A constitutive model based on damage mechanics and statistical strength theory for sandstone under water-rock coupling is proposed and verified by laboratory tests and numerical tests. The damage mechanism of sandstone under the coupling action of water and force is clarified based on the microscopic test of sandstone under the coupling action of water and rock in this paper. The stress state of sandstone is determined by analyzing the results of laboratory tests. Based on the energy theory method, the strength criterion of sandstone under the interaction of water and rock is obtained, which is introduced into the statistical damage constitutive model of sandstone considering the interaction of water and rock; thus, a relatively perfect damage constitutive model of sandstone considering the interaction of water and rock is established. Finally, the influence of the number of cycles on the model parameters was further analyzed by analyzing the model parameters and the water-rock coupling test. Compared with the existing test results, the model established in this paper can perfectly reflect the deterioration characteristics of sandstone under the coupling action of water and rock.

Goaf has become one of the most significant sources of hazard affecting the safety of metal and nonmetal mines. Evaluation of goaf stability is of paramount importance for mine safety production. First, 13 indices such as rock mass structure, geological structure, and goaf volume are selected based on engineering experience and literature review to assess the stability of goaf. These indices are classified according to the characteristics of each factor, and a stability evaluation system for underground mine goaf is constructed. Second, the analytic hierarchy process method based on group decision theory is utilized to calculate the subjective weight of each index. Additionally, the CRITIC method is used to calculate the objective weight of each index. Finally, game theory is used to combine the subjective and objective weights, thereby improving the accuracy of the index weight. The stability grade of the goaf is calculated using the normal cloud model. The FLAC3D numerical simulation is used to analyze the stability of the goaf and verify the accuracy of the model. The abovementioned model is utilized for assessing the stability of the goaf in the Duimenshan mine section. The results indicate that 90% of the goaf area is in a stable or relatively stable condition, while the remaining 10% is unstable. The evaluation outcomes were compared with FLAC3D numerical simulations, highlighting a scientific and reliable method with an accuracy rate of 90%.

In order to accurately judge the tendency of rock burst disaster and effectively guide the prevention and control of rock burst disaster, a rock burst intensity-grade prediction model based on the comprehensive weighting of prediction indicators and Bayesian optimization algorithm–improved-support vector machine (BOA-SVM) is proposed for the first time. According to the main factors affecting the occurrence and intensity of rock burst, the rock stress coefficient (σ_θ/σ_c), brittleness coefficient (σ_c/σ_t) and elastic energy index (W_et) are selected to construct the rock burst prediction indicator system. On the basis of the research of other scholars, according to the main performance and characteristics of rock burst, rock burst is divided into four intensity levels. The collected and sorted 120 sets of rock burst case data at home and abroad are taken as learning samples, and the T-SNE algorithm is used to perform dimensionality-reduction visualization processing on the sample data, visually display the distribution of samples of different grades, evaluate the representativeness of the sample data and prejudge the feasibility of the machine learning algorithm to distinguish different rock burst intensity levels. The combined improved analytic hierarchy process (IAHP) and Delphi method determine the subjective weight of the indicators; the combined entropy weight method and CRITIC method determine the objective weight of the indicator, and use the harmonic mean criterion of information theory to synthesize the subjective weight and objective weight of the indicator to obtain the comprehensive weight of the indicators. After weighted prediction indicators, a rock burst intensity-grade prediction model is constructed based on the support vector machine, and the hyperparameters of three types of support vector machines are improved by using the Bayesian optimization algorithm. Then, the prediction accuracy of different models is calculated by the random cross-validation method, and the feasibility and effectiveness of the rock burst intensity-grade prediction model is verified. In order to evaluate the generalization and engineering applicability of the proposed model, 20 groups of rock burst case data from the Maluping mine and Daxiangling tunnel are introduced to predict the rock burst intensity grade. The results show that the accuracy of the rock burst intensity-grade prediction model based on comprehensive weighting and BOA-SVM is as high as 93.30%, which is of higher accuracy and better effect than the ordinary model, and can provide warning information with a higher fault tolerance rate, which provides a new way of thinking for rock burst intensity-grade prediction.

Due to the impacts on the ecological environment, production safety and the economic benefits of large open-pit water deposits, there is no longer a single drainage method within the pit that can be used to meet the need for further deep mining. Therefore, curtain grouting technology is proposed for use in the prevention and control of mine water. The flow control equation of slurry in vertical fissures under the influence of self-weight stress was derived, and a three-dimensional visualized laminated jointed rock grouting model was developed independently to study the slurry transport mechanism, diffusion pattern, and sealing mechanism, which verified the correctness of the control equation. Field trials of industrial curtain grouting were also carried out at the mines, and the effectiveness of curtain water blockage was tested using visual analysis, inspection hole detection, and geophysical exploration methods. The results showed that after grouting the curtain, water-conducting cracks in the formation can be filled well and sealed, and the curtain has a good water-blocking effect. A complete set of diversified value-taking grouting systems is obtained, which can ensure the development of a green, safe and sustainable mine in the future.

To effectively mitigate the hazards posed by the blast waves of rock mass caving on closed walls during the mining process, a stability prediction method based on a random forest (RF) algorithm is proposed, which is designed to automatically identify key parameters. A machine learning model is developed using the algorithm, and its performance is evaluated through accuracy, precision, recall, and F1-score metrics. The probabilistic model of the objective function is constructed using the grid search hyperparameter optimization method, allowing for the selection of the most favorable hyperparameters for evaluation. The initial prediction accuracy of the RF algorithm model is 94.6%, indicating a strong predictive capability. Further adjustments to the base classifier, maximum depth, minimum number of leaves, and minimum number of samples enhance the model’s performance, resulting in an improved prediction accuracy of 95.9%. Finally, the optimized model is applied to predict the stability of the closed walls in the actual project, and the results are consistent with the on-site situation. This demonstrates that the random forest-based stability prediction model effectively forecasts the stability of closed walls in the actual project.