GNN-based spatial relationship modeling for automated scaffold component function recognition and intelligent compliance checking

Abstract

Manual scaffold inspection is inefficient and error-prone, particularly for complex and large-scale structures. Existing scan-to-BIM methods rely on hard-coded rules and the results lack sufficient semantic richness, limiting automation and scalability for comprehensive compliance checking. This paper presents an approach to integrating images and point clouds for automating scaffold component function recognition and compliance checking. Two scaffold graph designs—Tube Node Graph (TNG) and Tube-Plane Node Graph (TPNG)—are proposed, employing Graph Neural Networks (GNNs) to model spatial relationships and identify scaffold tube member functions. The primary distinction between TNG and TPNG is whether wall and ground plane elements are represented as nodes in the graph. Evaluation results show that TPNG outperforms TNG, achieving recognition accuracies of 84.86 % and 73.03 %, respectively. The proposed method enhances the efficiency and accuracy of scaffold compliance checking, providing an effective solution for automated inspection.