TL;DR
The proposed AFELA–machine learning framework provides a computationally efficient, reliable, and interpretable approach for tunnel stability assessment in sloping rock mass conditions.
Accurate prediction of the residual drift ratio of reinforced concrete bridge piers is challenging because conventional methods are computationally expensive, time-consuming, and unable to effectively capture complex nonlinear interactions among multiple influencing factors. To address these limitations, this study proposes an interpretable machine learning framework for predicting the residual drift ratio of reinforced concrete bridge piers. A comprehensive database containing 261 quasi-static experimental datasets was established, incorporating key structural and material parameters, including axial com-pression ratio, shear span ratio, stirrup ratio, longitudinal reinforcement ratio, material strengths, and geometric dimensions. Based on this database, six representative machine learning models were developed and systematically compared. Their predictive performance, robustness, and generalization capability were evaluated using multiple statistical metrics and Monte Carlo simulations. The results show that the CatBoost model consistently outperformed the other models, achieving an R2 value of 0.9629 on the test set while maintaining excellent stability under random data partitions. Furthermore, SHAP analysis was employed to interpret the trained model and quantify the contributions of individual input variables. Eight key factors influencing the residual drift ratio were identified, with the loading displacement ratio (θ) exhibiting the greatest influence. These findings demonstrate that the proposed framework provides an accurate, reliable, and interpretable tool for predicting the post-earthquake residual drift ratio of reinforced concrete bridge piers, offering valuable support for performance-based seismic design, post-earthquake damage assessment, and resilience-based bridge engineering.
The significance of rock brittleness is well‐recognized in the fields of geotechnical engineering and energy exploration. To enhance the predictive precision of rock brittleness, this paper proposes a Stacking integrated algorithm. This algorithm synergistically combines various meta models and foundational models, utilizing a suite of nine algorithm modes: Gaussian Process Regression, Support Vector Machine, Backpropagation Neural network, Extreme Learning Machine network, Decision Tree, Random Forest, Extreme Gradient Boosting, Lasso Regression, and Ridge Regression. Furthermore, Tuna and Bayesian optimization algorithms are utilized to refine the model's performance. Additionally, a new diversity index, k, based on the ratio of correlation coefficients, has been introduced to facilitate the optimal selection of base models for the Stacking integrated algorithm. The predictive accuracy of the Stacking integrated model, as determined by the proposed diversity index k, surpasses that of the finest individual base model and outperforms other sets of five integrated base models with an equivalent number of components. This underscores the efficacy of the diversity index k in guiding the selection of appropriate base models for the stacking process. The most effective model for predicting rock brittleness incorporates the Extreme Learning Machine network, Decision Tree, Random Forest and Extreme Gradient Boosting. This ensemble model demonstrates superior accuracy over the single best model, the Decision Tree, by reducing the average brittleness prediction error rate by 0.3745 and elevating the determination coefficient ( R 2 ) value from 0.9118 to 0.9629.When compared with the Particle Swarm Optimization model, this composite model achieves an increase of 0.1498 in R 2 .
The proposed framework combined a curated database, neural network-based curve prediction, and hyperparameter optimization, providing a robust approach for evaluating the soil arching effect, providing a robust approach for evaluating the soil arching effect.
Accurate prediction of the relative length of a hydraulic jump (Lj/d1) is essential for the safe and economical design of energy dissipation structures in open channels. In rough sloping channels, this prediction becomes challenging due to strong nonlinear interactions among inflow Froude number (Fr1), bed roughness height (h), and channel slope (θ), which are inadequately represented by conventional empirical equations. The objective of this research is to develop robust ML models for predicting Lj/d1 under combined rough and sloping bed situation and to find the most efficient modeling approach. The study utilized 452 experimental data consisting extensive range of Fr1 (2.49 to 7.62), h (0 to 30 mm), θ (0° to 6°). Four ML models such as ANN, RF, AdaBoost, and CatBoost were trained using 70% of the experimental data and tested on the remaining 30%. Model effectiveness was analyzed through graphical assessment, statistical evaluation, rank analysis, and SHapley Additive exPlanations based sensitivity analysis. Results demonstrated that all models attain high predictive accuracy; however, CatBoost performs better than others with excellent generalization, obtaining R² values of 0.9998 and 0.9952, MARE values of 0.0047 and 0.0201 during training and testing of experimental data, respectively. SHAP analysis validates Fr1 as the predominant parameter, followed by surface roughness and bed slope. The novelty of this research lies in the integrated application and comparison of multiple ML techniques, particularly CatBoost, for predicting hydraulic jump length in the combined rough sloping scenario, providing an accurate, interpretable, and practical framework for hydraulic engineering applications.