What's Happening?
Graph neural networks (GNNs) have been employed to improve text classification for fire-door defect inspection in pre-completion construction. By representing text as graphs, GNNs model document-term and term-term relations, capturing both global document context
and local lexical interactions. This approach contrasts with traditional sequential models and sequence-attention models, which primarily focus on token order. The study compared various GNN-based models, including BERT-GCN, TextING, TextGCN, and TensorGCN, evaluating their performance in terms of F1 score and accuracy. BERT-GCN emerged as the top performer, achieving the highest mean accuracy and F1 score, although it faced challenges in certain defect classes.
Why It's Important?
The application of GNNs in construction defect inspection represents a significant advancement in the field, offering a more nuanced and accurate method for identifying defects. This technology could lead to improved safety and quality in construction projects by enabling more precise defect detection and classification. The ability to automate and enhance defect inspection processes can reduce human error and increase efficiency, potentially leading to cost savings and faster project completion times. As the construction industry continues to adopt digital technologies, GNNs could play a crucial role in modernizing inspection and quality assurance practices.
What's Next?
Further development and refinement of GNN-based models could enhance their performance across all defect classes, addressing current challenges in capturing complex semantic or structural features. As these models become more robust, they may be integrated into standard construction inspection protocols, providing real-time decision support and improving overall project management. The success of GNNs in this application could also inspire their use in other areas of construction and engineering, where accurate defect detection is critical.
