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.
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.
Nga T. T. Nguyen, T. Nguyen, Tuan-Khoi Nguyen et al.· PLoS ONE· 0 citations
Ultra-High-Performance Concrete (UHPC) is a state-of-the-art concrete technology with exceptional qualities, including high compressive strength (CS) and durability. The CS, an essential property of UHPC, is determined through costly, time-consuming studies that require large amounts of material. To overcome these constraints, this work aimed to estimate the CS of UHPC using a range of single- and hybrid-machine learning (ML) methods. For this, five ML models, including Decision Tree (DT), Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), CatBoost, and a stacking Ensemble model combining these base models, were developed. The input space includes cement, silica fume, slag, fly ash, quartz powder, limestone powder, nano-silica, water, fine and coarse aggregate, fiber, superplasticizer, temperature, relative humidity, and age. The findings demonstrate that the Ensemble model achieved the best overall predictive performance across 20 Monte Carlo simulations, with a mean RMSE of 6.751 ± 0.316 MPa, MAE of 4.943 ± 0.219 MPa, and R² of 0.972 ± 0.002. According to the findings of 1D, 2D partial dependence plot (PDP), and SHAP analyses, age, cement, silica fume, water, sand, fiber, and superplasticizer were the primary variables influencing UHPC's CS.
M. H. Nguyen, Hai-Van Thi Mai, Ho-Ang Son· Journal of Science and Trans...· 0 citations
Ultra-high performance concrete (UHPC) has emerged as a revolutionary cementitious composite with exceptional mechanical properties and durability, yet its mixture design remains challenging due to complex nonlinear interactions among numerous constituents. Traditional experimental approaches are time-consuming, costly, and inefficient for optimizing UHPC formulations. Machine learning (ML) has recently gained significant attention as a powerful alternative for predicting UHPC performance and optimizing its mixture designs. This paper provides a comprehensive review of ML applications in UHPC, focusing on the prediction of compressive strength, flexural strength, workability, and durability properties. Various ML algorithms—including artificial neural networks (ANN), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGBoost), CatBoost, and Bayesian neural networks—are critically examined. The review synthesizes findings from over 200 published studies and multiple publicly available datasets comprising more than 2,000 UHPC mix designs. Key challenges, including data quality, model interpretability, and external validation, are discussed. Future research directions, such as physics-informed neural networks, generative AI for data augmentation, and explainable AI frameworks, are proposed to advance the field toward reliable and interpretable UHPC design.
Unknown authors· Journal of Computer Science...· 0 citations
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.
Rong Li, Teng Zhou, Siyu Lu et al.· Applied Sciences· 0 citations
Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness.
Javad Shayanfar, J. Barros· Journal of Composites Scienc...· 0 citations
Ultrahigh-performance concrete (UHPC) offers superior mechanical properties and durability but is constrained by high density, cost, and environmental impact due to its cement-intensive composition. This study presents a machine learning (ML)–driven multiobjective optimization framework for UHPC mix design, integrating waste aggregates, supplementary cementitious materials (SCMs), and performance-enhancing components to balance strength and sustainability. A robust data set combining 694 literature-derived and 87 experimental data points underpins the framework. Unsupervised anomaly detection (isolation forest) is employed to refine data quality, while ML techniques identify key parameters influencing UHPC properties. Predictive models, including artificial neural networks, random forests, and light gradient-boosting machine (LightGBM), are trained on full and reduced feature sets to ensure accuracy and generalizability. LightGBM, showing the best predictive performance, is embedded into single-objective and multiobjective optimization processes. Single-objective optimization achieves rapid and accurate convergence for individual performance targets. Multiobjective optimization yields diverse Pareto-optimal solutions, exposing trade-offs among strength, cost, and embodied carbon. Experimental validation confirms that optimized mixes improve performance while reducing environmental footprint. Two novel UHPC mixes were experimentally validated: (1) an SCM-based formulation that reduces clinker use while maintaining structural integrity; and (2) a UHPC mix incorporating waste glass aggregates, enhancing circularity and lowering carbon emissions. This study provides a scalable, data-driven approach to ecoefficient UHPC design, enabling intelligent, application-ready solutions that align with construction performance demands and global sustainability goals.
Yuhui Lyu, Fan Zheng, Haodong Ji et al.· Journal of materials in civi...· 0 citations
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