Comparison of artificial intelligence methods for predicting compressive strength of concrete

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dc.contributor.authorCihan, Mehmet Timur
dc.date.accessioned2022-05-11T14:03:07Z
dc.date.available2022-05-11T14:03:07Z
dc.date.issued2021
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractCompressive strength of concrete is an important parameter in concrete design. Accurate prediction of compressive strength of concrete can lower costs and save time. Therefore, thecompressive strength of concrete prediction performance of artificial intelligence methods (adaptive neuro fuzzy inference system, random forest, linear regression, classification and regression tree, support vector regression, k-nearest neighbour and extreme learning machine) are compared in this study using six different multinational datasets. The performance of these methods is evaluated using the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error criteria. Comparative results show that the adaptive neuro fuzzy inference system (ANFIS) is more successful in all datasets.
dc.identifier.doi10.14256/JCE.3066.2020
dc.identifier.endpage632
dc.identifier.issn0350-2465
dc.identifier.issn1333-9095
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85111693101
dc.identifier.scopusqualityQ4
dc.identifier.startpage617
dc.identifier.urihttps://doi.org/10.14256/JCE.3066.2020
dc.identifier.urihttps://hdl.handle.net/20.500.11776/4612
dc.identifier.volume73
dc.identifier.wosWOS:000674571200004
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorCihan, Mehmet Timur
dc.language.isoen
dc.publisherCroatian Soc Civil Engineers-Hsgi
dc.relation.ispartofGradevinar
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectartificial intelligence
dc.subjectregression
dc.subjectANFIS
dc.subjectconcrete compressive strength
dc.subjectmultinational data
dc.subjectSelf-Compacting Concrete
dc.subjectElastic-Modulus
dc.subjectSilica Fume
dc.subjectFly-Ash
dc.subjectPerformance
dc.subjectOptimization
dc.subjectMachine
dc.subjectSystem
dc.subjectAnfis
dc.subjectModel
dc.titleComparison of artificial intelligence methods for predicting compressive strength of concrete
dc.typeArticle

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