Slope Sliding Force Prediction via Belief Rule-Based Inferential Methodology

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. (C) 2021 The Authors. Published by Atlantis Press B.V.