• Title/Summary/Keyword: soil-structure-cavity interaction

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Seismic Response Analysis of Soil-Pile-Structure Interaction System considering the Underground Cavity (지중공동을 고려한 지반-말뚝-구조물 상호작용계의 지진응답해석)

  • 김민규;임윤묵;김문겸;이종세
    • Proceedings of the Earthquake Engineering Society of Korea Conference
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    • 2002.03a
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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.

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Seismic Response Analysis Method for 2-D Linear Soil-Structure Systemsusing Finite and Infinite Elements (유한요소와 무한요소를 사용한 2차원 선형 지반-구조물계의 지진응답해석법)

  • 김재민;윤정방;김두기
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.13 no.2
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    • pp.231-244
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    • 2000
  • This paper presents a dynamic analysis technique for a 2-D soil-structure interaction problem in the frequency domain, which can directly be applied as an analysis tool for seismic response analyses of underground structures, tunnels, embankments, and so on. In this method, the structure and near-field soil is modeled by the standard finite elements, while the unbounded far-field soil is represented using the dynamic infinite elements in the frequency domain. The earthquake-input motion is regarded as traveling P and SV waves which are incident vertically from the far-field of underlying half-space to the near-field of layered medium. The equivalent earthquake forces are then calculated utilizing so-called fixed-exterior-boundary-method and the free-field responses including displacements and tractions. For the verification of the present study, seismic response analyses are carried out for a multi-layered half-space free-field soil medium and a cylindrical cavity embedded in a homogeneous half-space. Comparisons of the present results with solutions by other approaches indicate that the proposed methodology gives accurate estimates. Finally, an application example of seismic response analysis for a subway station is presented, which demonstrates the applicability of the present study.

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Optimizing shallow foundation design: A machine learning approach for bearing capacity estimation over cavities

  • Kumar Shubham;Subhadeep Metya;Abdhesh Kumar Sinha
    • Geomechanics and Engineering
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    • v.37 no.6
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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.