The pore structure is an important factor of tunnel coating failure, cracking and water leakage. Some investigations on the statistical law of pores and pore networks have been conducted, but little quantitative analysis is observed on topology structure of the pore network, and even the pore structure of sandstone is complex and cross-scale distributed. Therefore, it is of theoretical and engineering significance to quantitatively characterize the connectivity of the pore network in sandstone. This study proposes a new complex network theory to analyze the three-dimensional nature of pore network structure in sandstone. The topological network structure, such as clustering degree, average path length and the module, which cannot be analyzed by traditional coordination number and fractal dimension methods, is analyzed. Numerical simulation results show that a scale-free network model is more suitable for describing the sandstone pore network than random models. The pore network of sandstone has good uniformity. The connectivity of sandstone pore networks has great potential for permeability enhancement. Therefore, this new method provides a way to deeply understand the pore connectivity characteristics of sandstone and to explore the distribution of crack grids in the arch of tunnel coatings.

A three-dimensional mathematical model of the working face in mine was set up by using computational fluid dynamics, and the moving and heat exchange of the multiphase flow in ventilation pipeline in mine was simulated. The distribution of the wall temperature was simplified, and the gas and grain are included in this paper. The coupling effect between the phases were considered in this model to reflect their relations. The changing of one phase according to the other one can be stated. The distribution of the temperature of the air without vapor and that of multiphase flow were compared. An effective tool for simulating the system of ventilation in mine and efficient running of it were stated.

The vector-based method is a technique that analyzes and processes data by vectoring them. It represents data points as multidimensional vectors that can be calculated and compared in a high-dimensional space. This method has significant advantages in prediction, mainly in the ability to process high-dimensional data, the effect of capturing nonlinear relationships, and the high efficiency on large datasets. First, the vector-based method effectively handles high-dimensional data, is not limited by the data dimension, and is suitable for complex multivariate analysis. Second, it is able to capture the nonlinear relationships between the data and identify potential patterns and trends through computation and comparisons in a high-dimensional space. Finally, the high efficiency of vector-based methods on large datasets enables it to quickly process and analyze large amounts of data and improve the accuracy and reliability of prediction. These characteristics make the vector-based method particularly applicable in mine pressure appearance prediction, which can more accurately analyze ore pressure data, identify potential risks, and then improve the level of mine safety management.