Te Xiao

Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation

Random finite element method (RFEM) provides a rigorous tool to incorporate spatial variability of soil properties into reliability analysis and risk assessment of slope stability. However, it suffers from a common criticism of requiring extensive computational efforts and a lack of efficiency, particularly at small probability levels (e.g., slope failure probability Pf < 0.001). To address this problem, this study integrates RFEM with an advanced Monte Carlo Simulation (MCS) method called “Subset Simulation (SS)” to develop an efficient RFEM (i.e., SS-based RFEM) for reliability analysis and risk assessment of soil slopes. The proposed SS-based RFEM expresses the overall risk of slope failure as a weighed aggregation of slope failure risk at different probability levels and quantifies the relative contributions of slope failure risk at different probability levels to the overall risk of slope failure. Equations are derived for integrating SS with RFEM to evaluate the probability (Pf) and risk (R) of slope failure. These equations are illustrated using a soil slope example. It is shown that the Pf and R are evaluated properly using the proposed approach. Compared with the original RFEM with direct MCS, the SS-based RFEM improves, significantly, the computational efficiency of evaluating Pf and R. This enhances the applications of RFEM in the reliability analysis and risk assessment of slope stability. With the aid of improved computational efficiency, a sensitivity study is also performed to explore effects of vertical spatial variability of soil properties on R. It is found that the vertical spatial variability affects the slope failure risk significantly.

Full probabilistic design of slopes in spatially variable soils using simplified reliability analysis method

ABSTRACT A simplified reliability analysis method is proposed for efficient full probabilistic design of soil slopes in spatially variable soils. The soil slope is viewed as a series system comprised of numerous potential slip surfaces and the spatial variability of soil properties is modelled by the spatial averaging technique along potential slip surfaces. The proposed approach not only provides sufficiently accurate reliability estimates of slope stability, but also significantly improves the computational efficiency of soil slope design in comparison with simulation-based full probabilistic design. It is found that the spatial variability has considerable effects on the optimal slope design.

Auxiliary Random Finite Element Method for Risk Assessment of 3-D Slope

Spatial variability of soil properties is one of the major uncertainties in geotechnical properties that significantly affect slope reliability and risk. To account for the effect of three-dimensional (3-D) spatial variability, an efficient random finite element method (RFEM), named auxiliary RFEM (ARFEM), is proposed for 3-D slope reliability analysis and risk assessment. The ARFEM consists of two steps: the preliminary analysis using a relatively coarse 3-D finiteelement model and subset simulation, and the target analysis using a detailed 3-D finite-element model and response conditioning method. Compared with direct Monte Carlo simulation-based RFEM, ARFEM can provide consistent reliability and risk estimates with much less computational efforts. In addition, it is found that both the horizontal and vertical spatial variability have significant, but different, impacts on 3-D slope reliability, risk and failure mechanisms.

Probabilistic Stratification Modeling in Geotechnical Site Characterization

AbstractStratification in geologic profiles is one of the most significant uncertainties in geotechnical site characterization. In this paper, a three-level probabilistic framework is proposed for ...

Subset simulation-based random finite element method for slope reliability analysis and risk assessment

Spatial variability is one of the most significant uncertainties in soil properties that affect the reliability of slope stability. It can be incorporated into slope reliability analysis and risk assessment through random finite element method (RFEM) in a rigorous manner. The great potential of RFEM in reliability analysis and risk assessment of soil slopes has been demonstrated in previous studies. Nevertheless, it often suffers from a common criticism of requiring extensive computational efforts and a lack of efficiency, particularly at small probability levels. This study develops an efficient RFEM that integrates RFEM with an advanced Monte Carlo Simulation method called “Subset Simulation (SS)”. By this means, the computational efficiency of calculating the failure probability and risk is significantly improved. This enhances the applications of RFEM in slope reliability analysis and risk assessment at small probability levels. In addition, the proposed SS-based RFEM also provides insights into the relative contributions of slope failure risk at different probability levels to the overall risk. Finally, the proposed approach is illustrated through a soil slope example. It is shown that the slope failure probability and risk can be evaluated properly using SS-based RFEM.