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dc.creatorSingh, Vijay P.
dc.creatorSwiatek, Dorota
dc.creatorTayfur, Gokmen
dc.creatorWita, Andrew
dc.date.accessioned2017-10-19T14:10:58Z
dc.date.available2017-10-19T14:10:58Z
dc.date.issued2005-06-01
dc.identifier.issn0733-9429
dc.identifier.urihttp://hdl.handle.net/1969.1/164665
dc.description.abstractA finite element method (FEM) and an artificial neural network (ANN) model were developed to simulate flow through Jeziorsko earthfill dam in Poland. The developed FEM is capable of simulating two-dimensional unsteady and nonuniform flow through a nonhomogenous and anisotropic saturated and unsaturated porous body of an earthfill dam. For Jeziorsko dam, the FEM model had 5,497 triangular elements and 3,010 nodes, with the FEM network being made denser in the dam body and in the neighborhood of the drainage ditches. The ANN model developed for Jeziorsko dam was a feedforward three layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the ANN model. The two models were calibrated and verified using the piezometer data collected on a section of the Jeziorsko dam. The water levels computed by the models satisfactorily compared with those measured by the piezometers. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM model. This case study offers insight into the adequacy of ANN as well as its competitiveness against FEM for predicting seepage through an earthfill dam body.en
dc.language.isoen_US
dc.subjectSeepageen
dc.subjectDamsen
dc.subjectEarthen
dc.subjectPolanden
dc.subjectNeural networksen
dc.subjectNumerical modelsen
dc.subjectCase reportsen
dc.titleCase Study: Finite Element Method and Artificial Neural Network Models for Flow through Jeziorsko Earthfill Dam in Polanden
dc.typeArticleen
local.departmentBiological and Agricultural Engineering (College of Agriculture and Life Sciences)en
dc.identifier.doi10.1061/(ASCE)0733-9429(2005)131:6(431)


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