As a powerful numerical analysis tool, PFC (Particle Flow Code) is widely applied to investigate the mechanical behavior of rock specimen or rock engineering under different stress states. To match the macroscopic properties of the PFC model with those of the rock, a set of micro-scale parameters of the model needs to be calibrated. Thus, this paper proposed an optimization method combining Box–Behnken experimental design and desirability function approach to quickly and accurately find the values of the micro-scale parameters. The sensitivity of the main micro-scale parameters (mean value of parallel-bond normal strength σ_c, ratio of particle normal to shear stiffness E_c, and Young’s modulus at each particle–particle contact k_n/k_s) and their interactions to the macroscopic responses (uniaxial compressive strength, Young’s modulus, and Poisson’s ratio) were thoroughly analyzed using response surface theory. After that, validation study was conducted on the calibrated model. The results manifest that the uniaxial compressive strength is extremely significantly affected by σ_c and k_n/k_s, the Young’s modulus is highly correlated with E_c and k_n/k_s, and the Poisson’s ratio is most significantly influenced by k_n/k_s. Additionally, the interaction of micro-scale parameters also has different impact upon the responses. Moreover, the simulated crack behavior around differently shaped openings in rock samples under uniaxial compression is found to be well agreeable with the experimental results, which verifies the reliability of the proposed method.

The construction of Green Mine has become an essential policy for the management of mineral resources in China. This study aims to propose a favorable approach to assess the performance of Green Mine. The evaluation criteria are recognized based on the specific characteristics of Green Mine. Considering that some of these criteria could not be mutually compensated, a novel multi-criteria decision making (MCDM) method is presented. It integrates the TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method with the elimination and choice translating reality (ELECTRE) method in a picture fuzzy environment. In most existent methods, the evaluation information of Green Mine is described by real numbers. However, in this study, picture fuzzy numbers (PFNs) are first used to express such information to effectively indicate the uncertainty and fuzziness in the evaluation process. Another innovation is that the entropy weights model and the best-worst method (BWM) are combined to determine the comprehensive criteria weights. The practicability and advantages of the proposed method are verified by a case study and a comparative analysis with other methods. In addition, the influences of some parameters are discussed using sensitivity analysis to judge the flexibility and robustness of the proposed method. The results show that the proposed approach is feasible for solving the non-compensatory problems of evaluation criteria and can provide references for the performance evaluation and management of Green Mine.

The accurate and reliable classification of rock mass is the basis of a reasonable engineering design. In the Xishan mining region of Sanshandao Gold Mine, three conventional rock mass classification methods of Tunneling Quality Index (Q), Rock Mass Rating (RMR) and China National Standard-basic quality (BQ), were compared in the burial depth area above 780 m, and it was discovered that the classification results of different rock mass classification methods had a low coincidence rate in the deep area; Therefore, this paper adopted entropy weight method, TOPSIS method and grey correlation analysis method to calculate the entropy weight and relative closeness of different methods in different middle sections. The study’s findings revealed that in the deep area, the relative closeness between each classification mass was: RMR > Q > BQ; Based on the above results, the IRMR method with modified RMR was selected for comprehensive analysis, and the concept of importance degree of evaluation index was defined; it was found that the importance degree of evaluation index of in-situ stress loss was the highest, while the importance degree of joint direction was the lowest; The “ETG” rock mass classification method based on “site-specific” is established, which provides a reference for the establishment of deep rock mass classification method.

The height of the water-flow fracture zone (WFZ) is an important reference for designing the size of a waterproof crown pillar. Once the WFZ is connected with the sea, there will be catastrophic consequences, especially for undersea mining. This study suggests using a rotating forest (RoF) model to predict the height of the WFZ for the evaluation of the size of a waterproof crown pillar. To train and test the RoF model, five indicators with major influencing factors on undersea safety mining were determined, 107 field-measured mining datasets were collected, 75 (70%) datasets were used for training, and 32 (30%) datasets were used for model testing. At the same time, the random forest ensemble algorithm (RFR) and support vector machine (SVM) models were introduced for comparison and verification; in the end, the tested results were evaluated by RMSE (root-mean-square error) and R². The comparison shows that the predicted results from the RoF model are significantly better than those from the RFR and SVM models. An importance analysis of the impact indicators shows that the mining height and depth have significant impacts on the prediction results. The development height of the WFZ in undersea safety mining was predicted via the RoF model. The predicted results via the RoF model were verified by field observations using panoramic borehole televiewers. The RoF prediction results are consistent with the observation results at all depths. Compared with the other two models, the RoF model has the smallest average absolute error at 2.87%. The results show that the RoF model can be applied to predict the height of the WFZ in undersea mining, which could be an effective way of minimizing the mineral resource waste in the study area and in other similar areas in the world under the premise of mine safety.

Considerable advances have been made from diverse perspectives in rockburst research to guarantee the safety of deep underground activities. However, to date, few reviews have been made to systematically analyze the overall research status of rockburst. This study is aimed at providing a scientometric review on rockburst research in hard rock from 2000 to 2019. First, a total of 430 papers were collected from the Web of Science Core Collection database. Then, the CiteSpace software was adopted to conduct a scientometric analysis from five main aspects. Finally, the existing knowledge domains and future directions were discussed. Based on the results of this study, main countries, institutions, authors, and journals of rockburst research were revealed, and some important papers, hot topics, and evolutionary trends were found out. This review contributes to the integrated knowledge map of rockburst research in hard rock and provides researchers a valuable and in-depth understanding in this domain.