• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2002년도 춘계 학술발표회 논문집
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• pp.117-124
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• 2002

The major purpose of this study is to determine the dynamic behavior of soil-pile-structure interaction system considering the underground cavity. For the analysis, a numerical method fur ground response analysis using FE-BE coupling method is developed. The total system is divided into two parts so called far field and near field. The far field is modeled by boundary element formulation using the multi-layered dynamic fundamental solution that satisfied radiational condition of wave. And this is coupled with near field modeled by finite elements. For the verification of dynamic analysis in the frequency domain, both forced vibration analysis and free-field response analysis are performed. The behavior of soil non-linearity is considered using the equivalent linear approximation method. As a result, it is shown that the developed method can be an efficient numerical method to solve the seismic response analysis considering the underground cavity in 2D problem.

• 한국전산구조공학회논문집
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• 제13권2호
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• pp.231-244
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• 2000

본 연구에서는 지하철구조물, 터널구조물 및 제방 등과 같은 2차원 지반-구조계의 지진응답해석을 위한 주파수영역 동적해석법을 제시하였다. 제시한 방법에서는 구조물과 구조물 주변 근역지반은 유한요소를 이용하고 원역지반은 주파수종속 동적무한요소를 이용하여 모형화하였다. 지진입력은 입력지진파를 수직으로 입사하는 P-파와 SV-파로 가정하여 자유장응답을 구하였으며 외부고정경계법을 적용하여 등가지진하중을 산정하였다. 본 연구기법의 검증을 위하여, 층상 자유장지반과 균질 반무한지반에 매입된 원동형 공동에 대하여 지진응답을 수행하였다. 이들을 다른 기법에 의한 해와 비교한 결과, 본 연구의 기법이 매우 정확함을 알 수 있었다. 마지막으로 지하철 역사의 지진응답해석 예제를 제시하여 본 연구의 적용성을 보였다.

• Geomechanics and Engineering
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• 제37권6호
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• pp.629-641
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• 2024

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.