Tunnel back break amount automatic calculation method based on Monte Carlo method

Abstract

The invention discloses a method for automatically calculating the over-excavation and under-excavation amount of a tunnel based on a Monte Carlo method, and belongs to the field of tunnel intelligent construction and blasting quality evaluation. According to the technical scheme, the method comprises the steps of obtaining a tunnel bare surrounding rock point cloud, and preprocessing point cloud data; generating a designed tunnel contour point cloud and registering the designed tunnel contour point cloud with an actual tunnel point cloud; convex hulls are generated for the two point clouds by using a convex hull algorithm, and a cuboid boundary box capable of wrapping the two convex hulls at the same time is established; a large number of points are randomly generated in the boundary box, and the position relation between the points and the two convex hulls is judged, so that it is determined that the points fall in an over-excavation area or an under-excavation area; by counting the ratio of the number of random points in the back-break area to the total number, the ratio of the irregular back-break area to the volume of the bounding box is obtained, and the ratio is multiplied by the volume of the bounding box to obtain the area body, so that the back-break amount which is difficult to measure and calculate in tunnel quality evaluation is accurately and quickly obtained. The method has the beneficial effects that the tunnel construction cost is reduced, and the construction efficiency and the safety of measurement personnel are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent tunnel construction and blasting quality assessment, and relates to an intelligent judgment method and an over- and under-break amount calculation method for a blasting tunnel, and in particular to an automatic calculation method for tunnel over- and under-break amount based on a Monte Carlo method. Background Art

[0002] In rock tunnel engineering, accurate calculation of overbreak and underbreak is a key link in tunnel excavation quality control. Traditionally, the measurement of overbreak and underbreak mainly relies on manual measurement, cross-sectional measurement, and geometric comparison. However, as tunnel design becomes increasingly complex, traditional measurement methods have gradually exposed shortcomings such as limited accuracy, low work efficiency, and difficulty in real-time feedback. For example, manual measurement requires surveyors to collect multiple cross-sectional data in the tunnel, which is not only time-consuming and labor-intensive, but also prone to measurement errors due to the complex terrain and construction environment. Cross-sectional measurement requires the acquisition of multiple cross-sectional data and subsequent comparative analysis, which makes it difficult to fully reflect the overbreak and underbreak situation in three-dimensional form.

[0003] In order to solve the above problems, the tunnel over-break and under-break measurement method based on laser scanner came into being. Laser scanners can quickly and accurately obtain three-dimensional point cloud data of the tunnel excavation surface. This method can more comprehensively describe the actual shape of the tunnel section by collecting a large number of points on the excavation surface. Compared with traditional section measurement, it has higher accuracy and efficiency. However, despite the high accuracy and fast acquisition speed of laser scanners, the traditional analysis method of over-break and under-break calculation based on three-dimensional point cloud data still faces some challenges in practical application.

[0004] At present, the over-excavation and under-excavation judgment method based on three-dimensional point cloud data usually compares the point cloud with the design model, calculates the distance from each point in the point cloud to the design model, and thus judges the existence of over-excavation or under-excavation and calculates the over-excavation and under-excavation amount based on the cross-sectional differential method. However, this method has certain shortcomings: the cross-sectional differential and integral methods usually construct the cross-sectional profile based on a limited number of measurement points. Since the three-dimensional shape of the tunnel excavation surface cannot be fully reflected, especially in rock tunnels or complex geological conditions, the intervals between points may mask tiny over-excavation and under-excavation details, thereby affecting the accuracy of the calculation. The distribution and density of the measurement points directly affect the calculation results, and insufficient points will lead to a decrease in accuracy.

[0005] Therefore, based on the three-dimensional laser point cloud data, a method for judging and calculating over-excavation and under-excavation with high precision, high efficiency and high flexibility is needed. Summary of the invention

[0006] In order to solve the above technical problems existing in the prior art, the present invention provides a method for automatically calculating the over-break and under-break of a tunnel based on the Monte Carlo method, which can accurately and efficiently judge and calculate the over-break area and over-break amount of a tunnel excavated by blasting.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for automatically calculating tunnel overbreak and underbreak based on Monte Carlo method, the specific steps are as follows:

[0009] S1. Use a laser scanner to obtain the exposed surrounding rock point cloud of the tunnel and pre-process the point cloud data;

[0010] S2, generating a point cloud of the designed tunnel outline according to the design drawing and registering it with the actual tunnel point cloud;

[0011] S3, using the convex hull algorithm to generate convex hulls for the two point clouds and establish a cuboid bounding box that can simultaneously wrap the two convex hulls;

[0012] S4. Randomly generate a large number of points within the bounding box. The position of each point can be determined by generating random x, y, z coordinates;

[0013] S5. For these random points, determine their positional relationship with the two convex hulls, so as to determine whether they fall in the over-excavation area or the under-excavation area;

[0014] S6. By counting the ratio of the number of random points in the over-under-digging area to the total number, the ratio of the irregular over-under-digging area to the volume of the bounding box can be obtained. The volume of the area can be obtained by multiplying the ratio by the volume of the bounding box.

[0015] Furthermore, the specific steps of step S1 are: according to the actual size and shape of the tunnel, select a suitable scanning position to ensure that the laser scanner can cover the tunnel excavation surface and obtain sufficiently dense and comprehensive point cloud data. Use laser scanning software or data processing tools to filter noise on the collected raw point cloud data to remove abnormal points, isolated points or invalid points caused by equipment errors and environmental factors.

[0016] Furthermore, the specific steps of step S2 are to extract the geometric parameters and coordinate information of the design section from the tunnel design drawings, including the shape, size, position and longitudinal extension direction of the tunnel section, to ensure that the generated point cloud accurately reflects the design model. Based on the design section data, dense section points are generated by setting a certain spacing, and the generated design tunnel contour point cloud is converted to the global coordinate system of tunnel construction or the same coordinate system as the actual point cloud to ensure that the two sets of data have a unified spatial reference. The registered design tunnel point cloud and the actual tunnel point cloud data are saved as the final model for subsequent over-excavation and under-excavation analysis.

[0017] Furthermore, the specific steps of step S3 are to calculate the convex hull of the actual tunnel point cloud using a commonly used convex hull generation algorithm. The convex hull algorithm will identify and connect the outermost points in the actual point cloud data to generate a minimum polyhedron containing all points. Save the vertex coordinates and face information of the generated convex hull of the design tunnel and the actual collected tunnel point cloud. Extract all vertices from the convex hulls of the actual and design tunnel point clouds, and determine the boundary ranges of the convex hull of the actual point cloud and the convex hull of the design point cloud respectively. According to the envelope point set, calculate its maximum and minimum coordinate values ​​in the three directions of x, y, and z to determine the six faces of the bounding box, namely the length, width, and height. Save the vertex and face information of the cuboid bounding box for use in the subsequent random point generation and over-excavation judgment process.

[0018] Furthermore, the specific steps of step S4 are: according to the bounding box generated in step S3, the minimum and maximum coordinate values ​​in the three directions of x, y, and z are obtained to define the spatial range of the bounding box. According to the required calculation accuracy and the volume of the bounding box, the density of the generated random points is determined, and according to the set density and the volume of the bounding box, the number of random points N to be generated in the bounding box is calculated. The x, y, and z coordinates of each random point are combined into a three-dimensional coordinate point.

[0019] Furthermore, the specific steps of step S5 are: for each random point, checking whether it is located in the convex hull of the two tunnels.

[0020] (1) Points falling within the actual tunnel convex hull: marked as R in .

[0021] (2) Points outside the actual tunnel convex hull: marked as R out .

[0022] (3) Points falling within the convex hull of the designed tunnel: marked as D in .

[0023] (4) Points outside the convex hull of the designed tunnel: marked as D out .

[0024] For the in and D out The random points are over-excavation points and are marked as R out , D in The points are under-excavation points. The coordinates of each random point and its classification results (over-excavation, under-excavation, and non-over-under-excavation) are saved as structured data for subsequent volume statistical analysis.

[0025] Furthermore, the specific steps of step S6 are to traverse the random point classification results and count the total number of random points marked as "over-digging area" and "under-digging area". Based on the proportion of random points in the over-digging and under-digging areas, the proportion is multiplied by the bounding box volume to obtain the volume of the over-digging and under-digging areas. The above method can quickly and accurately determine and calculate the over-digging and under-digging areas and the over-digging and under-digging amounts.

[0026] Beneficial effects of the present invention:

[0027] Compared with the prior art, the Monte Carlo method-based automatic calculation method for tunnel over-break and under-break has the following technical features or beneficial effects:

[0028] (1) Improve calculation accuracy: The traditional over-excavation and under-excavation judgment method based on three-dimensional point cloud data is limited by the number and distribution of measurement points, which may lead to a decrease in accuracy. However, the present invention uses the Monte Carlo method to randomly generate a large number of points in the bounding box and judge the positional relationship between these points and the tunnel convex hull, thereby more comprehensively reflecting the three-dimensional shape of the tunnel excavation surface. This method effectively avoids the accuracy problems caused by insufficient points or uneven distribution, and improves the accuracy of over-excavation and under-excavation calculations.

[0029] (2) Improved computing efficiency: Compared with traditional manual measurement and cross-sectional measurement, the present invention uses a laser scanner to quickly acquire point cloud data, and processes and analyzes it through an automated algorithm, which significantly improves computing efficiency. At the same time, the random point generation and judgment process of the Monte Carlo method can also be further optimized through parallel computing, further shortening the computing time.

[0030] (3) Enhanced flexibility: The method of the present invention is not limited to specific tunnel shapes or geological conditions and can be applied to tunnel projects in various complex geological environments. By adjusting the number and density of random points, the calculation accuracy and efficiency can be flexibly controlled to meet different engineering requirements.

[0031] (4) Providing a stable solution: Traditional methods often have difficulty ensuring the stability of calculation results when faced with complex geological conditions or irregular tunnel shapes. However, the present invention, through the statistical characteristics of the Monte Carlo method, can provide more stable and reliable over-break and under-break calculation results, providing strong support for tunnel engineering quality monitoring.

[0032] (5) Promote intelligent development: The implementation of the present invention promotes the intelligent development of tunnel engineering measurement. By integrating laser scanners and automated algorithms, automatic calculation and real-time monitoring of over-break and under-break are achieved, providing a strong guarantee for the safe and efficient construction of tunnel engineering.

[0033] The present invention provides a method for automatically calculating tunnel overbreak and underbreak based on the Monte Carlo method, which shows remarkable beneficial effects in improving calculation accuracy, enhancing calculation efficiency, enhancing flexibility, providing stable solutions and promoting intelligent development. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] 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:

[0035] Figure 1 It is the technical roadmap of the present invention;

[0036] Figure 2 The design contour point cloud image generated in Example 1;

[0037] Figure 3 It is the convex hull diagram of the two tunnel contours in Example 1;

[0038] Figure 4 This is a diagram showing the over-excavation and under-excavation judgment results of the present invention;

[0039] Figure 5 It is a schematic diagram of generating random points in a bounding box in the present invention. DETAILED DESCRIPTION

[0040] 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 1-5 The automatic calculation method of tunnel over-excavation and under-excavation based on the Monte Carlo 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.

[0041] The present invention provides a method for automatically calculating the amount of tunnel over-break and under-break based on the Monte Carlo method. Figure 1 As shown, the specific steps include:

[0042] S1. Use a laser scanner to obtain the exposed surrounding rock point cloud of the tunnel and pre-process the point cloud data.

[0043] S2. Generate a point cloud of the designed tunnel outline according to the design drawings and align it with the actual tunnel point cloud.

[0044] S3. Use the convex hull algorithm to generate convex hulls for the two point clouds and establish a rectangular bounding box that can simultaneously enclose the two convex hulls.

[0045] S4. Randomly generate a large number of points within the bounding box. The position of each point can be determined by generating random x, y, z coordinates.

[0046] S5. For these random points, determine their positional relationship with the two convex hulls to determine whether they fall in the over-excavation area or the under-excavation area.

[0047] S6. By counting the ratio of the number of random points in the over-under-digging area to the total number, the ratio of the irregular over-under-digging area to the volume of the bounding box can be obtained. The volume of the area can be obtained by multiplying the ratio by the volume of the bounding box.

[0048] Example 1

[0049] S1, first use a laser scanner to scan the exposed rock mass after blasting and excavation to obtain 3D point cloud data. According to the rock properties of the tunnel, ambient lighting conditions and scanning requirements, adjust the resolution, scanning angle and scanning distance of the laser scanner to ensure that the collected point cloud data has sufficient accuracy and density. During the scanning process, the stability of the scanner should be maintained, and the data noise caused by environmental vibration, human interference and other factors should be minimized.

[0050] Use laser scanning software or data processing tools to filter noise from the collected raw point cloud data, remove abnormal points, isolated points, or invalid points caused by equipment errors and environmental factors. Set a reasonable distance threshold to remove point cloud data that exceeds the scanning range, avoid errors caused by the edge of the point cloud, and ensure data accuracy.

[0051] S2, using CAD or tunnel design software, generates an ideal tunnel contour point cloud based on the design section data. By setting a certain spacing to generate dense section points, ensure that the design point cloud has sufficient resolution and continuity.

[0052] According to the actual length requirement of the tunnel, the design section is extended along the longitudinal direction of the tunnel to generate a complete three-dimensional design tunnel contour point cloud, providing a complete design model for subsequent registration. The design contour point cloud generated in this embodiment is as follows Figure 2 As shown. Convert the generated designed tunnel outline point cloud to the global coordinate system of tunnel construction or the same coordinate system as the actual point cloud to ensure that the two sets of data have a unified spatial reference for accurate registration. Check whether the scale of the designed point cloud is consistent with the actual tunnel size. If there is an error, make appropriate scale adjustments or fine-tuning to meet the registration accuracy requirements.

[0053] Further, S3, the outermost points in the actual point cloud data are identified and connected to generate the smallest polyhedron containing all the points, and the corresponding convex hulls are generated for both point clouds and the vertex coordinates and face information of the generated convex hulls of the designed tunnel and the actual collected tunnel point clouds are saved. The two convex hulls generated in this embodiment are as follows: Figure 3 shown.

[0054] Extract all vertices from the convex hulls of the actual and designed tunnel point clouds, and determine the boundary ranges of the convex hulls of the actual point clouds and the convex hulls of the designed point clouds. According to the envelope point set, calculate its maximum and minimum coordinate values ​​in the x, y, and z directions, denoted as

[0055] [x min ,x max ],[y min ,y max ],[z min ,z max ]

[0056] In this way, the six faces of the bounding box, i.e., the range of length, width, and height, are determined, and a certain range is reserved for the bounding box so that the bounding box completely encloses the two convex hulls. That is, the range of the bounding box is:

[0057] [x min -l,x max +l],[y min -l,y max +l],[z min -l,z max +l]

[0058] Where l is the reserved space. The schematic diagram of generating random points in the bounding box is as follows Figure 4 shown.

[0059] Save the vertex and face information of the cuboid bounding box for use in subsequent random point generation and over-excavation and under-excavation determination.

[0060] Further, S4 generates uniformly distributed random values ​​in the x, y, z directions, that is, randomly generates a coordinate between the minimum x, y, z value and the maximum x, y, z value. The x, y, z coordinates of each random point are combined into a three-dimensional coordinate point to generate a point set {P1(x1, y1, z1), P2(x2, y2, z2), ..., P n (x n ,y n ,z n )}. Check the distribution of the random point set to ensure uniform coverage within the bounding box to avoid bias in the random point generation algorithm affecting subsequent calculation results.

[0061] Further, S5, for each random point, check whether it is located in the convex hull of the two tunnels. The specific method for determining whether a point is in a convex hull is as follows: suppose each triangle of the convex hull consists of three vertices A i ,B i ,C i Definition, where i = 1, 2, ..., N represents the number of patches, and there are N patches in total. For each patch, calculate its normal vector The normal vector can be found by taking the cross product of the two edge vectors:

[0062]

[0063] Among them (B i -A i ) and (C i -A i ) are respectively side A i B i and side A i C i The × represents the vector cross product operation, the result is the normal vector perpendicular to the patch In order to ensure the consistency of the judgment direction, it is necessary to standardize the direction of the normal vector so that the normal vector always points to the outside of the convex hull. This can be set during the point cloud geometry construction process. For the point P to be judged, calculate the vector A between it and each point on the patch. i Vector And calculate the relative position scalar d from point P to the surface i ,Right now Projection in the direction of the normal vector:

[0064]

[0065] where · represents the vector dot product operation. i >0, point P is located outside the patch. Otherwise, it is located inside. If for all patches there is d i <0, then it can be determined that point P is inside all patches, that is, point P is inside the convex hull. On the contrary, if there is at least one d i >0, then point P is located outside the face, and therefore outside the convex hull. Through the above steps, a quick judgment of over-excavation and under-excavation can be made. The area inside the designed convex hull and outside the actual convex hull is the under-excavation area, and the area outside the designed convex hull and inside the actual convex hull is the over-excavation area. The over-excavation judgment result in this embodiment is as follows: Figure 5 shown.

[0066] Further, S6, traverse the random point classification results and count the total number of random points N marked as "over-excavation areas" o Similarly, count the total number of random points N marked as "under-digging areas" i. Volume of over-excavation area V o for:

[0067]

[0068] V box =abc (4)

[0069] Where V box is the volume of the bounding box, abc is the length, width and height of the bounding box, and N is the total number of random points. The above method can quickly and accurately determine and calculate the over-break and under-break area and the amount of over-break and under-break. The volume of the over-break and under-break area is output as the final calculation result for construction adjustment, progress statistics and engineering quality evaluation. By comparing the actual over-break and under-break volume with the design range, it can be used to guide subsequent tunnel excavation operations or make corresponding adjustments to the surrounding rock support.

[0070] The present invention improves the stability of the results, overcomes the shortcomings of the traditional point cloud over-excavation and under-excavation calculation method, and provides an efficient, accurate and stable solution for tunnel engineering quality monitoring.

[0071] 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 automatically calculating tunnel overbreak and underbreak based on Monte Carlo method, characterized in that: Here are the steps: S1. Use a laser scanner to obtain the exposed surrounding rock point cloud of the tunnel and pre-process the point cloud data; S2, generating a point cloud of the designed tunnel outline according to the design drawing and registering it with the actual tunnel point cloud; S3, using the convex hull algorithm to generate convex hulls for the two point clouds and establish a cuboid bounding box that can simultaneously wrap the two convex hulls; S4. Randomly generate a number of points within the bounding box; the position of each point is determined by generating random x, y, z coordinates; S5. For these random points, determine their positional relationship with the two convex hulls, so as to determine whether they fall in the over-excavation area or the under-excavation area; S6. By counting the ratio of the number of random points in the over-under-digging area to the total number, the ratio of the irregular over-under-digging area to the volume of the bounding box is obtained, and the volume of the area can be obtained by multiplying the ratio by the volume of the bounding box.

2. The method for automatically calculating tunnel overbreak and underbreak based on the Monte Carlo method according to claim 1, characterized in that: The specific steps of step S1 are: according to the actual size and shape of the tunnel, select a suitable scanning position to ensure that the laser scanner can cover the tunnel excavation surface and obtain sufficiently dense and comprehensive point cloud data; use laser scanning software or data processing tools to filter the noise of the collected original point cloud data to remove abnormal points, isolated points or invalid points caused by equipment errors and environmental factors.

3. The method for automatically calculating tunnel overbreak and underbreak based on Monte Carlo method according to claim 1, characterized in that: The specific steps of step S2 are: extracting the geometric parameters and coordinate information of the designed section from the tunnel design drawings to ensure that the generated point cloud accurately reflects the design model; based on the designed section data, generating dense section points by setting a certain spacing, converting the generated designed tunnel contour point cloud to the global coordinate system of tunnel construction or the same coordinate system as the actual point cloud to ensure that the two sets of data have a unified spatial reference; saving the aligned designed tunnel point cloud and the actual tunnel point cloud data as the final model respectively for subsequent over-excavation and under-excavation analysis.

4. The method for automatically calculating tunnel overbreak and underbreak based on Monte Carlo method as claimed in claim 3, characterized in that: The geometric parameters include the shape, size, position and longitudinal extension direction of the tunnel section.

5. The method for automatically calculating tunnel overbreak and underbreak based on Monte Carlo method according to claim 1, characterized in that: The specific steps of step S3 are: using a convex hull generation algorithm to calculate the convex hull of the actual tunnel point cloud; the convex hull algorithm identifies and connects the outermost points in the actual point cloud data to generate a minimum polyhedron containing all points; saving the vertex coordinates and surface information of the generated convex hull of the designed tunnel and the actual collected tunnel point cloud; All vertices are extracted from the convex hulls of the actual and designed tunnel point clouds, and the boundary ranges of the convex hulls of the actual point clouds and the designed point clouds are determined respectively; according to the envelope point set, the maximum and minimum coordinate values ​​in the three directions of x, y, and z are calculated to determine the six faces of the bounding box, namely the length, width, and height range; the vertex and face information of the cuboid bounding box is saved for use in the subsequent random point generation and over-excavation and under-excavation judgment process.

6. The method for automatically calculating tunnel overbreak and underbreak based on Monte Carlo method according to claim 1, characterized in that: The specific steps of step S4 are: according to the bounding box generated in step S3, obtain its minimum and maximum coordinate values ​​in the three directions of x, y, and z to define the spatial range of the bounding box; according to the required calculation accuracy and the volume of the bounding box, determine the density of generated random points, and according to the set density and volume of the bounding box, calculate the number N of random points to be generated in the bounding box; combine the x, y, and z coordinates of each random point into a three-dimensional coordinate point.

7. The method for automatically calculating tunnel overbreak and underbreak based on Monte Carlo method according to claim 1, characterized in that: The specific steps of step S5 are: for each random point, check whether it is located in the convex hull of the two tunnels: (1) Points falling within the actual tunnel convex hull: marked as R in ; (2) Points outside the actual tunnel convex hull: marked as R out ; (3) Points falling within the convex hull of the designed tunnel: marked as D in ; (4) Points outside the convex hull of the designed tunnel: marked as D out ; For the in and D out The random points are over-excavation points and are marked as R out , D in The points are under-excavation area points; the coordinates of each random point and its classification results are saved as structured data for subsequent volume statistical analysis.

8. The method for automatically calculating tunnel overbreak and underbreak based on Monte Carlo method according to claim 7, characterized in that: The classification results include over-break, under-break, and neither over-break nor under-break.

9. The method for automatically calculating tunnel overbreak and underbreak based on Monte Carlo method according to claim 1, characterized in that: The specific steps of step S6 are: traverse the random point classification results, count the total number of random points marked as "over-digging area" and "under-digging area"; based on the proportion of random points in the over-digging and under-digging areas, multiply the proportion by the volume of the bounding box to obtain the volume of the over-digging and under-digging areas.

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