A method, device, equipment and medium for simulating a grouting reinforcement process of a fractured rock mass

Abstract

This invention discloses a method, apparatus, equipment, and medium for simulating the grouting reinforcement process of fractured rock masses, applicable to the field of underground engineering. The method includes constructing a grouting model of the target fractured rock mass; retrieving and updating the grout flow network composed of fracture networks connected to the grouting pressure boundary; calculating the real-time diffusion state variables of the grout flow network based on the grout flow network; converting pressure components into nodal forces; calling a finite discrete element method (FEM) solver; updating the structural response state data of the rock mass grouting model based on the nodal forces and obtaining failed joint elements; updating the grout flow network based on the failed joint elements; and iteratively executing alternating solution steps until a preset termination condition is met, thereby completing the numerical simulation of the grouting-reinforcement process of fractured rock masses. This invention significantly reduces manual intervention and improves geometric fidelity, automatically identifies newly added failed joint elements and immediately incorporates them into the grout flow network, improving the accuracy of the grouting reinforcement simulation.

Description

Technical Field

[0001] This invention relates to the field of underground engineering technology, and in particular to a method, apparatus, equipment and medium for simulating the grouting reinforcement process of fractured rock masses. Background Technology

[0002] As the development of deep underground space continues to advance to depths of thousands of meters, problems such as sudden water inrush in highly permeable and low-strength fractured rock masses and large deformation and instability of surrounding rock occur frequently. Grouting reinforcement has become a key means to ensure construction safety and operational stability.

[0003] In existing technologies, most simulation schemes treat the grout as a Newtonian or simple non-Newtonian fluid with constant rheological parameters, neglecting the objective law that viscosity and yield stress increase rapidly over time due to cement hydration. Meanwhile, the continuous medium assumption struggles to characterize the local discontinuous behavior of fracture opening, slippage, and new fracture formation, while a purely discrete framework is insufficient to describe the continuous seepage characteristics of the grout in micro-fractures. Furthermore, existing studies often treat grout diffusion and consolidation reinforcement separately, lacking a quantitative assessment of the grout's bonding and repair effect on fracture surfaces after solidification. This leads to significant discrepancies between predicted diffusion range, post-reinforcement rock mass strength, and long-term permeability and actual field measurements, introducing uncertainties into engineering safety and economics.

[0004] Therefore, how to achieve reliable numerical simulation of the grouting process in fractured rock masses has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and medium for simulating the grouting reinforcement process in fractured rock masses, thereby solving the technical problem that existing technologies cannot accurately predict the diffusion range and reinforcement effect of grouting in deep fractured rock masses, and thus achieving the effect of reliably simulating the grouting-reinforcement behavior throughout the entire process and improving the accuracy of engineering safety assessment.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a method for simulating the grouting reinforcement process of fractured rock masses, comprising: Construct a rock mass grouting model of the target fractured rock mass and apply corresponding grouting boundary conditions to the rock mass grouting model.

[0007] The grout flow network, consisting of fracture networks connected to the grouting pressure boundary, is retrieved and updated using a flow network retrieval algorithm.

[0008] Based on the slurry flow network, the real-time diffusion state of the slurry in the slurry flow network is calculated using the improved cubic law and the linear fluid compressibility model applicable to time-varying Bingham-type slurries.

[0009] The pressure component in the real-time diffusion state of the slurry is applied to the boundary of the triangular finite element in a linear distribution form and converted into nodal forces.

[0010] The finite discrete element method solver is invoked to update the structural response state data of the rock mass grouting model based on the nodal forces and obtain the failed joint elements. The failed joint elements are then incorporated into the grout flow network.

[0011] Using the updated slurry flow network as input, the alternating solution steps of "improved cubic law and linear fluid compressible model - pressure to nodal force - finite discrete element mechanics solver - updated slurry flow network" are executed cyclically until the preset termination condition is met, thereby completing the numerical simulation of the grouting-reinforcement process of fractured rock mass.

[0012] Furthermore, the construction of the rock mass grouting model of the target fractured rock mass and the application of corresponding grouting boundary conditions to the rock mass grouting model include: Two-dimensional random fracture networks conforming to geostatistical characteristics are generated based on the Monte Carlo method.

[0013] The two-dimensional random fracture network is subjected to geometric topology optimization to obtain fracture geometry for mesh partitioning.

[0014] The fracture geometry is divided into triangular finite elements and joint elements to establish a continuous-discontinuous rock mass grouting model.

[0015] The grouting hole layout parameters are determined by probability sampling, and a time-varying grouting pressure is applied at the grouting hole boundary.

[0016] Further, the step of retrieving and updating the grout flow network composed of fracture networks connected to the grouting pressure boundary using a flow network retrieval algorithm includes: The flow network retrieval algorithm is used to identify all failed joint elements that are directly connected to the grouting pressure boundary, thus forming an initial grout flow network.

[0017] Based on the connectivity of the newly added failed joint units, the initial slurry flow network is updated to obtain the slurry flow network.

[0018] Furthermore, the real-time diffusion state parameters of the slurry include slurry flow rate, node pressure, and node saturation.

[0019] The calculation of the real-time diffusion state parameters of the slurry flow network based on the slurry flow network, using an improved cubic law and a linear fluid compressibility model suitable for time-varying Bingham-type slurries, includes: Using the slurry flow network as input data, the slurry flow rate of each flow channel is calculated using the improved cubic law applicable to time-varying Bingham-type slurries.

[0020] Substituting the slurry flow rate into a linear fluid compressible model, the nodal pressure and saturation are obtained through nodal balancing.

[0021] Further, the step of applying the pressure component of the real-time diffusion state quantity of the slurry in a linear distribution to the boundary of the triangular finite element and converting it into nodal forces includes: The pressure components of each node are extracted from the node pressure of the real-time diffusion state of the slurry.

[0022] The pressure component is coupled with the flow channel to generate a linearly distributed pressure load.

[0023] The pressure load is converted into concentrated forces acting on the nodes at both ends of the boundary of the triangular finite element, forming a nodal force vector.

[0024] Furthermore, the structural response state data includes nodal displacements, element stresses, and joint element states.

[0025] The process of calling the finite discrete element method (FEM) solver, updating the structural response state data of the rock mass grouting model based on the nodal forces, obtaining failed joint elements, and incorporating the failed joint elements into the grout flow network includes: The nodal force vectors are input into the finite discrete element mechanics solver to obtain nodal displacements, element stresses, and joint element states.

[0026] The newly added failure joint units are marked according to the structural response state data and incorporated into the slurry flow network.

[0027] Another embodiment of the present invention provides a device for simulating the grouting reinforcement process of fractured rock mass, comprising: The model building module is used to build a rock mass grouting model of the target fractured rock mass and apply corresponding grouting boundary conditions to the rock mass grouting model.

[0028] The network construction module is used to retrieve and update the grout flow network, which consists of fracture networks connected to the grouting pressure boundary, through a flow network retrieval algorithm.

[0029] The state quantity calculation module is used to calculate the real-time diffusion state quantities of the slurry flow network based on the slurry flow network, using the improved cubic law and linear fluid compressibility model applicable to time-varying Bingham type slurries.

[0030] The nodal force calculation module is used to apply the pressure component in the real-time diffusion state of the slurry to the boundary of the triangular finite element in a linear distribution form and convert it into nodal force.

[0031] The network update module is used to call the finite discrete element mechanics solver, update the structural response state data of the rock mass grouting model according to the nodal forces and obtain the failed joint elements, and incorporate the failed joint elements into the grout flow network.

[0032] The process simulation module is used to take the updated slurry flow network as input and repeatedly execute the alternating solution steps of "improved cubic law and linear fluid compressible model - pressure to nodal force - finite discrete element mechanics solver - update slurry flow network" until the preset termination condition is met, thereby completing the numerical simulation of the grouting-reinforcement process of fractured rock mass.

[0033] Furthermore, the model building module is specifically used for: Two-dimensional random fracture networks conforming to geostatistical characteristics are generated based on the Monte Carlo method.

[0034] The two-dimensional random fracture network is subjected to geometric topology optimization to obtain fracture geometry for mesh partitioning.

[0035] The fracture geometry is divided into triangular finite elements and joint elements to establish a continuous-discontinuous rock mass grouting model.

[0036] The grouting hole layout parameters are determined by probability sampling, and a time-varying grouting pressure is applied at the grouting hole boundary.

[0037] Another embodiment of the present invention provides a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the simulation method for grouting reinforcement of fractured rock mass as described above.

[0038] In another embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the simulation method for grouting reinforcement of fractured rock mass as described above.

[0039] Compared with existing technologies, the beneficial effects of the embodiments of the present invention are at least one of the following: The Monte Carlo method is used to rapidly generate a random fracture network that conforms to the statistical characteristics of the field, significantly reducing manual intervention and improving geometric fidelity, making the rock mass model before grouting closer to the actual geological conditions; the time-varying Bingham grout model, the improved cubic law, and the linear fluid compressibility model are used to achieve synchronous updates of grout flow rate, nodal pressure, and saturation, thereby enabling real-time capture of the diffusion front of the grout in the fracture and its evolution over time, significantly improving prediction accuracy compared to traditional fixed-parameter methods; through the "grouting diffusion-mechanical response-network update" cyclic mechanism, newly added failed joint units are automatically identified and immediately incorporated into the grout flow network after each mechanical calculation, ensuring that the influence of fracture aperture changes on the diffusion path is continuously included in subsequent calculations, significantly reducing the deviation in diffusion range caused by delayed network updates. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the steps of a simulation method for grouting reinforcement of fractured rock mass in one embodiment of the present invention. Figure 2 This is a schematic diagram illustrating two types of crack distributions that easily cause difficulties in mesh generation, and their optimization, in one embodiment of the present invention. Figure 3 This is a numerical model for grouting rock mass with discrete fracture network in one embodiment of the present invention; Figure 4 This is a schematic diagram of grouting flow network search in one embodiment of the present invention; Figure 5 This is a schematic diagram of grouting flow in one embodiment of the present invention; Figure 6 This is a time-varying grouting diffusion seepage-stress bidirectional coupling frame in one embodiment of the present invention; Figure 7 This is a schematic diagram of rock mass fracture grouting reinforcement and repair in one embodiment of the present invention; Figure 8 This is a structural block diagram of a simulation device for grouting reinforcement of fractured rock mass in one embodiment of the present invention; Figure 9 A structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0042] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0043] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0044] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0045] One embodiment of the present invention provides a simulation method for the grouting reinforcement process of fractured rock masses. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The diagram shows a flowchart of the simulation method for grouting reinforcement of fractured rock mass in one embodiment of the present invention, including steps S11-S16: S11. Construct a rock mass grouting model of the target fractured rock mass and apply corresponding grouting boundary conditions to the rock mass grouting model.

[0046] A two-dimensional random fracture network conforming to geostatistical characteristics is generated based on the Monte Carlo method. In numerical simulations of grouting in deep fractured rock masses, accurately reproducing the spatial uncertainty of the fracture system is a primary challenge. Field investigations show that while natural fractures are microscopically random and disordered, they exhibit significant directional dominance and scale-law characteristics (power-law length distribution, von Mises direction distribution, etc.) on a statistical scale. Traditional deterministic modeling methods struggle to simultaneously meet the dual requirements of "statistical similarity" and "geometric randomness." Therefore, this embodiment introduces a Monte Carlo random simulation framework: First, based on the joint rose diagram and survey line statistics, probability density functions for fracture length, orientation angle, and center point are established; then, uniform random numbers of [0,1] are generated using the linear congruence method, and after inverse transformation sampling, mapped to the aforementioned distribution, generating a batch of two-dimensional fracture sample sets that conform to statistical laws. Through the above probability sampling-mapping-arrangement process, all fracture segments are generated and combined in a two-dimensional plane at once, resulting in a two-dimensional random fracture network conforming to geostatistical characteristics. This network serves as the original geometric matrix for subsequent topology optimization and mesh generation.

[0047] The two-dimensional random fracture network is geometrically optimized to obtain the fracture geometry for mesh generation. Initial networks generated by Monte Carlo simulations often exhibit two types of ill-conditioned topologies: ① subparallel fractures intersect at extremely small acute angles, producing sharp triangles; ② fracture endpoints "virtually intersect," forming extremely short sides. These defects can lead to element distortion, excessively small time steps, and even numerical divergence during FDEM calculations. Therefore, this embodiment develops a fracture topology optimizer on the Python-AutoCAD joint platform: for the first type of defect, a "micro-translation method" is used, moving the fracture along the fracture normal by a distance ≤5% of the fracture length to increase the included angle to over 15°; for the second type of defect, an "endpoint extension-truncation method" is used, extending the fracture to a true intersection or deleting redundant segments if the endpoint spacing is <0.1 times the average element size. The optimized network maintains statistical characteristics and meets mesh generation quality indicators (minimum angle >20°, short side >0.05 m), and can be directly imported into Triangle / GMSH to generate continuous-discontinuous coupled computational meshes. After the above topology optimization process, the final fracture network meets the meshing quality requirements in terms of spatial continuity, angular distribution and side length ratio. The optimized network constitutes the "fracture geometry" described in this application and can be directly used for the subsequent discretization of triangular finite elements and joint elements.

[0048] A continuous-discontinuous rock mass grouting model is established by dividing the fracture geometry into triangular finite elements and joint elements. Considering the dual characteristics of "fractured rock mass = continuous matrix + discontinuous fractures," this embodiment employs a dual FDEM discretization strategy: ① Constant strain triangular finite elements are used for the rock matrix, with element sizes adaptively refined according to 1 / 5 to 1 / 3 of the fracture spacing to ensure accurate capture of the stress field at the fracture tip; ② Zero-thickness quadrilateral joint elements are embedded in the fracture surface, and the constitutive relation uses the Mohr-Coulomb softening model with tension truncation, which can simulate the entire process of fracture opening, slippage, and shear failure. The model boundary conditions are set with far-field stress based on measured in-situ stress values, the tunnel outline is set as a free surface, and the grouting pressure is subsequently coupled to the grouting hole boundary. The resulting continuous-discontinuous rock mass grouting model can simultaneously characterize: ① continuous elastic-plastic deformation of the rock mass; ② discontinuous displacement and propagation of fractures; ③ the three-phase coupling effect of grout, fractures, and matrix, laying a geometric and mechanical foundation for simulating the grouting diffusion-reinforcement process.

[0049] The grouting hole layout parameters are determined by probability sampling, and a time-varying grouting pressure is applied to the grouting hole boundaries. In actual engineering, the layout of grouting holes is often constrained by both "fracture development intensity" and "construction machinery limitations." This embodiment proposes a probability-engineering joint sampling algorithm: First, using the fracture density field as weight, Metropolis-Hastings importance sampling is used to generate a candidate set of grouting hole center points, ensuring that the probability of hole placement in high fracture density areas is increased by 3 to 5 times; then, a second screening is performed using engineering design rules (minimum hole spacing ≥ 3 times the hole diameter, hole opening distance from tunnel outline ≥ 1 m) as rejection criteria, finally obtaining a set of hole locations that balances statistical representativeness and construction feasibility. The geometric parameters of the grouting holes (hole diameter, inclination angle) follow an N(design value, construction error²) normal distribution and are determined by a single sampling.

[0050] The grouting pressure boundary adopts a three-segment time-varying function: the pressurization segment (0~t1) linearly increases to P_max, the pressure stabilization segment (t1~t2) remains constant, and the pressure relief segment (t2~t_end) linearly decreases to hydrostatic pressure. Among them, P_max, t1, and t2 are determined by inversion from field grouting tests. The pressure time history is coupled to the grouting hole wall through the FDEM boundary element method to realize the real hydraulic-mechanical coupled loading of the grout-fracture-matrix system.

[0051] S12. The grout flow network, which consists of fracture networks connected to the grouting pressure boundary, is retrieved and updated using a flow network retrieval algorithm.

[0052] The flow network retrieval algorithm retrieves all failed joint elements directly connected to the grouting pressure boundary, forming an initial grout flow network. Starting from the grouting hole boundary node, the algorithm traverses all failed joint elements, using a breadth-first search strategy to determine the direct connectivity between the failed element and the grouting hole. Failed elements sharing at least two common nodes are marked as CE3 state, and an adjacency list is constructed in memory, thus forming the initial grout flow network.

[0053] Subsequently, after each new mechanical solution step, the same search process is executed again to determine the connectivity of newly emerging failed joint elements: if the new failed element shares a common node with the existing CE3 set, it is incorporated into the initial slurry flow network, and the adjacency list and element state are updated in real time to obtain the slurry flow network that evolves over time.

[0054] S13. Based on the slurry flow network, the real-time diffusion state of the slurry in the slurry flow network is calculated using the improved cubic law and the linear fluid compressibility model applicable to time-varying Bingham-type slurries.

[0055] Specifically, the real-time diffusion state variables of the slurry in this embodiment include slurry flow rate, nodal pressure, and nodal saturation, which together characterize the transient seepage-mechanical behavior of the slurry in the fracture network. Slurry flow rate represents the volumetric flow rate through the flow channel per unit time, nodal pressure reflects the hydraulic effect of the slurry on the fracture wall, and nodal saturation describes the degree to which the slurry phase occupies the pore space within the fracture. These three variables are coupled through the mass conservation and momentum conservation equations, providing the necessary state variables for subsequent seepage-stress bidirectional iteration.

[0056] Based on the slurry flow network, the real-time diffusion state variables of the slurry flow network are calculated using the improved cubic law and linear fluid compressibility model applicable to time-varying Bingham-type slurries, as follows: First, the slurry flow network is used as input data. This network consists of an adjacency list composed of failed joint elements and element geometric parameters (equivalent hydraulic opening, channel length, and node coordinates). Then, the slurry flow rate of each flow channel is calculated using the improved cubic law. The equivalent hydraulic opening is determined based on the harmonic average of the openings of the nodes at both ends of the channel and the wedge correction coefficient. The yield stress and plastic viscosity are updated in real time with hydration time t according to Equation (5). The flow rate expression adopts a piecewise integral form when there is a slug flow zone to ensure the zero flow rate characteristic of Bingham slurry below the critical pressure gradient. The calculated flow rates of each channel are substituted into the node mass balance equation. Combined with the linear fluid compressibility model, the node pressure increment is solved iteratively by the node volume, slurry bulk modulus Kf, and time step Δt. The node saturation is updated according to the definition of saturation to complete the calculation of the real-time diffusion state of the slurry at the current time step.

[0057] S14. The pressure component in the real-time diffusion state of the slurry is applied to the boundary of the triangular finite element in a linear distribution form and converted into nodal forces.

[0058] When extracting the pressure components of each node from the nodal pressure of the real-time diffusion state of the slurry, all hydraulic nodes in the slurry flow network are traversed. Based on the nodal pressure values ​​calculated in the previous time step, the nodes are sequentially extracted and cached in array form according to their node numbers to ensure that each node has a unique corresponding pressure value. Then, hydrostatic pressure correction is applied to the virtual nodes on the boundary surface to make the pressure field meet the absolute pressure reference, providing a continuous and singular pressure distribution source for subsequent load conversion.

[0059] After extracting the nodal pressure components, these components are coupled with the flow channel to generate a linearly distributed pressure load, thus more realistically simulating the pressure exerted on the surrounding rock by the flow of slurry in the fracture. By analyzing the pressure at both ends of the flow channel, the distribution of the pressure load along the flow channel can be determined. This distribution is typically linear, meaning that the pressure load gradually changes from one end of the channel to the other.

[0060] Finally, the pressure load is converted into concentrated forces acting on the nodes at both ends of the boundary of the triangular finite element, forming a nodal force vector. This discretizes the continuously distributed pressure load, making it acceptable to the finite element model. In this way, the pressure load on the flow channel can be equivalent to concentrated forces acting on the nodes of the finite element, which can then be directly applied in the mechanical solution process. Moreover, this allows the model to more accurately reflect the effect of the grout on the rock mass during the grouting process.

[0061] S15. Call the finite discrete element method solver, update the structural response state data of the rock mass grouting model according to the nodal forces and obtain the failed joint elements, and incorporate the failed joint elements into the grout flow network.

[0062] Structural response state data is a key output in the simulation process, including nodal displacements, element stresses, and joint element states. These data collectively describe the mechanical behavior of the rock mass under grouting. Nodal displacements reflect the positional changes of points within the rock mass after being subjected to stress, element stresses show the stress levels experienced by each element within the rock mass, and joint element states indicate the opening and closing of fractures. These are all important indicators for assessing the stability of the rock mass.

[0063] The finite discrete element method (FEM) solver is invoked to update the structural response state data of the rock mass grouting model based on the nodal forces and obtain the failed joint elements. These failed joint elements are then incorporated into the grout flow network, including: The nodal force vectors are input into the finite discrete element method (FEM) solver. The solver uses the input nodal force vectors to update the mechanical behavior of the rock mass grouting model through complex mechanical calculations. In this process, the solver will comprehensively consider the continuity and discontinuity characteristics of the rock mass and simulate the response of the rock mass under grouting pressure, including the deformation and stress distribution of the rock mass, and finally obtain data such as nodal displacement, element stress and joint element state.

[0064] The newly added failed joint units are marked according to the structural response state data and incorporated into the slurry flow network to update and improve the slurry flow path. This ensures that the slurry flow network can reflect the latest state of the internal fractures of the rock mass in real time, thereby simulating the diffusion and distribution of slurry in the rock mass more accurately.

[0065] S16. Using the updated slurry flow network as input, repeatedly execute the alternating solution steps of "improved cubic law and linear fluid compressible model - pressure to nodal force conversion - finite discrete element mechanics solver - update slurry flow network" until the preset termination condition is met, thereby completing the numerical simulation of the grouting-reinforcement process of fractured rock mass.

[0066] Starting with the updated grout flow network, the simulation process enters a cyclical calculation phase, which consists of multiple alternating solution steps until a preset termination condition is met. This cyclical process is the core of the numerical simulation of the grouting-reinforcement process, ensuring that the model can dynamically reflect the interaction and evolution of the rock mass and grout during the grouting process.

[0067] First, the improved cubic law and a linear fluid compressibility model are used to calculate state parameters such as flow rate, pressure, and saturation in the slurry flow network. This step is based on the time-varying Bingham model of the slurry, which considers the non-Newtonian properties and compressibility of the slurry, thus enabling accurate simulation of the slurry flow behavior in the fracture. The calculation results include the distribution of the slurry in the flow network and the flow rate in each flow channel.

[0068] Next, the calculated slurry pressure is converted into nodal forces acting on the boundaries of the triangular finite element. This conversion involves discretizing the continuously distributed pressure load to make it acceptable to the finite element model. In this way, the pressure load on the flow channel can be equivalent to concentrated forces acting on the nodes of the finite element, and thus directly applied in the mechanical solution process.

[0069] Then, the finite discrete element method (FEM) solver is invoked to calculate the structural response of the rock mass based on the updated nodal forces. This includes updates to nodal displacements, element stresses, and joint element states. The solver comprehensively considers the continuity and discontinuity characteristics of the rock mass, simulating its response under grouting pressure, including deformation and stress distribution.

[0070] After obtaining new structural response data, the grout flow network needs to be updated based on this data. This involves identifying and labeling joint elements that have failed due to stress exceeding their bearing capacity and incorporating them into the grout flow network. This step ensures that the grout flow network can reflect the latest state of fractures within the rock mass in real time, thereby more accurately simulating the diffusion and distribution of grout within the rock mass.

[0071] This process is repeated until preset termination conditions are met, such as reaching a predetermined calculation time step, the grouting pressure dropping below a certain threshold, or the deformation and stress distribution of the rock mass reaching a stable state. In this way, the interaction and evolution of the rock mass and grout during the grouting process can be dynamically simulated, providing a scientific basis for the evaluation and optimization of grouting effects.

[0072] To more accurately disclose the technical content of the solution, another embodiment is provided to describe the solution in detail: In deep rock masses, joint and fracture networks generally exhibit high spatial heterogeneity and geometric randomness. Their distribution not only shows strong regional differences but is also influenced by multiple factors such as geostress, lithology, and tectonic activity. However, extensive field geological surveys and statistical studies have shown that although there is significant uncertainty in fracture distribution at the microscopic level, it usually exhibits certain statistical regularities at the macroscopic scale, that is, it has a specific probability density function or directional distribution trend. Therefore, probabilistic statistical methods can be used to construct statistically meaningful geometric models to approximately reproduce the spatial characteristics of natural fracture systems.

[0073] The Monte Carlo method, a simulation technique based on the law of large numbers and random sampling, can effectively capture the uncertainty characteristics of fracture networks by generating and evaluating a large number of independent random samples. It has been widely used in geotechnical engineering, geological modeling, and numerical simulation. The fracture networks generated using this method not only satisfy geostatistical characteristics but can also serve as important inputs for the preprocessing stage of numerical models (such as FDEM—the finite discrete element method). This section constructs a numerical modeling workflow for generating two-dimensional random fracture networks based on the Monte Carlo theoretical framework, thus providing a geometric basis for the semi-random continuous-discontinuous mechanical simulation of fractured rock masses.

[0074] The key to fracture network modeling lies in the reasonable sampling of fracture geometric parameters (such as length, orientation, and location), and the sampling process for these parameters relies on a specific probability distribution function (PDF). The core idea of ​​the Monte Carlo method is to establish a mapping relationship with random variables and use a computer to generate random samples that follow an expected distribution, thereby achieving an approximate reproduction of the fracture's geometric characteristics. The theoretical basis of this method is the law of large numbers and the central limit theorem, and its specific implementation process is as follows: (1) Generate a uniformly distributed random number in the interval [0,1]. Currently, commonly used methods for generating uniformly distributed random numbers include the linear congruential method, the mid-square method, the congruential method, and the shift-plus-command method. Among these, the linear congruential method has become one of the most widely used methods due to its simple structure, high computational efficiency, long cycle time, and uniform distribution of generated results. Its recursive formula is as follows: (1) in x n This represents the nth random number generated, mod represents the modulo operation, M is the modulus, and a is the multiplier. x n-1 This represents the random number generated in the previous step. c This is a constant term used to offset or adjust the generated random numbers. This refers to the generated random number within the interval [0,1].

[0075] (2) Generate random numbers that follow other distributions. Using the random numbers generated above within the interval [0,1], further generate random numbers that follow other distributions. For example, for a variable that follows a normal distribution, its probability density function is... (2) Its distribution function is: (3) Then the generated normal random numbers x for: (4) In the formula, For mathematical expectation, Standard deviation, r n The random number is uniformly distributed within [0,1] and is obtained by formula (1). Similarly, random numbers that satisfy other distributions can be obtained, such as exponential distribution, power law distribution, uniform distribution, etc.

[0076] Before randomly generating fractures, the scope of the problem domain is first defined to ensure that the fracture network is generated within a finite and controllable space, providing a foundation for subsequent calculations. Engineering geological data is analyzed and statistically analyzed to identify the dominant occurrences of joint fractures and group them accordingly. The density and quantity of joint fractures in each group are then calculated. Based on the probability distribution of the control parameters of the joint fractures, a series of random numbers conforming to this probability distribution are generated to create random fractures. Based on the research content of this paper, the following assumptions are made: (1) In the two-dimensional simulation, it is assumed that the trace of a crack is a straight line segment. Without considering the crack opening, a random crack can be completely determined by its length, orientation angle and center point. (2) The crack length, orientation angle, and center point all follow a specific distribution function obtained from statistical analysis. Generally speaking, the crack length follows a power law distribution, a normal distribution, a uniform distribution, etc.; the crack orientation angle follows a normal distribution, a uniform distribution, etc.; the center point generally follows a uniform distribution to avoid local over-density or sparseness.

[0077] (3) The number of cracks in each group can be obtained by multiplying the crack density obtained by statistics with the area of ​​the problem domain.

[0078] (4) The crack length may change due to boundary trimming.

[0079] Based on the above assumptions and the basic principles of the Monte Carlo method, a two-dimensional random fracture generation program was written in Python to obtain the initial fracture network topology information. The initial fracture network topology may contain two types of fracture distributions (including extremely sharp-angled and extremely short-side triangular elements) that easily cause numerical problems such as mesh generation difficulties: First, two subparallel (almost parallel) fractures intersect at a very small acute angle, such as... Figure 2 As shown in (a); secondly, the endpoint of crack a is very close to another crack b (or terminates before crack b but a and b do not intersect, or a and b intersect but only cross a very short distance). The initial crack network topology is optimized using AutoCAD software: for the first case, one of the cracks can be appropriately shifted to avoid intersection; for the second case, the endpoint of crack a needs to be appropriately extended to crack b, or the portion crossing crack b needs to be deleted, such as... Figure 2 (b). This leads to the optimized fracture network topology.

[0080] The reasonable setting of grouting boundary conditions is a key link in simulating the grouting diffusion behavior of fractured rock masses. Its core lies in the accurate characterization of the geometric parameters of the grouting holes and the dynamic characteristics of the grouting pressure. The arrangement of grouting holes needs to comprehensively consider the engineering requirements (such as the reinforcement range and the target grouting layer) and the statistical characteristics of the fracture network (such as the dominant direction and connectivity). Based on the random fracture network generated by the Monte Carlo method, the location of the grouting holes can be determined in the following two ways: (1) Deterministic hole layout: the coordinates are specified in advance according to the engineering design requirements (such as uniform hole layout or for specific fracture-dense areas); (2) Random hole layout: the center point of the grouting hole is generated by probability sampling in combination with the fracture density distribution function to ensure the spatial coupling between the grouting hole and the high-permeability fracture area. The geometric shape of the grouting hole (such as hole diameter and inclination angle) can be further set to a fixed value or follow a specific distribution (such as normal distribution to simulate construction error) based on the borehole measurement data or equipment parameters.

[0081] The applied grouting pressure needs to reflect the time-varying characteristics of the actual grouting process, such as... Figure 3 As shown. It can usually be described by piecewise functions or coupled fluid-solid mechanics models: (1) For simplified models, the grouting pressure can be set as constant pressure (such as linear boundary conditions) or dynamic pressure that increases stepwise with time (such as piecewise linear functions); (2) For high-precision simulation, the time-varying viscosity of the grout and the feedback effect of the change in the fissure aperture on the pressure need to be considered, and the pressure field distribution can be calculated by the Navier-Stokes equation or the equivalent porous medium model. In addition, the spatial non-uniformity of the grouting pressure can be corrected by the fissure permeability tensor, for example, by locally increasing the pressure gradient at the intersection and endpoint of the fissure to simulate the preferential diffusion path of the grout.

[0082] The rheological behavior of cement-based grouts is typically described using Newtonian or Bingham models. Compared to the Newtonian model, the Bingham model includes yield stress, which leads to a limiting diffusion distance for Bingham-type grouts, and the Bingham model degenerates into a Newtonian model when the yield stress is zero. Furthermore, due to hydration, the rheological properties of the grout (e.g., viscosity and yield stress) increase over time. Moreover, for fast-setting grouts, such as cement-water glass (CS) grout, the hydration rate is very rapid, and the grout hardens quickly, which significantly affects its diffusion process during grouting in rock fractures. Therefore, the Bingham model is used to describe the rheological properties of the grout, and the rheological parameters plastic viscosity and yield stress are treated as functions of time, as expressed below: (5) In the formula τ It is shear stress. η It is plastic viscosity. τ 0 is the yield shear stress. γ It is the shear rate. t This refers to the hydration time. Furthermore, considering that the FDEM-based grouting diffusion algorithm is an explicit time integration method, the time step size is crucial for simulating the time-varying Bingham grouting diffusion process. t plastic viscosity η and yield stress τ The value of 0 is directly substituted into the grouting diffusion algorithm to take into account the time-varying characteristics of the grout rheological properties.

[0083] Based on the aforementioned assumptions, the grouting slurry flows only within a fracture network composed of failed joint elements (original or newly generated fractures). Therefore, the retrieval and updating of the grouting flow network is crucial for solving the grouting flow problem. To this end, this section proposes a grouting flow network search algorithm to detect and update the grouting flow network, providing a necessary foundation for grouting diffusion simulation in fractured rock masses.

[0084] like Figure 4 As shown, joint elements can be divided into complete joint elements, currently unfilled failed joint elements, and filled failed joint elements (i.e., the grout flow network), corresponding to CE1, CE2, and CE3, respectively. The initial grout flow network consists of failed joint elements connected to the grouting holes. During the grout diffusion simulation, new cracks (new failed joint elements) may be generated under the grouting pressure. These will be classified as CE2, and the failed elements in CE2 will be checked to see if they are connected to the existing grout flow network. If so, the corresponding failed elements will be added to the grout flow network CE3; otherwise, they will remain classified as CE2. The above steps are repeated during the calculation to complete the detection and updating of the grout flow network.

[0085] Based on the assumption that grouting flow occurs within the fracture network of the rock mass, and the grouting flow network detected by the search algorithm described earlier, a solution for the grouting flow process in fractured rock mass can be established. Specifically, based on the time-varying Bingham model of cement-based grout, a solution framework for grouting flow is established to calculate and update the grout flow rate and pressure in the grouting flow network.

[0086] Based on the assumption that the grout flow in the fracture is laminar, an improved cubic law applicable to time-varying Bingham-type grout using a parallel plate model is employed to solve for the flow rate in the hydraulic element formed by the hydraulic nodes on both sides of the failed joint element. However, under the pressure of the grout, the two walls of the grout flow channel may not be parallel, but wedge-shaped (e.g., Figure 5 (As shown). Therefore, in order to satisfy the parallel plate assumption, an equivalent aperture is introduced, as shown below: (6) in a It is the equivalent hydraulic opening. a i and a j It is the hydraulic opening at both ends of the grouting channel ( Figure 5 (a)); a m It is the average hydraulic opening. a m = ( a i + a j ) / 2; r = a i / a j .

[0087] Based on the detected grout flow network, the grout penetration and diffusion process is simulated by introducing grout flow patterns. According to the Navier-Stokes equations, the flow velocity of the Bingham grout can be calculated as follows: (7) (8) in v It refers to the flow rate of the grout. Z It is the thickness of the choke region. J It is a pressure gradient, which can be achieved through... J = dp / dl Calculation. Therefore, flow channel nodes. i and j Traffic between q ij The calculation can be performed as follows: (9) in v ij It is a node i and j The flow rate of the slurry between them, J ij It is a node i and j The pressure gradient between them.

[0088] Due to the shear yield stress of Bingham-type grout, there exists a critical pressure gradient in the grout under external force. J 0 = 2 τ 0 / aThis is used to initiate slurry flow. Specifically, when the pressure gradient is less than the critical pressure gradient, Bingham-type slurry behaves as a non-flowable rigid body; when it is greater than the critical pressure gradient, the slurry behaves as a viscous fluid. Therefore, the flow rate can also be expressed as: (10) Then, by comparing with the nodes i The sum of the flows of all connected channels can be used to calculate the node. i Total inflow Q i : (11) In the formula j Represents all nodes i The nodes are connected. Finally, the fluid pressure is calculated based on a linear fluid compression model. Considering the pressure increase (or decrease) due to the increase (or decrease) in slurry saturation, the slurry pressure in the nodes can be calculated as follows: (12) in P t and P t-1 These are the time steps. t and t Grouting pressure at node -1; K f It is the bulk modulus of the slurry; Q t It is the time step t Total inflow to nodes; V t and V t−1 These are the time steps. t and t -1 node volume; S t It is the time step t The node saturation, Δ t It is the time step.

[0089] To ensure numerical convergence in the explicit grouting solution process, the time step size of each numerical calculation step must not exceed a time step threshold: (13) Where △ t 0 is the time step threshold. i and j Index all nodes and the flow channels connected to each node separately; V i It is a node i volume,L j It is a flow channel j The length.

[0090] After solving for the grout flow rate, the pressure at the hydraulic nodes can be calculated according to equation (12). The calculated grout pressure is applied to the two walls of the flow channel as a linearly distributed external load, which can be converted into concentrated nodal forces and added to the nodes of the triangular element. The concentrated nodal forces can be calculated as follows: (14) In formula F j It is the concentrated nodal force at both ends of the grouting flow channel; P i and P j It is a hydraulic node i and j Grouting pressure; L It is the length of the grouting flow channel.

[0091] To simulate the time-varying single-phase grouting diffusion process of fractured rock masses based on the FDEM numerical method, a grouting diffusion solution framework considering the time-varying characteristics of the grout was established based on FDEM. Furthermore, by alternately executing the FDEM mechanical solution framework and the grouting diffusion solution framework in each calculation step, the seepage-stress coupling process during grouting was realized. Figure 6 As shown, firstly, a numerical model for grouting in fractured rock mass is established and corresponding grouting boundary conditions are applied. Then, a flow network retrieval algorithm is used to retrieve and update the initial grout flow network composed of fracture networks connected to the grouting pressure boundary. In each subsequent step, this network also needs to be updated based on the calculation results of the mechanical solver from the previous time step (for fractured rock mass grouting in this paper, this mainly manifests as the grouting pressure increasing the fracture aperture and thus increasing the grout diffusion rate). Subsequently, based on the retrieved grout flow network, the improved cubic law and linear fluid compressibility model applicable to time-varying Bingham-type grout are used to calculate the grout flow rate, pressure, and saturation, among other state parameters. By treating the grout pressure as a linearly distributed load applied to the rock matrix (triangular finite element boundary), it is further transformed into nodal forces of the triangular finite elements. The mechanical solver performs calculations and analyses based on the obtained nodal forces, updating the nodal positions, joint element states, and contact states between finite elements at the end of each calculation time step. This updated information will be used to update the fracture network and its geometric properties, and then used for the next step of grout diffusion solution. By alternately executing the above-mentioned mechanical solver and grout diffusion solver at each calculation step, the simulation analysis of the time-varying grout diffusion process in fractured rock mass can be achieved.

[0092] Grouting reinforcement technology mainly involves injecting grout from grouting holes into fractured rock mass under certain grouting pressure. During this process, the grout undergoes a hydration reaction and solidifies, filling and bonding fractures that were originally unable to bear the load, thereby enhancing the strength and integrity of the rock mass and improving the overall bearing capacity of the tunnel surrounding rock. First, a developed grouting diffusion algorithm is used to simulate the diffusion process of grout in rock fractures and determine the final grouting diffusion range and distribution. Then, this section proposes a grouting reinforcement bonding repair algorithm based on FDEM to simulate the bonding repair process of the grout-filled fractures predicted by the grouting diffusion algorithm, achieving a refined simulation of the grouting diffusion and reinforcement process in the fractured rock mass. The specific grouting reinforcement repair simulation algorithm is described below: (1) such as Figure 7 As shown in (a), the grout diffuses and migrates within the rock mass fissures. This process and the final range of fissures filled by the diffusion can be predicted by the diffusion simulation algorithm proposed above. The joint units that the grout diffuses and fills are recorded, which is the diffusion path of the grout within the rock mass fissures. The joint units filled by the grout that are monitored and recorded are regarded as joint units that are repaired by grout bonding. All grout-filled and bonded joint units are retrieved and updated to establish the corresponding set of joint units.

[0093] (2) such as Figure 7 As shown in (b), all elements within the established bonded repair joint element set are subjected to bonded repair treatment. Specifically, all joint elements within the set are first activated, and the normal opening displacement and tangential slip displacement of the joint elements at this time are denoted as follows: O i and S i The following equivalent opening will be used when calculating the force on the joint element.

[0094] (15) In the formula, O and S These represent the actual opening and sliding displacements of the current joint element.

[0095] (3) All grouting-reinforced joint units within the joint unit set are reassigned with the mechanical strength parameters after bond repair to achieve the bond repair effect of the grout on rock fractures. Since there is relatively little theoretical and experimental research on the bond repair effect of grout, the current method mainly refers to existing literature on the enhancement of the mechanical properties of grout-reinforced grout-bound rock masses. The strength enhancement data provided by these studies will serve as the basis for improving the strength of the failed joint units filled by grout diffusion in the grouting reinforcement repair algorithm.

[0096] The simulation method for grouting reinforcement of fractured rock masses in this invention rapidly generates a random fracture network that conforms to the statistical characteristics of the field using the Monte Carlo method, significantly reducing manual intervention and improving geometric fidelity, making the rock mass model before grouting closer to the actual geological conditions. By using a time-varying Bingham grout model, an improved cubic law, and a linear fluid compressibility model, the method achieves synchronous updates of grout flow rate, nodal pressure, and saturation, thereby enabling real-time capture of the grout diffusion front in fractures and its evolution over time, significantly improving prediction accuracy compared to traditional fixed-parameter methods. Through a "grouting diffusion-mechanical response-network update" cyclic mechanism, newly added failed joint units are automatically identified and immediately incorporated into the grout flow network after each mechanical calculation, ensuring that the influence of fracture aperture changes on the diffusion path is continuously included in subsequent calculations, significantly reducing the deviation in diffusion range caused by delayed network updates.

[0097] This invention also provides a device for simulating the grouting reinforcement process of fractured rock masses, used to perform the simulation method for the grouting reinforcement process of fractured rock masses as described above. Figure 8 This is a structural block diagram of a simulation device for grouting reinforcement of fractured rock mass according to an embodiment of the present invention. The device includes: The model building module 21 is used to build a rock mass grouting model of the target fractured rock mass and apply corresponding grouting boundary conditions to the rock mass grouting model.

[0098] The network construction module 22 is used to retrieve and update the grout flow network composed of fracture networks connected to the grouting pressure boundary through a flow network retrieval algorithm.

[0099] The state quantity calculation module 23 is used to calculate the real-time diffusion state quantity of the slurry in the slurry flow network based on the slurry flow network, using the improved cubic law and linear fluid compressibility model applicable to time-varying Bingham type slurries.

[0100] The nodal force calculation module 24 is used to apply the pressure component in the real-time diffusion state of the slurry to the boundary of the triangular finite element in a linear distribution form and convert it into nodal force.

[0101] The network update module 25 is used to call the finite discrete element mechanics solver, update the structural response state data of the rock mass grouting model according to the nodal forces and obtain the failed joint elements, and incorporate the failed joint elements into the grout flow network.

[0102] The process simulation module 26 is used to take the updated slurry flow network as input and repeatedly execute the alternating solution steps of "improved cubic law and linear fluid compressible model - pressure to nodal force - finite discrete element mechanics solver - update slurry flow network" until the preset termination condition is met, thereby completing the numerical simulation of the grouting-reinforcement process of fractured rock mass.

[0103] Specifically, the model building module is used for: Two-dimensional random fracture networks conforming to geostatistical characteristics are generated based on the Monte Carlo method.

[0104] The two-dimensional random fracture network is subjected to geometric topology optimization to obtain fracture geometry for mesh partitioning.

[0105] The fracture geometry is divided into triangular finite elements and joint elements to establish a continuous-discontinuous rock mass grouting model.

[0106] The grouting hole layout parameters are determined by probability sampling, and a time-varying grouting pressure is applied at the grouting hole boundary.

[0107] The technical features and effects of the device proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be repeated here. Each module in the above-described device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0108] See Figure 9 This is a structural block diagram of a computer device provided in an embodiment of the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above-described embodiment of the simulation method for grouting reinforcement of fractured rock mass. Figure 1 Steps S11 to S16 as described above; or, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments, such as modules 21 to 26 of the simulation device for grouting and reinforcement of fractured rock mass.

[0109] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0110] The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0111] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting various parts of the computer device via various interfaces and lines.

[0112] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0113] If the modules integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0114] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0115] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform steps in the simulation method for grouting reinforcement of fractured rock mass as described in the above embodiments, for example... Figure 1 Steps S11 to S16 as described above.

[0116] In summary, compared with the prior art, the grouting reinforcement process simulation method, apparatus, computer equipment, and computer-readable storage medium provided by the embodiments of the present invention have the following beneficial effects: The Monte Carlo method is used to rapidly generate random fracture networks that conform to the statistical characteristics of the field, significantly reducing human intervention and improving geometric fidelity, making the rock mass model before grouting closer to the actual geological conditions. The time-varying Bingham grout model, the improved cubic law, and the linear fluid compressibility model are used to achieve synchronous updates of grout flow rate, nodal pressure, and saturation, thereby enabling real-time capture of the diffusion front of grout in fractures and its evolution over time, significantly improving prediction accuracy compared to traditional fixed-parameter methods. Through the "grouting diffusion-mechanical response-network update" cyclic mechanism, newly added failed joint elements are automatically identified after each mechanical calculation and immediately incorporated into the grout flow network, ensuring that the influence of fracture aperture changes on the diffusion path is continuously included in subsequent calculations, significantly reducing the deviation in diffusion range caused by network lag updates. The grouting reinforcement and repair simulation algorithm is used to simulate the bonding repair (reinforcement) process of fractures after grout solidification. After the grout diffuses and fills the cracks, the bonding and repair effect of the grout on the surrounding rock after solidification is simulated by activating the filled joint units and re-assigning them the mechanical strength parameters after bonding and repair. This allows for a comprehensive evaluation of the grouting reinforcement effect.

[0117] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for simulating the grouting reinforcement process of fractured rock mass, characterized in that, include: Construct a rock mass grouting model of the target fractured rock mass and apply corresponding grouting boundary conditions to the rock mass grouting model; The grout flow network, composed of fracture networks connected to the grouting pressure boundary, is retrieved and updated using a flow network retrieval algorithm. Based on the slurry flow network, the real-time diffusion state of the slurry in the slurry flow network is calculated using the improved cubic law and the linear fluid compressibility model applicable to time-varying Bingham-type slurries. The pressure component in the real-time diffusion state of the slurry is applied to the boundary of the triangular finite element in a linear distribution form and converted into nodal forces. The finite discrete element method solver is invoked to update the structural response state data of the rock mass grouting model based on the nodal forces and obtain the failed joint elements. The failed joint elements are then incorporated into the grout flow network. The failed joint elements are joint elements that fail due to stress exceeding the bearing capacity. Using the updated slurry flow network as input, the alternating solution steps of "calculating the real-time diffusion state of the slurry using the improved cubic law and the linear fluid compressible model - converting pressure into nodal forces - calling the finite discrete element mechanics solver - updating the slurry flow network" are executed repeatedly until the preset termination condition is met, thereby completing the numerical simulation of the grouting-reinforcement process of fractured rock mass.

2. The process simulation method of grouting reinforcement of fractured rock mass according to claim 1, characterized in that, The process of constructing a rock mass grouting model of the target fractured rock mass and applying corresponding grouting boundary conditions to the rock mass grouting model includes: Two-dimensional random fracture networks conforming to geostatistical characteristics are generated based on the Monte Carlo method; Geometric topology optimization is performed on the two-dimensional random fracture network to obtain fracture geometry for mesh partitioning; Based on the fracture geometry, triangular finite elements and joint elements are divided to establish a continuous-discontinuous rock mass grouting model; The grouting hole layout parameters are determined by probability sampling, and a time-varying grouting pressure is applied at the grouting hole boundary.

3. The simulation method for grouting reinforcement of fractured rock mass as described in claim 1, characterized in that, The step of retrieving and updating the grout flow network composed of fracture networks connected to the grouting pressure boundary using a flow network retrieval algorithm includes: The flow network retrieval algorithm is used to retrieve all failed joint elements that are directly connected to the grouting pressure boundary, forming an initial grout flow network. Based on the connectivity of the newly added failed joint units, the initial slurry flow network is updated to obtain the slurry flow network.

4. The simulation method for grouting reinforcement of fractured rock mass as described in claim 1, characterized in that, The real-time diffusion state parameters of the slurry include slurry flow rate, node pressure, and node saturation. The calculation of the real-time diffusion state parameters of the slurry flow network based on the slurry flow network, using an improved cubic law and a linear fluid compressibility model suitable for time-varying Bingham-type slurries, includes: Using the slurry flow network as input data, the slurry flow rate of each flow channel is calculated using the improved cubic law applicable to time-varying Bingham-type slurries. Substituting the slurry flow rate into a linear fluid compressible model, the nodal pressure and saturation are obtained through nodal balancing.

5. The simulation method for grouting reinforcement of fractured rock mass as described in claim 1, characterized in that, The step of applying the pressure component of the real-time diffusion state quantity of the slurry in a linear distribution to the boundary of the triangular finite element and converting it into nodal forces includes: Extract the pressure components of each node from the node pressure of the real-time diffusion state of the slurry. The pressure component is coupled with the flow channel to generate a linearly distributed pressure load; The pressure load is converted into concentrated forces acting on the nodes at both ends of the boundary of the triangular finite element, forming a nodal force vector.

6. The method of claim 5, wherein the process of grouting for fracture rock mass reinforcement is simulated by using the following equation: ###0002### where, K is the permeability of the fracture rock mass, n is the number of fractures, and a is the aperture of the fracture. The structural response state data includes nodal displacements, element stresses, and joint element states. The process of calling the finite discrete element method (FEM) solver, updating the structural response state data of the rock mass grouting model based on the nodal forces, obtaining failed joint elements, and incorporating the failed joint elements into the grout flow network includes: Inputting the nodal force vectors into a finite discrete element mechanics solver yields nodal displacements, element stresses, and joint element states. The newly added failure joint units are marked according to the structural response state data and incorporated into the slurry flow network.

7. A device for simulating the process of grouting reinforcement of fractured rock mass, characterized in that, include: The model building module is used to build a rock mass grouting model of the target fractured rock mass and apply corresponding grouting boundary conditions to the rock mass grouting model; The network construction module is used to retrieve and update the grout flow network composed of fracture networks connected to the grouting pressure boundary through a flow network retrieval algorithm. The state quantity calculation module is used to calculate the real-time diffusion state quantity of the slurry in the slurry flow network based on the slurry flow network, using the improved cubic law and linear fluid compressibility model applicable to time-varying Bingham type slurries. The nodal force calculation module is used to apply the pressure component in the real-time diffusion state of the slurry to the boundary of the triangular finite element in a linear distribution form and convert it into nodal force. The network update module is used to call the finite discrete element mechanics solver, update the structural response state data of the rock mass grouting model according to the nodal forces and obtain the failed joint elements, and incorporate the failed joint elements into the grout flow network, wherein the failed joint elements are joint elements that fail due to stress exceeding the bearing capacity; The process simulation module is used to take the updated grout flow network as input and repeatedly execute the alternating solution steps of "calculating the real-time diffusion state of the grout using the improved cubic law and the linear fluid compressible model - converting pressure into nodal forces - calling the finite discrete element mechanics solver - updating the grout flow network" until the preset termination condition is met, thereby completing the numerical simulation of the grouting-reinforcement process of fractured rock mass.

8. The simulation device for grouting reinforcement of fractured rock mass as described in claim 7, characterized in that, The model building module is specifically used for: Two-dimensional random fracture networks conforming to geostatistical characteristics are generated based on the Monte Carlo method; Geometric topology optimization is performed on the two-dimensional random fracture network to obtain fracture geometry for mesh partitioning; Based on the fracture geometry, triangular finite elements and joint elements are divided to establish a continuous-discontinuous rock mass grouting model; The grouting hole layout parameters are determined by probability sampling, and a time-varying grouting pressure is applied at the grouting hole boundary.

9. A computer device, comprising: The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the simulation method for grouting reinforcement of fractured rock mass as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and when a device where the computer readable storage medium is located executes the computer program, the crack rock mass grouting reinforcement process simulation method in any one of claims 1 to 7 is implemented.

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