Stacking ensemble models for data-driven intelligent modelling of compressive strength of sustainable recycled brick aggregate concrete (RBAC)
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, the applicability of base and stacking ensemble models for the data-driven prediction of the compressive strength (CS) of recycled brick aggregate concrete (RBAC) has been extensively evaluated. In this sense, a raw database consisting of 374 observations compiled from the literature was cleaned of outliers using the unsupervised learning algorithm Isolation Forest (IF), yielding a clean database of 324 observations with seven input features for use in the modeling process. During the modeling phase, a total of fourteen different prediction models were assessed, including four base ensemble machine learning (ML) models and ten stacking ensemble models built using various combinations of these models. To improve the interpretability of the model's decision mechanism and examine the marginal effect of each input feature on the prediction, Shapley Additive Explanations (SHAP)-based feature importance analysis and Individual Conditional Expectation (ICE) analysis were implemented. Lastly, to support the practical use of the developed models, a user-friendly graphical user interface (GUI) was designed, enabling engineers and field practitioners to make fast and reliable durability predictions for RBAC mixtures. Considering the comprehensive evaluations, all base learners developed in the test phase achieved an average R2 of 0.825 and RMSE of 3.22. In contrast, the stacking ensemble models improved these metrics, reaching an average R2 of 0.851 and RMSE of 2.98, thereby confirming the effectiveness of ensemble learning in enhancing prediction accuracy. Notably, the SM-2 model, derived from the binary combination of CB and GBM, demonstrated the best performance among all base and staking ensemble models with R = 0.927, R2= 0.857, RMSE = 2.91, and U95 = 4.13 during the test phase. Meanwhile, in general, it is remarkable that stacking models based on ternary combinations showed more stable and accurate predictions compared to those based on binary combinations. The SHAP and ICE-based explainability analyses revealed the marginal effects of each input feature on the predicted CS, providing valuable engineering insights for mixture optimization strategies. All in all, this study presents a robust and interpretable AI-based framework for the CS prediction of sustainable and eco-friendly RBAC, contributing significantly to the field of data-driven green concrete design.
Açıklama
Anahtar Kelimeler
Recycled brick aggregate concrete, Compressive strength, Stacking approach, Ensemble models, AI for sustainable materials
Kaynak
Materials Today Communications
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
49
