Predicting Architectural Design Parameters for Form Generation Using Machine Learning
With the growing integration of artificial intelligence in architectural practice, machine learning (ML) has emerged as a promising tool for enhancing design efficiency and decision-making. While ML techniques are well known for identifying complex patterns within large datasets, their application in predicting architectural design parameters—particularly for form generation—remains relatively underexplored. This study investigates the feasibility of a machine learning–based framework capable of predicting numerical design parameters to support the generation of architectural form in a controlled, human-centered design context.
Complexity of Architectural Form Generation
Architectural form generation is influenced by multiple interdependent factors, including spatial logic, functional requirements, and human-centered considerations. These factors introduce a high level of complexity that challenges conventional rule-based or deterministic design approaches. This section discusses how machine learning can address this complexity by learning hidden relationships between design inputs and outputs, enabling more adaptive and data-informed form generation strategies.
Parametric Design and Dataset Development
To support machine learning prediction, a single villa was designed using a parametric modeling approach, generating hundreds of design variations. This process ensured consistency while embedding human-centered design principles into the dataset. Four distinct datasets were derived from the generated samples, each targeting specific form-related and window-related parameters. This topic highlights the importance of dataset quality and structure in achieving reliable ML-based architectural predictions.
Machine Learning Algorithms and Prediction Tasks
The study applies a range of regression and classification algorithms to predict architectural parameters from the prepared datasets. Both continuous numeric predictions and categorical classifications are explored, reflecting real-world design decision scenarios. This section focuses on the comparative application of these algorithms and the rationale for selecting multiple learning strategies to evaluate prediction robustness across datasets.
Performance of Ensemble Learning Methods
Results indicate that ensemble learning methods outperform individual algorithms across all datasets. Regression models achieved high predictive accuracy, with R² values reaching up to 0.97, 0.79, and 0.99, while the best-performing classification model achieved 98% accuracy. This topic analyzes why ensemble approaches are particularly effective in architectural parameter prediction, emphasizing their ability to handle nonlinear relationships and reduce prediction variance.
Implications for Data-Driven Architectural Design
The findings demonstrate the strong potential of machine learning frameworks to predict architectural design parameters, provided that datasets are carefully designed and representative of design intent. This final section discusses the implications for future architectural workflows, where ML can support designers in early-stage form generation without replacing human creativity. The study contributes to the evolving discourse on data-driven and AI-assisted architectural design methodologies.
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