Highly efficient reliability analysis of anisotropic heterogeneous slopes: Machine Learning aided Monte Carlo method

• URL: http://arxiv.org/abs/2204.06098v1
• Date: Mon, 4 Apr 2022 16:28:53 GMT
• Title: Highly efficient reliability analysis of anisotropic heterogeneous slopes: Machine Learning aided Monte Carlo method
• Authors: Mohammad Aminpour, Reza Alaie, Navid Kardani, Sara Moridpour, Majidreza Nazem
• Abstract summary: This paper presents a highly efficient Machine Learning aided reliability technique. It is able to accurately predict the results of a Monte Carlo (MC) reliability study, and yet performs 500 times faster. The proposed technique reduces the computational time required for our study from 306 days to only 14 hours, providing 500 times higher efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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