BRIDGING BIM AND AI: A Graph-BIM Encoding Approach For Detailed 3D Layout Generation Using Variational Graph Autoencoder

Building Information Modelling (BIM) data provides an abundant source with hierarchical and detailed information on architectural elements. Nevertheless, transforming BIM data into an understandable format for AI to learn and generate controllable and detailed three-dimensional (3D) models remains a significant research challenge. This paper explores an encoding approach for converting BIM data into graph-structured data for AI to learn 3D models, which we define as Graph-BIM encoding. We employ the graph reconstruction capabilities of a Variational Graph Autoencoder (VGAE) for the unsupervised learning of BIM data to identify a suitable encoding method. vGaE's graph generation capabilities also reason for spatial layouts. Results demonstrate that VGAE can reconstruct BIM 3D models with high accuracy, and can reason the entire spatial layout from partial layout information detailed with architectural components. The primary contribution of this research is to provide a novel encoding approach for bridging AI and BIM encoding. The Graph-BIM encoding method enables low-cost, self-supervised learning of diverse BIM data, capable of learning and understanding the complex relationships between architectural elements. Graph-BIM provides foundational encoding for training general-purpose AI models for 3D generation.