An Optimization Approach Process of a 4-Story RC Building Structural Plan through Generative Design

This paper explores the application of parametric design principles using optimization methods to enhance seismic design of a 5-story reinforced concrete building. Through a detailed case study, the paper demonstrates the versatility of Simulated Annealing on building an algorithm that is based on FEA analysis potentially linked with parametric design by at the same time use a stochastic optimization method to tackle the problem. The introduction starts by a quick brief overview of parametric design and visual programming since in the recent years are becoming more and more common to architects and engineers for their user-friendly interface where users can build scripts by connecting blocks that represent functions. At this point it is important to emphasize some parametric design concepts, the previous studies on parametric design in related fields and therefore the corresponding key principles of Grasshopper in structural design. The methodology used is referred to the algorithm that involves the use of Simulated Annealing to iterate the different models and analysis by minimizing the torsional eccentricity on each floor after each iteration. Detailed explanation of how Grasshopper was employed in the design process and the visualization of the parametric design iterations are given. The results demonstrate the effectiveness of this approach, by presenting several design iterations which allows the engineers to have an optimal response against the seismic events just by arranging the dimensions and directions of columns, without changing the structural form. A discussion follows the interpretation of results based on the advantages and challenges of using Grasshopper for structural seismic response and an exploration of potential real-world applications. The main conclusion derives the summary of key findings, the implications of the study for future preliminary design concepts of the represented typology and also the encouragement for further research in the field of parametric design.