| Title |
Development of Automated Approach for Classifying Defect Types of Fire Door in Apartments |
| Authors |
엄익철(Eum, Ik-Chul) ; 왕승현(Wang, Seung-Hyeon) ; 유무영(Yoo, Moo-Young) ; 김재준(Kim, Jae-Jun) ; 김주형(Kim, Ju-Hyung) |
| DOI |
https://doi.org/10.5659/JAIK.2025.41.11.353 |
| Keywords |
Fire Door Dectect Detectction; Artificial Intelligence; Text Mining; Vectorization Methods; Deep Learning |
| Abstract |
Although fire door defect classification results have been widely applied in various areas?such as scheduling maintenance workers and setting
maintenance priorities?no previous studies have focused on fire door defects described in Korean text. This research addresses that gap by
examining the performance of various machine learning and deep learning techniques for text-based detection of fire door defects. A dataset
of 4,212 defect reports collected from 8,786 household units was annotated to include eight distinct defect types. Five traditional machine
learning models and three deep learning models were trained using three different vectorization approaches. The analysis centered on
evaluating classifier performance and the impact of vectorization methods. Among the traditional methods, LRwith N-grams achieved the
highest accuracy (81.89%), while an LSTM-RNN with word embeddings performed best among the deep learning models (74.97%). Overall,
the traditional models outperformed the deep learning models. For the test dataset, LRwith N-grams yielded an average F1-score of 82.18%
across all defect categories. These findings highlight the robustness of traditional methods for this particular context, while also suggesting
that further improvements could be realized through the use of broader datasets and more in-depth exploration of deep learning approaches. |