Prediction of Concrete Compressive Strength Using Mix Proportion and Curing Parameters
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Abstract
Compressive strength is the most critical mechanical property governing the structural performance of concrete, yet its accurate estimation before the standard 28-day testing period remains a persistent challenge for the construction industry. This study investigates the combined influence of mix-proportion variables (cement, water, fine aggregate, coarse aggregate, and superplasticizer content) and curing parameters (curing age, temperature, and relative humidity) on the compressive strength of normal and high-performance concrete. A dataset of 220 concrete mixtures was compiled and analysed using descriptive statistics, Pearson correlation analysis, and three predictive modelling techniques: multiple linear regression, second-degree polynomial regression, and random forest regression. The results indicate that the water-cement ratio and curing age are the two most influential parameters, exhibiting correlation coefficients of -0.61 and 0.62 with compressive strength, respectively. Among the models tested, the random forest regression algorithm achieved the highest predictive accuracy (R² = 0.951, RMSE = 2.92 MPa), substantially outperforming the linear (R² = 0.739) and polynomial (R² = 0.928) models. The findings confirm that non-linear, ensemble-based machine learning approaches capture the complex interactions between mix design and curing conditions more effectively than classical regression techniques. The proposed framework offers a practical, data-driven tool that engineers can use to estimate concrete compressive strength at the mix-design stage, reducing dependence on time-consuming laboratory trials and supporting more efficient and sustainable concrete production.
Article impact statement: Random forest regression, jointly trained on mix-proportion and curing variables, predicts concrete compressive strength far more accurately than classical regression, offering a practical mix-design decision-support tool.
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