Prediction of compressive and flexural strength of modified concrete using machine learning

Abstract

Accurate evaluation of the mechanical performance of modified concrete is important for mixture design and engineering application. This study developed machine-learning models to predict the compressive strength (CS) and flexural strength (FS) of rubber-polymer modified concrete using 50 experimental samples. Nine variables-cement, water, fine aggregate, coarse aggregate, superplasticizer, rubber content, polymer content, polymer solid content, and rubber particle size-were used as input features. Four regression models, namely linear regression, ridge regression, RBF kernel ridge regression, and bagged linear regression, were trained for two independent prediction tasks. Model performance was assessed by fivefold cross-validation using RMSE, MAE, and R2. Within a unified framework integrating data characterization, model comparison, and post-hoc interpretation, RBF kernel ridge regression achieved the best overall performance for both targets. For CS prediction, the RMSE, MAE, and R2 were 6.656, 4.921, and 0.824, respectively; for FS prediction, the corresponding values were 1.063, 0.644, and 0.802. Correlation analysis and ridge-coefficient interpretation showed that CS was more sensitive to coarse aggregate, water, superplasticizer dosage, and polymer solid content, whereas FS was more strongly influenced by fine aggregate, water, polymer solid content, and cement. These results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.