The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions

This study deals with development of artificial neural network (ANN) and multiple regression (MR) models that can be employed for estimating the critical factor of safety (Fs) value of homogeneous finite slopes. To achieve this, the Fs values of 675 homogenous finite slopes having different soil and slope parameters were calculated by using the simplified Bishop method and the minimum (critical) Fs value for each slope was determined and used in the ANN and MR models. The results obtained from ANN and MR models were compared with those obtained from the calculations. The values predicted from ANN models matched the calculated values much better than those obtained from MR models. Additionally, several performance indices such as determination coefficient (R2), variance account for (VAF), mean absolute error (MAE), and root mean square error (RMSE) were calculated; the receiver operating curves (ROC) were drawn, and the areas under the curves (AUC) were calculated to assess the prediction capacity of the ANN and MR models. ANN models have shown higher prediction performance than MR models based on the performance indices and the AUC values. The results demonstrated that the ANN models can be used at the preliminary stage of designing homogeneous finite slope.

► ANNs were used to predict the factor of safety value of homogeneous finite slopes.
► Fs values of 675 slopes with different soil and slope parameters were calculated.
► Constructed ANN models have shown higher prediction performance than MR models.
► ANN models can be used at the preliminary stage of designing the finite slope.