| id |
ijac202220203 |
| authors |
Dzieduszyński, Tomasz |
| year |
2022 |
| title |
Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions |
| source |
International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 196–215 |
| summary |
A substantial part of architectural and urban design involves processing of compositional interdependenciesand contexts. This article attempts to isolate the problem of spatial composition from the broader category ofsynthetic image processing. The capacity of deep convolutional neural networks for recognition and utilization of complex compositional principles has been demonstrated and evaluated under three scenariosvarying in scope and approach. The proposed method reaches 95.1%–98.5% efficiency in the generation ofcontext-fitting spatial composition. The technique can be applied for the extraction of compositionalprinciples from the architectural, urban, or artistic contexts and may facilitate the design-related decisionmaking by complementing the required expert analysis |
| keywords |
Spatial composition, architecture, convolutional neural network, ordering principles, machine learning, image generation, design, CAAD |
| series |
journal |
| references |
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