Prediction compressive strength of cement-based mortar containing metakaolin using explainable Categorical Gradient Boosting model

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dc.authoridArmağan Karamanlı / 0000-0003-3990-6515en_US
dc.authorscopusidArmağan Karamanlı / 55659970400en_US
dc.authorwosidArmağan Karamanlı / GWO-5489-2022en_US
dc.contributor.authorNguyen, Ngoc-Hien
dc.contributor.authorTong, Kien T.
dc.contributor.authorLee, Seunghye
dc.contributor.authorKaramanlı, Armağan
dc.contributor.authorVo, Thuc P.
dc.date.accessioned2022-10-31T06:16:08Z
dc.date.available2022-10-31T06:16:08Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Matematik Bölümüen_US
dc.description.abstractAlthough machine learning models have been employed for the compressive strength (CS) of cement-based mortar containing metakaolin, it is difficult to understand how they work due to “black-box” nature. In order to explain the involved mechanism, Categorical Gradient Boosting (CatBoost) model with feature importance, feature interaction, partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) is proposed in this paper. A dataset consisting of 424 samples with six input variables is used to build the CatBoost model, which has optimal performance by tuning a set of seven hyper-parameters using sequential model-based optimization. Five quantitative measures (R2, MAE, RMSE, a10-, a20-index) are employed to evaluate the accuracy and the obtained results are superior to the previous study. It is from feature importance that the most significant input variable involving the CS is water-to-binder ratio, followed by age of specimen and cement grade. The strongest feature interaction is between water-to-binder ratio and metakaolin. A comprehensive parametric study is carried out via SHAP and PDP to investigate the effects of all input variables on the CS of cement-based mortar.en_US
dc.identifier.citationNguyen, N. -., Tong, K. T., Lee, S., Karamanli, A., & Vo, T. P. (2022). Prediction compressive strength of cement-based mortar containing metakaolin using explainable categorical gradient boosting model. Engineering Structures, 269 doi:10.1016/j.engstruct.2022.114768en_US
dc.identifier.doi10.1016/j.engstruct.2022.114768en_US
dc.identifier.scopus2-s2.0-85136516083en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.engstruct.2022.114768
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3206
dc.identifier.volume269en_US
dc.identifier.wosWOS:000860590400005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKaramanlı, Armağan
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofEngineering Structuresen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCatBoosten_US
dc.subjectCement-Based Mortaren_US
dc.subjectPartial Dependence Ploten_US
dc.subjectSequential Model-based Optimizationen_US
dc.subjectShapley Additive exPlanationsen_US
dc.titlePrediction compressive strength of cement-based mortar containing metakaolin using explainable Categorical Gradient Boosting modelen_US
dc.typeArticleen_US

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