From an ESG standpoint, the DT supports auditable reporting by making key evidence streams measurable and traceable. Examples include documenting monitoring coverage, capturing response times to alerts, tracking completion of corrective actions, and maintaining a consistent record of risk assessments and their underlying assumptions. Importantly, the DT should preserve uncertainty and model limitations as part of the governance record to avoid creating misleading certainty. This governance‑oriented design addresses specialist feedback on impact and quality by positioning the framework as a system that can be evaluated through governance‑relevant metrics and integrated into operational decision workflows. To address the limitation that many DT proposals remain conceptual, this research defines a staged validation roadmap suitable for a TSF pilot. Phase A: Data Readiness and Monitoring Integrity. Establish reliable ingestion and governance controls; evaluate sensor coverage, latency, completeness, instrument health, and QA/QC. Phase B: Model Integration and Update Behavior. Integrate models with the data pipeline; calibrate using historical and incremental monitoring data; document uncertainty; establish drift detection and update periodicity. Phase C: Analytics Evaluation and Decision Usefulness. Test anomaly detection and alert logic; evaluate detection lead time, false alarm rate, missed detection rate, and actionability. Governance metrics should also be tracked, including decision traceability and evidence package retrievability for internal and independent reviews. This roadmap enables progressive demonstration of value while avoiding undocumented claims. A core requirement for strengthening operational impact is the use of measurable indicators that connect digital capabilities to tailings risk outcomes. The pilot roadmap operationalizes performance in four metric families: 1. Monitoring integrity metrics: data completeness, latency, instrument health, spatial coverage relative to critical TSF zones. 2. Model performance metrics: calibration error, drift indicators, update periodicity, sensitivity to boundary condition changes, reported with uncertainty bounds. 3. Analytics performance metrics: detection lead time, false alarm rate, missed detection rate, stability of alerts across updates. 4. Governance and assurance metrics: decision traceability, action closure rate, evidence package retrievability. By defining these metric families, DT can be evaluated as a governance system rather than an IT deliverable. Data heterogeneity is a principal barrier to geotechnical DT adoption. TSF digital twins must integrate time-series sensor data, spatial models, inspection narratives, laboratory results, and operational controls. A practical design principle is a common lifecycle data model with stable identifiers for physical entities, instruments, events, and risk objects.
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