Applying machine learning algorithms to architectural parameters for form generation

Architects have increasingly turned to Machine learning techniques to streamline various aspects of the architectural design process. Although ML excels at discovering intricate patterns, predicting architectural design parameters through ML remains relatively unexplored. The complexity of the design process entails aspects that affect the form generation process. This paper investigated the feasibility of a framework that employed ML algorithms to predict numeric values for creating 3D models. A single villa was designed parametrically to generate hundreds of samples ensuring a human-centered design process. Four datasets were created from the samples for predicting form and windows parameters. Various regression and classification algorithms were applied, with ensemble learning methods demonstrating impressive performance across all datasets. Regression tasks achieved high R2 of up to 0.97, 0.79, and 0.99 while the best classification algorithm achieved 98 % accuracy. These findings underscored the potential efficacy of the framework in predicting architectural parameters, contingent upon well-designed datasets.