Layer 1: Sensing and Data Acquisition. The sensing layer consolidates geotechnical instrumentation (pore pressure, deformation, settlement), hydrogeological and water-management measurements, survey and surface deformation data, inspection logs, and where applicable remote sensing such as UAV photogrammetry. The design requirement is to ensure data completeness, timestamp integrity, calibration metadata, and context tagging. Data acquisition should also include operational state variables that influence TSF behavior, such as deposition schedules, pond elevation, reclaim rates, and relevant construction activities. Layer 2: Integration and Data Governance. This layer provides the “single source of truth” across heterogeneous datasets by enforcing standardized schemas, metadata, QA/QC checks, and interoperability services. It supports lifecycle information management, enabling longitudinal analysis and ensuring that evidence used for decisions is retrievable and auditable. Integration should support both time-series and spatial data, linking sensor readings to GIS/BIM representations where appropriate, and linking inspection findings to specific TSF zones. Layer 3: Modelling and Simulation. The modelling layer maintains coupled geotechnical and hydrogeological models that represent TSF behavior and support scenario analysis, including seepage and consolidation representations, stability analyses, and deformation simulations. The design principle is to align models with monitoring: boundary conditions and parameter sets should be updateable using monitored data, while preserving model versioning and assumptions for traceability. Layer 4: Intelligence and Analytics. This layer applies statistical and machine-learning methods for anomaly detection, forecasting, and probabilistic risk inference. Analytics are designed to support early problem recognition and reduce cognitive load; they should not replace engineering judgment. Outputs should include uncertainty bounds and explainability artifacts where feasible. Layer 5: Visualization and Decision Support. This layer provides role-based dashboards, spatial maps, and scenario views that communicate current state, trends, and risk signals. Crucially, it embeds governance workflows: escalation protocols, action assignment, corrective action tracking, and documentation of decisions with linked evidence. The DT thus preserves an evidence trail supporting assurance and disclosure requirements. A TSF digital twin must align with governance structures and assurance processes, including roles and review mechanisms required by modern tailings standards. A DT supports these objectives by providing consistent and retrievable evidence of monitoring outcomes, model outputs, and corrective actions.
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