A Machine Learning Study on Internal Force Characteristics of the Anti-Slide Pile Based on the DOFS-BOTDA Monitoring Technology.

Chaoqun Wei,Meng Lu,Mengyao Yan,Kangqing Deng,Yueming Yin,Qinglu Deng

doi:10.3390/s22062085

Abstract

Long-term monitoring of constructed anti-slide piles can help in understanding the processes by which anti-slide piles are subjected to the thrust of landslides. This paper examined the landslide control project of Badong No. 3 High School. The internal force of an anti-slide pile subjected to long-term action of landslide thrust was studied by Distributed Optical Fiber Sensing (DOFS) technology. The BP neural network was used for model training on the monitored strain values and the calculated bending moment values. The results show the following: (1) The monitoring results of the sensor fibers reflect the actual situation more accurately than steel rebar meters do and can locate the position of the sliding zone more accurately. (2) The bending moments distributed along the anti-slide pile have staged characteristics under the long-term action of landslide thrust. Three stages can be summarized according to the development trend of the bending moment values. These three stages can be divided into two change periods of landslide thrust. (3) The model produced by the BP neural network training can predict the bending moment values. In this paper, the sensing fibers monitoring over a long time interval provides a basis for long-term performance analysis of anti-slide piles and stability evaluation of landslides. Using the BP neural network for training relevant data can provide directions for future engineering monitoring. More novel methods can be devised and utilized that will be both accurate and convenient.

Highlights

(2) The bending moments distributed along the anti-slide pile have staged characteristics under the long-term action of landslide thrust
The minimum error fields in the relative error curves of each group are distributed in the range of 15–27 m, and the relative errors fluctuate within 10%. This range happens to be the relatively large range of bending moment values, showing that the BP neural network model designed in this paper can predict the bending moment of the anti-slide pile with good effect
(2) Distributed Optical Fiber Sensing (DOFS) technology based on BOTDA gives full rein to advantages of full distribution, high precision, and long distance in monitoring landslide anti-slide piles

Summary

Introduction

Landslides are a geological hazard that is a worldwide issue. Each year the economic losses caused by landslides reach hundreds of billions of dollars [1]. Quasi-distributed and point-based monitoring methods provide incomplete data acquisition and cannot reflect the actual force of anti-slide piles. DOFS technology has become one of the most promising monitoring technologies owing to its fully distributed system, small size, corrosion resistance, and anti-electromagnetic interference [24–27] It can collect information such as strain and temperature at all points along the optical fiber in a structure. The strain and temperature data collected by the distributed sensing fibers and the steel rebar meters in the anti-slide pile are analyzed. The BP neural network model obtained through data training can predict internal force and shed light on the long-term performance and stability of the anti-slide pile. The appropriate parameters are set, and the corresponding microstrain and temperature values can be obtained through signal transmission and calculation of the instrument

BP Neural Network Algorithm

Bending Moment Distribution

Internal Force Training Model of the Anti-Slide Pile Based on Machine

Conclusions