• Geomechanics and Engineering
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• 제38권5호
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• pp.517-528
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• 2024

For numerous tunnelling projects implemented in urban areas due to limited space, it is crucial to take into account the interaction between the foundation, ground, and tunnel. In predicting the deformation of piled foundations and the ground during twin tunnel excavation, it is essential to consider various factors. Therefore, this study derived a prediction model for pile group settlement using machine learning to analyze the importance of various factors that determine the settlement of piled foundations during twin tunnelling. Laboratory model tests and numerical analysis were utilized as input data for machine learning. The influence of each independent variable on the prediction model was analyzed. Machine learning techniques such as data preprocessing, feature engineering, and hyperparameter tuning were used to improve the performance of the prediction model. Machine learning models, employing Random Forest (RF), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM, LGB) algorithms, demonstrate enhanced performance after hyperparameter tuning, particularly with LGB achieving an R² of 0.9782 and RMSE value of 0.0314. The feature importance in the prediction models was analyzed and P_N was the highest at 65.04% for RF, 64.81% for XGB, and P_CTC (distance between the center of piles) was the highest at 31.32% for LGB. SHAP was utilized for analyzing the impact of each variable. P_N (the number of piles) consistently exerted the most influence on the prediction of pile group settlement across all models. The results from both laboratory model tests and numerical analysis revealed a reduction in ground displacement with varying pillar spacing in twin tunnels. However, upon further investigation through machine learning with additional variables, it was found that the number of piles has the most significant impact on ground displacement. Nevertheless, as this study is based on laboratory model testing, further research considering real field conditions is necessary. This study contributes to a better understanding of the complex interactions inherent in twin tunnelling projects and provides a reliable tool for predicting pile group settlement in such scenarios.

• Wind and Structures
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• 제27권4호
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• pp.223-234
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• 2018

In this study, a simplified three-dimensional calculation model is developed for the dynamic analysis of soil-pile group-supertall building systems excited by wind loads using the substructure method. Wind loads acting on a 300-m building in different wind directions and terrain conditions are obtained from synchronous pressure measurements conducted in a wind tunnel. The effects of soil-structure interaction (SSI) on the first natural frequency, wind-induced static displacement, root mean square (RMS) of displacement, and RMS of acceleration at the top of supertall buildings are analyzed. The findings demonstrate that with decreasing soil shear wave velocity, the first natural frequency decreases and the static displacement, RMS of displacement and RMS of acceleration increase. In addition, as soil material damping decreases, the RMS of displacement and the RMS of acceleration increase.

• 한국터널지하공간학회 논문집
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• 제19권3호
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• pp.355-373
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• 2017

도심지에서의 터널굴착은 상부구조물과의 상호거동에 대한 이해가 필수적이다. 도심지에 사용중인 대부분의 구조물은 말뚝기초로 상부의 하중을 지지하고 있어, 터널 굴착 시 반드시 영향을 받는다. 따라서 본 연구에서는 실내모형시험을 통해 기존의 군말뚝 기초 하부 터널굴착에 따른 축력 분포와 지반의 거동을 분석하였다. 말뚝 기초는 2, 3 열 말뚝으로 가정되었으며, 말뚝 선단부와 터널 천단부의 이격거리는 터널직경에 대한 일반화를 위해 터널 직경(D) 대비 0.5D, 1.0D 그리고 1.5D로 고려되었다. 지반은 약 30%의 상대밀도(Dr)를 가지는 느슨한 사질토로 형성되었으며, 말뚝의 축력 분포를 측정하기 위해 말뚝에 변형률게이지(strain gauge)를 부착하였다. 또한, 이격거리에 따른 군말뚝의 침하와 인접지반의 침하를 변위센서(linear variable differential transformer; LVDT)와 다이얼게이지(dial gauge)를 통해 측정였으며, 터널굴착에 따른 지중의 변형을 근거리사진계측기법(close range photogrammetric technique)을 통해 측정하였다. 수치 해석을 통해 실내모형시험 및 근거리사진계측 결과와 비교 분석하였다. 본 연구에서는 체적손실율(volume loss; $V_L$) 개념을 이용하여 터널굴착을 모사하였으며, 1.5%로 적용되었다. 연구결과, 이격거리가 멀어질수록 말뚝의 축력감소는 작게 나타났으며, 침하량은 모두 유사한 경향을 나타내었다. 특히, 말뚝 선단부와 터널 천단부의 이격거리가 0.5D에서 1.0D로 증가할 때 축력과 침하량의 가장 큰 감소율이 가장 큰 것으로 나타났다.