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Open access
Aug 2026
Novel Interpretable Machine Learning Models for Predicting Compressive Strength of Nano-Silica Concrete
This study provides a robust, interpretable, and generalizable ML framework for optimizing nano-silica concrete mix design and highlights 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
Sep 2026
Machine-learning prediction and multi-objective optimization of concrete sulfate resistance
Sulfate attack progressively deteriorates concrete in marine, saline-soil, and sulfate-rich environments. This study developed an interpretable machine-learning and multi-objective optimization framework for sulfate-resistance-oriented concrete design. A literature-based dataset containing 744 records from 21 publications and 18 input features was compiled; 549 records were retained after outlier screening. Support vector regression, random forest, gradient boosting, and XGBoost models were evaluated using Bayesian hyperparameter optimization and random 5-fold cross-validation. XGBoost achieved the best performance ( R²=0.96, RMSE = 0.0429, and MAE = 0.0280). SHAP-based interpretability analysis revealed that at the same concentration, magnesium ions erode concrete more severely than sodium ions; water-reducing agent and sand content correlate positively with durability, while water-to-binder ratio and coarse aggregate amount correlate negatively. XGBoost was coupled with NSGA-II to optimize durability, cost, and carbon emissions. A balanced Pareto solution achieved a corrosion-resistance coefficient of 1.24, a cost of 312.24 CNY/m³, and carbon emissions 226.00 kgCO₂/m³.
Yi-Hang Guo, Jia-Yu Li, Li Li et al.
· Canadian journal of civil en... · 0 citations
Open access
Aug 2026
Bayesian-optimised machine learning for predicting aggressive environment resistance and service life of recycled aggregate geopolymer concrete
Developing reliable computational tools for durability and service-life assessment of concrete structures in aggressive environments is essential for advancing predictive modeling in structural engineering. This study introduces machine learning (ML)–based models for forecasting the sulfate and acid resistance of recycled aggregate geopolymer concrete (RGPC), produced with untreated and surface-treated recycled concrete aggregates (RCAs) through two mixing approaches. Three algorithms, i.e. Gaussian process regression (GPR), LSBoost ensemble, and Neural Network, were trained using nine input parameters related to material composition and exposure conditions, with durability indicators, namely mass loss rate (Kw) and compressive strength retention index (Kf), as outputs. A dataset of 336 experimentally tested RGPC specimens was used, applying Bayesian Optimisation for hyperparameter tuning and 5-fold cross-validation for generalisation. Among the models, the optimized GPR achieved the highest accuracy, confirmed by the lowest objective value. Feature importance analysis highlighted environmental cations, sulfate concentration, RCA replacement level, initial compressive strength, and exposure duration as the most influential factors governing degradation. The proposed Bayesian-optimized ML framework demonstrates a robust and generalizable method for predicting durability and service life of sustainable concretes, providing a valuable tool for simulation-driven design and durability-based performance assessment in mechanics and structural engineering.
P. Singh, Puja Rajhans
· Engineering Research Express · 0 citations
Open access
Sep 2026
Compressive Strength Prediction of Red Mud Concrete Using Explainable and Uncertainty-Aware Artificial Intelligence Models
Red mud, an alkaline industrial by-product of alumina refining generated in enormous volumes worldwide, poses a persistent environmental disposal challenge; using it as a partial cement replacement offers a promising route toward more sustainable concrete, but the resulting compressive strength is governed by complex, nonlinear interactions among the mix constituents that conventional empirical and regression-based models struggle to capture accurately. To address this challenge, the present study develops and compares four machine learning and deep learning models, namely the Deep Gradient Boosting Machine (DGBM), the Differentiable Neural Decision Tree (DNDT), Long Short-Term Memory (LSTM), and the Monte Carlo Dropout Neural Network (MCDNN), for the accurate and uncertainty-aware prediction of the compressive strength of red mud concrete. A dataset of 183 data points, compiled from the literature and supplemented with experimental results, was used to capture the influence of red mud content, curing period, and other mix parameters, including cement dosage, water content, and admixture proportions. The data were pre-processed prior to model training, and predictive performance was evaluated using R2, RMSE, MAE, and WMAPE, among other indicators. The results show that the deep learning models outperformed the tree-based models: LSTM achieved the highest accuracy (R2 = 0.942 on the testing dataset), while MCDNN additionally provided reliable uncertainty estimates alongside comparable prediction accuracy; DNDT and DGBM were comparatively less effective. Global sensitivity analysis identified fly ash and water content as the most influential contributors to strength development. By combining rigorous data-driven modeling with sensitivity and uncertainty analysis, this study contributes to the literature a validated, uncertainty-aware deep learning framework for sustainable concrete strength prediction, and offers practical value to the construction industry by providing engineers with a reliable, data-driven tool for optimizing red mud content in concrete mix design, thereby supporting the safe and wider industrial utilization of this problematic waste stream.
Pradeep Thangavel, D. R. Kumar, Prasoon Kumar et al.
· Buildings · 0 citations
Open access
Aug 2026
Innovative sustainable concrete with waste glass materials: an explainable machine learning for compressive strength prediction
Sustainable concrete incorporating waste glass materials has emerged as a promising solution to reduce environmental impacts associated with cement production and natural aggregate depletion. Accurate prediction of compressive strength (CS) is essential for optimizing such mixtures and ensuring structural reliability. In this study, five machine learning models: Random Forest (RF), K-Nearest-Neighbors (KNN), Adaptive-Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), and Extreme-Gradient-Boosting (XGBoost) were developed and optimized using Grid Search to predict the CS of concrete containing glass powder (GP) and glass sand (GS). A dataset of 270 experimental samples was utilized, incorporating eight input parameters, including curing duration, cement content, GP, GS, water, density, sand, and basalt. Among the models, LightGBM demonstrated superior predictive performance during testing, achieving a determination coefficient (R² = 0.964) and Root-Mean-Square-Error (RMSE = 2.05 MPa), followed by XGBoost (R² = 0.955) and RF (R² = 0.953). In contrast, KNN and AdaBoost exhibited comparatively lower performance. SHAP and Partial Dependence Plot (PDP) analyses identified curing duration and cement content as the most influential parameters, while water and GP exhibited negative effects on CS. To enhance practical applicability, the LightGBM model was deployed through a user-friendly GUI, enabling rapid and reliable prediction of CS and providing an accurate, interpretable, and practical decision-support tool for sustainable concrete mixture design.
Abdelrahman Shams, S. R. Wani, Eman Mousa et al.
· Scientific Reports · 0 citations
Comparative and Interpretable Machine Learning for Fly Ash Geopolymer Strength: Prediction, Validation and Optimization
An interpretable machine learning framework integrating Extreme Gradient Boosting with Shapley Additive Explanations to predict the 28-day compressive strength of fly ash-based geopolymer concrete (FA-GPC) is developed and experimentally validates.
Xiaoshuang Shi, Hao-Xiang Hu, Zhenhua Duan et al.
· 0 citations