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.
Accurate prediction of bond strength between reinforcement and concrete is critical for ensuring the structural reliability and durability of reinforced elements, particularly in emerging construction technologies such as three-dimensional concrete printing (3DCP). Traditional empirical and semi-empirical bond models are often limited by simplified assumptions and insufficient ability to capture complex nonlinear interactions among geometric, material, and reinforcement-related parameters. To address these limitations, this study proposes a comprehensive data-driven framework integrating ensemble machine learning models with systematic hyperparameter sensitivity analysis and explainable artificial intelligence techniques. An extensive experimental database comprising 550 samples and nine influential input variables was compiled and analysed. Random forest (RF), extreme gradient boosting (XGB), and AdaBoost (ADB) models were developed and rigorously optimized using multiple performance metrics, including RMSE, MAE, R², and CVRMSE. The results demonstrate that the XGB model significantly outperforms the other approaches, achieving superior accuracy and robust generalization. Residual diagnostics confirm unbiased predictions and stable error distributions. Furthermore, SHAP and partial dependence analyses provide transparent insights into the dominant influence of geometric ratios and reinforcement characteristics on bond strength. Finally, the optimal model was embedded into a user-friendly graphical interface to support practical engineering decision-making. The proposed framework offers an accurate, interpretable, and deployable solution for bond strength prediction in modern concrete construction.
Qaim Shah, Waheed Ali Khoso, Fawad Iqbal et al.· Discover Artificial Intellig...· 0 citations
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
The bond behavior between steel reinforcement and geopolymer concrete under cyclic loading is a critical factor for the performance of reinforced concrete structures in seismic regions. Traditional empirical models often fail to capture the complex, nonlinear interactions at the steel concrete interface, particularly under repeated loading conditions. This systematic literature review aims to synthesize and critically analyze existing research on machine learning-driven approaches for predicting and interpreting bond strength in this context. We systematically identified and evaluated studies that develop, validate, or apply machine learning algorithms including artificial neural networks, support vector machines, and ensemble methods to model bond-slip relationships, failure modes, and degradation mechanisms. The review methodology involved a structured search and thematic analysis of peer-reviewed articles, focusing on how these models incorporate key variables such as concrete compressive strength, fiber reinforcement type, confinement conditions, and loading history. Our analysis reveals that Several reviewed studies reported improved predictive performance of machine learning models compared with selected empirical equations. However, differences in datasets, validation strategies, and performance metrics limit direct comparison among studies and prevent definitive conclusions regarding consistent superiority and generalizability., achieving higher accuracy in predicting bond strength under both monotonic and cyclic regimes. Furthermore, we found that feature importance analyses from these models provide new insights into the relative influence of material properties for instance, the critical role of fiber volumetric ratio and lateral confinement in mitigating bond degradation under reversed cyclic loads. The review also identifies significant gaps, including the scarcity of experimental datasets for high-magnitude seismic loading and the limited generalizability of models across different geopolymer mix designs. We conclude that machine learning offers a powerful framework for advancing bond strength prediction in geopolymer concrete systems, but future work must prioritize the development of robust, transferable models trained on more diverse, large-scale cyclic test data. These findings provide a foundation for more reliable seismic design guidelines and inform the selection of machine learning strategies for structural performance assessment.
Qaim Shah, Waheed Ali Khoso, Mussa Umali· Journal of Infrastructure Pr...· 0 citations
The slant shear bond strength of the UHPC-NSC interface is a key parameter governing load transfer and structural reliability in composite concrete members. However, accurate prediction remains challenging because of strong nonlinear relationships among influencing parameters and the limited availability of experimental data. This study presents an optimized machine learning framework for reliable bond strength prediction by integrating Extra Trees Regressor (ETR) and CatBoost (CATB) with Grasshopper Optimization (GO) and Northern Goshawk Optimization (NG) for hyperparameter optimization. Model performance was evaluated using cross-validation and independent testing to ensure reliable generalization. Among the developed models, the optimized CATB-NG achieved the highest predictive accuracy with an R2 of 0.934, RMSE of 3.081 MPa, and MAE of 2.186 MPa. SHapley Additive exPlanations (SHAP) identified NSC surface treatment and compressive strength as the dominant factors influencing bond strength, while Individual Conditional Expectation (ICE) analysis revealed nonlinear feature interactions and threshold behaviors. To facilitate practical engineering applications, the optimized model was implemented in a graphical user interface (GUI) for real-time prediction with standardized feature encoding. The proposed framework provides an accurate, interpretable, and user-friendly tool for predicting UHPC-NSC interfacial bond strength and supports engineering design and decision making.
Sanjog Chhetri Sapkota, S. Adhikari, Nisha Panta et al.· Buildings· 0 citations
Accurate prediction of the mechanical strength of Basalt Fiber Reinforced Concrete (BFRC) is critical for structural design, safety assessment, and the advancement of sustainable infrastructure in civil engineering. Traditional prediction methods often fail to capture the nonlinear relationships between BFRC mix proportions and resulting strength characteristics, leading to unreliable estimations. To address this limitation, this study proposes the Optimized Moment Balanced Machine (OMBM), an advanced machine learning model developed to improve the predictive accuracy of BFRC strength parameters. The model was trained and evaluated using key input features, including cement content, silica fume, fly ash, superplasticizer, water, aggregate composition, and fiber property parameters. The performance of the OMBM was benchmarked against four established machine learning models, such as Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Linear Regression (LR). Results from ten-fold cross-validation show that OMBM consistently outperforms the comparison models across five evaluation metrics. It achieved the lowest RMSE (2.411), MAE (1.788), and MAPE (4.08%), along with the highest values for correlation coefficient (R = 0.978), and coefficient of determination (R2 = 0.956). Furthermore, the OMBM achieved a Reference Index (RI) score of 1.000, which confirms its position as the leading predictive model within this comparative framework. These results confirm the robustness and reliability of the proposed OMBM model, making it a highly effective tool for accurate strength prediction of BFRC. This approach offers significant potential for the advancement of sustainable infrastructure by enabling more accurate and efficient use of concrete materials.
R. R. Khasani, Ferry Hermawan, Yuliana Usman· IOP Conference Series: Earth...· 0 citations
Accurate prediction of the shear capacity of reinforced concrete (RC) beams strengthened with fabric-reinforced cementitious matrix (FRCM) systems remains challenging due to the complex interaction between geometry, internal reinforcement, and parameters related to the strengthening technique. This study proposes an interpretable and uncertainty-aware machine-learning framework for estimating the shear capacity of FRCM-strengthened beams. A database comprising 174 experimental beam tests collected from the literature was assembled and, to enrich the training space under limited experimental coverage, a hybrid tabular variational autoencoder (TVAE) framework was used to generate 6,000 synthetic samples from the training subset. The resulting experimental and augmented data sets were used to develop three machine-learning models: linear regression, support vector regression, and extreme gradient boosting. An existing analytical model was also evaluated for comparison. Among all approaches, the extreme gradient boosting model achieved the highest predictive accuracy, with
R
2
= 0.911 on the testing set and
R
2
= 0.949 for the complete data set, and also exhibited stable performance on the synthetic data set. Model interpretability was examined using shapley additive explanations (SHAP)-based explanations together with permutation-based importance analysis, which consistently identified effective depth as the most influential variable, followed by transverse-reinforcement ratio and shear span-to-depth ratio. To quantify feature-importance uncertainty, a fuzzy ensemble feature importance analysis was conducted. Effective depth exhibited the most stable importance pattern, whereas several FRCM-related parameters showed moderate importance with greater uncertainty. Introducing model-form uncertainty through a multimodel ensemble reduced the relative importance of several predictors. Contextual fuzzy rules further revealed distinct feature-state patterns associated with low, moderate, and high shear-capacity regimes. To improve practical applicability, the validated extreme gradient boosting model was further distilled into an explicit two-regime design-oriented formula with preliminary reliability calibration. Overall, integrating machine-learning prediction with fuzzy ensemble interpretability and TVAE-assisted design-oriented distillation 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