Machine Learning for Concrete Performance Prediction and Intelligent Optimization: A Comprehensive Review

Machine Learning Based Analysis of Industrially Produced Machine Learning Based Analysis of Industrially Produced Concrete Concrete

A hybrid experimental and machine learning framework for designing and predicting compressive strength of ultra-high-performance concrete

Ultra-high-performance concrete (UHPC) offers exceptional mechanical and durability properties but often relies on quartz powder, raising sustainability and occupational health concerns. This study introduces an integrated experimental-computational framework for predicting the compressive strength of UHPC and developing quartz-free mixtures. Experimentally, the effects of mixing sequence, sand characteristics, superplasticizer chemistry, and curing regime were investigated, leading to a quartz-free UHPC achieving 136 MPa at 28 days under heat-curing. A dataset of 550 UHPC compressive strength records was compiled, incorporating quantitative mix proportions and categorical variables (cement type, superplasticizer base, fiber type, and specimen geometry). Sixty-three machine learning models from tree-based, boosting, and support vector machine families were optimized using seven meta-heuristic algorithms. The Particle Swarm Optimization-tuned XGBoost model achieved the highest prediction accuracy (R2 = 0.897, RMSE = 7.63 MPa), followed by the Differential Evolution-optimized Random Forest (R2 = 0.867, RMSE = 8.70 MPa). SHapley Additive exPlanations (SHAP) analysis identified curing age as the most influential predictor after optimization. The proposed framework enables accurate and interpretable UHPC strength prediction and supports the design of safer and more sustainable quartz-free UHPC with reduced experimental effort.

Comparative machine learning models for predicting the compressive strength of ultra-high-performance concrete

Ultra-high-performance concrete (UHPC) exhibits exceptional mechanical properties and durability. However, its compressive strength is highly dependent on complex mix design parameters. While traditional experimental techniques and regression-based models are commonly used to evaluate UHPC compressive strength, machine learning approaches offer an efficient alternative for capturing complex nonlinear relationships. This study develops a machine learning–based framework to predict the compressive strength of UHPC and compares the predictive performance of five advanced algorithms: Extremely Randomized Trees (ER), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), CatBoost, and Artificial Neural Network (ANN). A comprehensive experimental database was utilized for training and validation purposes. Among the evaluated models, CatBoost achieved the best predictive performance, with a coefficient of determination (R²) exceeding 0.90, a root mean square error (RMSE) of approximately 4.5 MPa, and a mean absolute error (MAE) of approximately 3.6 MPa. However, subgroup residual analysis showed that the prediction reliability was not uniform across the full strength range. In particular, mixtures with compressive strength ≥180 MPa exhibited larger errors and systematic underprediction, mainly due to the limited number of ultra-high-strength samples in the compiled database. Therefore, the model is more reliable within well-represented strength ranges, while predictions in the ultra-high-strength region should be interpreted with caution. SHAP-based analysis, feature dependency analysis, and both Individual Conditional Expectation (ICE) and Partial Dependence Plots (PDP) were employed. These explainable AI techniques identified key variables and quantified their contributions to the compressive strength of UHPC. The findings demonstrate that interpretable machine learning can support preliminary UHPC mixture assessment by combining predictive performance with physically meaningful insights.

Machine Learning-Based Compressive Strength Prediction and Multi-Objective Optimization of Ultra-High Performance Concrete

The compressive strength of ultra-high-performance concrete (UHPC) is jointly influenced by multiple factors, including material composition, mixture proportion parameters, and curing regime. Conventional empirical methods are therefore insufficient to accurately characterize the highly nonlinear relationships involved. To improve the prediction accuracy of UHPC compressive strength and to achieve mixture proportion optimization that simultaneously considers mechanical performance, economic efficiency, and environmental impact, this study developed random forest (RF), artificial neural network (ANN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) models based on 810 publicly available UHPC experimental datasets. Model performance was evaluated using R2, RMSE, MAE, and MAPE. To enhance the robustness of model validation, repeated K-fold cross-validation, sensitivity analysis with different random seed splits, and benchmark model comparisons were further introduced. The results indicate that the XGBoost model achieved superior predictive performance on both the test set and robustness validation, with test-set R2, RMSE, MAE, and MAPE values of 0.9604, 7.77, 5.58, and 4.80, respectively. The model was further interpreted using SHAP, PDP, and ICE methods, and the results revealed that curing age, fiber content, silica fume content, and water-to-binder ratio were important variables affecting the compressive strength of UHPC. Furthermore, XGBoost was used as a surrogate model and coupled with NSGA-II and TOPSIS methods for multi-objective optimization. Under the constraints of compressive strength, water-to-binder ratio, superplasticizer-to-binder ratio, and absolute volume, a computationally recommended UHPC mixture proportion balancing strength, cost, and carbon emissions was obtained. This study provides a reproducible machine-learning-assisted approach for UHPC compressive strength prediction and low-carbon, cost-effective mixture proportion design.

Improving Composite Materials with Machine Learning: A Predictive Approach

Machine learning (ML) has become an important technology in the field of composite materials, offering efficientmethodsformaterialcharacterization, damageassessment, propertyprediction, anddesignoptimization. Conventional experimental techniques and physics-based simulations for predicting the complex behavior of composites can require considerable time, cost, and computational resources. In contrast, ML provides a data-driven approach capable of identifying hidden patterns, establishing relationships between variables, and generating accurate predictions. Algorithms such as Support Vector Regression (SVR), Random Forest Regression, and Linear Regression can process information related to material composition, filler characteristics, curing conditions, and environmental factors. This study examines the application of ML techniques to composite materials, particularly for predicting fracture toughness, characterizing damage, and optimizing mechanical properties. Correlation analysis reveals significant relationships between fracture toughness and important input parameters, demonstrating the usefulness of data-driven approaches in material development. Among the investigated techniques, Random Forest Regression demonstrates strong predictive capability and effectively represents complex material behavior. However, challenges including limited data availability, model interpretability, and generalization remain. Reliable ML performance depends on high-quality datasets, suitable feature selection, and effective model tuning. Overfitting can also affect prediction accuracy, making cross-validation and hyperparameter optimization essential. Integrating ML into composite material research can reduce experimental requirements, lower development costs, improve manufacturing efficiency, and accelerate the development of high-performance materials. Future research should emphasize larger datasets, explainable ML, and hybrid approaches combining ML with physics-based simulations.

Flowability prediction and artificial intelligence analysis of SCC based on machine learning

Six machine learning algorithms were employed to construct artificial intelligence models for the precise prediction of self-compacting concrete (SCC) flow properties, using a total of 158 sets of experimental data samples. A sensitivity analysis examining the relationships between feature parameters and flow properties was performed using Shapley additive explanations (SHAP). The mix proportion of SCC involved key feature parameters: the water-to-binder ratio, along with the proportions of cement, silica fume, slag, fly ash, fine aggregate, coarse aggregate, and water reducer. The flowability of SCC was quantitatively characterized by the results of the slump test. Based on the analysis of prediction errors and outcomes, the extreme gradient boosting (XGB) model was identified as exhibiting superior predictive accuracy and generalization performance, with the coefficient of determination (R2) reaching 0.927, and the explained variance of 0.936. Building upon the XGB intelligent algorithm, a graphical user interface-based interactive program was successfully developed to predict flowability using SCC mix proportions. The results of SHAP analysis indicated that the content of cement and silica fume exhibited a negative correlation with SCC flowability, while the content of fly ash and slag showed a positive correlation with SCC flowability. Compared to coarse aggregates, fine aggregates exerted a less pronounced effect on the flow characteristics of SCC. The dosage of water-reducing agent was the most significant positive factor affecting the flow properties of SCC.