Author

A. Mardani

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Open access Aug 2026

Machine Learning Approaches for Predicting Flow and Consistency Retention Performance in Cementitious Mixtures with Diverse Grinding Aid Dosages and Types

Grinding aids (GAs) are commonly utilized to enhance energy efficiency during clinker grinding and improve cement properties. However, alongside their benefits, GAs may lead to challenges such as reduced flow performance, loss of consistency retention, and increased demand for water or water-reducing admixtures in ceme...

Y. Kaya, V. Kobya, Naz Mardani et al. · Materials · 0 citations

Open access 2026

A Novel Stacking Ensemble Framework for Predicting Workability of Cement-Superplasticizer Systems With SHAP and LIME Interpretability

This study introduces XRES-GB, a novel stacking ensemble classifier that combines XGBoost, Random Forest, Extra Trees, and Support Vector Machine as base learners, with Gradient Boosting serving as the meta-model, and delivers high-fidelity predictions while maintaining interpretability.

Aybike Özyüksel Çiftçioğlu, Selin Özteber, Naz Mardani et al. · IEEE Access · 0 citations

Open access Jul 2026

Predicting the Workability of PCE-Modified Cement–Fly Ash Pastes with Explainable Machine Learning

Reliable assessment of polycarboxylate ether (PCE)–binder combinations requires predictive models whose interpolation performance is distinguished from their performance when an entire formulation is absent from training. This study examined 616 cement–fly ash pastes prepared using twenty-two in-house PCE formulations,...

Veysel Gider, S. Ekinci, Davut Izci et al. · Buildings · 0 citations