Review on the Monitoring and Early Warning Technology of Large-scale Unstable Rock Collapse

Large-scale rock collapse is a prevalent geological hazard in China, characterized by complex causes, wide distribution, strong concealment, sudden onset, and significant destructiveness, making early warning challenging to achieve. The instability mechanisms and warning models of rock collapse disasters vary widely. Scientific identification of damage to rock bridge structural surfaces is essential for early warning and prevention of these disasters. Conducting damage identification of rock bridge structural surfaces and comprehensive monitoring research, which includes static, dynamic, and environmental indicators (SDEI), is key to early monitoring and warning of large-scale rock collapses. The study of early warning mechanisms based on the identification of separation damage precursors is an effective means to enhance the timeliness of early warnings for such disasters. With the development of micro-electromechanical systems and cloud-edge collaboration technology, a new multivariate early warning paradigm is expected to emerge in the future. This paradigm would feature real-time linkage of dynamic stability evaluation, unstable working condition prediction, and failure time prediction models. It is also necessary to continuously enrich the early warning technology system for brittle failure disasters, such as large-scale rock collapses, to achieve real-time analysis of warning levels, stability status, unstable conditions, and timing of dangerous rock masses, effectively addressing the dual challenges of scientific and accurate prevention and control of large-scale rock collapse disasters and intelligent emergency decision-making. Finally, several development strategies and countermeasures are proposed to overcome the technical bottlenecks in current early monitoring and warning research. For instance, in the field of theoretical research on monitoring and early warning, it is crucial to investigate the causes and early warning mechanisms of brittle destruction disasters such as large-scale collapses. In terms of monitoring equipment development, promoting the development of domestic equipment that integrates dynamic indicators and SDEI is necessary. Building a monitoring and early warning index system based on the integration of multi-source information from SDEI is essential in the research on monitoring and early warning index systems. The development of intelligent monitoring and early warning technology based on cloud-edge integration should be steadily advanced. In terms of data collection for case studies of large-scale dangerous rock mass collapse disasters, comprehensively constructing the SDEI database for various large-scale dangerous rock mass collapse case monitoring samples should be undertaken, ultimately forming a virtuous development cycle of research on theoretical models of large-scale dangerous rock mass collapse disasters, development of monitoring equipment, construction of index systems, development of early warning technology, and database upgrading and improvement. These measures can provide some reference for better responding to large-scale rock collapse disasters in high-risk geological hazard areas.