Andreou, A., Kontovourkis, O., Solomou, S., Savvides, A., 2023.
Rethinking architectural design process using integrated parametric design and machine learning principles
Output Type: | Conference paper |
Presented at: | ECAADe 2023: Digital Design Reconsidered |
Publication: | Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) |
Venue: | Graz University of Technology, Austria |
Publisher: | ECAADe |
Dates: | 20/9/2023 - 23/9/2023 |
ISBN/ISSN: | 9789491207358 |
URL: | dx.doi.org/10.52842/conf.ecaade.2023.2.461 |
Volume/Issue: | 2 |
Pagination: | pp. 461-470 |
Artificial Intelligence (AI) has the potential to process vast amounts of subjective and conflicting information in architecture. However, it has mostly been used as a tool for managing information rather than as a means of enhancing the creative design process. This work proposes an innovative way to enhance the architectural design process by incorporating Machine Learning (ML), a type of Artificial Intelligence (AI), into a parametric architectural design process. ML would act as a mediator between the architects' inputs and the end-users' needs. The objective of this work is to explore how Machine Learning (ML) can be utilized to visualize creative designs by transforming information from one form to another - for instance, from text to image or image to 3D architectural shapes. Additionally, the aim is to develop a process that can generate comprehensive conceptual shapes through a request in the form of an image and/or text. The suggested method essentially involves the following steps: Model creation, Revisualization, Performance evaluation. By utilizing this process, end-users can participate in the design process without negatively affecting the quality of the final product. However, the focus of this approach is not to create a final, fully-realized product, but rather to utilize abstraction and processing to generate a more understandable outcome. In the future, the algorithm will be improved and customized to produce more relevant and specific results, depending on the preferences of end-users and the input of architects.