Hybrid digital twins combine physics-based simulations (virtual twin) with data-based analysis (digital twin), providing a simulation tool with predictive capabilities for damage detection, conditional simulations, and trend identification. In this work, we explore the hybrid digital twinning of steel-reinforced concrete structures using mixed-dimensional modelling, implementing both physical knowledge and in-situ data. The proposed hybrid twin is based on finite element methods with a consistent beam-to-solid volume coupling approach [1]. A model for steel-reinforced concrete is created using embedded 1D steel beam finite elements within elastoplastic 3D solid representation of concrete. Elastoplasticity is described with the Drucker Prager yield criterion. This approach allows physics-based modelling to capture the interaction between the reinforcement components and the concrete matrix of the investigated structure with reduced complexity and computational time while maintaining comparable accuracy to a fully resolved approach. The model can be efficiently combined with data-based analysis methods. In summary, the presented hybrid digital twin provides a powerful tool to predict and analyse the behaviour of reinforced concrete structures. It can help safeguard critical infrastructure like bridges, particularly within a structural health monitoring (SHM) ensemble [2]. REFERENCES [1] I. Steinbrecher, M. Mayr, M. J. Grill, J. Kremheller, C. Meier, A. Popp. A mortar-type finite element approach for embedding 1D beams into 3D solid volumes Computational Mechanics, 66:1377-1398, 2020. [2] T. Braml, J. Wimmer, Y. Varabei, S. Maack, S. Küttenbaum, T. Kuhn, M. Reingruber, A. Gordt, J. Hamm. Digitaler Zwilling: Verwaltungsschale BBox als Datenablage über den Lebenszyklus einer Brücke. Bautechnik, 99, 2021.