Prediction of Compressive Strength Using Support Vector Regression

dc.contributor.authorSai, Goutham J
dc.contributor.authorSingh, Vijay Pal
dc.coverage.issue1cs
dc.coverage.volume25cs
dc.date.accessioned2020-05-05T07:21:09Z
dc.date.available2020-05-05T07:21:09Z
dc.date.issued2019-06-24cs
dc.description.abstractAt the design stage of a structure, the members of adequate dimension and strength is provided. But with passage of time, the strength of the members reduces gradually due to exposure to environmental conditions and unexpected loadings other than for which the structure is designed. Non Destructive Testing (NDT) method provides a convenient and rapid method of determination of existing strength of concrete without subjecting the member to any damage. In the present study, Support Vector Regression (SVR) in Python has been used for the prediction of compressive strength of concrete. Three different NDT techniques have been used as input for the SVR model. A good co-relation between predicted strength and strength determined after crushing the concrete cubes has been achieved. It has also been observed that accuracy in the predicted strength is more in case of inputs from more than one NDT technique is used.en
dc.formattextcs
dc.format.extent51-56cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2018 vol. 25, č. 1, s. 51-56. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2019.1.051en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/186981
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/78cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectrebound hammeren
dc.subjectultrasonic pulse velocityen
dc.subjectpenetrationen
dc.subjectsupport vector regressionen
dc.subjectNDTen
dc.titlePrediction of Compressive Strength Using Support Vector Regressionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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