The seismic assessment of concrete gravity dams often requires repeated finite element (FE) analysis to investigate the influence of geometric modifications, material properties, loading conditions, and seismic scenarios. Conventional modelling workflows typically involve repetitive manual updates to geometric models, FE meshes, and analysis settings, reducing the efficiency of iterative studies. This paper presents a conceptual computational workflow for the development of analysis-ready Digital Twin models of concrete gravity dams through the integration of multiple digital modelling tools such as Rhino 3D, Grasshopper 3D, and TEDDY, and connecting the framework to FE analysis environment, such as the SOFiSTiK. Custom C# components were developed to stitch these tools together to provide and integrated environment based on the conceptual framework presented here. The proposed methodology combines parametric geometric modelling with automated FE model generation, enabling parameterized dam geometries to be transformed into analysis-ready structural models within a unified computational framework. The framework could then retrieve representative analysis results of the FE model back into the digital twin environment for visualization and post-processing. For demonstration purposes, the generated model of a dam was developed and analyzed through Modal and Response Spectrum Analysis, providing representative structural responses. The proposed workflow establishes a continuous information flow between parametric geometry generation, structural analysis, and result interpretation while maintaining compatibility with the existing SOFiSTiK environment. Rather than presenting a fully operational Digital Twin, the study establishes an extensible computational foundation for future development of dam-specific analysis components. Ongoing work focuses on expanding the workflow to support advanced seismic assessment of concrete gravity dams, including repeated parametric analyses, automated post-processing, fragility assessment, and quantitative risk-informed decision-making.