Weisheng Du

The anisotropy parameter of joint surfaces reflects the mechanical and physical properties of joint surfaces in different directions, which significantly affects their stability, permeability and the design reliability of rock engineering applications. To quantitatively characterize the anisotropic properties of joint surfaces, a novel method is proposed. This method integrates the morphological distribution characteristics of the actual contact joint surfaces with fractal theory. First, the 3D morphological features of the joint surfaces at various sampling intervals were acquired using 3D laser scanning. The results reveal that the surface morphology exhibits significant anisotropic characteristics and a pronounced sampling interval effect (SIE). Subsequently, the anisotropy of the 3D roughness parameter proposed by Ban et al. is investigated. To investigate the SIE on the accuracy of roughness characterization, a new 3D fractal roughness parameter is developed by incorporating fractal theory. Finally, a method is proposed to quantify the anisotropy based on the fractal features of the actual contact joint surface according to the variation of the fractal parameter in different directions. Furthermore, the size effect on fractal parameters in different shear directions is further investigated. The results demonstrate that the impact of the size effect on fractal parameters depends on the shear direction, and the parameters tend to stabilize with increasing observation scale. The anisotropy parameter proposed in this study is influenced by both normal stress and joint material properties, and it reflects the anisotropy of the actual contact 3D roughness under specific loading and material conditions.

The three-dimensional morphology characteristics of rock joints significantly influence their shear mechanical behavior. To accurately characterize the joint surface morphology, this study conducted 3D laser scanning on five groups of red sandstone joint specimens. The apparent dip angles of the joint surface morphology were then fitted using an existing distribution function. However, the results revealed limitations in the function’s ability to accurately fit the density distribution. To address this issue, this study proposes an improved apparent dip angle distribution function combining Weibull and exponential terms, which achieves superior fitting performance for both cumulative and density distributions. Based on the improved function, a novel three-dimensional roughness index is developed. This new index possesses clear physical significance and can effectively characterize the anisotropy of joint roughness. Its effectiveness is validated through comparison with conventional roughness indices. Furthermore, the new index shows strong correlations with shear strength-related parameters, indicating its potential applicability in the development of joint shear strength models.

The prevention of rockbursts is significant to ensure mining safety in deep coal mines. The multifactor analysis and a new quantitative evaluation method for rockbursts in coal mines are proposed in this study. In the aspect of rockburst analysis, a multifactor system of rockburst risk based on the material, stress, and large-scale geological structure is proposed. The factors influencing rockbursts in coal mines are analyzed by numerical simulations. Based on a standard mining model, three comparative models considering the rockburst tendency, high stress, and geological structure are established. The distribution of maximum principal stress and plastic zone during the mining process is compared. The reasons why these three types of factors are liable to trigger rockbursts lie in generating high-stress zones in surrounding rock masses. In the aspect of quantitative evaluation, the monitored microseismic signal is selected as the key indicator, and the daily frequency of microseisms is analyzed. A normal distribution function based on the daily frequency of microseisms is established. The interval of daily frequency of microseisms is set to judge whether the microseismic frequency is abnormal and then determine the rockburst risk of coal mines. Considering the results of multifactor analysis, it is proposed that the monitoring system combining microseisms with stress is the direction to accurately and quantitatively evaluate the rockburst risk in the future. This study makes specific explorations in the quantitative evaluation of rockburst risk in coal mines.