A landslide prediction model based on load-unload response ratio theory and its application

Abstract

Landslide is one of the most common geological disasters that 
seriously threatens human production and life. Hence it is of great 
significance to accurately predict landslide disasters. However, the 
process of landslide is accompanied by a series of extremely complex mechanical reactions, which are determined by various factors, such as the strength of rock and soil, the attitude and structure of stratum, the external disturbances and so on, and these 
factors are difficult to accurately capture. To predict the landslide 
accurately, a comprehensive landslide forecast model was proposed based on the load-unload response ratio (LURR) theory. In 
this model, sliding force inside the slope and displacement of the 
slope surface as the key parameters were measured. The sliding 
force, as the load-unload parameter, was obtained by a sliding 
perturbation remote monitoring (SPRM) system. While the displacement, as the load-unload response parameter, was measured 
by total station. Besides, the velocity and acceleration of displacement were also used as parameters to improve the accuracy of 
landslide hazard prediction. The LURR landslide prediction model 
was applied in Antaibao and Pingzhuang west open-pit slopes. 
Results show that the proposed model is accurate and reliable 
for landslide prediction. The force-displacement coupling model is 
more efficient in landslide prediction and early-warning, which is 
helpful to track the causes of landslide.