국내 굴착 암반 부지에 대한 심부 전단파속도 주상도 예측 모델 개발

Abstract

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.