Classification of artificial intelligence techniques for early architectural design stages.

Vissers-Similon, Elien; Dounas, Theodoros; De Walsche, Johan

Abstract

This paper provides a strategic classification of artificial intelligence (AI) techniques based on a systematic literature review and four levels of potential: the levels of input, output, collaboration and creativity. The classification demonstrates the potential and challenges of the AI techniques when used in early stages of architectural design. We aspire to help architects, researchers and developers to choose which AI techniques might be worth pursuing for specific tasks, optimising the use of today’s computational power in architectural design workflows. The results of the classification strongly indicate that Evolutionary Computing, Transformer Models and Graph Machine Learning hold the greatest potential for impact in early architectural design, and thus merit the attention to achieve that potential. Moreover, the classification assists with building multi-technique applications and helps to identify the most suitable AI technique for different circumstances such as the architect’s programming skills, the availability of training data or the nature of the design problem.

Citation

VISSERS-SIMILON, E., DOUNAS, T. and DE WALSCHE, J. 2024. Classification of artificial intelligence techniques for early architectural design stages. International journal of architectural computing [online], Online First. Available from: https://doi.org/10.1177/14780771241260857

• Journal Article Type: Article
• Acceptance Date: Aug 24, 2024
• Online Publication Date: Jul 25, 2024
• Deposit Date: Sep 19, 2024
• Publicly Available Date: Sep 19, 2024
• Journal: International journal of architectural computing
• Print ISSN: 1478-0771
• Electronic ISSN: 2048-3988
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1177/14780771241260857
• Keywords: Artificial intelligence; Early architectural design; Sketch design; Design support; Classification
• Public URL: https://rgu-repository.worktribe.com/output/2475306