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

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Tabar M. E., Katlav M., Türk K.

MATERIALS TODAY COMMUNICATIONS, cilt.44, ss.1-24, 2025 (SCI-Expanded, Scopus)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 44
• Basım Tarihi: 2025
• Doi Numarası: 10.1016/j.mtcomm.2025.112028
• Dergi Adı: MATERIALS TODAY COMMUNICATIONS
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
• Sayfa Sayıları: ss.1-24
• İnönü Üniversitesi Adresli: Evet

Özet

This 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.