A large shield mid-partition tunnel deformation detection method based on mobile laser scanning

Abstract

The present application relates to tunnel monitoring technology, and proposes a kind of large shield mid-partition wall tunnel deformation detection method based on mobile laser scanning.The method obtains ring point cloud by mobile laser scanning, and reconstructs three-dimensional point cloud in combination with mileage information.In the processing process, after point cloud segmentation, random sampling consistent algorithm and least square fitting are used to extract tunnel wall, mid-partition wall and walkway board geometric parameters, and walkway board features are identified by Hough straight line detection.Based on the inclination angle of mid-partition wall fitting straight line, coordinate correction is carried out, and transverse convergence point, mid-partition wall convergence point and key intersection are accurately calculated.Through the Euclidean distance of feature points, transverse and longitudinal convergence amount and convergence point height are calculated, and multiple scanning lines are used to improve the accuracy of weighted average.The present application effectively solves the problems of inaccurate positioning of transverse convergence point and difficult determination of vertical convergence point, has the advantages of simple method, high precision and strong practicability, and can provide technical support for large-diameter shield tunnel rapid deformation detection and field monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel monitoring, and particularly relates to a large shield mid-partition wall tunnel deformation detection method based on mobile laser scanning. BACKGROUND

[0002] In recent years, with the rapid advancement of urban rail transit and municipal infrastructure construction, the application of super-large diameter shield tunnels is increasingly widespread. In order to improve the utilization efficiency of underground space, improve the economy of construction, and meet the needs of traffic organization, structural stress performance, and operation and maintenance, the application of mid-partition wall structures in large-diameter shield tunnels is becoming more and more common. Research shows that the mid-partition wall has a significant impact on the overall stress distribution, deformation characteristics, and dynamic response of the tunnel, and has become one of the key technical elements in the design of super-large diameter shield tunnels.

[0003] Tunnel convergence deformation is an important indicator reflecting the morphological changes of tunnel structures under the action of the environment. Existing detection techniques have gradually developed from traditional total station measurement to three-dimensional laser scanning technology. Three-dimensional laser scanning has the advantages of fast data acquisition speed, high point cloud density, high precision, and simple operation, and has become an important means of obtaining tunnel deformation information. Among them, mobile three-dimensional laser scanning is particularly prominent in rail transit tunnel deformation detection due to its speed and efficiency, and has gradually become a key technology for rapid tunnel monitoring.

[0004] However, existing researches mostly focus on the extraction of convergence deformation of circular shield tunnels, and there are still few studies on the deformation monitoring and point distribution method of large-diameter shield mid-partition wall structure tunnels, especially in the aspect of using mobile laser scanning systems to realize rapid and accurate deformation detection. Especially in the curved section of the tunnel, due to the influence of the super-high track, the scanner coordinate system tilts with the track surface, and if the gravity direction is directly used as the vertical reference, the point cloud data will also be tilted. If no corresponding tilt correction is performed, the extraction of the convergence feature points will be deviated, thereby affecting the accuracy of the convergence deformation calculation. SUMMARY

[0005] The present application proposes a large shield mid-partition wall tunnel deformation detection method based on mobile laser scanning to solve the problems of inaccurate positioning of transverse convergence points and difficulty in determining vertical convergence points in the existing deformation detection of large-diameter shield tunnel mid-partition wall structures.

[0006] The method first collects tunnel point cloud data of each ring, and segments the point cloud to extract typical structures such as tunnel wall, partition wall and walkway plate, and fit to obtain corresponding geometric parameters. The point cloud posture correction is realized based on the inclination angle of the partition wall straight line, and the horizontal convergence point is accurately positioned combined with the geometric relationship between the partition wall and the tunnel wall, so that the horizontal convergence calculation is completed. Further, by extracting the intersection of the walkway plate and the partition wall, and the intersection of the partition wall and the tunnel wall, the vertical convergence point is obtained and the vertical convergence calculation is realized. Among them, the intersection of the walkway plate and the partition wall has good on-site distinguishability, and can be used as a reference datum for the layout of the horizontal convergence point, providing a basis for comparison for long-term manual monitoring, thereby providing technical support for the safe operation and maintenance of large-diameter shield tunnels.

[0007] The method of the application comprises the following steps:

[0008] S1, collecting tunnel point cloud data of each ring;

[0009] S2, segmenting the point cloud of each ring and obtaining geometric parameters, and dividing the point cloud into tunnel wall arc segments, partition walls, floors and walkway plates;

[0010] S3, extracting tunnel feature points according to the geometric parameters of each point cloud segment, the tunnel feature points including arc segment horizontal convergence points, partition wall horizontal convergence points, intersection points of walkway plates and partition walls, and intersection points of partition walls and tunnel wall arcs;

[0011] S4, calculating the horizontal convergence, vertical convergence and horizontal convergence point height according to the feature points.

[0012] Further, in the S1 step, the laser scanner adopts a two-dimensional spiral scanning mode, is installed on a moving carrier, travels at a uniform speed along a track, obtains a two-dimensional point cloud sequence of a cross section, and reconstructs a three-dimensional point cloud combined with mileage information.

[0013] Further, in the S2 step, the first ring point cloud is manually selected to complete the rough segmentation of the tunnel wall, the partition wall and the floor and record the segmentation position; the position is segmented in the subsequent ring; the tunnel wall arc segment and the partition wall straight segment are denoised and the geometric parameters are calculated by using the random sample consensus algorithm and the least square fitting algorithm; the point cloud perpendicular to the partition wall is extracted by using the Hough straight line detection in the partition wall segment, and the screening conditions are: (1) containing the largest number of scanning points; (2) the straight line is closer to the top of the tunnel, that is, the intercept is larger, and then the walkway plate straight line parameters are obtained by least square fitting of the straight line.

[0014] Further, in the S3 step, the inclination angle of the fitting straight line based on the partition wall is taken as a geometric correction reference, and the feature points include: (1) a circular arc segment transverse convergence point; (2) a partition wall transverse convergence point; (3) a walkway plate and partition wall intersection point, taken as a lower vertical convergence point and a transverse convergence point height calculation base point; and (4) a partition wall and tunnel wall circular arc intersection point, taken as an upper vertical convergence point.

[0015] Further, in the S4 step, the transverse convergence amount and the longitudinal convergence amount are calculated through the Euclidean distance between the transverse convergence point and the vertical convergence point, and the final convergence value of the ring is obtained based on the fitting accuracy and the weighted average of multiple scanning lines; and the transverse convergence point height can be calculated through the Euclidean distance between the walkway plate and partition wall intersection point and the partition wall transverse convergence point, and used to guide the layout of the field artificial monitoring points.

[0016] Compared with the prior art, the present application has the following advantages:

[0017] (1) The angle correction is realized through the partition wall fitting, so that the calculation deviation of the transverse convergence point caused by the curve segment super-elevation is avoided.

[0018] (2) The determination of the vertical convergence point and the field positioning of the transverse convergence point are realized through the walkway plate feature recognition, so that the problem that the vertical convergence point is difficult to determine is solved, and the practicability is strong.

[0019] The method of the present application is simple, stable and easy to implement, can synchronously obtain the parameters such as the transverse convergence, the longitudinal convergence and the convergence point height, the detection precision meets the engineering application requirements, and has important reference value for the rapid deformation detection and field monitoring point layout of the large shield partition wall form tunnel. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart of the method.

[0021] Figure 2 is a schematic diagram of a typical structure inside a tunnel.

[0022] Figure 3 is a rough segmentation diagram of the point cloud selected by the artificial point.

[0023] Figure 4 is a convergence solution schematic diagram. DETAILED DESCRIPTION

[0024] The preferred embodiments of the present application will be described below in conjunction with Figures 1-4 , and the present application will be further described.

[0025] Figure 1 As shown in the drawings, the present application comprises the following steps:

[0026] S1, collecting tunnel ring-by-ring point cloud data;

[0027] S2. Segment the ring-by-ring point cloud and obtain geometric parameters;

[0028] S3. Extract tunnel feature points based on the geometric parameters of each cloud segment. The tunnel feature points include the lateral convergence point of the arc segment, the lateral convergence point of the central partition wall, the intersection of the walkway slab and the central partition wall, and the intersection of the central partition wall and the arc of the tunnel wall.

[0029] S4. Calculate the lateral convergence, longitudinal convergence, and lateral convergence point height based on the feature points.

[0030] First, following step S1, use the mobile scanning system to acquire point clouds of the tunnel ring by ring. In the cross-section of each ring point cloud, such as... Figure 2 As shown, the typical internal structure of the tunnel includes the tunnel arc section 1, the straight section of the central partition wall 2, the walkway slab 3, and the floor slab 4.

[0031] Furthermore, in step S2, such as Figure 3 As shown, complete the manual selection of segment points (P1, P2, P3), as follows. Figure 4 As shown, the point cloud is first classified based on the point cloud segmentation points P1, P2, and P3, resulting in point clouds of the straight segment of the central partition wall (P1-P2) and the circular segment of the tunnel wall (P1-P3). The two point clouds are then subjected to noise removal and geometric parameter calculation using the Random Sample Consensus Algorithm (RANSAC) and the least squares fitting algorithm. This yields the following results for the straight segment L of the central partition wall: slope k, intercept b, and fitting accuracy σ1; and for the circular segment C of the tunnel wall: center o coordinates (X0, Y0), radius r, and fitting accuracy. .

[0032] Hough line detection was used to find a group of lines perpendicular to the central partition wall, and the following conditions were used for sorting and filtering: (1) the line containing the most scan points; (2) the line is closer to the top of the tunnel, i.e., its intercept is larger. The detected walkway slab line L b : Slope k b intercept b b Fitting accuracy .

[0033] Furthermore, in step S3, as Figure 4 As shown, the feature points are calculated using the following method:

[0034] Use equation (1) to convert the slope k of the line into the inclination angle θ of the line.

[0035] (1)

[0036] like Figure 4 As shown, in the fitted circular coordinate system, when the left side of the scanned point cloud is an arc segment (left line), the convergence point P of the arc segment is... CThe angle parameter can be calculated using the parametric equation of a circle. Then P C The coordinates are:

[0037] (2)

[0038] Through P C By drawing a perpendicular line from the point to the line segment, we can obtain the convergence point P of the central partition wall. L P can be calculated using equation (2). C The foot of the perpendicular to the line P L :

[0039] (3)

[0040] Let the walkway slab be straight L b The intersection point with the straight line L of the central partition wall is P. B Then P B The coordinates of the point are:

[0041] (4)

[0042] Let P1 be the intersection point of the arc segment C and the straight line L of the central partition wall. The coordinates of the intersection point P1 can be calculated by solving the equations of the line and the circle simultaneously. And taking the point with the larger y-value as the upper intersection point P1, the coordinates of intersection point P1 are:

[0043] (5)

[0044] Furthermore, in step S4, the calculation methods for the horizontal and vertical convergence deformations and convergence point heights of each ring are as follows:

[0045] The horizontal convergence R of the left line L Can be made by P C P L Euclidean distance calculation Its calculation accuracy can be determined by the circle fitting accuracy. With line fitting accuracy Union is represented as Vertical convergence V L Can be made by P B The Euclidean distance between P1 is calculated. Its calculation accuracy can be determined by the fitting accuracy of the circular arc segment. Fitting accuracy of the central partition wall Walkway slab fitting accuracy Union is represented as .

[0046] Since each ring contains many scan lines, in the calculation of each ring, m lines can be sampled on average in the middle of the ring, and the lateral convergence R of each line can be calculated separately. Li, vertical convergence V Li corresponding horizontal convergence accuracy , vertical convergence accuracy , let each ring horizontal convergence weight be , vertical convergence weight be , calculate each line weighted average value as the final horizontal convergence , vertical convergence .

[0047] When the right side of the scanning point cloud is a circular arc segment (right line), the angle parameter , the horizontal convergence , vertical convergence of the right line of the ring is calculated according to the above steps, and the final horizontal convergence , vertical convergence of the ring is .

[0048] In addition, the horizontal convergence point height is obtained by calculating the Euclidean distance between P L P B , which can guide the positioning of the horizontal convergence monitoring point P L on site. For manual convergence measurement, find the connection between the partition wall and the walkway plate on site, measure vertically upward , and then complete the layout of P L by pasting a reflective sheet.

Claims

1. A method for deformation detection of large shield midblock wall tunnels based on mobile laser scanning, characterized in that, The method comprises the following steps: S1, collecting tunnel point cloud data by ring; S2, segmenting point cloud by ring and obtaining geometric parameters, and dividing point cloud into tunnel wall circular arc segment, mid-partition wall, floor and walkway plate; S3, extracting tunnel feature points according to geometric parameters of each point cloud segment, wherein the tunnel feature points comprise circular arc segment transverse convergence point, mid-partition wall transverse convergence point, intersection of walkway plate and mid-partition wall, and intersection of mid-partition wall and tunnel wall circular arc; S4, calculating transverse convergence amount, longitudinal convergence amount and transverse convergence point height of the tunnel based on the feature points.

2. The method of claim 1, wherein, In the S1 step, a two-dimensional spiral scanning mode is adopted for the laser scanner, which is installed on a mobile carrier and uniformly travels along a track to obtain a two-dimensional point cloud sequence of a section, and three-dimensional point cloud data are reconstructed in combination with mileage information.

3. The method of claim 1, wherein, The S2 step comprises: completing rough segmentation of the tunnel wall, mid-partition wall and floor by manually selecting first ring point cloud and recording segmented positions; segmenting in subsequent rings according to the positions; adopting a random sample consensus algorithm and a least square fitting algorithm to denoise and solve geometric parameters of the tunnel wall arc segment and the mid-partition wall straight segment; adopting a Hough straight line detection algorithm to detect point cloud perpendicular to the mid-partition wall to realize extraction of walkway plate features, and the screening conditions are: (1) containing the largest number of scanning points; (2) the straight line is closer to the top of the tunnel, i.e., the intercept is larger, and then the straight line least square fitting is adopted to obtain straight line parameters of the walkway plate.

4. The method of claim 1, wherein, In the S3 step, the inclination angle of the mid-partition wall fitting straight line is taken as a geometric correction reference to calculate feature points, which comprise: (1) circular arc segment transverse convergence point; (2) mid-partition wall transverse convergence point; (3) intersection of walkway plate and mid-partition wall, which is taken as a lower vertical convergence point and a transverse convergence point height calculation base point; and (4) intersection of mid-partition wall and tunnel wall circular arc, which is taken as an upper vertical convergence point.

5. The method of claim 1, wherein, In the S4 step, transverse convergence and longitudinal convergence are calculated through the Euclidean distance between the transverse convergence point and the vertical convergence point in the feature points, and the final convergence amount of the ring is obtained based on fitting accuracy and weighted average of multiple scanning lines.

6. The method of claim 5, wherein, The transverse convergence point height is calculated through the Euclidean distance between the intersection of walkway plate and mid-partition wall and the mid-partition wall transverse convergence point in the feature points, which is used to guide the layout of field artificial monitoring points.

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