Danqing Song

Since infrastructure development has increasingly encountered gneiss, and its complex mechanical characteristics have brought engineering much hardship, research on gneiss has been booming. However, the stress concentration effect in gneiss has seldom been studied. This work found an under-discussed phenomenon and tried to explain it by the stress concentration effect. Specifically, triaxial compression experiments with different confined pressures on gneiss samples with different embedded textures and an improved Cosserat constitutive model were employed to study the textures in gneiss. The constitutive model was meant to simulate the experiment by adjusting the stress concentration effect in the texture areas in gneiss samples. The phenomenon found in the experiment for discussion was that the different textures in gneiss can cause different failure modes but not necessarily affect its compression strength. The phenomenon was then successfully reproduced by the finite element method with the improved Cosserat model by setting different stress concentration degrees in the textures. The experimental and simulation results revealed a potential mechanism to explain the phenomenon: the diverse failure modes observed in gneiss samples with identical strength may result from variations in stress concentration distributions. Lastly, a new method to trigger strain localization by setting stress concentration regions is proposed based on the analysis. This work may provide a different understanding of the failures in rocks with directional textures and inspire studies in localized stress concentration effects.

To solve the difficult problems of tailings dam instability and environmental pollution, multisource information perception, prediction and early warning technology for tailings dams are investigated. Taking a tailings pond in China as an example, a three-dimensional visualization intelligent management platform based on the spatiotemporal fusion of multisource big data is established to realize intelligent real-time monitoring, prediction and early warning of tailings dams. A monitoring system for air-space-ground integration was developed via high-resolution optical image recording, unmanned aerial vehicles (UAVs), radar, video surveillance and displacement sensors. This approach can realize pollution monitoring and efficient identification of high-risk areas. In addition, a machine learning algorithm is used to mine spatiotemporal data in detail. The Alibaba cloud platform was adopted to develop a data integration framework. Multisensor spatiotemporal data from tailings dams and multisource monitoring data from the environment were integrated. The integrated management of tailings dam environmental monitoring, pollution and stability assessment is realized. A stability prediction model for tailings dams based on multisource data is proposed. The temporal and spatial information of various forms of data is analysed from multiple levels and perspectives. Rapid prediction of the disaster situation and stability of tailings ponds is realized. Moreover, a tailings pollution assessment model based on a neural network is integrated with multisource information to establish a multifactor environmental pollution neural network assessment model and a multilevel warning platform. This work can provide technical support for intelligent monitoring and early warning systems for tailings ponds.