[논문]의사결정나무 및 랜덤포레스트 분류 모델을 이용한 교량 안전등급 예측

홍지수; 전세진

doi:10.12652/ksce.2023.43.3.0397

의사결정나무 및 랜덤포레스트 분류 모델을 이용한 교량 안전등급 예측
Prediction of Safety Grade of Bridges Using the Classification Models of Decision Tree and Random Forest 원문보기

KSCE Journal of Civil and Environmental Engineering Research = 대한토목학회논문집, v.43 no.3, 2023년, pp.397 - 411

홍지수 (아주대학교 건설시스템공학과) , 전세진 (아주대학교 건설시스템공학과)

초록
AI-Helper

국내에서 공용연수 30년 이상인 노후 교량의 수가 급증하고 있다. 이에 따라 교량 노후도, 상태 및 성능 예측을 바탕으로 한 첨단 유지관리 기술의 중요성이 점차 주목받고 있다. 이 연구에서는 머신러닝 기반의 의사결정나무 및 랜덤포레스트 분류 모델을 사용하여 교량의 안전등급을 예측하는 방법을 제안하였다. 일반국도상 교량 8,850개를 대상으로 해당 모델들을 혼동행렬, 균형 정확도, 재현율, ROC 곡선 및 AUC와 같이 여러가지 평가 지표를 통해 분석한 결과 전반적으로 랜덤포레스트가 의사결정나무보다 더 나은 예측 성능을 보유하였다. 특히 랜덤포레스트 중 랜덤 언더 샘플링 기법은 노후도가 비교적 커서 유지관리에 주의를 기울여야 하는 C, D등급 교량에 대해 재현율 83.4%로 다른 샘플링 기법들보다 예측 성능이 더 뛰어난 것으로 나타났다. 제안된 모델은 최근 점검이 실시되지 않은 교량들의 신속한 안전등급 파악 및 효율적이고 경제적인 유지관리 계획 수립에 유용하게 활용될 수 있을 것으로 기대된다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (19)

그림 Fig. 1. Bridges with a Service Period of More than 30 Years
표 Table 1. Status and Inspection/Examination Cycle According to Safety Grade
그림 Fig. 2. Development and Evaluation of Classification Model
그림 Fig. 3. Structure of Decision Tree-based Algorithm
그림 Fig. 4. Structure of Random Forest Algorithm
그림 Fig. 5. Pearson Correlation Coefficient of Features
그림 Fig. 6. Data Sampling Technique to Improve Imbalanced Data: (a) Random Under-sampling, (b) Random Over-sampling, (c) SMOTETomek Sampling
표 Table 2. Distribution of Safety Grade of Bridges Located in National Roads
표 Table 3. Hyper Parameters in Decision Tree and Random Forest
표 Table 4. Hyper Parameters in Decision Tree Model
그림 Fig. 7. Confusion Matrix
표 Table 5. Hyper Parameters in Random Forest Model
그림 Fig. 8. Results of Confusion Matrix: (a) Decision Tree, (b) Random Forest
표 Table 6. Evaluation of Predictive Performance in Decision Tree
표 Table 7. Evaluation of Predictive Performance in Random Forest
그림 Fig. 9. Comparison of ROC Curves: (a) Decision Tree, (b) Random Forest
그림 Fig. 10. Safety Grade According to Completion Year of Bridges
표 Table 8. Variable Importance and Permutation Feature Importance of the Random Forest Using Random Under-sampling
그림 Fig. 11. Safety Grade According to Average Daily Traffic

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