Interpretable machine learning algorithms to predict the axial capacity of frp-reinforced concrete columns

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dc.authorid0000-0001-7329-1230
dc.contributor.authorÇakıroğlu, Celal
dc.contributor.authorIslam, Kamrul
dc.contributor.authorBekdaş, Gebrail
dc.contributor.authorKim, Sanghun
dc.contributor.authorGeem, Zong Woo
dc.date.accessioned2022-11-04T08:34:29Z
dc.date.available2022-11-04T08:34:29Z
dc.date.issued2022
dc.departmentTAÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractFiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel rebars in reinforced concrete (RC) members due to their excellent corrosion resistance capability and enhanced mechanical properties. Extensive research works have been performed in the last two decades to develop predictive models, codes, and guidelines to estimate the axial load-carrying capacity of FRP-RC columns. This study utilizes the power of artificial intelligence and develops an alternative approach to predict the axial capacity of FRP-RC columns more accurately using data-driven machine learning (ML) algorithms. A database of 117 tests of axially loaded FRP-RC columns is collected from the literature. The geometric and material properties, column shape and slenderness ratio, reinforcement details, and FRP types are used as the input variables, while the load-carrying capacity is used as the output response to develop the ML models. Furthermore, the input-output relationship of the ML model is explained through feature importance analysis and the SHapely Additive exPlanations (SHAP) approach. Eight ML models, namely, Kernel Ridge Regression, Lasso Regression, Support Vector Machine, Gradient Boosting Machine, Adaptive Boosting, Random Forest, Categorical Gradient Boosting, and Extreme Gradient Boosting, are used in this study for capacity prediction, and their relative performances are compared to identify the best-performing ML model. Finally, predictive equations are proposed using the harmony search optimization and the model interpretations obtained through the SHAP algorithm.
dc.identifier.citationÇakıroğlu, C., Islam, K., Bekdaş, G., Kim, S., & Geem, Z. W. (2022). Interpretable machine learning algorithms to predict the axial capacity of frp-reinforced concrete columns. Materials, 15(8), 2742.
dc.identifier.doi10.3390/ma15082742
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85128679001
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12846/668
dc.identifier.volume15en_US
dc.identifier.wosWOS:000786856000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorÇakıroğlu, Celal
dc.language.isoen
dc.publisherMDPI-Multidisciplinary Digital Publishing Institute
dc.relation.ispartofMaterials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectFiber Reinforced Polymer (FRP) Rebaren_US
dc.subjectReinforced Concrete Columnsen_US
dc.subjectAxial Capacityen_US
dc.subjectFaserverstärktes Polymer (FRP) Bewehrungen_US
dc.subjectStahlbetonsäulenen_US
dc.subjectAxiale Kapazitäten_US
dc.subjectFiber Takviyeli Polimer (FRP) İnşaat Demirien_US
dc.subjectBetonarme Kolonlaren_US
dc.subjectEksenel Kapasiteen_US
dc.titleInterpretable machine learning algorithms to predict the axial capacity of frp-reinforced concrete columns
dc.typeArticle

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