A general framework for coupled hydro-mechanical modelling of rainfall-induced instability in unsaturated slopes with multivariate random fields. (November 2019)

• Record Type: Journal Article
• Authors: Masoudian, Mohsen S.; Hashemi Afrapoli, Mir Amid; Tasalloti, Ali; Marshall, Alec M.
• Is Part Of: Computers and geotechnics. Volume 115(2019)
• Publication Date: 2019-11
• Subjects: Rainfall-induced slope instability -- Unsaturated soil -- Hydro-mechanical coupling -- Random field -- Copula cross-correlation -- Lignite spoils; Engineering geology -- Data processing -- Periodicals; Soil mechanics -- Data processing -- Periodicals; Rock mechanics -- Data processing -- Periodicals; 624.1510285
• DOI: 10.1016/j.compgeo.2019.103162
• Languages: English
• ISSNs: 0266-352X

Abstract

Abstract: An accurate estimation of rainfall-induced instability of slopes for extremely nonhomogeneous materials such as lignite mine spoils is a major challenge. This paper investigates the stability of nonhomogeneous soil slopes with respect to slip surface development, size of sliding volume, and determination of safety factor. Specified dependent random variables are cross-correlated using a multivariate Gaussian copula, the use of which provides a faster and more accurate representation of the inter-dependent properties of randomly-distributed soil. A Monte-Carlo simulation is used to generate a series of multivariate random fields for slopes. These are then implemented in Abaqus and analysed under constant rainfall conditions using a fully coupled hydro-elasto-plastic model. The resulting stress, strain, pore pressure, and displacement data are further processed in MATLAB to evaluate critical slip surfaces and safety factors. Results indicate that the factor of safety in a homogenous case is overestimated compared to the nonhomogeneous condition, while the sliding volume is underestimated. Moreover, the factor of safety decreases as the rainfall simulation continues and the probability of failure increases to nearly 100% after 10 days of rainfall. The framework developed in this paper can provide guidance for conducting relatively inexpensive probabilistic analyses.