Journal of the Earthquake Engineering Society of Korea (한국지진공학회논문집)

Earthquake Engineering Society of Korea (한국지진공학회)
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Machine Learning-Based Rapid Prediction Method of Failure Mode for Reinforced Concrete Column

기계학습 기반 철근콘크리트 기둥에 대한 신속 파괴유형 예측 모델 개발 연구

  • Kim, Subin (Department of Architecture Engineering, Gyeongsang National University) ;
  • Oh, Keunyeong (Department of Building Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Shin, Jiuk (Department of Architecture Engineering, Gyeongsang National University)
  • 김수빈 (경상국립대학교 건축공학과) ;
  • 오근영 (한국건설기술연구원 건축연구본부) ;
  • 신지욱 (경상국립대학교 건축공학과)
  • Received : 2023.10.30
  • Accepted : 2023.12.15
  • Published : 2024.03.01
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Abstract

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.

Keywords

Acknowledgement

본 논문은 2023년도 교육부의 재원으로 한국연구재단의 지원(2021RIS-003)을 받아 수행된 지자체-대학 협력기반 지역혁신 사업 및 과학기술정보통신부의 재원으로 수행된 한국건설기술연구원 주요사업(No.20230146-001)의 일환으로 수행되었음.

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