Tunnel surrounding rock classification method, device, computer equipment and storage medium

Abstract

The present invention relates to the technical field of engineering survey, and discloses a method, device, computer equipment and storage medium for grading surrounding rock of a tunnel. The method for grading surrounding rock of a tunnel comprises: establishing a complete rock model according to target surrounding rock information of a target tunnel; analyzing multiple drilling positions of the complete rock model to obtain geometric parameters of multiple cracks in the target surrounding rock information; establishing a discrete crack network model according to the geometric parameters of the multiple cracks; establishing a comprehensive rock mass model according to the complete rock model and the discrete crack network model; determining rock mass strength data and elastic modulus data of target surrounding rock information according to the comprehensive rock mass model; calculating basic quality indicators of target rock mass according to rock mass strength data and elastic modulus data; wherein the basic quality indicators of target rock mass represent quality indicators for grading target surrounding rock information; and determining the grading level of target surrounding rock information according to the basic quality indicators of rock mass. The present invention realizes accurate grading of surrounding rock.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering investigation, and in particular to a classification method, device, computer equipment and storage medium for tunnel surrounding rock. Background Art

[0002] In tunnel excavation projects, the surrounding rocks whose stress state changes due to excavation are called surrounding rocks. When the tunnel is excavated, under the action of horizontal stress in the axial direction and stress in the direction perpendicular to the tunnel axis, the surrounding rocks will have destruction zones (rock crushing), damaged zones (rock weakening), disturbed zones (rock not weakening) and undisturbed zones (rock not changing), which will cause surrounding rock excavation damage zones. Among them, the destruction zones and damaged zones will cause trouble to tunnel excavation.

[0003] At present, the classification of surrounding rock is too rough, and the determination of destruction and damage areas is not accurate enough, resulting in incomplete support measures for surrounding rock, which can easily cause harm to the tunnel excavation process. Summary of the invention

[0004] In view of this, the present invention provides a method, device, computer equipment and storage medium for grading surrounding rocks of a tunnel to solve the problem that the grading of surrounding rocks is too rough.

[0005] In a first aspect, the present invention provides a method for grading tunnel surrounding rock, comprising: establishing a complete rock model according to target surrounding rock information of a target tunnel, the complete rock model representing a model of particles displaying the target surrounding rock information; analyzing multiple boreholes of the complete rock model to obtain geometric parameters of multiple cracks in the target surrounding rock information; establishing a discrete crack network model according to the geometric parameters of the multiple cracks, the discrete crack network model characterizing the crack positions at multiple boreholes; establishing a comprehensive rock mass model according to the complete rock model and the discrete crack network model, the comprehensive rock mass model representing comprehensive status information of the target surrounding rock information; determining rock mass strength data and elastic modulus data of the target surrounding rock information according to the comprehensive rock mass model; calculating basic quality indicators of the target rock mass according to the rock mass strength data and elastic modulus data; wherein the basic quality indicators of the target rock mass represent quality indicators for grading the target surrounding rock information; determining the grading level of the target surrounding rock information according to the basic quality indicators of the target rock mass.

[0006] The present invention establishes a complete rock model of particles showing target surrounding rock information according to target surrounding rock information of a target tunnel, analyzes multiple boreholes of the complete rock model, obtains geometric parameters of multiple cracks in the target surrounding rock information, establishes a discrete crack network model representing the crack positions at multiple boreholes according to the geometric parameters of the multiple cracks, and establishes a comprehensive rock model according to the complete rock model and the discrete crack network model. Therefore, the comprehensive rock model of the present invention includes both particles showing target surrounding rock information and crack positions representing multiple boreholes, so that the comprehensive rock model is more in line with the actual rock situation, and the classification of tunnel surrounding rock obtained according to the comprehensive rock model is also more accurate. The present invention calculates rock strength data and elastic modulus data according to an accurate comprehensive rock model, calculates target rock basic quality indicators representing the classification of target surrounding rock information according to the rock strength data and elastic modulus data, and determines the classification level of the target surrounding rock information according to the rock basic quality indicators. Compared with the related art, the present invention establishes a more accurate comprehensive rock model based on accurate data, calculates basic quality indicators of the target rock mass for classifying target surrounding rock information based on the comprehensive rock mass model, and classifies the target surrounding rock information based on the basic quality indicators of the target rock mass. The present invention determines the cracks in the target rock mass information and realizes the refined classification of the target surrounding rock information. According to the refined classification level, more complete support measures can be determined to ensure the smooth progress of the tunnel excavation process.

[0007] In an optional embodiment, a complete rock model is established based on target surrounding rock information of a target tunnel, including: obtaining mechanical data of the target surrounding rock information of the target tunnel; wherein the mechanical data represents data related to stress; determining horizontal stress distribution information and vertical stress distribution information of the target surrounding rock information based on the mechanical data; and establishing a complete rock model using a preset algorithm based on the horizontal stress distribution information and the vertical stress distribution information.

[0008] The present invention obtains mechanical data related to stress of target surrounding rock information of a target tunnel, determines horizontal stress distribution information and vertical stress distribution information of the target surrounding rock information according to the mechanical data, and establishes a complete rock model according to the horizontal stress distribution information and the vertical stress distribution information using a preset algorithm. The preset algorithm can effectively solve the problems of deformation, fracture and fracture development of particle aggregates. The complete rock model obtained by the present invention is more consistent with the stress distribution under actual conditions, thereby making subsequent mechanical analysis of the complete rock model more accurate.

[0009] In an optional implementation, a comprehensive rock model is established based on the complete rock model and the discrete fracture network model, including: superimposing the complete rock model and the discrete fracture network model to obtain the comprehensive rock model.

[0010] In an optional embodiment, the complete rock model is superimposed on the discrete fracture network model to obtain a comprehensive rock model, including: superimposing the fracture positions at multiple boreholes in the discrete fracture network model into the complete rock model to obtain the comprehensive rock model.

[0011] The present invention superimposes the fracture positions at multiple boreholes in the discrete fracture network model onto the complete rock model to obtain a comprehensive rock model. The comprehensive rock model of the present invention includes both the stress distribution in the complete rock model and the fracture positions at multiple boreholes in the discrete fracture network. When the comprehensive rock model is subsequently subjected to mechanical analysis, the obtained analysis results are more accurate and more consistent with the mechanical characteristics of the actual tunnel rock mass.

[0012] In an optional implementation, rock mass strength data and elastic modulus data of target surrounding rock information are determined based on the comprehensive rock mass model, including: performing uniaxial compression test simulation on the comprehensive rock mass model to obtain rock mass strength data and elastic modulus data.

[0013] In an optional embodiment, the basic quality index of the target rock mass is calculated based on the rock mass strength data and the elastic modulus data, including: determining a third mapping relationship between the basic quality index of the rock mass and the rock mass strength standard value based on a first mapping relationship between the acquired preset grading standard value and the basic quality index of the rock mass, and a second mapping relationship between the rock mass strength standard value and the preset grading standard value; determining the first basic quality index of the rock mass based on the rock mass strength data and the third mapping relationship; determining a fifth mapping relationship between the elastic modulus standard value and the preset grading standard value based on a fourth mapping relationship between the elastic modulus standard value and the basic quality index of the rock mass, and the first mapping relationship; determining the second basic quality index of the rock mass based on the elastic modulus data and the fifth mapping relationship; and forming the target basic quality index of the rock mass by the first basic quality index of the rock mass and the second basic quality index of the rock mass.

[0014] The present invention derives a third mapping relationship between the basic rock mass quality index and the rock mass strength standard value based on a first mapping relationship between a preset grading standard value and the basic rock mass quality index, and a second mapping relationship between the rock mass strength standard value and the preset grading standard value, and then substitutes the rock mass strength data into the third mapping relationship to obtain the first basic rock mass quality index; derives a fifth mapping relationship between the elastic modulus standard value and the basic rock mass quality index based on a fourth mapping relationship between the elastic modulus standard value and the basic rock mass quality index, and the first mapping relationship, and substitutes the elastic modulus data into the fifth mapping relationship to obtain the second basic rock mass quality index; the first basic rock mass quality index and the second basic rock mass quality index form a target rock mass basic quality index; the present invention derives and calculates the target rock mass basic quality index from the standard mapping relationship, so that the subsequent classification level of the target surrounding rock information determined according to the target rock mass basic quality index is more refined.

[0015] In an optional embodiment, after calculating the basic quality index of the target rock mass based on the rock mass strength data and the elastic modulus data, the method further includes: correcting the basic quality index of the target rock mass based on the basic quality index of the target rock mass and the obtained correction coefficient to obtain a corrected basic quality index of the rock mass; and determining the classification level of the target surrounding rock information based on the corrected basic quality index of the rock mass.

[0016] The present invention corrects the basic quality index of the target rock mass to obtain the corrected basic quality index of the rock mass, and determines the classification level of the target surrounding rock information according to the corrected basic quality index of the rock mass, so that the classification level of the target surrounding rock information is more accurate and more in line with the actual situation.

[0017] In a second aspect, the present invention provides a grading device for tunnel surrounding rock, comprising: a complete rock model establishment module, used to establish a complete rock model according to the target surrounding rock information of the target tunnel, the complete rock model represents a model of particles showing the target surrounding rock information; a discrete fracture network model establishment module, used to analyze multiple boreholes of the complete rock model to obtain geometric parameters of multiple fractures in the target surrounding rock information; a discrete fracture network model is established according to the geometric parameters of the multiple fractures, the discrete fracture network model characterizes the fracture positions at the multiple boreholes; a comprehensive rock model establishment module, used to establish a comprehensive rock model according to the complete rock model and the discrete fracture network model, the comprehensive rock model represents comprehensive status information of the target surrounding rock information; a data determination module, used to determine the rock strength data and elastic modulus data of the target surrounding rock information according to the comprehensive rock model; a basic quality index calculation module, used to calculate the basic quality index of the target rock according to the rock strength data and the elastic modulus data; wherein the basic quality index of the target rock represents the quality index for grading the target surrounding rock information; a grading grade determination module, used to determine the grading grade of the target surrounding rock information according to the basic quality index of the target rock.

[0018] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the tunnel surrounding rock grading method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the tunnel surrounding rock grading method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 is a schematic flow chart of a tunnel surrounding rock classification method according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of mechanical properties of granite with different weathering degrees according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of conversion between joint dip angle and apparent dip angle according to an embodiment of the present invention;

[0024] Figure 4 is a schematic diagram of a two-dimensional discrete fracture network according to an embodiment of the present invention;

[0025] Figure 5 is a schematic diagram of a comprehensive rock mass model according to an embodiment of the present invention;

[0026] Figure 6 is a schematic diagram of mechanical properties of comprehensive rock models of different sizes according to an embodiment of the present invention;

[0027] Figure 7 is a schematic flow chart of another tunnel surrounding rock classification method according to an embodiment of the present invention;

[0028] Figure 8 is a schematic flow chart of another method for grading surrounding rock of a tunnel according to an embodiment of the present invention;

[0029] Fig. 9 is a structural block diagram of a tunnel surrounding rock classification device according to an embodiment of the present invention;

[0030] Fig.10 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] The embodiment of the present invention provides a method for grading tunnel surrounding rocks, which achieves the effect of accurate grading of tunnel surrounding rocks by calculating basic quality indicators of target rock mass.

[0033] According to an embodiment of the present invention, an embodiment of a method for grading surrounding rock of a tunnel is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] In this embodiment, a tunnel surrounding rock classification method is provided, which can be used in a computer device, such as a server. Figure 1 : is a flow chart of a tunnel surrounding rock classification method according to an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps:

[0035] Step S101 : establishing a complete rock model according to target surrounding rock information of a target tunnel, wherein the complete rock model represents a model of particles showing the target surrounding rock information.

[0036] The target tunnel represents a tunnel to be constructed, and the target surrounding rock information represents information about surrounding rocks that require protective support. For example, the target surrounding rock information may be information related to granite surrounding rocks of different degrees of weathering.

[0037] In some optional embodiments, a complete rock model is established using a preset algorithm according to target surrounding rock information of the target tunnel. For example, the preset algorithm may be a discrete element method (PFC) 2D (ParticleFollowCode2Dimension, two-dimensional particle flow program) or PFC 3D (ParticleFollowCode3Dimension, three-dimensional particle flow program) is used to establish a complete rock model based on discrete elements.

[0038] In the embodiment of the present invention, the mechanical parameters and failure characteristics of the complete rock model can be obtained according to the complete rock model. For example, PFC 2D The software embedded FISH (Friendly Interactive SHell) language is used to establish a particle bonding model suitable for indoor experiments. A rectangular container is created, the particle friction coefficient is defined, floating particles are removed, parallel bonding is installed, and loading tests are carried out on the specimens to obtain the mechanical parameters and failure characteristics of the intact rock, such as Figure 2 The following is a schematic diagram of the mechanical properties of granite with different weathering degrees. Figure 2 In the equation, the horizontal axis is the failure characteristic ε, in %, and the vertical axis is the pressure σ, in megapascals (MPa). Figure 2 It can be seen that slightly weathered granite has the highest pressure and the smallest destructive characteristics, slightly to moderately weathered granite has medium pressure and medium destructive characteristics, and moderately weathered granite has the lowest pressure and the strongest destructive characteristics.

[0039] Step S102, analyzing multiple boreholes of the complete rock model to obtain geometric parameters of multiple fractures in the target surrounding rock information; establishing a discrete fracture network model based on the geometric parameters of the multiple fractures, the discrete fracture network model characterizing the fracture positions at the multiple boreholes.

[0040] The complete rock model includes multiple boreholes, each of which includes multiple fractures. Joint analysis is performed on the multiple boreholes of the complete rock model. The joints, also called fractures, are small fracture structures in which the rock blocks on both sides do not have significant displacement after the rock mass is fractured under stress.

[0041] In the embodiment of the present invention, joint analysis is performed on multiple boreholes of a complete rock model to obtain geometric parameters of multiple cracks in target surrounding rock information, wherein the geometric parameters include the inclination angle and trace length of the cracks, such as Figure 3 , which is a schematic diagram of the conversion between joint dip angle and apparent dip angle. The embodiment of the present invention can convert the joint dip angle into the apparent dip angle to visualize and analyze the joint dip angle.

[0042] As shown in Table 1, geometric parameters of multiple fractures in the target surrounding rock information are represented.

[0043] Table 1:

[0044]

[0045]

[0046] In some optional embodiments, random numbers are generated using the Monte Carlo method based on the geometric parameters of multiple fractures, a two-dimensional random discrete fracture network is constructed based on the random numbers, the two-dimensional random discrete fracture network is sampled and tested, and a discrete fracture network model at multiple boreholes is formed.

[0047] Among them, the Fish language is written using the particle flow code (PFC) software to generate the specified crack distribution type, and the probability distribution function of crack apparent inclination, trace length, line density and other parameters is generated according to the specified crack distribution type, thereby forming a two-dimensional discrete crack map, such as Figure 4 As shown, this is a schematic diagram of a two-dimensional discrete fracture network.

[0048] Step S103, establishing a comprehensive rock mass model based on the complete rock model and the discrete fracture network model, wherein the comprehensive rock mass model represents comprehensive status information of the target surrounding rock information.

[0049] In the embodiment of the present invention, the natural rock mass is a product of nature. During its long historical formation process, it has undergone various complex geological actions. Various structural surfaces of varying sizes, different occurrences, and different properties exist extensively inside it, showing strong nonlinearity and anisotropy. Therefore, a comprehensive rock model of the fractured rock mass is established by combining the complete rock model and the discrete fracture network model.

[0050] In some optional implementations, a comprehensive rock model is established based on the complete rock model and the discrete fracture network model, including: superimposing the complete rock model and the discrete fracture network model to obtain the comprehensive rock model.

[0051] Furthermore, the fracture locations at multiple boreholes in the discrete fracture network model are superimposed on the complete rock model to obtain a comprehensive rock mass model, such as Figure 5 The figure shows a schematic diagram of the comprehensive rock mass model. Figure 5 It includes both the characteristics of the complete rock model and Figure 4 Characteristics of a two-dimensional discrete fracture network.

[0052] In some optional embodiments, the mechanical properties of the fractured rock mass are related to the size of the rock mass. Generally, the larger the size of the rock mass, the lower the compressive strength and elastic modulus of the rock mass. However, when the size increases to a certain critical size, namely the representative elementary volume (REV), the compressive strength and elastic modulus tend to be stable. Only when the research scale of the fractured rock mass is larger than its REV, the equivalent parameters of the rock mass are representative. The particle radius has a certain influence on the macroscopic mechanical parameters of the model. The minimum scale particle number RES (Resolution) is defined as:

[0053] RES=(L / R min )[1 / (1+R max / R min )];

[0054] Among them, RES represents the number of particles with the smallest size, R min Indicates the minimum radius, R max represents the maximum radius, and L represents the height of the comprehensive rock mass model.

[0055] Establish comprehensive rock mass models of different volumes, such as Figure 6 The figure shows the mechanical properties of integrated rock models of different sizes. Generally, a large-sized integrated rock model is selected for mechanical analysis. The embodiment of the present invention ensures that the RES value remains unchanged by keeping the ratio of the model height L to the minimum radius and the ratio of the maximum and minimum radius unchanged, so as to reduce the influence of the particle size on the model calculation.

[0056] The embodiment of the present invention superimposes the fracture positions at multiple boreholes in the discrete fracture network model onto the complete rock model to obtain a comprehensive rock model. The comprehensive rock model of the embodiment of the present invention includes both the stress distribution in the complete rock model and the fracture positions at multiple boreholes in the discrete fracture network. When the comprehensive rock model is subsequently subjected to mechanical analysis, the obtained analysis results are more accurate and more consistent with the mechanical characteristics of the actual tunnel rock mass.

[0057] Step S104, determining rock mass strength data and elastic modulus data of target surrounding rock information according to the comprehensive rock mass model.

[0058] In some optional implementations, a uniaxial compression test simulation is performed on the comprehensive rock mass model to obtain rock mass strength data and elastic modulus data.

[0059] Among them, the rock property parameters and crack parameters of the rock mass at different drilling locations are different, and the number of cracks of the same size is also different. Therefore, uniaxial compression test simulations are carried out on multiple cracks to obtain multiple rock strength data and multiple elastic modulus data.

[0060] As shown in Table 2, the rock mass strength data and elastic modulus data representing the target surrounding rock information.

[0061] Table 2:

[0062]

[0063]

[0064] Step S105, calculating the basic quality index of the target rock mass according to the rock mass strength data and the elastic modulus data; wherein the basic quality index of the target rock mass represents the quality index for classifying the target surrounding rock information.

[0065] In some optional embodiments, a third mapping relationship between the basic rock quality index and the rock strength standard value is determined based on the first mapping relationship between the preset classification standard value and the basic rock quality index, and the second mapping relationship between the rock strength standard value and the preset classification standard value.

[0066] The first rock mass basic quality index is determined according to the rock mass strength data and the third mapping relationship.

[0067] According to the fourth mapping relationship between the standard value of the elastic modulus and the basic quality index of the rock mass, and the first mapping relationship, a fifth mapping relationship between the standard value of the elastic modulus and the preset classification standard value is determined.

[0068] The basic quality index of the second rock mass is determined according to the elastic modulus data and the fifth mapping relationship.

[0069] The first rock mass basic quality index and the second rock mass basic quality index constitute the target rock mass basic quality index.

[0070] Step S106, determining the classification level of the target surrounding rock information according to the basic quality index of the target rock mass.

[0071] In some optional implementations, the basic rock mass quality index includes the corresponding classification level of target surrounding rock information, and the classification level of the target surrounding rock information is obtained according to the basic rock mass quality index.

[0072] The method for grading the surrounding rock of a tunnel provided in this embodiment establishes a complete rock model of particles showing the target surrounding rock information according to the target surrounding rock information of the target tunnel, analyzes multiple boreholes of the complete rock model, obtains geometric parameters of multiple cracks in the target surrounding rock information, establishes a discrete crack network model representing the crack positions at multiple boreholes according to the geometric parameters of the multiple cracks, and establishes a comprehensive rock model according to the complete rock model and the discrete crack network model. Therefore, the comprehensive rock model of the embodiment of the present invention includes both particles showing the target surrounding rock information and crack positions representing multiple boreholes, so that the comprehensive rock model is more in line with the actual rock situation, and the classification of the tunnel surrounding rock obtained according to the comprehensive rock model is also more accurate. The present invention calculates rock strength data and elastic modulus data according to an accurate comprehensive rock model, calculates the target rock basic quality index representing the classification of the target surrounding rock information according to the rock strength data and the elastic modulus data, and determines the classification level of the target surrounding rock information according to the rock basic quality index. Compared with the related art, the embodiment of the present invention establishes a more accurate comprehensive rock model based on accurate data, calculates basic quality indicators of the target rock mass for classifying target surrounding rock information based on the comprehensive rock mass model, and classifies the target surrounding rock information based on the basic quality indicators of the target rock mass. The embodiment of the present invention determines the cracks in the target rock mass information and realizes the refined classification of the target surrounding rock information. According to the refined classification level, more complete support measures can be determined to ensure the smooth progress of the tunnel excavation process.

[0073] In this embodiment, another tunnel surrounding rock classification method is provided, which can be used in the above-mentioned computer device. Figure 7 is a flow chart of another tunnel surrounding rock classification method according to an embodiment of the present invention, such as Figure 7 As shown, the process includes the following steps:

[0074] Step S701, establishing a complete rock model according to target surrounding rock information of a target tunnel, wherein the complete rock model represents a model of particles showing the target surrounding rock information.

[0075] In some optional implementations, the above step S701 includes:

[0076] Step S7011, obtaining mechanical data of target surrounding rock information of the target tunnel; wherein the mechanical data refers to data related to stress.

[0077] Among them, the mechanical data include the position and direction of the maximum principal stress of the target tunnel, the vertical stress, and the direction of the tunnel axis.

[0078] Step S7012, determining the horizontal stress distribution information and the vertical stress distribution information of the target surrounding rock information according to the mechanical data.

[0079] Among them, the horizontal stress distribution information in the direction of the target tunnel axis and the vertical stress distribution information in the direction perpendicular to the target tunnel axis are converted according to the mechanical data.

[0080] Step S7013, establishing a complete rock model using a preset algorithm based on the horizontal stress distribution information and the vertical stress distribution information.

[0081] Among them, the preset algorithm can be discrete element method PFC 2D or PFC 3D .

[0082] The embodiment of the present invention obtains mechanical data related to stress of target surrounding rock information of a target tunnel, determines horizontal stress distribution information and vertical stress distribution information of the target surrounding rock information according to the mechanical data, and establishes a complete rock model according to the horizontal stress distribution information and the vertical stress distribution information using a preset algorithm. The preset algorithm can effectively solve the problems of deformation, fracture and fracture development of particle aggregates. The complete rock model obtained by the embodiment of the present invention is more consistent with the stress distribution under actual conditions, thereby making the subsequent mechanical analysis of the complete rock model more accurate.

[0083] Step S702: Analyze multiple boreholes in the complete rock model to obtain geometric parameters of multiple fractures in the target surrounding rock information; establish a discrete fracture network model based on the geometric parameters of the multiple fractures, and the discrete fracture network model represents the fracture positions at the multiple boreholes. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0084] Step S703: Establish a comprehensive rock mass model based on the complete rock model and the discrete fracture network model. The comprehensive rock mass model represents the comprehensive status information of the target surrounding rock information. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0085] Step S704: determining rock mass strength data and elastic modulus data of target surrounding rock information according to the comprehensive rock mass model.

[0086] In some optional implementations, the above step S704 includes:

[0087] Step S7041, perform uniaxial compression test simulation on the comprehensive rock mass model to obtain rock mass strength data and elastic modulus data.

[0088] In some optional implementations, micro-parameter calibration is usually performed based on macroscopic parameters such as rock mass strength data Rc, elastic modulus data E50, and Poisson's ratio V of the comprehensive rock mass model obtained through indoor uniaxial compression test simulation.

[0089] Step S705, calculating the basic quality index of the target rock mass according to the rock mass strength data and the elastic modulus data; wherein the basic quality index of the target rock mass represents the quality index for classifying the target surrounding rock information.

[0090] In some optional implementations, the above step S705 includes:

[0091] Step S7051, determining a third mapping relationship between the basic rock quality index and the rock strength standard value based on the acquired first mapping relationship between the preset classification standard value and the basic rock quality index, and the second mapping relationship between the rock strength standard value and the preset classification standard value.

[0092] The formula of the first mapping relationship is:

[0093] BQ = 80.786 + 6.0943RMR;

[0094] Among them, BQ represents the basic quality index of rock mass, and RMR represents the preset classification standard value.

[0095] The formula for the second mapping relationship is:

[0096] σ cmass =0.5e 0.06RMR ;

[0097] Among them, σ cmass It represents the standard value of rock mass strength, and RMR represents the preset classification standard value.

[0098] Therefore, according to the first mapping relationship and the second mapping relationship, the third mapping relationship can be derived, and the formula of the third mapping is:

[0099] BQ=80.786+101.57In(2σ cmass );

[0100] Among them, σ cmass It represents the standard value of rock mass strength, and BQ represents the basic quality index of rock mass.

[0101] Step S7052: Determine the basic quality index of the first rock mass according to the rock mass strength data and the third mapping relationship.

[0102] The rock mass strength data is substituted into the position of the rock mass strength standard value in the third mapping relationship to obtain the first rock mass basic quality index.

[0103] Step S7053, determining a fifth mapping relationship between the standard value of the elastic modulus and the preset classification standard value according to the fourth mapping relationship between the standard value of the elastic modulus and the basic quality index of the rock mass and the first mapping relationship.

[0104] The formula of the fourth mapping relationship is:

[0105] E m =2RMR-100RMR>55;

[0106]

[0107] Among them, E m It represents the standard value of elastic modulus and RMR represents the basic quality index of rock mass.

[0108] In some optional implementations, a fifth mapping relationship may be derived according to the fourth mapping relationship and the first mapping relationship, and the formula of the fifth mapping relationship is:

[0109] BQ=3.05E m +385.501E m >10GPa;

[0110] BQ=243.77lgE m +141.729E m ≤10GPa;

[0111] Among them, E m It represents the standard value of elastic modulus, BQ represents the basic quality index of rock mass, and GPa is the unit of the standard value of elastic modulus, Gigapascal.

[0112] Step S7054: determining the basic quality index of the second rock mass according to the elastic modulus data and the fifth mapping relationship.

[0113] The elastic modulus data is brought into the elastic modulus standard value position of the fifth mapping relationship to obtain the second rock mass basic quality index.

[0114] Step S7055, forming a target rock mass basic quality index from the first rock mass basic quality index and the second rock mass basic quality index.

[0115] The target rock mass basic quality index may be the first rock mass basic quality index, the second rock mass basic quality index, or the first rock mass basic quality index and the second rock mass basic quality index.

[0116] Step S706: Determine the classification level of the target surrounding rock information according to the basic quality index of the target rock mass. Figure 1 Step S106 of the illustrated embodiment will not be described in detail here.

[0117] Step S707: correct the target rock mass basic quality index according to the target rock mass basic quality index and the obtained correction coefficient to obtain a corrected rock mass basic quality index.

[0118] In some optional embodiments, when the surrounding rock is graded in detail, the basic rock quality index (BQ) is modified if one of the following situations occurs: first, there is groundwater; second, the stability of the surrounding rock is affected by weak structural surfaces and is controlled by a group; third, there is high initial stress.

[0119] The formula for calculating and modifying the basic quality index of rock mass is:

[0120] [BQ] = BQ - 100 (K1 + K2 + K3);

[0121] Among them, [BQ] represents the basic quality index of the modified rock mass, BQ represents the basic quality index of the target rock mass, K1 represents the correction factor of groundwater influence, K2 represents the correction factor of the influence of the main weak structural surface, and K3 represents the correction factor of the influence of the initial stress state. The values ​​of K1, K2 and K3 are obtained according to the specifications.

[0122] Step S708, determining the classification level of the target surrounding rock information according to the modified basic rock mass quality index.

[0123] The corrected basic rock mass quality index includes the corresponding classification level of target surrounding rock information, and the classification level of target surrounding rock information is obtained according to the basic rock mass quality index.

[0124] As shown in Table 3, the classification level of the target surrounding rock information is indicated.

[0125] Table 3:

[0126]

[0127] The embodiment of the present invention corrects the basic quality index of the target rock mass to obtain the corrected basic quality index of the rock mass, and determines the classification level of the target surrounding rock information according to the corrected basic quality index of the rock mass, so that the classification level of the target surrounding rock information is more accurate and more in line with the actual situation.

[0128] The tunnel surrounding rock classification method provided in the present embodiment derives a third mapping relationship between the basic rock mass quality index and the rock mass strength standard value according to a first mapping relationship between a preset classification standard value and the basic rock mass quality index, and a second mapping relationship between the rock mass strength standard value and the preset classification standard value, and then substitutes the rock mass strength data into the third mapping relationship to obtain the first basic rock mass quality index; derives a fifth mapping relationship between the elastic modulus standard value and the basic rock mass quality index according to a fourth mapping relationship between the elastic modulus standard value and the basic rock mass quality index, and the first mapping relationship, and substitutes the elastic modulus data into the fifth mapping relationship to obtain the second basic rock mass quality index; the first basic rock mass quality index and the second basic rock mass quality index form a target rock mass basic quality index; the embodiment of the present invention derives and calculates the target rock mass basic quality index by the standard mapping relationship, so that the classification level of the target surrounding rock information subsequently determined according to the target rock mass basic quality index is more refined.

[0129] In this embodiment, a method for grading surrounding rock of a tunnel is provided, which can be used in the above-mentioned computer device. Figure 8 is a flow chart of another tunnel surrounding rock classification method according to an embodiment of the present invention, such as Figure 8 As shown, the process includes:

[0130] A complete rock model is obtained based on the rock strength test at the boreholes in the tunnel site. A fracture network model is obtained based on the joint investigation and statistics at the boreholes. A large-volume comprehensive rock model is established based on the complete rock model and the fracture network model. The rock strength and elastic modulus of the rock mass at different boreholes are determined based on the large-volume comprehensive rock model. The empirical formulas of rock strength and elastic modulus and basic rock quality index (BQ) are derived. The basic rock quality index (BQ) is corrected according to the geological conditions to obtain the corrected basic quality index of surrounding rock. The fine classification of rock mass at different boreholes is determined based on the corrected basic quality index of surrounding rock.

[0131] In this embodiment, a grading device for tunnel surrounding rock is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0132] This embodiment provides a tunnel surrounding rock classification device, such as Fig. 9 As shown, including:

[0133] The complete rock model building module 901 is used to build a complete rock model according to the target surrounding rock information of the target tunnel. The complete rock model represents a model of particles showing the target surrounding rock information.

[0134] The discrete fracture network model building module 902 is used to analyze multiple boreholes of the complete rock model to obtain geometric parameters of multiple fractures in the target surrounding rock information; based on the geometric parameters of the multiple fractures, a discrete fracture network model is established, and the discrete fracture network model represents the fracture positions at the multiple boreholes.

[0135] The comprehensive rock mass model building module 903 is used to build a comprehensive rock mass model based on the complete rock model and the discrete fracture network model. The comprehensive rock mass model represents the comprehensive status information of the target surrounding rock information.

[0136] The data determination module 904 is used to determine the rock mass strength data and elastic modulus data of the target surrounding rock information according to the comprehensive rock mass model.

[0137] The basic quality index calculation module 905 is used to calculate the basic quality index of the target rock mass according to the rock mass strength data and the elastic modulus data; wherein the basic quality index of the target rock mass represents the quality index for classifying the target surrounding rock information.

[0138] The classification level determination module 906 is used to determine the classification level of the target surrounding rock information according to the basic quality index of the target rock mass.

[0139] In some optional implementations, the complete rock model building module 901 includes:

[0140] The mechanical data acquisition unit is used to acquire mechanical data of target surrounding rock information of the target tunnel; wherein the mechanical data represents data related to stress.

[0141] The stress distribution information determination unit is used to determine the horizontal stress distribution information and the vertical stress distribution information of the target surrounding rock information according to the mechanical data.

[0142] The complete rock model building unit is used to build a complete rock model using a preset algorithm according to the horizontal stress distribution information and the vertical stress distribution information.

[0143] In some optional implementations, the comprehensive rock model building module 903 includes:

[0144] The superposition unit is used to superimpose the complete rock model with the discrete fracture network model to obtain a comprehensive rock model.

[0145] In some optional implementations, the data determination module 904 includes:

[0146] The experimental unit is used to simulate the uniaxial compression experiment on the comprehensive rock model to obtain the rock strength data and elastic modulus data.

[0147] In some optional implementations, the basic quality indicator calculation module 905 includes:

[0148] The third mapping relationship determination unit is used to determine the third mapping relationship between the basic rock quality index and the rock strength standard value based on the acquired first mapping relationship between the preset classification standard value and the basic rock quality index, and the second mapping relationship between the rock strength standard value and the preset classification standard value.

[0149] The first rock mass basic quality index determining unit is used to determine the first rock mass basic quality index according to the rock mass strength data and the third mapping relationship.

[0150] The fifth mapping relationship determining unit is used to determine a fifth mapping relationship between the standard value of the elastic modulus and the preset classification standard value according to the fourth mapping relationship between the standard value of the elastic modulus and the basic quality index of the rock mass and the first mapping relationship.

[0151] The second rock mass basic quality index determining unit is used to determine the second rock mass basic quality index according to the elastic modulus data and the fifth mapping relationship.

[0152] The target rock mass basic quality index determination unit is used to form the target rock mass basic quality index from the first rock mass basic quality index and the second rock mass basic quality index.

[0153] In some optional embodiments, the tunnel surrounding rock grading device further comprises:

[0154] A correction unit, used for correcting the basic quality index of the target rock mass according to the basic quality index of the target rock mass and the obtained correction coefficient, so as to obtain a corrected basic quality index of the rock mass;

[0155] The classification level determination unit is used to determine the classification level of the target surrounding rock information according to the modified basic rock mass quality index.

[0156] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0157] The tunnel surrounding rock grading device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0158] The embodiment of the present invention also provides a computer device having the above Fig. 9 The grading device for the tunnel surrounding rock is shown.

[0159] See also Fig.10 , Fig.10 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.10 As shown, the computer device includes: one or more processors 1010, memory 1020, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 1010 is taken as an example.

[0160] The processor 1010 may be a central processing unit, a network processor or a combination thereof. The processor 1010 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0161] The memory 1020 stores instructions executable by at least one processor 1010, so that the at least one processor 1010 executes the method shown in the above embodiment.

[0162] The memory 1020 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 1020 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 1020 may optionally include a memory remotely arranged relative to the processor 1010, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0163] The memory 1020 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 1020 may also include a combination of the above types of memory.

[0164] The computer device also includes a communication interface 1030, which is used for the computer device to communicate with other devices or a communication network.

[0165] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0166] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for grading surrounding rock of a tunnel, characterized in that: The method comprises: Establishing a complete rock model according to target surrounding rock information of the target tunnel, wherein the complete rock model represents a model of particles showing the target surrounding rock information; Analyze multiple boreholes of the complete rock model to obtain geometric parameters of multiple fractures in the target surrounding rock information; establish a discrete fracture network model based on the geometric parameters of the multiple fractures, wherein the discrete fracture network model represents the fracture positions at the multiple boreholes; Establishing a comprehensive rock mass model based on the complete rock model and the discrete fracture network model, wherein the comprehensive rock mass model represents comprehensive status information of the target surrounding rock information; Determining rock mass strength data and elastic modulus data of the target surrounding rock information according to the comprehensive rock mass model; Calculate the basic quality index of the target rock mass according to the rock mass strength data and the elastic modulus data; wherein the basic quality index of the target rock mass represents the quality index for classifying the target surrounding rock information; The classification level of the target surrounding rock information is determined according to the basic quality index of the target rock mass.

2. The method according to claim 1, characterized in that The step of establishing a complete rock model according to target surrounding rock information of the target tunnel includes: Acquire mechanical data of the target surrounding rock information of the target tunnel; wherein the mechanical data represents data related to stress; Determine horizontal stress distribution information and vertical stress distribution information of the target surrounding rock information according to the mechanical data; The complete rock model is established using a preset algorithm according to the horizontal stress distribution information and the vertical stress distribution information.

3. The method according to claim 1 or 2, characterized in that: The step of establishing a comprehensive rock mass model based on the complete rock model and the discrete fracture network model comprises: The complete rock model is superimposed on the discrete fracture network model to obtain a comprehensive rock model.

4. The method according to claim 3, characterized in that The complete rock model and the discrete fracture network model are superimposed to obtain a comprehensive rock model, including: The fracture locations at the plurality of boreholes in the discrete fracture network model are superimposed on the complete rock model to obtain the comprehensive rock model.

5. The method according to claim 1 or 2, characterized in that: Determining the rock mass strength data and elastic modulus data of the target surrounding rock information according to the comprehensive rock mass model includes: A uniaxial compression test simulation is performed on the comprehensive rock mass model to obtain the rock mass strength data and the elastic modulus data.

6. The method according to claim 1 or 2, characterized in that: The step of calculating the basic quality index of the target rock mass according to the rock mass strength data and the elastic modulus data includes: Determine a third mapping relationship between the basic rock mass quality index and the rock mass strength standard value based on the first mapping relationship between the preset classification standard value and the basic rock mass quality index, and the second mapping relationship between the rock mass strength standard value and the preset classification standard value; Determining a first rock mass basic quality index according to the rock mass strength data and the third mapping relationship; Determine a fifth mapping relationship between the standard value of the elastic modulus and the preset classification standard value according to a fourth mapping relationship between the standard value of the elastic modulus and the basic quality index of the rock mass and the first mapping relationship; Determining a second rock mass basic quality index according to the elastic modulus data and the fifth mapping relationship; The first rock mass basic quality index and the second rock mass basic quality index constitute the target rock mass basic quality index.

7. The method according to claim 1 or 2, characterized in that: After calculating the basic quality index of the target rock mass according to the rock mass strength data and the elastic modulus data, the method further includes: According to the target rock mass basic quality index and the obtained correction coefficient, the target rock mass basic quality index is corrected to obtain a corrected rock mass basic quality index; The classification level of the target surrounding rock information is determined according to the modified rock mass basic quality index.

8. A tunnel surrounding rock grading device, characterized in that: The device comprises: A complete rock model building module, used to build a complete rock model according to target surrounding rock information of a target tunnel, wherein the complete rock model represents a model of particles showing the target surrounding rock information; A discrete fracture network model building module is used to analyze multiple boreholes of the complete rock model to obtain geometric parameters of multiple fractures in the target surrounding rock information; a discrete fracture network model is established according to the geometric parameters of the multiple fractures, and the discrete fracture network model represents the fracture positions at the multiple boreholes; A comprehensive rock mass model building module, used to build a comprehensive rock mass model based on the complete rock model and the discrete fracture network model, wherein the comprehensive rock mass model represents comprehensive status information of the target surrounding rock information; A data determination module, used to determine the rock mass strength data and elastic modulus data of the target surrounding rock information according to the comprehensive rock mass model; A basic quality index calculation module, used to calculate the basic quality index of the target rock mass according to the rock mass strength data and the elastic modulus data; wherein the basic quality index of the target rock mass represents a quality index for classifying the target surrounding rock information; The classification level determination module is used to determine the classification level of the target surrounding rock information according to the basic quality indicators of the target rock mass.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the tunnel surrounding rock grading method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the tunnel surrounding rock classification method according to any one of claims 1 to 7.

Images