Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength

Accurate estimation of concrete compressive strength is very important for the improvement of mix design, quality assurance, and compliance with engineering specifications. Most empirical traditional models have failed to capture the complex relationships inherent within varied constituents of concrete mixes. This paper develops a machine learning model for compressive strength prediction using mix design variables and curing age from a “Concrete Compressive Strength Dataset” obtained from the UCI Machine Learning Repository. After comprehensive data preprocessing and feature engineering, various regression and classification models were trained and evaluated, including gradient boosting, random forest, AdaBoost, k-nearest neighbors, linear regression, and neural networks. The gradient boosting regressor (GBR) achieved the highest predictive accuracy with an R² value of 0.94. Feature importance analysis showed that the water–cement ratio and age are the most crucial factors affecting compressive strength. Advanced methods such as SHapley Additive exPlanations (SHAP) values and partial dependence plots were used to attain deep insights about feature interaction with a view to enhancing interpretability and fostering trust in models. Results highlight the potential of machine learning models to improve concrete mix design with the aim of sustainable construction through the optimization of material usage and waste reduction. It is recommended that future research be undertaken with expanding datasets, more features, and richer feature engineering to enhance predictive power.