| id |
ecaade2021_150 |
| authors |
Song, Yanan and Yuan, Philip F. |
| year |
2021 |
| title |
A Research On Building Cluster Morphology Formation Based On Wind Environmental Performance And Deep Reinforcement Learning |
| source |
Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 335-344 |
| doi |
https://doi.org/10.52842/conf.ecaade.2021.1.335
|
| summary |
Nowadays, numerous researchers emphasize the significance of the environmen-tal performance-driven generative methodology. However, due to the complex coupling mechanism of environmental regulation factors, the existing optimiza-tion engines and applications are time-consuming and cumbersome. In this re-search, we propose a novel design methodology based on Deep Reinforcement Learning (DRL). This paper is divided into 3 sections, including theoretical framework, design strategy, and practical application. It first introduces an over-view of basic principles, illustrating the potential advantages of DRL in perfor-mance data-driven design. Based on this, the paper proposes a DRL-based gener-ative method. We point out a more specific discussion about the application and workflow of core DRL elements in architectural design. Finally, taking a grid-form urban space composed by multitude high-rise building blocks as an exam-ple, we present a application through a DRL agent to conduct numerous active wind environmental performance-based design tests. It is an interactive and gen-erative design method, owning multiple advantages of timeliness, convenience, and intelligence. |
| keywords |
Deep Reinforcement Learning; Environmental Performance Design; Generative Design; Building Cluster Formation |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (9,450,627 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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