A method for constructing a refined numerical model of random fractured karst tunnels

Abstract

The present invention provides a method for constructing a refined random fractured cave tunnel numerical model, including establishing a tunnel and stratum model according to the scope of the project, the distribution of the stratum, and the size of the tunnel; generating random numbers using the Monte Carlo method, establishing a discrete fracture network DFN template according to the fracture distribution characteristics and the probability density distribution function, cutting the tunnel and stratum model and converting it into a bonded block model; generating random numbers using the Monte Carlo method, determining the position and radius of the cave according to the cave distribution characteristics and establishing a random ball model, importing the random ball model into the bonded block model for cutting, and generating a random fractured cave model based on the difference set of Boolean operations. In the modeling process, the present invention comprehensively considers the distribution of fractures and caves in the surrounding rocks of the karst tunnel during the construction process, better simulates and analyzes the engineering mechanical behavior and stability in the karst stratum in the tunnel, thereby improving the safety and reliability of tunnel excavation construction.

Description

Technical Field

[0001] The invention relates to the technical field of karst geological engineering and geotechnical engineering, and in particular to a method for constructing a refined random fracture karst cave tunnel numerical model. Background Art

[0002] The formation of cracks is mainly caused by geological pressure, temperature changes or changes in the physical and mechanical properties of the rock itself, which provides a seepage path for water flow and promotes the development of caves. In addition, the development and distribution of cracks also control the overall mechanical properties of the rock mass, making it random, discontinuous and spatially anisotropic.

[0003] In soluble rock strata, in addition to the existence of weak structural surfaces such as cracks, caves are widely developed. Due to their irregular characteristics, the strata containing crack caves can be considered as an extremely uneven rock mass. The distribution of these crack cave structures has a great influence on the engineering mechanical properties and stability of the rock mass, which in turn poses a great safety hazard to tunnel excavation construction in this stratum.

[0004] Due to the complexity and changeability of geological processes, there is uncertainty in the formation of fissures and caves. In addition, fissures and caves have different shapes and complex distributions. Therefore, in engineering applications such as tunnel construction and mining, it is crucial to accurately describe the spatial distribution and mechanical properties of fissures and caves.

[0005] However, existing technologies have difficulty in accurately analyzing and describing rock masses in karst areas. For example, existing models have limitations in dealing with multi-scale fractures and cave distribution under complex geological conditions, and it is difficult to effectively simulate their interactions and their impact on overall mechanical properties.

[0006] Therefore, there is an urgent need for a numerical model construction method that can accurately describe the fractured cave body, so as to better analyze and simulate the engineering mechanical behavior and stability in the karst strata, thereby improving the safety and reliability of tunnel excavation construction. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a method for constructing a refined numerical model of a random fractured karst cave tunnel. During the modeling process, the present invention comprehensively considers the distribution of fractures and caves in the surrounding rocks during the construction of the karst tunnel, thereby meeting the needs of evaluating the safe distance between the cave and the tunnel during subsequent tunnel construction and operation and maintenance.

[0008] The technical solution of the present invention is: a method for constructing a refined random fracture cave tunnel numerical model, comprising the following steps:

[0009] S1) Establish tunnel and stratum model according to the scope of the project, stratum distribution and tunnel size;

[0010] S2) Determine the occurrence, size, i.e., trace length, position, opening, density, and corresponding probability density distribution functions of the rock mass joints and fissures according to the fissure distribution characteristics;

[0011] The Monte Carlo method is used to generate random numbers through a pseudo-random number generator (PRNG), and specific physical properties are assigned to each fracture during the simulation process based on the random numbers. A discrete fracture network (DFN) template is established based on the randomly generated attribute values ​​to accurately place each fracture in the tunnel and stratum model.

[0012] Then the tunnel and stratum models are cut using discrete fracture network (DFN) templates and converted into bonded block models (BBM);

[0013] S3) determining the location and radius of the cave according to the cave distribution characteristics and establishing a random ball model, using the Monte Carlo method to generate random numbers through a pseudo-random number generator PRNG, and assigning specific physical properties to each ball during the simulation process, and finally dividing it into surface meshes;

[0014] S4), importing the random ball model into the bonded block model BBM for cutting, and generating a random fracture cave model based on the difference set of Boolean operation.

[0015] Preferably, in step S1), before constructing the tunnel and stratum model, it is necessary to first quantify the engineering geological conditions, including the type of rock and its distribution range, fault folds, rock and soil physical and mechanical parameters, distribution characteristics of cracks, and development characteristics of caves.

[0016] Preferably, in step S1), the geotechnical physical and mechanical parameters include gravity, deformation modulus, Poisson's ratio, shear modulus, cohesion, internal friction angle, and tensile strength;

[0017] The distribution characteristics of the cracks include occurrence, size, i.e., trace length, and density;

[0018] The development characteristics of the cave include location, size, i.e., trace length, and volume karstity / line karstity.

[0019] Preferably, in step S1), the establishment of the tunnel and stratum model comprises the following steps:

[0020] S11), determining the distribution of the strata, and then determining the boundary range of the tunnel and stratum model, as well as the tunnel design size, shape and excavation method parameters to be adopted;

[0021] S12), importing the terrain data into Rhino through DEM, using built-in modeling tools to construct each stratum, and then using Boolean operations to merge or cut the stratum to obtain the stratum part of the tunnel and stratum model;

[0022] S13), using a curve tool to draw a cross-sectional and longitudinal profile of the tunnel, and then stretching or scanning the tunnel profile along a predetermined path to form a complete tunnel body;

[0023] S14) Use Boolean to integrate and inlay the tunnel and the stratum.

[0024] Preferably, in step S2), the elements of the discrete fracture network DFN template include fracture inclination, fracture inclination, fracture trace length, fracture position, and fracture density; wherein the fracture inclination and fracture inclination obey Fisher distribution, the fracture position obeys uniform distribution, and the fracture trace length obeys power law distribution.

[0025] Preferably, in step S2), the density parameter index of the rock mass joint fissure distribution is determined by the following formula:

[0026]

[0027] Where ρ3 represents the number of cracks per unit volume, i.e., the three-dimensional crack density; ρ1 represents the number of cracks per unit length, i.e., the one-dimensional line crack density; E(D 2 ) represents the expected value of the square of the crack diameter D; Ω represents the spatial region of integration, f(A) represents the direction function, and A represents the direction matrix;

[0028] Wherein, the direction matrix A is: A = cos(α i -μ);

[0029] In the formula, α i represents the crack tendency, μ represents the average crack tendency;

[0030] When the cracks are parallel to each other: f(A) = 1;

[0031] When the cracks are not parallel: f(a) = e κA ;

[0032] Where κ represents the concentration parameter of the crack.

[0033] Preferably, in step S2), the probability density function expression of the occurrence of the rock mass joint and fissure distribution is:

[0034]

[0035] Where α represents the crack inclination, β represents the crack dip angle, and κ represents the crack concentration parameter.

[0036] Preferably, in step S2), the probability density function expression of the size of the rock mass joint fissure distribution, i.e., the trace length, is:

[0037]

[0038] Where l represents the random variable of the crack trace length, a is the density term of the model with the scaling exponent; C represents the normalized parameter term; V represents the volume of the target area of ​​the crack;

[0039] Among them, the normalization parameter C is expressed as:

[0040]

[0041] Among them, l max , l min Represent the maximum and minimum values ​​of the trace length respectively.

[0042] Preferably, in step S2), the position probability density function expression of the rock mass joint fissure distribution is:

[0043]

[0044] Among them, y fi Indicates the Y coordinate value of the crack, y fmin With y fmax They respectively represent the lower and upper limits of the Y coordinate of the crack distribution in this area.

[0045] Preferably, in step S2), the probability density function expression of the opening degree of the rock mass joint fissure distribution is:

[0046]

[0047] Among them, b i represents the opening of the crack, μ b represents the mean value of crack opening, σ b Represents the standard deviation of the crack opening.

[0048] Preferably, in step S2), the generation of the bonded block model BBM specifically comprises the following steps:

[0049] S21), after defining the geometric and physical characteristics of the fracture, input the probability density function of the occurrence, location, size, i.e., trace length;

[0050] S22), generating target cracks, and then providing the number and density of generated target cracks;

[0051] S23), after generating the target crack, setting the crack opening;

[0052] S24), using the discrete fracture network DFN template to cut the tunnel surrounding rock model, the generated fractures will be coupled into the model to form a bonded block model BBM.

[0053] Preferably, in step S3), the constituent elements of the random ball model include the location of the cave, the height of the cave, the density of the cave and the relative distance between the cave and the tunnel, the density of the cave obeys Poisson distribution, and the relative distance between the cave and the tunnel obeys lognormal distribution; the Y coordinate of the location of the cave, that is, along the direction of tunnel excavation footage, obeys uniform distribution; and the X and Z coordinates of the location of the cave are jointly determined based on the angle between the center point of the ball and the perpendicular line of the tunnel centerline and the tangent plane in the X direction; wherein the angle obeys uniform distribution on the spatial plane; and the height of the cave obeys normal distribution.

[0054] Preferably, in step S3), the parameters of the random ball model to be generated are defined, including the spatial range of the balls, density parameters, radius distribution, the minimum allowable distance between balls, the spatial distribution range of tunnels, tunnel and distance parameters, and then the number of balls to be generated is determined, and then the random ball model is generated.

[0055] Preferably, in step S3), the lognormal distribution function expression of the relative position of the ball in space and the vertical distance from the Y axis is:

[0056]

[0057] In the formula, y hi represents the Y coordinate value of the ball, d i Represents the perpendicular distance between the center point coordinate of the ball and the Y axis, θ i represents the spatial angle between the vertical line of the ball center point and the Y axis and the X axis, x i represents the X coordinate value of the ball, z i Represents the Z coordinate value of the ball; σ c represents the standard deviation of the distance between the ball and the tunnel, μ c represents the mean distance between the ball and the tunnel, y max ,y min Indicates the upper and lower limits of the Y coordinate of the ball distribution in this area; lnd i Represents the logarithmic value of the distance between the center point of the ball and the perpendicular line of the Y axis.

[0058] Preferably, in step S3), the normal distribution function expression of the radius of the ball is:

[0059]

[0060] Among them, r irepresents the radius of the ball, r max is the maximum cave radius; σ r represents the standard deviation of the ball radius, μ r represents the mean radius of the spheres.

[0061] Preferably, in step S3), the Poisson distribution function expression of the density of the balls is:

[0062]

[0063] Where n represents the number of balls generated in a specific volume, λ represents the expected number of random balls generated in a certain area after multiple Poisson points, and ! represents factorial.

[0064] Preferably, in step S4), the random ball model is imported into the bonded block model BBM for cutting, and a random fracture cave model is generated based on the difference set of Boolean operation, which specifically includes the following steps:

[0065] S41), importing the geometric information of the ball into the bonded block model BBM;

[0066] S42), creating a geometric polygon group for the imported balls;

[0067] S43), performing Boolean cutting on the spheres, and grouping the blocks generated by the cutting;

[0068] S44), then perform a Boolean difference operation, delete the block and generate a random fracture cave model.

[0069] The beneficial effects of the present invention are:

[0070] 1. In the modeling process, the present invention comprehensively considers the distribution of cracks and caves in the surrounding rocks during the construction of the karst tunnel, so as to meet the needs of evaluating the safe distance between the cave and the tunnel during the subsequent tunnel construction and operation and maintenance;

[0071] 2. The present invention can establish a refined numerical model based on the statistical data of joints and fissures and the distribution of karst caves at the actual tunnel engineering site; it can better simulate and analyze the engineering mechanical behavior and stability of the karst strata in the tunnel, thereby improving the safety and reliability of tunnel excavation construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the process of the present invention;

[0073] Figure 2 This is a model diagram of the tunnel and stratum of the present invention;

[0074] Figure 3 This is a diagram of the generation of the fracture generation and bonding block BBM model of the present invention;

[0075] Figure 4 A schematic diagram of generating spatial coordinates of random balls of the present invention;

[0076] Figure 5 It is a schematic diagram of the generation of the random sphere model and the cave model of the present invention. DETAILED DESCRIPTION

[0077] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings:

[0078] like Figure 1 As shown, this embodiment provides a method for constructing a refined random fracture cave tunnel numerical model, comprising the following steps:

[0079] S1), according to the scope of the project, the distribution of the strata, and the size of the tunnel, a tunnel and strata model is established. The tunnel and strata model constructed in this embodiment is shown in FIG. Figure 2 As shown; and before constructing the tunnel and stratum model, it is necessary to quantify the engineering geological conditions, including the type of rock and its distribution range, fault folds, rock and soil physical and mechanical parameters, distribution characteristics of cracks, and development characteristics of caves.

[0080] Among them, the rock and soil physical and mechanical parameters described in this embodiment include density, deformation modulus, Poisson's ratio, shear modulus, cohesion, internal friction angle, and tensile strength.

[0081] The distribution characteristics of the cracks include occurrence, size (i.e., trace length), and density.

[0082] The development characteristics of the cave include location, size, i.e., trace length, and volume karstity / line karstity.

[0083] In this embodiment, the establishment of the tunnel and stratum model includes the following steps:

[0084] S11), determine the distribution of the strata, and then determine the boundary range of the tunnel and stratum model, as well as the tunnel design size, shape and excavation method parameters adopted; the tunnel unit grid size of the tunnel and stratum model is determined according to the cyclic excavation footage; the unit grid of the surrounding rock is larger than 4m, and grid division is performed after the grid size is determined;

[0085] S12), importing the terrain data into Rhino through DEM, using built-in modeling tools to construct each stratum, and then using Boolean operations to merge or cut the stratum to obtain the stratum part of the tunnel and stratum model;

[0086] S13), using a curve tool to draw a cross-sectional and longitudinal profile of the tunnel, and then stretching or scanning the tunnel profile along a predetermined path to form a complete tunnel body;

[0087] S14) Use Boolean to integrate and inlay the tunnel and the stratum.

[0088] S2) According to the crack distribution characteristics, the occurrence, size, i.e., trace length, position, opening, density and their corresponding probability density distribution functions of the rock mass joint crack distribution are determined; random numbers are generated by a pseudo-random number generator PRNG using the Monte Carlo method, and specific physical properties are assigned to each crack during the simulation according to the random numbers, and a discrete fracture network DFN template is established according to the randomly generated attribute values ​​to accurately place each fracture in the tunnel and stratum model; then the tunnel and stratum model is cut using the discrete fracture network DFN template and converted into a bonded block model BBM, see Figure 3 shown.

[0089] S3) Determine the location and radius of the cave according to the cave distribution characteristics and establish a random ball model. Generate random numbers using the Monte Carlo method through a pseudo-random number generator PRNG, and assign specific physical properties to each ball during the simulation process. Finally, divide it into surface meshes; see Figure 4 As shown.

[0090] S4), import the random ball model into the bonded block model BBM for cutting, and generate a random fracture cave model based on the difference set of Boolean operation, see Figure 5 shown.

[0091] As a preferred embodiment of this embodiment, in step S2), the elements of the discrete fracture network DFN template include fracture inclination, fracture inclination, fracture trace length, fracture position, and fracture density; wherein the fracture inclination and fracture inclination obey the Fisher distribution, the fracture position obeys the uniform distribution, and the fracture trace length obeys the power law distribution.

[0092] As a preferred embodiment of this invention, in step S2), the density parameter index of the rock mass joint fissure distribution is determined by the following formula:

[0093]

[0094] Where ρ3 represents the number of cracks per unit volume, i.e., the three-dimensional crack density; ρ1 represents the number of cracks per unit length, i.e., the one-dimensional line crack density; E(D 2 ) represents the expected value of the square of the crack diameter D; Ω represents the spatial region of integration, f(A) represents the direction function, and A represents the direction matrix;

[0095] Wherein, the direction matrix A is: A = cos(α i -μ);

[0096] In the formula, αi represents the crack tendency, μ represents the average crack tendency;

[0097] When the cracks are parallel to each other: f(A) = 1;

[0098] When the cracks are not parallel: f(A) = e κA ;

[0099] Where κ represents the concentration parameter of the crack.

[0100] As a preferred embodiment of the present invention, in step S2), the probability density function expression of the distribution of rock mass joints and fissures is:

[0101]

[0102] Where α represents the crack inclination, β represents the crack dip angle, and κ represents the crack concentration parameter.

[0103] As a preferred embodiment of the present invention, in step S2), the probability density function expression of the size of the rock mass joint fissure distribution, i.e., the trace length, is:

[0104]

[0105] Where l represents the random variable of the crack trace length, a is the density term of the model with the scaling exponent; C represents the normalized parameter term; V represents the volume of the target area of ​​the crack;

[0106] Among them, the normalization parameter C is expressed as:

[0107]

[0108] Among them, l max , l min Represent the maximum and minimum values ​​of the trace length respectively.

[0109] Preferably, in step S2), the position probability density function expression of the rock mass joint fissure distribution is:

[0110]

[0111] Among them, y fi Indicates the Y coordinate value of the crack, y fmin With y fmax They respectively represent the lower and upper limits of the Y coordinate of the crack distribution in this area.

[0112] As a preferred embodiment of the present invention, in step S2), the probability density function expression of the opening degree of the rock mass joint fissure distribution is:

[0113]

[0114] Among them, b i represents the opening of the crack, μ b represents the mean value of crack opening, σ b Represents the standard deviation of the crack opening.

[0115] As a preferred embodiment of the present invention, in step S2), the generation of the bonded block model BBM specifically comprises the following steps:

[0116] S21), after defining the geometric and physical characteristics of the fracture, input the probability density function of the occurrence, location, size, i.e., trace length;

[0117] S22), generating target cracks, and then providing the number and density of generated target cracks;

[0118] S23), after generating the target crack, setting the crack opening;

[0119] S24), using the discrete fracture network DFN template to cut the tunnel surrounding rock model, the generated fractures will be coupled into the model to form a bonded block model BBM.

[0120] As a preferred embodiment of this embodiment, in step S3), the constituent elements of the random ball model include the location of the cave, the height of the cave, the density of the cave and the relative distance between the cave and the tunnel, the cave density obeys the Poisson distribution, and the relative distance between the cave and the tunnel obeys the log-normal distribution; the Y coordinate of the location of the cave, that is, along the direction of tunnel excavation footage, obeys a uniform distribution; and the X and Z coordinates of the location of the cave are jointly determined based on the angle between the center point of the ball and the perpendicular line of the tunnel centerline and the tangent plane in the X direction; wherein the angle obeys a uniform distribution on the spatial plane; and the height of the cave obeys a normal distribution.

[0121] As a preferred embodiment of this embodiment, in step S3), the parameters of the random ball model to be generated are defined, including the spatial range of the balls, density parameters, radius distribution, the minimum allowable distance between balls, the spatial distribution range of tunnels, tunnel and distance parameters, and then the number of balls to be generated is determined, and then the random ball model is generated.

[0122] As a preferred embodiment of the present invention, in step S3), the logarithmic normal distribution function expression of the relative position of the ball in space and the vertical distance from the Y axis is:

[0123]

[0124] In the formula, y hi represents the Y coordinate value of the ball, d i Represents the perpendicular distance between the center point coordinate of the ball and the Y axis, θ irepresents the spatial angle between the vertical line of the ball center point and the Y axis and the X axis, x i represents the X coordinate value of the ball, z i Represents the Z coordinate value of the ball; σ c represents the standard deviation of the distance between the ball and the tunnel, μ c represents the mean distance between the ball and the tunnel, y max ,y min Indicates the upper and lower limits of the Y coordinate of the ball distribution in this area; lnd i Represents the logarithmic value of the distance between the center point of the ball and the perpendicular line of the Y axis.

[0125] As a preferred embodiment of this invention, in step S3), the normal distribution function expression of the radius of the ball is:

[0126]

[0127] Among them, r i represents the radius of the ball, r max is the maximum cave radius; σ r represents the standard deviation of the ball radius, μ r represents the mean radius of the spheres.

[0128] As a preferred embodiment of this invention, in step S3), the Poisson distribution function expression of the density of the balls is:

[0129]

[0130] Where n represents the number of balls generated in a specific volume, λ represents the expected number of random balls generated in a certain area after multiple Poisson points, and ! represents factorial.

[0131] As a preferred embodiment of the present invention, in step S4), the random ball model is imported into the bonded block model BBM for cutting, and a random fracture cave model is generated based on the difference set of Boolean operation, which specifically includes the following steps:

[0132] S41), importing the geometric information of the ball into the bonded block model BBM;

[0133] S42), creating a geometric polygon group for the imported balls;

[0134] S43), performing Boolean cutting on the spheres, and grouping the blocks generated by the cutting;

[0135] S44), then perform a Boolean difference operation to delete the block to generate a cave.

[0136] The above embodiments and descriptions are only for illustrating the principles and best embodiments of the present invention. Without departing from the spirit and scope of the present invention, the present invention may be subject to various changes and improvements, all of which fall within the scope of the present invention to be protected.

Claims

1. A method for constructing a refined random fracture cave tunnel numerical model, characterized in that: The following steps are involved: S1) Establish tunnel and stratum model according to the scope of the project, stratum distribution and tunnel size; S2) Determine the occurrence, size, i.e., trace length, position, opening, density, and corresponding probability density distribution functions of the rock mass joints and fissures according to the fissure distribution characteristics; The Monte Carlo method is used to generate random numbers through a pseudo-random number generator (PRNG), and specific physical properties are assigned to each fracture during the simulation process based on the random numbers. A discrete fracture network (DFN) template is established based on the randomly generated attribute values ​​to accurately place each fracture in the tunnel and stratum model. Then the tunnel and stratum models are cut using discrete fracture network (DFN) templates and converted into bonded block models (BBM); S3) determining the location and radius of the cave according to the cave distribution characteristics and establishing a random ball model, using the Monte Carlo method to generate random numbers through a pseudo-random number generator PRNG, and assigning specific physical properties to each ball during the simulation process, and finally dividing it into surface meshes; The constituent elements of the random ball model include the location of the cave, the height of the cave, the density of the cave and the relative distance between the cave and the tunnel. The density of the cave obeys Poisson distribution, and the relative distance between the cave and the tunnel obeys lognormal distribution. The Y coordinate of the location of the cave, that is, along the direction of tunnel excavation footage, obeys uniform distribution. The X and Z coordinates of the location of the cave are determined by the angle between the center point of the ball and the perpendicular line of the tunnel center axis and the tangent plane in the X direction. The angle obeys uniform distribution on the spatial plane. The height of the cave obeys normal distribution. Define the parameters of the random ball model to be generated, including the spatial range of the balls, density parameters, radius distribution, the minimum allowable distance between balls, the spatial distribution range of tunnels, tunnel and distance parameters, and then determine the number of balls to be generated, and then generate the random ball model; The lognormal distribution function expression of the relative position of the ball in space and the vertical distance from the Y axis is: In the formula, y hi represents the Y coordinate value of the ball, d i Represents the perpendicular distance between the center point coordinate of the ball and the Y axis, θ i represents the spatial angle between the center point of the ball and the vertical line of the Y axis and the X axis, x i represents the X coordinate value of the ball, z i Represents the Z coordinate value of the ball; σ c represents the standard deviation of the distance between the ball and the tunnel, μ c represents the mean distance between the ball and the tunnel, y max ,y min Indicates the upper and lower limits of the Y coordinate of the ball in the distribution area; lnd i Represents the logarithmic value of the distance between the center point coordinate of the ball and the perpendicular line of the Y axis; The normal distribution function expression of the radius of the ball is: Among them, r i represents the radius of the ball, r max is the maximum cave radius; σ r represents the standard deviation of the ball radius, μ r represents the mean value of the radius of the spheres; The Poisson distribution function expression of the density of the ball is: Where n represents the number of balls generated in a specific volume, λ represents the expected value of the number of random balls generated in a certain area after multiple Poisson points, and ! represents factorial; S4), importing the random ball model into the bonded block model BBM for cutting, and generating a random fracture cave model based on the difference set of Boolean operation.

2. The method for constructing a refined random fracture cave tunnel numerical model according to claim 1, characterized in that: In step S1), before constructing the tunnel and stratum model, it is necessary to quantify the engineering geological conditions, including the type of rock and its distribution range, fault folds, geotechnical physical and mechanical parameters, distribution characteristics of cracks, and development characteristics of caves.

3. The method for constructing a refined random fracture cave tunnel numerical model according to claim 2, characterized in that: In step S1), the establishment of the tunnel and stratum model includes the following steps: S11), determining the distribution of the strata, and then determining the boundary range of the tunnel and stratum model, as well as the tunnel design size, shape and excavation method parameters to be adopted; S12), importing the terrain data into Rhino through DEM, using built-in modeling tools to construct each stratum, and then using Boolean operations to merge or cut the stratum to obtain the stratum part of the tunnel and stratum model; S13), using a curve tool to draw a cross-sectional and longitudinal profile of the tunnel, and then stretching or scanning the tunnel profile along a predetermined path to form a complete tunnel body; S14) Use Boolean to integrate and inlay the tunnel and the stratum.

4. The method for constructing a refined random fracture cave tunnel numerical model according to claim 1, characterized in that: In step S2), the elements of the discrete fracture network DFN template include fracture inclination, fracture inclination, fracture trace length, fracture position, and fracture density; wherein the fracture inclination and fracture inclination obey the Fisher distribution, the fracture position obeys the uniform distribution, and the fracture trace length obeys the power law distribution.

5. The method for constructing a refined random fracture karst cave tunnel numerical model according to claim 1, characterized in that: In step S2), the density parameter index of the rock mass joint fissure distribution is determined by the following formula: Where ρ3 represents the number of cracks per unit volume, i.e., the three-dimensional crack density; ρ1 represents the number of cracks per unit length, i.e., the one-dimensional line crack density; E(D 2 ) represents the expected value of the square of the crack diameter D; Ω represents the spatial region of integration, f(A) represents the direction function, and A represents the direction matrix; Wherein, the direction matrix A is: A = cos(α i -μ); In the formula, α i represents the crack tendency, μ represents the average crack tendency; When the cracks are parallel to each other: f(A) = 1; When the cracks are not parallel: f(A) = e κA ; Where κ represents the concentration parameter of the crack; The probability density function expression of the occurrence of rock mass joint fissure distribution is: In the formula, α represents the crack inclination, β represents the crack inclination angle, κ represents the concentration parameter of the crack; The probability density function expression of the size of the rock mass joint fissure distribution, i.e., the trace length, is: Where l represents the random variable of the crack trace length, a is the density term of the model with the scaling exponent; C represents the normalized parameter term; V represents the volume of the target area of ​​the crack; Among them, the normalization parameter term C is expressed as: Among them, l max , l min Respectively represent the maximum and minimum values ​​of the trace length; The position probability density function expression of the rock mass joint fissure distribution is: Among them, y fi Indicates the Y coordinate value of the crack, y fmin With y fmax They represent the lower and upper limits of the Y coordinate of the crack distribution area respectively; The probability density function expression of the opening degree of the rock mass joint fissure distribution is: Among them, b i represents the opening of the crack, μ b represents the mean value of crack opening, σ b Represents the standard deviation of the crack opening.

6. The method for constructing a refined random fracture cave tunnel numerical model according to claim 1, characterized in that: In step S2), the generation of the bonded block model BBM specifically includes the following steps: S21), after defining the geometric and physical characteristics of the fracture, input the probability density function of the occurrence, location, size, i.e., trace length; S22), generating target cracks, and then providing the number and density of generated target cracks; S23), after generating the target crack, setting the crack opening; S24), using the discrete fracture network DFN template to cut the tunnel surrounding rock model, the generated fractures will be coupled into the model to form a bonded block model BBM.

7. The method for constructing a refined random fracture cave tunnel numerical model according to claim 1, characterized in that: In step S4), the random ball model is imported into the bonded block model BBM for cutting, and a random fracture cave model is generated based on the difference set of Boolean operation, which specifically includes the following steps: S41), importing the geometric information of the ball into the bonded block model BBM; S42), creating a geometric polygon group for the imported balls; S43), performing Boolean cutting on the spheres, and grouping the blocks generated by the cutting; S44), then perform a Boolean difference operation, delete the block and generate a random fracture cave model.

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