial risk evolution. This supports retrospective evaluation and the definition of preventive measures such as speed adjustments, signage, traffic flow modifications, or targeted operational controls. Conceptually, Stage 3 operates as a mine safety digital shadow, focused on historical hotspot visualization while establishing the foundation for a digital twin with predictive and simulation-based safety capabilities. The proposed algorithms were implemented in Python and deployed within the CAS server as part of an automated pipeline. This setup enabled continuous ingestion of alert data, real-time contextual severity estimation of interactions, and integration with the safety monitoring environment. 4. RESULTS AND DISCUSSION 4.1 Intelligent Mine Segmentation (Stage 1) The proposed segmentation approach was applied to telemetry data from mobile mining equipment to automatically derive functional zones within large-scale open-pit mining operations, including iron mining sites in Brazil and copper mining operations in Peru. The system identified zones corresponding to typical operational contexts, such as haulage routes, loading, dumping, and other activity areas. The segmentation was performed over a spatial discretization of the mine, where each spatial unit was assigned to a functional zone according to its dominant characteristics. A representative subset of the segmentation results was reviewed with safety and operations specialists, confirming the coherence of the identified zones and yielding an average spatial classification accuracy above 90% across the analyzed operations. Misclassifications were mainly observed in transition areas between zones, where functional boundaries are inherently diffuse. This behavior is consistent with data-driven spatial segmentation and highlights the need for a subsequent refinement step. Due to confidentiality constraints, detailed segmentation maps cannot be presented; however, validation with domain specialists confirmed the consistency of the identified zones across different operational contexts. 4.2 Intelligent CAS Alert Prioritization Using a Fuzzy Expert System (Stage 2) The fuzzy expert system for alert prioritization, described in Stage 2, was evaluated over a representative three-month operational period using aggregated data from Collision Avoidance System (CAS) logs from large-scale open-pit iron and copper mining operations in Brazil and
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