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

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dc.authoridCemiloglu, Ahmed/0000-0003-2633-0924
dc.authoridAzarafza, Mohammad/0000-0001-7777-3800
dc.authoridARSLAN, SIBEL/0000-0003-3626-553X
dc.authoridDerakhshani, Reza/0000-0001-7499-4384
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.accessioned2025-05-04T16:45:49Z
dc.date.available2025-05-04T16:45:49Z
dc.date.issued2023
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractFeatured Application AI application in UCS prediction for limestones of Maragheh. The 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 (R-2) reached 0.967, showing that the model's performance was impressively better than that of traditional fitting curves.
dc.identifier.doi10.3390/app13042217
dc.identifier.issn2076-3417
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85149283479
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app13042217
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35215
dc.identifier.volume13
dc.identifier.wosWOS:000938732100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250504
dc.subjectuniaxial compression strength
dc.subjectsupport vector machine
dc.subjectfitting curves
dc.subjectprediction performance
dc.subjectlimestone
dc.titleSupport Vector Machine (SVM) Application for Uniaxial Compression Strength (UCS) Prediction: A Case Study for Maragheh Limestone
dc.typeArticle

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