Enhancing the Analysis of Rheological Behavior in Clinker-Aided Cementitious Systems Through Large Language Model-Based Synthetic Data Generation.

This study investigates the parameters influencing the compatibility between cement and polycarboxylate ether (PCE) admixtures in cements produced with various types and dosages of grinding aids (GAs). A total of 29 cement types (including a control) were prepared using seven different GAs at four dosage levels, and 87 paste mixtures were produced with three PCE dosages. Rheological behavior was evaluated via the Herschel-Bulkley model, focusing on dynamic yield stress (DYS) and viscosity. The data were modeled using CNN, Random Forest (RF), and Neural Classification and Regression Tree (NCART), and each model was enhanced with synthetic data generated by Large Language Models (LLMs), resulting in CNN-LLM, RF-LLM, and NCART-LLM variants. All six variants were evaluated using R-squared, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Logcosh. This study is among the first to use LLMs for synthetic data augmentation. It augmented the experimental dataset synthetically and analyzed the effects on the study results. Among the baseline methods, NCART achieved the best performance for both viscosity (MAE = 1.04, RMSE = 1.33, R