논문 상세보기
Seismically deficient reinforced concrete(RC) structures experience reduced structural capacity and lateral resistance due to the increased axial loads resulting from green retrofitting and vertical extensions. To ensure structural safety, traditional performance assessment methods are commonly employed. However, the complexity of these evaluations can act as a barrier to the application of green retrofitting and vertical extensions. This study proposes a methodology for rapidly calculating the allowable axial force range of RC buildings by leveraging simplified structural details and seismic wave information. The methodology includes three machine-learning-based models: (1) predicting column failure modes, (2) assessing seismic performance under current conditions, and (3) evaluating seismic performance under amplified mass conditions. A machine learning model was specifically developed to predict the seismic performance of an RC moment frame building using structural details, gravity loads, failure modes, and seismic wave data as input variables, with dynamic response-based seismic performance evaluations as output data. Classifiers developed using various machine learning methodologies were compared, and two optimal ensemble models were selected to effectively predict seismic performance for both current and increased mass scenarios.
1. 서 론
2. 데이터 수집
2.1 입력변수 데이터세트
2.2 출력변수 데이터세트
3. ML 기반 질량 증가 내진성능 예측 모델 개발
3.1 입력변수 및 파생변수 선정
3.2 분류형 기계학습 방법론
3.3 성능평가지표
4. 동적해석 기반 축력허용범위 산정 방법론
4.1 건축물 성능평가
4.2 축력허용범위 산정 과정
4.3 대상 건축물 축력허용범위
5. 결 론
/ 감사의 글 /
/ REFERENCES /
