An explainable intelligent algorithm for the multiple performance prediction of cement-based grouting materials

Cement-based grouting material, distinguished by excellent fluidity and high strength, is widely used in the field of construction reinforcement, and anchoring of restraints. Owing to the high-performance requirements and complicated influencing factors of grouting material, the design of the mixing proportion has been challenging. This study proposes a machine learning (ML) based algorithmic framework integrating prediction, interpretation, and automatic hyperparameter tuning to identify the complex potential relationships between the mixing proportion parameters on the compressive strength and fluidity of cement-based grouting materials. The 442 compressive strength data and 217 fluidity data derived from both published literatures and laboratory experiments were collected to build a dataset for demonstrating the predictive performance of the ML models. The results indicated that the hyperparameter tuning technique via Bayesian Optimisation (BO) can significantly improve the time efficiency compared to grid search, reducing time consumption from 8000 s to 197 s with comparable accuracy. The optimal prediction results were obtained based on the XGBoost model with R2 = 0.93, RMSE = 7.37 for compressive strength, and R2 = 0.92, RMSE = 16.33 for fluidity. The SHapley Additive exPlainations (SHAP) is introduced to interpret the evaluation results and the influence of the various mix factors on grouting material from both global(model) and local(instance) perspectives. The suggested model can be seen as a function of influential input variables that help engineers conduct a rapid assessment and then in turn to optimize the design of the mixture proportion.