Tunnel back break identification and volume calculation method based on laser point cloud

Abstract

The invention discloses a tunnel back break identification and volume calculation method based on laser point cloud, and belongs to the technical field of tunnel intelligent construction and blasting quality evaluation. According to the technical scheme, point cloud data obtained through scanning are denoised; generating a design contour point cloud and aligning the design contour point cloud with an actual excavation tunnel center line; performing Delaunay tetrahedron on the actually acquired tunnel excavation point cloud and the designed contour point cloud; traversing the actual point cloud of the tunnel, and calculating a vector dot product formed between the point cloud and the triangular edge of the convex hull patch; judging an over-excavation and under-excavation area; and constructing a polyhedron by using the design contour point cloud and the actual excavation area point cloud which form the area, dividing the polyhedron into quadrangles, and calculating the volume of each quadrangle, thereby calculating the volume of each over-excavation and under-excavation area. The method has the beneficial effects that the over-excavation and under-excavation areas and the over-excavation and under-excavation amount which are difficult to calculate in tunnel blasting quality evaluation can be rapidly measured, and a tedious and time-consuming total station and a manual measurement method can be replaced; the method has the advantages of high precision, high efficiency, high flexibility and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent tunnel construction and blasting quality assessment, and relates to a method for judging and calculating the amount of tunnel over-break and under-break, and in particular to a method for identifying and calculating the volume of tunnel over-break and under-break based on laser point cloud. Background Art

[0002] During tunnel construction, overbreak and underbreak are difficult to avoid, especially in tunnels constructed using blasting methods. Accurate assessment of overbreak and underbreak volumes is crucial for stability and structural safety as well as construction cost maintenance. However, existing overbreak and underbreak judgments are time-consuming and dangerous, and mainly rely on manual experience and measurements. In some tunnels with rock burst risks, the exposed face is a high-risk area, and manual measurement of tunnel blasting contours may put surveyors in danger. Therefore, the tunnel overbreak and underbreak judgment and assessment method based on laser point cloud is an efficient and safe method.

[0003] Chinese patent application with publication number CN118135142A and Chinese patent with announcement number CN109598714B proposed a method for calculating over-excavation and under-excavation using an integral method. However, using an integral method will make it difficult to calculate irregular areas and it is difficult to avoid a certain loss of accuracy.

[0004] Therefore, it is necessary to develop a high-precision and fast method for judging and calculating over-excavation and under-excavation. Summary of the invention

[0005] In order to solve the above technical problems existing in the prior art, the present invention provides a method for identifying over-break and under-break of tunnels and calculating their volume based on laser point cloud, which can quickly and efficiently judge and calculate the over-break and under-break areas and amounts of over-break and under-break in tunnels excavated by blasting.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud, the specific steps are as follows:

[0008] S1. De-noise the scanned point cloud data first to avoid interference from over-excavation and under-excavation calculations;

[0009] S2, generating a design contour point cloud according to the tunnel design contour and aligning it with the center line of the actual excavated tunnel;

[0010] S3, Delaunay tetrahedronizing the actual collected tunnel excavation point cloud and the design contour point cloud;

[0011] S4, traverse the actual point cloud of the tunnel, calculate the vector dot product formed between the point cloud after Delaunay tetrahedronization and the triangular edge of the convex hull surface; judge whether the point cloud is inside or outside the design contour by the positive or negative value of the dot product, so as to judge the over-excavation and under-excavation area;

[0012] S5. For each area, a polyhedron is constructed from the design contour point cloud and the actual excavation area point cloud that constitute the area, and the polyhedron is divided into quadrilaterals. The volume of each quadrilateral is calculated, thereby calculating the volume of each over-excavation or under-excavation area.

[0013] Furthermore, the specific method of step S1 is to project the actual point cloud of the tunnel onto the cross section of the tunnel to perform data dimensionality reduction, divide the cross section into grids, count the number of point clouds in each grid and convert them into pixels, and use image processing filtering operations to filter out noise data in the point cloud.

[0014] Furthermore, the specific steps of step S2 are to establish a three-dimensional designed tunnel contour point cloud according to the designed excavation contour of the tunnel, and the density of the point cloud is similar to the density of the actual collected tunnel point cloud. The central axis of the designed tunnel contour is aligned with the central axis of the actual excavation tunnel contour.

[0015] Furthermore, the specific steps of step S3 are to determine the boundary of the calculation area according to the distribution of the point set and select a convex hull containing all points. Starting from an initial triangle, points are gradually added and the triangulation is updated. When adding each new point, check and ensure that the new triangle meets the Delaunay condition, that is, the circumscribed circle of any triangle should not contain other points. If not, the triangulation needs to be locally adjusted (such as edge flipping). Finally, a triangulation that meets the Delaunay condition is obtained.

[0016] Furthermore, the specific steps of step S4 are: for any face on the convex hull of the designed contour point cloud, use the direction vectors of the two side lengths to perform a cross product to obtain a new vector. The angle between the direction vectors of the two side lengths is required to be an obtuse angle. This ensures that the direction of the normal vector of the face on the convex hull obtained by the cross product is unified and all face the outside of the convex hull. Calculate the dot product of the point cloud of the actual excavated tunnel and the vector formed by any vertex of the face. According to the sign of the dot product, it is judged whether the point is inside or outside the designed contour. The points inside the displacement design contour are point clouds in the under-excavation area, and vice versa.

[0017] Furthermore, the specific steps of step S5 are: dividing the point clouds of the connected over-undercut areas into a whole, and forming a whole with the design contour point cloud corresponding to each area. Each whole is Delaunay tetrahedronized again, and the tetrahedronized polyhedron is divided into multiple simple geometric shapes (tetrahedrons), and the volumes of these simple shapes are calculated respectively, and finally their volumes are added to obtain the volume of each over-undercut area.

[0018] Beneficial effects of the present invention:

[0019] Compared with the prior art, the tunnel over-break and under-break identification and volume calculation method based on laser point cloud of the present invention has the following technical characteristics or beneficial effects:

[0020] (1) Improved calculation accuracy: Compared with the traditional integration method, the present invention can more accurately capture and process irregular areas in tunnel point cloud data through Delaunay tetrahedronization and subsequent precise volume calculation, thereby significantly improving the calculation accuracy of over-excavation and under-excavation volumes.

[0021] (2) Improve computing efficiency: Automated processing of laser point cloud data avoids the time-consuming and dangerous nature of manual measurement. This method provides a more efficient and safer over-break and under-break assessment method, especially in tunnel environments where there is a risk of rock burst.

[0022] (3) Enhanced safety: Through fully automated data processing and volume calculation processes, human intervention is reduced, thereby reducing the risk of surveyors’ exposure to hazardous environments and improving safety during the construction process.

[0023] (4) Flexibility and applicability: The method of the present invention is not only applicable to tunnels constructed by blasting, but can also be extended to other types of tunnel construction methods. At the same time, by adjusting the processing parameters of point cloud data, it can adapt to tunnel projects of different sizes, shapes and geological conditions.

[0024] (5) Easy integration and expansion: This method can be seamlessly integrated with the existing tunnel construction management system and laser scanning equipment, facilitating its application and promotion in actual projects.

[0025] (6) Cost-effectiveness: By accurately assessing the over-excavation and under-excavation volumes, the construction team can more effectively manage material usage and construction costs, avoid unnecessary waste, and thus improve the overall economic benefits of the project.

[0026] The present invention provides an efficient, accurate, safe and flexible method for identifying tunnel over-break and under-break and calculating volume, which is of great significance for improving the quality, safety and cost-effectiveness of tunnel construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below in combination with the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0028] Figure 1 It is the technical roadmap of the present invention;

[0029] Figure 2 A schematic diagram of generating a design contour point cloud for the present invention;

[0030] Figure 3 This is a schematic diagram of determining over-excavation and under-excavation results in Example 1. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Figure 1-3 The laser point cloud-based tunnel over-excavation and under-excavation identification and volume calculation method is further explained, and the technical solutions in the embodiments of the present application are clearly and completely described; obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments, and all other embodiments obtained by ordinary technicians in this field without making creative work based on the embodiments in the present application are within the scope of protection of the present application.

[0032] The present invention provides a method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud. The technical route is as follows: Figure 1 As shown, the specific steps include:

[0033] S1, the point cloud data obtained by scanning is first denoised to avoid interference from over-excavation and under-excavation calculations.

[0034] S2, generates a design contour point cloud based on the tunnel design contour and aligns it with the center line of the actual excavated tunnel.

[0035] S3, Delaunay tetrahedronization of the actual collected tunnel excavation point cloud and the designed contour point cloud.

[0036] S4, traverse the actual point cloud of the tunnel, calculate the vector dot product between the point cloud after Delaunay tetrahedronization and the triangular edge of the convex hull patch. The positive or negative value of the dot product is used to determine whether the point cloud is inside or outside the design contour, thereby determining the over-excavation and under-excavation area.

[0037] S5, for each area, construct a polyhedron with the design contour point cloud and the actual excavation area point cloud constituting the area, divide the polyhedron into quadrilaterals, calculate the volume of each quadrilateral, and thus calculate the volume of each over-excavation and under-excavation area.

[0038] Example 1

[0039] First, a laser scanner is used to scan the exposed rock mass after blasting and excavation to obtain three-dimensional point cloud data. In addition, the cross-sectional shape and size of the tunnel are obtained according to the tunnel design drawings.

[0040] S1, denoising the actual point cloud of the tunnel obtained. First, the actual point cloud of the tunnel is projected onto the cross section of the tunnel for data dimensionality reduction, the cross section is divided into grids, the number of point clouds in each grid is counted and converted into pixel grayscale values ​​of 0-255 according to the ratio, and the threshold filtering operation of image processing is used to filter out the noise data in the point cloud.

[0041] Preferably, the specific operation method of S2 is to determine the central axis of the tunnel based on a two-dimensional CAD file or a 3D model containing the geometric information of the tunnel. A Cartesian coordinate system is established with the central axis as the origin of the coordinates to determine the equation of each curve that constitutes the tunnel contour. According to the equation, tunnel contour points are uniformly generated on the tunnel contour. The specific schematic diagram is as follows: Figure 2 As shown. Duplicate the generated section points evenly along the central axis to form a full-length point cloud of the tunnel. Make sure to maintain the shape and orientation of the section when duplicating. Export the generated point cloud data to the desired format and then import it into the scanned laser point cloud model. Align the generated design contour point cloud with the central axis of the actual excavated tunnel point cloud.

[0042] S3, for the two point clouds, select a convex hull containing all the points respectively. Starting from an initial triangle, gradually add points and update the triangulation. When adding each new point, check and make sure that the new triangle meets the Delaunay condition, that is, the circumscribed circle of any triangle should not contain other points. If not, the triangulation needs to be locally adjusted (such as edge flipping). Finally, a triangulation that meets the Delaunay condition is obtained. The two point clouds are Delaunay tetrahedronized separately without interfering with each other.

[0043] Preferably, the specific operation method of S4 is to calculate the direction vector of any face on the convex hull according to the coordinates of the triangle vertices:

[0044]

[0045] Calculate the normal vector:

[0046]

[0047] The angle between the two selected vectors is an obtuse angle, which ensures that the normal vector points in one direction. For each point P of the actual tunnel excavation, calculate the vector from P to any vertex (such as A) of the patch:

[0048]

[0049] Compute the dot product:

[0050]

[0051] Determine the location of the point based on the sign of the dot product:

[0052] (1) If dot>0, the point is outside the design contour (over-excavation area).

[0053] (2) If dot<0, the point is inside the design contour (undercut area).

[0054] (3) If |dot|<ε, then the point is on the patch. Where ε = 10 -12

[0055] How to judge the over-break and under-break of tunnels Figure 3 As shown in Figure 2, the black area is the over-break area, and the white area is the under-break area. This method can identify and calculate the over-break and under-break areas and the over-break and under-break amounts in real time.

[0056] Preferably, S5, for the point clouds of the classified over-excavation and under-excavation areas, first identify and extract the connected point cloud data. A clustering algorithm can be used to automatically identify connected areas. Merge the point cloud of each over-excavation or under-excavation area with the corresponding design contour point cloud to form an overall point cloud. Ensure that the merged point cloud contains all necessary information for subsequent processing. Divide the generated tetrahedron into multiple simple tetrahedrons. The base of each tetrahedron is usually a triangle, and the vertex is a point in the merged point cloud. For each tetrahedron, the following volume formula is used for calculation:

[0057]

[0058] Among them A base is the area of ​​the base (i.e. the triangle):

[0059]

[0060] in, represents the unit normal vector of the bottom surface, The vectors obtained by representing the edges of the tetrahedron have a total volume of:

[0061]

[0062] Through these steps, the volume of overbreak and underbreak areas can be effectively calculated for subsequent analysis and evaluation.

[0063] The invention proposes a method for quickly and accurately judging overbreak and underbreak and calculating the overbreak and underbreak amount. The method has the advantages of flexibility, accuracy, safety and high efficiency.

[0064] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud, characterized in that: Here are the steps: S1. First, denoise the scanned point cloud data to avoid interference from over-excavation and under-excavation calculations; S2, generating a design contour point cloud according to the tunnel design contour and aligning it with the center line of the actual excavated tunnel; S3, Delaunay tetrahedronizing the actual collected tunnel excavation point cloud and the design contour point cloud; S4, traversing the actual point cloud of the tunnel, and calculating the vector dot product between the point cloud after Delaunay tetrahedronization and the triangular edges of the convex hull patch; The positive or negative value of the dot product is used to determine whether the point cloud is inside or outside the design contour, thereby determining the over-excavation or under-excavation area. S5. For each area, a polyhedron is constructed from the design contour point cloud and the actual excavation area point cloud that constitute the area, and the polyhedron is divided into quadrilaterals. The volume of each quadrilateral is calculated, thereby calculating the volume of each over-excavation or under-excavation area.

2. The method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud according to claim 1, characterized in that: The specific method of step S1 is: projecting the actual point cloud of the tunnel onto the cross section of the tunnel to reduce the dimension of the data, dividing the cross section into grids, counting the number of point clouds in each grid and converting them into pixels, and using image processing filtering operations to filter out noise data in the point cloud.

3. The method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud according to claim 1, characterized in that: The specific steps of step S2 are: establishing a three-dimensional designed tunnel contour point cloud according to the designed excavation contour of the tunnel, the density of the point cloud being the same as the density of the actual collected tunnel point cloud; aligning the central axis of the designed tunnel contour with the central axis of the actual excavation tunnel contour.

4. The method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud according to claim 1, characterized in that: The specific steps of step S3 are: according to the distribution of the point set, determine the boundary of the calculation area and select a convex hull containing all points; start from an initial triangle, gradually add points and update the triangulation; when adding each new point, check and ensure that the new triangle meets the Delaunay condition. If not, the triangulation needs to be locally adjusted to finally obtain a triangulation that meets the Delaunay condition.

5. The method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud according to claim 4, characterized in that: The new triangle satisfies the Delaunay condition if the circumcircle of any triangle contains no other points.

6. The method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud according to claim 4, characterized in that: Locally adjust the triangulation by edge flipping.

7. The method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud according to claim 1, characterized in that: The specific steps of step S4 are: for any facet on the convex hull of the designed contour point cloud, a new vector is obtained by cross-producting the direction vectors of the two side lengths; the angle between the direction vectors of the two side lengths is an obtuse angle; Calculate the dot product of the point cloud of the actual excavated tunnel and the vector formed by any vertex of the patch; determine whether the point is inside or outside the design contour based on the sign of the dot product; the points inside the displacement design contour are the point clouds of the under-excavation area, and vice versa.

8. The method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud according to claim 1, characterized in that: The specific steps of step S5 are: dividing the point clouds of the connected over- and under-break areas into a whole, and forming a whole with the design contour point cloud corresponding to each area; Delaunay tetrahedronizing each whole again, and the tetrahedronized polyhedron is divided into several simple geometric shapes, and the volumes of the geometric shapes are calculated respectively, and finally their volumes are added together to obtain the volume of each over- and under-break area.

9. The method for identifying tunnel over-break and under-break and calculating volume based on laser point cloud according to claim 8, characterized in that: The geometric shape is a tetrahedron.

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