WO2022174592A1 - 一种隧道形变实时监测方法及装置 - Google Patents

一种隧道形变实时监测方法及装置 Download PDF

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WO2022174592A1
WO2022174592A1 PCT/CN2021/121671 CN2021121671W WO2022174592A1 WO 2022174592 A1 WO2022174592 A1 WO 2022174592A1 CN 2021121671 W CN2021121671 W CN 2021121671W WO 2022174592 A1 WO2022174592 A1 WO 2022174592A1
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tunnel
structured light
monitoring
real
area
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PCT/CN2021/121671
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English (en)
French (fr)
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石峥映
王列伟
朱明�
连捷
黄友群
陆海东
李阳
刘洋
李沛遥
吴国强
夏宝前
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南京派光智慧感知信息技术有限公司
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Priority to JP2023532651A priority Critical patent/JP2023552171A/ja
Priority to AU2021428062A priority patent/AU2021428062B2/en
Priority to DE112021004827.9T priority patent/DE112021004827T9/de
Publication of WO2022174592A1 publication Critical patent/WO2022174592A1/zh
Priority to US18/446,004 priority patent/US11908121B2/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
    • E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
    • E21F17/18—Special adaptations of signalling or alarm devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0025—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of elongated objects, e.g. pipes, masts, towers or railways
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0041—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining deflection or stress
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0091—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by using electromagnetic excitation or detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G06T7/248—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments involving reference images or patches
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30184—Infrastructure

Definitions

  • the invention relates to the field of tunnel structure deformation monitoring, in particular to a method and device for real-time monitoring of tunnel deformation.
  • the shield method is mainly used for subway tunnel construction.
  • the cutter head is excavated, the slag is transported, the duct piece is attached, and the track is laid at the same time.
  • the structure is relatively stable and the cost is high.
  • the blasting method is mainly used in the construction of high-speed railway tunnels. First, blast the tunnel face. After blasting, the I-beam beam is erected and cement is poured to become the initial support area.
  • the deformation rate of the initial branch area is relatively large, and disasters such as tunnel settlement and convergence are very likely to occur, and real-time deformation monitoring is required.
  • cement After the initial support area, cement will be poured to form a permanent tunnel structure, which is called the closed area of the inverted arch and the secondary lining area.
  • the structure after this area belongs to a relatively stable area, and the tunnel structure is basically stable after the secondary lining is completed.
  • the structural deformation monitoring of tunnels under construction is usually measured by a total station.
  • tunnel construction it is necessary to install multiple reflective prisms (usually 3 to 7) at fixed intervals on the top of the tunnel by manual installation.
  • the installation of reflective prisms is inconvenient, and there are certain construction safety hazards.
  • the interval between each inspection is generally 2 to 4 hours. This inspection method is time-consuming and labor-intensive, and delays the construction progress.
  • the above-mentioned current total station monitoring method can only monitor 3 to 7 points in each section of the tunnel, which is very sparse, and the monitoring deformation time interval is relatively long. If a disaster accident occurs during the monitoring window period, it cannot effectively warn.
  • motion detection vehicles are usually used, and structured light sources and image sensors are deployed on the detection vehicles.
  • this detection method cannot be used in tunnels under construction that use blasting methods, and the detection period is long and non-real-time detection.
  • the present invention aims to provide a method and device for real-time monitoring of tunnel deformation, which can monitor the tunnel deformation in real time not only during the construction of the tunnel under construction, but also during the operation of the tunnel.
  • the real-time monitoring method for tunnel deformation of the present invention comprises the following steps:
  • the monitoring terminal observes all the structured light in the unstable area of the tunnel structure, and obtains the structured light curve in real time;
  • the data processing unit analyzes the changes of the structured light imaging, detects the deformation degree and offset distance of the tunnel in real time, and monitors the settlement and convergence of a single section of the tunnel, as well as the overall settlement disease of multiple sections.
  • step S3 includes the following steps:
  • the data processing unit uses the image recognition module to locate the structured light area and its edge;
  • the data processing unit extracts the center line of the structured light curve
  • the data processing unit automatically or manually updates the reference line formed by the center line
  • the data processing unit detects the structural state of the tunnel, and determines whether the tunnel single-section settlement, convergence, or multi-section overall settlement disaster occurs.
  • step S34 the method for the data processing unit to detect the state of the tunnel structure includes:
  • the present invention also provides a monitoring device for implementing the above-mentioned method for real-time monitoring of tunnel deformation, comprising a plurality of structured light sources and a monitoring terminal, wherein the plurality of structured light sources are erected in the unstable area of the tunnel structure;
  • the stabilization area communicates with and controls multiple structured light sources, and the monitoring terminal includes a data processing unit and multiple image sensors.
  • the structured light source is a multi-band light source
  • the detection terminal can perceive the multi-band light source
  • the measurement results of the multi-band light source are comprehensively analyzed to obtain the final measurement result.
  • the structured light source has a built-in sensor for detecting whether it is impacted, and when it is detected that the structured light source is impacted, the structured light source automatically readjusts the reference curve of the structured light.
  • the method and device for real-time monitoring of tunnel deformation of the present invention are based on improved structured light machine vision technology.
  • Multiple structured light sources are installed on the sidewall of the monitored area, and a monitoring terminal is installed in a relatively stable area.
  • the real-time change of the shape of the structured light on the inner wall of the tunnel can monitor the top settlement of the tunnel, the convergence of both sides, the local settlement, the overall settlement, the instability of the surrounding rock and other disasters.
  • the present invention can not only perform real-time tunnel deformation monitoring during the construction of the tunnel under construction, but also can monitor the tunnel deformation in real time in the operating tunnel.
  • the present invention does not need to install equipment on the top of the tunnel, and can replace the total station monitoring method commonly used in tunnels under construction. It has the characteristics of intensive monitoring points, automatic system operation and real-time monitoring.
  • FIG. 1 is a schematic diagram of a real-time monitoring device for tunnel deformation according to a preferred embodiment of the present invention.
  • FIG. 2 is a schematic diagram of the section division of the tunnel wall by the real-time monitoring method of tunnel deformation.
  • FIG. 3 is a schematic diagram of a tunnel structure state detected by a real-time monitoring method for tunnel deformation.
  • the real-time monitoring device for tunnel deformation includes a plurality of structured light sources 1 and a monitoring terminal 2 .
  • a plurality of structured light sources 1 are erected on the face and primary branch area to be monitored, and these areas are collectively referred to as the monitoring area or the unstable area D1.
  • the plurality of structured light sources 1 are arranged at equal intervals.
  • the structured light source 1 is a multi-band light source, and at the same time, the monitoring terminal 2 has a multi-band light source sensing function, and the measurement results of the multi-band light source are comprehensively analyzed to obtain the final measurement result.
  • the structured light source 1 has its own target, and the monitoring terminal 2 obtains the position change (settling or convergence) of each light source by observing the target.
  • the target is preferably a cross self-luminous infrared light source.
  • the structured light source 1 has a built-in sensor to detect whether it is hit. When it is detected, the structured light source 1 cooperates with the monitoring terminal 2 to automatically readjust the reference curve of the structured light.
  • the monitoring terminal 2 is erected in the closed area of the inverted arch or the secondary lining area D2, that is, the stable area.
  • the monitoring terminal 2 communicates with the plurality of structured light sources 1 in a wireless or wired manner, and controls the structured light sources 1 to be turned on at intervals according to detection requirements and on-site environment.
  • the structured light source 1 can be powered by a cable or by its own battery.
  • the monitoring terminal 1 includes a data processing unit and a plurality of image sensors, preferably two image sensors are used, one image sensor faces the monitoring area D1, and the other image sensor faces the secondary area D2.
  • multiple structured light sources 1 may also be erected in the secondary lining area D2, and the monitoring terminal 2 monitors the multiple structured light sources in the secondary lining area through an image sensor facing the secondary lining area D2.
  • the monitoring terminal 2 monitors the multiple structured light sources in the secondary lining area through an image sensor facing the secondary lining area D2.
  • the monitoring method of the above-mentioned tunnel deformation real-time monitoring device comprises the following steps:
  • the structured light source is installed at the bottom of the side wall of the tunnel.
  • the monitoring terminal observes all structured light L0 in the unstable region of the tunnel structure, and obtains the structured light curve in real time;
  • the data processing unit analyzes the changes of the structured light imaging, detects the deformation degree and offset distance of the tunnel in real time, and monitors the settlement and convergence of a single section of the tunnel, as well as the overall settlement of multiple sections and other diseases.
  • step S3 includes the following steps:
  • the data processing unit uses the image recognition module to locate the structured light area and its edge,
  • the data processing unit locates the structured light area by means of adaptive threshold processing, and uses a deep learning segmentation network to identify the edge of the structured light in the area, and the segmentation network preferably adopts a semantic segmentation model.
  • the data processing unit extracts the center line of the structured light curve
  • the data processing unit adopts a light stripe geometric distribution feature extraction algorithm, and distinguishes the upper and lower edges of the light stripe by edge detection, and the center line is the average of the coordinates of the two edge columns, and the edge detection preferably adopts the neighborhood method.
  • the data processing unit automatically or manually updates the reference line formed by the center line
  • the data processing unit includes a tunnel structure reference line setting module, which collects the center line of the structured light curve under the normal tunnel structure, saves the mask map, and updates.
  • the data processing unit detects the structural state of the tunnel, and determines whether a single-section subsidence, convergence, or multi-section overall subsidence of the tunnel occurs.
  • the above step S3 further includes: the data processing unit locates the target of the structured light source, calculates the pixel resolution in the X direction and the Y direction, obtains the distance corresponding to the pixel in the actual space, and preferentially uses a deep learning network semantic segmentation model for positioning.
  • the method for detecting the state of the tunnel structure by the data processing unit in the above step S34 includes:

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Abstract

一种隧道形变实时监测方法，包括：S1、在需要监测的隧道结构不稳定区域（D1）架设多个结构光光源（1），并且，在隧道结构相对稳定区域（D2）架设监测终端（2），监测终端（2）可与多个结构光光源（1）通信；S2、监测终端（2）观测隧道结构不稳定区域（D1）内的所有结构光（L0），并实时获取结构光曲线；S3、数据处理单元分析结构光成像变化，实时检测隧道形变程度及偏移距离，监测隧道单截面沉降、收敛、以及多截面整体沉降等病害。适用于在建隧道和运营隧道，监测点密集，能实时监测隧道的形变。还提供一种隧道形变实时监测装置。

Description

一种隧道形变实时监测方法及装置 技术领域
本发明涉及隧道结构形变监测领域，具体涉及一种隧道形变实时监测方法及装置。
背景技术
在建隧道的掘进方式主要有爆破和盾构两种方式。盾构方式主要用于地铁隧道建设，刀盘掘进、运渣、贴管片、铺设轨道同时进行，结构相对稳定，成本较高。爆破方式主要用于高铁隧道建设，先在掌子面进行爆破，爆破后立工字钢梁并浇筑水泥成为初支区域。初支区域的变形速率较大，极易发生隧道沉降、收敛等灾害，需要实时监测形变。初支区域之后会浇筑水泥形成永久隧道结构，称之为仰拱闭合区域和二衬区域，此区域之后结构属于相对稳定区域，完成二次衬砌后隧道结构基本稳定。
目前在建隧道结构形变监测通常使用全站仪进行测量。在建隧道掘进时，需要在隧道顶部通过人工安装方式，以固定间隔安装多个反光棱镜(通常为3～7个)，反光棱镜安装不方便，且有一定的施工安全隐患。在检测时，需停止施工并使用全站仪进行人工监测，每次检测间隔一般为2到4小时，这种检测方式费时费力，且耽误施工进度。
另外，上述现行的全站仪监测方法，在每一段隧道只能监测3到7个点，非常稀疏，并且监测形变时间间隔比较长，如果在监测窗口期发生灾害事故，则无法有效预警。
对于运营隧道，通常采用运动检测车的方式，在检测车上部署结构光源和图像传感器，但这种检测方式无法在采用爆破方式的在建隧道中使用，而且检测周期长，非实时检测。
因此，急需研发一种隧道结构形变监测方法，能够同时可以在运营隧道和在建隧道中方便部署，不需要在隧道顶部安装设备，并能够实时不中断地监测隧道结构形变，加大监测密度和频次。
发明内容
为解决现有技术中存在的问题，本发明旨在提出一种隧道形变实时监测方法及装置，其既能在在建隧道施工期间，又能在运营隧道期间实时地监测隧道形变。
为实现上述目的，本发明的隧道形变实时监测方法，包括如下步骤：
S1、在需要监测的隧道结构不稳定区域架设多个结构光光源，并且，在隧道结构相对稳定的二衬区域架设监测终端，监测终端与多个结构光光源通信；
S2、监测终端观测隧道结构不稳定区域内的所有结构光，并实时获取结构光曲线；
S3、数据处理单元分析结构光成像变化，实时检测隧道形变程度及偏移距离，监测隧道单截面沉降、收敛、以及多截面整体沉降病害。
在其中一种实施方式中，步骤S3包括如下步骤：
S31、数据处理单元利用图像识别模块定位结构光区域及其边缘；
S32、数据处理单元提取结构光曲线的中心线；
S33、数据处理单元自动或手动更新由中心线形成的基准线；
S34、数据处理单元检测隧道结构状态，判断是否发生隧道单截面沉降、收 敛、或者多截面整体沉降灾害。
在其中一种实施方式中，在步骤S34中，数据处理单元检测隧道结构状态的方法包括：
S341、划分隧道壁区间，分为左右侧壁和拱顶部分，左右侧壁检测横向和纵向位移，拱顶检测纵向位移；
S342、将实时结构光曲线中心线与基准线进行匹配，采用差值计算算法，比较掩码图不同区间基准线前后像素偏差，逐个比较像素横向或纵向位移变化，以最大偏差作为检测结果；
S343、采用组合线差异算法检测隧道多截面整体沉降，结合隧道前后多条结构光曲线中心线匹配结构，多条整体下移即为隧道多截面整体沉降病害；
S344、沉降量空间映射，通过像素量化将像素位移转换为空间偏移距离，获得实际沉降量，并根据位移方向确定病害类型。
本发明还提出一种实施上述的隧道形变实时监测方法的监测装置，包括多个结构光光源和一台监测终端，多个结构光光源架设在隧道结构不稳定区域；监测终端架设在隧道结构相对稳定区域，其与多个结构光光源通信，并控制多个结构光光源，监测终端包括数据处理单元和多个图像传感器。
在其中一种实施方式中，结构光光源为多波段光源，检测终端能感知多波段光源，多波段光源的测量结果综合分析得到最终测量结果。
在其中一种实施方式中，结构光光源内置传感器，用于检测自身是否被撞击，当检测到被撞击时，结构光光源自动重新调整结构光的基准曲线。
有益效果：
本发明的隧道形变实时监测方法及装置基于改进结构光机器视觉技术，在被监测区域侧壁安装多个结构光光源，以及在相对稳定区域安装监测终端，监 测终端的图像传感器检测被监测区域中隧道内壁上的结构光形状的实时变化，从而监测隧道顶部沉降、两侧收敛、局部沉降、整体沉降、围岩失稳等灾害。本发明不仅可以在在建隧道施工期间进行实时隧道形变监测，也可以在运营隧道中实时监测隧道形变，本发明无需在隧道顶部安装设备，其可以取代在建隧道常用的全站仪监测方法，具有监测点密集、系统自动运行、实时监测的特点。
附图说明
下面结合附图对本发明作进一步描写和阐述。
图1是本发明首选实施方式的隧道形变实时监测装置的示意图。
图2是隧道形变实时监测方法对隧道壁进行区间划分的示意图。
图3是隧道形变实时监测方法检测隧道结构状态的示意图。
具体实施方式
下面将结合附图、通过对本发明的优选实施方式的描述，更加清楚、完整地阐述本发明的技术方案。
如图1所示，本发明首选实施方式的隧道形变实时监测装置包括多个结构光光源1和一台监测终端2。
多个结构光光源1架设在需要监测的掌子面、初支区域，这些区域统称为监测区域或不稳定区域D1。优选地，多个结构光光源1等间距布置。结构光光源1为多波段光源，同时，监测终端2具备多波段光源感知功能，多波段光源的测量结果综合分析得到最终测量结果。结构光光源1自带靶标，监测终端2通过观测靶标获得每个光源自身的位置变化(沉降或收敛)。靶标优选为十字自发光红外光源。结构光光源1内置传感器，用于检测自身是否被撞击，当检测到被撞击时，结构光光源1配合监测终端2自动重新调整结构光的基准曲线
监测终端2架设在仰拱闭合区域或二衬区域D2，也即稳定区域。监测终端2通过无线或者有线的方式与多个结构光光源1通信，并根据检测需求和现场环境，控制结构光光源1间隔开启。结构光光源1可以通过有线供电或者自带电池供电。监测终端1包括数据处理单元和多个图像传感器，优选采用两个图像传感器，一个图像传感器面向监测区域D1，另一个图像传感器面向二衬区域D2。
在其他实施方式中，在二衬区域D2也可以架设多个结构光光源1，监测终端2通过面向二衬区域D2的图像传感器监测二衬区域的多个结构光光源。通过监测在二衬区域D2的多个结构光位移变化，可以获得监测终端2自身是否发生沉降或收敛，并校正在不稳定区域的多个结构光1的位移量。
上述隧道形变实时监测装置的监测方法，包括如下步骤：
S1、在需要监测的隧道结构不稳定区域架设多个结构光光源，并且，在隧道结构相对稳定的二衬区域架设一台监测终端，监测终端与多个结构光光源通信；
优选地，结构光光源安装在隧道侧壁底部。
S2、监测终端观测隧道结构不稳定区域内的所有结构光L0，并实时获取结构光曲线；
S3、数据处理单元分析结构光成像变化，实时检测隧道形变程度及偏移距离，监测隧道单截面沉降、收敛、以及多截面整体沉降等病害。
具体地，上述步骤S3包括如下步骤：
S31、数据处理单元利用图像识别模块定位结构光区域及其边缘，
优选地，数据处理单元通过自适应阈值处理方式定位结构光区域，利用深度学习分割网络识别区域内结构光的边缘，分割网络优选采用语义分割模型。
S32、数据处理单元提取结构光曲线的中心线，
优选地，数据处理单元采用光条纹几何分布特征提取算法，通过边缘检测区分光条纹上下两条边缘，中心线为两条边缘列坐标均值，边缘检测优选采用邻域法。
S33、数据处理单元自动或手动更新由中心线形成的基准线，
具体地，数据处理单元包括隧道结构基准线设置模块，通过采集隧道正常结构下结构光曲线的中心线，保存掩码图，进行更新。
S34、数据处理单元检测隧道结构状态，判断是否发生隧道单截面沉降、收敛、或者多截面整体沉降等病害。
优选地，上述步骤S3还包括：数据处理单元定位结构光光源的靶标，计算X方向和Y方向的像素分辨率，获得像素对应实际空间的距离，定位优先采用深度学习网络语义分割模型。
具体地，上述步骤S34数据处理单元检测隧道结构状态的方法包括：
S341、划分隧道壁区间，如图2所示，将隧道壁分为左侧壁d1、右侧壁d2、 和拱顶部分d3，左右侧壁主要检测横向和纵向位移，拱顶主要检测纵向位移；
S342、如图3所示，将实时结构光曲线中心线L2与基准线L1进行匹配，采用差值计算算法，比较掩码图不同区间基准线前后像素偏差，逐个比较像素横向或纵向位移变化，以最大偏差作为检测结果；
S343、采用组合线差异算法检测隧道多截面整体沉降，结合隧道前后多条结构光曲线中心线匹配结构，多条整体下移即为隧道多截面整体沉降病害；
S344、沉降量空间映射，通过像素量化将像素位移转换为空间偏移距离，获得实际沉降及收敛量，并根据位移方向确定病害类型。
上述具体实施方式仅仅对本发明的优选实施方式进行描述，而并非对本发明的保护范围进行限定。在不脱离本发明设计构思和精神范畴的前提下，本领域的普通技术人员根据本发明所提供的文字描述、附图对本发明的技术方案所作出的各种变形、替代和改进，均应属于本发明的保护范畴。本发明的保护范围由权利要求确定。

Claims (6)

  1. 一种隧道形变实时监测方法，其特征在于，包括如下步骤：
    S1、在需要监测的隧道结构不稳定区域和隧道结构相对稳定的二衬区域分别架设多个结构光光源，并且，在所述二衬区域架设监测终端，所述监测终端与所述多个结构光光源通信；
    S2、所述监测终端观测隧道结构不稳定区域和二衬区域内的所有结构光，并实时获取结构光曲线；
    S3、所述监测终端根据二衬区域的结构光位移变化，判断所述监测终端自身是否发生沉降或收敛，数据处理单元分析所述隧道结构不稳定区域的结构光成像变化，并根据二衬区域的结构光位移变化，校正所述隧道结构不稳定区域的结构光曲线的位移量，实时检测隧道形变程度及偏移距离，监测隧道单截面沉降、收敛、以及多截面整体沉降病害。
  2. 根据权利要求1所述的隧道形变实时监测方法，其特征在于，所述步骤S3包括如下步骤：
    S31、数据处理单元利用图像识别模块定位结构光区域及其边缘；S32、数据处理单元提取结构光曲线的中心线；
    S33、数据处理单元自动或手动更新由所述中心线形成的基准线；
    S34、数据处理单元检测隧道结构状态，判断是否发生隧道单截面沉降、收敛、或者多截面整体沉降灾害。
  3. 根据权利要求2所述的隧道形变实时监测方法，其特征在于，在所述步骤S34中，所述数据处理单元检测隧道结构状态的方法包括：
    S341、划分隧道壁区间，分为左右侧壁和拱顶部分，左右侧壁检测横向和纵向位移，拱顶检测纵向位移；
    S342、将实时结构光曲线中心线与基准线进行匹配，分析像素位移变化；
    S343、检测隧道多截面整体沉降，结合隧道前后多条结构光曲线中心线匹配结构，多条整体下移即为隧道多截面整体沉降病害；
    S344、沉降量空间映射，通过像素量化将像素位移转换为空间偏移距离，获得实际沉降及收敛量，并根据位移方向确定病害类型。
  4. 一种实施如权利要求1所述的隧道形变实时监测方法的监测装置，其特征在于，包括多个结构光光源和一台监测终端，
    所述多个结构光光源架设在隧道结构不稳定区域和隧道结构相对稳定的二衬区域；
    所述监测终端架设在隧道结构相对稳定的二衬区域，其与所述多个结构光光源通信，并控制所述多个结构光光源，所述监测终端包括数据处理单元和多个图像传感器。
  5. 根据权利要求4所述的监测装置，其特征在于，所述结构光光源为多波段光源，监测终端能感知多波段光源，多波段光源的测量结果综合分析得到最终测量结果。
  6. 根据权利要求4所述的监测装置，其特征在于，所述结构光光源内置传感器，用于检测自身是否被撞击，当检测到被撞击时，所述结构光光源自动重新调整结构光的基准曲线。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116906125A (zh) * 2023-09-06 2023-10-20 四川高速公路建设开发集团有限公司 基于数据同步传输算法的软岩隧道安全监测方法及系统
CN117211886A (zh) * 2023-11-07 2023-12-12 南京派光智慧感知信息技术有限公司 一种隧道掌子面多元信息一体化采集系统及其使用方法
CN117889823A (zh) * 2024-03-11 2024-04-16 福建省高速公路科技创新研究院有限公司 一种隧道运营期拱顶沉降的传递式监测方法

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112556600B (zh) 2021-02-22 2021-05-18 南京派光智慧感知信息技术有限公司 一种隧道形变实时监测方法及装置
CN113685189B (zh) * 2021-08-25 2023-02-17 同济大学 隧道掌子面开挖变形自动化测量方法
CN116022196B (zh) * 2023-02-13 2023-11-03 山东大学 一种基于计算机视觉的轨道变形监测系统

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001289620A (ja) * 2000-04-10 2001-10-19 Ohbayashi Corp トンネル内施工状態検知方法
US20030145658A1 (en) * 2002-01-11 2003-08-07 Gerhard Weithe Method and apparatus for surveying the geometry of tunnels
CN103017673A (zh) * 2012-12-26 2013-04-03 中铁二十四局集团有限公司 隧道围岩变形实时连续监测报警方法
CN103438823A (zh) * 2012-12-27 2013-12-11 广州市地下铁道总公司 一种基于视觉测量的隧道断面轮廊测量方法及装置
CN105809668A (zh) * 2016-01-15 2016-07-27 武汉武大卓越科技有限责任公司 基于线扫描三维点云的物体表面变形特征提取方法
CN109373926A (zh) * 2018-12-30 2019-02-22 江龙 隧道施工围岩变形连续监控报警方法以及监测系统
CN109681275A (zh) * 2019-01-20 2019-04-26 江龙 一种隧道施工围岩变形连续监控报警方法以及系统
CN111855664A (zh) * 2020-06-12 2020-10-30 山西省交通科技研发有限公司 一种可调节隧道病害三维检测系统
CN112097669A (zh) * 2020-11-17 2020-12-18 南京派光智慧感知信息技术有限公司 一种基于激光测距的隧道内结构形变的监测方法
CN112556600A (zh) * 2021-02-22 2021-03-26 南京派光智慧感知信息技术有限公司 一种隧道形变实时监测方法及装置

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100575872C (zh) * 2007-03-20 2009-12-30 中国空气动力研究与发展中心高速空气动力研究所 基于立体视觉的风洞模型外形监测方法
CN101458069B (zh) * 2008-12-30 2011-02-23 中铁二十四局集团有限公司 隧道围岩变形监测方法及其监测系统
US8845107B1 (en) * 2010-12-23 2014-09-30 Rawles Llc Characterization of a scene with structured light
US8972310B2 (en) * 2012-03-12 2015-03-03 The Boeing Company Method for identifying structural deformation
US9626568B2 (en) * 2013-11-26 2017-04-18 Rowan University Use of spatially structured light for dynamic three dimensional reconstruction and reality augmentation
CN204007533U (zh) * 2014-06-19 2014-12-10 樊晓东 隧道缺陷的全方位检测设备
CN105423940B (zh) * 2015-12-25 2017-12-26 同济大学 一种地铁隧道结构断面变形快速检测装置
CA3023641C (en) * 2016-01-15 2020-12-22 Wuhan Wuda Zoyon Science And Technology Co., Ltd Object surface deformation feature extraction method based on line scanning three-dimensional point cloud
US10560679B2 (en) * 2016-08-30 2020-02-11 Microsoft Technology Licensing, Llc Deformation detection and automatic calibration for a depth imaging system
CN107333046B (zh) * 2017-08-14 2019-06-25 成都中信华瑞科技有限公司 数据采集方法及隧道检测车
CN107655898B (zh) * 2017-10-10 2023-11-03 山西省智慧交通研究院有限公司 一种用于公路隧道检测的立体扫描机器人及其实施方法
CN110926339A (zh) * 2018-09-19 2020-03-27 山东理工大学 一种基于一次投影结构光平行条纹图案的实时三维测量方法
CN110161043B (zh) * 2019-05-10 2021-03-26 同济大学 一种地铁隧道结构综合检测车
CN111307043A (zh) * 2020-03-24 2020-06-19 上海勃发空间信息技术有限公司 一种基于结构光的位移监测系统

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001289620A (ja) * 2000-04-10 2001-10-19 Ohbayashi Corp トンネル内施工状態検知方法
US20030145658A1 (en) * 2002-01-11 2003-08-07 Gerhard Weithe Method and apparatus for surveying the geometry of tunnels
CN103017673A (zh) * 2012-12-26 2013-04-03 中铁二十四局集团有限公司 隧道围岩变形实时连续监测报警方法
CN103438823A (zh) * 2012-12-27 2013-12-11 广州市地下铁道总公司 一种基于视觉测量的隧道断面轮廊测量方法及装置
CN105809668A (zh) * 2016-01-15 2016-07-27 武汉武大卓越科技有限责任公司 基于线扫描三维点云的物体表面变形特征提取方法
CN109373926A (zh) * 2018-12-30 2019-02-22 江龙 隧道施工围岩变形连续监控报警方法以及监测系统
CN109681275A (zh) * 2019-01-20 2019-04-26 江龙 一种隧道施工围岩变形连续监控报警方法以及系统
CN111855664A (zh) * 2020-06-12 2020-10-30 山西省交通科技研发有限公司 一种可调节隧道病害三维检测系统
CN112097669A (zh) * 2020-11-17 2020-12-18 南京派光智慧感知信息技术有限公司 一种基于激光测距的隧道内结构形变的监测方法
CN112556600A (zh) * 2021-02-22 2021-03-26 南京派光智慧感知信息技术有限公司 一种隧道形变实时监测方法及装置

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116906125A (zh) * 2023-09-06 2023-10-20 四川高速公路建设开发集团有限公司 基于数据同步传输算法的软岩隧道安全监测方法及系统
CN116906125B (zh) * 2023-09-06 2023-12-29 四川高速公路建设开发集团有限公司 基于数据同步传输算法的软岩隧道安全监测方法及系统
CN117211886A (zh) * 2023-11-07 2023-12-12 南京派光智慧感知信息技术有限公司 一种隧道掌子面多元信息一体化采集系统及其使用方法
CN117211886B (zh) * 2023-11-07 2024-01-19 南京派光智慧感知信息技术有限公司 一种隧道掌子面多元信息一体化采集系统及其使用方法
CN117889823A (zh) * 2024-03-11 2024-04-16 福建省高速公路科技创新研究院有限公司 一种隧道运营期拱顶沉降的传递式监测方法

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