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<p>The escalating environmental impact of cement production has intensified the demand for sustainable alternatives in concrete formulations. This study in vestigates the predictive modeling of compressive strength (CS) in concrete incorporating glass powder (GP) as a partial cement substitute. A data set comprising 308 experimental samples with nine input variables including binder composition, curing age, particle size, and chemical constituents was analyzed using six machine learning (ML) models: Decision tree (DT), random forest (RF), extra trees (ET), k-nearest neighbors (KNN), support vector regression (SVR), and extreme gradient boosting. Each model was evaluated based on coefficient of determination (<i>R</i><sup>2</sup>), root mean square error (<i>RMSE</i>), mean absolute error, mean absolute percentage error, variance accounted for, root mean square error to standard deviation ratio, and weighted mean absolute percentage error to assess performance across 80% training and 20% testing splits. Among all models, the DT model exhibited the highest predictive accuracy with <i>R</i><sup>2</sup> = 0.97 (train) and 0.96 (test), <i>RMSE</i> = 3.80 MPa, and minimal residual spread, as confirmed by shapley additive explanations (SHAP) analysis and residual error plots. RF and ET models also performed robustly, benefiting from ensemble learning. Conversely, SVR and KNN displayed higher error margins, reflecting limitations in modeling nonlinear relationships in heterogeneous concrete systems. While the models show strong promise, future research should explore larger, multi-regional data sets and hybrid descriptors to improve generalizability and capture complex chemophysical interactions.</p>

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Interpretable machine learning framework for strength pre-diction of glass powder-enhanced concrete

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • Abdullah Faiz Al Asmari

摘要

The escalating environmental impact of cement production has intensified the demand for sustainable alternatives in concrete formulations. This study in vestigates the predictive modeling of compressive strength (CS) in concrete incorporating glass powder (GP) as a partial cement substitute. A data set comprising 308 experimental samples with nine input variables including binder composition, curing age, particle size, and chemical constituents was analyzed using six machine learning (ML) models: Decision tree (DT), random forest (RF), extra trees (ET), k-nearest neighbors (KNN), support vector regression (SVR), and extreme gradient boosting. Each model was evaluated based on coefficient of determination (R2), root mean square error (RMSE), mean absolute error, mean absolute percentage error, variance accounted for, root mean square error to standard deviation ratio, and weighted mean absolute percentage error to assess performance across 80% training and 20% testing splits. Among all models, the DT model exhibited the highest predictive accuracy with R2 = 0.97 (train) and 0.96 (test), RMSE = 3.80 MPa, and minimal residual spread, as confirmed by shapley additive explanations (SHAP) analysis and residual error plots. RF and ET models also performed robustly, benefiting from ensemble learning. Conversely, SVR and KNN displayed higher error margins, reflecting limitations in modeling nonlinear relationships in heterogeneous concrete systems. While the models show strong promise, future research should explore larger, multi-regional data sets and hybrid descriptors to improve generalizability and capture complex chemophysical interactions.