Prediction of the Sulfate Attack Resistance of Concrete Based on Machine-Learning Algorithms

Abstract

The thorough investigation into the evolution of concrete performance under sulfate attack environments holds significant importance for engineering applications in specific conditions. In this paper, a prediction model for the two evaluation indexes of sulfate attack resistance of concrete (SARC), namely compressive strength corrosion resistance coefficient and mass loss rate, is established based on four machine-learning algorithms: Support Vector Regression, Random Forest Regression, Gradient Boosting, and Extreme Gradient Boosting (XGB). A comparison of the various performances showed that the model based on the XGB algorithm had the strongest generalization ability and offered the best prediction of SARC (K test set R2=0.963, MLR test set R2=0.903). Feature importance and partial correlation analyses were performed for the two XGB models separately, and a graphical user interface was designed based on the two predictive models. The results reveal that the number of cycles, water-binder ratio, and cement content significantly influence the SARC. Moderately increasing cement, fly ash, and coarse aggregate content can enhance the SARC. Increasing the number of cycles, drying time, water-binder ratio, sand, and solution concentration will reduce the SARC. Therefore, measures such as moderately increasing the amount of cement, reducing the water-binder ratio, and increasing the fly ash content can be increased to improve the SARC, but overuse has no significant effect.