Journal of the Earthquake Engineering Society of Korea (한국지진공학회논문집)

Earthquake Engineering Society of Korea (한국지진공학회)
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Machine Learning-based Rapid Seismic Performance Evaluation for Seismically-deficient Reinforced Concrete Frame

기계학습 기반 지진 취약 철근콘크리트 골조에 대한 신속 내진성능 등급 예측모델 개발 연구

  • Kang, TaeWook (Department of Architecture Engineering, Gyeongsang National University) ;
  • Kang, Jaedo (Division of Safety and Infrastructure Research, The Seoul Institute) ;
  • Oh, Keunyeong (Department of Building Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Shin, Jiuk (Department of Architecture, Gyeongsang National University)
  • 강태욱 (경상국립대학교 건축공학과) ;
  • 강재도 (서울연구원 안전인프라연구실) ;
  • 오근영 (한국건설기술연구원 건축연구본부) ;
  • 신지욱 (경상국립대학교 건축공학과)
  • Received : 2024.02.23
  • Accepted : 2024.04.01
  • Published : 2024.07.01
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Abstract

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R2), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.

Keywords

Acknowledgement

본 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원 (RS-2024-00348713) 및 과학기술정보통신부의 재원으로 수행된 한국건설기술연구원 주요사업의 결과물임(No.20230146-001).

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