Study of flexural strength of concrete containing mineral admixtures based on machine learning.

In this paper, the prediction of flexural strength was investigated using machine learning methods for concrete containing supplementary cementitious materials such as silica fume. First, based on a database of suitable characteristic parameters, the flexural strength prediction was carried out using linear (LR) model, random forest (RF) model, and extreme gradient boosting (XGB) model. Subsequently, the influence of each input parameter on the flexural strength was analyzed using the SHAP model based on the optimal prediction model. The results showed that LR, RF, and XGB enhanced the accuracy of forecasting sequentially. Among the characteristic parameters, the most significant effect on the flexural strength of concrete is the water-binder ratio, and the water-binder ratio shows a negative correlation with flexural strength. The effect of maintenance age on flexural strength is second only to the water-binder ratio, and it shows a positive trend. When the amount of fly ash is less than 40% and the amount of slag or silica fume is less than 30%, the correlation between the amount of supplementary cementitious materials and flexural strength fluctuates and a positive peak in flexural strength is observed. However, at a dosage greater than the above, the supplementary cementitious materials all reduce flexural strength. The interaction interval and the degree of interaction between the supplementary cementitious materials and the cement content also differ in predicting flexural strength.