to compare model assumptions with current operating conditions. Water balance data may reside in separate spreadsheets. This fragmentation makes it harder to answer fundamental governance questions: what was known at the time of a decision, what evidence supported it, what assumptions were used, and what controls were selected. A DT designed for TSF risk governance addresses these questions by creating explicit linkages and retaining an auditable evidence trail. Geotechnical digital twin research is expanding, but the field remains in an early stage of adoption. Persistent barriers include the difficulty of modelling complex geotechnical processes, the computational resources required for coupled multi-physics simulations, interdisciplinary integration challenges, and insufficient standardization of data protocols and lifecycle information management. These constraints are directly relevant to TSFs, which require integrated representations of geological foundations, embankment materials, deposited tailings behavior, and hydrological boundary conditions. Recent work on tailings dam monitoring proposes DT+ML frameworks that combine real-time data collection, DT simulation, ML-based early detection and prediction, and decision support. Such frameworks align with TSF governance needs, but many remain conceptual or focus on isolated subsystems without full lifecycle integration. In addition, emerging sensing approaches expand feasible DT inputs. Distributed sensing approaches developed for infiltration monitoring in hydraulic structures illustrate high-continuity sensing combined with ML classification and physics-based DT modelling to detect and interpret infiltration activity; conceptually relevant to TSFs where seepage and infiltration are key stability drivers. UAV-based photogrammetry and image analysis provide high-resolution surface condition monitoring over large areas and can support detection of erosion, deformation, and geometric change, subject to robust data quality and governance. Digital twins in mineral processing also demonstrate relevant principles: process DTs can close control loops under variable ore conditions (e.g., adaptive dosing control informed by soft sensing), which matters for TSFs because tailings properties are influenced by upstream variability in ore characteristics and processing decisions (grain size distribution, density, chemistry). Therefore, where feasible, TSF digital twins should connect to mine-to-mill information flows rather than operate as isolated geotechnical replicas. This research proposes a layered DT design framework explicitly tailored to TSF risk governance. The DT is positioned as an operational risk‑governance instrument that supports continuous monitoring, scenario analysis, and evidence‑based decision‑making with traceability. The innovation is not simply the use of digital models, but the integration of (i) multi‑source sensing and data governance, (ii) coupled modelling aligned to monitoring, (iii) analytics that translate evidence streams into actionable risk signals, and (iv) workflows that embed decisions and actions into a persistent evidence trail. The framework is designed to be implementable as an engineered system: it defines functional layers, input/output contracts, update logic, and governance integration points. It is also designed to be empirically testable: it includes a pilot validation roadmap and performance indicators that can be measured in real deployments without making undocumented claims. This directly responds to specialist feedback that high innovation and strategic relevance must be complemented with empirical validation and measurable performance evidence.
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