| id |
caadria2020_045 |
| authors |
Zheng, Hao and Ren, Yue |
| year |
2020 |
| title |
Machine Learning Neural Networks Construction and Analysis in Vectorized Design Drawings |
| source |
D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 707-716 |
| doi |
https://doi.org/10.52842/conf.caadria.2020.2.707
|
| summary |
Machine Learning, a recently prevalent research domain in data prediction and analysis, has been widely used in a variety of fields. In the design field, especially for architectural design, a machine learning method to learn and generate design data as pixelized images has been developed in previous researches. However, proceeding pixelized image data will cause the problems of precision loss and calculation waste, since the geometric architectural design data is efficiently stored and presented as vectorized CAD files. Thus, in this article, the author developed a specific machine learning neural network to learn and predict design drawings as vectorized data, speeding up the learning and predicting process, while improving the accuracy. First, two necessary geometric tests have been successfully done, which shows the central concept of neural network construct. Then, a design rule prediction model was built to demonstrate the methods to optimize the neural network and data structure. Lastly, a generation model based on human-made design data was constructed, which can be used to predict and generate the bedroom furniture positions by inputting the boundary data of the room, door, and window. |
| keywords |
Machine Learning; Artificial Intelligence; Generative Design; Geometric Design |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (11,122,750 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:57 |
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