Abstract
Slope stability estimation is an engineering problem that involves several parameters. Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been successfully used in slope stability problem. However, there are some open issues for above-mentioned methods, which are very hard to overcome. For this reason, Gaussian Process Regression (GPR) which has a theoretical framework for obtaining the optimum hyperparameters self-adaptively has been used in slope stability problem. Without complicated mechanics computation process, through learning the empirical knowledge coming from real engineering, the complicated nonlinear mapping relationship between slope stability and its influencing factors was established easily using GPR. The results of test study indicate that the method is feasible, effective and simple to implement for slope stability evaluation. The results are better than previously published paper of ANN and SVM.
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