Design Space Exploration of Initial Structural Design Alternatives via Artificial Neural Networks

Increasing implementation of digital tools within a design process generates exponentially growing data in each phase, and inevitably, decision making within a design space with increasing complexity will be a great challenge for the designers in the future. Hence, this research aimed to seek potentials of captured data within a design space and solution space of a truss design problem for proposing an initial novel approach to augment capabilities of digital tools by artificial intelligence where designers are allowed to make a wise guess within the initial design space via performance feedbacks from the objective space. Initial structural design and modelling phase of a truss section was selected as a material of this study since decisions within this stage affect the whole process and performance of the end product. As a method, a generic framework was proposed that can help designers to understand the trade-offs between initial structural design alternatives to make informed decisions and optimizations during the initial stage. Finally, the proposed framework was presented in a case study, and future potentials of the research were discussed.
Citation Formats
O. Yetkin and A. Sorguç, “Design Space Exploration of Initial Structural Design Alternatives via Artificial Neural Networks,” presented at the 7th Conference on Education-and-Research-in-Computer-Aided-Architectural-Design-in-Europe (eCAADe) / 23rd Conference of the Iberoamerican-Society-Digital-Graphics (SIGraDi) ( SEP 11-13, 2019), Univ Porto, Fac Architecture, Porto, PORTUGAL, 2019, Accessed: 00, 2021. [Online]. Available: