| id |
acadia23_v3_179 |
| authors |
Jabi, Wassim; Leon, David Andres; Alymani, Abdulrahman; Behzad, Selda Pourali; Salamoun, Michelle |
| year |
2023 |
| title |
Exploring Building Topology Through Graph Machine Learning |
| source |
ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32. |
| summary |
Graph theory offers a powerful method for analyzing complex networks and relationships. When combined with machine learning, graph theory can provide valuable insights into the data generated by 3D models. This workshop integrated advanced spatial modeling and analysis with artificial intelligence, highlighting the importance of technological advancements in shaping the future of architecture and design. It introduced participants to novel workflows that link parametric 3D modeling with concepts of topology, graph theory, and graph machine learning. We used Topologicpy, an advanced spatial modeling and analysis software library designed for Architecture, Engineering, and Construction, paired with DGL, a powerful machine learning library that provides tools for implementing and optimizing graph neural networks (Figure 1). In essence, this process blends cutting-edge technologies and architectural principles that will shape the future of design. Participants learned how to use these workflows to convert 3D models into graphs, analyze their properties, and perform classification and regression tasks. Participants also explored how to create synthetic datasets based on generative and parametric workflows, and build and optimize graph neural networks for specific tasks. |
| series |
ACADIA |
| type |
workshop |
| full text |
 file.pdf (2,733,827 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/04/17 14:00 |
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