Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/6908
Title: Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers
Authors: Fırat, Mahmut.
Güngör, Mahmud.
Keywords: Artificial Neural Networks
Circular bridge piers
Generalized Regression Neural Networks
Scour depth prediction
Backpropagation
Bridge piers
Erosion
Piers
Regression analysis
Scour
Empirical formulas
Experimental studies
Feed-forward neural networks
Input variables
Multiple linear regressions
Training and testing
Neural networks
Publisher: Elsevier Ltd
Abstract: In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers. © 2008 Elsevier Ltd. All rights reserved.
URI: https://hdl.handle.net/11499/6908
https://doi.org/10.1016/j.advengsoft.2008.12.001
ISBN: 09659978 (ISSN)
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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