Browsing by Subject "EMBANKMENTS"

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    The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces
    (SHARIF UNIV TECHNOLOGY) Erzin, Y; Cetin, T
    This study deals with the development of Artificial Neural Network (ANN) and Multiple Regression (MR) models for estimating the critical factor of safety (F-s) 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 F-s values of this slope were calculated using the simplified Bishop method, and the minimum (critical) F-s 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. (C) 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
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    The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions
    (TECHNO-PRESS) Erzin, Y; Cetin, T
    In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the critical factor of safety (F-s) of the homogeneous finite slopes subjected to earthquake forces. To achieve this, the values of F-s 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) F-s 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 F-s of the homogeneous finite slopes subjected to earthquake forces.