The mechanical parameters of surrounding rock are an essential basis for roadway excavation and support design. Aiming at the difficulty in obtaining the mechanical parameters of surrounding rock and large experimental errors, the optimized BP neural network model is proposed in this paper. The mind evolutionary algorithm can adequately search the optimal initial weights and thresholds, while the neural network has the advantage of strong nonlinear prediction ability. So, the optimized BP neural network model (MEA-BP model) takes advantage of the two models. It can not only avoid the local extreme value problem but also improve the accuracy and reliability of the prediction results. Based on the orthogonal test method and finite element analysis method, training samples and test samples are established. The nonlinear relationship between rock mechanical parameters and roadway deformation is established by the BP model and MEA-BP model, respectively. The importance analysis of the three input variables shows that the ∆D is the most important input variable, while ∆BC has the smallest impact. The comparison of prediction performance between the MEA-BP model and BP model demonstrates that the optimized initial weights and thresholds can improve the accuracy of prediction value. Finally, the MEA-BP model has been well applied to predicting the mechanical parameter for the surrounding rock in the Pingdingshan mine area, which proves the accuracy and reliability of the optimized model.

Uniaxial compressive strength (UCS) is a critical parameter in the disaster prevention of engineering projects, requiring a large budget and a long time to estimate in different rocks or the early stage of a project. If predicted accurately, the UCS of rocks significantly affects geotechnical applications. This paper develops a dataset of 734 samples from previous studies on different countries’ magmatic, sedimentary, and metamorphic rocks. Within the study context, three main factors, point load index, P-wave velocity, and Schmidt hammer rebound number, are utilized to estimate UCS. Moreover, it applies extreme learning machines (ELM) to map the nonlinear relationship between the UCS and the influential factors. Five metaheuristic algorithms, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), butterfly optimization algorithm (BOA), and sparrow search algorithm (SSA), are used to optimize the bias and weight of ELM and thus enhance its predictability. Indeed, several performance parameters are utilized to verify the proposed models’ generalization capability and predictive performance. The minimum, maximum, and average relative errors of ELM achieved by the whale optimization algorithm (WOA-ELM) are smaller than the other models, with values of 0.22%, 72.05%, and 11.48%, respectively. In contrast, the minimum and mean residual error produced by WOA-ELM are less than the other models, with values of 0.02 and 2.64 MPa, respectively. The results show that the UCS values derived from WOA-ELM are superior to those from other models. The performance indices (coefficient of determination (R²): 0.861, mean squared error (MSE): 17.61, root mean squared error (RMSE): 4.20, and value account for (VAF): 91% obtained using the WOA-ELM model indicates high accuracy and reliability, which means that it has broad application potential for estimating UCS of different rocks.

Surrounding rock squeezing is a common geological disaster in underground excavation projects (e.g., TBM tunneling and deep mining), which has adverse effects on construction safety, schedule, and property. To predict the squeezing of the surrounding rock accurately and quickly, this study proposes a hybrid machine learning paradigm that integrates generative artificial intelligence and deep ensemble learning. Specifically, conditional tabular generative adversarial network is devised to solve the problems of data shortage and class imbalance for data augmentation at the data level, and the deep random forest is built based on the augmented data for subsequent squeezing classification. A total of 139 historical squeezing cases are collected worldwide to validate the efficacy of the proposed modeling paradigm. The results reveal that this paradigm achieves a prediction accuracy of 92.86% and a macro F₁-score of 0.9292. In particular, the individual F₁-scores on strong squeezing and extremely strong squeezing are more than 0.9, with excellent prediction reliability for high-intensity squeezing. Finally, a comparative analysis with traditional machine learning techniques is conducted and the superiority of this paradigm is further verified. This study provides a valuable reference for surrounding rock squeezing classification under a limited data environment.

Rockburst is a common dynamic geological disaster in underground mining and tunneling engineering, characterized by randomness, abruptness, and impact. Short-term evaluation of rockburst potential plays an outsize role in ensuring the safety of workers, equipment, and projects. As is well known, microseismic monitoring serves as a reliable short-term early-warning technique for rockburst. However, the large amount of microseismic data brings many challenges to traditional manual analysis, such as the timeliness of data processing and the accuracy of rockburst prediction. To this end, this study integrates artificial intelligence with microseismic monitoring. On the basis of a comprehensive consideration of class imbalance and multicollinearity, an innovative modeling framework that combines local outlier factor-guided synthetic minority oversampling and an extremely randomized forest with C5.0 decision trees is proposed for the short-term evaluation of rockburst potential. To determine the optimal hyperparameters, the whale optimization algorithm is embedded. To prove the efficacy of the model, a total of 93 rockburst cases are collected from various engineering projects. The results show that the proposed approach achieves an accuracy of 90.91% and a macro F₁-score of 0.9141. Additionally, the local F₁-scores on low-intensity and high-intensity rockburst are 0.9600 and 0.9474, respectively. Finally, the advantages of the proposed approach are further validated through an extended comparative analysis. The insights derived from this research provide a reference for microseismic data-based short-term rockburst prediction when faced with class imbalance and multicollinearity.