continuously evolving earth structures that store fine-grained residues and process water over long horizons. The technical complexity of TSFs arises from multi-physics interactions, including seepage and pore-pressure evolution, consolidation and desiccation, cyclic loading and seismic demand, deposition sequencing and beach geometry, and climate-driven variability in water balance. These drivers evolve over time and can interact in non-linear ways. Consequently, effective TSF management requires continuous situational awareness, disciplined engineering interpretation, and governance processes capable of acting on weak signals before they escalate. Industry 4.0 technologies such as IoT, big data, 5G connectivity, cloud/edge computing, and artificial intelligence, are accelerating the transition toward smart mines and data-driven operational governance. In smart mines, multidisciplinary datasets (geology, exploration, geometallurgy, environment, and survey) increasingly function as mining big data with high volume, variety, velocity, veracity, and value. 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
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