Semantic-aware quality assessment of building elements using graph neural networks

Abstract

Automated construction quality assessments often face challenges due to noise and occlusions in on-site data when independently comparing elements in as-designed building information models (ad-BIMs) with registered 3D point clouds. This paper proposes BIM-Graph Neural Network (BIM-GNN), a learning-based approach that leverages semantics in ad-BIM to enhance element-wise quality assessments. BIM-GNN transforms ad-BIM into a graph, where nodes represent BIM objects and edges capture their topological and spatial relationships. The node features incorporate ad-BIM semantics, scan alignments, and graph structure information, while edge attributes describe relationship types. BIM-GNN employs transductive learning to classify nodes as verified, deviated, missing, or no-data, capturing project-level context. Experimental results show that BIM-GNN outperforms baseline machine learning models by over 27% on the median weighted F1-Score and provides the ability to infer the status of partially observed and unobserved elements. This work inspires future research on semantic-aware automated construction control.