Structural design can be inefficient due to its manual and iterative nature, requiring costly and time-consuming revisions by experienced engineers; thus, there is a growing demand for automated and intelligent design approaches. In light of these factors, we propose a simplified model of shear wall buildings based on connected 3D beam columns to quickly derive the fundamental period, base shear, and moment. Our approach formulation considers an initial topologic configuration of the shear wall rectangular members, featuring intricate element connectivity and variable layout for the basement, first, and typical floors, offering a flexible approach to handle inputs from various sources, like grid-based layouts or vectorized image floor plans. Concerning the model, the column-based design yields an adequate estimation of the structure's fundamental translational period, with an R2 of around 0.7 and a roof displacement with an R2 of 0.85. Our findings, coupled with an average computing time of 1.6 s, enable the model integration into machine learning pipelines or the swift optimization of solutions by assembling fitness functions that consider parameters like building weight, roof displacement, or base shear and moment, ultimately reducing search space and saving costs in the design process.