Identification of sliding surface and classification of landslide warning based on the integration of surface and deep displacement under normal distribution theory

Abstract

Abstract Advanced identification of the potential sliding surface of a slope and accurate early warning are crucial prerequisites for effective management of landslides and timely and prevention of catastrophic accidents. This study analyzes the statistical characteristics of landslide displacement evolution. Based on the normal distribution theory, random variables of displacement velocity and acceleration with random errors are introduced into the analysis of surface displacement information, and random variables of relative displacement with random errors are introduced into the analysis of deep displacement information. When the random variables do not follow the normal distribution, the warning time can be obtained. Therefore, an advanced landslide classification warning method is established. The analysis results showed that analysis results from the April 30 landslide project at an open pit mine indicate that the earliest warning time for landslide initiation is 2020/2/19, while the earliest warnings for acceleration occur on 2020/4/15 and the fast acceleration on 2020/4/25. These three-level warning times align with reality, and the inferred slip surface position corresponds to the actual weak layer range. The primary power source driving landslide originates from behind the sliding body which subsequently pushes rock mass along weak layers near the south wing, north wing, and front in succession. Research findings can enhance landslide warning accuracy, facilitate advance identification of sliding surface, provide scientific basis for open-pit slope engineering design, as well as mitigate casualties and property losses.

Keywords

• Displacement monitoring
• Normal distribution
• Random variables
• Landslide warning
• Sliding surface identification