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        Self-Organizing Floor Plans

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        e5f9a0c7_cb23_485f_bcd3_457e690f4281.pdf (PDF, 3Mb)
        Author
        Carta, Silvio
        Attention
        2299/24977
        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.
        Publication date
        2021-07-23
        Published in
        Harvard Data Science Review HDSR
        Published version
        https://doi.org/10.1162/99608f92.e5f9a0c7
        Other links
        http://hdl.handle.net/2299/24977
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