ANN based investigations of reliabilities of the models for concrete under triaxial compression

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dc.authorid6603855148
dc.contributor.authorÖztekin E.
dc.date.accessioned20.04.201910:49:12
dc.date.accessioned2019-04-20T21:43:42Z
dc.date.available20.04.201910:49:12
dc.date.available2019-04-20T21:43:42Z
dc.date.issued2016
dc.departmentBayburt Üniversitesien_US
dc.description.abstractPurpose - A lot of triaxial compressive models for different concrete types and different concrete strength classes were proposed to be used in structural analyses. The existence of so many models creates conflicts and confusions during the selection of the models. In this study, reliability analyses were carried out to prevent such conflicts and confusions and to determine the most reliable model for normal- and high-strength concrete (NSC and HSC) under combined triaxial compressions. The paper aims to discuss these issues. Design/methodology/approach - An analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. After verifying the validity of the model by making comparisons with the models in the literature, reliabilities of all models were investigated. The Monte Carlo simulation method was used in the reliability studies. Artificial experimental data required for the Monte Carlo simulation method were generated by using artificial neural networks. Findings - The validity of the proposed model was verified. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Finally, the reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions. Research limitations/implications - Concrete compressive strength and lateral compression were taken as variables in the model. Practical implications - The reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions. Originality/value - A new analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Artificial experimental data were obtained by using artificial neural networks. Four different artificial neural networks were developed to generate artificial experimental data. They can also be used in the estimations of the strength of NSC and HSC under different triaxial loadings. © 2016 Emerald Group Publishing Limited.en_US
dc.identifier.doi10.1108/EC-03-2015-0065
dc.identifier.endpage2044
dc.identifier.issn0264-4401
dc.identifier.issue7
dc.identifier.scopus2-s2.0-84989870429en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2019
dc.identifier.urihttps://dx.doi.org/10.1108/EC-03-2015-0065
dc.identifier.urihttps://hdl.handle.net/20.500.12403/647
dc.identifier.volume33
dc.identifier.wosWOS:000386792700007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Ltd.
dc.relation.ispartofEngineering Computations (Swansea, Wales)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural network
dc.subjectConcrete compressive strength
dc.subjectMonte Carlo simulation
dc.subjectReliability
dc.subjectTriaxial compression
dc.subjectAnalytical models
dc.subjectCompaction
dc.subjectConcretes
dc.subjectData compression
dc.subjectHigh performance concrete
dc.subjectIntelligent systems
dc.subjectMonte Carlo methods
dc.subjectNeural networks
dc.subjectOffshore pipelines
dc.subjectReliability
dc.subjectConcrete compressive strength
dc.subjectConcrete strength
dc.subjectDesign/methodology/approach
dc.subjectLateral compression
dc.subjectMonte Carlo simulation methods
dc.subjectNormal- and high-strength concretes
dc.subjectReliability Index
dc.subjectTriaxial compression
dc.subjectCompressive strength
dc.subjectArtificial neural network
dc.subjectConcrete compressive strength
dc.subjectMonte Carlo simulation
dc.subjectReliability
dc.subjectTriaxial compression
dc.subjectAnalytical models
dc.subjectCompaction
dc.subjectConcretes
dc.subjectData compression
dc.subjectHigh performance concrete
dc.subjectIntelligent systems
dc.subjectMonte Carlo methods
dc.subjectNeural networks
dc.subjectOffshore pipelines
dc.subjectReliability
dc.subjectConcrete compressive strength
dc.subjectConcrete strength
dc.subjectDesign/methodology/approach
dc.subjectLateral compression
dc.subjectMonte Carlo simulation methods
dc.subjectNormal- and high-strength concretes
dc.subjectReliability Index
dc.subjectTriaxial compression
dc.subjectCompressive strength
dc.titleANN based investigations of reliabilities of the models for concrete under triaxial compressionen_US
dc.typeArticleen_US

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