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<p>Probabilistic slope stability analysis has proven effective in assessing the impact of uncertainty and variability in soil properties. In this paper, the probability density functions (PDFs) of the factor of safety (FOS) were determined for five limit equilibrium methods (namely Fellenius, Janbu simplified, Bishop simplified, Spencer and Morgenstern-Price), and the finite element method (FEM) using Monte Carlo simulation for single random variable conditions of the embankment. The random variables used for the limit equilibrium analysis are cohesion (<i>c</i>), friction angle (<i>φ</i>), and unit weight (<i>γ</i>). In contrast, Young’s modulus (<i>E</i>) is used instead of unit weight in finite element analysis (FEA). All the random variable parameters have been modelled as normal distributions except unit weight. Unit weight has been modelled as log-normal distribution. Further probabilistic analysis of embankment for the spatially varied condition has been performed under different pseudo-static loading using the random limit equilibrium method (RLEM) with the M&amp;P approach, and the random field having the least factor of safety for each pseudo-static loading has been validated using the random finite element method (RFEM). A further effect of cross-correlation between <i>c</i> and <i>φ</i> and correlation length (<i>θ</i><sub><i>h</i></sub> and <i>θ</i><sub><i>v</i></sub>) has also been studied using the Morgenstern-Price approach. A series of seismic time-history analyses have been performed on an embankment containing single random variable and spatially variable soil properties. Results are compared in terms of the mean value of PGA (m/s<sup>2</sup>) and standard deviation at different locations. The results revealed that stability analysis assuming single random variable conditions always has a high level of uncertainty in terms of FOS and critical seismic coefficient (<i>K</i><sub><i>y</i></sub>) as compared to spatially variable conditions. The deviation in PGA values with the change in random field sample is higher under spatially variable conditions as compared to single random variable soil conditions.</p>

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Probabilistic Seismic Slope Stability Analysis Using Limit Equilibrium and Finite Element Methods

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Lokesh Sharan Srivastava,
  • Pradipta Chakrabortty

摘要

Probabilistic slope stability analysis has proven effective in assessing the impact of uncertainty and variability in soil properties. In this paper, the probability density functions (PDFs) of the factor of safety (FOS) were determined for five limit equilibrium methods (namely Fellenius, Janbu simplified, Bishop simplified, Spencer and Morgenstern-Price), and the finite element method (FEM) using Monte Carlo simulation for single random variable conditions of the embankment. The random variables used for the limit equilibrium analysis are cohesion (c), friction angle (φ), and unit weight (γ). In contrast, Young’s modulus (E) is used instead of unit weight in finite element analysis (FEA). All the random variable parameters have been modelled as normal distributions except unit weight. Unit weight has been modelled as log-normal distribution. Further probabilistic analysis of embankment for the spatially varied condition has been performed under different pseudo-static loading using the random limit equilibrium method (RLEM) with the M&P approach, and the random field having the least factor of safety for each pseudo-static loading has been validated using the random finite element method (RFEM). A further effect of cross-correlation between c and φ and correlation length (θh and θv) has also been studied using the Morgenstern-Price approach. A series of seismic time-history analyses have been performed on an embankment containing single random variable and spatially variable soil properties. Results are compared in terms of the mean value of PGA (m/s2) and standard deviation at different locations. The results revealed that stability analysis assuming single random variable conditions always has a high level of uncertainty in terms of FOS and critical seismic coefficient (Ky) as compared to spatially variable conditions. The deviation in PGA values with the change in random field sample is higher under spatially variable conditions as compared to single random variable soil conditions.