Slope Sliding Force Prediction via Belief Rule-Based Inferential Methodology

Authors

Jing Feng¹, Xiaobin Xu^{1, *,} , Pan Liu¹, Feng Ma², Chengrong Ma³, Zhigang Tao^{4, 5, *}

¹School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

²Nanjing Smart Waterway Corp. Ltd, Nanjing, 210000, China

³College of Civil Engineering, Shaoxing University, Shaoxing, 312000, China

⁴State Key Laboratory for Geomechanics and Deep Underground Engineering, Beijing, 100083, China

⁵School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing, 100083, China

^*Corresponding author. Email: taozhigang@cumtb.edu.cn; xuxiaobin1980@hdu.edu.cn

DOI

10.2991/ijcis.d.210216.001How to use a DOI?

Keywords

Slope landslide; Sliding force; Belief rule base; SLP optimization algorithm; West–East Gas Pipeline Project

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.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Jing Feng
AU  - Xiaobin Xu
AU  - Pan Liu
AU  - Feng Ma
AU  - Chengrong Ma
AU  - Zhigang Tao
PY  - 2021
DA  - 2021/02/25
TI  - Slope Sliding Force Prediction via Belief Rule-Based Inferential Methodology
JO  - International Journal of Computational Intelligence Systems
SP  - 965
EP  - 977
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210216.001
DO  - 10.2991/ijcis.d.210216.001
ID  - Feng2021
ER  -