Instrumentalization of machine learning in architectural design

Abstract Applying Machine Learning (ML) has seen rapid progress in many disciplines, such as architectural design. Recent research reveals promising potential for integrating ML in solving design problems. This paper explored how ML can serve as a tool to guide designing action. It conducted thematic analyses of ML experiments in the architecture domain to build a framework addressing two key aspects: the tasks achieved and the required training dataset. The paper found that ML mainly performs design generation, optimization, and recognition via classification and clustering. Three scenarios of design generation using ML have started from pre-design requirements and constraints, conceptual design, or parametric design. ML can predict design features based on prescribed performance or calculate performance metrics, based on varied design options. Design recognition classifies or clusters designs to detect their styles, typologies, and morphologies, besides tracking the process of best practices. The specifications of training datasets vary in terms of data sources as real or virtual, techniques for representing datasets as visual or textual, and the relationships between input and output datasets as refinement, embodiment, sorting, or evaluation. The findings revealed that ML has a wide range of experimentation and opens more opportunities for further integration in the design process.