Bayesian-Optimized Ensemble Models for Geopolymer Concrete Compressive Strength Prediction with Interpretability Analysis

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Detalles Bibliográficos
Publicado en:	Buildings vol. 15, no. 20 (2025), p. 3667-3691
Autor principal:	Timur, Cihan Mehmet
Otros Autores:	Cihan Pınar
Publicado:	MDPI AG
Materias:	Mechanical properties Accuracy Concrete Regression analysis Artificial neural networks Optimization Graphical user interface Machine learning Performance evaluation Sodium hydroxide Decision trees User interface Bayesian analysis Artificial intelligence Support vector machines Predictions Geopolymers Neural networks Decision making Variables Algorithms Cement Ensemble learning Concrete properties Mathematical models Compressive strength
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Descripción
Resumen:	Accurate prediction of geopolymer concrete compressive strength is vital for sustainable construction. Traditional experiments are time-consuming and costly; therefore, computer-aided systems enable rapid and accurate estimation. This study evaluates three ensemble learning algorithms (Extreme Gradient Boosting (XGB), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), as well as two baseline models (Support Vector Regression (SVR) and Artificial Neural Network (ANN)), for this task. To improve performance, hyperparameter tuning was conducted using Bayesian Optimization (BO). Model accuracy was measured using R2, RMSE, MAE, and MAPE. The results demonstrate that the XGB model outperforms others under both default and optimized settings. In particular, the XGB-BO model achieved high accuracy, with RMSE of 0.3100 ± 0.0616 and R2 of 0.9997 ± 0.0001. Furthermore, Shapley Additive Explanations (SHAP) analysis was used to interpret the decision-making of the XGB model. SHAP results revealed the most influential features for compressive strength of geopolymer concrete were, in order, coarse aggregate, curing time, and NaOH molar concentration. The graphical user interface (GUI) developed for compressive strength prediction demonstrates the practical potential of this research. It contributes to integrating the approach into construction practices. This study highlights the effectiveness of explainable machine learning in understanding complex material behaviors and emphasizes the importance of model optimization for making sustainable and accurate engineering predictions.
ISSN:	2075-5309
DOI:	10.3390/buildings15203667
Fuente:	Engineering Database