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.

Landslides are usually caused by rainstorms and geological processes such as earthquakes and may have a massive impact on human production and life. The hazard chain of landslide–river blockage–outburst flood is the most common hazard chain caused by landslides. A database based on existing landslide cases was established in this study to investigate the assessment formulas of the risk of river blockage, dam stability, and peak flood discharge after a dam has broken. A risk assessment model of the landslide–river blockage–breaching hazard chain was established, including the downstream vulnerability. The case of the Baige landslide verified the applicability of the model. This model can be used in a landslide-prone area to predict whether a relatively massive river blockage will form after the landslide occurs, whether the landslide dam formed by the river blockage will breach in a short time, and the impact of the outburst flood on the downstream area.

Energy is the fundamental prerequisite for human survival and development, as well as the driving force behind the progress of human civilization

Aiming at the shortcomings of the BP neural network in practical applications, such as easy to fall into local extremum and slow convergence speed, we optimized the initial weights and thresholds of the BP neural network using the particle swarm optimization (PSO). Additionally, cloud computing service, web technology, cloud database and numerical simulation were integrated to construct an intelligent feedback analysis cloud program for underground engineering safety monitoring based on the PSO-BP algorithm. The program could conveniently, quickly, and intelligently carry out numerical analysis of underground engineering and dynamic feedback analysis of surrounding rock parameters. The program was applied to the cloud inversion analysis of the surrounding rock parameters for the underground powerhouse of the Shuangjiangkou Hydropower Station. The calculated displacement simulated with the back-analyzed parameters matches the measured displacement very well. The posterior variance evaluation shows that the posterior error ratio is 0.045 and the small error probability is 0.999. The evaluation results indicate that the intelligent feedback analysis cloud program has high accuracy and can be applied to engineering practice.

The fracture network of a deep geothermal reservoir forms the place for heat exchange between injected fluid and rock mass with high temperature. The fracture resistance ability of reservoir rocks will affect the formation of fracture-network structure, heat exchange and transmission characteristics, and reservoir mechanical stability. However, there are few reports on the fracture toughness and trajectory prediction of geothermal reservoirs with different depths. In this paper, the modified maximum tangential stress criterion (MMTS) is analyzed. The results show that the experimental data are significantly different from the theoretical estimate of MMTS under the influence of different occurrence depths. It is found that the fracture process zone (FPZ) seriously affects the accuracy of predicting fracture initiation angle and mixed-mode (I+II) fracture toughness by MMTS. The FPZ value, considering the influence of different occurrence depths, is modified, and the accuracy of MMTS in predicting the fracture mechanical characteristics of granite is improved. In addition, the mechanical test results show that the Brazilian splitting strength (σ_t) of granite fluctuates increase with the increase in temperature. With the increase in deviatoric stress, the Brazilian splitting strength and the Brazilian splitting modulus of rock show a trend of first increasing, then decreasing, and then increasing.