| id |
ijac201816203 |
| authors |
Anderson, Carl; Carlo Bailey, Andrew Heumann and Daniel Davis |
| year |
2018 |
| title |
Augmented space planning: Using procedural generation to automate desk layouts |
| source |
International Journal of Architectural Computing vol. 16 - no. 2, 164-177 |
| summary |
We developed a suite of procedural algorithms for space planning in commercial offices. These algorithms were benchmarked against 13,000 actual offices designed by human architects. The algorithm performed as well as an architect on 77% of offices, and achieved a higher capacity in an additional 6%, all while following a set of space standards. If the algorithm used the space standards the same way as an architect (a more relaxed interpretation), the algorithm achieved a 97% match rate, which means that the algorithm completed this design task as well as a designer and in a shorter time. The benchmarking of a layout algorithm against thousands of existing designs is a novel contribution of this article, and we argue that it might be a first step toward a more comprehensive method to automate parts of the office layout process. |
| keywords |
Office design, design augmentation, space planning, automation, office layout, desk layouts |
| series |
journal |
| email |
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| full text |
 file.pdf ( bytes) |
| references |
Content-type: text/plain
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| last changed |
2019/08/07 14:03 |
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