APPLICATION OF CONDITIONAL GAN FOR THE GENERATION OF ARCHITECTURAL PLANNING SOLUTIONS
Keywords:
generative models; architectural layout generation; spatial planningAbstract
DOI: https://doi.org/10.46296/ig.v9i17.0344
Abstract
The use of generative models with artificial intelligence in architecture is experiencing constant growth, with one main problem: generating design variants that are not always functionally or normatively feasible for construction. This research evaluates the use of the Conditional Generative Adversarial Network as an alternative to generative models in architecture, with the objective of generating models from a database of 400 pairs of architectural floor plans, which contain design criteria related to zoning and spatial distribution of residential buildings. The implementation of the cGAN model revealed that the generated images maintain fidelity to the reference floor plans, as well as demonstrating progressive and sequential learning during the training and image generation stages. The study also reveals that the use of these models can be adjusted to project-specific objectives, positioning itself as a tool capable of generating coherent designs and optimizing modeling time for repetitive and mechanical tasks in the development of floor plans.
Keywords: generative models; architectural layout generation; spatial planning.
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