| id |
caadria2019_449 |
| authors |
Lin, Yuqiong, Yao, Jiawei, Huang, Chenyu and Yuan, Philip F. |
| year |
2019 |
| title |
The Future of Environmental Performance Architectural Design Based on Human-Computer Interaction - Prediction Generation Based on Physical Wind Tunnel and Neural Network Algorithms |
| doi |
https://doi.org/10.52842/conf.caadria.2019.2.633
|
| source |
M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 633-642 |
| summary |
As the medium of the environment, a building's environment performance-based generative design cannot be separated from intelligent data processing. Sustainable building design should seek an optimized form of environmental performance through a complete set of intelligent induction, autonomous analysis and feedback systems. This paper analyzed the trends in architectural design development in the era of algorithms and data and the status quo of building generative design based on environmental performance, as well as highlighting the importance of physical experiments. Furthermore, a design method for self-generating environmental performance of urban high-rise buildings by applying artificial intelligence neural network algorithms to a customized physical wind tunnel is proposed, which mainly includes a morphology parameter control and environmental data acquisition system, code translation of environmental evaluation rules and architecture of a neural network algorithm model. The design-oriented intelligent prediction can be generated directly from the target environmental requirements to the architectural forms. |
| keywords |
Physical wind tunnel; neural network algorithms; dynamic model; environmental performance; building morphology self-generation |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (6,658,298 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:59 |
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