The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces
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Date
2012
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Abstract
This study deals with the development of Artificial Neural Network (ANN) and Multiple Regression (MR) models for estimating the critical factor of safety (Fs) value of a typical artificial slope subjected to earthquake forces. To achieve this, while the geometry of the slope and the properties of the man-made soil are kept constant, the natural subsoil properties, namely, cohesion, internal angle of friction, the bulk unit weight of the layer beneath the ground surface and the seismic coefficient, varied during slope stability analyses. Then, the Fs values of this slope were calculated using the simplified Bishop method, and the minimum (critical) Fs value for each case was determined and used in the development of the ANN and MR models. The results obtained from the models were compared with those obtained from the calculations. Moreover, several performance indices, such as determination coefficient, variance account for, mean absolute error and root mean square error, were calculated to check the prediction capacity of the models developed. The obtained indices make it clear that the ANN model has shown a higher prediction performance than the MR model. © 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
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Keywords
Anchorages (foundations) , Earthquakes , Forecasting , Mean square error , Neural networks , Safety factor , Soils , Critical factor of safeties , Determination coefficients , Earthquake force , Internal angle of frictions , Prediction performance , Root mean square errors , Simplified Bishop method , Slope stability analysis , artificial neural network , critical analysis , earthquake intensity , earthquake prediction , geometry , Slope stability