The malleability of concrete in combination with the foldability of textile reinforcement introduces a vast geometric design space for carbon-reinforced concrete systems. Particularly suitable for structural applications is the waterbomb origami tessellation which enables an integrated design approach synergizing the material and form to achieve high material utilization and structural performance. This facilitates the development of carbon-reinforced concrete (CRC) waterbomb modules that are both lightweight and material-efficient. Leveraging the inherent symmetry and tessellation compatibility of the waterbomb geometry, these modules can be assembled into floor and wall systems through a combination of topological interlocking and bolted connections, ensuring structural continuity, efficient stress transfer, and demountability for reuse. Designing waterbomb assemblies is challenging due to their geometric complexity, the multitude of interacting components, and the highly nonlinear structural behavior. To overcome these difficulties in an initial design step, the assembly is first decomposed into its constituting units, the waterbomb modules. On this basis, a finite element modeling approach is proposed in which the concrete and the textile-reinforcement are modeled explicitly in a resolved manner. This approach enables us to reliably capture the stress redistribution process and failure modes at the module level. Thus, this approach reduces both modeling complexity and computational cost. This framework provides the foundation for the systematic design and modeling of modular origami-inspired structural members like slabs or walls, where material and interior form are synergyzed to achieve high-performance of demountable carbon-reinforced concrete assemblies. The analysis presented in the paper will illuminate the correspondence between the material, form, and the resulting quasi-ductile failure process in complex modular structural systems. The results of the studies will support the design of a focused test setup for the validation of the predictions.