A graph-based computational tool for retrieving architectural precedents of Building and Ground Relationship (BGR Tool)

The use of neural networks to retrieve relevant images has become mainstream. However, retrieving images that contain specific spatial relationships remains a challenging task. Images alone are not sufficient to fully describe spatial and topological relationships, which are usually better represented as a graph made up of nodes and edges. This paper describes the development of a graph-based computational tool for retrieving architectural precedents that closely match the relationship between a building and its surrounding ground as detected in a designer's project. The tool, titled Building Ground Relationship (BGR), stems from a research project into Graph Machine Learning (GML) that used Deep Graph Convolutional Neural Networks (DGCNNs) to classify building and ground relationships. The neural network was trained using a large synthetic dataset of graphs and optimized through the fine-tuning of its hyperparameters. To verify its performance, a second surrogate model was built using the Deep Graph Library (DGL). The results were nearly identical, thus giving confidence that the model is highly optimized. In the development of the BGR tool, two primary technologies were utilized. In the first instance, the synthetic database was built in Rhino Grasshopper by generating variations of a parametric model. The dual graphs of these models were then automatically generated and exported using the (Blinded for peer review) software library. The second phase involved developing GML models used for predicting the class of the conceptual design, enabling the retrieval of the smaller case study. The results of this research point to the importance of topological representation and machine learning approaches in retrieving and classifying architectural precedents