  Machine Learning (ML) algorithms are increasingly used as surrogate models to
increase the efficiency of stochastic reliability analyses in geotechnical
engineering. This paper presents a highly efficient ML aided reliability
technique that is able to accurately predict the results of a Monte Carlo (MC)
reliability study, and yet performs 500 times faster. A complete MC reliability
analysis on anisotropic heterogeneous slopes consisting of 120,000 simulated
samples is conducted in parallel to the proposed ML aided stochastic technique.
Comparing the results of the complete MC study and the proposed ML aided
technique, the expected errors of the proposed method are realistically
examined. Circumventing the time-consuming computation of factors of safety for
the training datasets, the proposed technique is more efficient than previous
methods. Different ML models, including Random Forest (RF), Support Vector
Machine (SVM) and Artificial Neural Networks (ANN) are presented, optimised and
compared. The effects of the size and type of training and testing datasets are
discussed. The expected errors of the ML predicted probability of failure are
characterised by different levels of soil heterogeneity and anisotropy. Using
only 1% of MC samples to train ML surrogate models, the proposed technique can
accurately predict the probability of failure with mean errors limited to 0.7%.
The proposed technique reduces the computational time required for our study
from 306 days to only 14 hours, providing 500 times higher efficiency.
