Explainable ensemble algorithms with grey wolf optimization for estimation of the tensile performance of polyethylene fiber-reinforced engineered cementitious composite

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dc.contributor.authorTabar, Mehmet Emin
dc.contributor.authorKatlav, Metin
dc.contributor.authorTurk, Kazim
dc.date.accessioned2026-04-04T13:34:52Z
dc.date.available2026-04-04T13:34:52Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThis paper extensively examines the applicability of optimized ensemble machine learning (ML) algorithms via grey wolf optimization (GWO) to estimate the tensile performance of polyethylene fiber-reinforced engineered cementitious composites (PE-ECC). A robust and credible dataset is utilized for the establishment of the models based on available studies in the literature: The dataset includes 132 instances of PE-ECC mixes with 11 input features and 2 target output. Moreover, feature importance, Shapley additive explanation (SHAP) and partial dependence (PDP) analyses are implemented to enhance the explainability of the estimation models and to address the black box challenge of ML models. Based on the obtained results, the optimized extreme gradient boosting (XGBoost) and categorical boosting (CatBoost) models with GWO estimated the tensile performance of PE-ECC more effectively and accurately in comparison with other ensemble models. This has been extensively evaluated and proved through various approaches such as performance indicators, Taylor diagram, error analysis, and score analysis. To give a quantitative example, in the testing phase, for the prediction of tensile strain capacity, the GWO-XGBoost model reached the highest accuracy values with R2= 0.785 and RMSE= 1.077, whereas for the GWO-CatBoost model, these performance indicators were 0.764 and 1.129, respectively. In terms of tensile strength prediction, the GWO-XGBoost model achieved a high prediction accuracy with R2= 0.930 and RMSE= 1.004, while for the GWO-CatBoost model, R2 and RMSE were 0.932 and 0.987, respectively. Meanwhile, SHAP and PDP analyses were employed to identify the most influential features on output, and thus providing precious insight for designers to improve the tensile performance of PE-ECC. Additionally, a userfriendly graphical user interface (GUI) was constructed for estimating the tensile performance of PE-ECC and validated with new experimental datasets, illustrating the efficiency of the models. All in all, the importance of this work highlights the superior performance of the advanced GWO-ML models and GUI for estimating the tensile performance of PE-ECC and is thought to be a valuable contribution for further research in this area.
dc.identifier.doi10.1016/j.mtcomm.2025.112028
dc.identifier.issn2352-4928
dc.identifier.orcid0000-0001-9093-7195
dc.identifier.orcid0000-0002-3234-5340
dc.identifier.scopus2-s2.0-85218870096
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.mtcomm.2025.112028
dc.identifier.urihttps://hdl.handle.net/11616/109463
dc.identifier.volume44
dc.identifier.wosWOS:001436147100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofMaterials Today Communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectEngineered cementitious composites (ECC)
dc.subjectTensile performance
dc.subjectPolyethylene fiber (PE)
dc.subjectGrey wolf optimization (GWO)
dc.subjectEnsemble algorithm
dc.titleExplainable ensemble algorithms with grey wolf optimization for estimation of the tensile performance of polyethylene fiber-reinforced engineered cementitious composite
dc.typeArticle

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