Integration. The study focuses on advanced analytics and expert-driven reasoning to support collision-risk management in large-scale open-pit mining operations. The objective of the paper is to develop and validate a practical, context-aware methodology that supports the contextual interpretation and prioritization of CAS interaction alerts. The proposed framework combines automatic spatial segmentation, fuzzy expert systems, and graph-based aggregation to translate CAS interaction alerts into structured safety indicators using only standard CAS telemetry, without requiring additional sensors or infrastructure. The methodology was implemented within the CAS server environment and evaluated using operational data from large-scale open-pit iron and copper mining operations in Brazil and Peru. The analysis covers the full pipeline from interaction-level risk estimation to spatial hotspot identification across different operational zones. Without modifying the CAS alerting logic, the proposed system introduces an additional analytical layer that supports the interpretation of interaction risk according to site-specific operational conditions and safety practices, enabling each mine to prioritize alerts in alignment with its own operational risk perception. 3. METHODOLOGY The proposed framework is designed to interpret and contextualize alerts generated by a Collision Avoidance System through the estimation of collision risk associated with each interaction. It adopts a hybrid data-driven and expert-driven paradigm based exclusively on CAS data, including mobile equipment positioning telemetry and collision alert logs. From this single source, complementary artificial intelligence (AI) techniques are integrated: unsupervised clustering to construct spatial context, fuzzy expert systems for collision risk estimation, and graph-based models for severity aggregation and risk propagation. The resulting framework provides a coherent analytical methodology for the interpretation of CAS interaction alerts in complex mining environments, aligned with site-specific operational interpretations. As illustrated in Figure 1, the framework is structured into three sequential and interconnected stages. Stage 1 constructs the spatial context of the mine through intelligent segmentation. Stage 2 performs contextual interpretation and prioritization of CAS interaction alerts using a fuzzy multi-expert system. Stage 3 consolidates historical results to analyze spatial and temporal risk patterns, enabling the identification of operational hotspots from a preventive safety perspective.
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