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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1. 서 론
2. 데이터 수집
    2.1 입력변수 데이터세트
    2.2 출력변수 데이터세트
3. 기계학습 기반 내진성능예측 모델 개발
    3.1 입력변수 및 파생변수 선정
    3.2 분류형 기계학습 방법론
    3.3 성능평가지표
4. 결 론
/ 감사의 글 /
/ REFERENCES /

• 황희진(경상국립대학교 건축공학과 박사과정) | Hwang Heejin (Ph.D. Student, Department of Architectural Engineering, Gyeongsang National University)
• 오근영(한국건설기술연구원 건축연구본부 수석연구원) | Oh Keunyeong (Senior Researcher, Department of Building Research, Korea Institute of Civil Engineering and Building Technology)
• 이기학(세종대학교 건축공학과 교수, 공학박사) | Lee Kihak (Professor (PhD), Department of Architectural Engineering, Sejong University)
• 신지욱(경상국립대학교 건축공학과 부교수, 공학 박사) | Shin Jiuk (Associate Professor (PhD), Department of Architectural Engineering, Gyeongsang National University) Corresponding author