Application of Machine Learning for Prediction of Mechanical Properties of Mortars and Concretes

but.event.date25.01.2024cs
but.event.titleJuniorstav 2024cs
dc.contributor.authorPrudil, Matěj
dc.date.accessioned2024-05-07T08:53:20Z
dc.date.available2024-05-07T08:53:20Z
dc.date.issued2024-05-07cs
dc.description.abstractThis paper deals with the application of machine learning (ML) in the field of concrete technology. Two databases of test mortars and concretes were created from selected academic theses, which include mechanical properties in relation to their composition. These databases were used to develop two ML models that predict the mechanical properties of mortars and concretes depending on their composition. The mortar test database contains a total of 242 mechanical property records and the concrete test database contains 111 records. The materials in the database are CEM I, CEM II and CEM III cements combined with additives such as ground granulated blast furnace slag, high temperature fly ash and micro-ground limestone.en
dc.formattextcs
dc.format.extent1-9cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationJuniorstav 2024: Proceedings 26th International Scientific Conference Of Civil Engineering, s. 1-9. ISBN 978-80-86433-83-7.cs
dc.identifier.doi10.13164/juniorstav.2024.24085en
dc.identifier.isbn978-80-86433-83-7
dc.identifier.urihttps://hdl.handle.net/11012/245408
dc.language.isoencs
dc.publisherVysoké učení technické v Brně,Fakulta stavebnícs
dc.relation.ispartofJuniorstav 2024: Proceedings 26th International Scientific Conference Of Civil Engineeringcs
dc.relation.urihttps://juniorstav.fce.vutbr.cz/proceedings2024/
dc.rights© Vysoké učení technické v Brně,Fakulta stavebnícs
dc.rights.accessopenAccessen
dc.subjectConcrete technologyen
dc.subjectmachine learningen
dc.subjectmechanical propertiesen
dc.subjectcompressive strengthen
dc.subjectflexural strengthen
dc.titleApplication of Machine Learning for Prediction of Mechanical Properties of Mortars and Concretesen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta stavebnícs
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