Figure 34. AI‑Derived Movement Behaviour Clusters (Calderon, Barnes, 2025) This allowed engineers to clearly differentiate between normal background movement and early indicators of concern, providing a more confident basis for early-warning thresholds and enabling geotechnical teams to focus attention where it matters most, embedding these outcomes into the existent monitoring system as a complementary tool rather than modify the current systems. Figure 35. Examples of outcomes in terms of Displacement Rates by Geological Units using AI-tools (Calderon, Barnes, 2025) Taken together, the technical results of this work make clear that early-stage slope deformation is both detectable and interpretable when advanced analytics and geotechnical expertise are brought together in a structured way. The ability of SMA and AI driven Material No Failure Post Failure Pre + During Failure Pre Failure Post Failure Failure No Failure Velocity Distributions
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