Aug 2026· Proceedings of the Institution of Civil Engineers : Bridge Engineering· 0 citations· 93 references
TL;DR
A unified ML-based shear strength prediction model was developed that simultaneously captures the shear behaviour of both NC and UHPC beams, incorporating physically meaningful input features derived from the data set, thereby overcoming the limitations of separate empirical formulations.
Abstract
Accurate prediction of shear capacity in reinforced concrete beams is crucial for structural safety assessment. Conventional theoretical methods exhibit significant variability due to the complexity of shear failure mechanisms. This study presents an interpretable machine learning (ML) framework to enhance shear capacity prediction. A comprehensive database of 1175 beam specimens was developed, including normal concrete (NC) and ultra-high-performance concrete (UHPC) beams across three distinct cross-sectional geometries. The ML algorithms–support vector regression, artificial neural network, K-Nearest neighbors, decision tree, random forest, gradient boosting machine, light gradient boosting machine, adaptive boosting, categorical boosting, and extreme gradient boosting (XGBoost)–were optimized using 10-fold cross-validation and random search. The XGBoost algorithm demonstrated superior performance, achieving an R2 of 0.986 on the aggregated data set. Interpretability analysis with Shapley additive explanations identified beam depth (h), shear-span ratio (m), cross-sectional area (Ac) and fibre factor (λf) as critical features, highlighting their individual and interactive contributions. Moreover, a unified ML-based shear strength prediction model was developed that simultaneously captures the shear behaviour of both NC and UHPC beams, incorporating physically meaningful input features derived from the data set, thereby overcoming the limitations of separate empirical formulations. The proposed ML-based model significantly improved the accuracy of shear strength predictions compared to traditional empirical methods, enhancing reliability in structural design.
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
A comparative framework evaluating nine regression algorithms using the UCI Concrete Compressive Strength dataset, jointly integrating correlation-corrected statistical validation, multi-model Bayesian optimization, and domain-informed feature engineering with SHAP interpretation, rarely combined in prior concrete-strength studies.
Musthafa 'Abduh Fakhruddin, Sri Winarno, Acun Kardianawati· IDEALIS : InDonEsiA journaL...· 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
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
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
Accurate estimation of bond strength between steel reinforcement and geopolymer concrete is essential for the reliable design of sustainable reinforced concrete structures. However, the highly nonlinear interactions reduce the applicability and accuracy of conventional empirical models. This study proposes a Bayesian-optimized interpretable machine learning framework to predict the ultimate bond strength of reinforced geopolymer concrete using a comprehensive experimental database compiled from published studies. A dataset of 238 samples with 20 influential input variables was assembled to represent material properties, geopolymer chemistry, and specimen geometry. Six advanced machine learning algorithms, including Support Vector Regression (SVR), Random Forest (RF), Extra Trees Regressor (ETR), Gradient Boosting Machine (GBM), XGBoost, and CatBoost, were developed and systematically compared. Hyperparameter tuning was performed using Bayesian optimization to improve model performance. The results indicate that all models achieved strong predictive capability, while the optimized CatBoost model (BO-CatBoost) provided the best performance with testing metrics of R² = 0.950, MAE = 1.173, MAPE = 11.608%, and RMSE = 1.669. A comparative evaluation with existing empirical equations further demonstrated the superior accuracy and lower prediction variability of the proposed model. To enhance model transparency, SHAP-based explainability analysis was conducted to quantify the contribution of each input parameter. The global importance analysis revealed that compressive strength, the embedment length-to-bar diameter ratio, and the cover-to-bar diameter ratio are the most influential factors governing bond strength. Additional mixture-related parameters, including the alkaline solution-to-binder ratio, curing temperature, CaO content in the binder, and the SiO₂/Al₂O₃ ratio, also contribute to the bond mechanism by influencing geopolymerization and matrix densification. The proposed framework provides both high predictive accuracy and interpretable insights, demonstrating the potential of Bayesian-optimized interpretable machine learning to support the design and optimization of sustainable reinforced geopolymer concrete structures.
