Predicting Compressive Strength of Color Pigment Incorporated Roller Compacted Concrete via Machine Learning Algorithms: A Comparative Study

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dc.authoridCalis, Gokhan/0000-0001-7196-9407
dc.contributor.authorCalis, Gokhan
dc.contributor.authorYildizel, Sadik Alper
dc.contributor.authorKeskin, Ulku Sultan
dc.date.accessioned2024-01-22T12:22:14Z
dc.date.available2024-01-22T12:22:14Z
dc.date.issued2023
dc.departmentKMÜen_US
dc.description.abstractDue to its low albedo, traditional asphalt pavement contributes to the urban heat island effect. Color pigment added roller compacted high performance concrete is a novel approach to reducing the urban heat island effect through the use of paving materials. In this study, color pigment added roller compacted concrete specimens were produced and evaluate via the machine learning algorithms. Predicting compressive strength of concrete by utilization of machine learning methods is highly preferred method by scholars and professionals since ingredients' resources are intensive and time consuming. This research focused to predict the compressive strength of color pigment incorporated roller compacted concrete by applying multiple linear regression (ML), gradient boosting (GB), random forest (RF), support vector machines (SVM), artificial neural network (ANN) and bagging algorithms (BGG). A comprehensive database containing coarse aggregates, fine aggregate, water, cement and pigment amounts and density, age information as input parameters. The analysis results reveal that Bagging algorithm was able to obtain more satisfactory results than the other algorithms in predicting compressive strength (CS) of color pigment incorporated roller compacted concrete. In this algorithm, root mean square error (RMSE) was determined to be 1.53, R-2 to be 0.962, mean absolute error (MAE) to be 0.916, and mean absolute percentage error (MAPE) to be 0.033. ANN algorithm showed significant accuracy in prediction process with RMSE of 1.725, R-2 of 0.949, MAE of 1.144, and MAPE of 0.040. The lowest accuracy was obtained in SVM algorithm with RMSE of 26.910 R-2 of 0.512, MAE of 3.981, and MAPE of 0.040. Therefore, the present study can provide an efficient option for estimating the of color added Roller compacted concrete for pavements.en_US
dc.description.sponsorshipKonya Technical University Scientific Research Projects Coordinatorship [21104015]; Konya Technical Universityen_US
dc.description.sponsorship& nbsp;This research has been carried out with the support of Konya Technical University Scientific Research Projects Coordinatorship under Project Number of 21104015. The authors would like to thank Konya Technical University for their support.en_US
dc.identifier.doi10.1007/s42947-023-00321-y
dc.identifier.issn1996-6814
dc.identifier.issn1997-1400
dc.identifier.scopus2-s2.0-85166963960
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s42947-023-00321-y
dc.identifier.urihttps://hdl.handle.net/11492/7848
dc.identifier.wosWOS:001044183900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringernatureen_US
dc.relation.ispartofInternational Journal of Pavement Research and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzkmusnmz
dc.subjectRoller compacted concreteen_US
dc.subjectMachine learningen_US
dc.subjectLinear regressionen_US
dc.subjectRandom foresten_US
dc.subjectGradient boostingen_US
dc.subjectANNen_US
dc.subjectSupport vector machinesen_US
dc.subjectBagging regressoren_US
dc.titlePredicting Compressive Strength of Color Pigment Incorporated Roller Compacted Concrete via Machine Learning Algorithms: A Comparative Studyen_US
dc.typeArticle

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