CN116734757A - Tunnel surrounding rock deformation monitoring and early warning method based on unmanned aerial vehicle-mounted laser scanner - Google Patents
Tunnel surrounding rock deformation monitoring and early warning method based on unmanned aerial vehicle-mounted laser scanner Download PDFInfo
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- CN116734757A CN116734757A CN202310720260.5A CN202310720260A CN116734757A CN 116734757 A CN116734757 A CN 116734757A CN 202310720260 A CN202310720260 A CN 202310720260A CN 116734757 A CN116734757 A CN 116734757A
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- 238000006073 displacement reaction Methods 0.000 claims abstract description 13
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- 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
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- G—PHYSICS
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Abstract
The invention discloses a tunnel surrounding rock deformation monitoring and early warning method based on an unmanned aerial vehicle-mounted laser scanner, which comprises the following steps: b1, modeling a tunnel section; b2, dividing the measuring section according to the construction progress; b3, dividing a monitoring section according to the monitoring requirement, and scanning to establish a three-dimensional model; b4, utilizing a central axis determining method; b5, defining key monitoring points of tunnel sections as characteristic points; b6, judging the deformation condition of the section according to a preset deformation threshold value; b7: obtaining the deformation absolute value and deformation trend of each section surrounding rock; and B8, according to the spatial positions of the center points of the section models in different time periods. The method can judge the deformation early warning condition of the surrounding rock of the tunnel in advance only by extremely small calculated amount, thereby meeting the short-time-lag quasi-real-time early warning requirement and providing more time and space for the evacuation of tunnel constructors and the disposal of safety risk sources; and the displacement of the through axis can also be judged whether to meet the design requirement according to the spatial positions of the central intersection points of the section models in different time periods and the linear change of the tunnel.
Description
Technical Field
The invention relates to the technical field of tunnel surrounding rock construction monitoring and control, in particular to a tunnel surrounding rock deformation monitoring and early warning method based on unmanned aerial vehicle-mounted three-dimensional laser scanning point cloud.
Background
The monitoring and early warning of the tunnel surrounding rock is an essential important link of tunnel engineering, and the monitoring and early warning is accompanied with the full life cycle of the tunnel engineering. The deformation monitoring of the surrounding rock of the traditional tunnel mostly adopts static observation, and has the advantages of large workload, high cost, severe working environment, incapability of ensuring the safety of monitoring staff, large interference to other industrial and industrial construction and low efficiency. The unmanned aerial vehicle airborne three-dimensional laser scanning can perform all-weather all-dimensional scanning without interrupting the tunnel on the premise of not interfering with ground work, and compared with the traditional method, the method is safe, reliable, high in efficiency and accurate in data.
Generally, based on the monitoring technology of a three-dimensional laser scanner, equipment needs to be fixed in a tunnel for operation, or a track is pre-installed, so that the scanner can walk on the fixed track for operation, and meanwhile, an operator needs to assist at one side. This approach severely affects the construction in the tunnel, while the tunnel working environment cannot ensure the safety of the operators.
After the data of the existing three-dimensional laser scanner is acquired, noise reduction and thinning are needed to be carried out at one time, and comparison is carried out after all modeling is completed. The data processing capacity is very large, the monitoring result time lag is obvious, and the quasi-real-time dynamic rapid monitoring and early warning are difficult to realize.
Disclosure of Invention
Aiming at the technical defects, the invention aims to provide a tunnel surrounding rock deformation monitoring and early warning method based on an unmanned aerial vehicle-mounted laser scanner, wherein the unmanned aerial vehicle-mounted three-dimensional laser scanner does not need to build a fixed track in a tunnel, does not need to fix a testing working area and avoids the interference to construction; the coordinate conversion of point cloud data can be rapidly carried out through joint measurement with the point cloud data of the reference control point, the calculation of characteristic point cloud data of tunnel surrounding rock deformation monitoring is timely carried out, and rapid early warning judgment is carried out; the deformation early warning condition of the surrounding rock of the tunnel can be judged in advance only by extremely small calculated amount, so that the short-time-lag quasi-real-time early warning requirement is met, and more time and space are provided for evacuation of tunnel constructors and disposal of safety risk sources; and simultaneously, monitoring the linear displacement of the tunnel penetration.
In order to solve the technical problems, the invention adopts the following technical scheme:
the invention provides a tunnel surrounding rock deformation monitoring and early warning method based on an unmanned aerial vehicle-mounted laser scanner, which is characterized by comprising the following steps of:
b1, modeling a tunnel section according to tunnel design data;
b2, dividing measuring sections according to construction progress, and arranging at least 3 known control points in advance to serve as reference targets;
b3, dividing monitoring sections according to monitoring requirements, determining tunnel sections at equal mileage intervals in each monitoring section, arranging targets at positions of linear intersection points of tunnel section boundaries, and scanning to establish a three-dimensional model;
b4, extracting a tunnel center line by using a center line determining method; extracting center points of the central line and the tunnel section;
b5, defining key monitoring points of the tunnel section as characteristic points, wherein the characteristic points must comprise intersection points of design lines of the tunnel section boundary and vault monitoring points; preferentially calculating the distance from the point cloud of the characteristic point of each tunnel section boundary to the center point, and calculating the azimuth angle from the point cloud of the characteristic point of each tunnel section boundary to the center point;
b6, comparing the calculated distance and azimuth angle with the designed section, and judging the deformation condition of the section according to a preset deformation threshold; the displacement deformation of the boundary point cloud in three directions can be carried out;
b7: overlapping the models according to the model established by the monitoring time node to quickly obtain the deformation absolute value and the deformation trend of each section surrounding rock;
and B8, rapidly reacting the linear variation of the tunnel according to the spatial positions of the center points of the section models in different time periods.
Further, step B1 further includes: calculating the boundary point A of the design section i Distance and azimuth to the section center point O; the calculation data is stored as initial data.
Further, step B2 further includes: the reference target is a geodetic coordinate, and can be used as a reference point of other scanning point clouds, and the geodetic coordinate of any point cloud can be calculated through coordinate conversion.
Further, step B3 further includes: defining the key monitoring point of the tunnel section as a characteristic point A i The feature points must include intersection points of the design lines of the tunnel section boundaries and dome monitoring points.
Further, step B4 further includes: and extracting the central point of the tunnel section, and calculating the coordinates of the central point of the tunnel section according to the coordinates of the control point.
Further, the specific method for calculating the distance in step B5 is as follows: point cloud A 'of linear intersection of section boundaries' i (x′ i ,y′ i ,z′ i ) To the central point O i (x 0 ，y 0 ，z 0 ) Distance L between i ＝(A′ i -O i )·e i ，e i Is the unit vector in the central axis direction; azimuth calculation formula: cos<a，b>＝(a·b)/(|a|·|b|)。
Further, step B6 includes: preferentially calculating the distance and azimuth angle from the point cloud of the characteristic point of each tunnel section boundary to the center point cloud, comparing the calculation result with the initial section data, if the calculation result is lower than a threshold value, stopping iterative calculation, and enabling the section not to be early-warned; if the threshold value is exceeded or is close to the threshold value, setting the section as an early warning section, automatically calling other point cloud data of the section, modeling the tunnel section, comparing the tunnel section with initial data of the tunnel section, calculating displacement deformation of the point cloud in three axial directions, and calculating A '' i Relative to A i In azimuth angles of three axial directions, the three axial directions refer to three directions of x, y and z axes; and quickly finding out the specific deformation range and deformation amount of the section.
The specific method for calculating the three-way displacement deformation in the step B6 comprises the following steps:
A′ i (x′ i ,y′ i ,z′ i ) To A i (x i ,y i ,z i ) Distance L 'of (2)' i ，L′ i ＝|A′ i -A i |；
Three axial displacement variation calculations:
A′ i relative to A i Azimuth calculation in three axial directions:
further, tunnel sections are established according to the boundary point cloud of each section in the monitoring scanning mode, dynamic models of all sections are established according to time nodes and overlapped with the design models, and absolute deformation and deformation trend of the tunnel surrounding rock sections in a period of time are calculated.
Further, step B7 includes: and establishing a dynamic model of the center point cloud of all the sections according to the time nodes and the center point cloud coordinates of each section according to the monitoring scanning, and rapidly reflecting the tunnel center line shape change by comparing the dynamic model with the center point line shape of the design model.
The invention has the beneficial effects that: according to the method, a track is not required to be arranged in a tunnel, interference to other facilities and construction of the tunnel is avoided, all-weather scanning operation can be carried out at any time period, and all point cloud coordinates can be calculated through coordinate conversion by arranging known control points in advance; extracting the central intersection point of the central axis and the tunnel section; preferentially calculating the distance from the point cloud of the characteristic point of the tunnel section boundary to the intersection point of the section center; rapidly calculating azimuth angles from point clouds of characteristic points of each tunnel section boundary to a center intersection point; comparing the calculated data with the designed section; according to the preset deformation threshold, under the extremely short time lag condition, whether the deformation amount is overlarge or the deformation expansion trend occurs to the characteristic point deformation of the tunnel section boundary is rapidly judged, whether calculation of all point clouds of the tunnel section boundary to the center intersection point is automatically judged according to the deformation condition of the characteristic point deformation of the tunnel section boundary, and the early warning analysis speed and the early warning analysis efficiency can be improved. The deformation early warning condition of the surrounding rock of the tunnel can be judged in advance only by extremely small calculated amount, so that the short-time-lag quasi-real-time early warning requirement is met, and more time and space are provided for evacuation of tunnel constructors and disposal of safety risk sources. The invention can also rapidly reflect the linear change of the tunnel according to the space positions of the central intersection points of the section models in different time periods, and judge whether the displacement of the through axis meets the design requirement.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a tunnel surrounding rock deformation monitoring and early warning method based on an unmanned aerial vehicle-mounted laser scanner, which is provided by the embodiment of the invention;
FIG. 2 is a schematic view of a tunnel cross-section datum plane;
FIG. 3 is a schematic diagram of three-way displacement variation of point cloud of a tunnel section;
fig. 4 is a schematic diagram of the tunnel section point cloud azimuth variation.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A tunnel surrounding rock deformation monitoring and early warning method based on an unmanned aerial vehicle-mounted laser scanner comprises the following steps:
step one: according to a tunnel design drawing, a tunnel three-dimensional model is built, tunnel section geometric information is calculated, a certain mileage section is taken as an example, a center point O of the section is taken as a base point, a perpendicular surface W of a central line L of the tunnel is taken as a base point O, the center point O in the W surface is taken as an origin, the central line L is taken as a y axis, a horizontal line which is perpendicular to the central line L through the O point is taken as an x axis, a plumb line which is perpendicular to the central line L through the 0 point is taken as a z axis, a coordinate system is built, the W surface is taken as a reference plane, and a key monitoring point of the tunnel section is defined as a characteristic point A i The feature points must include intersection points of the design lines of the tunnel section boundaries, and other important monitoring points such as vaults. Calculating feature points A i Distance to O point L i Ao azimuth angle alpha A Reference is made to fig. 2.
Step two: setting and arranging 3 known control points on each measuring section to serve as reference targets; according to the monitoring and construction requirements, the absolute geodetic coordinates of the reference target can be imported into the point cloud, and the absolute geodetic coordinates of any point cloud can be obtained through coordinate conversion calculation.
Step three: and according to construction monitoring requirements, determining the distance between monitoring tunnel sections according to mileage, determining the corresponding mileage of the monitoring sections, and denoising or thinning the point cloud data. Extracting point cloud data of a corresponding mileage section, and determining a section center line L i Center point of section O i Tunnel section edgeLine intersection A 'of the boundaries' i By O i As the base point, cross the base point O i Line L of section i Is a vertical plane W of (2) i According to the method of the step one, a coordinate system of the point cloud section is established, and the linear intersection point A 'is calculated preferentially' i To O i Distance L 'of (2)' i ，A′ i O i Azimuth angle alpha of (2) Ai 。
Step four: for L' i And L i Alignment is carried out, and alpha is compared Ai And alpha A And comparing, namely quickly judging whether the section is set as an early warning section according to a preset early warning threshold value, and referring to fig. 3.
Step five: if the section exceeds the early warning threshold, setting the section as an early warning section, and monitoring the section point cloud A' i Displacement deformation in three axial directions is calculated A' i Relative to A i Fitting the boundary of the section at three axial azimuth angles, projecting the section onto a design reference plane W, and extracting surrounding rock deformation specific data and deformation range.
Step six: and (3) establishing a tunnel section dynamic model according to the monitoring time node sequence of each mileage section data, overlapping the tunnel section dynamic model with the design model, calculating the absolute deformation and deformation trend of the tunnel surrounding rock section in a period of time of each section, and pre-judging key monitoring sections or sections in advance to provide important decision information for safety risk control.
Step seven: extracting all section centers O in tunnel dynamic model i According to the coordinate calculation result of the second step, a tunnel center line shape model is established, and the model overlaps with the tunnel design center line shape, so that important reference information is provided for controlling the tunnel penetration line shape.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. The tunnel surrounding rock deformation monitoring and early warning method based on the unmanned aerial vehicle-mounted laser scanner is characterized by comprising the following steps of:
b1, modeling a tunnel section according to tunnel design data;
b2, dividing measuring sections according to construction progress, and arranging at least 3 known control points in advance to serve as reference targets;
b3, dividing monitoring sections according to monitoring requirements, determining tunnel sections at equal mileage intervals in each monitoring section, arranging targets at positions of linear intersection points of tunnel section boundaries, and scanning to establish a three-dimensional model;
b4, extracting a tunnel center line by using a center line determining method; extracting center points of the central line and the tunnel section;
b5, defining key monitoring points of the tunnel section as characteristic points, wherein the characteristic points comprise intersection points of design lines of the tunnel section boundary and vault monitoring points; preferentially calculating the distance from the point cloud of the characteristic point of each tunnel section boundary to the center point, and calculating the azimuth angle from the point cloud of the characteristic point of each tunnel section boundary to the center point;
b6, comparing the calculated distance and azimuth angle with the designed section, and judging the deformation condition of the section according to a preset deformation threshold; calculating displacement deformation and azimuth angles of the boundary point cloud in three axial directions;
b7: overlapping the models according to the model established by the monitoring time node to quickly obtain the deformation absolute value and the deformation trend of each section surrounding rock;
and B8, rapidly reacting the linear variation of the tunnel according to the spatial positions of the center points of the section models in different time periods.
2. The tunnel surrounding rock deformation monitoring and early warning method based on the unmanned aerial vehicle-mounted laser scanner, which is characterized by comprising the following steps of: the step B1 further includes: calculating the boundary point A of the design section i Distance and azimuth to the section center point O; storing the calculated data as initial data; a is that i (x i ,y i ,z i ) To the central point O i (x 0 ，y 0 ，z 0 ) Distance between: l (L) i ＝(A i -O i )·e i ，e i Is the unit vector in the central axis direction; azimuth angle alpha A The calculation formula: cos<a，b>＝(a·b)/(|a|·|b|)。
3. The tunnel surrounding rock deformation monitoring and early warning method based on unmanned aerial vehicle-mounted laser scanner according to claim 1 or 2, wherein the method comprises the following steps of: the step B2 further includes: the reference target is a geodetic coordinate, and can be used as a reference point of other scanning point clouds, and the geodetic coordinate of any point cloud can be calculated through coordinate conversion.
4. The tunnel surrounding rock deformation monitoring and early warning method based on the unmanned aerial vehicle-mounted laser scanner according to claim 3, wherein the method comprises the following steps of: the step B3 further includes: defining the key monitoring point of the tunnel section as a characteristic point A i The feature points must include intersection points of the design lines of the tunnel section boundaries and dome monitoring points.
5. The tunnel surrounding rock deformation monitoring and early warning method based on the unmanned aerial vehicle-mounted laser scanner, which is characterized by comprising the following steps of: the step B4 further includes: and extracting a central point of the tunnel section, and calculating the geodetic coordinates of the central point of the tunnel section according to the coordinates of the control points.
6. The tunnel surrounding rock deformation monitoring and early warning method based on the unmanned aerial vehicle-mounted laser scanner, which is characterized by comprising the following steps of: the specific method for calculating the distance in the step B5 is as follows: point cloud A 'of section boundary' i (x′ i ,y′ i ,z′ i ) To the central point O i (x 0 ，y 0 ，z 0 ) Distance between: l (L) i ＝(A′ i -O i )·e i ，e i Is the unit vector in the central axis direction; azimuth angle alpha Ai The calculation formula: cos<a，b>＝(a·b)/(|a|·|b|)。
7. Tunneling based on unmanned aerial vehicle on-board laser scanner according to claim 6The method for monitoring and early warning the deformation of the surrounding rock of the road is characterized by comprising the following steps of: the step B6 comprises the following steps: preferentially calculating the distance and azimuth angle from the point cloud of the characteristic point of each tunnel section boundary to the center point cloud, comparing the calculation result with the initial section data, if the calculation result is lower than a threshold value, stopping iterative calculation, and enabling the section not to be early-warned; if the threshold value is exceeded or is close to the threshold value, setting the section as an early warning section, automatically calling other point cloud data of the section, modeling the tunnel section, comparing the tunnel section with initial data of the tunnel section, calculating displacement deformation of the point cloud in three axial directions, and calculating A i ' relative to A i In azimuth angles of three axial directions, the three axial directions refer to three directions of x, y and z axes; quickly finding out the specific deformation range and deformation amount of the section;
the specific method for calculating the three-way displacement deformation in the step B6 comprises the following steps:
A′ i (x′ i ,y′ i ,z′ i ) To A i (x i ,y i ,z i ) Distance L 'of (2)' i ，L' i ＝|A′ i -A i |；
Three axial displacement variation calculations:
A′ i relative to A i Azimuth calculation in three axial directions:
8. the tunnel surrounding rock deformation monitoring and early warning method based on the unmanned aerial vehicle-mounted laser scanner, which is characterized by comprising the following steps of: the step B7 comprises the following steps: and establishing tunnel sections according to the boundary point cloud of each section in the monitoring scanning, establishing dynamic models of all sections according to time nodes, overlapping the dynamic models with the design models, and calculating absolute deformation and deformation trend of the tunnel surrounding rock sections in a period of time.
9. The tunnel surrounding rock deformation monitoring and early warning method based on the unmanned aerial vehicle-mounted laser scanner, which is characterized by comprising the following steps of: the step B8 comprises the following steps: and establishing a dynamic model of the center point cloud of all the sections according to the time nodes and the center point cloud coordinates of each section according to the monitoring scanning, and rapidly reflecting the tunnel center line shape change by comparing the dynamic model with the center point line shape of the design model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117073570A (en) * | 2023-10-12 | 2023-11-17 | 四川高速公路建设开发集团有限公司 | Tunnel deformation degree detection system and method based on unmanned aerial vehicle |
| CN117367302A (en) * | 2023-10-18 | 2024-01-09 | 深圳市水务工程检测有限公司 | Tunnel deformation monitoring safety early warning system and method based on three-dimensional laser scanning |
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- 2023-06-16 CN CN202310720260.5A patent/CN116734757A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117073570A (en) * | 2023-10-12 | 2023-11-17 | 四川高速公路建设开发集团有限公司 | Tunnel deformation degree detection system and method based on unmanned aerial vehicle |
| CN117073570B (en) * | 2023-10-12 | 2023-12-19 | 四川高速公路建设开发集团有限公司 | Tunnel deformation degree detection system and method based on unmanned aerial vehicle |
| CN117367302A (en) * | 2023-10-18 | 2024-01-09 | 深圳市水务工程检测有限公司 | Tunnel deformation monitoring safety early warning system and method based on three-dimensional laser scanning |
| CN117367302B (en) * | 2023-10-18 | 2024-08-09 | 深圳市水务工程检测有限公司 | Tunnel deformation monitoring safety early warning system and method based on three-dimensional laser scanning |
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