2026· Islamic University Journal of Applied Sciences· 0 citations
Abstract
The interfacial performance of advanced composites bars embedded in Ultra-High Performance Concrete (UHPC) is an important factor that controls load transfer and the performance of structural elements. Predicting bond strength is still difficult because it is affected by several factors, such as rebar type, bar profile, bar diameter, bonded length, cover depth, fiber content, UHPC compressive strength, and FRP tensile strength. Therefore, this study uses machine-learning models to estimate the the bonding capacity of FRP bars placed in UHPC Using a collected experimental database of 183 specimens from previous studies. Four machine-learning models were developed and compared, including Linear Regression, Random Trees, Multi-Layer Perceptron, and Locally Weighted Learning. The MLP model gave the best prediction performance, with a correlation coefficient of 0.9466, MAE of 2.3083 MPa, and RMSE of 3.0631 MPa. SHAP analysis showed that embedment length was the most influential variable, followed by bar surface condition, FRP tensile strength, and concrete cover. This confirms that FRP–UHPC bond behavior is controlled by the interaction between bonded length, surface condition, mechanical interlock, and confinement provided by UHPC. Overall, the developed explainable ML framework provides a useful tool for predicting FRP–UHPC bond strength and supporting future UHPC-specific bond models.
An optimized machine learning framework for reliable bond strength prediction by integrating Extra Trees Regressor and CatBoost with Grasshopper Optimization and Northern Goshawk Optimization for hyperparameter optimization is presented.
Sanjog Chhetri Sapkota, S. Adhikari, Nisha Panta et al.· Buildings· 1 citation
A comprehensive data-driven framework integrating ensemble machine learning models with systematic hyperparameter sensitivity analysis and explainable artificial intelligence techniques is proposed, demonstrating that the XGB model significantly outperforms the other approaches, achieving superior accuracy and robust generalization.
Qaim Shah, Waheed Ali Khoso, Fawad Iqbal et al.· Discover Artificial Intellig...· 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
This study presents a comparative evaluation of three machine learning models, XGBoost, AdaBoost, and LightGBM, for predicting the time-dependent bond strength between fiber-reinforced polymer (FRP) and timber in both normal and harsh environments. A dataset was compiled (79 for normal conditions and 265 for harsh environments) incorporating material properties, geometric parameters, exposure time, and solution pH as input features. Hyperparameter optimization was performed for each model, and performance was evaluated using R2, RMSE, MAE, and MSE metrics. SHAP analysis and Partial Dependence Plots were employed to interpret feature importance and model behavior. Under normal conditions, XGBoost achieved the highest predictive accuracy (testing R2 = 0.944, RMSE = 2.170, and MAE = 1.775), outperforming AdaBoost and LightGBM. However, in harsh environments, LightGBM demonstrated superior generalization, with the highest testing R2 of 0.797 and the lowest RMSE of 0.549 and MAE of 0.431, outperforming XGBoost and AdaBoost. AdaBoost exhibited severe overfitting under harsh conditions, with a training-to-testing R2 drop of 0.341. Feature importance analysis by SHAP analysis identified fiber tensile strength and exposure time as the most influential parameters governing bond performance. SHAP force plots demonstrated that fiber properties predominantly enhance bond strength, while pH consistently acts as a decreasing factor under harsh conditions. This research provides a robust predictive framework for FRP–timber bond durability, offering valuable insights for structural design and service life prediction in harsh environments.
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
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.