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<p>Defective fire doors in apartment buildings pose significant risks by undermining fire safety measures, enabling the rapid spread of smoke and fire, and potentially endangering residents’ lives. To address this critical safety issue, this research develops and evaluates four Graph Neural Network (GNN)-based text classification models—TextGCN, TextING, TensorGCN, and BERT-GCN—for 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.</p>

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Graph neural network–driven text classification for fire-door defect inspection in pre-completion construction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月23日

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  • Seunghyeon Wang

摘要

Defective fire doors in apartment buildings pose significant risks by undermining fire safety measures, enabling the rapid spread of smoke and fire, and potentially endangering residents’ lives. To address this critical safety issue, this research develops and evaluates four Graph Neural Network (GNN)-based text classification models—TextGCN, TextING, TensorGCN, and BERT-GCN—for 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.