Evaluation of the Impact of Different Combinations of Sustainable Concrete Materials on the Compressive Strength of Concrete Using Machine Learning Techniques

Nowadays, the application of Machine Learning (ML) techniques has gained significant popularity across various research domains. Concrete compressive strength (CCS) is a critical parameter for ensuring the long-term safety and durability of reinforced cement concrete (RCC) structures. Concrete is a composite material consisting of cementitious materials, fine aggregates, coarse aggregates, water, and chemical admixtures, and its compressive strength depends on the combination and proportion of these constituents. In this study, a dataset comprising 125 samples was used for the development of ML-based prediction models. The data were collected from laboratory experimental investigations conducted in the laboratory and the relevant published literature. Several ML algorithms, including Linear Regression, Ridge Regression, Lasso Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression (SVR), XGBoost, and Multi-Layer Perceptron (MLP) regression, were employed to predict the compressive strength of concrete.The results indicate that the MLP regressor achieved the highest coefficient of determination (R²) among all the models considered. It was also observed that the type of concrete materials used, selected input features, target variable, dataset size, and choice of ML algorithm significantly influence model performance. All results and graphical analyses were generated using Python programming on the Google Colab platform. The optimized performance metrics for the MLP regressor include an R² value of 0.839, a root mean square error (RMSE) of 6.656, and a mean absolute error (MAE) of 5.414.