Rockburst is a difficult problem in underground resource development and infrastructure construction. With scientific and technological progress, more research technologies and methods to prevent rockburst have been proposed and applied. Research content has become more comprehensive, and positive progress and achievements have been made. However, due to the complexity of rockburst control factors and the sudden occurrence of rockburst, the occurrence mechanism and accurate prediction of occurrence intensity and time are still difficult worldwide problems. In this paper, the research development and achievements of rockburst are reviewed. Firstly, various definitions and types of rockburst are briefly summarized. Then, research progress and achievements in four aspects are comprehensively reviewed, including methods and techniques of rockburst research (theoretical research, numerical simulation, physical model tests and in situ monitoring), mechanism of rockburst, classification and prediction of rockburst (empirical criteria, in situ monitoring, mathematical model approaches and rockburst chart) and prevention and control of rockburst. Of particular significance is that the classification and prediction with prevention and control are summarized in detail. Finally, limitations, deficiencies and some promising directions for future research are listed.

The internet often serves as a communication platform for users to freely express opinions. However, as it supports the quick and wide spread of public opinions, it often results in great uncertainty and is very difficult to control. As a result, evaluating the risk levels of internet public opinion becomes very challenging. The safety of internet public opinion affects the sustainability and stability of society. In this paper, a novel conceptual model of internet public opinion governance is proposed. The model can be used to grade risk levels by combining the Analytic Hierarchy Process Sort II (AHPSort II) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) under a fuzzy environment with triangular fuzzy sets. A numerical example is provided to verify the efficiency of the proposed method, and some managerial implications are also discussed.

By applying the Wavelet Relevance Vector Machine (WRVM) method, this research proposes the loose zone of roadway surrounding rock prediction. Based on the theory of relevance vector machine (RVM), the wavelet function is introduced to replace the original Gauss function as the model kernel function to form the WRVM. Five factors affecting the loose zone of roadway surrounding rock are selected as the model input, and the prediction model of the loose zone of roadway surrounding rock based on WRVM is established. By using cross-validation method, the kernel parameters of three kinds of wavelet relevance vector machines (RVMs) are calculated. By comparing and analyzing the root mean square (RMS) error of the test results of each predictive model, the advantages and accuracy of the model are verified. In practical engineering applications, the average relative prediction errors of the Mexican relevance vector machine, the Morlet relevance vector machine and the difference of Gaussian (DOG) relevance vector machine models are accordingly 4.581%, 4.586% and 4.575%. The square correlation coefficient of the predicted samples is 0.95 > 0.9, which further verifies the accuracy and reliability of the proposed method.

It is important to systematically investigate the similar materials with high similarity ratio and low strength in multilayer shale deposits, to provide a scientific basis and experimental basis for the research of underground mining of multilayer shale deposits. In this paper, using an orthogonal experimental method, the physical and mechanical parameters of different material proportions were analyzed with four control factors of mica powder/standard sand, filling material/bonding material, Portland cement/gypsum, silicone oil ratio. Twenty-five groups of material proportioning schemes were designed, and the density, porosity, compressive strength, and elastic modulus of each group of materials were measured. Through the range analysis and significance analysis, the influence of control factors on the material parameters was explored, and multivariate linear regression analysis of test results was carried to eliminate outliers. The result showed that the physical and mechanical parameters of similar materials prepared according to the proportioning scheme were widely distributed, which can meet the preparation requirements of similar materials with different lithologies. The density and compressive strength were most affected by the ratio of Portland cement/gypsum, the porosity was most affected by the ratio of filling material/bonding material, and the elastic modulus was mainly controlled by the silicone oil ratio. The proportioning scheme was applied to three similar prepared shale materials with large lithology differences. The error between actual similar constant and design similar constant of low strength similar material was less than 1.62%. The physical and mechanical parameters of similar materials were in good agreement with the original rock.

To combat the uncertainty of the multiple factors affecting roadway surrounding rock stability, five initial indexes are selected for reduction according to concept lattice theory: rock quality designation (RQD), uniaxial compressive strength (Rc), the integrity coefficient of rock mass, groundwater seepage, and joint condition. The aim of this study is to compute correlation coefficients among various indexes and verify the effectiveness of lattice reduction. Alpha stable distribution is used to replace the commonly used Gauss distribution in probabilistic neural networks. A prediction model for the stability of roadway surrounding rock is then established based on a concept lattice and improved probabilistic neural network. 100 groups of training sample data are plugged into this model one by one to examine its rationality. The established model is employed for engineering application prediction with ten indiscriminate sample groups from the Jianlinshan mining area of the Daye iron mine, revealing accuracy of up to 90%. This demonstrates that our prediction model based on a concept lattice and improved probabilistic neural network has high reliability and applicability.