Interpretable predictive modelling of basalt fiber reinforced concrete splitting tensile strength using ensemble machine learning methods and SHAP approach

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dc.contributor.authorÇakıroğlu, Celal
dc.contributor.authorAydın, Yaren
dc.contributor.authorÇakıroğlu, Celal
dc.contributor.authorBektaş, Gebrail
dc.contributor.authorGeem, Zong Woo
dc.date.accessioned2024-04-04T18:41:48Z
dc.date.available2024-04-04T18:41:48Z
dc.date.issued2023
dc.departmentTAÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractBasalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength.
dc.identifier.citationÇakıroğlu, C., Aydın, Y., Çakıroğlu, C., Bektaş, G., Geem, Zong W. (2023). Interpretable predictive modelling of basalt fiber reinforced concrete splitting tensile strength using ensemble machine learning methods and SHAP approach. Materials Today Communications, 16 (13), 1-18.
dc.identifier.doi10.3390/ma16134578
dc.identifier.endpage18en_US
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-85164791642
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1028
dc.identifier.volume16en_US
dc.identifier.wosWOS:001028234300001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.relation.ispartofMaterials Today Communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectFRPen_US
dc.subjectConcreteen_US
dc.subjectSplitting tensile strengthen_US
dc.subjectMachine learningen_US
dc.subjectXGBoosten_US
dc.subjectSHAPen_US
dc.titleInterpretable predictive modelling of basalt fiber reinforced concrete splitting tensile strength using ensemble machine learning methods and SHAP approach
dc.typeArticle

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