Jun 2026· Buildings· Vol 16, pp. 2602· 0 citations· 63 references
Abstract
Carbonation treatment can effectively address defects in recycled aggregates (RA) while achieving CO2 sequestration, thereby improving properties of recycled aggregate concrete (RAC). However, the compressive strength of carbonated recycled aggregate concrete (CRAC) is governed by complex interactions among multiple parameters, and existing machine learning (ML) studies often rely on heterogeneous literature data with limited parameter coverage, resulting in constrained predictive accuracy. To address this issue, this study established a robust ML framework for precise strength prediction. By integrating published literature with original experimental results, a dataset of 226 groups was constructed, incorporating 12 key parameters across RA properties, carbonation processes, mix proportions, and concrete age to systematically compare three ML models (GPR, SVM, EDT). To enhance model transparency, global sensitivity analysis used the SHapley Additive exPlanations (SHAP) method, while X-ray diffraction (XRD), scanning electron microscopy (SEM), and microhardness tests were employed to reveal reinforcement mechanisms at the phase, microstructural, and micromechanical levels, supporting the connection between intelligent prediction and mechanistic explanation. Results show that the GPR model exhibited the highest predictive performance and generalization capability (R2 = 0.98 for training, R2 = 0.94 for testing; RMSE = 1.08 MPa), outperforming comparative models in handling high-dimensional nonlinear relationships. SHAP analysis identified concrete age, water–cement (W/C) ratio, and the initial crush index of the RA as the primary factors, while carbonation process parameters, particularly relative humidity, carbonation pressure, and carbonation time, exerted significant regulatory effects on strength. XRD results qualitatively confirmed the formation of CaCO3 after carbonation, while SEM and microhardness analyses indicated that carbonation products contributed to pore filling and interfacial transition zone (ITZ) strengthening, providing a physical basis for both macroscopic performance improvement and model reliability. This study provides a scientific, data-driven solution for the mix design optimization and performance prediction of CRAC, delivering substantial environmental and economic benefits.
Abstract This study develops a robust framework for estimating the compressive strength of self-compacting concrete (SCC) incorporating recycled aggregates using supervised machine learning (ML) techniques. A comprehensive experimental database comprising 582 concrete mix designs was used, encompassing diverse input variables including binder content, water, coarse and fine aggregates, recycled aggregate proportion, superplasticizer dosage, and curing time. Seven ML algorithms—XGBoost, CatBoost, AdaBoost, Extra Trees, Bagging Regressor, K-Nearest Neighbors, and Radius Neighbors—were systematically trained using a stratified 70/15/15 data split and optimized via grid search with five-fold cross-validation. Model performance was evaluated using coefficient of determination (R 2), root mean squared error, and MAE across training, validation, and testing datasets. Among all models, XGBoost demonstrated the highest accuracy, achieving an average R 2 of 0.9799, RMSE of 2.87 MPa, and mean absolute error of 1.97 MPa. The Permutation Feature Importance analysis revealed that binder content, water, and coarse aggregate were the most influential predictors of strength. This study confirms that ensemble ML models, particularly XGBoost, can reliably predict the compressive strength of SCC with recycled aggregates, while offering transparent insights into material behavior. The results provide a valuable tool for sustainable mix design optimization and practical implementation in eco-efficient concrete construction.
A. Khan, M. D. Rasheed, Muhammad Huzaifa Naveed et al.· Data-Centric Engineering· 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, Abd Al-Kader A. Al Sayed· Buildings· 0 citations
Recycled concrete ultra-high performance concrete (RC-UHPC) prepared from recycled concrete sand (RCS) and recycled concrete powder (RCP) represents an important pathway for achieving the high-value utilization of construction and demolition waste. However, influenced by the material properties and compositional complexity, the application of RC-UHPC requires extensive performance optimization trials, a process that is not only inefficient but also accompanied by high costs. To address this challenge, this study introduces machine learning (ML) methods to improve the design efficiency of RC-UHPC, reduce reliance on conventional experimental methods, and facilitate its engineering application. A feature system comprising 11 key input variables, including cementitious materials, admixtures, and RCS with different particle sizes, was established. The performance of various models in predicting the 28-day compressive strength (CS28) of RC-UHPC was systematically evaluated, and the main influencing factors were identified through feature analysis. The results indicate that k-nearest neighbors, random forest, and gradient boosting regression trees are the optimal ML models, with mean R2 values of 0.974 ± 0.014, 0.954 ± 0.008, and 0.971 ± 0.007, respectively, under different random seeds. Feature analysis indicates that steel fibers and fly ash have a significant positive effect on CS28. In contrast, RCS and RCP exhibit a deteriorating effect, and no significant difference is observed in the effect of RCS with different particle sizes (0–1.18 mm and 0–2.36 mm) on CS28. This study analyzes the statistical associations between RC-UHPC performance and its influencing factors from a data-driven perspective. The results 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
This study presents a comprehensive comparative analysis of several machine learning (ML) models for predicting the compressive strength (CS) of nano-silica (NS)-enhanced concrete. A large dataset comprising 724 experimental mix designs was compiled from the literature, including various variables such as cement content, water-to-binder ratio, fine and coarse aggregates, nano-silica content, superplasticizer content, and curing time. Six ML algorithms were developed and evaluated: Interaction Model, Full Quadratic (FQ), Artificial Neural Network (ANN), M5P-Tree, Gradient Boosting (GB), and Random Forest (RF). Model performance was assessed using R², RMSE, MAE, scatter index (SI), and objective value (OBJ). Among all models, the RF model achieved the highest predictive accuracy, followed by ANN and GB models. Sensitivity analysis revealed curing time as the most influential factor, while partial dependence plots exhibited the nonlinear effect of nano-silica quantity, with an optimal strength response about 15 kg/m3. In addition, SHAP (SHapley Additive exPlanations) analysis was employed to enhance model interpretability, confirming the dominant influence of curing age and water-to-cement ratio on compressive strength prediction. Compared with many previous studies relying on limited datasets or single-model approaches, this study provides a robust, interpretable, and generalizable ML framework for optimizing nano-silica concrete mix design. The findings highlight the strong potential of ML, particularly ensemble models combined with explainable AI techniques, to improve prediction reliability, reduce trial-and-error experimentation, and support more cost-efficient and sustainable concrete design.
Yousif J. Bas, Jamal I. Kakrasul, Kamaran S. Ismail et al.· Engineering Research Express· 0 citations
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.
Jesús E. Altamiranda-Ramos, Alejandro Molina-Chegwin, Pau Coma-Busquets et al.· Buildings· 0 citations
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