A New Predictive Model for Uniaxial Compressive Strength of Rock Using Machine Learning Method: Artificial Intelligence-Based Age-Layered Population Structure Genetic Programming (ALPS-GP)

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dc.authoridÖzdemir, Engin/0000-0002-6043-0403
dc.authorwosidÖzdemir, Engin/ABG-7954-2020
dc.contributor.authorOzdemir, Engin
dc.date.accessioned2024-08-04T20:50:17Z
dc.date.available2024-08-04T20:50:17Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractUniaxial compressive strength (UCS) of rocks is the most commonly used parameter in geo-engineering application. However, this parameter is hard for measurement due to a time consuming and requires expensive equipment. Therefore, obtaining this value indirectly using non-destructive testing methods has been a frequently preferred method for a long time. In order to obtain multiple regression models, input parameters need many assumptions. Thus, the estimation of the mechanical properties of rocks using by machine learning methods has been investigated. In this study, UCS values of rocks were estimated by reformulating with artificial intelligence-based age-layered population structure genetic programming (ALPS-GP) which is one of machine learning methods. Artificial neural network (ANN) and ALPS-GP models were performed to predict UCS from porosity, Schmidt hammer hardness and ultrasonic wave velocity test methods. For this purpose, the mentioned three tests (porosity, Schmidt hammer hardness and P-wave velocity) were carried out on ten different stones from Turkey. ANN was performed to evaluate this new technique. Reliability of UCS values determined by models was checked with mean absolute error (MAE), coefficient of determination (R-2), root mean square error (RMSE) and variance account for (VAF) values. These values were calculated as 1.64, 0.98, 2.11 and 99.81 for ANN, and 2.11, 0.98, 2.50 and 97.86 for ALPS-GP, respectively. It was observed that both methods used were quite successful in UCS estimation. The most important advantage of the ALPS-GP model is providing an equation for UCS estimation. In the light of the obtained findings, it has been revealed that this equation derived from ALPS-GP can be used in UCS estimation processes of similar rock types (limestone, dolomite and onyx).en_US
dc.identifier.doi10.1007/s13369-021-05761-x
dc.identifier.endpage639en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85107483528en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage629en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-021-05761-x
dc.identifier.urihttps://hdl.handle.net/11616/99974
dc.identifier.volume47en_US
dc.identifier.wosWOS:000658277500008en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligence-based age-layered population structure genetic programming (ALPS-GP)en_US
dc.subjectArtificial neural networken_US
dc.subjectUniaxial compressive strengthen_US
dc.subjectP-wave velocitiesen_US
dc.subjectSchmidt hardnessen_US
dc.titleA New Predictive Model for Uniaxial Compressive Strength of Rock Using Machine Learning Method: Artificial Intelligence-Based Age-Layered Population Structure Genetic Programming (ALPS-GP)en_US
dc.typeArticleen_US

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