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To address this critical safety issue, this research develops and evaluates four Graph Neural Network (GNN)-based text classification models\u2014TextGCN, TextING, TensorGCN, and BERT-GCN\u2014for the automatic identification of fire-door defects. By systematically optimizing both general and model-specific hyperparameters, a comprehensive evaluation involving 1008 model variants was conducted using multiple performance metrics. Among these, the optimized BERT-GCN model demonstrated superior performance, achieving notable F1 scores on the test dataset across various defect categories: frame gap (91.28%), door closer adjustment (90.52%), contamination (70.75%), dent (90.21%), scratch (90.34%), sealing components (90.29%), mechanical operation components (90.29%), and others (69.99%). Overall, BERT-GCN achieved an average F1 score of 85.46%, surpassing the performance of 2,430 other evaluated text classification models. These results highlight the strong potential and effectiveness of GNN-based approaches for enhancing safety management practices in construction environments.<\/jats:p>","DOI":"10.1038\/s41598-025-28120-4","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T12:21:55Z","timestamp":1766492515000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Graph neural network\u2013driven text classification for fire-door defect inspection in pre-completion construction"],"prefix":"10.1038","volume":"15","author":[{"given":"Seunghyeon","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"28120_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.firesaf.2022.103589","author":"M Calayir","year":"2022","unstructured":"Calayir, M., Selamet, S. & Wang, Y. 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