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dc.contributor.authorCeryan, Nurcihan
dc.contributor.authorOkkan, Umut
dc.contributor.authorSamui, Pijush
dc.contributor.authorCeryan, Şener
dc.date.accessioned2019-10-22T11:03:32Z
dc.date.available2019-10-22T11:03:32Z
dc.date.issued2013en_US
dc.identifier.issn0363-9061
dc.identifier.issn1096-9853
dc.identifier.urihttps://doi.org/10.1002/nag.2154
dc.identifier.urihttps://hdl.handle.net/20.500.12462/9123
dc.description.abstractIn the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS-SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS-SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS-SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS-SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS-SVM model resulted in error reductions relative to that of the other models.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.isversionof10.1002/nag.2154en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectTensile Strength Modelingen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectLeast Squares Support Vector Machinesen_US
dc.subjectArtificial Neural Networksen_US
dc.titleModeling of tensile strength of rocks materials based on support vector machines approachesen_US
dc.typearticleen_US
dc.relation.journalInternational Journal for Numerical and Analytical Methods in Geomechanicsen_US
dc.contributor.departmentBalıkesir Meslek Yüksekokuluen_US
dc.contributor.authorID0000-0003-2906-6479en_US
dc.contributor.authorID0000-0001-7359-8718en_US
dc.contributor.authorID0000-0003-1284-3825en_US
dc.identifier.volume37en_US
dc.identifier.issue16en_US
dc.identifier.startpage2655en_US
dc.identifier.endpage2670en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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