A study on the prediction of displacement in the accelerated deformation stage of the creep bedding rock landslides

Abstract

In view of the complexity, randomness, and uncertainty of landslide evolution, it causes substantial damage to the environment every year, in the absence of a mature model for landslide displacement prediction. Most of bedding landslides are characterized by creep, and its displacement in the accelerated deformation stage changes with time in line with the law extremely similar to that of rock creep. From the perspective of the rock creep theory, a nonlinear viscous component is proposed, and a model for predicting the displacement in the accelerated deformation stage regarding creep landslides is established by combining it with the plastic component. The cumulative displacement-time series data at the monitoring points is input as the parameters, and the model parameters are obtained via the nonlinear fitting of the model parameters, and thereafter the prediction of landslide displacement in the accelerated deformation stage is rendered possible. Taking the landslide on the South Slope of West Open-pit Mine in Fushun and the Jimingsi landslide as the research cases, the newly proposed displacement prediction model is employed to analyze and predict the cumulative displacement of landslides. By comparing the measured and predicted values at the selected prediction time points in the initial stage of accelerated deformation and in the pre-sliding stage, it is found that, in these two landslides, the maximum relative error (RE) at each monitoring point and each prediction time point in the initial stage and the pre-sliding stage of accelerated deformation is respectively less than 2% and less than 7%, and that the correlation coefficient of predicted and measured values of displacement in the phase of accelerated deformation is greater than 0.99, and the results show that the proposed model for predicting the displacement of creep bedding rock landslides has satisfactory accuracy and applicability, and can meet the demand of landslide displacement prediction and serve as an effective way to prevent the environmental risk posed by landslides.