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, R
dc.contributor.author Keshavarzy, A
dc.contributor.author Gazni, R
dc.date.accessioned 2012-02-02T09:26:43Z
dc.date.issued 2010-01
dc.identifier.citation Journal of Software Engineering and Applications, 2010, 3 (10), pp. 944 - 964
dc.identifier.issn 1945-3116
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.
dc.publisher Scientific Research Publishing, Inc.
dc.subject Artificial Neural Network, Scour Depth, Ordinary Kriging, Inverse Distance Weighting, Bridge Piers, Bed Sill
dc.subject Artificial Neural Network, Scour Depth, Ordinary Kriging, Inverse Distance Weighting, Bridge Piers, Bed Sill
dc.title Application of Artificial Neural Network, Kriging, and Inverse Distance Weighting Models for Estimation of Scour Depth around Bridge Pier with Bed Sill
dc.type Journal Article
dc.description.version Published
dc.parent Journal of Software Engineering and Applications
dc.journal.volume 10
dc.journal.volume 3
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 0803 Computer Software
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
dc.description.keywords Science & Technology
dc.description.keywords Life Sciences & Biomedicine
dc.description.keywords Environmental Sciences
dc.description.keywords Marine & Freshwater Biology
dc.description.keywords Environmental Sciences & Ecology
dc.description.keywords ENVIRONMENTAL SCIENCES
dc.description.keywords MARINE & FRESHWATER BIOLOGY
dc.description.keywords ballast-water treatment
dc.description.keywords biological invasions
dc.description.keywords shipping
dc.description.keywords toxic dinoflagellate cysts
dc.description.keywords TANK SEDIMENTS
dc.description.keywords MICROORGANISMS
dc.description.keywords DINOPHYCEAE
dc.description.keywords TRANSPORT
dc.description.keywords INVASION
dc.description.keywords Artificial Neural Network, Scour Depth, Ordinary Kriging, Inverse Distance Weighting, Bridge Piers, Bed Sill
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group /University of Technology Sydney/Strength - Built Infrastructure


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