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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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