Explainable ensemble learning models for the rheological properties of self-compacting concrete

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dc.authorid0000-0001-7329-1230
dc.contributor.authorÇakıroğlu, Celal
dc.contributor.authorBekdaş, Gebrail
dc.contributor.authorKim, Sanghun
dc.contributor.authorGeem, Zong Woo
dc.date.accessioned2023-03-08T12:19:59Z
dc.date.available2023-03-08T12:19:59Z
dc.date.issued2022
dc.departmentTAÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractSelf-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable's effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity.
dc.identifier.citationÇakıroğlu, C., Bekdaş, G., Kim, S., & Geem, Z. W. (2022). Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete. Sustainability, 14(21), 14640.
dc.identifier.doi10.3390/su142114640
dc.identifier.issue21en_US
dc.identifier.scopus2-s2.0-85147882916
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12846/705
dc.identifier.volume14en_US
dc.identifier.wosWOS:000884524400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÇakıroğlu, Celal
dc.language.isoen
dc.publisherMDPI-Multidisciplinary Digital Publishing Institute
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectPlastic Viscosityen_US
dc.subjectSelf-Compacting Concreteen_US
dc.subjectYield Stressen_US
dc.subjectV-Funnel Flowen_US
dc.subjectPlastische Viskositäten_US
dc.subjectSelbstverdichtender Betonen_US
dc.subjectFließspannungen_US
dc.subjectV-Trichter Fließenen_US
dc.subjectPlastik Viskoziteen_US
dc.subjectKendiliğinden Yerleşen Betonen_US
dc.subjectAkma Gerilmesien_US
dc.subjectV-Huni Akışıen_US
dc.titleExplainable ensemble learning models for the rheological properties of self-compacting concrete
dc.typeArticle

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