A Stepwise Discrimination Method of Multi-Index in Landslide Stability Monitoring. (13th October 2022)

• Record Type: Journal Article
• Authors: Chen, Hao; Wu, Honggang
• Other Names: Xue Yi Academic Editor.
• Is Part Of: Geofluids. Volume 2022(2022)
• Publication Date: 2022-10-13
• Subjects: Hydrogeology -- Periodicals; Sedimentary basins -- Periodicals; Fluids -- Migration -- Periodicals; Groundwater flow -- Periodicals; Geothermal resources -- Periodicals; Fluid dynamics -- Periodicals; Earth -- Crust -- Periodicals; 551.49
• DOI: 10.1155/2022/9962143
• Languages: English
• ISSNs: 1468-8115

Abstract

Abstract : The deep displacement monitoring can directly reflect the deformation information inside the slope and can provide an important evidence for the identification of landslide stability. Based on the monitoring data on deep displacement of borehole, there are many monitoring indicators that can reflect the slope state. However, these indicators have their own applicability, and they are independent of each other, which cannot fully reflect the true movement state of the landslide. Therefore, it is necessary to establish a scientific method to give full play to the strengths of each monitoring index and improve the accuracy of identification for landslide stability. Here, the near-surface accumulative displacement, displacement rate, kinetic energy, and the rate of change of kinetic energy are selected as main monitoring indicators to establish a multi-index stepwise discrimination method for landslide stability in this study. This method analyzes the total deformation characteristics and movement trend of landslide from three aspects: surface displacement, internal displacement rate, and slope energy. Relied on the monitoring data on deep displacement of borehole of a landslide in Wushan County, Chongqing, China, this study finds that a single index is easily disturbed by external factors, causing the abnormal mutation in curve which affects analysis of landslide deformation. The variation characteristics of curve among multiple indexes can be mutually corroborated, effectively identify the abnormal fluctuation of the curve, and avoid the identification errors of landslide movement state. In addition, we also found that when there is an obvious sliding surface on the slope, the displacement rate curve will cluster at the sliding surface along the depth direction in this study. And this feature can be used as an important basis for the identification of sliding surface. The method proposed in this study can provide reference and suggestions for the actual treatment of landslide and monitoring data on deep displacement mining.