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id ijac201816406
authors As, Imdat; Siddharth Pal and Prithwish Basu
year 2018
title Artificial intelligence in architecture: Generating conceptual design via deep learning
source International Journal of Architectural Computing vol. 16 - no. 4, 306-327
summary 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.
keywords Architectural design, conceptual design, deep learning, artificial intelligence, generative design
series journal
email as@hartford.edu
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