Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials

Abstract

Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix design. In this study, experimental testing was conducted to create a dataset of 233 samples, including fluidity, dynamic yield stress, and plastic viscosity of cement-based materials. The proportions of cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), and sand were selected as inputs. Machine learning (ML) methods were employed to establish predictive models for these three early workability indicators. To improve prediction capability, optimized hybrid models, such as Particle Swarm Optimization (PSO)-based CatBoost and XGBoost, were adopted. Furthermore, the influence of individual input variables on each workability indicator of the cement-based material was examined using Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. This study provides a novel reference for achieving rapid and accurate control of cement-based material workability.