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 ho...