In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

1. 서론
2. 프레임 구조의 비선형 동적해석
    2.1 프레임 구조
    2.2 동적 해석
3. 구조물 응답 데이터 확보
4. 기계학습 예측모델
    2.1 Decision Tree
    2.1 Random Forest
    2.3 시계열 교차 검증 기법
    2.4 평가기법
5. 수치해석
    4.1 2-story steel space frame
    4.2 6-story steel space frame
6. 결론
References

• 이승혜(딥러닝 건축연구소, 세종대학교 건축공학과 조교수, 공학박사) | Lee Seunghye (Deep Learning Architecture Research Center Dept. of Architectural Engineering, Sejong University)
• 이재홍(딥러닝 건축연구소, 세종대학교 건축공학과 교수, 공학박사) | Lee Jaehong (Deep Learning Architecture Research Center Dept. of Architectural Engineering, Sejong University) Corresponding author