Method and system for judging grade of surrounding rock in front of tunnel face

Abstract

The invention provides a method and a system for judging the grade of surrounding rock in front of a tunnel face, and relates to the technical field of tunnel geology advanced forecasting, and the specific scheme comprises the following steps: collecting trace length information of each structural surface on the tunnel face in a tunnel excavation process; clustering the structural surfaces on the tunnel face into a deterministic structural surface and a random structural surface based on the trace length information; according to the track length information of the deterministic structural plane and the random structural plane, respectively predicting the track length of the deterministic structural plane and the track length of the random structural plane of the front non-excavated tunnel face; according to the track length information obtained through prediction, the rock mass fracture rate of the front unexcavated tunnel face is calculated, and the surrounding rock grade is judged; according to the method, the surrounding rock level of the front unexcavated part is predicted according to the structural plane trace length information on the excavated tunnel face, a scientific basis is provided for tunnel dynamic design and surrounding rock level change, the construction progress can be accelerated, and the construction cost is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of tunnel geological advance prediction, and in particular relates to a method and system for judging the grade of surrounding rock in front of a tunnel face. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The evaluation of tunnel surrounding rock grade is of great significance to the construction of tunnel projects. It is of great significance to the determination of specific tunnel construction methods and the selection of supporting structure parameters such as anchors and arch frames, and has an important impact on the cost and progress of construction.

[0004] During the tunnel construction process, although the engineering geological conditions will be surveyed and a surrounding rock quality evaluation report will be given in the early stage of construction, the surrounding rock quality evaluation results in the early stage of construction often have large errors compared with the actual construction conditions, which makes it difficult to meet the needs of tunnel dynamic design and construction, thus affecting the construction period and even causing unnecessary casualties.

[0005] Existing methods for judging the grade of surrounding rock ahead, such as the method for predicting the grade of surrounding rock ahead of the tunnel face based on the principle of spatial mapping and the method for predicting the grade of surrounding rock ahead based on advanced geological forecast data, can predict the grade of surrounding rock ahead of the tunnel face to a certain extent. However, the processing of prediction data is highly professional, time-consuming, and difficult for technical personnel to master, and lacks certain practical application value. Therefore, it is urgent to invent a method for quickly and easily judging the grade of surrounding rock ahead of the tunnel face to provide a practical theoretical basis for actual construction. Summary of the invention

[0006] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method and system for judging the surrounding rock grade in front of the tunnel face. The surrounding rock grade of the unexcavated part in front is predicted according to the structural surface trace length information on the excavated tunnel face, which provides a scientific basis for the dynamic design of the tunnel and the change of surrounding rock grade, and is conducive to speeding up the construction progress and reducing construction costs.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0008] A first aspect of the present invention provides a method for determining the grade of surrounding rock in front of a tunnel face.

[0009] A method for determining the grade of surrounding rock in front of a tunnel face, comprising:

[0010] During tunnel excavation, the trace length information of each structural surface on the tunnel face is collected;

[0011] Based on the trace length information, the structural surfaces on the tunnel face are clustered into deterministic structural surfaces and random structural surfaces.

[0012] According to the trace length information of the deterministic structural surface and the random structural surface, the trace length of the deterministic structural surface and the trace length of the random structural surface of the unexcavated tunnel face in front are predicted respectively;

[0013] Based on the predicted trace length information, the rock fracture rate of the unexcavated tunnel face ahead is calculated to determine the surrounding rock grade.

[0014] Furthermore, the trace length information of each structural surface on the tunnel face is collected during the tunnel excavation process, specifically:

[0015] Use 3D laser scanner to scan the tunnel face;

[0016] The trace length information of the structural surface on the tunnel face is obtained from the scanned image.

[0017] Furthermore, the clustering of the structural surfaces on the tunnel face into deterministic structural surfaces and random structural surfaces is specifically as follows:

[0018] The K-Means clustering method is used to divide the structural surface trace lengths on the tunnel face into two categories.

[0019] Furthermore, the deterministic structural surface trace length and the random structural surface trace length of the unexcavated tunnel face are predicted respectively, specifically:

[0020] The trace lengths of the deterministic structural surface and the random structural surface on each excavated tunnel face are summed up respectively to obtain the sum of the trace lengths of the deterministic structural surface and the sum of the trace lengths of the random structural surface on each tunnel face;

[0021] According to the sum of the trace lengths of the deterministic structural surfaces and the sum of the trace lengths of the random structural surfaces, the autoregressive model is used to predict the sum of the trace lengths of the deterministic structural surfaces and the sum of the trace lengths of the random structural surfaces of the unexcavated tunnel face in front.

[0022] Furthermore, the calculation of the rock mass fissure ratio of the unexcavated tunnel face is specifically as follows:

[0023] According to the sum of the trace lengths of the structural surfaces on the unexcavated tunnel face, the trace length value contained in the unit area of ​​the tunnel face is calculated as the rock mass fracture ratio;

[0024] Furthermore, the sum of the structural surface trace lengths on the unexcavated tunnel face is obtained by adding the sum of the deterministic structural surface trace lengths and the sum of the random structural surface trace lengths.

[0025] Furthermore, the surrounding rock grade is determined based on the relationship between the rock mass fissure ratio and the rock mass quality grade to obtain the surrounding rock grade.

[0026] A second aspect of the present invention provides a system for determining the grade of surrounding rock in front of a tunnel face.

[0027] A system for judging the grade of surrounding rock in front of a tunnel face, comprising an information collection module, a structural surface classification module, a trace length prediction module and a grade judgment module:

[0028] The information collection module is configured to: collect trace length information of each structural surface on the tunnel face during tunnel excavation;

[0029] The structural surface classification module is configured to: cluster the structural surfaces on the tunnel face into deterministic structural surfaces and random structural surfaces based on the trace length information;

[0030] The trace length prediction module is configured to: predict the trace length of the deterministic structural surface and the trace length of the random structural surface of the unexcavated tunnel face in front respectively according to the trace length information of the deterministic structural surface and the random structural surface;

[0031] The grade judgment module is configured to calculate the rock mass fracture rate of the unexcavated tunnel face ahead based on the predicted trace length information and make a judgment on the surrounding rock grade.

[0032] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a method for determining the grade of surrounding rock in front of a tunnel face as described in the first aspect of the present invention.

[0033] A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a method for determining the grade of surrounding rock in front of a tunnel face as described in the first aspect of the present invention are implemented.

[0034] One or more of the above technical solutions have the following beneficial effects:

[0035] The present invention predicts the surrounding rock grade of the unexcavated part ahead based on the structural surface trace length information on the excavated face, which can provide a scientific basis for tunnel dynamic design and surrounding rock grade change, and is conducive to accelerating construction progress and reducing construction costs.

[0036] The information relied on by the present invention for predicting the surrounding rock grade of the unexcavated part in front of the tunnel face is the trace length value of the structural surface on each excavated tunnel face, and does not require information such as the inclination and dip angle of the structural surface. Therefore, the method for predicting the surrounding rock grade in front of the tunnel face provided by the present invention is simple and clear, and can be easily applied in engineering.

[0037] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0039] Figure 1 This is a flow chart of the method of the first embodiment. DETAILED DESCRIPTION

[0040] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0042] Embodiment 1

[0043] In one embodiment of the present disclosure, a method for determining the grade of surrounding rock in front of a tunnel face is provided, which combines the rock mass structural surface information on multiple excavated tunnel faces to quantitatively predict the grade of surrounding rock in the unexcavated part in front of the tunnel, such as Figure 1 As shown, the following steps are included:

[0044] Step S1: During tunnel excavation, the trace length information of each structural surface on the tunnel face is collected.

[0045] Collect rock mass structural surface information on multiple excavated tunnel faces. Specifically, the popular 3D laser scanner can be used to scan the face, and then software can be used to process it to obtain the trace length information of the structural surface on the face. A geological compass can also be used for manual cataloging.

[0046] Specifically, during the tunnel excavation process using the drill and blast method, trace length information is collected for the tunnel face exposed during each excavation to obtain the trace lengths of all structural surfaces on the tunnel face. Since the scales of structural surfaces vary, the structural surfaces required to be collected in the present invention are grade II to IV structural surfaces specified in the field of rock mechanics.

[0047] It should be pointed out that the rock mass structural surface information required to be collected in this embodiment is mainly trace length information, and the structural surface inclination, dip angle and other information frequently used in the prior art are not used in this embodiment.

[0048] Step S2: Based on the trace length information, the structural surfaces on the tunnel face are clustered into deterministic structural surfaces and random structural surfaces.

[0049] After the structural surface trace length information on each tunnel face is counted, the rock structural surface on each tunnel face is classified.

[0050] The K-Means clustering method is a relatively mature and widely used one-dimensional data clustering algorithm. That is, it only needs to provide all the trace length values ​​on each tunnel face, set the K value to 2, and divide the trace lengths of the structural surfaces on the tunnel face into two categories: deterministic structural surfaces and random structural surfaces. The classification standard is the length of the structural surface trace length. The larger the trace length value is, the deterministic structural surface is, and the smaller the trace length value is, the random structural surface is.

[0051] When K-Means is applied to the structural surface trace length clustering in this embodiment, the distance required by the algorithm itself can be the Euclidean distance in the usual sense.

[0052] Step S3: According to the trace length information of the deterministic structural surface and the random structural surface, the trace length of the deterministic structural surface and the trace length of the random structural surface of the unexcavated tunnel face ahead are predicted respectively.

[0053] Based on the deterministic structural surface information and random structural surface information collected on multiple tunnel faces, the development of the deterministic structural surface and random structural surface in the unexcavated part in front of the tunnel is predicted. The specific prediction method is to use the autoregressive model in machine learning for prediction, which is as follows:

[0054] Sum the trace lengths of the deterministic structural surfaces on each excavated tunnel face to obtain the sum of the trace lengths of the deterministic structural surfaces on each tunnel face;

[0055] Sum the trace lengths of the random structural surfaces on each excavated tunnel face to obtain the sum of the trace lengths of the random structural surfaces on each tunnel face;

[0056] According to the sum of the trace lengths of the deterministic structural surfaces and the sum of the trace lengths of the random structural surfaces on multiple tunnel faces that have been obtained, the autoregressive model is used to predict the sum of the trace lengths of the deterministic structural surfaces and the sum of the trace lengths of the random structural surfaces of the tunnel face that has not been excavated ahead.

[0057] The autoregressive model is a time series prediction model, which predicts future observations based on past observations. In this embodiment, the structural surface trace length information of the excavated face is the past observation value, and the trace length information of the unexcavated face of the tunnel ahead is the future observation value. It should be pointed out that this embodiment requires that the number of past observations is at least three, that is, the trace length information of the unexcavated face ahead is inferred based on at least three trace length information of the excavated faces.

[0058] Specifically, taking the deterministic trace length as an example (the trace length of the random structural surface is similar to it), it is assumed that the sum of the trace lengths of the deterministic structural surfaces on the n tunnel faces is l1, l2, ..., l n , the corresponding excavation faces are numbered 1, 2, ..., n, respectively. Then the sum of the deterministic structural surface trace lengths of the (n+1)th face is l n+1 , expressed as:

[0059] ln+1=a+·b(n+1)

[0060]

[0061] Where i is the number of the tunnel face, is the deterministic structural surface trace length of the tunnel face numbered i, is the average value of the tunnel face number, is the average value of the sum of the deterministic trace lengths of each face, a and b are the coefficients of the formula, which can be calculated from the previous data.

[0062] Step S4: Based on the predicted trace length information, the rock mass fracture rate of the unexcavated tunnel face ahead is calculated to determine the surrounding rock grade.

[0063] The rock mass fracture ratio in this embodiment is defined as the length of the rock mass structural surface contained in the unit area of ​​the tunnel face. The length of the rock mass structural surface here is the sum of the trace length value of the deterministic structural surface and the trace length value of the random structural surface in this embodiment.

[0064] Specifically, the sum of the predicted deterministic structural surface trace lengths and the sum of the random structural surface trace lengths are added to obtain the sum of the structural surface trace lengths on the unexcavated tunnel face.

[0065] The sum of the trace lengths of the structural surfaces on the unexcavated face is used to calculate the trace length value contained in the unit area of ​​the face as the rock mass fracture ratio. The rock mass fracture ratio is used as a quantitative indicator for rapid identification of the surrounding rock grade ahead. The surrounding rock of the unexcavated part ahead of the tunnel is graded. The specific reference standards are shown in Table 1.

[0066] Table 1 Reference classification standards for rock mass fissure ratio

[0067]

[0068] Embodiment 2

[0069] In one embodiment of the present disclosure, a system for judging the grade of surrounding rock in front of a tunnel face is provided, including an information collection module, a structural surface classification module, a trace length prediction module and a grade judgment module:

[0070] The information collection module is configured to: collect trace length information of each structural surface on the tunnel face during tunnel excavation;

[0071] The structural surface classification module is configured to: cluster the structural surfaces on the tunnel face into deterministic structural surfaces and random structural surfaces based on the trace length information;

[0072] The trace length prediction module is configured to: predict the trace length of the deterministic structural surface and the trace length of the random structural surface of the unexcavated tunnel face in front respectively according to the trace length information of the deterministic structural surface and the random structural surface;

[0073] The grade judgment module is configured to calculate the rock mass fracture rate of the unexcavated tunnel face ahead based on the predicted trace length information and make a judgment on the surrounding rock grade.

[0074] Embodiment 3

[0075] The purpose of this embodiment is to provide a computer-readable storage medium.

[0076] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for determining the grade of surrounding rock in front of a tunnel face as described in the first embodiment of the present disclosure.

[0077] Embodiment 4

[0078] The purpose of this embodiment is to provide an electronic device.

[0079] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in a method for determining the grade of surrounding rock in front of a tunnel face as described in the first embodiment of the present disclosure are implemented.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining the grade of surrounding rock in front of a tunnel face, characterized in that: include: During tunnel excavation, the trace length information of each structural surface on the tunnel face is collected; Based on the trace length information, the structural surfaces on the tunnel face are clustered into deterministic structural surfaces and random structural surfaces. According to the trace length information of the deterministic structural surface and the random structural surface, the trace length of the deterministic structural surface and the trace length of the random structural surface of the unexcavated tunnel face in front are predicted respectively; Based on the predicted trace length information, the rock fracture rate of the unexcavated tunnel face ahead is calculated to determine the surrounding rock grade.

2. A method for determining the grade of surrounding rock in front of a tunnel face as claimed in claim 1, characterized in that: The trace length information of each structural surface on the tunnel face is collected during the tunnel excavation process, specifically: Use 3D laser scanner to scan the tunnel face; The trace length information of the structural surface on the tunnel face is obtained from the scanned image.

3. A method for determining the grade of surrounding rock in front of a tunnel face as claimed in claim 1, characterized in that: The clustering of the structural surfaces on the tunnel face into deterministic structural surfaces and random structural surfaces is specifically as follows: The K-Means clustering method is used to divide the structural surface trace lengths on the tunnel face into two categories.

4. A method for determining the grade of surrounding rock in front of a tunnel face as claimed in claim 1, characterized in that: The deterministic structural surface trace length and the random structural surface trace length of the unexcavated tunnel face are predicted respectively, specifically: The trace lengths of the deterministic structural surface and the random structural surface on each excavated tunnel face are summed up respectively to obtain the sum of the trace lengths of the deterministic structural surface and the sum of the trace lengths of the random structural surface on each tunnel face; According to the sum of the trace lengths of the deterministic structural surfaces and the sum of the trace lengths of the random structural surfaces, the autoregressive model is used to predict the sum of the trace lengths of the deterministic structural surfaces and the sum of the trace lengths of the random structural surfaces of the unexcavated tunnel face in front.

5. The method for determining the grade of surrounding rock in front of a tunnel face according to claim 1, characterized in that: The calculation of the rock mass fissure rate of the unexcavated tunnel face is specifically as follows: According to the sum of the trace lengths of the structural surfaces on the unexcavated tunnel face, the trace length value contained in the unit area of ​​the tunnel face is calculated as the rock mass fracture rate.

6. A method for determining the grade of surrounding rock in front of a tunnel face as claimed in claim 5, characterized in that: The sum of the structural surface trace lengths on the unexcavated tunnel face is obtained by adding the sum of the deterministic structural surface trace lengths and the sum of the random structural surface trace lengths.

7. The method for determining the grade of surrounding rock in front of a tunnel face according to claim 1, characterized in that: The surrounding rock grade is determined based on the relationship between the rock mass fissure ratio and the rock mass quality grade.

8. A system for judging the grade of surrounding rock in front of the tunnel face, characterized in that: It includes information collection module, structural surface classification module, trace length prediction module and grade judgment module: The information collection module is configured to: collect trace length information of each structural surface on the tunnel face during tunnel excavation; The structural surface classification module is configured to: cluster the structural surfaces on the tunnel face into deterministic structural surfaces and random structural surfaces based on the trace length information; The trace length prediction module is configured to: predict the trace length of the deterministic structural surface and the trace length of the random structural surface of the unexcavated tunnel face in front respectively according to the trace length information of the deterministic structural surface and the random structural surface; The grade judgment module is configured to calculate the rock mass fracture rate of the unexcavated tunnel face ahead based on the predicted trace length information and make a judgment on the surrounding rock grade.

9. An electronic device, comprising: a memory for non-transitory storage of computer readable instructions; as well as a processor for executing the computer readable instructions, When the computer-readable instructions are executed by the processor, the method described in any one of claims 1 to 7 is executed.

10. A storage medium, characterized in that: The computer-readable instructions are non-transitory stored, wherein when the non-transitory computer-readable instructions are executed by a computer, the method of any one of claims 1 to 7 is performed.

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