Powder bed 3D printing of concrete offers unprecedented design freedom for the fabrication of complex structural components. It allows the construction of overhangs and eliminates the need for formwork, making it attractive for digital construction and the realization of non-standard geometries. To fully exploit this design freedom, topology optimization becomes an essential tool. It enables a structurally efficient material distribution, helping to minimize material usage while maintaining mechanical performance. However, applying topology optimization to printed concrete components introduces two major challenges: first, the need to account for numerous constraints; second, the occurrence of geometric deviations between the as-design model and the as-printed part is a consistent issue [1], which may undermine structural performance. To address the first challenge, a topology optimization framework has been developed that incorporates finite element analysis and supports multiple relevant constraints. These include anisotropic stress constraints in tension and compression, geometric constraints to protect specific functional surfaces from modification, and manufacturing constraints that enforce minimum member sizes. The framework was tested for different objective functions such as compliance or mass minimization, enabling an exploration of trade-offs between stiffness, weight, and material efficiency. Additionally, sample prints were realized. To tackle the issue of geometric deviations, 3D scanning techniques are used to capture the as-built geometry of printed specimens. Precise registration and alignment of scanned point clouds with the reference CAD models is challenging [2], but allows for accurate quantification of geometric discrepancies. By evaluating multiple deviation metrics and comparing different print settings, insights into the underlying sources of variation are obtained. These findings are further integrated into the optimization process through a feedback loop that anticipates and compensates for systematic shape deviations, thereby aligning computational design more closely with fabrication realities. In contrast to manual post-processing [3], this iterative approach enhances the precision and reliability of powder bed 3D-printed concrete, advancing the structural quality and predictability of digitally manufactured components.