CN117934484A - Tunnel face surrounding rock level judging method, equipment and storage medium - Google Patents
Tunnel face surrounding rock level judging method, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a method, equipment and a storage medium for judging the surrounding rock level of a tunnel face, which comprise the following steps: acquiring and storing tunnel face matched data from a mobile terminal in a wireless mode, wherein the tunnel face matched data comprises image information of a tunnel face; performing point clouding and gridding treatment on the image information to construct a rock mass digital surface model; based on the rock mass digital surface model, extracting the interfacial distance information of the tunnel face structure through clustering; obtaining structural surface roughness information of the face based on the rock mass digital surface model; and calculating a surrounding rock grade evaluation index based on the surface interval information and the structural surface roughness information to realize surrounding rock grade judgment. Compared with the prior art, the method has the advantages of improving the judging efficiency of the surrounding rock level, along with high accuracy, high flexibility and the like.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, equipment and a storage medium for judging the surrounding rock level of a tunnel face.
Background
The mountain tunnel is designed and constructed mainly by adopting a new Otto method, and the core idea is to fully utilize the self-bearing capacity of surrounding rock, restrict the deformation of the surrounding rock by adopting means such as anchor spraying support and the like, and dynamically adjust the design and the construction by monitoring and measuring. However, the geological conditions of actual engineering are often complex and changeable, and the untimely and inexact acquisition of geological information brings great difficulty to dynamic design and safe and efficient construction. Therefore, in the actual construction process, geological information acquisition, tunnel dynamic design and construction are extremely easy to be disjointed, intelligent means are needed to be used for enabling the three, acquisition, design and construction are integrated into a seamless whole, and intelligent transformation and upgrading of tunnel construction are realized. Therefore, the determination of the surrounding rock level of the face is a precondition for intelligent design and construction of the tunnel, and the content of the determination needs to be studied in depth to assist intelligent construction of the tunnel.
The international rock mechanics society considers that the discontinuous surface of the rock mass plays a decisive role in the mechanical properties of the rock mass to a great extent, and the water leakage of the discontinuous surface of the rock mass is used as one of ten key indexes for quantitatively describing the properties of the discontinuous surface of the rock mass. In addition, the main surrounding rock quality evaluation systems (BQ, RMR, Q, GSI and the like) all take the state (water quantity, water pressure, position, shape) of discontinuous surface water leakage as one of key calculation parameters. On the one hand, the existing detection and prediction technology for bad geology such as underground water and the like based on a geological advanced prediction method mostly performs qualitative analysis in a certain range near a detected surface, and is difficult to accurately identify and extract the water leakage state of the rock mass surface near an excavated surface; on the other hand, due to the diversity of rock mass characteristics near the excavated surface, an effective method for directly and finely identifying the water leakage state of the discontinuous surface of the rock mass is not available. At present, a plurality of researches adopt a machine learning convolutional neural network image recognition method, progress is made on the recognition of the seepage water of the concrete material of the tunnel segment or lining, and a reference can be provided for the recognition of the seepage water of the discontinuous surface of the follow-up research rock mass.
At present, the geotechnical engineering field has a plurality of researches aiming at the influence of fillers on the mechanical properties of rock mass, including the structural surface shear property research, the structural surface fracture stress-seepage coupling research, the structural surface fracture shear-seepage characteristic research and the like. However, identification algorithms directed to the composition and nature of rock mass discontinuous surface fills have been less studied. But referring to the existing mechanical property test and machine learning algorithm for the filler rock mass, the detection of the filler based on the image recognition technology can be primarily tried, and the recognition of the in-situ rock mass filler is further realized.
In summary, a method for quickly judging the surrounding rock level of the tunnel face is lacking currently, so as to solve or partially solve the problems existing in the prior art.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method, equipment and a storage medium for judging the surrounding rock level of a tunnel face so as to improve the judging efficiency of the surrounding rock level.
The aim of the invention can be achieved by the following technical scheme:
The invention provides a method for judging the level of surrounding rock of a tunnel face, which comprises the following steps:
Acquiring and storing tunnel face matched data from a mobile terminal in a wireless mode, wherein the tunnel face matched data comprises image information of a tunnel face;
performing point clouding and gridding treatment on the image information to construct a rock mass digital surface model;
Based on the rock mass digital surface model, extracting the interfacial distance information of the tunnel face structure through clustering;
obtaining structural surface roughness information of the face based on the rock mass digital surface model;
And calculating a surrounding rock grade evaluation index based on the surface interval information and the structural surface roughness information to realize surrounding rock grade judgment.
As a preferable technical solution, the extracting process of the inter-surface distance information includes:
Extracting the trace lines in the rock mass digital surface model, calculating the main direction of each trace line, grouping the trace lines through clustering, and calculating the average main direction of each trace line in each group;
and drawing the test line by interaction with a user, calculating the intersection point of the test line and the trace, and obtaining the average distance between the intersection points in each group of trace as the surface distance information.
As a preferable technical scheme, the traces are clustered based on a K-means clustering algorithm.
As a preferred embodiment, the average distance is calculated by the following formula:
wherein, Is the average spacing within a group,/>To the first/>, of the line and the trace within the groupIntersection points/>Representing the intersection/>And intersection/>Distance between/>Is the number of intersection points,/>Is a small group number.
As a preferable technical solution, the calculating process of the structural surface roughness information includes:
equidistant cutting is carried out on the rock mass digital surface model, a two-dimensional roughness contour line is obtained, and the roughness coefficient of the rock joint surface of the roughness contour line is calculated;
and calculating the roughness coefficient average value of the rock joint surface to obtain the three-dimensional roughness, and taking the three-dimensional roughness as the roughness information of the structural surface.
As a preferred technical scheme, the method further comprises:
Outputting the surrounding rock grade evaluation index to a mobile terminal for visual display.
As an optimal technical scheme, the tunnel face matching data also comprises a line name, mileage information, collector information, rock mass saturated compressive strength information, initial ground stress state information and groundwater water outlet state information.
As a preferable technical solution, the acquiring of the image information of the face includes the following steps:
And presetting a distance in front of the palm face, determining internal parameters of a camera through a field test, and acquiring images with different angles at the same position as image information by using the fixed internal parameters.
In another aspect of the present invention, there is provided a tunnel face surrounding rock level determination apparatus, including:
the data acquisition mobile terminal is used for acquiring tunnel face matched data, wherein the tunnel face matched data comprises image information of a tunnel face;
The data cloud end is used for acquiring and storing the tunnel face matched data in a wireless mode from the mobile end;
The analysis cloud end is used for carrying out point cloud and meshing processing on the image information, constructing a rock mass digital surface model, extracting the face spacing information of a tunnel face structure through clustering based on the rock mass digital surface model, obtaining the structural face roughness information of the face based on the rock mass digital surface model, calculating the surrounding rock grade evaluation index based on the face spacing information and the structural face roughness information, and transmitting the surrounding rock grade evaluation index to the data cloud end to realize surrounding rock grade judgment.
In another aspect of the invention, a computer-readable storage medium is provided, comprising one or more programs for execution by one or more processors of an electronic device, the one or more programs comprising instructions for performing the tunnel face surrounding rock level determination method described above.
Compared with the prior art, the invention has the following beneficial effects:
(1) Improve the judgement efficiency of country rock level: according to the application, after the image information of the face is acquired, the face spacing information and the roughness information of the structural face are respectively obtained by constructing the rock mass digital surface model, so that the evaluation index of the surrounding rock grade is calculated in an auxiliary manner, the determination of the surrounding rock grade is realized, the whole process is highly automated, and the determination efficiency of the surrounding rock grade is effectively improved.
(2) The accuracy of the obtained face image is high: aiming at the problems of more dust, dark light, limited shooting range and short shooting time of a tunnel construction site, the application determines the internal parameters of the digital camera by standardizing the shooting position and adopting a field test method, thereby being capable of constructing a stable and high-precision three-dimensional point cloud model of the tunnel face.
(3) The flexibility is high: the tunnel face surrounding rock level judging device provided by the invention has sharing property and independence, can be flexibly deployed in any analysis platform with a micro-service architecture, and overcomes the dependence of intelligent analysis systems such as tunnel remote diagnosis and the like on business programs in the present stage.
Drawings
Fig. 1 is a schematic flow chart of determining a surrounding rock level of a tunnel face in an embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Aiming at the problems existing in the prior art, the embodiment provides a method for judging the surrounding rock level of a tunnel face, and referring to fig. 1, the method comprises the following steps:
step S1, shooting a face through a mobile terminal application program and filling related matched information (including a mark section, mileage and time), so as to acquire the face image and the matched information. The method comprises the following substeps.
And S11, shooting time is two time periods after manual danger elimination or after the steel arch support. The shooting position is 3-4 m away from the front of the face, and the whole picture can be fully distributed on the rock mass.
Step S12, determining the internal parameters of the digital camera by adopting a field test method and fixing the internal parameters of the camera, thereby solving the problems of time consumption and inaccuracy of repeatedly calibrating the camera on site. Setting basic shooting parameters of a camera, focusing the rock mass of the face at the position 3-4 m in front of the face, and winding the internal parameters of the camera in the focusing environment protection certificate carrying process by using a transparent adhesive tape.
Aiming at the special environment (more dust, dark light and limited shooting range) of a tunnel construction site and the construction process (short shooting time reserved for workers) of a new Otto method, the standardized shooting method for realizing binocular three-dimensional reconstruction by adopting a digital camera in the tunnel construction process to obtain the three-dimensional point cloud model of the tunnel face is provided.
In the embodiment, a camera is used for collecting surrounding rock images of the face, 8 images are sampled at the same position according to different specified angles, the currently collected pictures are pushed through a mobile terminal, and the corresponding line names, mileage, collector, rock mass saturated compressive strength, initial ground stress state and groundwater water outlet state are filled in.
Step S2, the data is transmitted to a data management platform (namely a data cloud) of the cloud through a network, integrated storage of the data is carried out, and standardized data operation service is provided.
And step S3, performing point clouding and gridding on the image information rapidly in a cloud service mode, and storing the image information as a point cloud file.
And S4, based on a spatial contraction algorithm and a tensor voting algorithm, three-dimensional space mutation feature point identification and analysis are carried out.
And S5, extracting information such as rock mass contour coordinates, super-underexcavation quantity, discontinuous surface occurrence, distribution, opening degree, roughness and the like.
The extraction process of the structural face spacing information of the face comprises the following steps:
In step S51, the trajectories are automatically identified in a digital surface model of the rock mass (rock mass DSM) using techniques based on tensor voting theory and its optimization processing ("grouping", "growing algorithm extraction trajectory segments", "connection trajectory segments" and "linearization trajectory").
Step S52, calculating the main direction of each trace by main element analysis(i=1，2，3，…)。
Step S53, adopting a K-means clustering algorithm to perform clustering according to the main direction of the trace(I=1, 2,3 …) groups the traces.
Step S54, calculating the average principal direction of the traces in each subgroup(K is the subgroup number). For each subgroup, the direction of its line is/>Therefore, the position of a point (for example, can be determined by mouse manual interaction) can be determined, and the measuring line/>, can be automatically drawnAnd calculate the intersection point/>, of the line and the trace in the group(I=1, 2,3, …, n), where n is the number of intersections, thereby calculating the average spacing/>, of the panelAs shown in formula (1).
（1）
In the formula,Is the intersection/>And intersection/>Distance between them.
The structural surface roughness information extraction comprises the following steps:
step S51, equidistant cutting is performed on the rock mass DSM to obtain a two-dimensional roughness profile.
Step S52, calculating root mean square of different contour linesAnd converted to a rock joint surface roughness coefficient (JRC value) by equation (2).
（2）
And S53, adopting an average value of the roughness coefficients of the rock joint surfaces of a plurality of two-dimensional contour lines to approximately replace the three-dimensional roughness, and finishing the extraction of the roughness information of the structural surface.
And S6, calculating a correction BQ value (rock mass basic quality index value), an RMR (rock mass geomechanical classification) evaluation index and a GSI (rock mass geological intensity) evaluation index of the surrounding rock in real time by adopting a multi-source data fusion analysis method combining exploration, design and construction based on the extracted face geological information.
In the step, the surrounding rock intelligent sketch analysis service returns joint information, roughness information, rock mass integrity information and main weak structural plane attitude information based on the extracted spliced three-dimensional point cloud data of the face.
And S7, extracting and analyzing data by using cloud analysis service to judge the surrounding rock level of the face and obtain a judging result.
Specifically, in this step, the surrounding rock BQ value is calculated by the formula (3).
（3）
Wherein R c is uniaxial saturated compressive strength; k v is the rock mass integrity coefficient; k 1 is the groundwater influence correction coefficient; k 2 is the groundwater influence correction coefficient; k 3 is the initial pressure state influence correction coefficient.
And S8, finally, pushing the judging result to a front-end display interface of the data cloud and the data acquisition mobile terminal through a platform information pushing service, so as to realize data feedback and provide support for judging geological environment of the engineering site.
The method has the following characteristics:
① Intelligent collection of tunnel face information: the digital photogrammetry analysis mainly comprises two modes of directly carrying out image interpolation processing analysis on the excavated surface photo, obtaining the space point cloud data of the excavated surface by utilizing a three-dimensional imaging technology, and then carrying out analysis processing on the point cloud data. In the aspect of two-dimensional image analysis and research of excavation face photos, at present, a crack joint model is built by combining tunnel construction excavation face photos mainly through edge detection and boundary processing, and a geological sketch is automatically generated.
② Intelligent extraction of geological information: and automatically reconstructing the two-dimensional photos into a three-dimensional rock surface point cloud through a virtual multi-view algorithm. Automatically identifying space occurrence and performing joint face optimization intelligent grouping through an adaptive normal vector algorithm based on neighborhood point allocation and a rapid optimization clustering algorithm based on improved density peak values; and automatically extracting the tunnel face trace and roughness through a three-dimensional trace extraction algorithm based on a feature point shrinking technology and a three-dimensional roughness extraction algorithm based on a k-means++ contour technology.
Example 2
Referring to fig. 1, on the basis of embodiment 1, this embodiment provides a tunnel face surrounding rock level determination device, including:
The data acquisition mobile terminal is used for acquiring the tunnel face matched data, wherein the tunnel face matched data comprise image information and description information (scale section, mileage, time and the like) of the tunnel face, and information reminding and result displaying are carried out after the result is received.
The data cloud end is used for acquiring and storing tunnel face matched data in a wireless mode from the mobile end, wherein the data are stored in a recording and indexing mode.
The analysis cloud end is used for carrying out point clouding and meshing processing on the image information, constructing a rock mass digital surface model, extracting the face spacing information of the tunnel face structure through clustering based on the rock mass digital surface model, obtaining the structural face roughness information of the face based on the rock mass digital surface model, calculating the surrounding rock grade evaluation index based on the face spacing information and the structural face roughness information, and transmitting the surrounding rock grade evaluation index to the data cloud end to realize surrounding rock grade judgment.
Example 3
The present embodiment provides a computer-readable storage medium including one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing the tunnel face surrounding rock level determination method as described in embodiment 1.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method for judging the level of surrounding rock of a tunnel face is characterized by comprising the following steps:
Acquiring and storing tunnel face matched data from a mobile terminal in a wireless mode, wherein the tunnel face matched data comprises image information of a tunnel face;
performing point clouding and gridding treatment on the image information to construct a rock mass digital surface model;
Based on the rock mass digital surface model, extracting the interfacial distance information of the tunnel face structure through clustering;
obtaining structural surface roughness information of the face based on the rock mass digital surface model;
And calculating a surrounding rock grade evaluation index based on the surface interval information and the structural surface roughness information to realize surrounding rock grade judgment.
2. The method for determining the level of surrounding rock of tunnel face according to claim 1, wherein the extracting process of the face-to-face distance information comprises the following steps:
Extracting the trace lines in the rock mass digital surface model, calculating the main direction of each trace line, grouping the trace lines through clustering, and calculating the average main direction of each trace line in each group;
and drawing the test line by interaction with a user, calculating the intersection point of the test line and the trace, and obtaining the average distance between the intersection points in each group of trace as the surface distance information.
3. The method for judging the level of surrounding rock of tunnel face according to claim 2, wherein the traces are clustered based on a K-means clustering algorithm.
4. The method for determining the surrounding rock level of the tunnel face according to claim 2, wherein the average distance is calculated by the following formula:
,
wherein, Is the average spacing within a group,/>To the first/>, of the line and the trace within the groupIntersection points/>Representing the intersection/>And intersection/>Distance between/>Is the number of intersection points,/>Is a small group number.
5. The method for determining the surrounding rock level of the tunnel face according to claim 1, wherein the calculating process of the structural surface roughness information comprises the following steps:
equidistant cutting is carried out on the rock mass digital surface model, a two-dimensional roughness contour line is obtained, and the roughness coefficient of the rock joint surface of the roughness contour line is calculated;
and calculating the average value of the roughness coefficients of the rock joint surface to obtain the three-dimensional roughness, and taking the three-dimensional roughness as the roughness information of the structural surface.
6. The method for determining the level of surrounding rock of a tunnel face according to claim 1, further comprising:
Outputting the surrounding rock grade evaluation index to a mobile terminal for visual display.
7. The method for determining the surrounding rock level of the tunnel face according to claim 1, wherein the tunnel face matching data further comprises a line name, mileage information, collected person information, rock mass saturation compressive strength information, initial ground stress state information and groundwater water outlet state information.
8. The method for determining the surrounding rock level of the tunnel face according to claim 1, wherein the step of acquiring the image information of the face comprises the steps of:
And presetting a distance in front of the palm face, determining internal parameters of a camera through a field test, and acquiring images with different angles at the same position as image information by using the fixed internal parameters.
9. A tunnel face surrounding rock level determination apparatus, comprising:
the data acquisition mobile terminal is used for acquiring tunnel face matched data, wherein the tunnel face matched data comprises image information of a tunnel face;
The data cloud end is used for acquiring and storing the tunnel face matched data in a wireless mode from the mobile end;
The analysis cloud end is used for carrying out point cloud and meshing processing on the image information, constructing a rock mass digital surface model, extracting the face spacing information of a tunnel face structure through clustering based on the rock mass digital surface model, obtaining the structural face roughness information of the face based on the rock mass digital surface model, calculating the surrounding rock grade evaluation index based on the face spacing information and the structural face roughness information, and transmitting the surrounding rock grade evaluation index to the data cloud end to realize surrounding rock grade judgment.
10. A computer readable storage medium comprising one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing the tunnel face surrounding rock level determination method of any one of claims 1-8.
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| CN202410338817.3A CN117934484A (en) | 2024-03-25 | 2024-03-25 | Tunnel face surrounding rock level judging method, equipment and storage medium |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103065051A (en) * | 2012-12-31 | 2013-04-24 | 中国铝业股份有限公司 | Method for performing grading and sectionalizing on rock mass automatically |
| CN112950771A (en) * | 2021-04-02 | 2021-06-11 | 中国铁建重工集团股份有限公司 | Detection method and system for surrounding rock structure and related components |
| CN113256082A (en) * | 2021-05-07 | 2021-08-13 | 中交第二航务工程局有限公司 | Intelligent geological sketch and geological evaluation method for tunnel face |
| CN113378909A (en) * | 2021-06-07 | 2021-09-10 | 武汉科技大学 | Grading characterization method, device and medium for roughness coefficient of rock joint surface |
| CN113553763A (en) * | 2021-07-12 | 2021-10-26 | 中铁二十局集团有限公司 | Tunnel surrounding rock rapid grading method and system |
| CN117540476A (en) * | 2023-11-21 | 2024-02-09 | 中铁第一勘察设计院集团有限公司 | Extremely hard rock tunnel structural surface and unfavorable combination spatial distribution prediction method and system thereof |
-
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- 2024-03-25 CN CN202410338817.3A patent/CN117934484A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103065051A (en) * | 2012-12-31 | 2013-04-24 | 中国铝业股份有限公司 | Method for performing grading and sectionalizing on rock mass automatically |
| CN112950771A (en) * | 2021-04-02 | 2021-06-11 | 中国铁建重工集团股份有限公司 | Detection method and system for surrounding rock structure and related components |
| CN113256082A (en) * | 2021-05-07 | 2021-08-13 | 中交第二航务工程局有限公司 | Intelligent geological sketch and geological evaluation method for tunnel face |
| CN113378909A (en) * | 2021-06-07 | 2021-09-10 | 武汉科技大学 | Grading characterization method, device and medium for roughness coefficient of rock joint surface |
| CN113553763A (en) * | 2021-07-12 | 2021-10-26 | 中铁二十局集团有限公司 | Tunnel surrounding rock rapid grading method and system |
| CN117540476A (en) * | 2023-11-21 | 2024-02-09 | 中铁第一勘察设计院集团有限公司 | Extremely hard rock tunnel structural surface and unfavorable combination spatial distribution prediction method and system thereof |
Non-Patent Citations (1)
| Title |
|---|
| 陈建琴 等: "基于点云的岩体间距与粗糙度的自动化提取", 地下空间与工程学报, 28 February 2017 (2017-02-28), pages 133 - 140 * |
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