IIn the context of site response analysis, the use of shear wave velocity (  ) profiles that consider the seismological rock (  ≥ 3,000 m/s) depth is recommended. This study proposes regression analysis and machine learning-based models to predict deep   profiles for a specialized excavated rock site in South Korea. The regression model was developed by modifying mathematical expressions from a previous study and analyzing the correlation between   and model variables to predict deep   beyond 50 m. The machine learning models, designed using tree-based algorithms and a fully connected hierarchical structure, were developed to predict   from 51 m to 300 m at 1 m intervals. These models were validated by comparing them with measured deep   profiles and accurately estimating the trend of deep   variations. The proposed prediction models are expected to improve the accuracy of ground motion predictions for a specialized excavated rock site in Korea.

A B S T R A C T
1. 서 론
2. 자료 수집
3. 회귀분석 기반의 예측 모델 개발
    3.1 개발 방법
    3.2 모델 평가
4. 머신러닝 기반의 예측 모델 개발
    4.1 개발 방법
    4.2 모델 평가
5. 결 론
감사의 글
REFERENCES

• 김지은(울산과학기술원 지구환경도시건설공학과 대학원생) | Kim Jieun (Graduate Research Assistant, Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology)
• 김병민(울산과학기술원 지구환경도시건설공학과 부교수) | Kim Byungmin (Associate Professor, Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology)
• 조영규(울산과학기술원 지구환경도시건설공학과 연구교수) | Cho Youngkyu (Research Professor, Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology) Corresponding author