Autocompletion of Architectural Spatial Configurations Using Case-Based Reasoning, Graph Clustering, and Deep Learning

Viktor Eisenstadt, Christoph Langenhan, Jessica Bielski, Ralph Bergmann, Klaus-Dieter Althoff

Abstract: This paper presents an approach for autocompletion of architectural building designs in the form of graph-based floor plans during the early design phase. We utilize established case-based reasoning methods, such as subgraph matching and transformational adaptation, further we employ supervised and unsupervised machine learning techniques, such as graph clustering and graph neural networks. Combining those methods into a single approach, the goal is to predict possibly missing spaces in architectural designs of housing buildings, supporting the acceleration of the early design process of architects to make it more sustainable, while enriching it with the recent developments of artificial intelligence. The approach was validated by a performance evaluation and a user study with participation of representatives of the architecture domain.