Evaluating the strength and deformability properties of Misis fault breccia using artificial neural networks

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dc.authorid0000-0001-7903-143X
dc.authorid0000-0003-2488-7817
dc.contributor.authorKahraman, S.
dc.contributor.authorGunaydin, O.
dc.contributor.authorAlber, M.
dc.contributor.authorFener, M.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2009
dc.departmentNiğde ÖHÜ
dc.description.abstractSince the preparation of smooth specimens from the fault breccias are usually difficult and expensive, the development of some predictive models for the geomechanical properties of fault breccias will be useful. In this study, artificial neural networks (ANNs) analysis was applied on the data pertaining to Misis fault breccia to develop some predictive models for the uniaxial compressive strength (UCS) and elastic modulus (E) from the indirect methods. The developed ANNs models were also compared with the regression models. As a result of ANNs analysis, very good models were derived for both UCS and E estimation. It was shown that ANNs models were more reliable than the regression models. Concluding remark is that UCS and E values of Misis fault breccia can reliably be estimated from the indirect methods using ANNs analysis. (c) 2008 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2008.08.002
dc.identifier.endpage6878
dc.identifier.issn0957-4174
dc.identifier.issue3
dc.identifier.scopus2-s2.0-58349084247
dc.identifier.scopusqualityQ1
dc.identifier.startpage6874
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2008.08.002
dc.identifier.urihttps://hdl.handle.net/11480/5070
dc.identifier.volume36
dc.identifier.wosWOS:000263817100136
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFault breccia
dc.subjectUniaxial compressive strength
dc.subjectElastic modulus
dc.subjectPhysical and textural properties
dc.subjectArtificial neural networks
dc.titleEvaluating the strength and deformability properties of Misis fault breccia using artificial neural networks
dc.typeArticle

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