Slope Sliding Force Prediction via Belief Rule-Based Inferential Methodology

Abstract

Slope sliding force can be measured by an anchor cable sensor with the negative Poisson's ratio (NPR) property. It is capable of reflecting the stability of the slope intuitively. Thus, predicting the variation trend of the sliding force is able to achieve early warning for landslide disaster, thereby avoiding losses to the lives and property of the people. In this paper, due to the uncertain variation of the sliding force, a belief rule-based (BRB) sliding force prediction model is established to describe the nonlinear and uncertain relationship between the history/current sliding force and the future sliding force. In this model, the activated belief rules are fused by adopting the evidence reasoning (ER) algorithm. And based on the fused results, the sliding force at a future time can be predicted accurately. Moreover, considering the variation of the sliding force on different slopes or different monitoring points in the same slope, a parameter transfer strategy of BRB model together with a corresponding online update method are proposed to achieve the adaptive design of the BRB prediction model. Finally, the effectiveness of the proposed sliding force prediction methods has been verified by experiments on the sub-section of the China West-East Gas Pipeline Project.

Load Transfer Behavior and Failure Mechanism of Bird’s Nest Anchor Cable Anchoring Structure

“…In view of the limitation of traditional anchor cables with respect to mechanical properties and severe engineering environments, new anchor cables with extraordinary mechanical properties should be developed. Existing NPR anchor cables have solved the deformation problem of deep, soft rock roadways [20,21]. The deformation capacity increases to 3000-4000 mm, and the maximum constant resistance is 567.7-800 kN [22].…”