MEMS三维形变监测仪在涉铁工程中的应用
Application of MEMS Three-dimensional Deformation Monitoring Instrument in Deformation Monitoring of Rail Transit
王磊1
作者信息
- 1. 中国铁路设计集团有限公司,天津 300251
- 折叠
摘要
研究MEMS三维形变监测仪的设备组成、单个节段与多个节段工作原理和数据解算原理,形成一种深层水平位移和沉降自动化监测的方法.在某铁路隧道边坡 15 个监测点进行深层水平位移监测实验,对比 MEMS 三维形变监测仪埋设前后与人工监测的数据结果,两种方式的最大误差为1.97 mm;在某铁路U形槽进行沉降监测实验,对比MEMS三维形变监测仪与单点沉降计 80 期自动化监测的数据结果,两种方式的最大误差为2.96 mm.研究结果表明,相较于传统深层水平位移和沉降监测,利用MEMS三维形变监测仪能够快速、高频率、高精度及时获取监测区域三维变化数据,弥补列车通行期间无法进行人工监测的不足,实时掌握运营期间构筑物的三维变化.
Abstract
This paper studied the equipment composition,working principles of single and multiple segments,and data calculation principles of MEMS(Micro-Electro-Mechanicl-Systems)three-dimensional deformation monitoring instrument,forming a method for automatic monitoring of deep horizontal displacement and settlement.The research results were used to conduct deep horizontal displacement monitoring experiments at 15 monitoring points on a certain tunnel slope.The experiment compared the data results of MEMS three-dimensional deformation monitoring instrument before and after burial with manual monitoring,and the maximum error of the two methods was 1.97 mm.Settlement monitoring experiments on a U-shaped railway subgrade was conducted.The experiment compared the data results of MEMS three-dimensional deformation monitoring instrument and single point settlement meter for 80 periods of automated monitoring,and the maximum error of the two methods was 2.96 mm.The research results show that,compared with traditional deep horizontal displacement and settlement monitoring,the use of MEMS three-dimensional deformation monitoring instruments can quickly,high-frequency,high-precision and timely obtain three-dimensional change data in the monitoring area of rail transit,compensate for the lack of manual monitoring during train operation,and grasp the three-dimensional changes of structures in real time during operation.
关键词
涉铁工程/传感器/三维形变监测/加速度计/自动化监测Key words
involving railway engineering/sensors/3D deformation monitoring/accelerometers/automated monitoring引用本文复制引用
出版年
2024
NETL
NSTL
万方数据