Flowability prediction and artificial intelligence analysis of SCC based on machine learning
TL;DR
Six machine learning algorithms were employed to construct artificial intelligence models for the precise prediction of self-compacting concrete (SCC) flow properties, and the extreme gradient boosting (XGB) model was identified as exhibiting superior predictive accuracy and generalization performance.
Abstract
Six machine learning algorithms were employed to construct artificial intelligence models for the precise prediction of self-compacting concrete (SCC) flow properties, using a total of 158 sets of experimental data samples. A sensitivity analysis examining the relationships between feature parameters and flow properties was performed using Shapley additive explanations (SHAP). The mix proportion of SCC involved key feature parameters: the water-to-binder ratio, along with the proportions of cement, silica fume, slag, fly ash, fine aggregate, coarse aggregate, and water reducer. The flowability of SCC was quantitatively characterized by the results of the slump test. Based on the analysis of prediction errors and outcomes, the extreme gradient boosting (XGB) model was identified as exhibiting superior predictive accuracy and generalization performance, with the coefficient of determination (R2) reaching 0.927, and the explained variance of 0.936. Building upon the XGB intelligent algorithm, a graphical user interface-based interactive program was successfully developed to predict flowability using SCC mix proportions. The results of SHAP analysis indicated that the content of cement and silica fume exhibited a negative correlation with SCC flowability, while the content of fly ash and slag showed a positive correlation with SCC flowability. Compared to coarse aggregates, fine aggregates exerted a less pronounced effect on the flow characteristics of SCC. The dosage of water-reducing agent was the most significant positive factor affecting the flow properties of SCC.