Download PDFOpen PDF in browserAchieving an Optimized Solution for Structural Design of Single-Storey Steel Buildings using Generative Design MethodologyEasyChair Preprint 521610 pages•Date: March 24, 2021AbstractThe significant capabilities of emerging technologies need to be studied to better understand how they can be used to enhance the efficiency of the structural design process. Software already used in the industry are evolving, and some applications are utilizing the power of machine learning and artificial intelligence. Various companies are starting to invest in these technologies and are searching for solutions to reduce component mass, improve structural performance, and minimize manufacturing process time. Currently, the Steel Centre at the University of Alberta is researching these technologies' applications towards typical structural designs. Industry consultation is being conducted to map out current industry practices and logistics. A literature review of various optimization algorithms and past studies on the application of generative design (GD) is being performed. In addition, a single-storey case study is being conducted that involves developing an automation tool in Grasshopper that generates warehouse geometry according to user inputs. S-Frame, an advanced structural analysis software, is being integrated into the design tool. Wallacei, an evolutionary solver, is being used to input design objectives and constraints, resulting in optimizing the key parameters. This automation tool aims to assist in developing a deep understanding of the possibilities of GD towards structural optimization, and specifically towards single-storey structures in Canada, which would lead to the creation of extremely efficient structures. Lastly, the case study preliminary results are highlighted in this paper along with future development and research. Keyphrases: Artificial Intelligence, Automation, Evolutionary Algorithms, Optimization, Structural Engineering, generative design
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