| id |
caadria2024_509 |
| authors |
Kim, Frederick Chando, Yang, Hong-bin, Johanes, Mikhael and Huang, Jeffrey |
| year |
2024 |
| title |
Deep Winning Form: Machine investigation of architectural quality |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.273
|
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 273–282 |
| summary |
This paper showcases the development of Arch-Form, a platform that enables the investigation of underutilization of knowledge from architectural competitions, specifically within the Swiss architecture system. The aim is to leverage machine learning to analyse and understand architectural forms from school competition data spanning the past 20 years. The original contribution of this study lies in transforming competition results into a machine-learnable format, using 622 massing models to create 'architectural' point clouds. This methodology involves using 3D Adversarial Autoencoders (3dAAE) to encode and reconstruct these point clouds, experimenting with various structured formats such as uniform, horizontal and vertical g-codes. The main conclusion drawn is that machine learning can significantly aid in understanding and predicting architectural form preferences, documenting trends, and transformations in design. This approach enhances the computability of architectural forms. It offers a new perspective on how machines interpret and generate architectural data, contributing to a more comprehensive understanding of architectural evolution and societal preferences in design. |
| keywords |
Architectural Form, Architecture Competition, Machine Learning, Digital Representation, Point Clouds |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,516,107 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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