| Title |
Prediction of Safety Grade of Bridges Using the Classification Models of Decision Tree and Random Forest |
| Authors |
홍지수(Hong, Jisu) ; 전세진(Jeon, Se-Jin) |
| DOI |
https://doi.org/10.12652/Ksce.2023.43.3.0397 |
| Keywords |
머신러닝; 의사결정나무; 랜덤포레스트; 교량 안전등급; 유지관리 Machine learning; Decision tree; Random forest; Safety grade of bridges; Maintenance |
| Abstract |
The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected. |