Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances

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dc.authoridSezer, Ebru Akcapinar -- 0000-0002-9287-2679; Gokceoglu, Candan -- 0000-0003-4762-9933; Gokceoglu, Candan -- 0000-0003-4762-9933;
dc.contributor.authorYeşiloğlu Gültekin, Nurgül
dc.contributor.authorGökceoğlu, C.
dc.contributor.authorSezer, Ebru Akçapınar
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-29T19:26:18Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-29T19:26:18Z
dc.date.issued2013
dc.departmentMühendislik Fakültesi
dc.description.abstractThe main goal of this study is to develop some prediction models for the UCS of six different granitic rocks selected from Turkey. During the modeling stage of the study, various approaches such as multiple regression, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are applied to estimate UCS. Tensile strength (sigma(t)), block punch index (BPI), point load index (Is((50))) and P-wave velocity (V-p) are considered as the input parameters for the models. In the study, total 75 cases including all inputs and output are used. In accordance with the analyses employed in the study, and considering the inputs, three different models are constructed as tensile strength and P-wave velocity (Model 1), BPI and P-wave velocity (Model 2), Is((50)) and P-wave velocity (Model 3) to estimate UCS. Performance assessments show that ANFIS is the better predictive tool than the other methods employed, and Model 1 is the better model for the prediction of UCS. The results show that the models developed can be used as preliminary stages of rock engineering assessments because the models developed herein have high prediction performances. It is evident that such prediction studies provides not only some practical tools but also understanding of the controlling index parameters of UCS of rocks. (C) 2013 Elsevier Ltd. All rights reserved.
dc.description.sponsorshipHacettepe University Scientific Research Unit, Ankara, Turkey [08D07602 002]
dc.description.sponsorshipThis study was supported by the Hacettepe University Scientific Research Unit, Ankara, Turkey with Project no. 08D07602 002. Also, the authors are grateful to the reviewers for their constructive comments.
dc.identifier.doi10.1016/j.ijrmms.2013.05.005
dc.identifier.endpage122en_US
dc.identifier.issn1365-1609
dc.identifier.scopusqualityQ1
dc.identifier.startpage113en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijrmms.2013.05.005
dc.identifier.urihttps://hdl.handle.net/20.500.12451/5529
dc.identifier.volume62en_US
dc.identifier.wosWOS:000323533900014
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMühendislik Fakültesi
dc.relation.ispartofInternational Journal of Rock Mechanics and Mining Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectGranite
dc.subjectUniaxial Compressive Strength
dc.subjectPrediction Method
dc.subjectANFIS
dc.subjectANN
dc.subjectMultiple Regression
dc.titlePrediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances
dc.typeArticle

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