BIM-based construction quality assessment using Graph Neural Networks

Automated construction quality control and as-built verification often involve comparing 3D point clouds captured on-site with as-designed Building Information Models (ad-BIM) at the individual element level. However, signal noise and occlusions, common in data captured from cluttered job sites, can negatively affect the performance of these methods that overlook the semantic relationships between elements. In this paper, we introduce a novel approach to automated quality control that enhances element-wise quality assessments by exploiting semantics in BIM. The proposed method represents ad-BIM as a graph by encoding elements' topological and spatial relationships. Exploiting this representation, we propose a Graph Neural Networks (GNNs)-based algorithm to infer element-wise built quality status. Our method significantly outperforms classical methods and allows for inference on partially observed or unobserved elements.