A method for identifying and calculating the volume of tunnel over-excavation and under-excavation based on laser point clouds.

Abstract

A method for identifying and calculating the volume of over- and under-excavation in tunnels based on laser point clouds belongs to the field of intelligent tunnel construction and blasting quality assessment technology. Technical solution: The scanned point cloud data is denoised; a design contour point cloud is generated and aligned with the centerline of the actual excavated tunnel; the actual collected tunnel excavation point cloud and the design contour point cloud are Delaunay tetrahedralized; the actual tunnel point cloud is traversed, and the vector dot product formed between the point cloud and the triangular sides of the convex hull is calculated; over- and under-excavation areas are identified; a polyhedron is constructed from the design contour point cloud and the actual excavated area point cloud constituting the area; the polyhedron is divided into quadrilaterals, and the volume of each quadrilateral is calculated, thereby calculating the volume of each over- and under-excavation area. Beneficial effects: This invention can quickly measure over- and under-excavation areas and quantities that are difficult to calculate in tunnel blasting quality assessment, and can replace cumbersome and time-consuming total station and manual measurement methods; this method has the advantages of high precision, high efficiency, and high flexibility.

Description

Technical Field

[0001] This invention relates to the field of intelligent tunnel construction and blasting quality assessment technology, and to a method for judging and calculating the over-excavation and under-excavation volume of tunnels, particularly a method for identifying and calculating the over-excavation and under-excavation volume of tunnels based on laser point clouds. Background Technology

[0002] Over-excavation and under-excavation are difficult to avoid during tunnel construction, especially in tunnels constructed using blasting methods. Accurate assessment of over-excavation and under-excavation volumes is crucial for stability, structural safety, and cost control. However, existing methods for determining over-excavation and under-excavation are time-consuming and dangerous, relying primarily on manual experience and measurements. In tunnels with rockburst risks, the area near the exposed working face is a high-risk zone, and manually measuring the tunnel blasting profile could endanger surveyors. Therefore, a laser point cloud-based method for determining and assessing tunnel over-excavation and under-excavation is an efficient and safe approach.

[0003] Chinese patent application with publication number CN118135142A and Chinese patent with publication number CN109598714B propose a method for calculating over- and under-excavation using an integral method. However, using an integral method makes it difficult to calculate irregular areas, which inevitably leads to 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] To address the aforementioned technical problems in existing technologies, this invention provides a method for identifying and calculating the over- and under-excavation volume of tunnels based on laser point clouds. This method can quickly and efficiently determine and calculate the over- and under-excavation areas and amounts in blasted excavation tunnels.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for identifying over-excavation and under-excavation and calculating the volume of tunnels based on laser point clouds, the specific steps of which are as follows:

[0008] S1. The point cloud data obtained from the scan is first denoised to avoid interference from over-drilling and under-drilling calculations;

[0009] S2. Generate a point cloud of the tunnel design outline and align it with the center line of the actual excavated tunnel.

[0010] S3. Convert the actual tunnel excavation point cloud and the design outline point cloud into Delaunay tetrahedrons.

[0011] S4. Traverse the actual point cloud of the tunnel and calculate the vector dot product between the point cloud after Delaunay tetrahedronization and the triangular sides of the convex hull patch; determine whether the point cloud is inside or outside the design contour by the sign of the dot product, thereby determining the over-excavation and under-excavation areas.

[0012] S5. For each region, construct a polyhedron from the design outline point cloud and the actual excavation area point cloud that make up the region. Divide the polyhedron into quadrilaterals and calculate the volume of each quadrilateral, thereby calculating the volume of each over-excavated and under-excavated region.

[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 reduce the dimensionality of the data, 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 the noisy data in the point cloud.

[0014] Further, step S2 involves establishing a three-dimensional point cloud of the designed tunnel profile based on the tunnel's designed excavation outline. The density of this point cloud is similar to that of the actual collected tunnel point cloud. The central axis of the designed tunnel profile is then aligned with the central axis of the actual excavated tunnel profile.

[0015] Further, step S3 involves determining the boundary of the computational domain based on the distribution of the point set and selecting a convex hull that contains all points. Starting with an initial triangle, points are added incrementally, and the triangulation is updated accordingly. When adding each new point, the Delaunay condition is checked and ensured: the circumcircle of any triangle should not contain any other points. If this condition is not met, local adjustments to the triangulation are needed (e.g., edge flipping). Finally, a triangulation satisfying the Delaunay condition is obtained.

[0016] Further, step S4 involves the following steps: For any facet on the convex hull of the design contour point cloud, a new vector is obtained by cross-product of the direction vectors of the two side lengths. The angle between the two direction vectors must be obtuse. This ensures that the direction of the normal vector of the facet on the convex hull obtained by cross-product is uniform, both pointing outwards from the convex hull. The dot product of the vector formed by the point cloud of the actual excavated tunnel and any vertex of the facet is calculated. The sign of the dot product determines whether the point is inside or outside the design contour. Points displaced inside the design contour are considered under-excavated area point clouds, while those outside are considered over-excavated area point clouds.

[0017] Further, step S5 involves dividing the point cloud of the connected over-excavation and under-excavation areas into a whole, and forming a whole with the design contour point cloud corresponding to each area. Each whole is then Delaunay tetrahedralized again. The tetrahedralized polyhedron is divided into multiple simple geometric shapes (tetrahedrons). The volume of each simple shape is calculated, and finally, the volumes are added together to obtain the volume of each over-excavation and under-excavation area.

[0018] The beneficial effects of this invention are:

[0019] Compared with existing technologies, the tunnel over-excavation and under-excavation identification and volume calculation method based on laser point clouds described in this invention has the following technical features or beneficial effects:

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

[0021] (2) Improve computational efficiency: The use of laser point cloud data for automated processing avoids the time-consuming and dangerous nature of manual measurement. Especially in tunnel environments with rock burst risks, this invention provides a more efficient and safer method for assessing over-excavation and under-excavation.

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

[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 be adapted to tunnel projects of different sizes, shapes and geological conditions.

[0024] (5) Easy to integrate and expand: This method can be seamlessly integrated with existing tunnel construction management systems and laser scanning equipment, which facilitates its application and promotion in actual engineering projects.

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

[0026] This invention provides an efficient, accurate, safe and flexible method for identifying and calculating the volume of tunnel over-excavation and under-excavation, which is of great significance for improving the quality, safety and cost-effectiveness of tunnel construction. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

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

[0029] Figure 2 A schematic diagram illustrating the generation of the design contour point cloud for this invention;

[0030] Figure 3 This is a schematic diagram illustrating the results of judging over-excavation and under-excavation in Example 1. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The following description, in conjunction with the accompanying drawings... Figure 1-3 The method for identifying and calculating the volume of tunnel over-excavation and under-excavation based on laser point clouds is further explained, and the technical solutions in the embodiments of this application are clearly and completely described. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0032] This invention provides a method for identifying and calculating the volume of tunnel over-excavation and under-excavation based on laser point clouds, the technical route of which 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-drilling and under-drilling 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 converts the actual tunnel excavation point cloud and the design contour point cloud into a Delaunay tetrahedron.

[0036] S4: Traverse the actual point cloud of the tunnel and calculate the vector dot product between the point cloud after Delaunay tetrahedronization and the triangular sides of the convex hull patch. The sign of the dot product determines whether the point cloud is inside or outside the design profile, thus identifying the over- or under-excavation area.

[0037] S5. For each region, construct a polyhedron from the design outline point cloud and the actual excavation area point cloud that make up the region. Divide the polyhedron into quadrilaterals and calculate the volume of each quadrilateral, thereby calculating the volume of each over-excavated and under-excavated region.

[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 shape and size of the tunnel cross-section are obtained according to the tunnel design drawings.

[0040] S1. Denoising the obtained actual tunnel point cloud. First, the actual tunnel point cloud is projected onto the tunnel cross-section for data dimensionality reduction. The cross-section is divided into grids, the number of points in each grid is counted, and the points are converted into pixel grayscale values ​​of 0-255 according to the ratio. Noisy data in the point cloud is filtered out using image processing threshold filtering.

[0041] Preferably, the specific operation method of S2 is as follows: Based on a two-dimensional CAD file or 3D model containing the tunnel's geometric information, determine the tunnel's central axis. Establish a Cartesian coordinate system with the central axis as the origin, and determine the equation of each curve segment that makes up the tunnel profile. Based on the equation, uniformly generate tunnel profile points on the tunnel profile, as illustrated in the diagram below. Figure 2 As shown. The generated cross-sectional points are uniformly replicated along the central axis to form the full-length point cloud of the tunnel. Ensure the shape and orientation of the cross-sections are maintained during replication. 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 profile point cloud with the central axis of the actual excavated tunnel point cloud.

[0042] S3: For two point clouds, select a convex hull containing all points in each. Starting with an initial triangle, gradually add points and update the triangulation. When adding each new point, check and ensure the new triangle satisfies the Delaunay condition, i.e., the circumcircle of any triangle should not contain other points. If not, local adjustments to the triangulation are needed (e.g., edge flipping). The final triangulation satisfies the Delaunay condition. The two point clouds are then subjected to Delaunay tetrahedralization operations independently, without interference.

[0043] Preferably, the specific operation method of S4 is as follows: for any facet on the convex hull, calculate its direction vector based on the coordinates of the triangle vertices:

[0044]

[0045] Calculate the normal vector:

[0046]

[0047] The selected two vectors have an obtuse angle between them to ensure that the normal vector points in the same direction. For each point P where the tunnel is actually excavated, calculate the vector from P to any vertex of the patch (e.g., A):

[0048]

[0049] Calculate the dot product:

[0050]

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

[0052] (1) If dot>0, then the point is outside the design outline (overcut area).

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

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

[0055] The judgment of over-excavation and under-excavation of tunnels is as follows: Figure 3 As shown in the diagram, black represents over-excavation areas, and white represents under-excavation areas. This method can identify and calculate over-excavation and under-excavation areas and amounts in real time.

[0056] Preferably, in step S5, for the point clouds of the classified over-excavated and under-excavated areas, firstly, the connected point cloud data are identified and extracted. Clustering algorithms can be used to automatically identify connected areas. The point cloud of each over-excavated or under-excavated area is merged with the corresponding design contour point cloud to form a unified point cloud. Ensure that the merged point cloud contains all necessary information for subsequent processing. The generated tetrahedron is divided into multiple simple tetrahedrons. The base of each tetrahedron is typically a triangle, and the vertex is a point in the merged point cloud. For each tetrahedron, the volume is calculated using the following formula:

[0057]

[0058] Where A base It is the area of ​​the base (i.e., the triangle):

[0059]

[0060] in, This represents the unit normal vector of the base. The total volume of the vectors representing the edges of the tetrahedron is:

[0061]

[0062] These steps allow for the efficient calculation of the volume of over-excavated and under-excavated areas, facilitating subsequent analysis and evaluation.

[0063] This invention proposes a method for quickly and accurately determining and calculating over- and under-excavation amounts. This method offers advantages such as flexibility, accuracy, safety, and efficiency.

[0064] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for identifying and calculating the volume of tunnel over-excavation and under-excavation based on laser point clouds, characterized in that, The steps are as follows: S1. First, the point cloud data obtained from the scan is denoised to avoid interference from over-excavation and under-excavation calculations; the actual point cloud of the tunnel is projected onto the cross-section of the tunnel to reduce the dimensionality of the data, the cross-section is divided into grids, the number of point clouds in each grid is counted and converted into pixels, and image processing filtering operations are used to filter out the noisy data in the point cloud. S2. Generate a design contour point cloud based on the tunnel design contour and align it with the center line of the actual excavated tunnel; establish a three-dimensional design tunnel contour point cloud based on the tunnel design excavation contour, wherein the density of the point cloud is the same as the density of the actual collected tunnel point cloud. Align the centerline of the designed tunnel profile with the centerline of the actual excavated tunnel profile. S3. Delaunay tetrahedronize the actual tunnel excavation point cloud and the design contour point cloud; determine the boundary of the computational region based on the distribution of the point set, and select a convex hull containing all points; starting from an initial triangle, gradually add points and update the triangulation; when adding each new point, check and ensure that the new triangle satisfies the Delaunay condition. If it does not, the triangulation needs to be locally adjusted until a triangulation that satisfies the Delaunay condition is obtained; a new triangle satisfies the Delaunay condition if the circumcircle of any triangle does not contain other points; locally adjust the triangulation by edge flipping. S4. Traverse the actual point cloud of the tunnel and calculate the vector dot product between the point cloud after Delaunay tetrahedronization and the triangular sides of the convex hull patch. The sign of the dot product determines whether the point cloud is inside or outside the design contour, thus identifying over- or under-excavation areas. For any facet on the convex hull of the design contour point cloud, a new vector is obtained by cross-product of the direction vectors of the two sides, with the angle between the two direction vectors being obtuse. The dot product of the new vector obtained by cross-product of the direction vectors of the two sides and the vector formed by the point cloud of the actual excavated tunnel and any vertex of the facet is calculated. The sign of the dot product determines whether the point is inside or outside the design contour. Points that are displaced inside the design contour are considered under-excavation point clouds, while those outside are considered over-excavation point clouds. S5. For each region, construct a polyhedron from the design outline point cloud and the actual excavation area point cloud that make up the region. Divide the polyhedron into multiple quadrilaterals and calculate the volume of each quadrilateral to calculate the volume of each over- or under-excavation region. Divide the point clouds of connected over- or under-excavation regions into a whole, and form a whole with the design outline point cloud corresponding to each region. Delaunay tetrahedronize each whole again. The tetrahedronized polyhedron is divided into several simple geometric shapes. Calculate the volume of each geometric shape, and finally add their volumes to obtain the volume of each over- or under-excavation region. The geometric shape is a tetrahedron.

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