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dc.contributor.authorCeryan, Nurcihan
dc.contributor.authorCan, Nuray Korkmaz
dc.date.accessioned2021-03-04T09:04:15Z
dc.date.available2021-03-04T09:04:15Z
dc.identifier.citationCeryan N., Can N. K. , "Prediction of The Uniaxial Compressive Strength of Rocks Materials", HANDBOOK OF RESEARCH ON TRENDS AND DIGITAL ADVANCES IN ENGINEERING GEOLOGY, ss.31-96, 2018
dc.identifier.issn2326-6155
dc.identifier.othervv_1032021
dc.identifier.otherav_6686379b-e3b7-46cf-9531-e3597e83bdbf
dc.identifier.urihttp://hdl.handle.net/20.500.12627/71191
dc.identifier.urihttps://doi.org/10.4018/978-1-5225-2709-1.ch002
dc.description.abstractThis study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.
dc.language.isoeng
dc.subjectMÜHENDİSLİK, JEOLOJİK
dc.subjectMühendislik ve Teknoloji
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.titlePrediction of The Uniaxial Compressive Strength of Rocks Materials
dc.typeMakale
dc.relation.journalHANDBOOK OF RESEARCH ON TRENDS AND DIGITAL ADVANCES IN ENGINEERING GEOLOGY
dc.contributor.departmentBalıkesir Üniversitesi , ,
dc.identifier.startpage31
dc.identifier.endpage96
dc.contributor.firstauthorID249417


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