CN117147552A - Rock slag grading analysis method for TBM tunnel - Google Patents
Rock slag grading analysis method for TBM tunnel Download PDFInfo
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- CN117147552A CN117147552A CN202311418842.4A CN202311418842A CN117147552A CN 117147552 A CN117147552 A CN 117147552A CN 202311418842 A CN202311418842 A CN 202311418842A CN 117147552 A CN117147552 A CN 117147552A
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- 239000002893 slag Substances 0.000 title claims abstract description 78
- 239000011435 rock Substances 0.000 title claims abstract description 75
- 238000004458 analytical method Methods 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 30
- 230000000694 effects Effects 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 10
- 239000003086 colorant Substances 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 8
- 230000001502 supplementing effect Effects 0.000 claims description 8
- 239000002689 soil Substances 0.000 claims description 6
- 239000002245 particle Substances 0.000 claims description 5
- 238000003709 image segmentation Methods 0.000 claims description 4
- 230000005641 tunneling Effects 0.000 claims description 4
- 241001270131 Agaricus moelleri Species 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000005498 polishing Methods 0.000 claims description 2
- 239000011800 void material Substances 0.000 claims description 2
- 238000010276 construction Methods 0.000 abstract description 7
- 238000012360 testing method Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/10—Image acquisition
- G06V10/12—Details of acquisition arrangements; Constructional details thereof
- G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
- G06V10/141—Control of illumination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W30/00—Technologies for solid waste management
- Y02W30/50—Reuse, recycling or recovery technologies
- Y02W30/91—Use of waste materials as fillers for mortars or concrete
Abstract
The invention relates to a rock slag grading analysis method for a TBM tunnel, which belongs to the technical field of civil engineering and is used for solving the problems that the conventional analysis test is usually carried out indoors and has a long period, and grading parameters cannot be provided for drivers in time as operation references; compared with the prior art, the method for verifying the TBM tunnel slag grading is reasonable and effective by reasonably simplifying the slag shape, and can provide guidance for the construction process of the TBM.
Description
Technical Field
The invention belongs to the technical field of civil engineering, and particularly relates to a rock slag grading analysis method for a TBM tunnel.
Background
In recent years, along with the vigorous development of tunnel construction technology, TBM construction has become a main construction method for mountain tunnel construction in China, the grading parameters of the rock slag are required to be obtained, the traditional mode is determined through a screening test, namely, the rock slag passes through standard screens with different apertures to obtain the weight of the rock slag in each grain size range, and finally, grading characteristic curves are drawn and related parameters are obtained;
because the analysis test is usually carried out indoors and the period is longer, and the rock slag is transported to the outside of the hole from the face through the belt, the method can not provide grading parameters for drivers in time as operation references even more than half an hour when the tunneling distance is longer, the image recognition technology comprises face recognition and commodity recognition to be greatly broken through along with the development of the computer field at present, and the image recognition technology is fully applied to the fields of safety inspection, identity verification, mobile payment, intelligent retail cabinets and the like, and the image recognition technology is tried to be applied to the acquisition of the rock slag grading parameters in consideration of the advantages of high working efficiency, small space occupation and the like of the technology.
In view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a rock slag grading analysis method for a TBM tunnel, which is used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the rock slag grading analysis method for the TBM tunnel is characterized by comprising the following steps of:
step S1: a set of image acquisition system is developed by combining the belt characteristics, and comprises a light supplementing lamp and a high-speed camera for acquiring slag soil information;
step S2: image segmentation is carried out after rock residue images are acquired so as to improve the recognition effect, and RGB color modes are selected for image recognition so as to separate rock residue from a belt;
step S3: the shape of the rock slag is equivalent to a cuboid, the shape of the rock slag is reasonably simplified, and the application effect of an equivalent method is analyzed;
step S4: and four characteristic parameters of curvature coefficient, non-uniformity coefficient, maximum particle size and roughness index are selected to describe the rock slag, and the rock slag level identification effect is verified through comparison analysis with the actually measured slag soil grading characteristic parameters.
Further, in step S1, the image acquisition system is disposed above the rear supporting belt by 50cm and 60m from the tunnel face, and includes a light supplement lamp and a high-speed camera.
Further, in step S1, the shooting range of the high-speed camera is 50×50cm, the light compensating lamp polishes in the vertical direction, the recognition accuracy of the system is improved by improving the overall brightness of the image, and the rock slag on the belt is shot in the TBM stable tunneling stage.
Further, in step S2, the image is divided into a plurality of disjoint areas with obvious differences according to the characteristics of geometric shapes, colors, gray scales, spatial textures and the like, so that the image main body is separated from the image background, all colors are obtained by the variation of the three primary color channels of red, green and blue and the superposition of the three primary color channels, and the RGB color modes are selected for image recognition.
Further, in step S2, the RGB image is binarized by using a local threshold segmentation method according to the following formula,
where i and j are the abscissa of the pixel,andbefore and after binarizationGray value 0 represents black, gray value 255 represents white, and T is the segmentation threshold.
Further, in step S2, the rock slag and the void are separated by binarization, and the separated rock slag area is secondarily divided by adopting a watershed division method.
Further, in step S3, the pixel area of the rock slag is first obtainedMaximum Euclidean distance between two pixel units in rock residue regionThe pixel length in the graph is calculated by the following formula，；
To compare the measured length with the pixel length, the following is scaled:
where, l is the true size of the image,for the pixel size of the image, a and b are a respectively p And b p Is the actual size of (a);
after the two-dimensional size of the rock slag is obtained, mapping the two-dimensional image into a three-dimensional image, respectively equating slag pieces into cuboids, respectively taking a and b for the equivalent cuboids, simultaneously introducing a dimensionless coefficient lambda, and defining the ratio of the width to the thickness of the rock slag.
Further, in step S4, identifying the rock slag grading parameters under each surrounding rock category, and comparing the obtained identification result with the actually measured result;
when the error is positive, the corresponding actual measurement value is underestimated as the representative recognition result,
when the error is negative, the representative recognition result overestimates the measured value.
Compared with the prior art, the invention has the beneficial effects that:
when the method is used, the grading condition of the rock slag can be predicted to a certain extent, the actually measured grading curve under each working condition is basically consistent with the recognition result, the absolute error of the mass accumulation rate is within 4 percent, the recognition effect of the method on the non-uniform coefficient is optimal, the relative error is less than 2.8 percent, the maximum particle size is less than 6.8 percent, and the maximum relative error of the curvature coefficient and the non-uniform coefficient is 17.1 percent and 14.3 percent respectively. The method for grading the TBM tunnel rock slag is proved to be reasonable and effective, and can provide a certain guide for the construction process of the TBM.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a flow chart of a method for analyzing the slag composition of a TBM tunnel according to the present invention.
FIG. 2 is a schematic diagram of an image recognition system according to the present invention;
FIG. 3 is a graph showing the contrast of images of rock slag after image segmentation according to the present invention;
FIG. 4 is a two-dimensional simplified diagram of the shape of the rock slag in accordance with the present invention;
FIG. 5 is a three-dimensional simplified diagram of the slag shape according to the present invention;
FIG. 6 is a graph showing the comparison of the identification result and the actual measurement result according to the present invention;
FIG. 7 is a graph showing the comparison of the identification result and the actual measurement result according to the present invention;
FIG. 8 is a graph showing the comparison of the identification result and the actual measurement result according to the present invention;
FIG. 9 is a graph showing the comparison of the identification result and the actual measurement result according to the present invention;
fig. 10 is a graph showing comparison between the identification result and the actual measurement result according to the present invention.
Reference numerals: 1. a light supplementing lamp; 2. a high speed camera.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The invention provides a rock slag grading analysis method for a TBM tunnel, which comprises steps S1-S4.
Step S1, a set of image acquisition system is developed by combining the characteristics of a belt and mainly comprises a light supplementing lamp 1 and a high-speed camera 2 for acquiring muck information.
The method comprises the following specific steps:
as shown in fig. 2, an image acquisition system developed in connection with belt features.
The image acquisition system is arranged above the rear supporting belt 50cm, is approximately 60m away from the tunnel face, comprises a light supplementing lamp 1 and a high-speed camera 2, the shooting range of the camera is 50 multiplied by 50cm, the light supplementing lamp is used for polishing along the vertical direction, the recognition precision of the system is improved by improving the overall brightness of an image, and rock slag on the belt is shot in a TBM stable tunneling stage.
Example two
And S2, performing image segmentation after obtaining the rock residue image so as to improve the recognition effect, selecting an RGB color mode for image recognition, and separating the rock residue (image main body) from the belt (image background).
The method comprises the following specific steps:
as shown in fig. 3, a slag identification map is obtained by dividing the image.
The image is divided into a plurality of disjoint areas with obvious differences according to the characteristics of geometric shapes, colors, gray scales, space textures and the like, so that the image main body is separated from the image background. The RGB color mode obtains almost all colors perceived by human vision through the change of the three primary colors (red, green and blue) and the mutual superposition of the three primary colors, is one of the most widely applied color systems, has wide application scene in view of the clear physical meaning, and selects the RGB color mode for image recognition.
The RGB image is binarized by a local threshold segmentation method according to the following formula.
Where i and j are the abscissa of the pixel,andbefore and after binarizationGray value 0 represents black, gray value 255 represents white, and T is the segmentation threshold.
Through binarization, the rock slag and the gaps are separated, but the distance between slag blocks is relatively short in the application process, and even the slag blocks are shielded, so that slag blocks with larger quantity and size can exist in the rock slag identification result. Therefore, the separated rock residue area is subjected to secondary segmentation by adopting a watershed segmentation method.
Example III
And S3, the shape of the rock slag is equivalent to a cuboid, the shape of the rock slag is reasonably simplified, and the application effect of the equivalent method is analyzed.
The method comprises the following specific steps:
as shown in fig. 4 to 5, the equivalent shape of the rock slag after the shape simplification is performed.
Firstly, obtaining the pixel area of rock slagMaximum Euclidean distance between two pixel units in rock residue regionThe pixel length in the graph is calculated by，。
To compare the measured length with the pixel length, the following is scaled:
where, l is the true size of the image,for the pixel size of the image, a and b are a respectively p And b p Is a practical size of the (c) device.
After the two-dimensional size of the rock slag is obtained, mapping the two-dimensional image into a three-dimensional image, respectively equating slag pieces into cuboids, respectively taking a and b for the equivalent cuboids, simultaneously introducing a dimensionless coefficient lambda, and defining the ratio of the width to the thickness of the rock slag.
Example IV
And S4, selecting four characteristic parameters of curvature coefficient, non-uniformity coefficient, maximum particle size and roughness index to describe the rock slag, and verifying the rock slag level identification effect by comparing and analyzing with the actually measured slag soil grading characteristic parameters.
As shown in fig. 6, a comparison graph of predicted values and measured values of the slag soil parameters under different surrounding rock categories is shown.
The method comprises the following specific steps:
by identifying the rock slag grading parameters under each surrounding rock category, the obtained identification result is compared with the actual measurement result. When the error is positive, the recognition result underestimates the corresponding actual measurement value, and when the error is negative, the recognition result overestimates the actual measurement value. In general, the rock slag image recognition method can predict the grading condition of the rock slag to a certain extent, the actually measured grading curve under each working condition is basically consistent with the recognition result, and the absolute error of the mass accumulation rate is within 4%. The method has the advantages that the identification effect on the non-uniform coefficient is optimal, the relative error is smaller than 2.8%, the maximum particle size is smaller than 6.8%, and the maximum relative error of the curvature coefficient and the non-uniform coefficient is 17.1% and 14.3% respectively. The method for grading the TBM tunnel rock slag is proved to be reasonable and effective, and can provide a certain guide for the construction process of the TBM.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The rock slag grading analysis method for the TBM tunnel is characterized by comprising the following steps of:
step S1: a set of image acquisition system is developed by combining the belt characteristics, and comprises a light supplementing lamp (1) and a high-speed camera (2) for acquiring slag soil information;
step S2: image segmentation is carried out after rock residue images are acquired so as to improve the recognition effect, and RGB color modes are selected for image recognition so as to separate rock residue from a belt;
step S3: the shape of the rock slag is equivalent to a cuboid, the shape of the rock slag is reasonably simplified, and the application effect of an equivalent method is analyzed;
step S4: and four characteristic parameters of curvature coefficient, non-uniformity coefficient, maximum particle size and roughness index are selected to describe the rock slag, and the rock slag level identification effect is verified through comparison analysis with the actually measured slag soil grading characteristic parameters.
2. The rock slag grading analysis method for the TBM tunnel according to claim 1, wherein in the step S1, the image acquisition system is arranged above the rear supporting belt by 50cm and 60m from the tunnel face, and comprises a light supplementing lamp (1) and a high-speed camera (2).
3. The rock slag grading analysis method for the TBM tunnel according to claim 1, wherein in the step S1, the shooting range of the high-speed camera (2) is 50×50cm, the light supplementing lamp (1) is used for polishing in the vertical direction, the identification precision of the system is improved by improving the overall brightness of an image, and the rock slag on the belt is shot in the TBM stable tunneling stage.
4. The method for analyzing the rock slag gradation of the TBM tunnel according to claim 1, wherein in the step S2, the image is divided into a plurality of disjoint areas with obvious differences according to the characteristics of geometric shapes, colors, gray scales, space textures and the like, so that the image main body is separated from the image background, all colors are obtained by the change of three primary color channels of red, green and blue and the superposition of the three primary color channels, and the RGB color modes are selected for image recognition.
5. A rock mass grading analysis method for TBM tunnel according to claim 1, wherein in step S2, RGB image is binarized by local threshold segmentation method according to the following formula,in the formula, i and j are the abscissa and the ordinate of the pixel point, < >>And->Before and after binarization->Gray value 0 represents black, gray value 255 represents white, and T is the segmentation threshold.
6. The method for analyzing the rock slag gradation of the TBM tunnel according to claim 1, wherein in the step S2, the rock slag and the void are separated by binarization, and the separated rock slag area is secondarily divided by a watershed dividing method.
7. The method for analyzing the rock slag gradation of a TBM tunnel according to claim 1, wherein in step S3, the pixel area of the rock slag is first obtainedMaximum Euclidean distance between two pixel units in rock slag area>The pixel length +.in the graph is calculated by the following formula>，/>；
，/>To compare the measured length with the pixel length, the following is scaled:
，/>wherein l is the true size of the image, < +.>For the pixel size of the image, a and b are a respectively p And b p Is the actual size of (a);
after the two-dimensional size of the rock slag is obtained, mapping the two-dimensional image into a three-dimensional image, respectively equating slag pieces into cuboids, respectively taking a and b for the equivalent cuboids, simultaneously introducing a dimensionless coefficient lambda, and defining the ratio of the width to the thickness of the rock slag.
8. The method for analyzing the rock slag gradation of the TBM tunnel according to claim 1, wherein in the step S4, the identification result obtained by identifying the rock slag gradation parameters under each surrounding rock category is compared with the actually measured result;
when the error is positive, the corresponding actual measurement value is underestimated as the representative recognition result,
when the error is negative, the representative recognition result overestimates the measured value.
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