Generating floor plan diagrams using a self-organising adjacency graph

In this study, we introduce a novel self-organising map algorithm-based approach to floor plan diagram generation. A graph distance function was used instead of the standard Euclidean distance, together with a graph grid that fits inside any floor plan shape. The cosine distance was used to determine the best-matching neuron in the training process. Machine learning was then used to optimise the diagram on the fly; specifically, an unsupervised learning technique automatically generated initial floor plan diagrams without the need for an existing training dataset. The proposed method uses an adjacency matrix that describes the relationship between spaces. As a vital part of the process, the user can initialise fixed positions for some zones and elements, such as the entrance, thereby influencing the adjacency zoning localisation outcome. A Mathematica interface allows users to set input entrance positions, draw floor plan outlines and specify the direct relations between spaces. To validate the feasibility of the proposed approach, we performed an experiment using a residential apartment. The main contribution of this study was in improving the automation of the initial planning and design stages, which are the first crucial stages in designing floor plans.