Assessment of cement characteristics affecting rheological properties of cement pastes

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Mardanı Aghabaglou A., Kankal M., Nacar S., Felekoğlu B., Ramyar K.

Neural Computing & Applications, vol.1, no.19, pp.1-22, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 1 Issue: 19
  • Publication Date: 2021
  • Doi Number: 10.1007/s00521-021-05925-8
  • Journal Name: Neural Computing & Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.1-22
  • Keywords: Cementitious systems, Rheological properties, Multivariate adaptive regression splines, Multiple additive regression trees
  • Bursa Uludag University Affiliated: Yes


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), waterreducing

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.