| id |
caadria2024_128 |
| authors |
Bauscher, Erik, Dai, Anni, Elshani, Diellza and Wortmann, Thomas |
| year |
2024 |
| title |
Learning and Generating Spatial Concepts of Modernist Architecture via Graph Machine Learning |
| 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 1, pp. 159–168 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.159
|
| summary |
This project showcases a use case away from most other research in the field of generative AI in architecture. We present a workflow to generate new, three-dimensional spatial configurations of buildings by sampling the latent space of a graph auto-encoder. Graph representations of three-dimensional buildings can store more data and hence reduce the loss of information from building to machine learning model compared to image- and voxel-based representations. Graphs do not only represent information about elements (nodes/pixels/etc.) but also the relationships between elements (edges). This is specifically helpful in architecture where we define space as an assemblage of physical elements which are all somehow connected (i.e., wall touches floor). Our method generates valuable, logical and original geometries that represent the architectural style chosen in the training data. These geometries are highly different from any image-based generation process and justify the importance of graph-based 3D geometry generation of architecture via machine learning. The method also introduces a novel conversion process from architecture to graph, an adapted decoder architecture, and a physical prototype to control the generation process, all making generative machine learning more applicable to a real-world scenario of designing a building. |
| keywords |
generative 3D architecture, generative graph machine learning, graph-based architecture, human-computer interaction, graph autoencoder, latentwalk |
| series |
CAADRIA |
| email |
erikbauscher5@gmail.com |
| full text |
 file.pdf (1,518,534 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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