339 Digital Twins operationalize this transformation by linking physical assets and processes to continuously synchronized virtual representations for monitoring, simulation, and decision support. For TSFs, the value proposition of digital twins is especially strong because governance frameworks such as the GISTM require an integrated knowledge base, risk-based monitoring systems, accountability structures, emergency preparedness, and public disclosure across the full lifecycle. The DT can act as an enabling mechanism to unify technical evidence, modelling outputs, and decision records into a traceable system. Yet, geotechnical DT adoption remains nascent due to modelling complexity, computational demands, interoperability barriers, and the absence of standardized protocols and lifecycle platforms. This research translates these cross-cutting issues into a TSF-specific DT framework with clear functional layers, governance integration points, and an empirically testable validation roadmap. TSFs have unique risk characteristics compared with many industrial assets. They are spatially extensive; their material properties evolve as deposition proceeds; and their safety depends on both engineered controls (e.g., drainage features, raise geometry, construction quality) and operational controls (e.g., deposition plans, water reclaim, pond management). Many TSF failure mechanisms manifest through gradual precursors (changes in seepage regime, deformation trends, beach geometry, or erosion) that may be detectable if monitoring is sufficiently continuous and interpreted within an integrated model. The GISTM codifies an integrated approach to tailings management through auditable requirements emphasizing an interdisciplinary knowledge base, robust monitoring, governance accountability, emergency readiness, and public disclosure across the facility lifecycle. The practical implication is that TSF decision-making must be evidence-based, timely, and traceable, and must integrate technical, environmental, and social considerations. A DT can support these expectations by providing a single, continually updated representation of TSF state and risk context; but only if it is designed as a governance system rather than a dashboard. In current practice, TSF information is frequently fragmented. Instrumentation readings may be stored in monitoring platforms separate from geotechnical interpretation notes. Inspection findings may be documented in unstructured formats without linkage to time-series sensor data. Numerical models may be executed episodically and stored as standalone files, making it difficult 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.
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