The research into the road performance of coal gangue is of great significance for the consumption of coal gangue and reducing pollution. In this paper, the coal gangues were prepared by separation and crushing processes, and their gradations were also optimized. Aiming to identify the possible problems of coal gangue as a pavement base, an unconfined compressive strength test, a splitting test, a freeze–thaw test, and a drying shrinkage test of cement stabilized gangue with varying cement amounts were carried out, and the test results were compared and analyzed. The test results showed that the maximum dry density and optimum moisture content (OMC) of the optimized cement stabilized gangue and cement stabilized macadam increased with cement content. The maximum dry density and OMC of cement stabilized macadam were larger than that of cement stabilized gangue with the same cement content. The optimized 7-day unconfined compressive strength of cement stabilized gangue can meet the requirements for a secondary and lower highway base and subbase. The OMC and cement content are the critical factors affecting the compressive strength loss rate of cement stabilized gangue after freeze–thaw cycles. The smaller the OMC of cement stabilized gangue and the larger the cement content, the lower the compressive strength loss rate. With an increase in cement content, the drying shrinkage strain of cement stabilized gangue increased. The results show that a cement content of 4% is optimal for the cement stabilized coal gangue, which can be used for the light traffic base and heavy traffic subbase of class II and below highways. It provides a basis, guide, and reference for the application of coal gangue materials in a high-grade highway base.

The in situ stress distribution is one of the driving factors for the design and construction of underground engineering. Numerical analysis methods based on artificial neural networks are the most common and effective methods for in situ stress inversion. However, conventional algorithms often have some drawbacks, such as slow convergence, overfitting, and the local minimum problem, which will directly affect the inversion results. An intelligent inverse method optimizing the back-propagation (BP) neural network with the particle swarm optimization algorithm (PSO) is applied to the back analysis of in situ stress. The PSO algorithm is used to optimize the initial parameters of the BP neural network, improving the stability and accuracy of the inversion results. The numerical simulation is utilized to calculate the stress field and generate training samples. In the application of the Shuangjiangkou Hydropower Station underground powerhouse, the average relative error decreases by about 3.45% by using the proposed method compared with the BP method. Subsequently, the in situ stress distribution shows the significant tectonic movement of the surrounding rock, with the first principal stress value of 20 to 26 MPa. The fault and the lamprophyre significantly influence the in situ stress, with 15–30% localized stress reduction in the rock mass within 10 m. The research results demonstrate the reliability and improvement of the proposed method and provide a reference for similar underground engineering.

Surrounding rock deformation and consequent support failure are the most prominent issues in red-bed rock tunnel engineering and are mainly caused by the effects of unloading, rheology, and swelling. This study investigated the mechanical responses of two kinds of red-bed mudstone and sandstone under unloading conditions via laboratory observation. Volume dilation was observed on the rocks during unloading, and the dilatancy stress was linear with the initial confining pressure. However, the ratios of dilatancy stress to peak stress of the two rocks kept at a range from 0.8 to 0.9, regardless of confining pressures. Both the elastic strain energy and the dissipated energy evolved synchronously with the stress–strain curve and exhibited conspicuous confining pressure dependence. Special attention was paid to the evolution behavior of the dilatancy angle. The dilatancy angle changed linearly during unloading. When the confining pressure was 10 MPa, the dilatancy angle of mudstone decreased from 26.8° to 12.5° whereas the dilatancy angle of sandstone increased from 34.6° to 51.1°; when the confining pressure rose to 25 MPa, the dilatancy angle of mudstone and sandstone decreased from 45.8° to 17.4° and increased from 21.7° to 39.5°, respectively. To further understand the evolution of the dilatancy angle, we discussed the links between the variable dilatancy angle and the processes of rock deformation and energy dissipation.