Compressive strength prediction of cement base under sulfate attack by machine learning approach

Cement-based materials in underground structures, bridge foundation, and offshore platforms often face sulfate environments. It has been widely reported that the compressive strength (CS) of these materials post sulfate attack is crucial for structural stability and durability. However, existing experimental and analytical methods require extensive resources and lengthy period. For addressing this challenge, therefore, a novel stacking model is proposed to accurately predict the CS of cement base under sulfate attack in this study. The stacking model integrates Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGB) as base learners, with Linear Regression (LR) as the meta-learner. In this novel model training, the 330 data sets used are obtained from the relevant literatures. The water-cement ratio, cement content, water content, sand content, sulfate concentration, wetting temperature, wetting time, drying temperature, drying time, exposure time are selected as inputs while the output is CS of cement-based materials subjected to sulphate deterioration. To improve the forecast capability, particle swarm optimization (PSO) is adopted for optimizing hyperparameters of stacking model, which can be implemented by reducing the discrepancy between model prediction and measured CS. By comparing with the results of experiments and the solely machine learning (ML) models, it is found that the stacking model optimized by PSO presents the best prediction accuracy of R²=0.992, RMSE=2.248 MPa, and MAE=1.782 MPa. Moreover, the examination of the influence of individual input variables on the CS of cement-based material subjected to sulfate attack is conducted through the application of Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. The PSO optimized stacking model proposed in this study can effectively predict the CS of cement-based materials under sulfate attack. Our study provides a new reference for future research in this field.