Support Vector Machine (SVM) Application for Uniaxial Compression Strength (UCS) Prediction: A Case Study for Maragheh Limestone

dc.authorid0000-0003-2633-0924tr
dc.authorid0000-0002-5066-3836tr
dc.authorid0000-0003-3626-553Xtr
dc.authorid0000-0003-4972-2840tr
dc.authorid0000-0002-9072-7179tr
dc.authorid0000-0001-7777-3800tr
dc.authorid0000-0001-7499-4384tr
dc.contributor.authorCemiloglu, Ahmed
dc.contributor.authorZhu Licai
dc.contributor.authorArslan, Sibel
dc.contributor.authorXu,Jinxia
dc.contributor.authorYuan,Xiaofeng
dc.contributor.authorAzarafza, Mohammad
dc.contributor.authorDerakhshani, Reza
dc.date.accessioned2024-02-28T12:19:56Z
dc.date.available2024-02-28T12:19:56Z
dc.date.issued09.02.2023tr
dc.departmentSağlık Bilimleri Fakültesitr
dc.description.abstractThe geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R2) reached 0.967, showing that the model’s performance was impressively better than that of traditional fitting curves.tr
dc.identifier.endpage14tr
dc.identifier.issue2217tr
dc.identifier.scopus2-s2.0-85149283479en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage1tr
dc.identifier.urihttps://www.mdpi.com/2076-3417/13/4/2217
dc.identifier.urihttps://hdl.handle.net/20.500.12418/14416
dc.identifier.volume13tr
dc.identifier.wosWOS:000938732100001en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofApplied Sciencesen_US
dc.relation.publicationcategoryUluslararası Editör Denetimli Dergide Makaletr
dc.rightsinfo:eu-repo/semantics/openAccesstr
dc.titleSupport Vector Machine (SVM) Application for Uniaxial Compression Strength (UCS) Prediction: A Case Study for Maragheh Limestoneen_US
dc.typeArticleen_US

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