As mineral resource development moves deeper into the earth, mine dynamic disasters, represented by rockbursts, occur frequently. Due to the combined effects of in situ rock stress state and geostress conditions, it is difficult to obtain reliable prediction results using traditional empirical criteria or single-index prediction methods. To address these issues, this paper constructs a rockburst sample database based on the existing literature, including maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength, elastic energy index, stress coefficient, and brittleness coefficient. Secondly, six typical machine learning algorithms are selected for rockburst level classification research. Then, to address the problem of imbalanced sample distribution, the SMOTE oversampling method is introduced to balance the data, and Bayesian optimization and cross-validation are combined to optimize the model hyperparameters. The results show that optimized XGBoost models exhibit high accuracy and stability in rockburst level discrimination, and accuracy reached 0.7664, recall was 0.7664, macro-P was 0.7664, and macro-F1 was 0.7662. Furthermore, taking the BO-XGBoost model as an example, the SHAP method is introduced to analyze the interpretability of the model’s prediction results. The results show that the elastic energy index and stress-related indices are the main controlling factors affecting the intensity of rockburst; they accounted for 25.75% and 22.68%, respectively. Based on the above research results, this paper further explores the ideas for rockburst safety management and prevention from the aspects of energy control, stress regulation, and optimization of rock mass structural characteristics, providing theoretical basis and technical support for the scientific formulation of rockburst risk identification, level prediction, and safety prevention and control measures in deep underground engineering.
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.
The compressive strength of ultra-high-performance concrete (UHPC) is jointly influenced by multiple factors, including material composition, mixture proportion parameters, and curing regime. Conventional empirical methods are therefore insufficient to accurately characterize the highly nonlinear relationships involved. To improve the prediction accuracy of UHPC compressive strength and to achieve mixture proportion optimization that simultaneously considers mechanical performance, economic efficiency, and environmental impact, this study developed random forest (RF), artificial neural network (ANN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) models based on 810 publicly available UHPC experimental datasets. Model performance was evaluated using R2, RMSE, MAE, and MAPE. To enhance the robustness of model validation, repeated K-fold cross-validation, sensitivity analysis with different random seed splits, and benchmark model comparisons were further introduced. The results indicate that the XGBoost model achieved superior predictive performance on both the test set and robustness validation, with test-set R2, RMSE, MAE, and MAPE values of 0.9604, 7.77, 5.58, and 4.80, respectively. The model was further interpreted using SHAP, PDP, and ICE methods, and the results revealed that curing age, fiber content, silica fume content, and water-to-binder ratio were important variables affecting the compressive strength of UHPC. Furthermore, XGBoost was used as a surrogate model and coupled with NSGA-II and TOPSIS methods for multi-objective optimization. Under the constraints of compressive strength, water-to-binder ratio, superplasticizer-to-binder ratio, and absolute volume, a computationally recommended UHPC mixture proportion balancing strength, cost, and carbon emissions was obtained. This study provides a reproducible machine-learning-assisted approach for UHPC compressive strength prediction and low-carbon, cost-effective mixture proportion design.
The proposed AFELA–machine learning framework provides a computationally efficient, reliable, and interpretable approach for tunnel stability assessment in sloping rock mass conditions.
Ground settlement induced by shield tunnel boring machine (TBM) excavation is a major geotechnical concern in urban tunneling because it may affect the safety of adjacent structures and underground infrastructure. In this study, machine learning models were developed to predict the maximum settlement induced by shield TBM excavation using a three-dimensional numerical analysis database comprising 320 simulation cases generated from combinations of tunnel diameter (D), ground elastic modulus (E), face pressure (FP), and backfill pressure (BP). Random forest (RF) and extreme gradient boosting (XGBoost) models were developed and compared with an existing regression-based settlement prediction equation. Predictive performance and generalization capability were evaluated using random split and GroupKFold validation techniques. Under random split validation, RF achieved the highest predictive performance, with a coefficient of determination of 0.997 and a root mean square error of 0.438 mm, followed by XGBoost. Both machine learning models outperformed the existing settlement prediction equation. However, model performance decreased substantially under GroupKFold validation, indicating limited generalization capability under unseen D–E grouped conditions. The results demonstrate that the developed machine learning models provide accurate predictions within the range of tunnel–ground conditions represented by the adopted numerical analysis database. The findings highlight the importance of evaluating both predictive performance and generalization capability, particularly when machine learning models developed from numerical analysis databases are applied beyond the conditions represented in the training database.