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February 1, 2018SOILS AND FOUNDATIONS121 citationsOpen Access

Modeling of non-stationary random field of undrained shear strength of soil for slope reliability analysis

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SJShui‐Hua JiangJHJinsong Huang

Key Points

  • The aim is to propose a non-stationary random field model to characterize the spatial variability of the undrained shear strength of soil with depth.
  • Developed a non-stationary random field model for soil property assessment.
  • Conducted a subset simulation to evaluate slope reliability under undrained conditions.
  • Performed sensitivity studies to compare the new model with existing stationary and non-stationary models.
  • The proposed model effectively captures changes in undrained shear strength with depth.
  • Sensitivity studies show significant advantages over stationary models and existing non-stationary models.

Abstract

The spatial variability of soil properties is often assumed to be modeled as stationary or weakly stationary random fields in slope reliability analyses. However, abundant site-specific data have revealed that the mean and standard deviation of soil properties, such as the undrained shear strength of soil, change with depth. Thus, the non-stationary characteristics of soil properties need to be properly accounted for. The aim of this paper is to propose a non-stationary random field (RF) model for the characterization of the spatial variability and the depth-dependent nature of the undrained shear strength of soil. With the proposed model, the uncertainties of the trend and fluctuating components can be modeled individually. As an example, a clay slope under undrained conditions is investigated to illustrate the proposed model. A subset simulation is carried out to evaluate the slope reliability incorporating the non-stationary characteristics of soil properties. The advantages of the proposed model, relative to the existing non-stationary RF models and the commonly-used stationary RF model in the literature, are demonstrated through a series of sensitivity studies.

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Cite This Study

Jiang et al. (2018) studied this question.

synapsesocial.com/papers/6a2395b6c1f1c7a6bca036d6https://doi.org/10.1016/j.sandf.2017.11.006
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