Prediction of Manufactured-Sand Concrete Compressive Strength Using Hybrid ML Models and Dream Optimization Algorithm

This study proposes a predictive framework for the compressive strength (CS) of manufactured-sand concrete (MSC), integrating six machine learning (ML) models—artificial neural network (ANN), random forest (RF), extreme learning machine (ELM), kernel-ELM (KELM), support vector regression (SVR), and extreme gradient boosting (XGBoost) with the newly developed Dream optimization algorithm (DOA) for hyperparameter tuning. A database of 306 samples with eight features is used to train and test models. Results demonstrate that all models achieved satisfactory predictive accuracy, with the DOA-RF model exhibiting the best performance on the testing dataset (R2 = 0.9755, RMSE = 2.7836, MAE = 2.1716, WI = 0.9933). The DOA-XGBoost model also yielded competitive results, whereas DOA-ELM showed relatively weaker performance. Compared with existing optimization-based approaches, the proposed DOA-RF model significantly reduced RMSE and MAE, validating the effectiveness of the DOA. SHAP analysis further revealed that the water-to-binder ratio (W/B) and curing age (CA) are the most influential factors in predicting MSC strength. Overall, this work not only establishes an accurate and interpretable predictive tool but also underscores the potential of novel optimization algorithms to advance data-driven concrete design and sustainable construction practices.