CN113592873A - Method for measuring and calculating RQD value of surrounding rock based on virtual drilling, electronic equipment and medium - Google Patents

Method for measuring and calculating RQD value of surrounding rock based on virtual drilling, electronic equipment and medium Download PDF

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CN113592873A
CN113592873A CN202111167915.8A CN202111167915A CN113592873A CN 113592873 A CN113592873 A CN 113592873A CN 202111167915 A CN202111167915 A CN 202111167915A CN 113592873 A CN113592873 A CN 113592873A
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傅金阳
王浩宇
王宇
阳军生
祝志恒
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Central South University
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Abstract

The invention discloses a virtual drilling hole-based surrounding rock RQD value measuring and calculating method, electronic equipment and a medium, wherein a tunnel face joint crack distribution diagram is obtained through a superpixel segmentation algorithm and a superpixel block-based boundary resolution algorithm, then a plurality of virtual measurement drill holes are arranged, finally the total length of an interval with the block length being more than 10cm in each virtual measurement drill hole is calculated, the surrounding rock RQD values of all drill holes are obtained, and a weighted average value is taken as the tunnel face surrounding rock RQD value. The method does not depend on professional measuring equipment, does not need actual drilling, can measure and calculate the RQD value of the current tunnel face only through the image shot by the handheld camera, and has the advantages of low cost, high speed and no damage.

Description

Method for measuring and calculating RQD value of surrounding rock based on virtual drilling, electronic equipment and medium
Technical Field
The invention relates to the field of tunnel engineering, in particular to a method for measuring and calculating a RQD value of surrounding rock based on virtual drilling, electronic equipment and a medium.
Background
The tunnel surrounding rock stability classification is an engineering processing means for dividing an infinite rock mass sequence into different stability levels according to a plurality of indexes such as rock hardness degree, rock mass integrity degree and the like. Through the development of the last century, underground engineers at home and abroad explore and practice a great deal of surrounding rock stability grading. The rock basic Quality index RQD (rock Quality designation) refers to a method for drilling in rock by using an NX drill bit with the diameter of more than or equal to 75mm and a double-layer core pipe, continuously coring, calculating the percentage of the cumulative length of the core with the length of more than 10cm (inclusive) in the total length of a drilled hole, and grading the stability of surrounding rock according to the percentage. The rock base quality indicator RQD also appears as an intermediate parameter in geomechanical classification (RMR) and other methods.
The traditional RQD measuring and calculating method has the following problems:
1. the working time consumption is large, the efficiency is low, and the construction can not be quickly completed in the tunnel construction period;
2. the cost is high due to the dependence on drilling equipment;
3. the accuracy of the RQD value may be affected by core fracture caused by late factors such as core drilling operation, manual work and the like;
4. on the IV-level and V-level surrounding rock working surface, effective measurement is difficult to carry out.
In order to solve the existing problems, for example, chinese patent publication No. CN111622737A discloses a method for quickly determining a formation rock RQD based on borehole sound waves, which includes performing borehole coring and sound wave testing on pilot holes, determining a critical wave velocity V' p according to the distribution of the pilot hole sound waves and the distribution of the pilot hole RQD, and further quickly calculating a zone measurement hole RQD along the depth of the borehole.
Disclosure of Invention
The invention provides a method, electronic equipment and a medium for measuring and calculating a RQD value of a surrounding rock based on a virtual borehole, which are used for directly processing a tunnel face image by using a computer vision algorithm and measuring and calculating the RQD value, and aims to solve the technical problem that the RQD of the existing rock basic quality index depends on actual drilling and cannot quickly and accurately obtain the RQD value.
In order to achieve the technical purpose, the technical scheme of the invention is that,
a method for measuring and calculating a RQD value of surrounding rock based on virtual drilling comprises the following steps:
performing superpixel segmentation on a tunnel face image, and then calculating the weight of edges among superpixel blocks according to the mean value and the variance of each superpixel block in an LAB color space and the mean value of Sobel filter response values on the boundary among the superpixel blocks to generate a region adjacency graph; marking the boundary of the superpixel block corresponding to the edge with the weight not greater than the set threshold in the region adjacency graph as a digestion state; finally, regarding all boundaries of the superpixel blocks in a non-digestion state in the region adjacency graph as rock mass joint fractures, and obtaining a face joint fracture distribution graph;
step two, arranging a plurality of virtual measuring drill holes parallel to the central axis of the face at equal intervals on the distribution map of the joint cracks of the face;
and step three, calculating the RQD value of the surrounding rock of each virtual measuring drill hole according to the length of a block body formed by dividing the joint crack of the tunnel face in each virtual measuring drill hole, and taking the weighted average value of the RQD values of the surrounding rocks of all the virtual measuring drill holes as the RQD value of the tunnel face surrounding rock.
In the method for measuring and calculating the RQD value of the surrounding rock based on the virtual borehole, in the first step, the superpixel segmentation comprises the following steps:
firstly, converting a tunnel face image from an RGB space to an LAB space, then randomly arranging a plurality of seed points on the image, taking the square root of the total number of image pixel points divided by the number of the seed points as a step length, calculating the distance measurement between other pixel points and the seed points in a range of twice the step length around each seed point, and finally selecting the seed point with the minimum distance measurement as a clustering center for each pixel point, thereby taking the pixel point with the same clustering center as a super pixel block.
In the first step, the calculation formula of the weight of the edges between the superpixel blocks is as follows:
Figure 307078DEST_PATH_IMAGE001
wherein the content of the first and second substances,a，brespectively representing superpixel blocksaAndb，μ i andξ i respectively representing the mean and covariance in the superpixel LAB space,λ 1andλ 2are all normalized coefficients and are obtained by the following steps,sobel（i) Represent an image iniThe sobel filter response values at the points,Irepresenting the area covered by the boundary between two super-pixel blocks.
In the first step, a region adjacency graph is generated by taking coordinates of all pixels in a single superpixel block as nodes and taking weight values of two adjacent superpixel blocks as edges.
In the second step, the virtual measurement borehole is arranged in a mode that:
calculating the actual length corresponding to a single pixel in an image according to the focal length of a camera, the size of a sensor of the camera and the distance from the camera to the tunnel face of the tunnel;
secondly, on the distribution diagram of the joint crack of the tunnel face, drawing an upper arch line and a lower arch line of the tunnel face according to the actual length corresponding to the single pixel obtained in the step I, and symmetrically arranging virtual measurement drill holes at intervals by taking the upper arch line of the tunnel as a starting point and the lower arch line as an end point and parallel to the central axis of the tunnel face.
According to the method for measuring and calculating the RQD value of the surrounding rock based on the virtual drilling, in the step I, the actual length corresponding to a single pixel is as follows: (camera sensor size/pixel size of image) × (camera distance from tunnel face/camera focal length).
In the third step, the method for measuring and calculating the RQD value of the surrounding rock on the tunnel face comprises the following steps:
1) respectively calculating RQD value of each virtual measurement borehole, namely, the blocks formed by dividing the virtual measurement borehole by the joint crack of the face, and the accumulated length of all the blocks larger than 10cmL’ i()And virtually measuring the borehole lengthL i()Percentage of the ratio of the above.
2) Taking the weighted average of the RQD values of all the virtual measurement drilling holes to obtain the RQD value of the face.
In the third step, the weighted average of the RQD values of the surrounding rocks of all the virtual measurement boreholes is calculated by taking the length of each virtual measurement borehole as a weight value.
An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the aforementioned methods.
A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the aforementioned method.
The method has the technical effects that a special measuring device is not needed, actual drilling is not needed, the RQD value of the current tunnel face can be measured and calculated only through an image shot by a handheld camera, and the method has the advantages of low cost, rapidness and no damage.
The invention will be further explained with reference to the drawings.
Drawings
FIG. 1 is a layout view of virtual drilling holes on a face of a rock of the present invention;
FIG. 2 is a region adjacency diagram of the present invention;
FIG. 3 is a schematic view of a region adjacency graph visualization according to the present invention;
FIG. 4 is a profile of a facet joint fracture according to the present invention;
in the figure: 1. a super-pixel block; 2. a superpixel block boundary; 3. a non-resolved superpixel block boundary; 4. a central axis of a tunnel face; 5. an arch line is arranged on the tunnel face of the tunnel; 6. a tunnel face lower arch line; 7. the borehole is measured virtually.
Fig. 5 is a diagram illustrating RQD values for a wall rock according to the present invention.
Detailed Description
Referring to fig. 1, in this embodiment, an image of a tunnel face is shot first, and when the scene shooting is performed, the scene topographic conditions should be fully utilized to take a picture of a lens perpendicular to the tunnel face, and a shooting declination is controlled within a range of 5 ° as much as possible, so that a projection deviation and a shielding deviation are reduced or eliminated as much as possible, and the reliability and accuracy of the collected image are ensured;
and then performing superpixel segmentation on the shot tunnel face image, wherein the method adopts an SLIC algorithm, namely a K-means clustering algorithm is adopted to cluster superpixels so as to realize superpixel segmentation. Firstly, converting a tunnel face image from an RGB space to an LAB space:
Figure 974820DEST_PATH_IMAGE002
wherein the content of the first and second substances,U、V、Windicating the scaling factors 0.950546, 1.0, 1.088754, respectively.
u i 、v i 、w i Is the intermediate variable that is the variable between,r i 、g i 、b i is the RGB value of the pixel point and,l i 、a i 、b i is the LAB value of the pixel point, T is the matrix transposition symbol,tis a function variable. A pixel point in the imagepiExpressed as:
Figure 971595DEST_PATH_IMAGE003
whereinxiAndyirepresenting pixelsSpatial coordinates.
Then randomly arranging a plurality of seed points on the image, taking the square root of the total number of image pixel points divided by the number of the seed points as a step length, and taking the number of the arranged seed points as the number of the seed pointsKTotal number of image pixels isNThen the step length S is:
Figure 292854DEST_PATH_IMAGE004
then, the distance measure of other pixel points and the seed point within the range of two times of the step length around each seed point is calculated, namely, the distance measure is used for one seed pointp i Other pixel points within the 2S rangep j Calculating a distance metricD（i，j）：
Figure 516025DEST_PATH_IMAGE005
WhereinmAndknormalized coefficients representing color distance and spatial distance respectively,D lab representing the Euclidean distance of two pixel points in the LAB space,D xy represents the spatial distance of two points in XY coordinates.
And finally, selecting a seed point with the minimum distance measurement as a clustering center for each pixel point, so that the pixel points with the same clustering center are used as a superpixel block. The resulting superpixel segmentation map is shown in fig. 1.
The weights of the edges between superpixel blocks are then calculated based on the mean and variance of the superpixel blocks in the LAB color space, and the mean of the Sobel filter response values at the boundaries between the superpixel blocks:
Figure 846513DEST_PATH_IMAGE001
wherein the content of the first and second substances,a，brespectively representing superpixel blocksaAndb，μ i andξ i respectively representMean and covariance in the superpixel LAB space,λ 1andλ 2are all normalized coefficients, in this embodimentλ 11000 is taken, and 200 is taken.sobel（i) Represent an image iniThe sobel filter response values at the points,Irepresenting the area covered by the boundary between two super-pixel blocks.
Referring to fig. 2, after the weights of the edges are obtained, a region adjacency graph is generated, where the region adjacency graph is generated by using the coordinates of all pixels in a single superpixel block as nodes and using the weight values of two adjacent superpixel blocks as edges. Taking the region adjacency graph shown in fig. 2 as an example, A, B, C, D, E are five nodes respectively storing the coordinates of all pixels in a single superpixel block, and the lines connecting these nodes are edges, where the superpixel blocks corresponding to C and E are not adjacent in space, so that the weights cannot be calculated, and there is no edge between them.
Referring to fig. 3, the region adjacency graph is visualized to form an image as shown in fig. 3, wherein the lighter the color of the edge is, the higher the weight thereof is.
Then, the superpixel block boundary 2 corresponding to the edge whose weight is not greater than the set threshold is marked as the resolution state, wherein the set threshold is defined as the average value of all weights in the region adjacency graph in the present embodiment. And finally, regarding all non-resolved superpixel block boundary lines 3 in the region adjacency graph as rock mass joint fractures, thereby obtaining a palm surface joint fracture distribution graph, wherein as shown in fig. 4, a superpixel block boundary line 2 in the graph is a light-colored curve, and the superpixel block boundary line 2 surrounds the boundary line to form a superpixel block 1. The non-resolved superpixel block boundaries 3 are represented by broken lines.
Referring to fig. 4, next, a plurality of virtual measurement drill holes 7 parallel to the central axis 4 of the tunnel face are arranged on the distribution diagram of the joint fissure of the tunnel face at equal intervals. In this embodiment, the actual length corresponding to a single pixel in the image is first calculated according to the focal length of the camera, the size of the sensor of the camera, and the distance from the camera to the tunnel face of the tunnel, where the actual length corresponding to the single pixel is: (camera sensor size/pixel size of image) × (camera distance from tunnel face/camera focal length). And then drawing an upper arch line 5 of the tunnel face and a lower arch line 6 of the tunnel face according to the actual length corresponding to the single pixel obtained in the step I on the distribution diagram of the joint crack of the tunnel face, taking the upper arch line 5 of the tunnel face as a starting point and the lower arch line 6 of the tunnel face as an end point, and symmetrically arranging virtual measuring drill holes 7 at intervals in parallel with the central axis 4 of the tunnel face. In general, the virtual measuring boreholes 7 are arranged in 5-8 and distributed as evenly as possible over the face.
Referring to fig. 5, the RQD value of the surrounding rock of each virtual survey borehole is calculated according to the length of the block in each virtual survey borehole, which is divided by the fracture of the face joint, and since the virtual borehole is divided into a plurality of segments by boundaries which are not resolved, some segments are less than 10cm in length, i.e., segments which are shown as darker colors in the figure, and are not included in the calculation. I.e. calculating the cumulative length of all blocks greater than 10cmL’ i()And virtually measuring the borehole lengthL i()And obtaining the RQD value of the surrounding rock for each virtual survey borehole. And then taking the weighted average of the RQD values of the surrounding rocks of all the virtual measurement drilled holes as the RQD value of the tunnel face surrounding rock. Wherein the weighted average is calculated using the length of each of the virtual measurement boreholes as a weight value.
The invention also provides an electronic device and a computer readable medium according to the embodiment of the invention.
Wherein electronic equipment includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the aforementioned methods.
In specific use, a user can interact with a server which is also used as a terminal device through an electronic device which is used as the terminal device and based on a network, and functions of receiving or sending messages and the like are realized. The terminal device is generally a variety of electronic devices provided with a display device and used based on a human-computer interface, including but not limited to a smart phone, a tablet computer, a notebook computer, a desktop computer, and the like. Various specific application software can be installed on the terminal device according to needs, including but not limited to web browser software, instant messaging software, social platform software, shopping software and the like.
The server is a network server for providing various services, such as a background server for providing corresponding computing services for the received tunnel face images transmitted from the terminal device. The method and the device realize the RQD value calculation of the surrounding rock on the basis of the scheme of the invention for the received tunnel face image, and return the final calculation result to the terminal equipment.
The method for measuring and calculating the RQD of the surrounding rock provided by this embodiment is generally performed by the server, and in practical applications, the terminal device can also directly perform the RQD measurement and calculation of the surrounding rock under the condition that the requirement is satisfied.
Similarly, the computer-readable medium of the present invention stores thereon a computer program, which when executed by a processor implements the RQD value estimation method for a surrounding rock according to an embodiment of the present invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for measuring and calculating a RQD value of surrounding rock based on virtual drilling is characterized by comprising the following steps:
performing superpixel segmentation on a tunnel face image, and then calculating the weight of edges among superpixel blocks according to the mean value and the variance of each superpixel block in an LAB color space and the mean value of Sobel filter response values on the boundary among the superpixel blocks to generate a region adjacency graph; marking the boundary of the superpixel block corresponding to the edge with the weight not greater than the set threshold in the region adjacency graph as a digestion state; finally, regarding all boundaries of the superpixel blocks in a non-digestion state in the region adjacency graph as rock mass joint fractures, and obtaining a face joint fracture distribution graph;
step two, arranging a plurality of virtual measuring drill holes parallel to the central axis of the face at equal intervals on the distribution map of the joint cracks of the face;
and step three, calculating the RQD value of the surrounding rock of each virtual measuring drill hole according to the length of a block body formed by dividing the joint crack of the tunnel face in each virtual measuring drill hole, and taking the weighted average value of the RQD values of the surrounding rocks of all the virtual measuring drill holes as the RQD value of the tunnel face surrounding rock.
2. The method for measuring and calculating the RQD value of the surrounding rock based on the virtual borehole of claim 1, wherein the first step of performing superpixel segmentation comprises the following steps of:
firstly, converting a tunnel face image from an RGB space to an LAB space, then randomly arranging a plurality of seed points on the image, taking the square root of the total number of image pixel points divided by the number of the seed points as a step length, calculating the distance measurement between other pixel points and the seed points in a range of twice the step length around each seed point, and finally selecting the seed point with the minimum distance measurement as a clustering center for each pixel point, thereby taking the pixel point with the same clustering center as a super pixel block.
3. The virtual borehole-based RQD value measurement and calculation method for the surrounding rock according to claim 1, characterized in that: in the first step, the calculation formula of the weight of the edges between the superpixel blocks is as follows:
Figure 694601DEST_PATH_IMAGE001
wherein the content of the first and second substances,a，brespectively representing superpixel blocksaAndb，μ i andξ i respectively representing the mean and covariance in the superpixel LAB space,λ 1andλ 2are all normalized coefficients and are obtained by the following steps,sobel（i) Represent an image iniPoint of interestThe sobel filter response value of (1),Irepresenting the area covered by the boundary between two super-pixel blocks.
4. The method for RQD value estimation of a virtual borehole-based wall rock as defined in claim 1, wherein in step one, the region adjacency graph is generated by using coordinates of all pixels in a single superpixel block as nodes and weight values of two adjacent superpixel blocks as edges.
5. The virtual borehole-based RQD value measurement and calculation method for the surrounding rock according to claim 1, characterized in that: in the second step, the arrangement mode of the virtual measurement drilling holes is as follows:
calculating the actual length corresponding to a single pixel in an image according to the focal length of a camera, the size of a sensor of the camera and the distance from the camera to the tunnel face of the tunnel;
secondly, on the distribution diagram of the joint crack of the tunnel face, drawing an upper arch line and a lower arch line of the tunnel face according to the actual length corresponding to the single pixel obtained in the step I, and symmetrically arranging virtual measurement drill holes at intervals by taking the upper arch line of the tunnel as a starting point and the lower arch line as an end point and parallel to the central axis of the tunnel face.
6. The method for measuring and calculating the RQD value of the surrounding rock based on the virtual borehole according to the claim 5, wherein in the step (i), the actual length corresponding to a single pixel is as follows: (camera sensor size/pixel size of image) × (camera distance from tunnel face/camera focal length).
7. The virtual borehole-based RQD value measurement and calculation method for the surrounding rock according to claim 1, characterized in that: in the third step, the method for measuring and calculating the RQD value of the surrounding rock on the tunnel face comprises the following steps:
1) respectively calculating RQD value of each virtual measurement borehole, namely, the blocks formed by dividing the virtual measurement borehole by the joint crack of the face, and the accumulated length of all the blocks larger than 10cmL’ i()And a virtual measuring drillLength of holeL i()Percent of the ratio;
2) taking the weighted average of the RQD values of all the virtual measurement drilling holes to obtain the RQD value of the face.
8. The virtual borehole-based RQD value measurement and calculation method for the surrounding rock according to claim 1, characterized in that: in the third step, the weighted average of the RQD values of the surrounding rocks of all the virtual measurement boreholes is calculated by using the length of each virtual measurement borehole as a weight value.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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CN113920141A (en) * 2021-12-15 2022-01-11 中南大学 Rock integrity coefficient calculation method and device and storage medium
CN115656053B (en) * 2022-10-19 2024-05-31 山东大学 Rock mineral content testing method and system

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CN106296678A (en) * 2016-08-03 2017-01-04 黄河勘测规划设计有限公司 RQD based on boring optical image technology analyzes method
CN112132403A (en) * 2020-08-25 2020-12-25 绍兴文理学院 RQD based on photogrammetry and BQ inversiontOptimal threshold t solving method
CN112241711A (en) * 2020-10-22 2021-01-19 东北大学 Intelligent method for identifying RQD from borehole core photo

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US20080283738A1 (en) * 2004-05-28 2008-11-20 Stephan Peters Device for Examining Rotor Drilled Holes
US20140139879A1 (en) * 2012-11-22 2014-05-22 Kyocera Document Solutions Inc. Image Forming Apparatus That Allows for a Multi-Operation
CN106296678A (en) * 2016-08-03 2017-01-04 黄河勘测规划设计有限公司 RQD based on boring optical image technology analyzes method
CN112132403A (en) * 2020-08-25 2020-12-25 绍兴文理学院 RQD based on photogrammetry and BQ inversiontOptimal threshold t solving method
CN112241711A (en) * 2020-10-22 2021-01-19 东北大学 Intelligent method for identifying RQD from borehole core photo

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113920141A (en) * 2021-12-15 2022-01-11 中南大学 Rock integrity coefficient calculation method and device and storage medium
CN113920141B (en) * 2021-12-15 2022-02-11 中南大学 Rock integrity coefficient calculation method and device and storage medium
CN115656053B (en) * 2022-10-19 2024-05-31 山东大学 Rock mineral content testing method and system

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