| id |
ijac201816202 |
| authors |
Tamke, Martin; Paul Nicholas and Mateusz Zwierzycki |
| year |
2018 |
| title |
Machine learning for architectural design: Practices and infrastructure |
| source |
International Journal of Architectural Computing vol. 16 - no. 2, 123-143 |
| summary |
In this article, we propose that new architectural design practices might be based on machine learning approaches to better leverage data-rich environments and workflows. Through reference to recent architectural research, we describe how the application of machine learning can occur throughout the design and fabrication process, to develop varied relations between design, performance and learning. The impact of machine learning on architectural practices with performance-based design and fabrication is assessed in two cases by the authors. We then summarise what we perceive as current limits to a more widespread application and conclude by providing an outlook and direction for future research for machine learning in architectural design practice. |
| keywords |
Machine learning, robotic fabrication, design-integrated simulation, material behaviour, feedback, Complex Modelling |
| 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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