Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

1. 서 론
2. 실험 데이터베이스
    2.1 철근콘크리트 기둥 데이터베이스
    2.2 파괴거동에 영향을 미치는 입력변수
3. 기계학습방법론
4. 분류모델성능평가지표
5. 결 론
/ 감사의 글 /
/ REFERENCES /

• 김수빈(경상국립대학교 건축공학과 석사과정) | Kim Subin (Student, Department of Architecture Engineering, Gyeongsang National University)
• 오근영(한국건설기술연구원 건축연구본부 수석연구원) | Oh Keunyeong (Senior Researcher, Department of Building Research, Korea Institute of Civil Engineering and Building Technology)
• 신지욱(경상국립대학교 건축공학과 조교수(공학박사)) | Shin Jiuk (Assistant Professor(PhD), Department of Architecture Engineering, Gyeongsang National) Corresponding author