| id |
caadria2022_233 |
| authors |
Dai, Sida, Kleiss, Michael, Alani, Mostafa and Pebryani, Nyoman |
| year |
2022 |
| title |
Reinforcement Learning-Based Generative Design Methodology for Kinetic Facade |
| 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. 151-160 |
| doi |
https://doi.org/10.52842/conf.caadria.2022.1.151
|
| summary |
This paper presents a reinforcement learning (RL) based design method for kinetic facades to optimize the movement direction of shading panels. Included with this research is a case study on the Westin Peachtree Plaza in Atlanta, USA to examine the effectiveness of the proposed design method in a real-life context. Optimization of building performance has been given increased attention due to the significant impact buildings have on energy consumption and carbon emissions. Further, building performance is closely related to the "Sustainable Cities and Communities‚ mentioned in SDG11. Results show that the novel design method improved the building performance by reducing solar radiation and glare and illustrate the potential of RL in tackling complex design problems in the architectural field. |
| keywords |
reinforcement learning, kinetic facade, generative design, design methodology, SDG 11 |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (2,666,090 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/07/22 07:34 |
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