Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Structural Plan Schema Generation Through Generative Adversarial Networks
https://orcid.org/http://orcid.org/0000-0002-7497-9261
https://orcid.org/http://orcid.org/0000-0003-1144-6588
https://orcid.org/http://orcid.org/0000-0002-7270-2058
This paper suggests a workflow that generates floor plans with structural elements. Generating structural layouts in a BIM environment with the implementation of a machine learning method allows a future projection for fast and easy exploration of multiple design options. Pix2Pix, a Generative Adversarial Networks (GAN) model, takes the wall layout as input and generates a structural layout by learning from existing knowledge used to generate a decision support system for structural layout generation. The paper also suggest an additional script as a fine-adjustment model to refine the structural layout based on predetermined structural rules. This script increases the accuracy of the structural layouts generated by the GAN algorithm. Based on the test dataset, the research demonstrates a 64% success rate in providing structural schema assistance. Considering the results, this study seems to have the potential to be a supportive application in the early design phase.
Structural Plan Schema Generation Through Generative Adversarial Networks
https://orcid.org/http://orcid.org/0000-0002-7497-9261
https://orcid.org/http://orcid.org/0000-0003-1144-6588
https://orcid.org/http://orcid.org/0000-0002-7270-2058
This paper suggests a workflow that generates floor plans with structural elements. Generating structural layouts in a BIM environment with the implementation of a machine learning method allows a future projection for fast and easy exploration of multiple design options. Pix2Pix, a Generative Adversarial Networks (GAN) model, takes the wall layout as input and generates a structural layout by learning from existing knowledge used to generate a decision support system for structural layout generation. The paper also suggest an additional script as a fine-adjustment model to refine the structural layout based on predetermined structural rules. This script increases the accuracy of the structural layouts generated by the GAN algorithm. Based on the test dataset, the research demonstrates a 64% success rate in providing structural schema assistance. Considering the results, this study seems to have the potential to be a supportive application in the early design phase.
Structural Plan Schema Generation Through Generative Adversarial Networks
https://orcid.org/http://orcid.org/0000-0002-7497-9261
https://orcid.org/http://orcid.org/0000-0003-1144-6588
https://orcid.org/http://orcid.org/0000-0002-7270-2058
This paper suggests a workflow that generates floor plans with structural elements. Generating structural layouts in a BIM environment with the implementation of a machine learning method allows a future projection for fast and easy exploration of multiple design options. Pix2Pix, a Generative Adversarial Networks (GAN) model, takes the wall layout as input and generates a structural layout by learning from existing knowledge used to generate a decision support system for structural layout generation. The paper also suggest an additional script as a fine-adjustment model to refine the structural layout based on predetermined structural rules. This script increases the accuracy of the structural layouts generated by the GAN algorithm. Based on the test dataset, the research demonstrates a 64% success rate in providing structural schema assistance. Considering the results, this study seems to have the potential to be a supportive application in the early design phase.
Structural Plan Schema Generation Through Generative Adversarial Networks
Nexus Netw J
Kösenciğ, Kamile Öztürk (Autor:in) / Okuyucu, Elif Bahar (Autor:in) / Balaban, Özgün (Autor:in)
Nexus Network Journal ; 26 ; 409-427
01.06.2024
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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