Numerical calculation method, device, equipment and program product for surface roughness of grouting body

Abstract

The application discloses a grouting body surface roughness numerical calculation method, device, equipment and program product, and the method comprises the following steps: obtaining the porosity and grading curve of the soil body in the grouting area; determining the modeling information based on the grading curve, and constructing a cubic sample box by using discrete element software; generating spherical sample particles in the cubic sample box according to the porosity and the grading curve, and then running the model by parameter assignment until balance is reached to obtain a target sample; collecting a plurality of cross sections in the target sample; quantifying the total rough volume of each cross section based on all the spherical sample particles on the cross section; based on the total rough volume, the surface roughness of each cross section is obtained by using the principle of sand filling method; and the average value of the surface roughness of all the cross sections is obtained to obtain the grouting body surface roughness. The application replaces the preparation and test measurement of the physical sample by discrete element numerical simulation, greatly improves the efficiency and repeatability of the roughness measurement, and can be widely applied to the field of electric digital data processing technology.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and in particular to a method, apparatus, equipment and program product for numerically calculating the surface roughness of grout bodies. Background Technology

[0002] Grouting technology is now widely used, and soil grouting can serve functions such as reinforcement and seepage prevention. After grouting, a grout body of a certain size is often formed. The shape of the grout body is usually simplified to a cylinder or sphere for ease of calculation. For fine-grained soils, this simplification has little impact. However, for coarse-grained soils, especially larger gravelly soils, due to the larger particle size, when the overall size of the grout body is small, the surface roughness of the grout body is large. The uneven surface interlocks with the surrounding soil particles. In this case, simplifying the surface of the grout body to a cylindrical or spherical surface to analyze its interfacial mechanical behavior with the soil often leads to significant errors. It is necessary to consider the influence of the grout body surface roughness on its interfacial mechanical behavior. The first step in analyzing the influence of surface roughness is to quantitatively measure the surface roughness.

[0003] Surface roughness measurement involves two aspects: preparing representative samples and selecting an appropriate method to measure the surface roughness of the samples. For sample preparation, the sample size often needs to be at least 10 times the maximum particle size to mitigate the size effect. For coarse-grained soils, especially gravelly soils, the large particle size leads to excessive sample preparation costs and makes sample movement very inconvenient. Furthermore, if samples are prepared by pouring cement grout for curing under actual working conditions, the resulting samples can only be used for a single measurement and are subsequently discarded, resulting in significant material waste. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, electronic device, storage medium, and program product for calculating the surface roughness of grout bodies, aiming to solve at least one problem in the prior art.

[0005] To achieve the above objectives, one aspect of this invention proposes a method for numerically calculating the surface roughness of a grouting body, the method comprising:

[0006] Obtain the porosity and gradation curves of the soil in the area to be grouted;

[0007] The modeling information is determined based on the gradation curve, and then a cubic sample box is constructed using discrete element software.

[0008] Spherical sample particles are generated in a cubic sample box based on porosity and gradation curves. Then, the model is run by assigning parameters until equilibrium is reached to obtain the target sample.

[0009] Multiple cross sections were collected from the target specimen;

[0010] The total roughness volume of each cross section is obtained by quantifying all spherical sample particles on the cross section.

[0011] Based on the total rough volume, the surface roughness of each cross section is obtained by converting it using the sand filling method principle. The surface roughness of the grout body is then obtained based on the average surface roughness of all cross sections.

[0012] In some embodiments, obtaining the porosity and gradation curves of the soil in the area to be grouted includes the following steps:

[0013] Obtain the natural dry density and specific gravity of the soil in the area to be grouted;

[0014] The solidity of the soil in the area to be grouted is determined based on the ratio of natural dry density to specific gravity.

[0015] Porosity is determined based on the complementary value of solids relative to 1;

[0016] Obtain soil sample data from multiple real soil samples in the soil mass of the area to be grouted;

[0017] Based on soil sample data, the gradation curve of the soil in the area to be grouted was obtained by sieve analysis.

[0018] In some embodiments, modeling information is determined based on the gradation curve, and then a cubic sample box is constructed using discrete element method software, including the following steps:

[0019] The maximum particle size of the soil in the grouting area is determined based on the gradation curve.

[0020] Using a preset multiple of the maximum particle size as the side length, a cubic sample box is constructed in the discrete element method software using the wall generation command.

[0021] In some embodiments, spherical sample particles are generated in a cubic sample box according to the porosity and gradation curves, and then the model is run by assigning parameters until equilibrium is reached to obtain the target sample, including the following steps:

[0022] Porosity was used as the first control condition, and the volume percentage of particles of different sizes was determined based on the gradation curve as the second control condition.

[0023] Based on the first and second control conditions, particles are randomly placed in a cubic sample box, and an initial particle system model is iteratively constructed using discrete element software.

[0024] The initial particle system model is parameterized by setting the wall contact friction coefficient and mechanical parameters, and then, combined with the preset sample unbalanced force ratio, the initial particle system model is run to a mechanical equilibrium state to obtain the target sample.

[0025] In some embodiments, multiple cross-sections are collected from the target specimen, including the following steps:

[0026] The maximum particle size is used as a constraint to determine the preset interval; wherein, the maximum particle size is determined based on the gradation curve, and the preset interval is greater than the maximum particle size.

[0027] Based on a preset interval, multiple cross-sections are collected at equal intervals between the top and bottom surfaces of the target sample.

[0028] In some embodiments, the total roughness volume of each cross section is obtained by quantifying all spherical sample particles on the cross section, including the following steps:

[0029] Obtain the center coordinates and particle radii of all spherical sample particles in the target sample, as well as the cross-sectional position of each section;

[0030] The first distance between the spherical sample particles and different cross sections is obtained based on the center coordinates and cross-sectional positions.

[0031] When the first distance between the target spherical sample particle and the target cross section is less than the particle radius of the target spherical sample particle, the target spherical sample particle is divided into the target cross section, and the second distance between the top of the target spherical sample particle and the target cross section is quantified based on the particle radius and the first distance.

[0032] Specifically, when the center coordinates of the target spherical sample particle are above the cross-sectional position of the target section, the second distance is obtained based on the sum of the particle radius and the first distance; when the center coordinates of the target spherical sample particle are below the cross-sectional position of the target section, the second distance is obtained based on the difference between the particle radius and the first distance.

[0033] The first cross section in the target specimen is taken as the first candidate cross section;

[0034] The first spherical sample particle on the first candidate cross section is taken as the candidate particle;

[0035] Based on the particle radius and second distance corresponding to the candidate particles, the rough volume of the candidate particles is calculated using the spherical cap formula.

[0036] Take the next spherical sample particle on the first candidate section as the candidate particle, return to execute the step of calculating the rough volume of the candidate particle using the spherical cap formula based on the particle radius and second distance corresponding to the candidate particle, until all spherical sample particles on the first candidate section have been traversed.

[0037] The total roughness volume is calculated by summing the roughness of all spherical sample particles on the first candidate cross section.

[0038] Take the next cross section in the target sample as the first candidate cross section, and return to execute the step of taking the first spherical sample particle on the first candidate cross section as the candidate particle, until the total roughness volume of each cross section in the target sample is obtained.

[0039] In some embodiments, the surface roughness of each cross-section is obtained based on the total roughness volume using the sand-filling method principle, including the following steps:

[0040] The first cross section in the target specimen is used as the second candidate cross section;

[0041] Traverse the second distance of all spherical sample particles on the second candidate section and use the largest second distance as the calculated height;

[0042] The area of ​​the cross section is obtained by squared the side length of the target sample;

[0043] Subtract the total roughness volume of the second candidate section from the product of the area and the calculated height to obtain the volume of standard sand to be poured in.

[0044] The surface roughness of the second candidate cross section is obtained based on the ratio of the volume to the area of ​​the injected standard sand.

[0045] Take the next cross section in the target sample as the second candidate cross section, and return to perform the second distance step of traversing all spherical sample particles on the second candidate cross section until the surface roughness of each cross section in the target sample is obtained.

[0046] To achieve the above objectives, another aspect of the present invention provides a numerical calculation device for the surface roughness of a grouting body, the device comprising:

[0047] The first module is used to obtain the porosity and gradation curve of the soil in the area to be grouted;

[0048] The second module is used to determine modeling information based on the gradation curve, and then use discrete element software to construct a cubic sample box.

[0049] The third module is used to generate spherical sample particles in a cubic sample box based on porosity and gradation curves, and then run the model through parameter assignment until equilibrium is reached to obtain the target sample.

[0050] The fourth module is used to collect multiple cross-sections in the target specimen;

[0051] The fifth module is used to obtain the total roughness volume of each cross section based on the quantification of all spherical sample particles on the cross section;

[0052] The sixth module is used to convert the surface roughness of each cross section based on the total roughness volume using the sand filling method principle, and to obtain the surface roughness of the grout body based on the average surface roughness of all cross sections.

[0053] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0054] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0055] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0056] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method, apparatus, electronic device, storage medium, and program product for numerically calculating the surface roughness of grouting bodies. This scheme obtains the porosity and gradation curve of the soil in the area to be grouted; determines modeling information based on the gradation curve, and then constructs a cubic sample box using discrete element software; generates spherical sample particles in the cubic sample box according to the porosity and gradation curve, and then runs the model through parameter assignment until equilibrium is reached to obtain the target sample; collects multiple cross sections in the target sample; quantifies the total roughness volume of each cross section based on all spherical sample particles on the cross section; converts the total roughness volume into the surface roughness of each cross section using the sand filling method principle, and obtains the surface roughness of the grouting body based on the average surface roughness of all cross sections. This invention achieves significant advantages by replacing physical sample preparation and experimental measurement with discrete element numerical simulation: First, it avoids the problems of high material consumption, bulky samples, and inconvenient transportation caused by preparing large-size physical samples, thus significantly reducing costs and operational difficulties; Second, the numerical model can be reused an unlimited number of times, and rapid analysis of multiple sections and working conditions can be achieved with a single modeling, effectively eliminating the serious material waste caused by the single use of samples in traditional methods; Third, parametric modeling can flexibly and efficiently simulate soils with different gradations and porosities, thereby greatly improving the efficiency and repeatability of roughness measurement, providing a reliable and convenient technical means for in-depth research on the mechanical behavior of the grout-soil interface. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of an implementation environment for the method of numerically calculating the surface roughness of a grout body provided in an embodiment of the present invention;

[0058] Figure 2 This is a flowchart illustrating a method for numerically calculating the surface roughness of a grout body according to an embodiment of the present invention.

[0059] Figure 3This is a schematic diagram of the overall process of the numerical calculation method for the surface roughness of the grout body provided in the embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram of a modeling example of a cubic sample box provided in an embodiment of the present invention;

[0061] Figure 5 This is a schematic diagram of a modeling example of generating spherical sample particles in a sample box, provided by an embodiment of the present invention.

[0062] Figure 6 This is a schematic diagram of a cross-sectional example of the configuration section provided in an embodiment of the present invention;

[0063] Figure 7 This is a schematic diagram of a modeling example of a configuration section provided in an embodiment of the present invention;

[0064] Figure 8 This is a schematic diagram illustrating the principle of the distance from the particle center coordinates to the cross section provided in this embodiment of the invention;

[0065] Figure 9 This is a schematic diagram of a modeling example of all particles on a cross section provided in an embodiment of the present invention;

[0066] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0068] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0069] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0071] In related technologies, surface roughness measurement involves two aspects: preparing representative samples and selecting appropriate methods to measure the surface roughness of the samples. For sample preparation, the sample size often needs to be at least 10 times the maximum particle size to mitigate the size effect. For coarse-grained soils, especially gravelly soils, the large particle size leads to significant sample preparation costs, and the excessive weight makes sample movement very inconvenient. Furthermore, if samples are prepared by pouring cement grout for curing under actual working conditions, the resulting samples can only be used for a single measurement and are subsequently discarded, resulting in substantial material waste.

[0072] In view of this, this invention provides a method, apparatus, equipment, and program product for numerically calculating the surface roughness of grouting bodies. This method involves: acquiring the porosity and gradation curve of the soil in the area to be grouted; determining modeling information based on the gradation curve; constructing a cubic sample box using discrete element method software; generating spherical sample particles within the cubic sample box according to the porosity and gradation curve; running the model through parameter assignment until equilibrium is reached to obtain the target sample; collecting multiple cross-sections within the target sample; quantifying the total roughness volume of each cross-section based on all spherical sample particles; converting the total roughness volume using the sand filling method to obtain the surface roughness of each cross-section; and obtaining the surface roughness of the grouting body based on the average surface roughness of all cross-sections. This invention achieves significant advantages by replacing physical sample preparation and experimental measurement with discrete element numerical simulation: First, it avoids the problems of high material consumption, bulky samples, and inconvenient transportation caused by preparing large-size physical samples, thus significantly reducing costs and operational difficulties; Second, the numerical model can be reused an unlimited number of times, and rapid analysis of multiple sections and working conditions can be achieved with a single modeling, effectively eliminating the serious material waste caused by the single use of samples in traditional methods; Third, parametric modeling can flexibly and efficiently simulate soils with different gradations and porosities, thereby greatly improving the efficiency and repeatability of roughness measurement, providing a reliable and convenient technical means for in-depth research on the mechanical behavior of the grout-soil interface.

[0073] It is understood that the numerical calculation method for the surface roughness of grouting bodies provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0074] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0075] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0076] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0077] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0078] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a numerical calculation method for the surface roughness of a grout body. The following description uses the application of this numerical calculation method for the surface roughness of a grout body in server 101 as an example. It can be understood that this numerical calculation method for the surface roughness of a grout body can also be applied to terminal 102.

[0079] Reference Figure 2 , Figure 2 This is an optional flowchart of the numerical calculation method for the surface roughness of grout provided in the embodiments of the present invention. The execution subject of the numerical calculation method for the surface roughness of grout can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S600.

[0080] Step S100: Obtain the porosity and gradation curves of the soil in the area to be grouted;

[0081] It should be noted that, in some embodiments, step S100 may include the following steps: obtaining the natural dry density and specific gravity of the soil in the area to be grouted; determining the solids ratio of the soil in the area to be grouted based on the ratio of natural dry density to specific gravity; determining the porosity based on the complementary value of the solids ratio to 1; obtaining soil sample data of multiple real soil samples in the soil in the area to be grouted; and obtaining the gradation curve of the soil in the area to be grouted by sieving based on the soil sample data.

[0082] For example, in some specific implementations, the natural soil mass can first be abstracted as a composite consisting of a solid particle skeleton and pores (filled with water or air). Its total volume V is decomposed into two parts: the solid particle volume Vs and the pore volume Vv, i.e.: V = Vs + Vv. This is the geometric basis for all analyses.

[0083] Furthermore, two key measurable parameters are introduced:

[0084] 1. Natural dry density ρd: It is a measure of the total mass (M) corresponding to the total volume (V) (ρd=M / V). It reflects the overall compactness of the soil, including pores.

[0085] 2. Specific gravity (Gs): Essentially a reflection of the density (ρs) of the solid particles themselves (Gs=ρs / ρw), taking ρw=1g / cm³. 3 When Gs is equal to ρs, it reflects the density of the mineral particles that make up the soil skeleton.

[0086] The key logical bridge lies in the fact that the total mass M of the soil almost entirely comes from solid particles (ignoring the mass of water in the soil pores). Therefore, the total mass can also be expressed as the product of particle density and particle volume: M = ρs × Vs ≈ Gs × Vs.

[0087] Now, we have two expressions for the total mass M:

[0088] Overall: M = ρd × V;

[0089] From the perspective of particles: M = Gs × Vs;

[0090] Since it is the same pile of soil, their total mass should be equal, so we establish the following equation:

[0091] ρd×V=Gs×Vs;

[0092] Therefore, the proportion of solid particle volume to the total volume (i.e., the solidity ratio) can be calculated:

[0093] Vs / V=ρd / Gs;

[0094] Finally, according to the volume decomposition model, the sum of the pore volume fraction (porosity n) and the solid volume fraction should be 1:

[0095] n = Vv / V = 1 - Vs / V;

[0096] Substituting the solids ratio obtained in the previous step into the formula, we get the final formula:

[0097] n = 1 - ρd / Gs;

[0098] The ratio between the macroscopically measured "overall average dry density" ρd and the "intrinsic particle density" Gs of the constituent material can be used to infer the degree of compactness of the solid material in the total volume. The difference between this degree of compactness and the ideal completely dense state (porosity of 0) is the proportion of space occupied by pores, i.e., porosity.

[0099] The input / application data basis for the sieving method is representative, dry, loose-state raw soil samples obtained from engineering sites or laboratories. Its core task is to quantify the size distribution of solid particles in the soil. This distribution data is the fundamental basis for constructing discrete element samples in the entire numerical simulation method.

[0100] Step S200: Determine modeling information based on the gradation curve, and then use discrete element software to construct a cubic sample box;

[0101] It should be noted that in some embodiments, step S200 may include the following steps: determining the maximum particle size of the soil in the area to be grouted based on the gradation curve; using a preset multiple of the maximum particle size as the side length, constructing a cubic sample box in the discrete element software through the wall generation command.

[0102] For example, in some specific implementations, a cubic sample box with a side length L of 12 times the maximum particle size of the soil particles can be constructed in the discrete element software using the wall generation command (this value can be adjusted according to actual application requirements; this value is only a preferred example).

[0103] Step S300: Based on the porosity and gradation curve, spherical sample particles are generated in the cubic sample box, and then the model is run by assigning parameters until equilibrium is reached to obtain the target sample.

[0104] It should be noted that in some embodiments, step S300 may include the following steps: using porosity as a first control condition, and determining the volume percentage of particles of different sizes based on the gradation curve as a second control condition; based on the first and second control conditions, randomly placing particles in a cubic sample box, and iteratively constructing an initial particle system model using discrete element software; assigning parameter values ​​to the initial particle system model using preset wall contact friction coefficients and mechanical parameters, and then, in conjunction with preset sample unbalanced force ratios, running the initial particle system model to a mechanical equilibrium state to obtain the target sample.

[0105] For example, in some specific embodiments, spherical sample particles can be generated in the sample box according to the soil gradation curve and porosity. The particle and wall contact friction coefficients (fric) are assigned, the effective modulus (emod) and stiffness ratio (krat) of the contact model are assigned, the sample unbalanced force ratio (ratio-average) is set to 1e-3, and the model is run until equilibrium is reached. Specifically, this embodiment of the invention represents the core operation steps in discrete element modeling, the purpose of which is to "replicate" a sample in the numerical world with the same particle composition and density as real soil. The entire process can be decomposed into two main stages: generating particles and assigning parameters to achieve equilibrium.

[0106] First stage: Particles are generated based on gradation and porosity;

[0107] The goal of this stage is to fill the prepared empty sample box with spherical particles that meet the requirements.

[0108] Data input and transformation:

[0109] Input 1: Grading curve. It provides the "size formula" of the particle group, which is the volume percentage of particles of different sizes.

[0110] Input 2: Porosity (n). It determines the "density" of the particle packing.

[0111] Conversion: In discrete element method (DEM) software (such as PFC, EDEM, etc.), gradation is usually represented by defining the particle size distribution. For example, multiple particle "groups" (such as diameters of 60mm, 40mm, 20mm, etc.) can be set, and the proportion of each group of particles in the generated volume can be specified. This proportion needs to be calculated based on the gradation curve to ensure that the volume distribution of particles in the generated sample is consistent with the curve.

[0112] Particle generation method (“particle flow” method):

[0113] Within the sample chamber space, particles are randomly generated using an algorithm. Initially, the particles can be in an overlapping state (to quickly fill the space).

[0114] The generation process is controlled by a target porosity or target bulk density. The software iteratively generates or deletes particles and runs a preliminary, fast relaxation calculation to initially disperse the particles. The porosity of the final initial packing is basically consistent with the input n value.

[0115] Key logic: The gradation curve determines "how big the particles are and how many of each type," while porosity determines "how tightly they are packed." The combination of the two ensures that the geometric composition (particle size distribution and spacing) of the numerical sample is equivalent to that of a real soil sample.

[0116] Second stage: Assigning contact parameters and balancing the system;

[0117] The goal of this stage is to bring the particle system generated in the previous step, which may be in an unstable overlapping state, to a stable, naturally stacked mechanical equilibrium state under the influence of gravity and other forces.

[0118] Assign contact parameters:

[0119] The coefficient of friction (fric) between particles and between particles and the wall: This is a key parameter controlling the ease with which particles slide. In this invention, a large value (e.g., 0.5 or higher) can be assigned to it in order to quickly reach equilibrium. A larger coefficient of friction will cause the particles to stop rolling and sliding more quickly, thereby significantly reducing the calculation time required for the model to reach static equilibrium.

[0120] Effective modulus (emod) and stiffness ratio (krat) of the contact model:

[0121] The effective modulus (emod) determines how easily particles deform upon contact (contact stiffness).

[0122] The stiffness ratio (krat) is usually the ratio of tangential stiffness to normal stiffness.

[0123] It should be noted that the purpose of this method is to obtain static geometric surface roughness, not to simulate real mechanical behavior. Therefore, these stiffness parameters do not affect the final roughness calculation result (roughness is a geometric quantity); their role is only to maintain the stability of the numerical calculation and affect the rate at which equilibrium is reached. These values ​​can also be determined empirically.

[0124] Run to balance:

[0125] Set the gravitational acceleration and run the model.

[0126] The unbalanced force ratio of the monitoring system. This ratio is defined as the ratio of the unbalanced resultant force on all particles in the system to the total contact force.

[0127] Set a convergence criterion: When the unbalanced force ratio drops below a very small threshold (e.g., 1e-3 or 5e-3), the system is considered to have reached static equilibrium. At this point, the particles no longer have macroscopic motion, their spatial arrangement stabilizes, and a numerical sample with a representative rough surface is formed that can be used for subsequent cross-sectional analysis.

[0128] Step S400: Collect multiple cross sections in the target sample;

[0129] It should be noted that in some embodiments, step S400 may include the following steps: determining a preset interval using the maximum particle size as a constraint condition; wherein the maximum particle size is determined based on the gradation curve, and the preset interval is greater than the maximum particle size; and based on the preset interval, collecting multiple cross-sections at equal intervals between the top and bottom surfaces of the target sample.

[0130] For example, in some specific implementations, taking the definition of 5 calculation sections as an example, they can be located at 1 / 6, 1 / 3, 1 / 2, 2 / 3, and 5 / 6 of the sample box height from bottom to top. The distance between two adjacent calculation sections should be greater than one times the maximum particle size (applicable to all gradations, the purpose being to ensure that the particle with the maximum particle size located between two sections is not included in the calculation of the upper and lower sections at the same time), so as to ensure that all particles are included in the calculation at most once, ensuring the independence of the 5 calculation sections; there can be more than 3 calculation sections (the setting can be adjusted according to the actual application requirements), the purpose of which is to further reduce the error by taking the average value of multiple calculations.

[0131] Step S500: The total roughness volume of each cross section is obtained by quantifying all spherical sample particles on the cross section;

[0132] It should be noted that in some embodiments, step S500 may include the following steps: obtaining the center coordinates and particle radii of all spherical sample particles in the target sample, as well as the cross-sectional position of each cross-section; quantizing the first distance between the spherical sample particles and different cross-sections based on the center coordinates and cross-sectional positions; when the first distance between the target spherical sample particle and the target cross-section is less than the particle radius of the target spherical sample particle, dividing the target spherical sample particle into the target cross-section, and quantizing the second distance between the top of the target spherical sample particle and the target cross-section based on the particle radius and the first distance; wherein, when the center coordinates of the target spherical sample particle are above the cross-sectional position of the target cross-section, the second distance is obtained based on the sum of the particle radius and the first distance; when the center coordinates of the target spherical sample particle are below the cross-sectional position of the target cross-section, the second distance is based on the difference between the particle radius and the first distance. The process involves: obtaining the first cross-section of the target sample as the first candidate cross-section; selecting the first spherical sample particle on the first candidate cross-section as a candidate particle; calculating the rough volume of the candidate particle using the spherical segment formula based on the particle radius and second distance; selecting the next spherical sample particle on the first candidate cross-section as a candidate particle, and returning to execute the step of calculating the rough volume of the candidate particle using the spherical segment formula based on the particle radius and second distance, until all spherical sample particles on the first candidate cross-section have been traversed; summing the rough volumes of all spherical sample particles on the first candidate cross-section as the total rough volume; selecting the next cross-section of the target sample as the first candidate cross-section, and returning to execute the step of selecting the first spherical sample particle on the first candidate cross-section as a candidate particle, until the total rough volume of each cross-section in the target sample is obtained.

[0133] For example, in some specific embodiments, all particles are traversed, and the distance Di from the center coordinates of the particles to a certain cross section is calculated. When the distance Di is less than the particle radius Ri, the particle is considered to be on the cross section. The volume of the part of the particle above the cross section is calculated using the spherical cap formula as the rough volume Vi of the particle. The rough volumes of all particles on the cross section are summed to obtain the total rough volume V of the cross section. At the same time, the distance hi between the top of each particle on the cross section and the cross section is calculated (when the center of the particle is below the calculated cross section, hi = Ri - Di; when the center of the particle is above the calculated cross section, hi = Ri + Di).

[0134]

[0135] Step S600: Based on the total rough volume, the surface roughness of each cross section is obtained by converting it using the sand filling method principle, and the surface roughness of the grout body is obtained based on the average surface roughness of all cross sections.

[0136] It should be noted that in some embodiments, the surface roughness of each cross-section is obtained based on the total roughness volume using the sand-filling method principle, which may include the following steps: taking the first cross-section in the target sample as the second candidate cross-section; traversing the second distance of all spherical sample particles on the second candidate cross-section and taking the largest second distance as the calculated height; obtaining the area of ​​the cross-section based on the square of the side length of the target sample; subtracting the total roughness volume of the second candidate cross-section from the product of the area and the calculated height to obtain the volume of standard sand poured; obtaining the surface roughness of the second candidate cross-section based on the ratio of the volume of standard sand poured to the area; taking the next cross-section in the target sample as the second candidate cross-section, and returning to the step of traversing the second distance of all spherical sample particles on the second candidate cross-section until the surface roughness of each cross-section in the target sample is obtained.

[0137] For example, in some specific embodiments, the maximum value of the distance hi between the top of each particle and the cross-section is taken as the calculation height H (the calculation height H is max(hi)). Then, based on the principle of surface roughness measurement using the sand-filling method, Ra = Vs / S, where Ra is the surface roughness, Vs is the volume of standard sand poured in, and S is the area of ​​the measurement cross-section. In the discrete element sample box, Vs = L 2 ×HV, S=L 2 Substituting the values, we get Ra = HV / L 2 The roughness of the five cross sections was calculated sequentially, and the average value was taken as the surface roughness of the sample.

[0138] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0139] To address the shortcomings of existing technologies, this invention proposes a numerical calculation method for the surface roughness of grouting bodies, such as... Figure 3 As shown, the method of this embodiment of the invention can be implemented through the following process steps:

[0140] (1) The natural dry density and specific gravity of the soil in the area to be grouted were obtained by test, the porosity of the soil was calculated, and the gradation curve of the soil was obtained by sieve analysis.

[0141] (2) For example Figure 4 As shown, a cubic sample box with a side length L of 12 times the maximum particle size of the soil was constructed in the discrete element method software using the wall generation command.

[0142] (3) such as Figure 5As shown, spherical sample particles are generated in the sample chamber according to the soil gradation curve and porosity. The contact friction coefficient (fric) of the particles and the wall is assigned, as are the effective modulus (emod) and stiffness ratio (krat) of the contact model. The sample unbalanced force ratio (ratio-average) is set to 1e-3, and the model is run until equilibrium is reached. The mechanical parameters do not affect the final result; this invention is only for measuring surface roughness, which is a physical property and does not involve mechanical aspects. These parameters only affect the time required for model equilibrium and can be selected empirically. Alternatively, the friction coefficient can be increased to shorten the equilibrium time. Theoretically, a smaller unbalanced force ratio is better, but this increases the required model equilibrium time. Sensitivity analysis shows that values ​​below 5e-3 have little impact on calculation accuracy.

[0143] (4) such as Figure 6 and Figure 7 As shown, five calculation sections are defined, located from bottom to top at 1 / 6, 1 / 3, 1 / 2, 2 / 3, and 5 / 6 of the sample box height. The distance between two adjacent calculation sections should be greater than one times the maximum particle size (applicable to all gradations, the purpose being to ensure that the particle with the maximum particle size located between two sections is not included in the calculation of the upper and lower sections simultaneously), to ensure that all particles are included in the calculation at most once, thus ensuring the independence of the five calculation sections.

[0144] (5) such as Figure 8 and Figure 9 As shown, iterate through all particles and calculate the distance D from the particle center coordinates to a certain cross section. When the distance D is less than the particle radius Ri, the particle is considered to be on the cross section. Calculate the volume of the part of the particle above the cross section using the spherical cap formula as the rough volume Vi of the particle. Sum the rough volumes of all particles on the cross section to obtain the total rough volume V of the cross section. At the same time, calculate the distance hi between the top of each particle and the cross section, and take the maximum value as the calculation height H (the calculation height H is max(hi). When the center of the particle is below the calculation cross section, hi = Ri - Di; when the center of the particle is above the calculation cross section, hi = Ri + Di).

[0145] ;

[0146] (6) The calculation principle of surface roughness measurement based on the sand filling method is Ra=Vs / S, where Vs is the volume of standard sand poured in and S is the area of ​​the measurement section. In the discrete element sample box, Vs=L 2 ×HV, S=L 2 Substituting the values, we get Ra = HV / L 2 The roughness of the five cross sections was calculated sequentially, and the average value was taken as the surface roughness of the sample.

[0147] In some specific application scenarios, with a maximum particle size of 60 mm and a porosity of 0.35, sample boxes of different sizes were generated, and the surface roughness was calculated as shown in Table 1 below. As the side length of the sample box increases, the surface roughness value gradually stabilizes. When the side length of the sample box reaches 0.7 m, the surface roughness error is within 5%, and the calculation accuracy meets the requirements. As the side length continues to increase, the calculation accuracy does not change significantly, but the calculation workload increases significantly. Therefore, considering both the calculation accuracy and the calculation workload, the optimal value for the sample box size is 12 times the maximum particle size. 12 times the maximum particle size is suitable for soils with different porosities and gradations, but the application scenarios of this invention are mainly in the range of coarse-grained soils, i.e., the content of coarse-grained groups (particle size 0.075-60 mm) is greater than 50%. The surface roughness of fine-grained soils is not large and can usually be simplified to planar treatment. The soil classification refers to the "Engineering Classification Standard for Soil" GB / T50145.

[0148] Table 1

[0149]

[0150] In summary, this invention generates samples using discrete element method (DEM) software. Assuming the final diffusion surface of the grout during injection is a smooth plane, generating a plane within the sample yields a randomly rough surface. Then, based on the sand-filling method for measuring surface roughness, the surface roughness value is accurately obtained through mathematical calculation. Specifically, obtaining surface roughness through numerical calculation avoids extensive experimentation, saving manpower and resources.

[0151] The core of this invention lies in generating a sample in discrete element method (DEM) software. Assuming the final diffusion surface of the grout is a smooth plane, the surface roughness value of that plane is obtained by calculating the particle volume on that plane. In actual grouting, the grout diffuses uniformly from the injection point outwards. After solidification, on the outermost side of the grout body, soil particles are partially encased in grout, some protruding outwards, forming an irregularly convex surface. The purpose of this invention is to measure the roughness of this surface. Therefore, it is assumed that the grout diffusion forms a smooth plane. A smooth plane means ignoring the roughness that may occur during grout solidification itself, mainly considering the roughness caused by particles. The roughness measured in this invention is relative to the outer surface of the grout body, without considering the influence of the shape (curved surface) of the outer surface itself. A calculation section is created in the DEM software. This section, along with the particles on it, simulates the surface geometry of the grout body formed when the grout spreads from bottom to top to that plane. This step is similar to completing the sample preparation in an experimental process. Multiple calculation sections are created, i.e., multiple samples are prepared for repeated measurements. The subsequent roughness calculation follows the process of simulating the sand cone method measurement principle.

[0152] The most widely used method for measuring the surface roughness of concrete structures is the sand filling method. First, a baffle is used to surround the structure. Then, standard sand is laid on the surface of the structure up to the highest point of the surface. The surface of the sand is leveled, and then the laid sand is collected and its volume is measured. The volume of the sand divided by the area of ​​the laid surface is the surface roughness.

[0153] Most methods for measuring surface roughness of structures rely on experimental means. This invention proposes a numerical calculation method that can significantly reduce workload and obtain surface roughness values ​​more quickly and efficiently.

[0154] This invention also provides a numerical calculation device for the surface roughness of a grout body, which can implement the above-described method. This device may include:

[0155] The first module is used to obtain the porosity and gradation curve of the soil in the area to be grouted;

[0156] The second module is used to determine modeling information based on the gradation curve, and then use discrete element software to construct a cubic sample box.

[0157] The third module is used to generate spherical sample particles in a cubic sample box based on porosity and gradation curves, and then run the model through parameter assignment until equilibrium is reached to obtain the target sample.

[0158] The fourth module is used to collect multiple cross-sections in the target specimen;

[0159] The fifth module is used to obtain the total roughness volume of each cross section based on the quantification of all spherical sample particles on the cross section;

[0160] The sixth module is used to convert the surface roughness of each cross section based on the total roughness volume using the sand filling method principle, and to obtain the surface roughness of the grout body based on the average surface roughness of all cross sections.

[0161] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0162] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0163] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0164] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes:

[0165] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0166] The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001.

[0167] Input / output interface 1003 is used to implement information input and output;

[0168] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0169] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0170] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0171] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0172] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0173] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0174] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0175] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0176] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0177] The present invention provides a method, apparatus, electronic device, storage medium, and program product for numerically calculating the surface roughness of grouting bodies. This method involves: acquiring the porosity and gradation curve of the soil in the area to be grouted; determining modeling information based on the gradation curve; constructing a cubic sample box using discrete element method software; generating spherical sample particles within the cubic sample box according to the porosity and gradation curve; running the model through parameter assignment until equilibrium is reached to obtain the target sample; collecting multiple cross-sections within the target sample; quantifying the total roughness volume of each cross-section based on all spherical sample particles; converting the total roughness volume using the sand filling method to obtain the surface roughness of each cross-section; and obtaining the surface roughness of the grouting body based on the average surface roughness of all cross-sections. This invention achieves significant advantages by replacing physical sample preparation and experimental measurement with discrete element numerical simulation: First, it avoids the problems of high material consumption, bulky samples, and inconvenient transportation caused by preparing large-size physical samples, thus significantly reducing costs and operational difficulties; Second, the numerical model can be reused an unlimited number of times, and rapid analysis of multiple sections and working conditions can be achieved with a single modeling, effectively eliminating the serious material waste caused by the single use of samples in traditional methods; Third, parametric modeling can flexibly and efficiently simulate soils with different gradations and porosities, thereby greatly improving the efficiency and repeatability of roughness measurement, providing a reliable and convenient technical means for in-depth research on the mechanical behavior of the grout-soil interface.

[0178] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0179] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0180] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0181] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0182] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method for numerically calculating surface roughness of a cast body, characterized by, The method includes the following steps: Obtain the porosity and gradation curves of the soil in the area to be grouted; Based on the gradation curve, modeling information is determined, and then a cubic sample box is constructed using discrete element software. Spherical sample particles are generated in the cubic sample box according to the porosity and the gradation curve. Then, the model is run by assigning parameters until equilibrium is reached to obtain the target sample. Multiple cross-sections were collected from the target sample; The total roughness volume of each cross section is obtained by quantifying all the spherical sample particles on the cross section. Based on the total rough volume, the surface roughness of each cross section is obtained by converting it using the sand filling method principle, and the surface roughness of the grout body is obtained based on the average value of the surface roughness of all cross sections. The step of determining modeling information based on the gradation curve and then constructing a cubic sample box using discrete element method software includes the following steps: The maximum particle size of the soil in the area to be grouted is determined based on the gradation curve. The cube sample box is constructed in the discrete element software by using a wall generation command with a preset multiple of the maximum particle size as the side length. The process of generating spherical sample particles in the cubic sample box based on the porosity and the gradation curve, and then running the model through parameter assignment until equilibrium is reached to obtain the target sample includes the following steps: The porosity is used as the first control condition, and the volume percentage of particles of different sizes is determined based on the gradation curve as the second control condition. Based on the first control condition and the second control condition, particles are randomly placed in the cubic sample box, and an initial particle system model is iteratively constructed using the discrete element software. The initial particle system model is parameterized by setting the wall contact friction coefficient and mechanical parameters, and then, combined with the preset sample unbalanced force ratio, the initial particle system model is run to a mechanical equilibrium state to obtain the target sample.

2. The method of claim 1, wherein, Obtaining the porosity and gradation curves of the soil in the area to be grouted includes the following steps: Obtain the natural dry density and specific gravity of the soil in the area to be grouted; The solidity of the soil in the area to be grouted is determined based on the ratio of the natural dry density to the specific gravity. The porosity is determined based on the complementary value of the solids ratio to 1; Obtain soil sample data from multiple real soil samples in the soil of the area to be grouted; Based on the soil sample data, the gradation curve of the soil in the area to be grouted is obtained by sieve analysis.

3. The method of claim 1, wherein, The process of collecting multiple cross-sections from the target sample includes the following steps: The maximum particle size is used as a constraint condition to determine the preset interval; wherein, the maximum particle size is determined based on the gradation curve, and the preset interval is greater than the maximum particle size; Based on the preset interval, multiple cross sections are collected at equal intervals between the top and bottom surfaces of the target sample.

4. The method of claim 1, wherein, The process of quantifying the total roughness volume of each cross section based on all the spherical sample particles on the cross section includes the following steps: Obtain the center coordinates and particle radii of all spherical sample particles in the target sample, as well as the cross-sectional position of each cross section; Based on the center coordinates and the cross-sectional position, the first distance between the spherical sample particles and the different cross-sections is obtained; When the first distance between the target spherical sample particle and the target cross section is less than the particle radius of the target spherical sample particle, the target spherical sample particle is divided into the target cross section, and the second distance between the top of the target spherical sample particle and the target cross section is quantified based on the particle radius and the first distance; Wherein, when the center coordinates of the target spherical sample particle are above the cross-sectional position of the target cross-section, the second distance is obtained based on the sum of the particle radius and the first distance; when the center coordinates of the target spherical sample particle are below the cross-sectional position of the target cross-section, the second distance is obtained based on the difference between the particle radius and the first distance. The first cross section in the target specimen is taken as the first candidate cross section; The first spherical sample particle on the first candidate cross section is taken as the candidate particle; Based on the particle radius corresponding to the candidate particle and the second distance, the rough volume of the candidate particle is calculated using the spherical cap formula; Take the next spherical sample particle on the first candidate cross section as the candidate particle, and return to the step of calculating the rough volume of the candidate particle using the spherical cap formula based on the particle radius and the second distance corresponding to the candidate particle, until all the spherical sample particles on the first candidate cross section have been traversed. The total roughness volume is the sum of the roughness of all the spherical sample particles on the first candidate cross section. Using the next cross section in the target sample as the first candidate cross section, the process returns to the step of using the first spherical sample particle on the first candidate cross section as the candidate particle, until the total roughness volume of each cross section in the target sample is obtained.

5. The method of claim 4, wherein, The process of obtaining the surface roughness of each cross-section based on the total roughness volume using the sand-filling method includes the following steps: The first cross section of the target specimen is used as the second candidate cross section; The second distance of all spherical sample particles on the second candidate cross section is traversed, and the largest second distance is used as the calculated height; The area of ​​the cross section is obtained based on the square of the side length of the target sample; Subtracting the total roughness volume of the second candidate section from the product of the area and the calculated height yields the volume of standard sand to be poured in. The surface roughness of the second candidate cross section is obtained based on the ratio of the volume of the injected standard sand to the area. The next cross section in the target sample is taken as the second candidate cross section, and the step of traversing the second distance of all the spherical sample particles on the second candidate cross section is returned to be performed until the surface roughness of each cross section in the target sample is obtained.

6. A numerical calculation device for the surface roughness of a grouting body, characterized in that, The device includes: The first module is used to obtain the porosity and gradation curve of the soil in the area to be grouted; The second module is used to determine modeling information based on the gradation curve, and then use discrete element software to construct a cubic sample box. The third module is used to generate spherical sample particles in the cubic sample box according to the porosity and the gradation curve, and then run the model through parameter assignment until equilibrium is reached to obtain the target sample. The fourth module is used to collect multiple cross-sections in the target sample; The fifth module is used to quantify the total roughness volume of each cross section based on all the spherical sample particles on the cross section; The sixth module is used to convert the total rough volume into the surface roughness of each cross section using the sand filling method principle, and to obtain the surface roughness of the grout body based on the average value of the surface roughness of all the cross sections. The step of determining modeling information based on the gradation curve and then constructing a cubic sample box using discrete element method software includes the following steps: The maximum particle size of the soil in the area to be grouted is determined based on the gradation curve. The cube sample box is constructed in the discrete element software by using a wall generation command with a preset multiple of the maximum particle size as the side length. The process of generating spherical sample particles in the cubic sample box based on the porosity and the gradation curve, and then running the model through parameter assignment until equilibrium is reached to obtain the target sample includes the following steps: The porosity is used as the first control condition, and the volume percentage of particles of different sizes is determined based on the gradation curve as the second control condition. Based on the first control condition and the second control condition, particles are randomly placed in the cubic sample box, and an initial particle system model is iteratively constructed using the discrete element software. The initial particle system model is parameterized by setting the wall contact friction coefficient and mechanical parameters, and then, combined with the preset sample unbalanced force ratio, the initial particle system model is run to a mechanical equilibrium state to obtain the target sample.

7. An electronic device, comprising: The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 5.

8. A computer program product, characterised in that, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1 to 5.

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