Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

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dc.authoridTeymen, Ahmet/0000-0001-7952-1025
dc.contributor.authorTeymen, Ahmet
dc.contributor.authorMenguc, Engin Cemal
dc.date.accessioned2024-11-07T13:24:37Z
dc.date.available2024-11-07T13:24:37Z
dc.date.issued2020
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is(50)) and P-wave velocity (V-p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PIat was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively. (C) 2020 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
dc.identifier.doi10.1016/j.ijmst.2020.06.008
dc.identifier.endpage797
dc.identifier.issn2095-2686
dc.identifier.issn2212-6066
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85087763002
dc.identifier.scopusqualityQ1
dc.identifier.startpage785
dc.identifier.urihttps://doi.org/10.1016/j.ijmst.2020.06.008
dc.identifier.urihttps://hdl.handle.net/11480/14217
dc.identifier.volume30
dc.identifier.wosWOS:000591871800005
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofInternational Journal of Mining Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectUniaxial compressive strength
dc.subjectAdaptive neuro-fuzzy inference system
dc.subjectMultiple regression
dc.subjectArtificial neural network
dc.subjectGenetic expression programming
dc.titleComparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks
dc.typeArticle

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