University of Hertfordshire

By the same authors

Self-Organizing Floor Plans

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Self-Organizing Floor Plans. / Carta, Silvio.

In: Harvard Data Science Review HDSR, Vol. 3, 23.07.2021.

Research output: Contribution to journalArticlepeer-review

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@article{17f432dd85464a3f9dc1f2b416397058,
title = "Self-Organizing Floor Plans",
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.",
author = "Silvio Carta",
note = "{\textcopyright} 2021 by the author(s). The editorial is licensed under a Creative Commons Attribution (CC BY 4.0) International license (https://creativecommons.org/licenses/by/4.0/legalcode)",
year = "2021",
month = jul,
day = "23",
doi = "10.1162/99608f92.e5f9a0c7",
language = "English",
volume = "3",
journal = "Harvard Data Science Review HDSR",
issn = "2644-2353",
publisher = "MIT Press",

}

RIS

TY - JOUR

T1 - Self-Organizing Floor Plans

AU - Carta, Silvio

N1 - © 2021 by the author(s). The editorial is licensed under a Creative Commons Attribution (CC BY 4.0) International license (https://creativecommons.org/licenses/by/4.0/legalcode)

PY - 2021/7/23

Y1 - 2021/7/23

N2 - 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.

AB - 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.

U2 - 10.1162/99608f92.e5f9a0c7

DO - 10.1162/99608f92.e5f9a0c7

M3 - Article

VL - 3

JO - Harvard Data Science Review HDSR

JF - Harvard Data Science Review HDSR

SN - 2644-2353

ER -