A grouting body surface roughness calculation method, device, equipment and product

Abstract

This invention discloses a method, apparatus, equipment, and product for calculating the surface roughness of grout bodies. The method includes: acquiring the porosity and particle size distribution curves of the soil in the area to be grouted; extracting target parameters from the particle size distribution curves and determining the average particle size of different particle groups; obtaining preset coefficients characterizing the linear relationship of the parameters through discrete element simulation based on the particle size distribution curves; and quantifying the surface roughness based on the target parameters and porosity, combined with the preset coefficients. This invention allows for rapid prediction of roughness using only conventional geotechnical test data before grouting construction, avoiding the high cost, heavy labor, and material waste associated with preparing large-scale solid grout body samples. This invention provides reliable roughness input parameters for accurately analyzing the complex interfacial mechanical behavior between grout bodies and surrounding soil in coarse-grained soils, contributing to improved safety and economy in engineering design, and can be widely applied in the field of electronic 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 product for calculating the surface roughness of grout bodies. Background Technology

[0002] Grouting technology is widely used in engineering fields such as soil reinforcement and seepage prevention. The grout body formed after grouting is often simplified to a cylinder or sphere for ease of mechanical analysis. For fine-grained soils, this simplification has little impact; however, for coarse-grained soils, especially those containing large particles (such as gravel), when the grout body size is relatively small, its surface becomes significantly rough due to particle protrusion. This rough surface, interlocking with the surrounding soil particles, is a key factor affecting interfacial mechanical behavior (such as shear strength). If the grout body surface is still simplified to a smooth curved surface for analysis, it will lead to significant errors in the prediction of interfacial mechanical properties (such as pull-out resistance and frictional resistance). Therefore, quantitatively calculating the surface roughness of the grout body is an important prerequisite for accurately analyzing its interfacial mechanical behavior.

[0003] Currently, surface roughness measurement mainly relies on physical testing methods, with the "sand cone method" being the most typical. This method requires setting up a baffle around the surface to be tested, pouring standard sand until it covers the highest protrusion, leveling it, measuring the volume of the sand, and then calculating the average roughness depth. However, this method has significant limitations: First, to obtain representative results and mitigate the "size effect," the sample size needs to be more than 10 times the maximum particle size of the soil. For coarse-grained soils, this means preparing large, heavy samples, resulting in high material consumption, high preparation costs, and extreme inconvenience in movement and operation. Second, if actual grout samples are prepared by injecting grout under actual working conditions, each sample can only be used for one destructive measurement and is then discarded, causing serious waste of materials and resources. In addition, existing technologies lack theoretical methods for directly and quickly predicting roughness based on the basic physical properties of soil, leading to low efficiency in engineering design and analysis. 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 calculating the surface roughness of a grouting body, the method comprising:

[0006] Obtain the porosity and particle size distribution curves of the soil in the area to be grouted; wherein the soil in the area to be grouted includes particle groups of different sizes;

[0007] Target parameters are extracted from the particle size distribution curve, and the average particle size of different particle size groups is determined; the target parameters include the maximum particle size of the particles in the particle group and the volume fraction of different particle size groups.

[0008] Based on the particle size distribution curve, preset coefficients characterizing the linear relationship of the parameters are obtained through discrete element simulation; wherein, the preset coefficients include the first target coefficient and the second target coefficient;

[0009] The surface roughness is obtained by quantification based on the target parameters and porosity, combined with preset coefficients.

[0010] In some embodiments, obtaining the porosity and particle size distribution curves of the soil in the area to be grouted includes the following steps:

[0011] Obtain the natural dry density and specific gravity of the soil in the area to be grouted;

[0012] The solidity of the soil in the area to be grouted is determined based on the ratio of natural dry density to specific gravity.

[0013] Porosity is determined based on the complementary value of solids relative to 1;

[0014] Obtain soil sample data from multiple particle sizes in the soil mass of the area to be grouted;

[0015] Based on soil sample data, the particle size distribution curve of the soil in the area to be grouted was obtained by sieve analysis.

[0016] In some embodiments, extracting target parameters from particle size distribution curves includes the following steps:

[0017] The maximum particle size is obtained from the particle size distribution curve;

[0018] Based on the pre-defined particle size range, the percentage of particle size corresponding to the particle size boundary values ​​of different particle size groups is collected from the particle size distribution curve; wherein, the particle size boundary values ​​include the upper limit value and the lower limit value of particle size.

[0019] The volume fraction is obtained by calculating the mass fraction of each particle size group based on the difference between the particle size percentage corresponding to the upper limit and the particle size percentage corresponding to the lower limit.

[0020] In some embodiments, determining the average particle size of different particle size groups includes the following steps:

[0021] The particle size boundary values ​​for different particle size groups are determined based on the pre-defined particle size range; wherein, the particle size boundary values ​​include the upper limit value and the lower limit value of the particle size.

[0022] Based on the upper and lower limits of particle size, the average particle size of each particle size group is obtained through averaging calculations; the averaging calculations include arithmetic average calculations and geometric average calculations.

[0023] In some embodiments, when the preset coefficient is the first target coefficient, the preset coefficient characterizing the linear relationship of the parameters is obtained by discrete element simulation based on the particle size distribution curve, including the following steps:

[0024] Based on the particle size distribution curve, a first virtual soil mass is generated using discrete element method software; wherein, the first virtual soil mass includes spherical particles corresponding to each size particle group.

[0025] By using gravity deposition or compression algorithms in discrete element software, the first virtual soil mass is made to achieve porosity, thus obtaining the first target soil mass.

[0026] A first cross section is randomly constructed in the first target soil mass, and one side is taken as the first measurement reference surface;

[0027] Obtain the center coordinates and particle radii of all spherical particles on the first cross section;

[0028] The first measurement space height of each spherical particle on the first cross section from the first measurement reference plane is obtained based on the center coordinates and particle radius quantization.

[0029] Wherein, when the center coordinates are located on one side of the first measurement reference plane, the first measurement space height is the sum of the particle radius and the first distance, and the first distance represents the distance between the center coordinates and the first cross section; when the center coordinates are located on the other side of the first measurement reference plane, the first measurement space height is the difference between the particle radius and the first distance.

[0030] The maximum value of the height of the first measurement space is taken as the first fitted value, and the maximum particle size is taken as the second fitted value. The first candidate coefficient is obtained based on the ratio of the first fitted value to the second fitted value.

[0031] Return to the step of randomly constructing a first cross section in the first target soil and taking one side as the first measurement reference surface, until a first number of first candidate coefficients are obtained;

[0032] The first candidate coefficients of the first quantity are averaged to obtain the first target coefficient.

[0033] In some embodiments, when the preset coefficient is the second target coefficient, the preset coefficient characterizing the linear relationship of the parameters is obtained by discrete element simulation based on the particle size distribution curve, including the following steps:

[0034] Based on the particle size distribution curve, a second virtual soil mass is generated using discrete element method software; the second virtual soil mass includes spherical particles corresponding to each particle size group.

[0035] By using gravity deposition or compression algorithms in discrete element software, the second virtual soil mass is made to achieve porosity, thus obtaining the second target soil mass.

[0036] A second cross section is randomly constructed in the second target soil mass, and one side is taken as the second measurement reference surface;

[0037] Obtain the center coordinates and particle radii of all spherical particles on the second cross section;

[0038] The second measurement space height of each spherical particle on the second cross section from the second measurement reference plane is obtained based on the center coordinates and particle radius quantization.

[0039] Wherein, when the center coordinates are located on one side of the second measurement reference plane, the height of the second measurement space is the sum of the particle radius and the second distance, and the second distance represents the distance between the center coordinates and the second cross section; when the center coordinates are located on the other side of the second measurement reference plane, the height of the second measurement space is the difference between the particle radius and the second distance.

[0040] Based on the second distance and particle radius, the radius of the circle intercepted by the second section of each spherical particle is calculated using the Pythagorean theorem, and then the area of ​​the circle intercepted by the second section of each spherical particle is calculated.

[0041] Based on the height of the second measurement space and the particle radius, the spherical cap volume of each spherical particle located on one side of the second measurement reference plane is calculated using the spherical cap formula;

[0042] The total circular area corresponding to each particle group on the second cross section is obtained based on the circular area statistics, and the total spherical volume corresponding to each particle group on one side of the second measurement reference plane is obtained based on the spherical volume statistics.

[0043] The second candidate coefficient for each particle group is obtained by dividing the ratio of the total volume of the spherical segment to the total area of ​​the spherical segment by the average particle size.

[0044] Return to the step of randomly constructing a second cross section in the second target soil and taking one side as the second measurement reference surface, until a second number of second candidate coefficients are obtained;

[0045] The second candidate coefficients of the second number are averaged to obtain the second target coefficient.

[0046] In some embodiments, surface roughness is quantified based on target parameters and porosity, combined with preset coefficients, including the following steps:

[0047] The product of the first target coefficient and the maximum particle size is used as the first parameter value;

[0048] The weighted particle size of each particle group is obtained by multiplying the volume fraction of the same particle group with the average particle size.

[0049] The weighted particle sizes of all particle groups are summed to obtain the cumulative particle size.

[0050] The second parameter value is obtained by multiplying the accumulated particle size, the second target coefficient, and the porosity parameter; wherein, the porosity parameter is determined based on the complementary value of porosity to 1.

[0051] The surface roughness is obtained based on the difference between the first parameter value and the second parameter value;

[0052] The expression for surface roughness is:

[0053] ;

[0054] In the formula, Indicates surface roughness; Indicates the first target coefficient; Indicates the maximum particle size; Indicates the second objective coefficient; Indicates porosity; This represents the volume fraction of the i-th particle group; This represents the average particle size of the i-th particle group.

[0055] To achieve the above objectives, another aspect of the present invention provides a device for calculating the surface roughness of a grouting body, the device comprising:

[0056] The first module is used to obtain the porosity and particle size distribution curves of the soil in the area to be grouted; wherein, the soil in the area to be grouted includes particle groups of different sizes;

[0057] The second module is used to extract target parameters from the particle size distribution curve and determine the average particle size of different particle size groups; wherein, the target parameters include the maximum particle size of the particles in the particle group and the volume fraction of different particle size groups.

[0058] The third module is used to obtain preset coefficients representing the linear relationship of parameters by solving the discrete element simulation based on the particle size distribution curve; wherein the preset coefficients include the first target coefficient and the second target coefficient.

[0059] The fourth module is used to quantify the surface roughness based on the target parameters and porosity, combined with preset coefficients.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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 calculating the surface roughness of a grouting body. This solution obtains the porosity and particle size distribution curves of the soil in the area to be grouted; wherein the soil in the area to be grouted includes particle groups of different sizes; target parameters are extracted from the particle size distribution curves, and the average particle size of different particle groups is determined; wherein the target parameters include the maximum particle size in the particle group and the volume fraction of different particle groups; based on the particle size distribution curves, preset coefficients characterizing the linear relationship of the parameters are obtained through discrete element simulation; wherein the preset coefficients include a first target coefficient and a second target coefficient; and the surface roughness is quantified based on the target parameters and porosity, combined with the preset coefficients. This invention establishes a theoretical quantitative relationship between the surface roughness of the grout and the most basic physical properties of the soil (particle size distribution, porosity), realizing a paradigm shift from "physical test measurement" to "theoretical model calculation." Using this method, roughness can be quickly predicted before grouting construction using only conventional geotechnical test data, avoiding the high cost, heavy labor, and material waste associated with preparing large-scale solid grout samples. This method provides reliable roughness input parameters for accurately analyzing the complex interfacial mechanical behavior between the grout and the surrounding soil in coarse-grained soil, contributing to improved safety and economy in engineering design. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of an implementation environment for the method of calculating the surface roughness of a grout body provided in an embodiment of the present invention;

[0065] Figure 2 This is a flowchart illustrating a method for calculating the surface roughness of a grout body according to an embodiment of the present invention;

[0066] Figure 3 This is a schematic diagram illustrating the principle and structure of the sand-filling method provided in an embodiment of the present invention;

[0067] Figure 4 This is a schematic diagram of a data fitting example for coefficient A provided in an embodiment of the present invention;

[0068] Figure 5 This is a schematic diagram of a data fitting example for coefficient B provided in an embodiment of the present invention;

[0069] Figure 6 This is a schematic diagram of the structure of a grout surface roughness calculation device provided in an embodiment of the present invention;

[0070] Figure 7This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0071] 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.

[0072] 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.”

[0073] 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.

[0074] 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.

[0075] In related technologies, existing technologies lack theoretical methods for directly and quickly predicting roughness based on the basic physical properties of soil, resulting in low efficiency in engineering design and analysis.

[0076] In view of this, embodiments of the present invention provide a method, apparatus, equipment, and product for calculating the surface roughness of a grouting body. This method involves obtaining the porosity and particle size distribution curves of the soil in the grouting area; wherein the soil in the grouting area includes particle groups of different sizes; extracting target parameters from the particle size distribution curves and determining the average particle size of different particle groups; wherein the target parameters include the maximum particle size in the particle group and the volume fraction of different particle groups; based on the particle size distribution curves, obtaining preset coefficients characterizing the linear relationship of the parameters through discrete element simulation; wherein the preset coefficients include a first target coefficient and a second target coefficient; and quantifying the surface roughness based on the target parameters and porosity, combined with the preset coefficients. This invention establishes a theoretical quantitative relationship between the surface roughness of the grout and the most basic physical properties of the soil (particle size distribution, porosity), realizing a paradigm shift from "physical test measurement" to "theoretical model calculation." Using this method, roughness can be quickly predicted before grouting construction using only conventional geotechnical test data, avoiding the high cost, heavy labor, and material waste associated with preparing large-scale solid grout samples. This method provides reliable roughness input parameters for accurately analyzing the complex interfacial mechanical behavior between the grout and the surrounding soil in coarse-grained soil, contributing to improved safety and economy in engineering design.

[0077] It is understood that the surface roughness calculation method for 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 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a method for calculating the surface roughness of a grout body. The following description uses the application of this method to server 101 as an example. It can be understood that this method can also be applied to terminal 102.

[0083] Reference Figure 2 , Figure 2 This is an optional flowchart of the grout surface roughness calculation method provided in the embodiments of the present invention. The execution subject of the grout surface roughness calculation method 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 S400.

[0084] Step S100: Obtain the porosity and particle size distribution curves of the soil in the area to be grouted;

[0085] The soil in the area to be grouted includes particle sizes of different specifications;

[0086] 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 particle groups in the soil in the area to be grouted; and obtaining the particle size distribution curve of the soil in the area to be grouted by sieving based on the soil sample data.

[0087] For example, in some specific embodiments, when obtaining porosity, the natural dry density ρd and the specific gravity of soil particles Gs are first measured. The solids ratio of the soil is calculated according to the formula ρd / Gs, then the porosity n = 1 – solids ratio. When obtaining the particle size distribution curve, representative soil samples are collected, and according to the "Standard for Geotechnical Testing Methods", particles with a diameter greater than 0.075 mm are subjected to sieve analysis using standard sieves, and fine particles can be analyzed using the densitometer method. Finally, a cumulative particle size distribution curve is plotted with particle size as the abscissa (logarithmic scale) and the cumulative percentage of soil particles smaller than a certain particle size as the ordinate.

[0088] Specifically, the embodiments of the present invention clarify the standard and reliable acquisition methods for the two core input parameters, porosity and particle size distribution curves, making the implementation of the entire technical solution solid and in line with engineering practice standards. The parameter acquisition methods of the present invention are all conventional tests in the field of geotechnical engineering, which are easy to implement and can ensure that the method of the present invention can be widely promoted and applied.

[0089] Step S200: Extract target parameters from particle size distribution curves and determine the average particle size of different particle size groups;

[0090] The target parameters include the maximum particle size in the particle group and the volume fraction of different particle sizes.

[0091] It should be noted that, in some embodiments, extracting target parameters from the particle size distribution curve may include the following steps: collecting the maximum particle size from the particle size distribution curve as the maximum particle size; based on the pre-divided particle size range, collecting the particle size percentage corresponding to the particle size boundary values ​​of different particle size groups from the particle size distribution curve; wherein, the particle size boundary values ​​include an upper limit particle size value and a lower limit particle size value; and obtaining the mass fraction corresponding to each particle size group as the volume fraction based on the difference between the particle size percentage corresponding to the upper limit particle size value and the particle size percentage corresponding to the lower limit particle size value.

[0092] When the particle size boundary value only has an upper limit value, such as the smallest particle group divided within the particle size range, the particle size percentage corresponding to the upper limit value can be directly used as the volume fraction of the corresponding particle group. When the particle size boundary value only has a lower limit value, such as the largest particle group divided within the particle size range, the difference between 1 (i.e., 100%) and the particle size percentage corresponding to the lower limit value can be used as the volume fraction of the corresponding particle group.

[0093] For example, in some specific embodiments, when extracting target parameters, the particle size corresponding to a cumulative percentage of 100% can be directly found from the particle size distribution curve, which is the maximum particle size Dmax. The particle size range is pre-divided according to standards (such as GB / T50145-2007) (e.g., coarse gravel: 20-60mm). On the gradation curve, the cumulative percentage corresponding to the upper limit particle size (e.g., 60mm) of a certain particle size group (e.g., 85%) and the cumulative percentage corresponding to the lower limit particle size (e.g., 20mm) (e.g., 65%) are read. The difference between the two (20%) is the mass fraction of that particle size group (coarse gravel), which is used as an approximation of its volume fraction Pi in this invention.

[0094] Specifically, the embodiments of the present invention provide specific and operable steps for accurately extracting the maximum particle size and volume fraction of each particle group from a standard gradation curve, reducing subjective errors in the parameter extraction process. In particular, by combining standardized particle group division with gradation curve readings, the embodiments of the present invention can achieve standardization and streamlining of parameter extraction, thereby ensuring the consistency and repeatability of the calculation process.

[0095] It should be noted that, in some embodiments, determining the average particle size of different particle size groups may include the following steps: determining the particle size boundary values ​​of different particle size groups based on a pre-divided particle size range; wherein, the particle size boundary values ​​include an upper limit value and a lower limit value; and obtaining the average particle size of each particle size group by averaging based on the upper limit value and the lower limit value; wherein, the averaging operation includes arithmetic average operation and geometric average operation.

[0096] Specifically, when the particle size boundary value only has an upper limit value, such as the smallest particle group divided within the particle size range, the average particle size of the particle group is determined by the first multiple of the upper limit value (the first multiple is less than 1); when the particle size boundary value only has a lower limit value, such as the largest particle group divided within the particle size range, the average particle size of the particle group is determined by the second multiple of the lower limit value (the first multiple is greater than 1).

[0097] For example, in some specific embodiments, the average particle size of each particle group is determined. At that time, it is based on the pre-defined particle size range. For the arithmetic mean calculation, the upper and lower limits of the particle group are directly added together and then divided by 2. For example, the average particle size of the coarse gravel group (20mm, 60mm) is... = (20+60) / 2 = 40mm. For particle groups with a large range of particle sizes, the geometric mean can also be used, i.e. = √(upper limit × lower limit), where √ represents the square root operation.

[0098] Specifically, the embodiments of the present invention provide a clear mathematical method for calculating the average particle size, making the concept of "average particle size" completely clear and unambiguous from definition to calculation; specifically, the present invention enhances the adaptability of the method to soils with different particle size distribution characteristics by providing two optional schemes, namely arithmetic mean and geometric mean, and can improve the calculation flexibility.

[0099] Step S300: Based on the particle size distribution curve, the preset coefficients representing the linear relationship of the parameters are obtained by discrete element simulation.

[0100] The preset coefficients include the first target coefficient and the second target coefficient;

[0101] It should be noted that in some embodiments, when the preset coefficient is the first target coefficient, step S300 may include the following steps: generating a first virtual soil body based on the particle size distribution curve using discrete element software; wherein, the first virtual soil body includes spherical particles corresponding to each size particle group; using the gravity deposition or compression algorithm of the discrete element software, making the first virtual soil body reach the porosity to obtain the first target soil body; randomly constructing a first cross section in the first target soil body and taking one side as the first measurement reference surface; obtaining the center coordinates and particle radii of all spherical particles on the first cross section; quantizing the distance of each spherical particle on the first cross section from the first measurement reference surface based on the center coordinates and particle radii; wherein, when the center coordinates are located at the first target coefficient, the first measurement space height of each spherical particle on the first cross section from the first measurement reference surface is obtained; wherein, when the center coordinates are located at the first target coefficient, the first target soil body is obtained; and ... On one side of a measurement reference plane, the first measurement space height is the sum of the particle radius and the first distance, where the first distance represents the distance between the center coordinate and the first cross section. When the center coordinate is located on the other side of the first measurement reference plane, the first measurement space height is the difference between the particle radius and the first distance. The maximum value of the first measurement space height is taken as the first fitted value, and the maximum particle size is taken as the second fitted value. The first candidate coefficient is obtained based on the ratio of the first fitted value to the second fitted value. The process returns to the step of randomly constructing the first cross section in the first target soil and taking one side as the first measurement reference plane until a first number of first candidate coefficients are obtained. The first target coefficient is obtained by averaging the first number of first candidate coefficients.

[0102] For example, in some specific implementations, when calibrating the first target coefficient A, the following steps are performed: Based on the target gradation, a "first virtual soil body" containing spherical particles of the corresponding size is generated in discrete element software. Gravity deposition or compression is simulated using software, and the model is adjusted until its porosity reaches the true value, forming the "first target soil body". An infinitely extending plane is randomly generated within this soil body as the "first cross-section," and one side is designated as the "first measurement reference plane." All particles intersecting this cross-section are identified, and their center coordinates and radius R are obtained. For each intersecting particle, if its center is located on the same side of the measurement reference plane, its "first measurement space height" H_single = (distance d from the center to the cross-section) + R; if it is located on the opposite side, then H_single = R - d. The maximum value of H_single among all particles is taken as H for this simulation. The actual diameter D_max_sim of the largest particle in the soil body is recorded. The single simulation value A_single = H / D_max_sim is calculated. Repeat the above process of generating and calculating the cross-section hundreds of times, and finally take the arithmetic mean of all A_single to obtain the calibrated coefficient A (for near-spherical particles, this value is about 0.94).

[0103] Specifically, this embodiment of the invention calibrates the key coefficient A through discrete element numerical simulation. It utilizes numerical simulation to replace numerous physical experiments for parameter calibration, resulting in extremely low cost, high efficiency, precise control of variables, and reliable results. By clarifying the physical meaning of coefficient A (related to the maximum protrusion height) and its numerical source, this embodiment enhances the persuasiveness and rigor of the entire theoretical model.

[0104] It should be noted that in some embodiments, when the preset coefficient is the second target coefficient, step S300 may include the following steps: generating a second virtual soil body based on the particle size distribution curve using discrete element software; wherein, the second virtual soil body includes spherical particles corresponding to each particle size group; using the gravity deposition or compression algorithm of the discrete element software, making the second virtual soil body reach the porosity to obtain the second target soil body; randomly constructing a second cross section in the second target soil body and taking one side as the second measurement reference surface; obtaining the center coordinates and particle radii of all spherical particles on the second cross section; quantizing the distance of each spherical particle on the second cross section from the second measurement reference surface based on the center coordinates and particle radii; wherein, when the center coordinates are located on one side of the second measurement reference surface, the second measurement space height is the sum of the particle radius and the second distance, and the second distance characterizes the distance between the center coordinates and the second cross section; when the center coordinates are located on the other side of the second measurement reference surface, the second measurement space height is... The second distance is the difference between the particle radius and the second measurement space height. Based on the second distance and particle radius, the radius of the circle intercepted by the second section for each spherical particle is calculated using the Pythagorean theorem, and then the area of ​​the circle intercepted by the second section for each spherical particle is calculated. Based on the second measurement space height and particle radius, the volume of the spherical cap on one side of the second measurement reference plane for each spherical particle is calculated using the spherical cap formula. Based on the circular area statistics, the total circular area corresponding to each particle group on the second section is obtained, and based on the spherical cap volume statistics, the total volume of the spherical cap corresponding to each particle group on one side of the second measurement reference plane is obtained. Based on the ratio of the total spherical cap volume to the total circular area divided by the average particle size, the second candidate coefficient for each particle group is obtained. The process returns to the step of randomly constructing a second section in the second target soil and taking one side as the second measurement reference plane until a second number of second candidate coefficients are obtained. The second number of second candidate coefficients are averaged to obtain the second target coefficient.

[0105] For example, in some specific embodiments, when calibrating the second target coefficient B, the following steps are performed: Similarly, a "second virtual soil" and a "second target soil" conforming to the target gradation and porosity are generated and prepared. A "second cross-section" and a "second measurement reference surface" are randomly generated. For each intersecting particle, the distance d from its center to the cross-section and the radius R are calculated. The circular area S_single of the particle intercepted by the cross-section is calculated as π*(R). 2 – d 2 Calculate the spherical cap volume V_single of the particle located on one side of the measurement reference plane (this can be calculated using the spherical cap height formula). Classify the particles by group, and sum the S_single of all intersecting particles within each group to obtain the total cross-sectional area of ​​that group. The total protrusion volume of the particle group is obtained by summing V_single. For each particle group, calculate the ratio ( / ) / (in The average particle size of the particle group is used to obtain the contribution value of this group in this simulation. The contribution values ​​of all particle groups are averaged to obtain the single simulation value B_single. After repeating the simulation hundreds of times, the arithmetic mean of all B_singles is taken to obtain the calibration coefficient B (for near-spherical particles, this value is about 0.53).

[0106] Specifically, this invention uses precise geometric calculations (area, volume) at the particle scale to fit macroscopic statistical laws, revealing the microscopic mechanism of surface roughness formation and giving the model a solid physical foundation. The coefficient B calibrated in this invention has clear statistical significance, representing the average effect of particle shape and arrangement on roughness.

[0107] Step S400: Based on the target parameters and porosity, the surface roughness is quantified using a preset coefficient.

[0108] It should be noted that, in some embodiments, step S400 may include the following steps: using the product of a first target coefficient and the maximum particle size as a first parameter value; obtaining the weighted particle size of each particle group based on the product of the volume fraction and the average particle size of the same particle group; summing the weighted particle sizes of all particle groups to obtain the accumulated particle size; obtaining a second parameter value based on the product of the accumulated particle size, the second target coefficient, and the porosity parameter; wherein, the porosity parameter is determined based on the complementary value of porosity to 1; obtaining the surface roughness based on the difference between the first parameter value and the second parameter value; wherein, the expression for the surface roughness is:

[0109] ;

[0110] In the formula, Indicates surface roughness; Indicates the first target coefficient; Indicates the maximum particle size; Indicates the second objective coefficient; Indicates porosity; This represents the volume fraction of the i-th particle group; This represents the average particle size of the i-th particle group.

[0111] For example, in some specific implementations, the final surface roughness is calculated. When calculating, follow the formula step by step: first calculate the first term. Then calculate the weighted particle size for each particle group. And sum to get Then calculate the second term. Finally, subtracting the second term from the first term yields the predicted surface roughness. For example, for a certain soil mass, n=0.334, =60mm, calculated =23.52mm, take A=0.94, B=0.53, then = 0.94*60 –0.53*(1-0.334)*23.52 ≈ 48.10mm.

[0112] Specifically, this invention provides a complete and closed mathematical expression for calculating the final roughness. The formula is concise, with a clear physical meaning (maximum protrusion contribution minus correction terms due to pore size and average particle size), and is easy to implement through programming or manual calculation. Verification through embodiments shows that the calculation results of the formula have an error of less than 5% compared to physical experimental measurements, significantly improving computational efficiency while ensuring engineering accuracy. This invention's ability to transform complex surface morphology problems into functional relationships with basic soil parameters is key to its convenient and rapid prediction capabilities.

[0113] 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.

[0114] To address the shortcomings of existing technologies, this invention proposes a theoretical calculation method that calculates the surface roughness of the grout body based on the most basic physical properties of the soil (particle size distribution and porosity), saving significant manpower and resources.

[0115] It should be noted that the calculation method of this invention basically assumes that: the soil is uniform, that is, the spatial distribution of soil particles in the soil is uniform; the final diffusion surface of the slurry is a smooth plane, that is, the roughness caused by the solidification of the slurry is ignored.

[0116] In actual grouting, the grout diffuses evenly from the grouting holes to the surrounding area, eventually forming a columnar or spherical grout body depending on the arrangement of the grouting holes. Some of the outermost particles of the grout body are wrapped by the grout, while others protrude to the outside. When the particle size is small, this protrusion can be ignored, and the entire outer surface can be simplified as a smooth curved surface. However, for soil with larger particles, this simplification will lead to an underestimation of the interfacial strength between the grout body and the soil. Therefore, the influence of surface roughness needs to be considered. This roughness is relative to the roughness of the final diffusion surface of the grout, so the diffusion surface can be treated as a plane.

[0117] In some specific embodiments, the method for calculating the surface roughness of the grout body according to the present invention can be implemented as follows:

[0118] (1) The natural dry density ρd and specific gravity Gs of the soil in the area to be grouted were obtained by test, and the porosity n of the soil was calculated.

[0119] (2) Assuming there is a random grouting surface in the soil, since the soil is homogeneous, when the grouting surface is large enough, the area (Si) of each particle group intercepted by this section should be consistent with the ratio of the volume fraction (Pi) of each particle group in space (when the specific gravity of each particle group is consistent, i.e., the mass fraction of the corresponding gradation curve). Thus, we can obtain:

[0120]

[0121] The specific gravity of particles from different grain groups within the same stratum generally does not differ significantly, because small particles are often formed by the breakup of larger particles. Therefore, the mass fraction from the gradation curve can usually be directly used as the volume fraction for calculation. However, the specific gravity of particles may differ between different strata. It is clear that when the specific gravity of particles differs significantly, the mass fraction needs to be converted to obtain the true volume fraction.

[0122] (3) Based on the principle of surface roughness measurement using the sand cone method:

[0123]

[0124] Where Vs is the volume of standard sand poured in, and S is the area of ​​the measurement section.

[0125] The outer surface of the grout body formed by grouting is often curved, while the sand filling method is suitable for measuring the roughness of a plane, so it is not applicable to the surface roughness of the grout body.

[0126] The method yields the average roughness, and the principle is the same as that of the sand filling method. Both represent the average depth of the surface to be measured. The units of formula (2) and formula (9) are also lengths, and they are also corresponding in terms of dimensions.

[0127] like Figure 3 As shown, the volume of standard sand poured in is equal to the total volume (V) of the measurement space minus the volume of soil particles within the measurement space (the space from the measurement section to the uppermost part of the soil particles intercepted by that section). Measure the volume of soil particles in space This should be the sum of the volumes of the soil particles above the measured cross-section. Therefore:

[0128]

[0129] in, Let be the volume of the portion of the i-th grain group located above the cross section.

[0130] Since the soil is homogeneous, the porosity at a certain cross-section of the soil should be consistent with the porosity of the soil in space. Therefore, the area of ​​soil particles on the cross-section is... The following relationship exists between the cross-sectional area S and the cross-sectional area S:

[0131]

[0132] Let be the area intercepted by the cross section of the i-th particle group;

[0133] Substituting formulas 3 and 4 into formula 2, we get:

[0134]

[0135] Here, H represents the height of the measurement space.

[0136] Spherical particles are used to simulate soil particles. Given the gradation and porosity, soil is simulated and generated in discrete element software. A cross section is randomly given, and the farthest distance between the particles intersecting the cross section and the cross section is extracted (corresponding to the measurement space height H in formula (5)). The relationship between the distance and the maximum particle size of the soil is obtained as follows: Figure 4 As shown, a strong linear relationship can be observed between it and the maximum particle size of the soil. That is:

[0137]

[0138] For soils with nearly spherical soil particles, A is taken as 0.94.

[0139] Based on the average particle size of the particle group Instead of the particle size of each particle in the particle group, when the soil gradation and porosity are determined, a cross-section is randomly selected, and the area of ​​each particle group on the cross-section is a constant value. The total volume of the soil particles of each particle group intercepted by the cross-section is the same for the portion above and below the cross-section, which is also a constant value. Next, using numerical simulation, spherical particles are used to simulate soil particles to generate a soil with a certain gradation and porosity. A cross-section is randomly generated, and the volume of a certain particle group intersecting the cross-section located at the top of the cross-section is extracted. Area intersecting with the cross section and the average particle size of the particle group The value, after fitting, is as follows: Figure 5 As shown, a strong linear relationship was found between the two, namely:

[0140]

[0141] For soils with nearly spherical soil particles, B is set to 0.53.

[0142] H is the maximum depth of the standard sand layer required to cover the highest protrusion on the surface.

[0143] Numerical simulation using discrete element method (DEM) software provided a more intuitive solution for the values ​​of A and B. A represents the height of the maximum protrusion on the grout surface, ideally equal to the maximum particle diameter, at which point the particle is tangent to the measurement cross-section, and A is set to 1. However, the condition for determining if a particle is on the cross-section is that the distance from the particle to the cross-section is less than r, i.e., the particle intersects the cross-section. Therefore, H corresponds to the distance from the cross-section to the particle with the smallest intersection area among the largest particles, plus the particle's radius.

[0144] The value of coefficient B was also verified more intuitively through discrete element numerical simulation. This value is equal to the expected value of the ratio of the volume V of the spherical cavity cut by the random cross section, the area S of the intersection of the sphere and the cross section, and the diameter D, i.e., E(V / S / D). Since V approaches the volume of the sphere when S approaches zero, the ratio is infinite and the function diverges, making it difficult to calculate through theoretical derivation. However, an approximate solution can be obtained in the discrete element method using a finite number of particles.

[0145] The values ​​of A and B are directly solved by discrete element method. This value is more reasonable than the value fitted by formula 9. In addition, the definition of A and B comes from the analysis of the results rather than the previous assumptions. The final roughness prediction accuracy is also within 5%.

[0146] Let the area of ​​soil particles on the cross section If the value is 1, then the area Si of each particle group is numerically equal to the volume fraction Pi of each particle group, which gives us:

[0147]

[0148] Where Dw is the weighted particle size of the soil.

[0149] Substituting formulas 6 and 8 into formula 5, we get:

[0150]

[0151] Where A and B are dimensionless constants. For soil particles with a shape close to spherical, A is 0.94 and B is 0.53 (this value is for soil particles with a shape close to spherical; for soil particles with special geometry, the parameter values ​​need to be re-determined through experiments).

[0152] For soil particles that are flaky, under the influence of gravity, the particles tend to lie flat rather than stand upright. Therefore, when the grouting surface is parallel to the horizontal plane, the height H of the measurement space is related to the thickness of the flaky particles, and the corresponding values ​​of A and B will decrease. However, when the grouting surface is perpendicular to the horizontal direction, the height of the measurement space is related to the length of the particles, and the value of A will increase.

[0153] Furthermore, the values ​​of A and B are also related to the definition of particle size D. If sieving is used as the standard, particles that can pass through a sieve with a certain aperture are considered to have a particle size smaller than that aperture. In this case, the particle size values ​​will be smaller. Therefore, for needle-shaped particles, the value of A will increase, even to a value greater than 1, while the value of B will decrease. For this type of particle, We need to take the length of the long side of the particle, and Using the equivalent particle size would be more reasonable, and the same applies to flaky particles.

[0154] The following uses independent experimental data (refer to Table 1 below) to verify the formula, and the error between theory and experiment is within 5%.

[0155] Table 1

[0156]

[0157] The particle size range of each particle group in the soil is defined in 3.0.2 of the "Engineering Classification Standard for Soil" GB / T50145-2007 (as shown in Table 2 below). This allows for a more detailed classification, which will improve the calculation accuracy, but will also require more preliminary work.

[0158] Table 2

[0159]

[0160] In summary, this invention establishes the relationship between the surface roughness of the grout body and the maximum particle size, the weighted particle size of the soil, and the porosity using two physical indicators: particle size distribution and porosity. This allows for a simple and rapid calculation of surface roughness. Specifically, this invention establishes a calculation expression for the surface roughness of the grout body using the two most fundamental physical indicators of soil particle size distribution and porosity. The method is simple and fast, avoiding extensive experimentation and saving manpower and resources.

[0161] like Figure 6 As shown, this embodiment of the invention also provides a grout surface roughness calculation device 900, which can implement the above-described method. This device may include:

[0162] The first module 910 is used to obtain the porosity and particle size distribution curves of the soil in the area to be grouted; wherein, the soil in the area to be grouted includes particle groups of different sizes;

[0163] The second module 920 is used to extract target parameters from the particle size distribution curve and determine the average particle size of different particle size groups; wherein, the target parameters include the maximum particle size of the particles in the particle group and the volume fraction of different particle size groups.

[0164] The third module 930 is used to obtain preset coefficients representing the linear relationship of parameters by solving discrete element simulation based on the particle size distribution curve; wherein the preset coefficients include the first target coefficient and the second target coefficient;

[0165] The fourth module 940 is used to quantify the surface roughness based on the target parameters and porosity, combined with preset coefficients.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] like Figure 7 As shown, Figure 7 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes:

[0170] 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.

[0171] 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.

[0172] Input / output interface 1003 is used to implement information input and output;

[0173] 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.).

[0174] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0175] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0176] 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.

[0177] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0178] 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.

[0179] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0180] 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.

[0181] 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.

[0182] The present invention provides a method, apparatus, electronic device, storage medium, and program product for calculating the surface roughness of grouting bodies. This method acquires the porosity and particle size distribution curves of the soil in the grouting area. The soil in the grouting area includes particle groups of different sizes. Target parameters are extracted from the particle size distribution curves, and the average particle size of the different particle groups is determined. The target parameters include the maximum particle size in the particle group and the volume fraction of the different particle groups. Based on the particle size distribution curves, preset coefficients characterizing the linear relationship of the parameters are obtained through discrete element simulation. These preset coefficients include a first target coefficient and a second target coefficient. The surface roughness is quantified based on the target parameters and porosity, combined with the preset coefficients. This invention establishes a theoretical quantitative relationship between the surface roughness of the grout and the most basic physical properties of the soil (particle size distribution, porosity), realizing a paradigm shift from "physical test measurement" to "theoretical model calculation." Using this method, roughness can be quickly predicted before grouting construction using only conventional geotechnical test data, avoiding the high cost, heavy labor, and material waste associated with preparing large-scale solid grout samples. This method provides reliable roughness input parameters for accurately analyzing the complex interfacial mechanical behavior between the grout and the surrounding soil in coarse-grained soil, contributing to improved safety and economy in engineering design.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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 appropriate combinations thereof.

[0187] 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 calculating the surface roughness of a grouting body, characterized in that, The method includes the following steps: Obtain the porosity and particle size distribution curves of the soil in the area to be grouted; wherein the soil in the area to be grouted includes particle groups of different sizes; Target parameters are extracted from the particle size distribution curve, and the average particle size of the particle groups of different specifications is determined; wherein, the target parameters include the maximum particle size of the particles in the particle group and the volume fraction of the particle groups of different specifications; Based on the particle size distribution curve, preset coefficients characterizing the linear relationship of the parameters are obtained by discrete element simulation; wherein, the preset coefficients include a first target coefficient and a second target coefficient; The surface roughness is obtained by quantification based on the target parameters and the porosity, combined with the preset coefficients. The process of obtaining the porosity and particle size distribution curves of the soil in the grouting area 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 of multiple particle groups in the soil of the area to be grouted; Based on the soil sample data, the particle size distribution curve of the soil in the area to be grouted is obtained by sieve analysis. The step of quantifying the surface roughness based on the target parameter and the porosity, combined with the preset coefficient, includes the following steps: The product of the first target coefficient and the maximum particle size is used as the first parameter value; The weighted particle size of each particle group is obtained by multiplying the volume fraction of the same particle group with the average particle size. The weighted particle sizes of all the particle groups are summed to obtain the cumulative particle size. The second parameter value is obtained by multiplying the accumulated particle size, the second target coefficient, and the porosity parameter; wherein the porosity parameter is determined based on the complementary value of the porosity to 1. The surface roughness is obtained based on the difference between the first parameter value and the second parameter value; The expression for the surface roughness is as follows: ； In the formula, Indicates surface roughness; Indicates the first target coefficient; Indicates the maximum particle size; Indicates the second objective coefficient; Indicates porosity; This represents the volume fraction of the i-th particle group; This represents the average particle size of the i-th particle group.

2. The method according to claim 1, characterized in that, The extraction of target parameters from the particle size distribution curve includes the following steps: The maximum particle size is obtained from the particle size distribution curve and taken as the maximum particle size. Based on the pre-defined particle size range, the percentage of particle size corresponding to the particle size boundary values ​​of different particle sizes of the particle group is collected from the particle size distribution curve; wherein, the particle size boundary values ​​include an upper limit value and a lower limit value of particle size. The mass fraction corresponding to each particle size group is obtained as the volume fraction based on the difference between the particle size percentage corresponding to the upper limit of particle size and the particle size percentage corresponding to the lower limit of particle size.

3. The method according to claim 1, characterized in that, Determining the average particle size of the particle groups of different specifications includes the following steps: The particle size boundary values ​​for different particle sizes are determined based on the pre-defined particle size range; wherein, the particle size boundary values ​​include an upper limit particle size value and a lower limit particle size value; Based on the upper limit and lower limit of particle size, the average particle size of each particle group of the specified size is obtained by averaging; wherein, the averaging operation includes arithmetic average operation and geometric average operation.

4. The method according to claim 1, characterized in that, When the preset coefficient is the first target coefficient, the step of obtaining the preset coefficient characterizing the linear relationship of the parameters through discrete element simulation based on the particle size distribution curve includes the following steps: Based on the particle size distribution curve, a first virtual soil mass is generated using discrete element method software; wherein, the first virtual soil mass includes spherical particles corresponding to each particle size group. The first virtual soil body is made to reach the porosity by using the gravity deposition or compression algorithm of the discrete element software, thereby obtaining the first target soil body; A first cross section is randomly constructed in the first target soil mass, and one side is taken as the first measurement reference surface; Obtain the center coordinates and particle radii of all spherical particles on the first cross section; Based on the center coordinates and the particle radius, the first measurement space height of each spherical particle on the first cross section from the first measurement reference surface is obtained by quantization. Wherein, when the center coordinate is located on one side of the first measurement reference plane, the first measurement space height is the sum of the particle radius and the first distance, where the first distance represents the distance between the center coordinate and the first cross section; when the center coordinate is located on the other side of the first measurement reference plane, the first measurement space height is the difference between the particle radius and the first distance; The maximum value of the height of the first measurement space is used as the first fitted value, and the maximum particle size is used as the second fitted value. The first candidate coefficient is obtained based on the ratio of the first fitted value to the second fitted value. Return to the step of randomly constructing a first cross section in the first target soil and taking one side as the first measurement reference surface, until a first number of the first candidate coefficients are obtained; The first candidate coefficients of the first number are averaged to obtain the first target coefficient.

5. The method according to claim 1, characterized in that, When the preset coefficient is the second target coefficient, the process of obtaining the preset coefficient characterizing the linear relationship of the parameters through discrete element simulation based on the particle size distribution curve includes the following steps: Based on the particle size distribution curve, a second virtual soil mass is generated using discrete element method software; wherein, the second virtual soil mass includes spherical particles corresponding to each particle size group. The second virtual soil body is made to reach the porosity by using the gravity deposition or compression algorithm of the discrete element software, thereby obtaining the second target soil body; A second cross section is randomly constructed in the second target soil mass, and one side is taken as the second measurement reference surface; Obtain the center coordinates and particle radii of all the spherical particles on the second cross section; Based on the center coordinates and the particle radius, the second measurement space height of each spherical particle on the second cross section from the second measurement reference surface is obtained by quantization. Wherein, when the center coordinate is located on one side of the second measurement reference plane, the height of the second measurement space is the sum of the particle radius and the second distance, where the second distance represents the distance between the center coordinate and the second cross section; when the center coordinate is located on the other side of the second measurement reference plane, the height of the second measurement space is the difference between the particle radius and the second distance; Based on the second distance and the particle radius, the radius of the circle intercepted by the second cross section for each spherical particle is calculated using the Pythagorean theorem, and then the area of ​​the circle intercepted by the second cross section for each spherical particle is calculated. Based on the height of the second measurement space and the particle radius, the spherical cap volume of each spherical particle located on one side of the second measurement reference plane is calculated using the spherical cap formula; Based on the statistical analysis of the circular area, the total circular area corresponding to each of the particle groups on the second cross section is obtained; based on the statistical analysis of the spherical cap volume, the total spherical cap volume corresponding to each of the particle groups on one side of the second measurement reference plane is obtained. The second candidate coefficient for each particle group is obtained by dividing the ratio of the total volume of the spherical cap to the total area of ​​the circle by the average particle size. Return to the step of randomly constructing a second cross section in the second target soil and taking one side as the second measurement reference surface, until a second number of the second candidate coefficients are obtained; The second candidate coefficients of the second number are averaged to obtain the second target coefficient.

6. A device for calculating the surface roughness of a grouting body, characterized in that, The device includes: The first module is used to obtain the porosity and particle size distribution curves of the soil in the area to be grouted; wherein the soil in the area to be grouted includes particle groups of different sizes; The second module is used to extract target parameters from the particle size distribution curve and determine the average particle size of the particle groups of different specifications; wherein, the target parameters include the maximum particle size of the particles in the particle group and the volume fraction of the particle groups of different specifications; The third module is used to obtain preset coefficients representing the linear relationship of the parameters by solving the discrete element method based on the particle size distribution curve; wherein the preset coefficients include a first target coefficient and a second target coefficient. The fourth module is used to quantify the surface roughness based on the target parameters and the porosity, combined with the preset coefficients. The process of obtaining the porosity and particle size distribution curves of the soil in the grouting area 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 of multiple particle groups in the soil of the area to be grouted; Based on the soil sample data, the particle size distribution curve of the soil in the area to be grouted is obtained by sieve analysis. The step of quantifying the surface roughness based on the target parameter and the porosity, combined with the preset coefficient, includes the following steps: The product of the first target coefficient and the maximum particle size is used as the first parameter value; The weighted particle size of each particle group is obtained by multiplying the volume fraction of the same particle group with the average particle size. The weighted particle sizes of all the particle groups are summed to obtain the cumulative particle size. The second parameter value is obtained by multiplying the accumulated particle size, the second target coefficient, and the porosity parameter; wherein the porosity parameter is determined based on the complementary value of the porosity to 1. The surface roughness is obtained based on the difference between the first parameter value and the second parameter value; The expression for the surface roughness is as follows: ； In the formula, Indicates surface roughness; Indicates the first target coefficient; Indicates the maximum particle size; Indicates the second objective coefficient; Indicates porosity; This represents the volume fraction of the i-th particle group; This represents the average particle size of the i-th particle group.

7. An electronic device, characterized in that, 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, characterized 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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