| 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 |
|
| full text |
 file.pdf (1,518,534 bytes) |
| references |
Content-type: text/plain
|
Alexander, C. (1977)
 A Pattern Language
, Oxford University Press
|
|
|
|
|
Alymani, A., Jabi, W., & Corcoran, P. (2022)
Graph machine learning classification using architectural 3D topological models
, SIMULATION: Transactions of The Society for Modeling and Simulation International, Online, 1-15. https://doi.org/10.1177/00375497221105894
|
|
|
|
|
Beetz, J., Leeuwen, J. van, & Vries, B. de. (2009)
IfcOWL: A case of transforming EXPRESS schemas into ontologies
, AI EDAM, 23(1), 89-101. https://doi.org/10.1017/S0890060409000122
|
|
|
|
|
Carpo, M. (2017)
The Second Digital Turn
, MIT Press. https://mitpress.mit.edu/9780262534024/the-second-digital-turn/
|
|
|
|
|
de las Heras, L.-P., Terrades, O. R., Robles, S., & Sanchez, G. (2015)
CVC-FP and SGT: A new database for structural floor plan analysis and its groundtruthing tool
, International Journal on Document Analysis and Recognition (IJDAR), 18(1), 15-30. https://doi.org/10.1007/s10032-014-0236-5
|
|
|
|
|
del Campo, M. (2022)
Deep House-Datasets, estrangement, and the problem of the new
, Architectural Intelligence, 1(1), 12. https://doi.org/10.1007/s44223-022-00013-w
|
|
|
|
|
Elshani, D., Hernandez, D., Lombardi, A., Siriwardena, L., Schwinn, T., Fisher, A., Staab, S., Menges, A., & Wortmann, T. (2023)
Building Information Validation and Reasoning Using Semantic Web Technologies
, M. Turrin, C. Andriotis, & A. Rafiee (Eds.), Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries (pp. 470-484). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-37189-9_31
|
|
|
|
|
Elshani, D., Lombardi, A., Fisher, A., Hernandez, D., Staab, S., & Wortmann, T. (2022)
Knowledge Graphs for Multidisciplinary Co-Design: Introducing RDF to BHoM
, ESWC - LDAC 2022
|
|
|
|
|
Hamilton, W. L. (2020)
Fast Graph Representation Learning with PyTorch Geometric
, Morgan and Claypool Publishers
|
|
|
|
|
Hillier, B. (1996)
Inductive Representation Learning on Large Graphs
, Cambridge University Press
|
|
|
|
|
Hu, R., Huang, Z., Tang, Y., van Kaick, O., Zhang, H., & Huang, H. (2020)
Graph2Plan: Learning Floorplan Generation from Layout Graphs
, ACM Transactions on Graphics, 39(4). https://doi.org/10.1145/3386569.3392391
|
|
|
|
|
Koh, I. (2020)
CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis (arXiv:1904
, Springer
|
|
|
|
|
McGlinn, K., & Pauwels, P. (Eds.). (2022)
Buildings and Semantics: Data Models and Web Technologies for the Built Environment
, CRC Press. https://doi.org/10.1201/9781003204381
|
|
|
|
|
Mitchell, M. (2020)
Artificial intelligence-A guide for thinking humans
, Penguin Books
|
|
|
|
|
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Chintala, S. (2019)
PyTorch: An Imperative Style
, High-Performance Deep Learning Library (arXiv:191201703). arXiv. https://doi.org/10.48550/arXiv.1912.01703
|
|
|
|
|
Rasmussen, M. H., Lefrançois, M., Schneider, G., & Pauwels, P. (2020)
BOT: The Building Topology Ontology of the W3C Linked Building Data Group
, Semantic Web. https://doi.org/10.3233/SW-200385
|
|
|
|
|
Wu, W., Xiao-Ming, F., Tang, R., Wang, Y., Qi, Y.-H., & Liu, L. (2019)
Neurosymbolic AI -- Why, What, and How
, ACM Transactions on Graphics, 38, 234:1-234:12. https://doi.org/10.1145/3355089.3356556
|
|
|
|
|
Zhong, X., Koh, I., & Fricker, P. D. P. (2023)
Building-GNN: Exploring a co-design framework for generating controllable 3D building prototypes by graph and recurrent neural networks
, Digital Design Reconsidered: Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023), 431-440. https://doi.org/10.52842/conf.ecaade.2023.2.431
|
|
|
|
| last changed |
2024/11/17 22:05 |
|
