Open AccessThe use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces
Author(s) -
Yusuf Erzin,
Tülin Çetin
Publication year - 2012
Publication title -
scientia iranica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.299
H-Index - 51
eISSN - 2345-3605
pISSN - 1026-3098
DOI - 10.1016/j.scient.2012.02.008
Subject(s) - factor of safety , cohesion (chemistry) , artificial neural network , mean squared error , slope stability , friction angle , subsoil , mathematics , coefficient of determination , geotechnical engineering , geology , statistics , computer science , soil science , physics , quantum mechanics , machine learning , soil water
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