Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

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dc.authorscopusid50161145100
dc.contributor.authorCihan, Mehmet Timur
dc.date.accessioned2022-05-11T14:03:06Z
dc.date.available2022-05-11T14:03:06Z
dc.date.issued2019
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractMachine learning methods have been successfully applied to many engineering disciplines. Prediction of the concrete compressive strength (fc) and slump (S) is important in terms of the desirability of concrete and its sustainability. The goals of this study were (i) to determine the most successful normalization technique for the datasets, (ii) to select the prime regression method to predict the fc and S outputs, (iii) to obtain the best subset with the ReliefF feature selection method, and (iv) to compare the regression results for the original and selected subsets. Experimental results demonstrate that the decimal scaling and min-max normalization techniques are the most successful methods for predicting the compressive strength and slump outputs, respectively. According to the evaluation metrics, such as the correlation coefficient, root mean squared error, and mean absolute error, the fuzzy logic method makes better predictions than any other regression method. Moreover, when the input variable was reduced from seven to four by the ReliefF feature selection method, the predicted accuracy was within the acceptable error rate. © 2019 M. Timur Cihan.
dc.identifier.doi10.1155/2019/3069046
dc.identifier.issn1687-8086
dc.identifier.scopus2-s2.0-85076620083
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1155/2019/3069046
dc.identifier.urihttps://hdl.handle.net/20.500.11776/4605
dc.identifier.volume2019
dc.identifier.wosWOS:000501766300002
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorCihan, Mehmet Timur
dc.language.isoen
dc.publisherHindawi Limited
dc.relation.ispartofAdvances in Civil Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titlePrediction of Concrete Compressive Strength and Slump by Machine Learning Methods
dc.typeArticle

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