| Title |
A Suggestion of the Direction of Construction Disaster Document Management through Text Data Classification Model based on Deep Learning |
| Authors |
Kim, Hayoung ; Jang, YeEun ; Kang, HyunBin ; Son, JeongWook ; Yi, June-Seong |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2021.22.5.073 |
| Keywords |
Construction Safety; CNN; Deep Learning; Classification Model; Disaster Data |
| Abstract |
This study proposes an efficient management direction for Korean construction accident cases through a deep learning-based text data classification model. A deep learning model was developed, which categorizes five categories of construction accidents: fall, electric shock, flying object, collapse, and narrowness, which are representative accident types of KOSHA. After initial model tests, the classification accuracy of fall disasters was relatively high, while other types were classified as fall disasters. Through these results, it was analyzed that 1) specific accident-causing behavior, 2) similar sentence structure, and 3) complex accidents corresponding to multiple types affect the results. Two accuracy improvement experiments were then conducted: 1) reclassification, 2) elimination. As a result, the classification performance improved with 185.7% when eliminating complex accidents. Through this, the multicollinearity of complex accidents, including the contents of multiple accident types, was resolved. In conclusion, this study suggests the necessity to independently manage complex accidents while preparing a system to describe the situation of future accidents in detail. |