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.
Highlights
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 article introduces some of the techniques currently used by designers to generate automatic building floor plans and spatial configurations in general, by using machine learning and neural networks
In his work on automatic production of floor plans and other architectural spaces, Swahn developed a workflow whereby he scraps a number of webpages for images to generate a training data set to be used with Pix2Pix (Isola et al, 2017), a generative adversarial network built for image-to-image translation
Summary
This article discusses how the space in which we live can be designed by algorithms instead of humans, with designers working out their projects driven by computer logic instead of Euclidean geometry. The argument suggested by this article is that designers in both cases are still in full control of their creative work, especially considering the effort they make in preparing and curating the data set, modeling the problem, setting up the entire workflow, evaluating results at any stage of the process to ensure consistency and validity and, being responsible for the final results obtained Designers, especially those interested in computational design (that is, simplifying, a subset of design where computers are heavily used), consider selforganizing plans and generative design in general as one of the future directions for development. With the analysis included in this work we want to offer a robust counterargument to the first view, explaining the extent to which the idea of computers as a substitute for designers is simplistic, but fundamentally flawed
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