한국지진공학회논문집 (Journal of the Earthquake Engineering Society of Korea)

  • 제29권3호
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  • Pages.151-161
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  • 2025
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  • 1226-525X(pISSN)
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  • 2234-1099(eISSN)
한국지진공학회 (Earthquake Engineering Society of Korea)
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기계학습 기반 철근콘크리트 모멘트골조 신속 내진성능 예측 모델 개발

Machine Learning-Based Rapid Prediction Method for Seismic Performance of Reinforced Concrete Moment Frames

  • 황희진 (경상국립대학교 건축공학과 ) ;
  • 오근영 (한국건설기술연구원 건축연구본부 ) ;
  • 이기학 (세종대학교 건축공학과 ) ;
  • 신지욱 (경상국립대학교 건축공학과 )
  • Hwang, Heejin (Department of Architectural Engineering, Gyeongsang National University) ;
  • Oh, Keunyeong (Department of Building Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Lee, Kihak (Department of Architectural Engineering, Sejong University) ;
  • Shin, Jiuk (Department of Architectural Engineering, Gyeongsang National University)
  • 투고 : 2024.10.28
  • 심사 : 2024.12.26
  • 발행 : 2025.05.01
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초록

Existing reinforced concrete buildings with seismically deficient columns experience reduced structural capacity and lateral resistance due to increased axial loads from green remodeling or vertical extensions aimed at reducing CO2 emissions. Traditional performance assessment methods face limitations due to their complexity. This study aims to develop a machine learning-based model for rapidly assessing seismic performance in reinforced concrete buildings using simplified structural details and seismic data. For this purpose, simple structural details, gravity loads, failure modes, and construction years were utilized as input variables for a specific reinforced concrete moment frame building. These inputs were applied to a computational model, and through nonlinear time history analysis under seismic load data with a 2% probability of exceedance in 50 years, the seismic performance evaluation results based on dynamic responses were used as output data. Using the input-output dataset constructed through this process, performance measurements for classifiers developed using various machine learning methodologies were compared, and the best-fit model (Ensemble) was proposed to predict seismic performance.

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과제정보

본 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원(RS-2024-00348713) 및 과학기술정보통신부의 재원으로 수행된 한국건설기술연구원 주요사업의 결과물임(No. 20240190-001).

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