Modeling uniaxial compressive strength of some rocks from turkey using soft computing techniques

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dc.authoridEren Sarıcı, Didem/0000-0003-2639-5226
dc.authoridÖzdemir, Engin/0000-0002-6043-0403
dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authorwosidEren Sarıcı, Didem/ABG-8471-2020
dc.authorwosidÖzdemir, Engin/ABG-7954-2020
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.contributor.authorGul, Enes
dc.contributor.authorOzdemir, Engin
dc.contributor.authorSarici, Didem Eren
dc.date.accessioned2024-08-04T20:49:14Z
dc.date.available2024-08-04T20:49:14Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractUniaxial compressive strength (UCS) is substantially used mechanical parameters to observe and classification of rocks, but this test is subsersive, taking a long time and required well equipped laboratory conditions and properly prepared samples. Therefore it is important to estimate this parameter from other physico-mechanical rock parameters that are nondestructive, easy to prepare samples and required less time. Machine learning methods which are among these methods and increase their importance and validty are Multilayer Perceptron Neural Network (MLPNN), M5 Model Tree (M5MT), Extreme Learning Machine (ELM) methods. In this study, Brazilian tensile strength, ultrasonic P-wave velocity, shore hardness tests of different rock types (Basalt, limestone, dolostone) were performed. The results were used for estimating UCS using MLPNN, M5MT, ELM methods. The validation of models were checked root mean squared error (RMSE), mean absolute error (MAE), variance account for (VAF) and coefficient of determination (R-2) and a10-index. Weights and bias values for MLPNN and ELM approaches and the tree structure for the M5MT method are presented. The result indicated MLPNN model outperforms the other models. Based on the result of predictive models with RMSE, MAE, VAF and R-2 equal to RMSE: 1.3421, MAE: 0.7985, VAF: 99.7409, R-2: 0.9982%.en_US
dc.identifier.doi10.1016/j.measurement.2020.108781
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-85099176798en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2020.108781
dc.identifier.urihttps://hdl.handle.net/11616/99713
dc.identifier.volume171en_US
dc.identifier.wosWOS:000614791100002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMeasurementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRocken_US
dc.subjectUniaxial Compressive Strengthen_US
dc.subjectMultilayer Perceptron Neural Networken_US
dc.subjectM5 Model Treeen_US
dc.subjectExtreme Learning Machine Algorithmen_US
dc.titleModeling uniaxial compressive strength of some rocks from turkey using soft computing techniquesen_US
dc.typeArticleen_US

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