Floor plan graph learning for generative design of residential buildings: a discrete denoising diffusion model

Floor planning, as one of the most important considerations in building design, often involves intensive trial-and-error processes with many constraints considered simultaneously. Artificial intelligence (AI) generative design solutions being developed are hampered by two shortcomings. Firstly, the vast topological knowledge embedded in existing floor plans has been largely unexplored and is thus wasted. Secondly, an efficient methodological instrument to learn the topological knowledge for generative design is lacking. This paper aims to develop a graph learning methodology to learn graph knowledge from floor plans and generate knowledge ready for building generative design. A discrete denoising diffusion model (D3M) that can learn topology graphs via its bi-directional structure of 'corruption and denoise' is developed and trained using more than 80,000 floor plans from a large-scale dataset. It is found that the D3M can learn the knowledge from floor plans and present it as various building floor topologies, which are evaluated in a preliminary case study as reliable and useful for generating real-life building floor plans. The research provides a design knowledge management framework that can be further implemented in academic works and design practices through some mainstreaming or commercializing efforts.