Carta, Silvio ORCID: https://orcid.org/0000-0002-7586-3121 (2021) Self-organizing floor plans. Harvard Data Science Review (HDSR), 3 (3.3). pp. 1-35. ISSN 2688-8513 (Print), 2644-2353 (Online) (doi:10.1162/99608f92.e5f9a0c7)

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Abstract

This article introduces and comments on some of the techniques currently used by designers to generate automatic building floor plans and spatial configurations in general, with emphasis on machine learning and neural networks models. This is a relatively new tendency in computational design that reflects a growing interest in advanced generative and optimization models by architects and building engineers. The first part of this work contextualizes self-organizing floor plans in architecture and computational design, highlighting their importance and potential for designers as well as software developers. The central part discusses some of the most common techniques with concrete examples, including Neuro Evolution of Augmenting Topologies (NEAT) and Generative Adversarial Networks (GAN). The final section of the article provides some general comments considering pitfalls and possible future developments, as well as speculating on the future of this trend.

Item Type:	Article
Uncontrolled Keywords:	self-organizing floor plans; computational design; architecture; machine learning; generative adversarial networks; artificial neural networks
Subjects:	N Fine Arts > NA Architecture N Fine Arts > NC Drawing Design Illustration Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Liberal Arts & Sciences Faculty of Liberal Arts & Sciences > School of Design (DES)
Last Modified:	18 Oct 2023 07:52
URI:	http://gala.gre.ac.uk/id/eprint/44202

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