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dc.creatorKostić, Srdan
dc.creatorVasović, Nebojša
dc.creatorMarinković, Boban
dc.date.accessioned2021-03-10T13:30:46Z
dc.date.available2021-03-10T13:30:46Z
dc.date.issued2017
dc.identifier.issn0305-215X
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/3690
dc.description.abstractA new approach is proposed for the robust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulation. The essence of the suggested procedure lies in the reliable prediction of concrete strength as a simple function of unit mass, water/cement ratio, age and superplasticizer content. The derived model provides sufficiently accurate results for the calibration and verification phases, the latter of which is conducted using data that were not used for model development. The results of additional analysis indicate that residuals in the calibration and verification stages have a normal distribution. Is its also shown that the uncertainty of estimated coefficient values has a statistically insignificant effect on concrete strength, confirming the reliability of the proposed model. Moreover, analysis of the effect of laboratory measurement errors indicates the robustness of concrete compressive strength against the variation in measured parameter values.en
dc.publisherTaylor & Francis Ltd, Abingdon
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/176016/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/171017/RS//
dc.rightsrestrictedAccess
dc.sourceEngineering Optimization
dc.subjectConcrete compressive strengthen
dc.subjectresponse surface methodology (RSM)en
dc.subjecthistorical dataen
dc.subjectprediction modelen
dc.subjectMonte Carlo simulationen
dc.titleRobust optimization of concrete strength estimation using response surface methodology and Monte Carlo simulationen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage877
dc.citation.issue5
dc.citation.other49(5): 864-877
dc.citation.rankM22
dc.citation.spage864
dc.citation.volume49
dc.identifier.doi10.1080/0305215X.2016.1211432
dc.identifier.rcubconv_5252
dc.identifier.scopus2-s2.0-84981164273
dc.identifier.wos000396745600009
dc.type.versionpublishedVersion


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