Assessment of cement characteristics affecting rheological properties of cement pastes

Abstract

In this study, the cement-based parameters affecting CEMI portland cements-polycarboxylate ether-based high-range water-reducing (HRWR) admixtures compatibility were investigated. For this purpose, eight CEMI cements and three commercial HRWR admixtures were used. The rheological properties of 112 paste mixtures with different admixture dosages and water/cement (W/C) ratios were determined in accordance with Herschel–Bulkley model. Then after, using the experimental data, proper models were established to predict the dynamic yield stress and final viscosity of the pastes. In addition to cement characteristics (such as fineness, compound composition and equivalent alkali content), water-reducing admixture content and its solid material content as well as water/cement ratio of the pastes were considered as input data. Multivariate adaptive regression splines (MARS) and multiple additive regression trees (MART) methods were used in the models. Besides, artificial neural network (ANN) and conventional regression analysis (CRA) including linear, power, and exponential functions were applied to determine the accuracy of the heuristic regression methods. Three statistical indices, root-mean-square error, mean absolute error, and Nash–Sutcliffe, were used to evaluate the performance of the models. Modeling findings indicated that the model with the lowest error for both of the rheological variables in the testing set is the MART, followed by ANN, MARS, and CRA-Exponential methods. The most effective cement characteristics causing incompatibility, hence detraction of paste rheological properties, in decreasing order, were determined as cement fineness, C3S, C3A and equivalent alkali contents. C4AF and C2S contents of the cement were found to have less effect on the cement–admixture incompatibility. It will be possible to determine the rheological properties of mixtures containing different cements without conducting an experimental study by using the model based on MART method.