Abstract:
Structural optimization of solids aims to find the optimal designs of structures by minimizing a constrained objective function such as the material compliance within a given problem domain. This constrained optimization problem is subjected to a set of displacement and load boundary conditions which in turn will be minimized with respect to a structural parameter. Although various structural optimization techniques have a
sound mathematical basis, the practical constructability of optimal designs poses a great challenge in the manufacturing stage. The recent development in additive manufacturing partially side-steps this problem predominantly in the domain of Mechanical Engineering. However, in Civil Engineering structures, there is a great possibility of utilizing these optimization tools, especially in precast constructions. Currently, there is only
a limited number of unified frameworks which output ready to manufacture parametric Computer-Aided Design (CAD) of the optimal designs. From a generative design perspective, it is essential to have a single platform that outputs a structurally optimized CAD model because CAD models are an integral part of most industrial product
development and manufacturing stages. This study focuses on developing a novel unified workflow handling both topology and size optimization in a single parametric platform (Rhino-Grasshopper) which outputs a readyto-manufacture CAD model with the assessment of their structural integrity. In the proposed method, the first topology optimized pixel model is generated for any two-dimensional problem and converted into a one-pixelwide chain model using skeletonization. From the obtained skeleton, a spatial frame structure is extracted, and its members are sized optimally. Finally, the CAD model is generated using Constructive Solid Geometry (CSG) trees and its structural performance is assessed. In addition, industry-standard structural sections can be
assigned to the CAD model to be analyzed and designed in accordance with standard codes of practice.
