The applied design of recycled aggregate concrete (RAC) with supplementary cementitious materials (SCMs) requires reliable estimation of 28-day flexural strength (FS28) before trial batching. This is challenging because RAC–SCM mixtures involve nonlinear interactions among binder chemistry, aggregate replacement, water-to-binder ratio, and admixture dosage. However, most predictive models focus on compressive strength or sustainability optimization, while fewer address FS28 using chemically informed descriptors and independent validation. This study developed and externally validated an XGBoost framework for FS28 prediction. The methodology combined binder characterization by XRF, XRD, SEM, and particle-size analysis; reactivity descriptors; database development; modeling; and experimental validation. A database of 397 mixtures from 22 sources was refined to 382 observations for training and testing, and the model was validated with 58 RAC–SCM mixtures and 174 prismatic specimens tested according to ASTM C78/C78M-22. XGBoost achieved R2 values of 91.61%, 80.75%, and 77.62% for training, testing, and validation, with RMSE values of 0.559, 0.835, and 0.364 MPa. Compared with the best alternative models, XGBoost reduced RMSE by 8.6% in testing and 9.5% in validation. Interpretability analysis identified binder reactivity, water-to-binder ratio, aggregate composition, cement content, SCM replacement, and superplasticizer dosage as key factors.
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
This study aimed to identify an Alkali-Activated Slag (AAS) mortar mixture balancing flowability and compressive strength as a sustainable alternative to Portland cement. Twenty-seven mixtures varying activator dosage, modulus, water-to-binder ratio, aggregate-to-binder ratio, and superplasticizer content were evaluated using a Taguchi L27 design, ANOVA, and PCR-TOPSIS optimization. Activator dosage was most influential (70.90% contribution), followed by aggregate-to-binder ratio (10.12%), while modulus and water-to-binder ratio played minor roles. The optimal mixture (flow 115%, strength 48.85 MPa) was validated by an independent replicate batch, with an estimated 79% lower CO2 footprint than a Portland-cement control. Reaction mechanisms were not confirmed microstructurally. Unlike prior single-response approaches, this study combines statistical validation, experimental confirmation, and environmental estimation for a more rigorous mortar design basis.
Andi Arham Adam, Bayu Rahmat Ramadhan, Isa Hanif Emeraldy et al.· Revista ALCONPAT· 0 citations
: Microbially Induced Carbonate Precipitation (MICP) is an environmentally friendly technique for sandy soil stabilization. However, the cementation performance is governed by multiple coupled factors and complex experimental procedures, making accurate prediction challenging. In this study, a dual-objective XGBoost prediction model suitable for small-sample scenarios is developed from 77 sets of laboratory data to rapidly estimate unconfined compressive strength (UCS) and calcium carbonate content (CCC) separately. A mechanism-guided feature engineering strategy is adopted to construct three cross features, including urease activity coupled with curing time, calcium carbonate content combined with curing time, and urea-calcium concentration, together with five key influencing parameters. Five-fold cross-validation is used to ensure model stability. In the UCS model, soil particle size fraction (29.94%) and urease activity (20.85%) dominate, while in the CCC model, soil particle size fraction (22.47%) and urease activity (17.63%) prevail, both align well with fundamental MICP mechanisms. The CCC model achieves a coefficient of determination (R 2 ) of 0.6342 and a mean absolute error (MAE) of 2.92%, showing reliable predictive ability. The UCS model achieved an R 2 of 0.7991 and a MAE of 844.83 kPa. However, due to the mathematical amplification of relative error, a small portion of low-strength specimens produced abnormally high MAPE (116.60%), limiting the formal engineering design of the UCS model. SHAP (SHapley Additive exPlanations) analysis is further employed to enhance model interpretability and to quantitatively clarify the marginal contributions and interaction effects of the input features. The proposed framework offers a valuable reference for parameter analysis and mechanistic interpretation of MICP-treated soils. At the same time, the larger prediction deviation for UCS highlights the intrinsic uncertainty of strength evolution in such complex multi-factor systems.
Alkali-activated recycled aggregate concrete (AARAC) offers a sustainable alternative to traditional concrete but suffers from complex, non-linear mechanical behavior that challenges conventional prediction methods. This study develops and compares five machine learning models, linear regression (LR), M5P, Random Forest (RF), K-Nearest Neighbors (KNN) and XGBoost, for predicting the compressive strength (Cs), flexural strength (Fs), splitting tensile strength (Ss), pull-out bond strength (PT), and water absorption (Wa%) of AARAC. A dataset of 360 experimental samples, incorporating natural aggregate, recycled concrete aggregate (RCA), cement block aggregate (CBA), water-to-cement ratio (W/C), alkaline treatment status, and slump, was used. Models were evaluated via train/test split (80/20) and 10-fold cross-validation using R2, MAE, RMSE, and MAPE. Random Forest achieved the highest test R2 (0.8736) and lowest test MAPE (1.418%) and XGBoost (R2 = 0.8605, MAPE = 1.557%). KNN and M5P performed moderately, while LR was the weakest (R2 = 0.6958, MAPE = 2.147%). All tree-based models exhibited overfitting, with training R2 up to 0.98. Scatter plot analysis revealed systematic underprediction by RF for Cs (constant offset of ~2 MPa) and increasing bias for PT, Ss, and Wa% at higher values. XGBoost gave perfect predictions for PT and Wa% but underpredicted Cs and Fs. K-fold cross-validation confirmed XGBoost as the most robust (mean R2 = 0.9844). Correlation analysis showed W/C strongly increases Wa% (r = 0.80) and decreases PT (r = −0.73); RCA negatively affects mechanical properties, while CBA and alkaline treatment improve them. The study concludes that ensemble tree models, particularly Random Forest, are superior for AARAC prediction, but systematic bias requires post hoc calibration.
Ahmed D. Almutairi, A. A. Al Sayed· Buildings· 0 citations
This study analyzes the statistical associations between RC-UHPC performance and its influencing factors from a data-driven perspective and can provide scientific guidance for mix design and performance regulation of RC-UHPC in engineering applications.
Ye Xu, Qi Wu· Engineering Research Express· 0 citations
Self-compacting concrete (SCC) incorporating ceramic waste powder (CWP) as a partial cement replacement
offers a route to reduce both landfill burden and embodied carbon in construction, but characterising the combined effect
of mix design variables on the full suite of fresh and hardened properties normally demands extensive laboratory testing.
This study evaluates Gaussian Process Regression (GPR) as a data-efficient surrogate modelling approach for predicting
twelve fresh and mechanical properties of CWP-based SCC from three mix design inputs: cement content, CWP content,
and water-to-binder (w/b) ratio. A dataset of twenty-one experimentally characterised SCC mixes, spanning slump flow,
T500 time, V-funnel time, L-box ratio, segregation resistance, compressive strength (7, 28, and 90 days), split tensile
strength (7, 28, and 90 days), and 28-day flexural strength, was modelled using independent GPR models with a Matern
5/2 kernel and white-noise term, with performance assessed exclusively through leave-one-out cross-validation (LOOCV)
given the small sample size. Eleven of the twelve properties were predicted with LOOCV coefficients of determination (R2
)
between 0.80 and 0.99, with the strongest performance observed for slump flow and split tensile strength (R2 ≥ 0.96).
Twenty-eight-day flexural strength was the exception, with a negative LOOCV R2
indicating that the three mix design
inputs alone do not explain its variability in this dataset. The results demonstrate that GPR, combined with rigorous
LOOCV validation and explicit predictive uncertainty, is a viable and transparent tool for mix-design-stage property
prediction in small experimental SCC datasets, while also illustrating the diagnostic value of LOOCV in identifying
properties that require additional explanatory variables.
P. Joshi, D. Parekh· International Journal of Inn...· 0 citations
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