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dc.contributor.advisorTayfur, Gökmenen
dc.contributor.authorErsayın, Denizen
dc.date.accessioned2014-07-22T13:51:08Z
dc.date.available2014-07-22T13:51:08Z
dc.date.issued2006en
dc.identifier.urihttp://hdl.handle.net/11147/3228
dc.descriptionThesis (Master)--Izmir Institute of Technology, Civil Engineering, Izmir, 2006en
dc.descriptionIncludes bibliographical references (leaves: 73-75)en
dc.descriptionText in English; Abstract: Turkish and Englishen
dc.descriptionx, 75 leavesen
dc.description.abstractDams are structures that are used especially for water storage , energy production, and irrigation. Dams are mainly divided into four parts on the basis of the type and materials of construction as gravity dams, buttress dams, arch dams, and embankment dams. There are two types of embankment dams: earthfill dams and rockfill dams. In this study, seepage through an earthfill dam's body is investigated using an artificial neural network model. Seepage is investigated since seepage both in the dam's body and under the foundation adversely affects dam's stability. This study specifically investigated seepage in dam.s body. The seepage in the dams body follows a phreatic line. In order to understand the degree of seepage, it is necessary to measure the level of phreatic line. This measurement is called as piezometric measurement. Piezometric data sets which are collected from Jeziorsko earthfill dam in Poland were used for training and testing the developed ANN model. Jeziorsko dam is a non-homogeneous earthfill dam built on the impervious foundation. Artificial Neural Networks are one of the artificial intelligence related technologies and have many properties. In this study 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 artificial neural network model. In the line of the purpose of this research, the locus of the seepage path in an earthfill dam is estimated by artificial neural networks. MATLAB 6 neural network toolbox is used for this study.en
dc.language.isoengen
dc.publisherIzmir Institute of Technologyen
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.lccTC543 .E73 2006en
dc.subject.lcshEarth damsen
dc.subject.lcshDams--Data processingen
dc.subject.lcshNeural networks (Computer science)en
dc.titleStudying seepage in a body of earth-fill dam by (Artifical Neural Networks) ANNsen
dc.typemasterThesisen
dc.contributor.departmentIzmir Institute of Technology. Civil Engineeringen
dc.description.rfc1766Text in English; Abstract: Turkish and English.en
dc.relation.publicationcategoryTezen_US


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