An investigation on anisotropic soil slope stability by LS-SVM and LEM approaches

What is it about?

This study aims to use regression Least-Squares Support Vector Machine (LS-SVM) as a probabilistic model to determine the Factor of Safety (FS) and Probability of Failure (PF) of anisotropic soil slopes. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. This method increases the computational performance of low-probability analysis significantly. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of Machine Learning (ML) named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method. This research utilizes ML techniques to predict slope failure. Combining LS-SVM and LEM offers a unique and innovative approach to this critical aspect of geotechnical engineering. Furthermore, the paper addresses the anisotropic behavior of soil in slope stability analysis.

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