The fresh-state behavior of cementitious systems is governed by complex interactions between cement mineralogy and the molecular architecture of polycarboxylate ether-based superplasticizers (PCEs). Predicting workability indicators such as mini-slump and flow time across varying cement C3A contents and PCE types remains challenging, and existing machine learning approaches rarely combine structurally diverse learning principles with explainable decision mechanisms. 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. The framework was applied to an experimental dataset comprising 171 observations spanning 22 input variables, including cement mineralogical composition, physical characteristics, and PCE molecular parameters. Mini-slump and flow time values were converted into binary workability classes using threshold values selected according to practical workability limits, and the resulting class imbalance was addressed via a model-specific strategy combining Random Oversampling and cost-sensitive class weighting. Model performance was evaluated through 5-fold stratified group cross-validation and benchmarked against eleven conventional classifiers. XRES-GB achieved mean test accuracies of 0.91 and 0.92 for mini-slump and flow time classification, respectively, outperforming all competing models in both accuracy and fold-to-fold consistency. SHAP analyses identified Water Reducer Admixture dosage, cement content, and density as the most influential explanatory variables, although their relative importance varied between the mini-slump and flow time classification tasks. LIME explanations further confirmed that individual predictions align with established cement-admixture interaction mechanisms. The proposed framework delivers high-fidelity predictions while maintaining interpretability, offering a data-driven tool for optimizing cement-PCE compatibility in mixture design.
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, four fly ash replacement levels (0, 15, 30, and 45 wt %), seven PCE dosages (0.50–2.00 wt % of binder), and a fixed water-to-binder ratio of 0.35. Marsh funnel flow time was measured for all mixtures, whereas mini-slump measurements were available for 252 mixtures comprising nine PCE formulations. Ten regression algorithms were evaluated using 3 × 5-fold repeated cross-validation and nine non-algebraically redundant input variables. XGBoost achieved R2 = 0.946 ± 0.027 and RMSE = 8.40 s for flow time, and R2 = 0.778 ± 0.061 and RMSE = 0.471 cm for mini-slump. These random-resampling results describe interpolation among formulations represented in the training folds. When each PCE chemistry was withheld in turn, the mean R2 was 0.865 ± 0.198 for flow time and 0.034 ± 0.530 for mini-slump. Performance also decreased when the boundary fly ash levels were withheld. SHAP and permutation analyses identified PCE dose and fly ash replacement as the strongest predictors, while the formulation-level descriptors made smaller contributions. These findings represent associations learned from the present dataset and do not constitute direct evidence of adsorption or dispersion mechanisms. At a nominal 90% level, split-conformal intervals achieved empirical coverages of 85.5% for flow time—moderately below the nominal level, within finite-sample binomial fluctuation—and 90.2% for mini-slump on a random test partition; across fifty repeated train–calibration–test splits, the mean coverages were 89.4 ± 3.6% and 90.1 ± 5.8%. The flow-time model is suitable for preliminary screening within the investigated factor ranges, whereas the mini-slump model should be restricted to interpolation among the sampled formulations.
Veysel Gider, S. Ekinci, Davut Izci et al.· Buildings· 0 citations
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