Characterization of Stationary and Nonstationary Random Fields with Different Copulas on Undrained Shear Strength of Soils: Probabilistic Analysis of Embankment Stability on Soft Ground

Abstract

Soil properties are known to have high spatial variability and often fluctuate with depth. The objective of this study was to investigate the effects of using different models to simulate the spatial variability of undrained shear strength (su) to calculate the failure probability of an embankment on soft ground. Two-dimensional random fields of su were generated based on one Gaussian and two non-Gaussian copulas, with stationary and nonstationary assumptions. Statistical parameters of su variation—mean, coefficient of variance, and scale fluctuation (correlation length)—were estimated from simulated and field data. Monte Carlo probabilistic analyses were performed on embankment stability based on both stationary and nonstationary random fields and all copula approaches; results showed more frequent embankment failures at low water levels in the embankment ditch. In particular, the nonstationary random field (su increases with depth) simulations more closely reflected real observed data, with higher probabilities of slope failure and lower mean factor of safety than the stationary random field simulations. Additionally, the non-Gaussian copulas provided simulated data that more accurately reflected observed field data, highlighting the importance of copula selection when characterizing soil parameter random fields.