A comparative assessment of bagging ensemble models for modeling concrete slump flow

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dc.contributor.authorAydoğmuş, Hacer Yumurtacı
dc.contributor.authorErdal, Halil İbrahim
dc.contributor.authorKarakurt, Onur
dc.contributor.authorNamlı, Ersin
dc.contributor.authorTürkan, Yusuf S.
dc.contributor.authorErdal, Hamit
dc.date.accessioned2021-02-19T21:16:16Z
dc.date.available2021-02-19T21:16:16Z
dc.date.issued2015
dc.departmentALKÜ
dc.descriptionNamli, Ersin/0000-0001-5980-9152
dc.description.abstractIn the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.
dc.identifier.doi10.12989/cac.2015.16.5.741
dc.identifier.endpage757en_US
dc.identifier.issn1598-8198
dc.identifier.issn1598-818X
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage741en_US
dc.identifier.urihttps://doi.org/10.12989/cac.2015.16.5.741
dc.identifier.urihttps://hdl.handle.net/20.500.12868/349
dc.identifier.volume16en_US
dc.identifier.wosWOS:000367921100005
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor0-belirlenecek
dc.language.isoen
dc.publisherTechno-Press
dc.relation.ispartofComputers And Concrete
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectbagging (bootstrap aggregating)
dc.subjectclassification and regression trees
dc.subjectensemble learning
dc.subjectmultilayer perceptron
dc.subjectsupport vector machines
dc.titleA comparative assessment of bagging ensemble models for modeling concrete slump flow
dc.typeArticle

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