CN114993203B - Tunnel deformation monitoring method based on primary support unequal thickness - Google Patents
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Abstract
The invention discloses a tunnel deformation monitoring method based on primary support unequal thickness, and relates to the technical field of tunnel deformation monitoring. After the surrounding rock of the tunnel and the surface data of the primary support are scanned and acquired through a three-dimensional laser scanning technology of non-contact measurement, a tunnel primary support model containing primary support thickness data can be established and obtained, and abnormal points of the tunnel are marked according to the difference of primary support thickness, so that the monitoring can be performed in a targeted manner during the later-stage tunnel monitoring, the workload of the later-stage monitoring is effectively saved, and the data acquisition speed of the monitoring is accelerated. Meanwhile, the relation between the primary support thickness and the tunnel deformation is conveniently researched according to the monitoring data and the tunnel primary support model. The uneven thickness of the primary support caused by the surrounding rock overexcavation phenomenon is considered, so that the method not only accords with the actual engineering condition, but also can effectively monitor the local stress concentration caused by overexcavation and the potential safety hazard caused by insufficient primary support thickness caused by underexcavation.
Description
Technical Field
The invention relates to the technical field of tunnel deformation monitoring, in particular to a tunnel deformation monitoring method based on primary support unequal thickness.
Background
The tunnel overexcavation directly affects the stability of surrounding rock and construction cost of the tunnel, but the control of the overexcavation is always one of the heavy difficulties of field technicians in control. In the process of excavation, the phenomenon of overexcavation caused by blasting is difficult to avoid, and the overexcavation is excessive, so that the problem of stress concentration can be generated, and the stability of surrounding rock is affected; the undermining can lead to insufficient thickness of the primary support, and hidden danger is generated on engineering quality and safety. However, in the case of tunnel deformation monitoring, the primary support is often considered to be of equal thickness, which is clearly not the case in practical engineering. Therefore, it is very practical and important engineering significance to monitor deformation by considering the primary costs for unequal thickness due to the influence of undermining.
The traditional tunnel super-undermining detection and deformation monitoring mainly take a manual measurement mode, adopts equipment such as a total station, a section meter and a measuring robot, and performs full-section measurement on the tunnel in a point-by-point and section-by-section mode, and the method has the advantages of low detection efficiency, long time consumption and great consumption of manpower and material resources. In addition, the measurement accuracy of the traditional detection method is easily influenced by tunnel environment factors, the conditions of overall form change and the like of a tunnel cannot be reflected, and the actual requirements of a large number of detections in the tunnel excavation process cannot be met.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a tunnel deformation monitoring method based on primary support unequal thickness, which has the advantages of non-contact measurement, high data precision and high data acquisition speed.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the tunnel deformation monitoring method based on the primary support unequal thickness comprises the following steps:
s1: scanning surrounding rocks of a tunnel to be detected by using a laser scanner to obtain surrounding rock point cloud data;
s2: performing coordinate axis unified conversion on surrounding rock point cloud data to obtain the axial direction of a tunnel to be detected;
s3: calculating to obtain the actual radius of the tunnel according to the axis direction of the tunnel to be detected, and calculating to obtain the super-underexcavation quantity of the tunnel to be detected by using the actual radius of the tunnel and the theoretical radius of the tunnel;
s4: scanning again by using a laser scanner after the primary support construction of the tunnel to be detected is completed, so as to obtain the point cloud data of the primary support surface of the tunnel;
s5: establishing a tunnel primary support model according to the tunnel primary support surface point cloud data and the surrounding rock point cloud data, and marking abnormal thickness points of the tunnel primary support model through the super-underexcavation quantity;
s6: after the tunnel is completed, scanning and detecting the positions corresponding to the thickness abnormal points for a plurality of periods by using a laser scanner, and comparing the monitoring data of each period to obtain the deformation condition of the surface of the tunnel to be detected.
After the surrounding rock of the tunnel and the surface data of the primary support are scanned and acquired through a three-dimensional laser scanning technology of non-contact measurement, a tunnel primary support model containing primary support thickness data can be established and obtained, and abnormal points of the tunnel are marked according to the difference of primary support thickness, so that the monitoring can be performed in a targeted manner during the later-stage tunnel monitoring, the workload of the later-stage monitoring is effectively saved, and the data acquisition speed of the monitoring is accelerated. Meanwhile, the relation between the primary support thickness and the tunnel deformation is conveniently researched according to the monitoring data and the tunnel primary support model.
Further, in step S2, preprocessing is required before performing coordinate axis conversion on surrounding rock point cloud data, where the preprocessing of surrounding rock point cloud data includes one or more of point cloud denoising, point cloud thinning or point cloud simplification. The data noise generated by too bad field working environment can be removed by preprocessing the point cloud data; and redundant points in the point cloud data can be removed on the premise of not affecting the whole characteristics of the point cloud of the tested main body.
Further, in step S2, the coordinate axes of the surrounding rock point cloud data are uniformly converted into the earth coordinate axes, and the specific method comprises the following steps:
a1: before the laser scanner scans the tunnel to be detected in the step S1, the total station pair is usedThe laser scanner performs positioning, and the position coordinate in the earth coordinate system is recorded as (x) by the origin of the total station 0 ，y0，z 0 ) And calculates an x-axis direction vector (x 1 ，y 1 ，z 1 ) And a y-axis direction vector (x 2 ，y 2 ，z 2 )；
A2: converting a scanning coordinate system of surrounding rock point cloud data into a geodetic coordinate system: surrounding rock point cloud data are acquired by a laser scanner in a segmented scanning way for a tunnel to be detected, and coordinates of a scanning coordinate system of each segment of surrounding rock point cloud data are recorded as (x) i '，y i '，z i ') the coordinates of the earth coordinate system corresponding to the scanning coordinate system of the surrounding rock point cloud data of each section are recorded as (x) i ，yi，z i ) The conversion method comprises the following steps:
wherein θ is the angle at which the scan coordinate system rotates about the z-axis, α is the angle at which the scan coordinate system rotates about the x-axis, and γ is the angle at which the scan coordinate system rotates about the y-axis; t (T) 1 A rotation matrix for the z-axis direction; t (T) 2 A rotation matrix in the x-axis direction; t (T) 3 Rotating the matrix for the y-axis direction;
a3: fitting circle center coordinates of surrounding rock point cloud data on tunnel cross sections by using a RANSAC algorithm, respectively projecting the circle center coordinates on any cross section onto a xoz surface and a yoz surface of a geodetic coordinate system, and sequencing projection points of the circle center coordinates of all cross sections according to the advancing direction of the tunnel;
a4: fitting projection points on a xoz surface and a yoz surface of the geodetic coordinate system respectively by using cubic spline interpolation to obtain two curves respectively positioned on a xoz surface and a yoz surface of the geodetic coordinate system;
a5: and integrating the two curves to obtain the axial direction of the tunnel to be detected.
Further, the method for integrating the two curves comprises the following steps:
taking the point (x) on any one of the curves on the xoz plane q ，z q ) Taking the point (y) on the same z coordinate of the curve on the yoz plane q ，z q ) Points (x) q ，z q ) Sum point (y) q ，z q ) Integrated as (x) q ，y q ，z q ) A new curve can be obtained, and the new curve is the axial direction of the tunnel to be detected.
Further, in step S3, the method for calculating the super-underexcavation amount of the tunnel to be detected includes:
wherein, (x) xoy ,y xoy ) The contour coordinates (x) of surrounding rock point cloud data on the section of the tunnel to be detected r ，y r ) Is the center coordinates, r, of a geodetic coordinate system on the same tunnel section to be detected 0 D is the theoretical radius of the tunnel, and d is the value of the surrounding rock point cloud data from the theoretical contour of the tunnel to be detected;
when d is more than 0, the tunnel to be detected is overdrawn in the area, and the overdrawing value is |d|; when d is less than 0, namely the tunnel to be detected is undermined in the area, and the undermining value is |d|; when d=0, no undermining of the tunnel to be detected occurs in this region.
Further, the marking method of the thickness outlier in step S5 is as follows:
the actual thickness T of the primary support is the design thickness of the primary support, wherein the actual thickness T of the primary support is the surface point cloud data of the primary support minus the surrounding rock point cloud data of the primary support;
when T-T < d <0, marking the area as a thickness ultrathin abnormal point;
when d is less than or equal to T-T <0, marking the area as an abnormal point with thinner thickness;
when 0<T-t is less than or equal to d, marking the area as an abnormal point with thicker thickness;
when 0< d < T-t, marking the area as an extra-thick abnormal point;
when T-t=0, this area is not marked as a thickness outlier.
Further, in step S6, after scanning the position corresponding to the thickness abnormal point, a tunnel surface model is built, and the method for judging the tunnel deformation condition includes the following steps:
s61: equally dividing the tunnel surface model into m sections with the length p along the central axis direction, wherein p is more than 0.01 and less than 0.1m; dividing the tunnel surface model into n sections with an angle a along the clockwise annular direction of the cross section from the bottom of the left tunnel sidewall, wherein a is more than 0 degrees and less than 2 degrees;
at this time, the tunnel surface model is equally divided into m×n unit meshes, and the number is W by the row number where the unit meshes are located i，j ，(i＝1，2，...，m；j＝1，2，...，n；)；
S62: with centre O of the ith segment on the tunnel axis i (i=1, 2.., m) is the projection center, the cell grid W i，j For projecting the projection reference plane, the unit grid W i，j The projection range of the point cloud model on the surface of the tunnel in the k period is P i，j，k ，(i＝1，2，...，m；j＝1，2，...，n；k＝1，2，...，q；)；P i，j，k Is a plane quadrangle;
s63: projection center O i To a plane quadrilateral P i，j，k Is pi, j, k, then the cell grid W in the tunnel surface model i，j The deformation of the tunnel structure in the (k+1) th period relative to the (k) th period of the region corresponding to the projection range is delta i，j，k+1 ＝p i，j，k+1 -p i，j，k ；
If delta i，j，k+1 > 0, then cell grid W i，j The tunnel in the projection range is detected as being outwardly distended and deformed compared with the last scanning,
if delta i，j，k+1 <0, then cell grid W i，j The tunnel in the projection range is detected as inward shrinkage deformation compared with the last scanning;
if delta i，j，k+1 =0, then cell grid W i，j The tunnel in the projection range is not deformed compared with the previous scanning detection.
Further, the tunnel deformation condition judging method further includes S64: tunnel structure deformation delta of k+1 stage relative to k stage all cell grids i，j，k+1 And establishing a corresponding proportional relation with the color gradation, and manufacturing a tunnel surface deformation gray scale model graph.
The tunnel surface deformation gray scale model graph is obtained by establishing a proportional relation between the detected tunnel structure deformation quantity and the color level of each period and the previous period, so that the tunnel deformation can be dynamically and visually realized, and monitoring staff can conveniently determine the deformation form of the tunnel.
The beneficial effects of the invention are as follows:
1. according to the invention, after the surrounding rock of the tunnel and the surface data of the primary support are scanned and acquired by a three-dimensional laser scanning technology of non-contact measurement, a tunnel primary support model containing primary support thickness data can be established, and the tunnel is marked with abnormal points according to the difference of primary support thickness, so that the monitoring can be carried out in a targeted manner in the later-stage tunnel monitoring, the workload of the later-stage monitoring is effectively saved, and the data acquisition speed of the monitoring is accelerated. Meanwhile, the relation between the primary support thickness and the tunnel deformation is conveniently researched according to the monitoring data and the tunnel primary support model.
2. The uneven thickness of the primary support caused by the surrounding rock overexcavation phenomenon is considered, so that the method not only accords with the actual engineering condition, but also can effectively monitor the local stress concentration caused by overexcavation and the potential safety hazard caused by insufficient primary support thickness caused by underexcavation.
Drawings
FIG. 1 is a schematic diagram of the conversion of a scan coordinate system and a geodetic coordinate system;
FIG. 2 is a schematic view of a tunnel surrounding rock super-undercut section;
FIG. 3 is a schematic diagram of a surrounding rock surface model;
fig. 4 is a schematic cross-sectional view of a tunnel primary support.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
The tunnel deformation monitoring method based on the primary support unequal thickness comprises the following steps:
s1: scanning surrounding rocks of a tunnel to be detected by using a laser scanner to obtain surrounding rock point cloud data;
preprocessing is needed before coordinate axis conversion is carried out on surrounding rock point cloud data, and the preprocessing of the surrounding rock point cloud data comprises one or more of point cloud denoising, point cloud thinning or point cloud simplification. The data noise generated by too bad field working environment can be removed by preprocessing the point cloud data; and redundant points in the point cloud data can be removed on the premise of not affecting the complete characteristics of the point cloud of the tested main body. And establishing a surrounding rock surface profile model according to the preprocessed surrounding rock point cloud data, wherein the surrounding rock surface profile model is shown in fig. 3.
S2: performing coordinate axis unified conversion on surrounding rock point cloud data to obtain the axial direction of a tunnel to be detected;
the coordinate axes of surrounding rock point cloud data are uniformly converted into the earth coordinate axes, and the concrete method comprises the following steps:
a1: before the laser scanner scans the tunnel to be detected in the step S1, the laser scanner is positioned by using the total station, and the origin of the total station is recorded as the position coordinates (x 0, y0, z) under the geodetic coordinate system 0 ) And calculates an x-axis direction vector (x 1 ，y 1 ，z 1 ) And a y-axis direction vector (x 2 ，y 2 ，z 2 )；
A2: converting a scanning coordinate system of surrounding rock point cloud data into the groundCoordinate system: surrounding rock point cloud data are acquired by segmented scanning of a tunnel to be detected by a laser scanner, and as shown in fig. 1, the coordinates of a scanning coordinate system of each segment of surrounding rock point cloud data are recorded as (x) i '，y i '，z i ') the coordinates of the earth coordinate system corresponding to the scanning coordinate system of the surrounding rock point cloud data of each section are recorded as (x) i ，y i ，z i ) The conversion method comprises the following steps:
wherein θ is the angle at which the scan coordinate system rotates about the z-axis, α is the angle at which the scan coordinate system rotates about the x-axis, and γ is the angle at which the scan coordinate system rotates about the y-axis; t (T) 1 A rotation matrix for the z-axis direction; t (T) 2 A rotation matrix in the x-axis direction; t (T) 3 Rotating the matrix for the y-axis direction;
a3: fitting circle center coordinates of surrounding rock point cloud data on tunnel cross sections by using a RANSAC algorithm, respectively projecting the circle center coordinates on any cross section onto a xoz surface and a yoz surface of a geodetic coordinate system, and sequencing projection points of the circle center coordinates of all cross sections according to the advancing direction of the tunnel;
a4: fitting projection points on a xoz surface and a yoz surface of the geodetic coordinate system respectively by using cubic spline interpolation to obtain two curves respectively positioned on a xoz surface and a yoz surface of the geodetic coordinate system;
a5: and integrating the two curves to obtain the axial direction of the tunnel to be detected.
The integration method of the two curves comprises the following steps:
taking the point (x) on any one of the curves on the xoz plane q ，z q ) Taking the point (y) on the same z coordinate of the curve on the yoz plane q ，z q ) Points (x) q ，z q ) Sum point (y) q ，z q ) Integrated as (x) q ，y q ，z q ) A new curve can be obtained, and the new curve is the axial direction of the tunnel to be detected.
S3: calculating to obtain the actual radius of the tunnel according to the axis direction of the tunnel to be detected, and calculating to obtain the super-underexcavation quantity of the tunnel to be detected by using the actual radius of the tunnel and the theoretical radius of the tunnel;
the method for calculating the super-underexcavation amount of the tunnel to be detected comprises the following steps:
wherein, (x) xoy ,y xoy ) The contour coordinates (x) of surrounding rock point cloud data on the section of the tunnel to be detected r ，y r ) Is the center coordinates, r, of a geodetic coordinate system on the same tunnel section to be detected 0 D is the theoretical radius of the tunnel, and d is the value of the surrounding rock point cloud data from the theoretical contour of the tunnel to be detected;
as shown in FIG. 2, when d is more than 0, the tunnel to be detected is overdrawn in the area, and the overdrawing value is d|; when d is less than 0, namely the tunnel to be detected is undermined in the area, and the undermining value is |d|; when d=0, no undermining of the tunnel to be detected occurs in this region.
S4: scanning again by using a laser scanner after the primary support construction of the tunnel to be detected is completed to obtain the point cloud data of the primary support surface of the tunnel; when the tunnel is used as a primary support, the thickness of the primary support is unequal due to the fact that surrounding rock is underexcavated, and the thickness section of the primary support is shown in fig. 4.
S5: establishing a tunnel primary support model according to the tunnel primary support surface point cloud data and the surrounding rock point cloud data, and marking abnormal thickness points of the tunnel primary support model through the super-underexcavation quantity; the marking method of the thickness abnormal point comprises the following steps:
the actual thickness T of the primary support is the design thickness of the primary support, wherein the actual thickness T of the primary support is the surface point cloud data of the primary support minus the surrounding rock point cloud data of the primary support;
when T-T < d <0, marking the area as a thickness ultrathin abnormal point;
when d is less than or equal to T-T <0, marking the area as an abnormal point with thinner thickness;
when 0<T-t is less than or equal to d, marking the area as an abnormal point with thicker thickness;
when 0< d < T-t, marking the area as an extra-thick abnormal point;
when T-t=0, this area is not marked as a thickness outlier.
S6: and after the tunnel is completed, performing multi-period scanning detection on the position corresponding to the thickness abnormal point by using a laser scanner, and comparing the monitoring data of each period to obtain the deformation condition of the tunnel to be detected.
The tunnel surface model is built after scanning the corresponding position of the thickness abnormal point in each period, and the tunnel deformation condition judging method comprises the following steps:
s61: equally dividing the tunnel surface model into m sections with the length p along the central axis direction, wherein p is more than 0.01 and less than 0.1m; dividing the tunnel surface model into n sections with an angle a along the clockwise annular direction of the cross section from the bottom of the left tunnel sidewall, wherein a is more than 0 degrees and less than 2 degrees;
at this time, the tunnel surface model is equally divided into m×n unit meshes, and the number is W by the row number where the unit meshes are located i，j ，(i＝1，2，...，m；j＝1，2，...，n；)；
In the specific implementation, the whole tunnel is scanned to establish a tunnel surface model, and when the tunnel deformation condition of the tunnel surface at the non-thickness abnormal point is judged, the non-thickness abnormal point position of the tunnel surface model is equally divided into m 'sections with the length of p' along the central axis direction, wherein p 'is larger than p, and m' is smaller than m.
S62: with centre O of the ith segment on the tunnel axis i (i=1, 2.., m) is the projected center cell grid W i，j For projecting the projection reference plane, the unit grid W i，j In the first placeThe projection range of the point cloud model on the surface of the k-period tunnel is P i，j，k ，(i＝1，2，...，m；j＝1，2，...，n；k＝1，2，...，q；)；P i，j，k Is a plane quadrangle;
s63: projection center O i To a plane quadrilateral P i，j，k Is p i，j，k Then the unit grid W in the tunnel surface model i，j The deformation of the tunnel structure in the (k+1) th period relative to the (k) th period of the region corresponding to the projection range is delta i，j，k+1 ＝p i，j，k+1 -p i，j，k ；
If delta i，j，k+1 > 0, then cell grid W i，j The tunnel in the projection range is detected as being outwardly distended and deformed compared with the last scanning,
if delta i，j，k+1 <0, then cell grid W i，j The tunnel in the projection range is detected as inward shrinkage deformation compared with the last scanning;
if delta i，j，k+1 =0, then cell grid W i，j The tunnel in the projection range is not deformed compared with the previous scanning detection.
S64: and establishing a corresponding proportional relation between the deformation delta i, j and k+1 of the tunnel structure of the k+1 phase relative to all unit grids of the k phase and the tone scale, and manufacturing a tunnel surface deformation gray scale model graph. The tunnel surface deformation gray scale model graph is obtained by establishing a proportional relation between the detected tunnel structure deformation quantity and the color level of each period and the previous period, so that the tunnel deformation can be dynamically and visually realized, and monitoring staff can conveniently determine the deformation form of the tunnel.
Claims (5)
1. The tunnel deformation monitoring method based on the primary support unequal thickness is characterized by comprising the following steps of:
s1: scanning surrounding rocks of a tunnel to be detected by using a laser scanner to obtain surrounding rock point cloud data;
s2: performing coordinate axis unified conversion on surrounding rock point cloud data to obtain the axial direction of a tunnel to be detected;
s3: calculating to obtain the actual radius of the tunnel according to the axis direction of the tunnel to be detected, and calculating to obtain the super-underexcavation quantity of the tunnel to be detected by using the actual radius of the tunnel and the theoretical radius of the tunnel;
s4: scanning again by using a laser scanner after the primary support construction of the tunnel to be detected is completed, so as to obtain the point cloud data of the primary support surface of the tunnel;
s5: establishing a tunnel primary support model according to the tunnel primary support surface point cloud data and the surrounding rock point cloud data, and marking abnormal thickness points of the tunnel primary support model through the super-underexcavation quantity;
s6: after the tunnel is completed, scanning and detecting the positions corresponding to the thickness abnormal points for a plurality of periods by using a laser scanner, and comparing the monitoring data of each period to obtain the deformation condition of the surface of the tunnel to be detected;
the marking method of the thickness abnormal point in the step S5 comprises the following steps:
the actual thickness T of the primary support is the design thickness of the primary support, wherein the actual thickness T of the primary support is the surface point cloud data of the primary support minus the surrounding rock point cloud data of the primary support;
when T-T < d <0, marking the area as a thickness ultrathin abnormal point;
when d is less than or equal to T-T <0, marking the area as an abnormal point with thinner thickness;
when 0<T-t is less than or equal to d, marking the area as an abnormal point with thicker thickness;
when 0< d < T-t, marking the area as an extra-thick abnormal point;
when T-t=0, this area is not marked as a thickness outlier;
in the step S2, preprocessing is required before coordinate axis conversion is performed on surrounding rock point cloud data, where the preprocessing of the surrounding rock point cloud data includes one or more of point cloud denoising, point cloud thinning or point cloud simplification;
in the step S2, coordinate axes of surrounding rock point cloud data are uniformly converted into earth coordinate axes, and the specific method comprises the following steps:
a1: before the laser scanner scans the tunnel to be detected in the step S1, the laser scanner is positioned by using the total station, and the origin of the total station is recorded as the position coordinate (x) under the geodetic coordinate system 0 ，y 0 ，z 0 ) And calculates an x-axis direction vector (x 1 ，y 1 ，z 1 ) And a y-axis direction vector (x 2 ，y 2 ，z 2 )；
A2: converting a scanning coordinate system of surrounding rock point cloud data into a geodetic coordinate system: surrounding rock point cloud data are acquired by a laser scanner in a segmented scanning way for a tunnel to be detected, and coordinates of a scanning coordinate system of each segment of surrounding rock point cloud data are recorded as (x) i '，y i '，z i ') the coordinates of the earth coordinate system corresponding to the scanning coordinate system of the surrounding rock point cloud data of each section are recorded as (x) i ，y i ，z i ) The conversion method comprises the following steps:
wherein θ is the angle at which the scan coordinate system rotates about the z-axis, α is the angle at which the scan coordinate system rotates about the x-axis, and γ is the angle at which the scan coordinate system rotates about the y-axis; t (T) 1 A rotation matrix for the z-axis direction; t (T) 2 A rotation matrix in the x-axis direction; t (T) 3 Rotating the matrix for the y-axis direction;
a3: fitting circle center coordinates of surrounding rock point cloud data on tunnel cross sections by using a RANSAC algorithm, respectively projecting the circle center coordinates on any cross section onto a xoz surface and a yoz surface of a geodetic coordinate system, and sequencing projection points of the circle center coordinates of all cross sections according to the advancing direction of the tunnel;
a4: fitting projection points on a xoz surface and a yoz surface of the geodetic coordinate system respectively by using cubic spline interpolation to obtain two curves respectively positioned on a xoz surface and a yoz surface of the geodetic coordinate system;
a5: and integrating the two curves to obtain the axial direction of the tunnel to be detected.
2. The tunnel deformation monitoring method based on the primary support unequal thickness according to claim 1, wherein the method for integrating the two curves is as follows:
taking the point (x) on any one of the curves on the xoz plane q ，z q ) Taking the point (y) on the same z coordinate of the curve on the yoz plane q ，z q ) Points (x) q ，z q ) Sum point (y) q ，z q ) Integrated as (x) q ，y q ，z q ) A new curve can be obtained, and the new curve is the axial direction of the tunnel to be detected.
3. The method for monitoring tunnel deformation based on unequal primary support thickness according to claim 1, wherein the method for calculating the super-underexcavation amount of the tunnel to be detected in step S3 is as follows:
wherein, (x) xoy ,y xoy ) The contour coordinates (x) of surrounding rock point cloud data on the section of the tunnel to be detected r ，y r ) Is the center coordinates, r, of a geodetic coordinate system on the same tunnel section to be detected 0 D is the theoretical radius of the tunnel, and d is the value of the surrounding rock point cloud data from the theoretical contour of the tunnel to be detected;
when d is more than 0, the tunnel to be detected is overdrawn in the area, and the overdrawing value is |d|; when d is less than 0, namely the tunnel to be detected is undermined in the area, and the undermining value is |d|; when d=0, no undermining of the tunnel to be detected occurs in this region.
4. The method for monitoring tunnel deformation based on unequal primary support thickness according to claim 1, wherein in the step S6, a tunnel surface model is built after scanning the position corresponding to the thickness abnormal point in each period, and the method for judging tunnel deformation condition comprises the following steps:
s61: equally dividing the tunnel surface model into m sections with the length p along the central axis direction, wherein p is more than 0.01 and less than 0.1m; dividing the tunnel surface model into n sections with an angle a along the clockwise annular direction of the cross section from the bottom of the left tunnel sidewall, wherein a is more than 0 degrees and less than 2 degrees;
at this time, the tunnel surface model is equally divided into m×n unit meshes, and the number is W by the row number where the unit meshes are located i，j ，(i＝1，2，...，m；j＝1，2，...，n；)；
S62: with centre O of the ith segment on the tunnel axis i (i=1, 2.., m) is the projection center, the cell grid W i，j For projecting the projection reference plane, the unit grid W i，j The projection range of the point cloud model on the surface of the tunnel in the k period is P i，j，k ，(i＝1，2，...，m；j＝1，2，...，n；k＝1，2，...，q；)；P i，j，k Is a plane quadrangle;
s63: projection center O i To a plane quadrilateral P i，j，k Is p i，j，k Then the unit grid W in the tunnel surface model i，j The deformation of the tunnel structure in the (k+1) th period relative to the (k) th period of the region corresponding to the projection range is delta i，j，k+1 ＝p i，j，k+1 -p i，j，k ；
If delta i，j，k+1 > 0, then cell grid W i，j The tunnel in the projection range is detected as being outwardly distended and deformed compared with the last scanning,
if delta i，j，k+1 <0, then cell grid W i，j The tunnel in the projection range is detected as inward shrinkage deformation compared with the last scanning;
if delta i，j，k+1 =0, then cell grid W i，j The tunnel in the projection range is not deformed compared with the previous scanning detection.
5. The method for monitoring tunnel deformation based on unequal primary support thickness according to claim 4, wherein the method for judging tunnel deformation condition further comprises S64:
and establishing a corresponding proportional relation between the deformation delta i, j and k+1 of the tunnel structure of the k+1 phase relative to all unit grids of the k phase and the tone scale, and manufacturing a tunnel surface deformation gray scale model graph.
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