Physics-guided ensemble machine learning framework for slope failure prediction

Tree Based Machine Learning Framework for Stability Evaluation of Square Tunnels in Sloping Rock

The Evolution and Future of Machine Learning in Rock Slope Stability: A Systematic Review and Meta-Analysis

Physics-Informed Hybrid Machine Learning Model for Carbonation Depth Prediction in Concrete through Residual Correction and Variogram Analysis of Response Surfaces

A hybrid residual correction framework that integrates a physics-based carbonation model with a stacked ensemble of machine learning algorithms: gradient boosted regression trees (GBRT), support vector regression (SVR), and Gaussian process regression (GPR), combined through an XGBoost metamodel, demonstrating that the residual-based metamodel reproduced observed carbonation depths with higher accuracy.

Statistical and Sensitivity Evaluation of Advanced Computational Models for Predicting Rock Mechanical Properties

Interpretable Machine Learning for Predicting Residual Drift Ratios in Reinforced Concrete Bridge Piers

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.

Comparison of Performances of Machine Learning and Deep Learning Models for Prediction of Creep Rupture Life

Accurate prediction of creep rupture life is essential for ensuring the long-term reliability of high-temperature components in power generation and petrochemical industries. Selecting appropriate data-driven models for limited and heterogeneous creep datasets remains a critical challenge, as conventional accuracy-based comparisons do not fully capture model behavior under varying service conditions. This study presents a unified evaluation framework for systematically comparing multiple machine learning and deep learning models for creep rupture life prediction of 2.25Cr–1Mo steel. The framework integrates predictive accuracy, prediction reliability, regime-specific error analysis, and computational efficiency, enabling a comprehensive assessment beyond global error metrics. The input feature space is reduced from seventeen to eight physically meaningful variables without loss of predictive performance. To further assess model robustness, prediction errors are analyzed across four distinct rupture life regimes, revealing significant variations in model behavior that are not reflected in aggregate metrics. Results indicate that support vector regression (SVR) provides the most consistent overall performance across all regimes and offers a strong balance between accuracy and computational efficiency. Among deep learning models, a Bayesian neural network (BNN) achieves competitive predictive performance while additionally enabling uncertainty estimation. These findings demonstrate that, for small tabular creep datasets, appropriately regularized models outperform complex neural network architectures, highlighting the importance of matching model complexity to dataset characteristics. This study is limited to a single steel grade, moderate dataset size, and extrapolation beyond trained stress and temperature ranges, which are key directions for future work.