  Random field Monte Carlo (MC) reliability analysis is a robust stochastic
method to determine the probability of failure. This method, however, requires
a large number of numerical simulations demanding high computational costs.
This paper explores the efficiency of different machine learning (ML)
algorithms used as surrogate models trained on a limited number of random field
slope stability simulations in predicting the results of large datasets. The MC
data in this paper require only the examination of failure or non-failure,
circumventing the time-consuming calculation of factors of safety. An extensive
dataset is generated, consisting of 120,000 finite difference MC slope
stability simulations incorporating different levels of soil heterogeneity and
anisotropy. The Bagging Ensemble, Random Forest and Support Vector classifiers
are found to be the superior models for this problem amongst 9 different models
and ensemble classifiers. Trained only on 0.47% of data (500 samples), the ML
model can classify the entire 120,000 samples with an accuracy of %85 and AUC
score of %91. The performance of ML methods in classifying the random field
slope stability results generally reduces with higher anisotropy and
heterogeneity of soil. The ML assisted MC reliability analysis proves a robust
stochastic method where errors in the predicted probability of failure using %5
of MC data is only %0.46 in average. The approach reduced the computational
time from 306 days to less than 6 hours.
