A soil body grouting diffusion simulation method

Abstract

The present application relates to a kind of soil grouting diffusion simulation method, bidirectional real-time data interaction of continuous term and discrete term is executed in simulation process, fluid dynamic load information is sent to discrete term model in each calculation time step by continuous term model, discrete term model determines the state of adhesive key with fluid dynamic load and mutual contact force between particles, simulates the migration of particle, and the position and void information of particle in current time step are sent to continuous term model, so that continuous term model updates fluid dynamic load information of next time step;It can accurately capture the dynamic evolution of porosity due to particle rearrangement under the action of grouting pressure, and the seepage path redirection of slurry with dynamic change of pore, can effectively solve the problem that traditional single simulation method is difficult to accurately simulate the flow or movement process of slurry and soil simultaneously, ensure the simulation precision of grouting diffusion under complex geological conditions.

Description

Technical Field

[0001] This invention relates to the field of grouting simulation technology, specifically to a soil grouting diffusion simulation method. Background Technology

[0002] Grouting technology is widely used in engineering fields such as foundation reinforcement and tunnel grouting. Its core objective is to significantly improve the bearing capacity of foundation soil or tunnel surrounding rock, thereby ensuring the safety and stability of various engineering structures.

[0003] Traditional grouting methods rely heavily on empirical guidance such as grouting specifications, lacking precise numerical simulations and theoretical support. This makes them unsuitable for the refined design requirements of grouting under complex geological conditions, and easily leads to grouting blind spots. To effectively avoid grouting blind spots and ensure that foundation reinforcement achieves the expected results, it is urgent to introduce precise numerical simulation technology to deeply explore the grout diffusion law and provide a scientific basis for engineering design.

[0004] As slurry is a continuous fluid, while soil is a discrete solid, the interaction mechanism between the two is extremely complex. It is difficult for a single software to accurately simulate their flow and motion processes simultaneously, which brings great challenges to the simulation work.

[0005] The pore structure and particle size distribution of different soils vary significantly, which makes the diffusion path, speed and range of the slurry different, further increasing the difficulty and complexity of the simulation.

[0006] The complex fluid-structure interaction mechanism and the diversity of soils mean that traditional single simulation methods cannot meet engineering requirements, necessitating a new coupling technology to overcome this bottleneck.

[0007] Therefore, it is necessary to study a soil grouting diffusion simulation method. Summary of the Invention

[0008] Therefore, the purpose of this invention is to provide a soil grouting diffusion simulation method, which can effectively solve the problem that traditional single simulation methods are difficult to simultaneously and accurately simulate the flow or movement process of grout and soil.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A soil grouting diffusion simulation method includes the following steps: S100: Establish a coupled simulation environment; A discrete term model of soil particles and a continuous term model of slurry fluid are constructed. In the discrete term model, a contact model is used to set the constitutive relationship of interparticle adhesion, and a critical failure stress threshold is set for the adhesive bond. S200: Two-way coupled iterative computation; During the simulation, bidirectional real-time data interaction between continuous and discrete terms is performed within each computation time step: The continuous term model sends hydrodynamic load information to the discrete term model; The discrete term model uses hydrodynamic loads and interparticle contact forces to determine the bond state, simulate particle migration, and send the particle position and void information of the current time step to the continuous term model, so that the continuous term model can update the hydrodynamic load information of the next time step. S300: Simulation Result Analysis; The discrete particle cementation state is analyzed, the density and gradient of the bonding bonds are calculated, and the grouting radius is identified and fitted. S400: Predictive model building; Repeated simulations were performed with different injection pressures as independent variables to calculate the diffusion physical limit data under different grouting pressure conditions. Through nonlinear regression analysis, a prediction function for grouting pressure and grouting radius was established.

[0010] Furthermore, in step S100, a discrete term model is established through the following steps: S110: Geometric modeling; A geometric model conforming to the characteristics of pile foundation grouting is constructed to characterize the simulation space of grouting influence; S120: Particle setting; Particles are generated for the geometric model, and the discrete properties of soil particles are defined. S130: Set the constitutive relationship for interparticle adhesion; A bonding model was used to simulate the distribution of cementation state between soil particles, and a critical failure stress threshold was set for the bonding bonds; a foundation contact model was used to handle the calculation of collision forces between particles.

[0011] Furthermore, step S200 specifically involves bidirectional coupled iterative calculation through the following steps: S210: Calculate fluid force loads; The continuous term model first calculates the flow field data, and then calculates the fluid force load acting on all discrete particles at the current time step based on the flow field data and the position and porosity information of the particles, and sends the fluid force load information to the discrete term model. S220: Calculation of internal forces of discrete terms; The discrete term model uses fluid force load as the external force driving source and calculates the resultant force on the particles based on the basic contact model and adhesion model; S230: Particle state update; Based on Newton's second law, the acceleration, velocity and position changes of particles are solved according to the resultant force, realizing the dynamic migration simulation of particles under the combined action of flow field drive and contact constraint. The particle position and void information at this time step are calculated, and the updated particle information is fed back to the continuous term model. S240: Continuous term model update calculation; The continuous term model corrects the flow field equations for the next time step using source terms and sends the updated hydrodynamic loads to the discrete term model, completing one coupling cycle.

[0012] Furthermore, in step S200, the resultant force acting on the particle is calculated, and based on Newton's second law, the acceleration, velocity, and position changes of the particle are solved according to the resultant force to simulate the particle migration.

[0013] Furthermore, the critical failure stress threshold includes critical normal stress and critical tangential stress; when the combined stress on the particle exceeds the critical normal stress or critical tangential stress, the bond is determined to be broken.

[0014] Furthermore, the porosity information includes porosity and grid cell density; The particle mass m within the computational domain of a specified volume Δv is extracted using a discrete term model, and the mesh element density ρ is calculated. Δv =m / Δv; By calculating the actual particle volume V particle =m / ρ, where ρ is the actual density of the particle; Then, the porosity C = 1 - V is calculated. particle / Δv.

[0015] Furthermore, in step S300, damage evolution data of the bonding bonds are extracted, and based on the density and gradient distribution characteristics of the bonding bonds, the central cavity zone, the splitting and mixing zone, and the initial stable zone are divided from the soil grouting reinforcement influence domain.

[0016] Furthermore, the outer edge envelope of the split mixing zone is identified and defined as the farthest diffusion boundary of the grout in the soil, and the radius of the farthest diffusion boundary is identified and defined as the grouting radius.

[0017] Furthermore, when constructing both discrete and continuous term models, boundary settings are performed, with the top and bottom surfaces set as periodic boundaries and the remaining surfaces set as walls. The geometric model is a sector; the boundary setting of the continuous term also uses the grouting port as the pressure inlet and the outer arc surface of the sector as the pressure outlet.

[0018] The beneficial effects of the above technical solution are: (1) This invention constructs a discrete term model of soil particles and a continuous term model of grout fluid respectively and establishes a coupling calculation mechanism. The continuous term model sends the fluid dynamic load information to the discrete term model in real time. The discrete term model simulates the migration of particles with the fluid dynamic load and the mutual contact force between particles, and sends the position and porosity information of the particles at the current time step to the continuous term model, so that the continuous term model updates the fluid dynamic load information of the next time step, forming a cyclic coupling calculation, realizing the continuous closed-loop feedback of "squeezing-displacement-pore update" of grout on soil particles. Compared with the traditional method that regards soil as a constant volume medium, this method can accurately capture the dynamic evolution of porosity caused by particle rearrangement under grouting pressure, as well as the seepage path redirection caused by the dynamic change of pores. It can effectively solve the problem that the traditional single simulation method is difficult to accurately simulate the flow or movement process of grout and soil at the same time, and ensure the simulation accuracy of grout diffusion simulation under complex geological conditions.

[0019] (2) By introducing an interparticle bonding model, this invention introduces bonding bonds between adjacent particles. On the one hand, it provides stiffness constraints in the normal and tangential directions between particles, enabling the simulation model to truly reflect the initial structural strength of the soil when it is in static equilibrium before grouting, optimize the particle force scenario, and improve the simulation fidelity. On the other hand, by setting a critical failure stress threshold for the bonding bonds, the splitting and diffusion process of the grout in the soil is mapped to the dynamic failure process of the bonding bonds, realizing the dynamic monitoring of the "splitting-cracking" state of the soil. In addition, the bonding model provides a key source of microscopic data for the grout diffusion range. The fracture distribution of bonding bonds in the calculation domain can be statistically analyzed to intuitively and accurately characterize the disturbance effect of the grout on the soil skeleton and the spatial distribution state of the grout inside the soil. It can identify and accurately extract the splitting front edge of the grout and the grout diffusion boundary, providing scientific and objective support for the subsequent mechanism research of grout diffusion radius, grouting effect evaluation, and grouting pressure parameter optimization. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the geometric model; Figure 2 A schematic diagram of generating particles for discrete terms; Figure 3 This is a schematic diagram of continuous term grid partitioning; Figure 4 A schematic diagram is set for the boundary of continuous terms; Figure 5 A schematic diagram illustrating the coupled operation of discrete and continuous terms; Figure 6 For discrete term particle cloud diagrams; Figure 7 For continuous terms, streamline diagram; Figure 8The simulation results are for an injection pressure of 0.1 MPa. Figure 9 The simulation results are for an injection pressure of 0.2 MPa. Figure 10 The simulation results are for an injection pressure of 0.3 MPa. Figure 11 The simulation results are for an injection pressure of 0.4 MPa. Figure 12 The simulation results are for an injection pressure of 0.5 MPa. Figure 13 The simulation results are for an injection pressure of 0.6 MPa. Figure 14 This is a fitted curve of injection pressure versus injection radius. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: This embodiment aims to provide a soil grouting diffusion simulation method, mainly used for simulating soil grouting, addressing the problem that traditional single simulation methods cannot accurately simulate the flow or movement process of grout and soil simultaneously. It includes the following steps: S100: Establish a coupled simulation environment; The coupled simulation environment includes a discrete model simulating soil particles and a continuous model simulating slurry fluid.

[0022] The discrete term model is established through the following steps: In this embodiment, EDEM software is used to establish a discrete term model to simulate the movement and interaction of soil particles. The main governing equation is Newton's second law.

[0023] Establishing a discrete term model mainly includes the following steps: S110: Geometric modeling; First, as Figure 1 A geometric model conforming to the characteristics of pile foundation grouting is constructed using 3D modeling software (such as UG). The diffusion pattern of the grout is usually a near-spherical cap. In this embodiment, a 60° sector is constructed as the simulation space for the grouting effect. The center of the sector is set as the axis of the grouting hole, the injection diameter is 20mm, the inner diameter of the model is 40.0mm, the outer diameter is 350.0mm, and the model height is 150.0mm.

[0024] S120: Particle setting; generates particles for the geometric model. For example... Figure 2 The "Particle Factory" plugin generates soil particles in the geometric model in one go, with the number of particles set to 85,711 and the half-diameter of each particle set to 2.5 mm.

[0025] The discrete properties of soil particles are mainly described by Poisson's ratio, density, and shear modulus. We assume a Poisson's ratio of 0.2 and a density of 1800 kg / m³. 3 , shear modulus 1e7Pa.

[0026] Defining only soil mechanical parameters is insufficient for a discrete description of the discrete phase; more importantly, contact type settings and parameter assignments are required. To simulate the physical and mechanical interactions between soil particles and the splitting effect caused by grouting, a refined definition of interparticle contacts is necessary.

[0027] S130: Set the constitutive relationship for interparticle bonding; the Hertz-Mindlin (no slip) model is used for the basic contact model to handle the calculation of collision forces between particles. The Bonding model is selected to simulate the cementation state between soil particles. This is the core of the grouting splitting simulation. The contact type is bonding, no slip, and standard rolling friction settings. Specific contact and bonding parameters are shown in the table below: .

[0028] S140: Discrete term boundary settings; to enable the small sector model to equivalently simulate deep geological environments, the top and bottom surfaces of the sector are set as periodic boundaries to eliminate boundary effects in the height direction and simulate strata of infinite depth. All geometric surfaces except the top and bottom surfaces are set as walls, physically contacting the particles. The walls act as rigid constraints, providing lateral stress, allowing particles to slide on the walls. This ensures computational convergence while more easily characterizing the radial scouring effect of the particles. In this embodiment, pressure settings are also applied to each boundary to describe the gravity acting on the model.

[0029] This embodiment establishes a continuous term model using the following method: In this embodiment, Fluent software is used to construct a continuous term model of the injected grout to simulate and solve the flow and pressure transmission process of the grout in the soil pores.

[0030] The slurry parameters are CFD fluid parameters. The water-cement ratio needs to be set before calculation. The water-cement ratio only needs to be corrected for the dynamic viscosity parameter. That is, the dynamic viscosity of the fluid is different under different water-cement ratios. It is only necessary to assign different parameters to the dynamic viscosity for calculation.

[0031] CFD calculations use fluid density, viscosity coefficient, and particle density for characterization. The physical model employs the Eulerian+DDPM (Eulerian-Dense Discrete Phase Model), which effectively handles the momentum exchange between high-concentration grout and a large number of discrete soil particles, simulating the infiltration and pushing behavior of grout in dense particle groups. The standard k-ε model is selected as the turbulence model to capture the fluid turbulence characteristics and energy dissipation near the grouting port caused by high-pressure injection.

[0032] The fluid properties mainly include fluid density, viscosity coefficient, and particle density. The fluid density is set to 1760 kg / m³, viscosity coefficient to 0.06 kg / m³, and particle density to 1800 kg / m³.

[0033] The mesh is the carrier of fluid computation, and its quality directly affects the accuracy of pressure transmission. Therefore, it is necessary to discretize the geometric model into a mesh.

[0034] like Figure 3 In this embodiment, a non-uniform mesh is used for the sector-shaped geometric model: a locally denser mesh area at the grouting inlet and a gently diffusing area in the remaining regions. Since the pressure gradient and velocity vector changes most drastically at the inner arc grouting inlet, the mesh size in this region (around the inner diameter R40.0 mm) is fined to 6 mm. As the grouting radius increases, the flow field changes become more gradual, so the mesh size in regions far from the inlet is set to 10-15 mm to balance computational accuracy and efficiency. After meshing, the minimum volume of the mesh in the model is approximately 7.092229 × 10⁻⁶. 1 mm, with a maximum volume of approximately 5.479211 × 10. 3 mm.

[0035] To drive fluid flow and simulate a realistic formation discharge environment, CFD fluid dynamics boundaries need to be set for the geometric model, similar to the discrete term boundaries, such as... Figure 4 The top and bottom surfaces are set as periodic boundaries, and the remaining surfaces are set as walls to ensure a continuous pressure gradient for the fluid in the height direction, simulating the flow characteristics of deeply buried strata. Furthermore, the grouting port is set as a pressure inlet, i.e. Figure 4 The blue arrow in the image indicates the gauge pressure condition; the pressure outlet is set to be located on the outer arc surface of the sector, i.e. Figure 4 The red arrow indicates that the gauge pressure is set to 0.

[0036] S200: Two-way coupled iterative computation; like Figure 5After the discrete and continuous term models are set up respectively, a two-way data interaction mechanism is established through a dedicated coupling interface, such as the EDEM Coupling Interface, and dynamic simulation iteration is started. Through the high-frequency two-way data synchronization link between the continuous and discrete term solvers, the real-time exchange of hydrodynamic loads and particle space states is completed in each calculation time step.

[0037] The continuous term model sends the hydrodynamic load information to the discrete term model; the discrete term model uses the hydrodynamic load and the mutual contact force between particles to determine the bond state, simulate particle migration, and send the particle position and void information of the current time step to the continuous term model, so that the continuous term model updates the hydrodynamic load information of the next time step.

[0038] Specifically, within each computation time step, the following bidirectional coupled iterative computations are performed: S210: Calculate fluid force loads; The solver in the continuous term model first calculates the flow field data such as the pressure gradient, local fluid velocity, and fluid viscosity. Then, based on the flow field data and the position and porosity information of the particles, it calculates the fluid force load acting on all discrete particles at the current time step and sends the fluid force load information to the discrete term model as an external driving source.

[0039] S220: Discrete term force calculation; The discrete term model uses fluid force load as the external force driving source, calculates the force acting on a single particle in each time step, and analyzes the migration of the particle under the action of fluid force load.

[0040] Based on the bond model, before the bond bonds break, the particles transfer normal and tangential stiffness constraints through "bond disks." This constraint simulates the initial structural strength of the soil before it is damaged by grout. Therefore, in this embodiment, the bond bonds are not only the connection between particles, but also a physical representation of the initial structural strength of the soil. The grouting process is the dynamic breaking process of the bond bonds by the hydrodynamic load of the grout.

[0041] Based on the Hertz-Mindlin model, particle contact is detected and contact forces are calculated. According to the contact effect of Hertz-Mindlin theory, when particles undergo physical collision or overlap, nonlinear elastic restoring forces and damping forces are calculated based on the normal overlap and tangential displacement. Then, the lateral sliding resistance of particles during the extrusion process is simulated using preset static friction coefficients (0.2) and dynamic friction coefficients (0.1). Based on the Hertz-Mindlin model and the force-in plugin in EDENN software, the resultant force on the particles is calculated.

[0042] The discrete term model monitors the synthetic stress inside the bonding bonds in real time. When it exceeds the critical threshold of 26,000 Pa, the structure undergoes brittle fracture, thus simulating the physical characteristics of split grouting. After the bonding bonds break, the particles migrate to the periphery, forming voids or even cavities. During the migration process, the particles collide with other particles, causing the particle to experience reverse resistance and giving other particles momentum.

[0043] S230: Based on Newton's second law, the dynamic migration simulation of particles under the combined action of flow field drive and contact constraint is realized by solving the acceleration, velocity and position changes of particles according to the resultant force, and the particle distribution state and adhesion state are updated.

[0044] Due to the extremely strong fluid force, it directly breaks through the threshold (26000 Pa), causing a large number of bonding bonds to break and forming a central cavity region. The fluid force decreases with the diffusion distance, and as the interionic force is transmitted, the net force on the particles continuously decreases with the diffusion distance until it can no longer break through the threshold and remains in the gaps of the outer particles, forming new bonding bonds. The outer particles are compacted, forming a clear grouting radius boundary.

[0045] Finally, the updated particle position and void information at the current time step are calculated, and the updated particle information is fed back to the continuous term model.

[0046] Porosity information includes porosity and mesh cell density. The particle mass *m* within a specified volume Δv computational domain is extracted using the EDEM discrete term model, and the mesh cell density *ρ* is calculated. Δv =m / Δv.

[0047] By calculating the actual particle volume V particle =m / ρ, where ρ is the actual density of the particles (1800 kg / m³). 3 ).

[0048] Then, the porosity C = 1 - V is calculated. particle / Δv. Porosity refers to the ratio of the space occupied by fluid (void volume) within a grid cell to the total volume of the grid cell. It is used to correct the effective flow cross-sectional area of ​​the fluid in the computational domain, thereby achieving a geometrical characterization of fluid flow resistance.

[0049] S240: Continuous term model update calculation; Based on the updated particle information, the continuous term model corrects the source terms of the flow field equations for the next time step and sends the updated hydrodynamic loads to the discrete term model, completing one coupling cycle.

[0050] Then, bidirectional coupled iterative calculations are performed repeatedly until the simulation is completed.

[0051] S300: Simulation Result Analysis; Using injection pressure as the independent variable, simulations were repeatedly performed to analyze the changes in soil porosity (density, compaction) under different pressures. Figure 6 The distribution of particles under slurry impact is shown. Particles near the injection port have a higher velocity gradient (green and light blue areas), indicating that a large number of bonds have been broken at this point. Driven by fluid load, the particles migrate to the periphery, forming a clear tendency to split cavities. Figure 7 The permeation of grout in soil pores is demonstrated. The line diffuses radially outward from the grouting port, and the flow velocity gradually decreases with increasing radial distance, verifying the aforementioned physical model that the hydrodynamic load weakens with increasing diffusion radius. like Figure 8-13 This embodiment simulates six working conditions: 0.1 MPa, 0.2 MPa, 0.3 MPa, 0.4 MPa, 0.5 MPa, and 0.6 MPa, as the sampling simulation range. In other embodiments, this embodiment can be extended to other grouting pressure conditions. The same constant injection time is set for all working conditions to ensure the comparability of diffusion radii.

[0052] This embodiment extracts the damage evolution data of the bonding bonds and divides the soil grouting reinforcement influence domain obtained from the simulation according to the bonding bond density and gradient. The dividing lines mainly include the inner boundary line and the outer boundary line, and then divide the central cavity area, the splitting mixed area and the initial stable area.

[0053] Reference Figures 8-13 Under different pressures, both density and gradient gradually increase along the injection direction and remain unchanged after reaching a steady state.

[0054] Taking 0.3 MPa as an example, refer to Figure 10 The density in the elliptical arc (top view) shows that the density is low in the area within 0.1 of the y-axis, with almost no particles present, indicating that there is only a fluid phase. This indicates that this is the central cavity area. Due to the extremely high fluid pressure in the central area adjacent to the grouting hole, the soil particles are completely pushed away, and the bonding bonds are almost completely broken, forming a net cavity filled with grout.

[0055] refer to Figure 10 In the density + elliptical arc (top view), within the 0.1-0.14 region on the y-axis, specifically the purple area within the density + elliptical arc (top view), the bond density gradually increases, indicating a splitting and mixing zone between particles and fluid. This signifies significant particle movement and substantial fluid turbulence. The splitting and mixing zone is located between the central cavity and the initial stable zone, representing an area of ​​drastic fluctuation in the bond gradient (rate of change). The grout is forcibly intruding into the soil, causing partial bond failure, thus forming a splitting and mixing zone between the grout and soil particles.

[0056] In the green area after 0.14, the bond density remains significantly high, and the impact of grouting on the bond density in this area is minimal or even negligible, forming an initial stable zone.

[0057] A distinct dense boundary forms between the splitting mixing region and the initial stable region, which is the outer envelope of the splitting mixing region, as shown in the figure. Figure 10 The red envelope surface from the perspective view or the red arc from the top view is identified and defined as the farthest diffusion boundary of the grout in the soil, and the radius of the farthest diffusion boundary is identified and defined as the grouting radius.

[0058] like Figure 14 By integrating and analyzing simulation results under different pressure conditions, a pressure-radius mapping relationship is established in the coordinate system. Through numerical fitting, a high-precision prediction function is obtained within the sampling simulation range (0.1-0.6 MPa). The coefficient of determination R 2 (R-squared) = 0.9999.

Claims

1. A soil grouting diffusion simulation method, characterized in that: Includes the following steps: S100: Establish a coupled simulation environment; A discrete term model of soil particles and a continuous term model of slurry fluid are constructed. In the discrete term model, a contact model is used to set the constitutive relationship of interparticle adhesion, and a critical failure stress threshold is set for the adhesive bond. S2 00: Two-way coupled iterative computation; During the simulation, bidirectional real-time data interaction between continuous and discrete terms is performed within each computation time step: The continuous term model sends hydrodynamic load information to the discrete term model; The discrete term model uses hydrodynamic loads and interparticle contact forces to determine the bond state, simulate particle migration, and send the particle position and void information of the current time step to the continuous term model, so that the continuous term model can update the hydrodynamic load information of the next time step. S300: Simulation Result Analysis; The discrete particle cementation state is analyzed, the density and gradient of the bonding bonds are calculated, and the grouting radius is identified and fitted. S400: Predictive model building; Repeated simulations were performed with different injection pressures as independent variables to calculate the diffusion physical limit data under different grouting pressure conditions. Through nonlinear regression analysis, a prediction function for grouting pressure and grouting radius was established.

2. The soil grouting diffusion simulation method according to claim 1, characterized in that: In step S100, the discrete term model is established through the following steps: S110: Geometric modeling; A geometric model conforming to the characteristics of pile foundation grouting is constructed to characterize the simulation space of grouting influence; S120: Particle setting; Particles are generated for the geometric model, and the discrete properties of soil particles are defined. S130: Set the constitutive relationship for interparticle adhesion; A bonding model was used to simulate the distribution of cementation state between soil particles, and a critical failure stress threshold was set for the bonding bonds; a foundation contact model was used to handle the calculation of collision forces between particles.

3. The soil grouting diffusion simulation method according to claim 2, characterized in that: Step S200 specifically involves bidirectional coupled iterative calculation through the following steps: S210: Calculate fluid force loads; The continuous term model first calculates the flow field data, and then calculates the fluid force load acting on all discrete particles at the current time step based on the flow field data and the position and porosity information of the particles, and sends the fluid force load information to the discrete term model. S220: Calculation of internal forces of discrete terms; The discrete term model uses fluid force load as the external force driving source and calculates the resultant force on the particles based on the basic contact model and adhesion model; S230: Particle state update; Calculate the particle position and void information at this time step, and feed the updated particle information back to the continuous term model; S240: Continuous term model update calculation; The continuous term model corrects the flow field equations for the next time step using source terms and sends the updated hydrodynamic loads to the discrete term model, completing one coupling cycle.

4. The soil grouting diffusion simulation method according to claim 3, characterized in that: In step S230, the resultant force acting on the particle is calculated, and based on Newton's second law, the acceleration, velocity, and position changes of the particle are solved according to the resultant force to simulate the particle migration.

5. The soil grouting diffusion simulation method according to claim 4, characterized in that: The critical failure stress threshold includes critical normal stress and critical tangential stress; when the combined stress on the particles exceeds the critical normal stress or critical tangential stress, the bond is determined to be broken.

6. A soil grouting diffusion simulation method according to any one of claims 1-5, characterized in that: The void information includes porosity and grid cell density; The particle mass m within the computational domain of a specified volume Δv is extracted using a discrete term model, and the mesh element density ρ is calculated. Δv =m / Δv; By calculating the actual particle volume V particle =m / ρ, where ρ is the actual density of the particle; Then, the porosity C = 1 - V is calculated. particle / Δv.

7. A soil grouting diffusion simulation method according to any one of claims 1-5, characterized in that: In step S300, damage evolution data of the bonding bonds are extracted. Based on the bonding bond density and gradient distribution characteristics, the central cavity zone, splitting and mixing zone, and initial stable zone are divided from the soil grouting reinforcement influence domain.

8. The soil grouting diffusion simulation method according to claim 7, characterized in that: The outer edge envelope of the split mixing zone is identified and defined as the farthest diffusion boundary of the grout in the soil, and the radius of the farthest diffusion boundary is identified and defined as the grouting radius.

9. A soil grouting diffusion simulation method according to any one of claims 2-5, characterized in that: When constructing both discrete and continuous term models, boundary settings are performed, with the top and bottom surfaces set as periodic boundaries and the remaining surfaces set as walls. The geometric model is a sector; the boundary setting of the continuous term also uses the grouting port as the pressure inlet and the outer arc surface of the sector as the pressure outlet.

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