| id |
caadria2023_88 |
| authors |
Shi, Yinyi, Chen, Jinhui, Hu, Guangzhi and Wang, Cunyuan |
| year |
2023 |
| title |
Prediction and Optimisation of the Typical Airport Terminal Corridor Façade Shading Using Integrated Machine Learning and Evolutionary Algorithms |
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 59–68 |
| doi |
https://doi.org/10.52842/conf.caadria.2023.1.059
|
| summary |
For airport terminal buildings, the use of large glass curtain walls is beneficial to introduce daylight, but it also tends to cause excessive partial illumination, uncomfortable glare, and an increase in energy load. An appropriate façade shading design is important to help improve indoor environment performance and comfort. In this research, integrated machine learning algorithms were used to train a total of 2187 data samples of louvre shading and film shading to build a performance prediction model for adjustable façade shading of the typical terminal corridor, enabling rapid simulation and optimisation. The results show that the optimisation objectives are more closely related to the adjustable shading components and their façade areas. Besides, the optimal solutions for film and louvre shading are more influenced by the energy and light environment indicators respectively. Different target weights have a different impact on the selection of preferred solutions. This research further enriches the parametric model library and provides a reference for future design decisions and performance evaluation of airport terminal façade shading. |
| keywords |
Airport terminal corridor, Façade shading, Machine learning, Multi-objective optimisation, Performance prediction, SDG9 |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (2,957,603 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/06/15 23:14 |
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