Artificial intelligence in architecture: Generating conceptual design via deep learning

Imdat As*, Siddharth Pal, Prithwish Basu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

84 Citations (Scopus)

Abstract

Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, we present the research of an alternative graph-based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, we present a function-driven deep learning approach to generate conceptual design. We trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, we explored the application of generative adversarial networks to generate entirely new and unique designs.

Original languageEnglish
Pages (from-to)306-327
Number of pages22
JournalInternational Journal of Architectural Computing
Volume16
Issue number4
DOIs
Publication statusPublished - 1 Dec 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2018.

Keywords

  • Architectural design
  • artificial intelligence
  • conceptual design
  • deep learning
  • generative design

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