The rockburst phenomenon occurs in dry red sandstone under high in situ stress, and the rockburst effect is weaker for a water-bearing rock. The rockburst effect on red sandstone with different water contents is analyzed in this paper. A true triaxial testing machine is used to conduct the loading, and acoustic emission recording equipment and a high-speed camera are used to monitor the acoustic signal inside the rock and the rock-caving situation throughout the entire process in order to analyze the characteristics of the acoustic emissions and the ejection form of the rockburst. The results show that rockburst occurs in dry red sandstone and 50% saturated red sandstone but not in saturated red sandstone. The phrase characteristics of the stress–strain curve of the dry rock vary more significantly than those of the water-bearing rock, and the elastic strain energy inside the rock decreases gradually as the water content increases. The double peak of the acoustic emissions curve occurs during the failure process of the dry rock and gradually transitions to a stepped pattern as the water content increases. The ejected fragments of dry red sandstone during the rockburst are abundant and large. The true triaxial test results illustrate the characteristic effect of the rockburst on red sandstone with different water contents, reveal the failure mode and ejection characteristics of red sandstone with different water contents, and demonstrate the influence of the water content on the rockburst characteristics of red sandstone. The results of this study provide a theoretical reference for the study of the rockburst mechanisms of similar hard rocks.
Areas with vulnerable ecological environments often breed many geological disasters, especially landslides, which pose a severe threat to the safety of people’s lives and property in these areas. To aid in landslide prevention and mitigation, an approach combining the coefficient of determination method (CF) and a deep neural network (DNN) were proposed in this study for landslide susceptibility evaluation. The deep neural network can excavate the deep features of samples and improve the accuracy of the susceptibility model. In addition, the logistic regression model (LRM) and support vector machine (SVM) were selected to create landslide susceptibility maps for comparison, which also involved the coefficient of determination method (CF). Based on landslide remote sensing interpretation and field investigations, a spatial database of mudstone landslides in the Xining area was established. Eight different conditional factors, including the elevation, slope, slope aspect, undulation, curvature, watershed, distance from a fault, and distance from a road, in the study area were selected as the evaluation factors to evaluate the susceptibility. The results revealed that four factors (i.e., the ground elevation, curvature, distance from a fault, and distance from a road) had relatively significant influences on the landslide susceptibility in the study area. Finally, the confusion matrix was used to evaluate the accuracy of the results obtained using the three methods, and the optimal result was selected to evaluate the landslide susceptibility in the study area. It was found that the combined CF-DNN method was more suitable for evaluating the landslide susceptibility in this area. Landslide susceptibility zoning was conducted to divide the study area into four sensitivity levels: low (32.65%), medium (35.12%), high (22.44%), and extremely high (9.79%) susceptibility. The high-risk areas were primarily distributed in the high-elevation areas along the eastern edge of the Huangshui Basin.
The stability of the surrounding rock analysis and evaluation during tunnel construction is the basis of tunnel construction risk control. In this paper, we focus on the stability of a large-scale transportation tunnel complex during its construction in a densely-populated urban area. The tunnel complex includes seven shallow-buried tunnels with large cross-sections. In order to gain insight into the excavation influence of the different tunnels, stability analyses were first carried out using FLAC3D numerical simulation. Results showed that the tunnels were subjected to heave and crown settlement induced by adjacent excavation. Also, stress concentrated in the rock blocks connecting different tunnels. Subsequently, a bench-scale model test was performed to understand the failure of the rock blocks and to examine the accuracy of the numerical simulation. The test results agreed well with the numerical simulation. Based on the numerical and test results, the mechanism of the rock blocks failure was explained and construction risk control technology to stabilize the rock blocks was proposed. The construction risk analyses revealed: 1) tunnels are subjected to significant heave due to the excavation of tunnels located above; 2) the stability of the rock blocks is the paramount determinant for stabilizing the whole tunnel complex; 3) ensuring rock blocks to be in a state of triaxial stress is conducive to its stability and hence the stability of multiple tunnels.
Goaf ground collapse has great constraints on people’s lives, property safety, and social development within the influence scope because of its concealment and suddenly happening characteristics. The high-density resistivity method is used to explore the goaf of a quarry in Xiangtan City, Hunan Province. The surface subsidence of the goaf is analyzed using the apparent resistivity inversion section diagram and the comprehensive analysis results. The filling water or sediment in the goaf is reflected as a low-resistivity abnormal body, with a resistivity change that is significantly different from the resistivity change of the surrounding bedrock and the contour fluctuation. The morphological characteristics and geological conditions of the underground abnormal body in the goaf are deduced. Based on the geophysical prospecting method, geological disasters such as goaf ground collapse can be explored. From the combined analysis and processing of inversion data, the geological structure and stratum information of goaf can be inferred, which provides a theoretical basis for further disaster prevention.