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A comparative assessment of tree-based predictive models to estimate geopolymer concrete compressive strength

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Abstract

Fly ash-based geopolymer concrete (FA-GPC) is a material that might be utilized to build a more sustainable construction industry; therefore, this paper aims to develop a novel approach for its compressive strength (CS) prediction. To achieve this goal, three tree-based machine learning methods, namely, Radom Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost), were developed based on experimental databases considering fourteen key factors of FA-GPC. The results indicated that XGBoost outperformed other methods, as proved by excellent prediction metrics. The sensitivity analysis of XGBoost models was then discussed utilizing feature importance, mean Shapley additive explanations (SHAP), and Beeswarm-SHAP values. The findings indicated that FA content had the most crucial impact on the CS, followed by SiO2 and NaOH contents, among the other variables examined. Finally, the SHAP dependence plots technique was utilized to quantitatively discuss feature interactions and contributions to the CS of FA-GPC.

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  1. University of Transport Technology, Hanoi, 100000, Vietnam

    May Huu Nguyen, Hai-Van Thi Mai, Son Hoang Trinh & Hai-Bang Ly

  2. Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan

    May Huu Nguyen

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Nguyen, M.H., Mai, HV.T., Trinh, S.H. et al. A comparative assessment of tree-based predictive models to estimate geopolymer concrete compressive strength. Neural Comput & Applic 35, 6569–6588 (2023). https://doi.org/10.1007/s00521-022-08042-2

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