Study on Displacement Back Analysis of Spatial Variability of Soil Slope

Abstract

The in situ soil properties exhibit natural spatial variability due to various factors and are difficult to be estimated based on limited test data. In this paper, a probabilistic back analysis method is adopted to characterize the spatial variability of soil parameters based on the responses of displacement. The Karhunen-Loeve (K-L) expansion method is used to discretize the random field of soil spatial variability. A finite number of basic random variables from the truncated K-L expansion are the random variables to be back estimated using the Markov Chain Monte Carlo (MCMC) method. To improve computational efficiency, a surrogate model based on the polynomial chaos expansion (PCE) is established to substitute the finite element model of slope stability analysis. A hypothetical example with a slope subject to surcharge load is presented to illustrate the accuracy and efficiency of the method. The spatially varied elastic moduli are estimated using the responses of displacement. It is found that the back analysis based on horizontal displacements is more accurate than that based on vertical displacements. When the correlation length decreases, the accuracy of the estimated field is reduced. The vertical correlation length has a greater effect on the estimation of soil spatial variability than the horizontal correlation length. When the coefficient of variation increases, the accuracy of the estimated field is reduced.