| id |
caadria2022_411 |
| authors |
Yang, Xuyou, Bao, Ding Wen, Yan, Xin and Zhao, Yucheng |
| year |
2022 |
| title |
OptiGAN: Topological Optimization in Design Form-Finding With Conditional GANs |
| doi |
https://doi.org/10.52842/conf.caadria.2022.1.121
|
| source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 121-130 |
| summary |
With the rapid development of computers and technology in the 20th century, the topological optimisation (TO) method has spread worldwide in various fields. This novel structural optimisation approach has been applied in many disciplines, including architectural form-finding. Especially Bi-directional Evolutionary Structural Optimisation (BESO), which was proposed in the 1990s, is widely used by thousands of engineers and architects worldwide to design innovative and iconic buildings. To integrate topological optimisation with artificial intelligence (AI) algorithms and to leverage its power to improve the diversity and efficiency of the BESO topological optimisation method, this research explores a non-iterative approach to accelerate the topology optimisation process of structures in architectural form-finding via conditional generative adversarial networks (GANs), which is named as OptiGAN. Trained with topological optimisation results generated through Ameba software, OptiGAN is able to predict a wide range of optimised architectural and structural designs under defined conditions. |
| keywords |
BESO (bi-directional evolutionary structural optimisation), Artificial Intelligence, Deep Learning, Topological Optimisation, Form-Finding, GAN (Generative Adversarial Networks), SDG 12, SDG 9 |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,579,693 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/07/22 07:34 |
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