Efficient numerical-simulation-based slope reliability analysis considering spatial variability

Currently, numerical-simulation-based slope reliability analysis (NSB-SRA) considering spatial variability is still a time-consuming task. To address this problem, this study proposed an efficient numerical-simulation-based slope reliability analysis method. Dual dimensionality reduction technique is firstly employed to greatly reduce random variables that are required for establishing a limit-equilibrium-analysis-based multivariate adaptive regression splines (MARS) model. Then, response conditioning method is used to select the failure samples predicted by MARS model as samples for performing NSB-SRA. Finally, the proposed method is validated through two spatially variable slope examples. The results show that MARS + FDM is an efficient solution to perform NSB-SRA, especially for low-probability-level NSB-SRA problem. Besides, NSB-SRA is necessary for cases of horizontal scale of fluctuation, smaller vertical of fluctuation, larger variability of undrained shear strength, and stronger positive cross-correlation between cohesion and internal friction angle because neglecting NSB-SRA will lead to an unreliable assessment on slope reliability.

Automated summary

In this study, an efficient numerical-simulation-based slope reliability analysis (NSB-SRA) method considering spatial variability is proposed. By employing dual dimensionality reduction and response conditioning techniques, the computational time is reduced and the accuracy of slope reliability assessment is improved.