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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

吴迪, 梁泰铭, 吴静, 吴建建, 易杨, 娄万鹏. 基于MEMS传感器的滑坡监测算法设计与试验[J]. 地质科技通报, 2024, 43(6): 39-50. doi: 10.19509/j.cnki.dzkq.tb20240214
引用本文: 吴迪, 梁泰铭, 吴静, 吴建建, 易杨, 娄万鹏. 基于MEMS传感器的滑坡监测算法设计与试验[J]. 地质科技通报, 2024, 43(6): 39-50. doi: 10.19509/j.cnki.dzkq.tb20240214
WU Di, LIANG Taiming, WU Jing, WU Jianjian, YI Yang, LOU Wanpeng. Design and experiment of landslide monitoring algorithm based on MEMS sensor[J]. Bulletin of Geological Science and Technology, 2024, 43(6): 39-50. doi: 10.19509/j.cnki.dzkq.tb20240214
Citation: WU Di, LIANG Taiming, WU Jing, WU Jianjian, YI Yang, LOU Wanpeng. Design and experiment of landslide monitoring algorithm based on MEMS sensor[J]. Bulletin of Geological Science and Technology, 2024, 43(6): 39-50. 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
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

Design and experiment of landslide monitoring algorithm based on MEMS sensor

More Information
  • 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。

     

  • 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

    图 2  滑坡简化模型

    at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

    图 3  滑坡室内模型试验装置

    Figure 3.  Indoor model test device for landslide

    图 4  砂土颗粒级配曲线

    Figure 4.  Sand particle grading curve

    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)

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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
    下载: 导出CSV
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  • 收稿日期:  2024-04-30
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