Investigation of Discontinuities Spacing Histograms by The Use Machine Learning Method

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dc.authorid0000-0002-0493-7787en_US
dc.contributor.authorUzun, Yusuf
dc.contributor.authorTuranboy, Alparslan
dc.contributor.authorTezel, Gülay
dc.date.accessioned2020-12-16T07:08:40Z
dc.date.available2020-12-16T07:08:40Z
dc.date.issued2012en_US
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractDiscontinuities are major geological features in the rock mass and discontinuity spacing is one of the important parameters in describing the rock mass. Relation between discontinuity spacing and relative spacing has described by different curve fittings. These curve fittings will show the type (negative exponential, log-normal or normal distribution) of the statistical distribution as histograms. Discontinuity spacing and frequency data obtained at a field site in southern Seydişehir (Turkey). Sampling methods vary from one study to another (core sampling, scan-line survey, aerial photograpy). In this study, the possible distributions of discontinuity spacing along a straight line through a rock mass are considered. In this study, 5 different drilling sampling have been used. We have examined discontinuity spacing and relative spacing relations that obtained from these core sampling with using machine learning method. Machine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data. Minitab (LEAD Technologies, Inc) and Weka (Waikato Environment for Knowledge Analysis) software where preferred in all analyses and interprets. Results of study, different empirical equation for each histogram have been constituted. Machine learning method has been treated on the obtained equations and reached interesting outputs.en_US
dc.identifier.citationUzun, Y. Tezel, G. Turanboy, A., (2012). Investigatıon of Discontinuities Spacing Histograms By The Use Machine Learning Method. Journal of Selcuk University Natural and Applied Science, 1, (1), 53-71.en_US
dc.identifier.endpage71en_US
dc.identifier.issn2147-3781en_US
dc.identifier.issue1en_US
dc.identifier.startpage53en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/40784
dc.identifier.volume1en_US
dc.institutionauthorTuranboy, Alparslan
dc.institutionauthorTezel, Gülay
dc.language.isotren_US
dc.publisherSelçuk Üniversitesien_US
dc.relation.ispartofJournal of Selcuk University Natural and Applied Scienceen_US
dc.relation.publicationcategoryMakale - Ulusal - Editör Denetimli Dergien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectDiscontinuityen_US
dc.subjectDiscontinuity Analysisen_US
dc.subjectDiscontinuity Distributionen_US
dc.subjectMachine Learningen_US
dc.titleInvestigation of Discontinuities Spacing Histograms by The Use Machine Learning Methoden_US
dc.typeArticleen_US

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