Graphitecture:Utilizing AI and Graph Theory in the Architecture Design Process

Abstract

This thesis investigates a graph-based generative AI model for architectural design, transforming abstract graph representations into detailed architectural forms. It addresses a crucial gap in computer-aided design research, overcoming the limitations of traditional image-based methods in capturing architectural compositions. In this model, nodes represent programs and edges denote adjacency, facilitating the creation of diverse domestic structures and their grouping into clusters through graph similarity analysis. The model also showcases the ability to explore extensive design possibilities from a single input graph, thus inspiring the design process. The methodology is demonstrated through the design of collective housing communities, employing a multi-scaled graph system and various graph algorithms. The objective is to create vibrant living spaces characterized by social diversity and architectural variety, thereby activating urban environments. Ultimately, this thesis harmonizes the dichotomy between functional rationality and aesthetic integrity in architectural forms, advancing design exploration by integrating computational techniques with deep learning.