Application of Artificial Neural Network, Kriging, and Inverse Distance Weighting Models for Estimation of Scour Depth around Bridge Pier with Bed Sill

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dc.contributor.author Homayoon, Rahman en_US
dc.contributor.author Keshavarzy, Alireza en_US
dc.contributor.author Gazni, Reza en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T09:26:43Z
dc.date.available 2012-02-02T09:26:43Z
dc.date.issued 2010 en_US
dc.identifier 2011000719 en_US
dc.identifier.citation Homayoon Rahman, Keshavarzi Alireza, and Gazni Reza 2010, 'Application of Artificial Neural Network, Kriging, and Inverse Distance Weighting Models for Estimation of Scour Depth around Bridge Pier with Bed Sill', Scientific Research Publishing, Inc., vol. 3, no. 10, pp. 944-964. en_US
dc.identifier.issn 1945-3116 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/15115
dc.description.abstract This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth. en_US
dc.language en_US
dc.publisher Scientific Research Publishing, Inc. en_US
dc.title Application of Artificial Neural Network, Kriging, and Inverse Distance Weighting Models for Estimation of Scour Depth around Bridge Pier with Bed Sill en_US
dc.parent Journal of Software Engineering and Applications en_US
dc.journal.volume 3 en_US
dc.journal.number 10 en_US
dc.publocation USA en_US
dc.identifier.startpage 944 en_US
dc.identifier.endpage 964 en_US
dc.cauo.name FEIT.School of Civil and Environmental Engineering en_US
dc.conference Verified OK en_US
dc.for 080300 en_US
dc.personcode 0000072953 en_US
dc.personcode 105641 en_US
dc.personcode 0000072952 en_US
dc.percentage 100 en_US
dc.classification.name Computer Software en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Artificial Neural Network, Scour Depth, Ordinary Kriging, Inverse Distance Weighting, Bridge Piers, Bed Sill en_US


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