| id |
caadria2021_080 |
| authors |
Yang, Xuyou and Xu, Weishun |
| year |
2021 |
| title |
A Tool for Searching Active Bending Bamboo Strips in Construction via Deep Learning |
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 463-472 |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1.463
|
| summary |
As an alternative material for construction, the structural use of bamboo in architecture is commonly associated with active bending. However, as natural material, the deformation of unprocessed bamboo strips is affected by the distribution of nodes, whose impact on deformation is difficult to precisely programme for each individual case and thus often causes discrepancies between generic digital simulation and construction. This research proposes a tool for searching active bending bamboo strips via deep leaning based on a multi-task neural network. The tool is able to predict both the number and locations of nodes suggested on bamboo strips according to a target curve as tool input. By approximating the prediction, users can find a strip that is most likely to deform into the desired geometry. |
| keywords |
neural network; active bending; neural architecture search (NAS); bamboo; material behaviour |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (4,220,354 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:57 |
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