Previous studies have shown that GAN has made some progress in the generation of campus layout plan, but the result is single output for single input condition. This paper hopes to make some attempts and explorations on the diversity generation of campus planning layout design by machine learning. Based on Pix2Pix model, this paper proposes a method to divide image channels so that both the campus function bubble diagram and the site boundary can both become the input conditions. There is a strong correspondence between the campus functional bubble diagram and the campus layout. The main idea of this study is to control the generated results by changing the input of the campus functional bubble diagram, so that we can have a diversity layout of campus according to the same site conditions. In the experiment, we train thirty samples of campus planning layout design, and finally evaluate the generated results in a qualitative and quantitative way, which proves that the generated results are relatively ideal. This research enables designers to participate in the process of machine learning generative design to control the generation results.


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    Titel :

    Exploration on Diversity Generation of Campus Layout Based on GAN


    Weitere Titelangaben:

    Computational Design and Robotic Fabrication


    Beteiligte:
    Yuan, Philip F. (Herausgeber:in) / Chai, Hua (Herausgeber:in) / Yan, Chao (Herausgeber:in) / Li, Keke (Herausgeber:in) / Sun, Tongyue (Herausgeber:in) / Liu, Yubo (Autor:in) / Zhang, Zhilan (Autor:in) / Deng, Qiaoming (Autor:in)

    Kongress:

    The International Conference on Computational Design and Robotic Fabrication ; 2022 June 27, 2022 - July 05, 2022



    Erscheinungsdatum :

    04.04.2023


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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