Jul 2026· Journal of Composites Science· Vol 10, pp. 377· 0 citations· 34 references
TL;DR
A physics-guided machine learning framework that integrates domain-informed feature engineering, conditional synthetic data augmentation, and stacking ensemble learning to predict the chloride diffusion coefficient of concrete from Rapid Chloride Migration (RCM) test data is presented.
Abstract
Chloride-induced corrosion is one of the principal causes of deterioration in reinforced concrete infrastructure, making accurate prediction of chloride diffusion coefficients essential for durability assessment and service-life design. Existing machine learning models often suffer from limited experimental datasets and insufficient incorporation of engineering knowledge, restricting their predictive capability and generalization. This study presents a physics-guided machine learning framework that integrates domain-informed feature engineering, conditional synthetic data augmentation, and stacking ensemble learning to predict the chloride diffusion coefficient of concrete from Rapid Chloride Migration (RCM) test data. Physics-guided features were developed to represent fundamental transport mechanisms and binder characteristics, while synthetic data augmentation was employed to improve data coverage and enhance model robustness. The final stacking ensemble combined CatBoost, XGBoost, Random Forest, and Linear Regression through a Ridge Regression meta-learner. The proposed framework achieved a coefficient of determination (R2) of 0.903, with an RMSE of 1.321 and an MAE of 0.920 on an independent holdout dataset, outperforming all individual machine learning models. Ablation analysis demonstrated that synthetic data augmentation was the primary contributor to performance improvement, while ensemble learning provided additional gains in predictive accuracy and robustness. Model interpretability using SHapley Additive exPlanations (SHAP) identified slag content, water-to-binder ratio, and porosity-related variables as the dominant factors governing chloride diffusion predictions, consistent with established durability mechanisms. The proposed framework provides an accurate and interpretable tool for chloride diffusion prediction that supports durability assessment, service-life estimation, and the design of sustainable concrete mixtures.
A hybrid residual correction framework that integrates a physics-based carbonation model with a stacked ensemble of machine learning algorithms: gradient boosted regression trees (GBRT), support vector regression (SVR), and Gaussian process regression (GPR), combined through an XGBoost metamodel, demonstrating that the residual-based metamodel reproduced observed carbonation depths with higher accuracy.
Ankit Rai, Umesh Kumar Sharma, R. Ball· Journal of materials in civi...· 0 citations
An interpretable and uncertainty-aware machine-learning framework for estimating the shear capacity of FRCM-strengthened beams enables accurate, transparent, and uncertainty-aware assessment of shear capacity in FRCM-strengthened concrete beams.
Xiangsheng Liu, G. Figueredo, G. Gordon et al.· Journal of composites for co...· 0 citations
Accurate prediction of concrete compressive strength is essential for mixture design, quality control, and the broader use of supplementary cementitious materials in low-carbon construction. Fly ash concrete is particularly challenging to model because its strength development is affected by nonlinear interactions among binder composition, water–binder relationships, admixture dosage, and material characteristics. To address this problem, this study proposes a Dominant Learner with Adaptive Mixing (DLAM) framework for data-driven strength prediction. DLAM uses inner cross-validation to identify the most reliable learner from a pool of machine learning models and introduces a validation-controlled Ridge calibration step to exploit complementary information among candidate predictions. The calibration branch is adopted only when it improves the inner-validation root mean squared error (RMSE), thereby reducing the risk of unnecessary model combination and performance degradation. The framework is evaluated using a leakage-free repeated outer/inner validation protocol on a fly ash concrete dataset and is further examined on an independent public concrete strength dataset. DLAM is compared with individual learners, adaptive model-averaging baselines, and Stacking. The results show that DLAM achieves the lowest mean RMSE among the focused comparators on both datasets, with a clear improvement on the external dataset and a more modest gain on the fly ash dataset. These findings demonstrate that validation-controlled calibration provides a transparent and robust way to enhance machine-learning-based concrete strength prediction, especially when different learners capture complementary aspects of the mixture–strength relationship.
Results indicate that support vector regression (SVR) provides the most consistent overall performance across all regimes and offers a strong balance between accuracy and computational efficiency, and a Bayesian neural network (BNN) achieves competitive predictive performance while additionally enabling uncertainty estimation.
Muhammad Bilal Jan, Zengchao Wu, Mengyu Chai· Metals· 0 citations
Accurate prediction of concrete compressive strength is essential for effective mix design, quality control, and structural performance assessment. Conventional empirical models often exhibit limited accuracy due to the complex and nonlinear interactions among concrete constituents.
This study investigates the applicability of several machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset comprising 1030 concrete mixtures. Linear regression was adopted as a baseline model and compared with support vector regression, random forest regression, and artificial neural networks.
The performance of machine learning models was meticulously assessed using the coefficient of determination, root mean square error, and mean absolute error. Additionally, the models underwent five-fold cross-validation to evaluate their robustness and generalization capabilities. The results unambiguously demonstrate that machine learning models significantly outperform linear regression models.
Cross-validation results confirm the stability and reliability of the developed models. Feature importance analysis reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established concrete material behavior. The findings demonstrate that machine learning models, particularly random forest regression, can serve as effective supporting tools for preliminary concrete mix design and performance evaluation.
S. Rouabah· ITEGAM- Journal of Engineeri...· 0 citations
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
