Predictive modeling for compressive strength of blended cement concrete using hybrid machine learning models

Blended cement concrete, which incorporates supplementary cementitious materials alongside cement as a binder, is widely recognized as a sustainable solution in the construction industry. However, accurate prediction of its properties can be challenging due to its complex composition and numerous design parameters. Machine learning algorithms are commonly employed to streamline this process, saving both time and resources. In this study, a novel approach was undertaken by utilizing hybrid models that integrate support vector regression with optimization techniques, including the firefly algorithm, particle swarm optimizer, and grey wolf optimizer, to predict the compressive strength of blended cement concrete. A comprehensive dataset comprising 1287 data points, and 11 input variables was used for model training and validation. The hybrid models demonstrated superior performance, with mean absolute error values below 6 MPa and root mean square error values below 9 MPa for both training and validation phases. Additionally, the correlation coefficient values for all models exceeded 0.87, indicating strong predictive accuracy. SHAP analysis revealed that the water-to-binder ratio was the most significant factor influencing compressive strength, with a SHAP value of 9.8. The findings suggest that these hybrid machine-learning models offer an effective tool for optimizing the compressive strength prediction of blended cement concrete in sustainable construction practices, ensuring both resource efficiency and reliability. Future research could explore alternative machine learning algorithms or novel hybrid combinations to further enhance model performance. Investigating these approaches may address current limitations and potentially lead to more accurate and efficient predictions of the compressive strength of blended cement concrete.