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

Abstract

In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the critical factor of safety (<TEX>$F_s$</TEX>) of the homogeneous finite slopes subjected to earthquake forces. To achieve this, the values of <TEX>$F_s$</TEX> in 5184 nos. of homogeneous finite slopes having different slope, soil and earthquake parameters were calculated by using the Simplified Bishop method and the minimum (critical) <TEX>$F_s$</TEX> for each of the case was determined and used in the development of the ANN and MR models. The results obtained from both the models were compared with those obtained from the calculations. It is found that the ANN model exhibits more reliable predictions than the MR model. Moreover, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed. Also, the receiver operating curves were drawn, and the areas under the curves (AUC) were calculated to assess the prediction capacity of the ANN and MR models developed. The performance level attained in the ANN model shows that the ANN model developed can be used for predicting the critical <TEX>$F_s$</TEX> of the homogeneous finite slopes subjected to earthquake forces.