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dc.creatorBojović, Dragan
dc.creatorNikolić, Dragan
dc.creatorJanković, Ksenija
dc.creatorLončar, Ljiljana
dc.date.accessioned2022-04-18T15:05:59Z
dc.date.available2022-04-18T15:05:59Z
dc.date.issued2012
dc.identifier.issn2217-8139
dc.identifier.urihttp://rims.institutims.rs/handle/123456789/156
dc.description.abstractMnogi parametri utiču na karakteristike svežeg i očvrslog betona. Jedan od najvažnijih svakako jeste poroznost betona. Poroznost svežeg betona meri se količinom uvučenog vazduha. Uticaj uvučenog vazduha na beton i njegovu pritisnu čvrstoću istraživali su mnogi svetski istraživači. Na bazi tih istraživanja, izdvojene su dve, u praksi najviše korišćene, formule. Tehnike mekog programiranja, a posebno neuronske mreže, s formiranim bazama podataka laboratorijskih ispitivanja betona, otvaraju novi pristup u predviđanju uticaja količine uvučenog vazduha u svežem betonu na njegovu čvrstoću pri pritisku.sr
dc.description.abstractMany parameters influence on the characteristics of fresh and hardened concrete. One of the most important characteristic of concrete is its porosity. Measure the porosity of fresh concrete is measured by the amount of entrained air. The effect of entrained air in concrete on compressive strength investigated by many authors. On the bases these works we have two formulas applicable in practice. Soft programming techniques especialy neural networks and the formation of databases related to the testing in laboratories for concrete opened up new approaches in predicting the impact of the quantity of entrained air in concrete on compressive strength.en
dc.publisherDruštvo za ispitivanje i istraživanje materijala i konstrukcija Srbije, Beograd
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.sourceGrađevinski materijali i konstrukcije
dc.subjectuvučen vazduhsr
dc.subjectneuronske mrežesr
dc.subjectčvrstoća pri pritiskusr
dc.subjectbetonsr
dc.subjectneural networksen
dc.subjectentrapped airen
dc.subjectconcreteen
dc.subjectcompressive strengthen
dc.titleEvaluation of air content on concrete compressive strength with classical approach and neural networksen
dc.typearticle
dc.rights.licenseBY-SA
dc.citation.epage54
dc.citation.issue1
dc.citation.other55(1): 47-54
dc.citation.rankM51
dc.citation.spage47
dc.citation.volume55
dc.identifier.fulltexthttp://rims.institutims.rs/bitstream/id/44/153.pdf
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_rims_156
dc.type.versionpublishedVersion


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