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.

在这项研究中，研究了水泥基参数对CEMI波特兰水泥与聚羧酸盐醚基高范围减水剂的相容性的影响。为此，使用了八种CEMI水泥和三种商用HRWR外加剂。根据Herschel–Bulkley模型确定了112种不同掺合料和水/水泥（W / C）比的糊状混合物的流变特性。然后，使用实验数据，建立适当的模型来预测糊料的动态屈服应力和最终粘度。除水泥特性（如细度，化合物组成和当量碱含量）外，还将减水掺合料含量及其固体材料含量以及糊料的水灰比作为输入数据。在模型中使用了多元自适应回归样条（MARS）和多重加性回归树（MART）方法。此外，人工神经网络（ANN）和常规回归分析（CRA）包括线性，幂和指数函数被用来确定启发式回归方法的准确性。三个统计指标，均方根误差，平均绝对误差和Nash–Sutcliffe，用于评估模型的性能。建模结果表明，测试集中两个流变变量中误差最小的模型是MART，其次是ANN，MARS和CRA-Exponential方法。导致不相容性，从而降低浆料流变性能的最有效的水泥特性（按降序排列）确定为水泥细度C 人工神经网络（ANN）和包括线性，幂和指数函数的常规回归分析（CRA）被用于确定启发式回归方法的准确性。三个统计指标，均方根误差，平均绝对误差和Nash–Sutcliffe，用于评估模型的性能。建模结果表明，测试集中两个流变变量中误差最小的模型是MART，其次是ANN，MARS和CRA-Exponential方法。导致不相容性，从而降低浆料流变性能的最有效的水泥特性（按降序排列）确定为水泥细度C 人工神经网络（ANN）和包括线性，幂和指数函数的常规回归分析（CRA）被用于确定启发式回归方法的准确性。三个统计指标，均方根误差，平均绝对误差和Nash–Sutcliffe，用于评估模型的性能。建模结果表明，测试集中两个流变变量中误差最小的模型是MART，其次是ANN，MARS和CRA-Exponential方法。导致不相容性，从而降低浆料流变性能的最有效的水泥特性（按降序排列）确定为水泥细度C 并采用指数函数确定启发式回归方法的准确性。三个统计指标，均方根误差，平均绝对误差和Nash–Sutcliffe，用于评估模型的性能。建模结果表明，测试集中两个流变变量中误差最小的模型是MART，其次是ANN，MARS和CRA-Exponential方法。导致不相容性，从而降低浆料流变性能的最有效的水泥特性（按降序排列）确定为水泥细度C 并采用指数函数确定启发式回归方法的准确性。三个统计指标，均方根误差，平均绝对误差和Nash–Sutcliffe，用于评估模型的性能。建模结果表明，测试集中两个流变变量中误差最小的模型是MART，其次是ANN，MARS和CRA-Exponential方法。导致不相容性，从而降低浆料流变性能的最有效的水泥特性（按降序排列）确定为水泥细度C 建模结果表明，测试集中两个流变变量中误差最小的模型是MART，其次是ANN，MARS和CRA-Exponential方法。导致不相容性，从而降低浆料流变性能的最有效的水泥特性（按降序排列）确定为水泥细度C 建模结果表明，测试集中两个流变变量中误差最小的模型是MART，其次是ANN，MARS和CRA-Exponential方法。导致不相容性，从而降低浆料流变性能的最有效的水泥特性（按降序排列）确定为水泥细度C₃ S，C ₃ A和等效碱含量。发现水泥中的C ₄ AF和C ₂ S含量对水泥与掺合料不相容性的影响较小。通过使用基于MART方法的模型，无需进行实验研究就可以确定包含不同水泥的混合物的流变特性。