Aug 2026· Buildings· Vol 16, pp. 3081· 1 citation· 72 references
TL;DR
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.
Abstract
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.
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
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.
Abdulaziz Alqurashi· Islamic University Journal o...· 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.
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
ABSTRACT This study establishes four popular data-driven techniques – Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB) for predicting the flexural strength (FS) of high-performance steel fiber-reinforced cementitious composites (FRCCs). A database containing 156 experimental records was used to train and test predictive models with nine feasible input variables. The results demonstrated that the GB model was the best predictor for estimating the FS of FRCCs. The GB model maintained satisfactory predictive accuracy after 10-fold cross-validation, achieving an average R2 of 0.834 on the validation folds generated from the training dataset. The predictive capability of GB model remained stable at 600 Monte Carlo simulations. The Shapley Additive Explanations (SHAP) method and partial dependence plots (PDP) indicated that fiber volume content was the most influential factor affecting FS predictions. To validate the accuracy of the GB model, a single-point case study was conducted on three specimens subjected to a three-point bending load. The discrepancy between the experimental values and GB predictions corresponded to an absolute prediction error of 1.21%, highlighting the accuracy of the developed model. Finally, a cloud-based web application was developed to provide a convenient tool for practical FS prediction of FRCCs.
Duy-Liem Nguyen, Tan-Duy Phan· Journal of Structural Integr...· 0 citations
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.
Qizhi Xu, Yan Tang, Shimin Ding et al.· Proceedings of the Instituti...· 0 citations