基于MEMS传感器的滑坡监测算法设计与试验

吴迪; 梁泰铭; 吴静; 吴建建; 易杨; 娄万鹏

doi:10.19509/j.cnki.dzkq.tb20240214

基于MEMS传感器的滑坡监测算法设计与试验

doi: 10.19509/j.cnki.dzkq.tb20240214

吴迪^1,,
梁泰铭¹,
吴静²,
吴建建^{1, 2, ,},
易杨¹,
娄万鹏¹

1.
桂林电子科技大学建筑与交通工程学院, 广西桂林 541004
2.
自然资源部南方石山地区矿山地质环境修复工程技术创新中心, 南宁 530028

基金项目:

国家自然科学基金项目 42067044

广西重点研发计划项目桂科AB23026028

江西省交通运输厅科技项目 2022H0030

南方石山地区矿山地质环境修复工程技术创新中心开放课题 NFSS2023024

详细信息

作者简介:
吴迪, E-mail: wudi@guet.edu.cn

通讯作者:
吴建建, E-mail: 1049003625@qq.com

中图分类号: P642.22
计量
- 文章访问数: 262
- PDF下载量: 61
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-30
- 录用日期: 2024-09-04
- 修回日期: 2024-08-30

Design and experiment of landslide monitoring algorithm based on MEMS sensor

WU Di^1
,,
LIANG Taiming¹,
WU Jing²,
WU Jianjian^{1, 2
, ,},
YI Yang¹,
LOU Wanpeng¹

1.
School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
2.
Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area, Ministry of Natural Resources, Nanning 530028, China

More Information

Author Bio:
WU Di, E-mail: wudi@guet.edu.cn

Corresponding author: WU Jianjian, E-mail: 1049003625@qq.com

摘要

摘要:
针对当前工程上土体滑坡位移监测稳定性与准确性差、覆盖难、成本高等难题, 提出了一种基于微机电系统(micro-electro-mechanical system, 简称MEMS)传感器技术的土体滑坡内部位移监测方法。考虑斜坡岩土体形变过程的运动特性, 设计了消除MEMS传感器加速度数据随机偏差和固定偏差的时域加速度积分算法。为验证该位移监测方法的有效性和实用性, 设计开展了2组室内牵引式滑坡模型试验及对应有限元仿真计算, 利用MEMS技术进行试验边坡土体内部位移监测, 结合有限元软件计算结果对试验MEMS数据结果进行对比分析, 评估算法的准确性和可靠性。结果表明: 基于该算法的MEMS土体滑坡监测位移值, 在水平方向和竖向的平均相对误差最小值分别为0.09%和0.50%, 具有较高的准确度, 满足实际工程需要。研究成果为土体滑坡监测提供了新的思路, 也为MEMS传感器在滑坡/边坡防护工程中应用提供理论基础。
- MEMS传感器 /
- 土体滑坡 /
- 加速度积分 /
- 模型试验 /
- 位移监测
Abstract: Objective
To address current engineering challenges such as poor stability and accuracy, limited coverage, and high costs associated with soil landslide displacement monitoring, a novel method based on microelectromechanical system (MEMS) sensor technology is proposed.
Methods
By considering the kinematic characteristics of slope deformation, a time-domain acceleration integration algorithm is designed to correct random and systematic errors in the acceleration data collected by MEMS sensor. To verify the effectiveness and practicality of this displacement monitoring method, two indoor landslide model tests and corresponding finite element simulation calculations were designed and conducted. MEMS technology was used to monitor the internal displacement of the soil for the test slopes, and the results were compared with the the finite element simulation outputs to evaluate the accuracy and reliability of the algorithm.
Results
The findings indicate that the MEMS-based soil landslide displacement monitoring achieved a minimum average relative error of 0.09% in the horizontal direction and 0.50% in the vertical direction, demonstrating high accuracy and suitability for practical engineering applications.
Conclusion
The research results provide a novel approach to soil landslide monitoring and provides a theoretical foundation for the integrating MEMS sensors in landslide and slope safety engineering.
- MEMS sensor /
- soil landslide /
- acceleration integral /
- model test /
- displacement monitoring
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 时域积分基础处理流程

a.加速度；s.位移；v.速度

Figure 1. Basic processing flow of time-domain integration

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图 2 滑坡简化模型

a_t为加速度真实记录；β为滑坡岩土体滑动角度，(°)；α为斜坡的坡角，(°)；a′ _k为去除直流分量的加速度信号输出采集值

Figure 2. Simplified model of a landslide

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图 3 滑坡室内模型试验装置

Figure 3. Indoor model test device for landslide

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图 4 砂土颗粒级配曲线

Figure 4. Sand particle grading curve

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图 5 MEMS传感器(a)、机电百分表(b)和静态应变仪(c)

Figure 5. MEMS sensor(a), electromechanical dial gauge(b), and static strain gauge(c)

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图 6 砂土滑坡室内模型试验示意图(单位：mm)

Figure 6. Schematic diagram of indoor model test for a sand landslide

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图 7 试验前后边坡土体变化情况对比

Figure 7. Comparison of slope soil changes before and after the experiment

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图 8 2组室内模型箱试验布设点位传感器位移变化

Figure 8. Displacement changes of sensors positioned in two sets of indoor model box test

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图 9 2组试验各传感器水平位移-时间曲线

Figure 9. Displacement time curves in the horizontal direction for each sensor in the first and second groups of experiments

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图 10 2组试验各传感器竖向位移-时间曲线

Figure 10. Displacement time curves in the vertical direction for each sensor in the first and second groups of experiments

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图 11 机电百分表与PLAXIS数值模拟数据对比

Figure 11. Comparison of numerical simulation data between electromechanical dial gauge and PLAXIS

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图 12 PLAXIS数值模拟边坡水平位移和竖向位移云图

Figure 12. Cloud map of horizontal and vertical displacements of slope from PLAXIS numerical simulation

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图 13 PLAXIS数值模拟边坡总位移云图

Figure 13. Cloud map of total displacement of slope from PLAXIS numerical simulation

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图 14 第1组试验各传感器与PLAXIS数值模拟所得水平位移和竖向位移-时间曲线对比

Figure 14. Comparison of displacement time curves in the horizontal and vertical directions obtained from each sensor in the first group of experiments and PLAXIS numerical simulations

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图 15 第2组试验各传感器与PLAXIS数值模拟所得水平位移和竖向位移-时间曲线对比

Figure 15. Comparison of displacement time curves in the horizontal and vertical directions obtained from each sensor in the second group of experiments and PLAXIS numerical simulations

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表 1 试验砂样物理力学指标

Table 1. Physical and mechanical indices of test sand samples

土体类型	密度/ (g·cm^-3)	最大干密度/ (g·cm^-3)	含水率/ %	内摩擦角/(°)	黏聚力/ kPa
砂土	1.61	1.68	1.03	32.8	0.2

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