| id |
ecaade2020_018 |
| authors |
Sato, Gen, Ishizawa, Tsukasa, Iseda, Hajime and Kitahara, Hideo |
| year |
2020 |
| title |
Automatic Generation of the Schematic Mechanical System Drawing by Generative Adversarial Network |
| source |
Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 403-410 |
| doi |
https://doi.org/10.52842/conf.ecaade.2020.1.403
|
| summary |
In the front-loaded project workflow, mechanical, electrical, and plumbing (MEP) design requires precision from the beginning of the design phase. Leveraging insights from as-built drawings during the early design stage can be beneficial to design enhancement. This study proposes a GAN (Generative Adversarial Networks)-based system which populates the fire extinguishing (FE) system onto the architectural drawing image as its input. An algorithm called Pix2Pix with the improved loss function enabled such generation. The algorithm was trained by the dataset, which includes pairs of as-built building plans with and without FE equipment. A novel index termed Piping Coverage Rate was jointly proposed to evaluate the obtained results. The system produces the output within 45 seconds, which is drastically faster than the conventional manual workflow. The system realizes the prompt engineering study learned from past as-built information, which contributes to further the data-driven decision making. |
| keywords |
Generative Adversarial Network; MEP; as-built drawing; automated design; data-driven design |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (3,398,509 bytes) |
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Content-type: text/plain
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| last changed |
2022/06/07 07:57 |
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