Jul 2026· Materials Research Express· Vol 13, pp. 155501· 0 citations· 59 references
Physics
Abstract
This study proposes a material-centered predictive framework for fly ash (FA)–based cementitious composites by integrating mixture composition, early-age mechanical properties, and microstructural parameters to model the evolution of compressive strength (CS). A bio-inspired hybrid regression approach, the Enzyme Action Optimizer–enhanced Extreme Learning Machine (EAO-ELM), is introduced to capture the nonlinear relationships governing strength development. In the proposed hybrid model, the random assignment of input–hidden layer weights and biases—the principal source of prediction variance in conventional ELM—is replaced by a population-based global search that minimizes the training loss directly. This improves the conditioning of the hidden-layer output matrix and stabilizes the generalization capability of the model, which explains the performance advantage of EAO-ELM over conventional ELM-based and ensemble learning approaches. Beyond serving as a computational technique, the proposed model provides insight into material behavior and supports performance-driven mixture design. To the best of the authors’ knowledge, this work represents the first application of the EAO to cementitious materials. To systematically evaluate how material-related information influences predictive accuracy, three scenario-based information schemes were constructed. These scenarios enable a comprehensive assessment of the contribution of early-age mechanical properties to long-term strength estimation. Across all scenarios, the EAO-ELM model consistently outperformed classical and ensemble regression methods, achieving an improvement of approximately 36% over the strongest model (Random Forest) in the full-information case. Interpretability analyses using SHapley Additive exPlanations (SHAP) and Partial Dependence Plot further revealed that 7 d CS, cement content, and FA ratio exert the greatest influence on strength prediction, whereas water absorption and ultrasonic pulse velocity (UPV) play secondary roles. Overall, the proposed framework offers a accurate, interpretable, and adaptive means of understanding strength development in sustainable cementitious systems and provides subject to external validation on independent datasets, a promising decision-support aid for optimizing mixture design in construction materials engineering.
Geopolymer concrete (GPC) is a sustainable alternative to Portland cement concrete; however, complex geopolymerization mechanisms and nonlinear strength development under ambient curing make mixture optimization challenging. This study develops a chemistry-informed data-driven framework to predict the 28-day compressive strength of ambient-cured slag/fly ash–based GPC. A dataset of 151 mixtures was compiled incorporating eight input parameters, including key precursor oxide ratios (SiO₂/CaO, SiO₂/Al₂O₃, and CaO/Al₂O₃), which are rarely considered in existing predictive models. Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models were developed and compared. The optimal ANN model (8–2–2–1 architecture) achieved superior predictive accuracy (R² = 0.93, MAE = 2.82), while the GEP model (R² = 0.77, MAE = 5.55) produced an explicit mathematical equation suitable for practical applications. Model reliability was verified experimentally using four new mix designs. Sensitivity analysis identified the SiO₂/CaO ratio as the most influential parameter governing strength development in ambient-cured GPC.
A. Sabry, Sabry A. Ahmed, Mohamed K. Ismail et al.· Discover Materials· 0 citations
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.
A comprehensive data-driven framework integrating ensemble machine learning models with systematic hyperparameter sensitivity analysis and explainable artificial intelligence techniques is proposed, demonstrating that the XGB model significantly outperforms the other approaches, achieving superior accuracy and robust generalization.
Qaim Shah, Waheed Ali Khoso, Fawad Iqbal et al.· Discover Artificial Intellig...· 0 citations
In this study, machine learning approaches were used to predict the electrical conductivity (EC) of conductive cementitious composites under data-limited conditions. Data scarcity was addressed via data augmentation based on a tabular variational autoencoder (TVAE), and multiple machine learning models were trained and evaluated using a consistent set of input variables and validation procedures. Results showed that prediction performance differed across model types. Tree-based and ensemble learning models exhibited stable predictive behavior after data augmentation and hyperparameter optimization, whereas distance-, kernel-, and neural network-based models were more sensitive to changes in data distribution and optimization strategies. TVAE-based data augmentation had a model-dependent effect on prediction. In addition, Shapley additive explanations analysis indicated that the mixing amount of conductive materials and fly ash content were among the most influential factors governing EC, with fly ash content exhibiting nonlinear, value-dependent contribution patterns. These findings provide insights into model-dependent prediction behavior and variable contribution characteristics in EC prediction for conductive cementitious composites under data-limited conditions.
Jihye Sung, Min-Chang Kang, J. Hu et al.· International Journal of Con...· 0 citations
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
An integrated framework combining improved optimization algorithms with the Shapley Additive Explanations (SHAP) method is developed, providing a robust scientific basis and a practical computational tool for predicting concrete strength, facilitating the deep integration of machine learning with civil engineering practice.
Juanjuan Quan, Kunlin Liu, Hao Su et al.· Buildings· 1 citation
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