Abstract
Automatic design tools are being developed to assist designers handle tedious work at scale. However, knowledge gaps still exist in harnessing deep learning models to learn from human experience for more efficient design generation while keeping the data understandable and interoperable. Moreover, human-in-the-loop approach is largely neglected, which are essential for more user-centered design. This research utilizes graph data to parametrically represent housing designs and graph-representative deep generative models for design generation, which provides an interactive design approach for the users at every step. This method would facilitate the human-centered design process by returning feasible and parametric housing design alternatives.
Original language | English |
---|---|
Title of host publication | Proceedings of the 2024 European Conference on Computing in Construction |
Editors | Marijana Srećković, Mohamad Kassem, Ranjith Soman, Athanasios Chassiakos |
Publisher | European Council on Computing in Construction (EC3) |
Pages | 469-477 |
Number of pages | 9 |
ISBN (Electronic) | 978-9-083451-30-5 |
DOIs | |
Publication status | Published - 2024 |
Event | European Conference on Computing in Construction, EC3 2024 - Minoa Palace Resort, Chania, Greece Duration: 14 Jul 2024 → 17 Jul 2024 https://ec-3.org/conference2024/ https://engineering.esteco.com/events/2024-european-conference-on-computing-in-construction |
Publication series
Name | Proceedings of the European Conference on Computing in Construction |
---|---|
Publisher | European Council on Computing in Construction (EC3) |
Volume | 2024 |
ISSN (Electronic) | 2684-1150 |
Conference
Conference | European Conference on Computing in Construction, EC3 2024 |
---|---|
Abbreviated title | 2024 EC3 |
Country/Territory | Greece |
City | Chania |
Period | 14/07/24 → 17/07/24 |
Internet address |
Keywords
- generative housing design
- interactive artificial intelligence
- graph neural networks
- generative adversarial networks