Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network

This paper presents prediction of minimum factor of safety (FS) against slope failure in clayey soils using artificial neural network (ANN). Two multilayer perceptron ANN models were used to predict the minimum factor of safety using different data sets of geometric and shear strength parameters and based on the four well-known methods of Fellenius (Ordinary), Bishop, Janbu, and Spencer, respectively. The input parameters used to train and test the two ANN models include the reciprocal of slope tangent β, angle of internal friction of soil φ (^o), height of the slope H (m), cohesion of the soil c (kN/m²), unit weight of the soil γ (kN/m³) and the stability number m (c/γH). The output parameter for both ANN is the FS of the slope. The number of hidden layers and the number of neurons in each hidden layer were determined by trial and error to achieve the best results. It is observed that both ANN predictions are very close to the FS calculated by each of the corresponding four methods, separately. However, the ANN model with the scaled down number of input parameters showed better performance and the best one has a normalized mean square error of 0.0073, mean absolute percent error (MAPE) of 1.52 % and correlation coefficient (r) of 0.9966. It is concluded that such ANN models are reliable, simple and valid computational tools for predicting the FS and for assessing the stability of slopes of clayey soil. Six known case studies that are based on different methods were used to further test and validate the accuracy of the ANN model. It was observed that the ANN model predictions of FS of the case studies were very accurate with MAPE of 3.72 % for all methods combined. Based on the developed ANN model, a parametric study was then carried out to investigate the influence of the slope angle (β), stability number (m) and angle of internal friction (φ) on the factor of safety and slope stability of clayey soil.