Machine learning prediction of cement paste flow time: Effects of cement composition and superplasticizer properties

Özteber S., Mardani N., Kobya V., Göç G. T., Çavdur F., Mardani A.

ENGINEERING SCIENCE AND TECHNOLOGY, AN INTERNATIONAL JOURNAL, cilt.84, ss.1-12, 2026 (SCI-Expanded, Scopus)

Özet

The rheological behavior of fresh cement paste is strongly influenced by both the phase composition of the portland cement and the molecular structure of polycarboxylate ether-based superplasticizers. Predicting flow time from these compositional variables remains challenging due to the nonlinear nature of the underlying interactions. This study employs tree-based ensemble machine learning models to predict the flow time of cement paste using a dataset comprising 616 observations and 14 input features that encompass cement phase components (C3S, C2S, C3A, C4AF) and key characteristics of the superplasticizer. Three algorithms, Gradient Boosting, Random Forest, and Extra Trees, were trained, optimized via grid search with k-fold cross-validation, and evaluated on an independent test set. Among these, the Extra Trees model delivered the highest predictive accuracy, achieving an R2 of 0.976 and a mean absolute error of 1.24 s. SHAP (SHapley Additive exPlanations) analysis was subsequently applied to interpret the model outputs. The results identify water-reducing admixture dosage as the single most influential predictor of flow time, followed at a considerable distance by superplasticizer molecular weight and cement specific surface area. Other material parameters, including C4AF content and pH, exerted only marginal effects within the investigated range. The findings demonstrate that ensemble learning methods, when coupled with interpretability techniques, can effectively capture the complex relationships governing cement paste rheology and provide a reliable, data-driven framework to support mixture proportioning and admixture compatibility assessment.

The rheological behavior of fresh cement paste is strongly influenced by both the phase composition of the portland cement and the molecular structure of polycarboxylate ether-based superplasticizers. Predicting flow time from these compositional variables remains challenging due to the nonlinear nature of the underlying interactions. This study employs tree-based ensemble machine learning models to predict the flow time of cement paste using a dataset comprising 616 observations and 14 input features that encompass cement phase components (C3S, C2S, C3A, C4AF) and key characteristics of the superplasticizer. Three algorithms, Gradient Boosting, Random Forest, and Extra Trees, were trained, optimized via grid search with k-fold cross-validation, and evaluated on an independent test set. Among these, the Extra Trees model delivered the highest predictive accuracy, achieving an R2 of 0.976 and a mean absolute error of 1.24 s. SHAP (SHapley Additive exPlanations) analysis was subsequently applied to interpret the model outputs. The results identify water-reducing admixture dosage as the single most influential predictor of flow time, followed at a considerable distance by superplasticizer molecular weight and cement specific surface area. Other material parameters, including C4AF content and pH, exerted only marginal effects within the investigated range. The findings demonstrate that ensemble learning methods, when coupled with interpretability techniques, can effectively capture the complex relationships governing cement paste rheology and provide a reliable, data-driven framework to support mixture proportioning and admixture compatibility assessment.