## 1 Introduction
Nevertheless, the majority of these studies do not consider the combined impact of slope geometry and non-associative plasticity models, which the present study explores. LEA methods evolved from the analytical ones developed in 1930s towards their implementation in computer software that is able to compute both the Factor of Safety (FoS) and the location of the critical slip surface more quickly and accurately. In contrast to LEA, PA allows for the uncertainty of input variables to be quantified and incorporated into the analysis, so besides an approximate value of the FoS, an approximate value of the failure probability is also obtained. PA should not be viewed as a substitute for the classic deterministic methods, but as a complementary approach. Several existing ML methods that have been used for slope stability analyses in the last decade were reviewed in [48]. Out of these, artificial neural networks [49] are the most common, with Support Vector Machine [50] and various Regression Algorithms [51] also being extensively used. Finally, NMA can simulate the behavior of complex, real-life geotechnical problems using software code, and provides the most accurate values for the FoS. The computational power available today allows multiple parameters to be taken into account, like internal friction angles, material cohesion, slope height, slope angle, pore water pressure, and the unit weight of rock and soil, etc. NMA can employ continuum numerical modeling techniques, such as the finite element method (FEM), finite difference (FD), and the boundary element method (BEM); discontinu modeling, such as the discrete elements method (DEM) and the universal distinct element code (UDEC); and coupled continuum and discontinuum modeling for very complex conditions, including the combined finite-discrete element method (FDEM). The DEM was introduced [52] as a discontinuum-based method that is capable of modeling and simulating the failure of discontinuous media such as jointed rock slopes [53; 54], and can be coupled successfully with the Shear Strength Reduction Method [55]. In their comprehensive review [56], [PERSON] et al. emphasize that the SSRM is adopted in the majority of studies that use FEM, DEM, or FD to analyze waste dump slope stability. For the current study, SSRM was chosen for its ability to handle non-linear material behavior more effectively compared to LEA, which is especially important in the case of mine waste dumps, considering their heterogenous and loose structure. Several scientists like [PERSON] et al. [57], [PERSON] et al. [58], and [PERSON] et al. [59] presented several advantages of this method over traditional ones, concluding that in the case of the SSRM when failure occurs 'naturally', the location of the critical failure surface is obtained without the need for a trial and error or the limitations of the interslice shears as in LEA, and it can consider complex factors that affect slope stability. The SSRM was proposed initially by [PERSON] et al. [60] and involves progressively reducing the shear strength of the material in small increments, to bring the slope to the state of limit equilibrium (failure). The series of iterations are carried out using trial values of the strength reduction factor in order to reduce the values of cohesion and the internal angle of friction, until either the slope failure occurs or a limiting condition is obtained following the Mohr-Coulomb failure criterion. Since its initial use, various developments and variations of the method have been proposed. Scholars like [PERSON] [61], [PERSON] [62], [PERSON] et al. [63], and [PERSON] et al. [64] made the first improvements in the early 90s. More recently, [PERSON] et al. [65] modified and used the SSRM to prove its feasibility for the evaluation of locally loaded slopes, by combining the numerical method with physical studies. The safety and stability of an open-pit mine waste dump from Tibet was assessed by [PERSON] et al.