Machine learning based prediction and analysis of bond strength between steel reinforcement and geopolymer concrete under cyclic loading for seismic applications: a review

Interpretable machine learning with Bayesian optimization for bond strength prediction of steel reinforcement in geopolymer concrete

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.

Assessment of Shear Strength and Failure Mechanisms in Exterior Reinforced Concrete Beam–Column Joints Using Machine Learning and Explainable Artificial Intelligence

The seismic performance of reinforced concrete (RC) beam–column joints depends on both shear strength and failure mechanisms, the assessment of which remains challenging because of complex interactions among geometric, material, loading, and reinforcement parameters. This study presents a data-driven framework for assessing the shear strength and failure mechanisms of exterior RC beam–column joints. A database comprising 210 experimental specimens was systematically compiled from published studies. Seventeen input variables were selected based on structural mechanics, seismic design provisions, and previous experimental investigations. Machine learning models were developed for shear strength prediction and failure mode classification. SHAP was employed to interpret the trained models, while symbolic regression derived an interpretable design-oriented equation. On the independent test set, XGBoost achieved the highest shear strength prediction (R2 = 0.973, RMSE = 40.09 kN), whereas the Support Vector Machine achieved 80.5% classification accuracy. The results indicate that the governing parameters for failure mechanisms differ from those controlling shear strength. Joint shear capacity was primarily influenced by geometric dimensions and longitudinal reinforcement ratios, whereas axial load ratio and joint transverse reinforcement had a greater influence on failure mechanisms. These findings highlight the importance of simultaneously assessing shear strength and failure mode in RC beam–column joints.

Integrating strain-orthogonal phase-field modeling and machine learning for fracture prediction in steel fiber reinforced concrete

This paper aims to investigate the effects of steel fiber position, inclination angle and length on the fracture behavior of steel fiber reinforced concrete (SFRC), while reducing the computational burden associated with high-fidelity phase-field fracture simulations. A computational framework integrating phase-field modeling (PFM) and machine learning (ML) is developed. A strain-orthogonal phase-field formulation is employed to simulate interfacial damage and crack propagation in SFRC across different concrete grades and fiber configurations. Based on these simulations, a dataset of 357 samples is generated, comprising peak load (Pmax), critical displacement (U) and mechanical work (W). Gradient-boosting algorithms, including CatBoost, LightGBM and XGBoost, are trained and optimized using Bayesian optimization with five-fold cross-validation to construct efficient surrogate models. The results show that CatBoost consistently provides the highest prediction accuracy, achieving test R2 values of 0.999 for peak load, 0.986 for critical displacement, and 0.942 for mechanical work. The developed ML surrogates enable near-instantaneous prediction of fracture responses, offering a substantial reduction in computational cost compared with standalone phase-field simulations. Model interpretation based on SHAP reveals that matrix stiffness and initial crack length dominate peak load, while fiber inclination plays a more significant role in post-peak mechanical work and energy dissipation. This study proposes a hybrid phase-field–machine learning framework for efficient fracture analysis of SFRC. By exploiting high-fidelity numerical simulations as a data source for surrogate modeling, the proposed approach enables rapid parametric studies and optimization of fiber-reinforced concrete systems within a computational engineering context.

MICP and fiber-reinforced soil improvement: mechanisms, engineering performance, and machine learning prediction—a review

Explainable ensemble learning framework for bond strength prediction in 3D printed concrete structures

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.

Predictive modeling and design optimization of concrete properties reinforced with basalt fiber under harsh environment

Basalt fiber reinforced concrete (BFRC) has recently attracted increased attention for improving durability, mechanical strength, and chemical resistance concerning harsh environmental conditions. The most noticeable gap is left in being able to predict long-term performance accurately and optimize that performance because of the complexities that arise from the multiscale interactions between fibers, matrix, and environmental stressors. This study, therefore, offers a highly unified and multiscale machine learning framework by pulling together five disaggregated analytical models into a single predictive-optimization pipeline prearranged for basalt fiber reinforced concrete. The physics-augmented graph attention transformer network (P-GATNet) is expected to embed interfacial physics within graph-based message passing to capture load-driven mechanical responses, resulting in highly accurate strength and fracture evolution predictions (e.g., flexural R² ≈ 0.97). The spectral decomposition assisted degradation model uses Hilbert-Huang-based spectral analysis, which then decouples degradation mechanisms for accurately forecasting alkali resistance and damage kinetics with an error of less than 4.5%. The multi-agent physics reinforcement optimizer (MAPRO) jointly optimizes the strength and chemical performance by modeling competing failure mechanisms via cooperative agents. For improved representation of features, the deep morphological encoder with multi-modal fusion (DME-MMF) marries image-derived morphological embeddings with experimental tabular data, thus enhancing the interpretability and accuracy of predictions. Lastly, the transformer-based inverse composite generator enables reverse material design by producing feasible basalt fiber reinforced concrete formulations that satisfy predetermined strength and durability targets at an approximate success rate of 93%. This approach improves predictive fidelity, interpretability, and design in basalt fiber reinforced concrete.