Improving forecasting of concrete strength using advanced machine learning methods

Abstract This study presents an improved technique that uses many machine-learning models to estimate the compressive strength of concrete. The goal of the project is to increase the precision of strength predictions based on the age and composition of concrete mixes. Cement, fly ash, water, superplasticizer, coarse and fine aggregate, and sample age are among the materials. Megapascals (MPa) are used to quantify compressive strength. To determine the connections between mix proportions, age, and strength, a variety of blends were examined. Machine learning techniques including Random Forest, XGBoost, AdaBoost, Bagging, Support Vector Regression, and Linear Regression were used. The efficiency of the model was assessed using performance indicators such as accuracy, R-squared (R2), Mean Absolute Error (MAE), and Mean Squared Error (MSE). With an MAE of 2.2, MSE of 10.5, R2 of 0.94, MAPE of 8.5, RMSE of 3.25, and accuracy of 0.92, XGBoost (optimized) performed the best. This model performed noticeably better than others, highlighting how machine learning may improve predictions of compressive strength and optimize the composition of concrete, thus promoting the fields of materials science and civil engineering.