Joint surface peak shear strength prediction method, device, equipment, medium and program product

Abstract

The embodiment of the application provides a joint surface peak shear strength prediction method, device, equipment, medium and program product, the method comprises the following steps: scanning the original rock joint surface to obtain the original rock joint point cloud data; acquire a description function, and describe the distribution of microconvex bodies in the original rock joint surface by combining the joint point cloud data with the description function; determine the distribution function density and any normal stress; determine the average value of the actual contact joint microconvex body inclination according to the initial peak dilatancy angle of the initial state when no cyclic shear occurs, and determine the peak dilatancy angle when a plurality of cyclic shears occur; according to the number of cyclic shears, a peak shear strength prediction model of the joint surface is determined; the model is simple to calculate, can realize high-precision prediction only by a small number of parameters, and is suitable for engineering application.

Description

Technical Field

[0001] This application relates to the field of joint surface quantification technology, and more specifically, to a method, apparatus, equipment, medium, and program product for predicting the peak shear strength of joint surfaces. Background Technology

[0002] In geotechnical engineering, the shear strength of structural surfaces is a key factor affecting engineering stability. The roughness of joint surfaces directly determines the dilatation effect during shearing, thus affecting the peak shear strength. However, in practical engineering, structural surfaces are often subjected to cyclic shear loads, such as earthquakes, blasting vibrations, or mechanical loading. These cyclic loads differ from single static shear; they cause joint surfaces to undergo multiple repeated shearing events, rather than a single shearing event. This repeated cyclic shearing significantly alters the mechanical behavior of joint surfaces, leading to continuous wear of micro-protrusions and roughness degradation, thereby reducing the peak shear strength. Therefore, studying the shear strength of structural surfaces under cyclic loading is of great significance for accurately predicting their performance under complex working conditions and ensuring engineering safety. However, traditional prediction models are not sufficiently applicable under cyclic shear loading, failing to comprehensively consider the nonlinear degradation due to the number of cycles, the effect of actual contact micro-protrusions, nonlinear wear characteristics, and the compressive effect of normal stress. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, equipment, medium, and program product for predicting the peak shear strength of joint surfaces, in order to solve the problem that existing traditional prediction models are not applicable under cyclic shear loads and fail to comprehensively consider the nonlinear degradation of the number of cycles, the effect of actual contact micro-protrusions, nonlinear wear characteristics, and the compressive effect of normal stress.

[0004] In a first aspect, embodiments of this application provide a method for predicting the peak shear strength of joint surfaces, including:

[0005] Scan the original rock joint surface to obtain the original rock joint point cloud data;

[0006] Obtain the description function, and use the description function in combination with joint point cloud data to describe the distribution of micro-protrusions in the original rock joint surface;

[0007] Determine the distribution function density and any normal stress;

[0008] The average tilt angle of the joint micro-protrusions in actual contact is determined based on the initial peak dilatation angle of the initial state before cyclic shearing occurs, and the peak dilatation angle during several cyclic shearings is determined.

[0009] A prediction model for the peak shear strength of the joint surface is determined based on the number of cyclic shearing cycles.

[0010] In the above implementation process, the embodiments of this application scan the original rock joint surface to obtain the original rock joint point cloud data; obtain the describing function, and describe the distribution of micro-protrusions in the original rock joint surface by combining the describing function with the joint point cloud data; determine the distribution function density and any normal stress; determine the average dip angle of the actually contacting joint micro-protrusions based on the initial peak dilatation angle of the initial state before cyclic shearing, and determine the peak dilatation angle during several cyclic shearings; determine the peak shear strength prediction model of the joint surface based on the number of cyclic shearings; solve the problem of ignoring the influence of non-contact micro-elements in traditional models by calculating the average dip angle of the actually contacting micro-protrusions; introduce the number of cyclic shearings to accurately reflect the dynamic characteristics of rapid wear in the early stage of cyclic shearing and gradual smoothing in the later stage; introduce normal stress to reflect the roughness degradation and dilatation angle reduction caused by the flattening of micro-protrusions under high normal stress; the model calculation is simple, can achieve high-precision prediction, and is suitable for engineering applications.

[0011] Furthermore, the scanning of the original rock joint surface to obtain the original rock joint point cloud data includes:

[0012] By setting the sampling interval and using a 3D laser scanner to scan the original rock joint surface, point cloud data of the original rock joint represented by axial coordinates is obtained.

[0013] In the above implementation process, point cloud data of joint surfaces of the original rock are obtained so as to determine various parameters and models of the joint surfaces in the future.

[0014] Furthermore, the step of obtaining the description function and describing the distribution of micro-protrusions in the original rock joint surface using the description function in conjunction with joint point cloud data includes:

[0015] Determine the correspondence between the effective shear angle and the joint shear strength;

[0016] Joint morphology is represented by triangular mesh elements. The geometric relationship between the triangular elements of the joint surface and the shear direction is determined, and the effective shear dip angle is calculated.

[0017] ;

[0018] in, ; ;for Effective shear tilt angle; Let the dip angle be the angle of the triangular element of the joint surface. Let be the angle between the joint surface dip and the shear direction, t be the shear direction vector, and N be the element outward normal vector. The out-of-plane normal vector is the shear plane. The projection vector of the shear direction onto the shear plane;

[0019] Determine the relationship between the ratio of the sum of the areas of infinitesimal elements whose effective dip angle is greater than the effective shear dip angle to the sum of the joint surface areas and the effective shear dip angle:

[0020] ;

[0021] in, It is the ratio of the total area of ​​joint morphology surfaces with an effective dip angle greater than 0 to the total area of ​​joint surfaces; The joint roughness parameter controls the shape of the dip angle distribution curve;

[0022] This is the core part of the cumulative distribution function;

[0023] The larger the joint roughness parameter, the higher the joint surface roughness, and the greater the distance between the vertex of the micro-protrusion and the shear surface.

[0024] In the above implementation process, the actual contact micro-protrusions are screened by combining triangular mesh generation and geometric algorithms to ensure that only the effective contact area participates in the strength calculation, thereby improving the prediction accuracy of the subsequent model.

[0025] Furthermore, determining the distribution function density includes:

[0026] right Taking the derivative, we obtain the probability density function:

[0027] ;

[0028] Determining any normal stress includes:

[0029] Assume that the minimum tilt angle among all contact micro-protrusions is the tilt angle of the micro-protrusion that yields under a given load;

[0030] Depend on The minimum tilt angle among all contact micro-protrusions is obtained:

[0031] ;

[0032] in, Normal stress, i.e., the vertical stress applied to the joint surface; This represents the uniaxial compressive strength of the rock, which is a benchmark stress value. The normalized normal stress.

[0033] In the above implementation process, the compression effect of normal stress is accurately described by the linear stress term, reflecting the roughness degradation and reduction of shear dilatation angle caused by the flattening of micro-protrusions under high normal stress.

[0034] Furthermore, determining the average tilt angle of the joint micro-protrusions in actual contact based on the initial peak dilatation angle of the initial state before cyclic shearing includes:

[0035] ;

[0036] Among them, the average tilt angle of the joint micro-protrusions in actual contact is the initial peak dilatation angle. Integral to 90°;

[0037] Determine the peak shear dilatation angle during several cycles of shearing:

[0038] ;

[0039] in, This refers to the nonlinear degradation factor caused by cyclic shearing; is the cyclic degradation rate; m is the number of cyclic shearing cycles; denoted as the compressive degradation factor caused by normal stress; k is the stress degradation coefficient. Normal stress; It represents the uniaxial compressive strength of the rock. The normalized normal stress.

[0040] In the above implementation process, the average tilt angle of the actual contact micro-protrusions was taken into account, which made up for the shortcomings of previous models that did not fully reflect the real contact behavior.

[0041] Furthermore, the prediction model for determining the peak shear strength of the joint surface based on the number of cyclic shearing cycles includes:

[0042] ;

[0043] in, The shear strength of the joint surface after n shear cycles; This is the basic friction angle.

[0044] In the above implementation process, the model calculation is simple, and only a small number of parameters are needed to achieve high-precision prediction, making it suitable for engineering applications.

[0045] Secondly, embodiments of this application provide a device for predicting the peak shear strength of joint surfaces, comprising:

[0046] The data acquisition module is used to scan the original rock joint surfaces and obtain the original rock joint point cloud data;

[0047] The distribution description module is used to obtain the description function and describe the distribution of micro-protrusions in the original rock joint surface by combining the description function with joint point cloud data.

[0048] The parameter determination module is used to determine the distribution function density and any normal stress;

[0049] The shear determination module is used to determine the average tilt angle of the joint micro-protrusions in actual contact based on the initial peak shear dilatation angle of the initial state when no cyclic shearing has occurred, and to determine the peak shear dilatation angle during several cyclic shearings.

[0050] The model determination module is used to determine the peak shear strength prediction model of the joint surface based on the number of cyclic shearing cycles.

[0051] Thirdly, embodiments of this application provide an electronic device, including:

[0052] The system includes a processor, a memory, and a bus. The processor is connected to the memory via the bus. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, they are used to implement the joint surface peak shear strength prediction method as described above.

[0053] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a server, implements the method for predicting the peak shear strength of joint surfaces as described above.

[0054] Fifthly, embodiments of this application provide a computer program product, the computer program product including instructions, which, when executed by a computer, cause the computer to perform the method described above. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart illustrating a method for predicting the peak shear strength of a joint surface provided in an embodiment of this application;

[0057] Figure 2 A digital model diagram of the joint surface provided in this application embodiment for a method to predict the peak shear strength of the joint surface;

[0058] Figure 3 A schematic diagram of a joint surface triangular micro-element provided for a method of predicting the peak shear strength of a joint surface according to an embodiment of this application;

[0059] Figure 4 This is a schematic diagram of the structure of a joint surface peak shear strength prediction device provided in an embodiment of this application;

[0060] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0061] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0062] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0063] In geotechnical engineering, the shear strength of structural surfaces is a key factor affecting engineering stability. The roughness of joint surfaces directly determines the dilatation effect during shearing, thus influencing peak shear strength. However, in practical engineering, structural surfaces are often subjected to cyclic shear loads, such as earthquakes, blasting vibrations, or mechanical loading. These cyclic loads differ from single static shear; they cause joint surfaces to undergo multiple repeated shearing events, rather than a single shearing event. This repeated cyclic shearing significantly alters the mechanical behavior of joint surfaces, leading to continuous wear of micro-protrusions and roughness degradation, thereby reducing peak shear strength. Therefore, studying the shear strength of structural surfaces under cyclic loading is of great significance for accurately predicting their performance under complex working conditions and ensuring engineering safety.

[0064] Furthermore, in practical engineering, not all joint surface elements participate in contact and shear. During shearing, only some micro-protrusions (i.e., actual contact micro-protrusions) directly bear the shear force, while the contribution of non-contact elements to the shear strength is limited. Therefore, it is crucial to consider the distribution and behavior of actual contact micro-protrusions to more realistically reflect the shear characteristics of joint surfaces. Ignoring the role of actual contact micro-protrusions will cause the prediction model to deviate from reality, reducing prediction accuracy.

[0065] Furthermore, the wear of micro-protrusions during cyclic shearing exhibits nonlinear characteristics. In the initial stage of the cycle, the micro-protrusions wear rapidly, and the roughness decreases significantly; as the number of cycles increases, the wear rate gradually slows down, and the roughness tends to stabilize. This nonlinear wear directly affects the dilatation angle and shear strength. Traditional models (such as the Barton or Grasselli models) are mostly based on static conditions and fail to fully describe this dynamic degradation process, leading to inaccurate predictions. Therefore, a nonlinear wear mechanism needs to be introduced to accurately capture the impact of cyclic shearing on joint roughness.

[0066] Meanwhile, the compressive effect of normal stress is also an important factor that cannot be ignored. During cyclic shearing, normal stress flattens the micro-protrusions on the joint surface, reducing their height and roughness, thereby further weakening the dilatation effect and shear strength. Especially under high normal stress, the compressive deformation of micro-protrusions is more significant, and the degradation effect on roughness is more pronounced. Traditional models usually simplify the compressive effect of normal stress, failing to accurately reflect its influence on micro-protrusion wear and shear strength, leading to prediction bias.

[0067] In summary, traditional prediction models are insufficiently applicable under cyclic shear loading, failing to comprehensively consider the nonlinear degradation of the number of cycles, the effect of actual contact micro-protrusions, nonlinear wear characteristics, and the compressive effect of normal stress. Therefore, there is an urgent need for a peak shear strength prediction method that can comprehensively consider these factors to improve the reliability and safety of engineering designs under complex working conditions.

[0068] This application proposes a method for predicting the peak shear strength of joint surfaces to solve the above-mentioned problems.

[0069] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for predicting the peak shear strength of a joint surface, provided in an embodiment of this application. The method includes:

[0070] 100. Scan the original rock joint surface to obtain the original rock joint point cloud data.

[0071] Optionally, the sampling interval can be set, and a 3D laser scanner can be used to scan the original rock joint surface to obtain the original rock joint point cloud data represented by the X, Y, and Z axis coordinates. Please refer to [reference needed]. Figure 2 This allows us to acquire point cloud data of the joint surfaces of the original rock, so that we can subsequently determine the various parameters and models of the joint surfaces.

[0072] As is understandable, the sampling interval refers to the horizontal or vertical distance between two adjacent scanning points during the scanning process, which directly affects the density and accuracy of point cloud data. The sampling interval can be set according to requirements.

[0073] 200. Obtain the description function, and use the description function in combination with joint point cloud data to describe the distribution of micro-protrusions in the original rock joint surface.

[0074] Specifically, determine the correspondence between the effective shear angle and the joint shear strength; please refer to... Figure 3 The joint morphology is represented by triangular mesh elements. The geometric relationship between the triangular mesh elements of the joint surface and the shear direction is determined, and the effective shear dip angle is calculated.

[0075] ;

[0076] in, ; ;for Effective shear tilt angle; Let the dip angle be the angle of the triangular element of the joint surface. Let be the angle between the joint surface dip and the shear direction, t be the shear direction vector, and N be the element outward normal vector. The out-of-plane normal vector is the shear plane. This is the projection vector of the shear direction onto the shear plane.

[0077] Determine the relationship between the ratio of the sum of the areas of infinitesimal elements whose effective dip angle is greater than the effective shear dip angle to the sum of the joint surface areas and the effective shear dip angle:

[0078] ;

[0079] in, It is the ratio of the total area of ​​joint morphology surfaces with an effective dip angle greater than 0 to the total area of ​​joint surfaces; The joint roughness parameter controls the shape of the dip angle distribution curve;

[0080] This is the core part of the cumulative distribution function;

[0081] The larger the joint roughness parameter, the higher the joint surface roughness, and the greater the distance between the vertex of the micro-protrusion and the shear surface, that is, the steeper the inclination angle distribution of the micro-protrusion. The joint roughness parameter is usually determined by fitting experimental data.

[0082] The above-described relationship between the ratio of the total area of ​​micro-elements with an effective dip angle greater than the effective shear dip angle to the total area of ​​the joint surface and the effective shear dip angle is determined using a double exponential form. This effectively captures the nonlinear distribution law of the apparent dip angle of the joint surface. Compared with traditional linear or single exponential models, this function can more accurately describe the gradual change of the dip angle of micro-protrusions from low to high, conforming to the geometric characteristics of actual joint surfaces. By introducing a joint roughness parameter, the distribution function can reflect the differences in roughness of different joint surfaces. The magnitude of the joint roughness parameter directly affects the steepness of the distribution curve, making the model applicable to various joint surfaces ranging from smooth to highly rough. This distribution function can adapt to the characteristics of joint surfaces under complex working conditions, such as different shear directions and roughness levels. Compared with traditional models (such as the Grasselli model), this distribution function is more flexible in describing the distribution of micro-elements facing shear and is suitable for dynamic loading scenarios such as cyclic shearing.

[0083] Using the obtained point cloud data, combined with step 200, the three-dimensional topographic parameters of a specific shear direction under different sampling intervals are obtained: joint roughness parameters.

[0084] Therefore, by combining triangular mesh generation with geometric algorithms to screen actual contact micro-protrusions, we can ensure that only the effective contact area participates in the strength calculation, thereby improving the prediction accuracy of subsequent models.

[0085] 300. Determine the distribution function density and any normal stress.

[0086] Understandably, to further determine the distribution function density, the above formula is differentiated. The distribution function density can more precisely reflect the density of joint micro-protrusions at different apparent dip angles, which is crucial for accurately characterizing the microscopic features of the joint surface. In subsequent analyses, this helps to more accurately understand the distribution of actual contact micro-protrusions, thereby improving the accuracy of predicting the peak shear strength of the joint surface.

[0087] Specifically, determining the distribution function density includes:

[0088] right Taking the derivative, we obtain the probability density function:

[0089] .

[0090] It should be noted that the distribution function density, in its physical sense, precisely reflects the rate of change of the apparent dip angle distribution of joint micro-protrusions. In practical rock engineering applications, analyzing this distribution function density clearly reveals the density of micro-protrusions at different apparent dip angles. For example, a higher distribution function density in certain areas indicates a denser distribution of micro-protrusions near that apparent dip angle; while in areas with a lower distribution function density, the distribution of micro-protrusions is relatively sparse. This refined analysis of micro-protrusion distribution provides crucial theoretical support for accurately determining the average dip angle of actual contact joint micro-protrusions and for constructing a more accurate prediction model for the peak shear strength of joint surfaces.

[0091] Specifically, determining any normal stress includes:

[0092] Understandably, for fully coupled joints without infill, when subjected to a normal load, a small portion of the joint's contact micro-protrusions will undergo elastic deformation, with yielding occurring in the initially contacting micro-protrusions. As the normal stress continues to increase, more micro-protrusions will come into contact and yield, a process that continues until the area of ​​the contacting micro-protrusions is large enough to withstand a given normal load. Therefore, it can be assumed that the minimum tilt angle among all contacting micro-protrusions is the tilt angle of the micro-protrusion that just yields under a given load.

[0093] Depend on The minimum tilt angle among all contact micro-protrusions is obtained:

[0094] ;

[0095] in, Normal stress, i.e., the vertical stress applied to the joint surface; This represents the uniaxial compressive strength of the rock, which is a benchmark stress value. The normalized normal stress.

[0096] More specifically, normal stress is the vertical stress applied to the joint surface, which affects the compression and shear behavior of the micro-protrusion; the higher the normal stress, the easier it is for the micro-protrusion to be flattened, resulting in a decrease in roughness and shear dilatation angle.

[0097] Thus, the compressive effect of normal stress is accurately described by the linear stress term, reflecting the roughness degradation and reduction of shear dilatation angle caused by the flattening of micro-protrusions under high normal stress.

[0098] 400. Determine the average tilt angle of the joint micro-protrusions in actual contact based on the initial peak dilatation angle of the initial state before cyclic shearing, and determine the peak dilatation angle during several cyclic shearings.

[0099] Specifically, determining the average tilt angle of the joint micro-protrusions in actual contact based on the initial peak dilatation angle of the initial state before cyclic shearing includes:

[0100] ;

[0101] Among them, the average tilt angle of the joint micro-protrusions in actual contact is the initial peak dilatation angle. Integral to 90°;

[0102] Determine the peak shear dilatation angle during several cycles of shearing:

[0103] ;

[0104] in, This refers to the nonlinear degradation factor caused by cyclic shearing; is the cyclic degradation rate; m is the number of cyclic shearing cycles; denoted as the compressive degradation factor caused by normal stress; k is the stress degradation coefficient. Normal stress; It represents the uniaxial compressive strength of the rock. The normalized normal stress.

[0105] More specifically, the peak dilatation angle is the equivalent inclination angle contributed by the dilatation effect caused by roughness during the joint surface shearing process, directly affecting the peak shear strength. This angle increases with the number of cycles (n) and the normal stress. It degenerates as its quantity increases.

[0106] Initial peak dilatation angle (unit: degrees): Represents the initial state before cyclic shearing occurs (i.e., the dilatation angle when n=0 and normal stress degradation is not considered). It is usually determined by the average initial actual contact micro-protrusion tilt angle, reflecting the original roughness characteristics of the joint surface.

[0107] Nonlinear degradation factor caused by cyclic shearing (unitless): exponential form The wear and roughness degradation of jointed micro-protrusions during cyclic shearing are described; the exponential form reflects the nonlinear characteristics of the wear: the degradation is faster in the early stage and tends to level off in the later stage.

[0108] Cyclic degradation rate (unit: dimensionless, depending on the definition of n): controls the rate at which the roughness of the micro-protrusion degrades with the number of cycles; the higher the cyclic degradation rate, the faster the degradation, and it is usually determined by fitting through cyclic shear tests, reflecting the wear resistance and shear conditions of the joint material.

[0109] Cyclic shear count (unit: dimensionless, integer): indicates the number of cyclic shears experienced by the joint surface, such as repeated shearing caused by earthquakes, blasting vibrations, or mechanical loading; an increase in the number of cyclic shears leads to wear of micro-protrusions, reducing roughness and shear dilatation angle.

[0110] Compression degradation factor caused by normal stress (unitless): This term describes the compressive and shearing effects of normal stress on the micro-protrusion; as the normal stress increases, the micro-protrusion is flattened and the shear dilatation angle decreases. A linear form approximates this physical process.

[0111] Stress degradation coefficient (unit: dimensionless): reflects the degree of influence of normal stress on the roughness degradation of micro-protrusions; the larger the stress degradation coefficient, the stronger the inhibitory effect of normal stress on the dilatation angle. It is determined by experimental fitting and is usually related to rock material and joint characteristics.

[0112] Normal stress (unit: MPa or Pa): The vertical stress applied to the joint surface, which affects the compression and shear behavior of micro-protrusions; the higher the normal stress, the easier it is for the micro-protrusions to be flattened, resulting in a decrease in roughness and shear dilatation angle.

[0113] Uniaxial compressive strength of rock (unit: MPa or Pa): a reference stress value used to normalize normal stress. .

[0114] Normalized normal stress (unitless): through Normalizing the normal stress makes it easier to compare the effects of different stress levels on the degradation of micro-convexities.

[0115] Therefore, the embodiments of this application are computationally simple and easy to apply in engineering: the formula contains only two main parameters (λ and k), which greatly reduces the difficulty of fitting and calculation compared to the original complex form (involving the Weibull function and multiple parameters). Exponential form and linear stress terms It facilitates rapid calculations and is suitable for quick on-site assessments in engineering projects.

[0116] This application's embodiments retain the core physical mechanism: fully considering the nonlinear degradation of the number of cycles: through exponential terms This describes the nonlinear wear effect of micro-protrusions under cyclic shearing; the exponential form effectively captures the trend of the wear rate gradually decreasing with increasing cycle number, consistent with the physical law of joint roughness degradation in practice. The compressive effect of normal stress is fully considered: through linear terms... This reflects the compressive and shearing effects of normal stress on the micro-protrusion; as the normal stress increases, the micro-protrusion is flattened, the peak shear dilatation angle decreases, and the linear form can effectively approximate this effect within a reasonable range.

[0117] The embodiments of this application have strong applicability: the simplified formula is applicable to most cyclic shear scenarios, and the parameters λ and k can be fitted with a small amount of experimental data, reducing experimental costs. The model has a small prediction error for moderate cycle counts and normal stress ranges (typically within 5%-10%), making it suitable for preliminary engineering design.

[0118] The embodiments of this application fully consider two factors: 1. Nonlinear degradation of the number of iterations:

[0119] Exponential form The nonlinear process of micro-protrusion wear is described. In the initial stage of the cycle, wear is rapid (significant decrease); as the number of cycles increases, the wear rate slows down (consistent with the physical law of micro-protrusions gradually becoming smoother); consistent with experimental observations, the surface roughness of the joint decreases rapidly in the early stages of the cycle and then tends to stabilize. 2. Compression effect of normal stress: linear term It reflects the compressive effect of normal stress on micro-protrusions; higher normal stress will flatten the micro-protrusions, reducing the equivalent tilt angle and peak shear dilatation angle; this method is simple but effective, capturing the direct effect of stress on roughness degradation, and is applicable to most engineering stress ranges.

[0120] 500. Determine the prediction model for the peak shear strength of the joint surface based on the number of cyclic shearing cycles.

[0121] Specifically, the prediction model for determining the peak shear strength of the joint surface based on the number of cyclic shearing cycles includes:

[0122] ;

[0123] in, The shear strength of the joint surface after n shear cycles; The basic friction angle is obtained from the tilt test and is expressed in degrees.

[0124] Therefore, the model is simple to calculate and requires only a few parameters to achieve high-precision prediction, making it suitable for engineering applications.

[0125] As described above, the embodiments of this application scan the original rock joint surface to obtain the original rock joint point cloud data; obtain the describing function, and describe the distribution of micro-protrusions in the original rock joint surface by combining the describing function with the joint point cloud data; determine the distribution function density and any normal stress; determine the average dip angle of the actually contacting joint micro-protrusions based on the initial peak dilatation angle of the initial state before cyclic shearing, and determine the peak dilatation angle during several cyclic shearings; determine the peak shear strength prediction model of the joint surface based on the number of cyclic shearings; solve the problem of neglecting the influence of non-contact micro-elements in traditional models by calculating the average dip angle of the actually contacting micro-protrusions; introduce the number of cyclic shearings to accurately reflect the dynamic characteristics of rapid wear in the early stage of cyclic shearing and gradual smoothing in the later stage; introduce normal stress to reflect the roughness degradation and dilatation angle reduction caused by the flattening of micro-protrusions under high normal stress; the model calculation is simple, can achieve high-precision prediction, and is suitable for engineering applications.

[0126] Existing technologies neglect the dynamic screening mechanism of actually contacting micro-protrusions: Traditional models (such as the Barton and Grasselli models) typically assess the shear strength of joint surfaces based on the statistical characteristics of the overall joint morphology, failing to distinguish between micro-protrusions actually involved in shearing and non-contact areas. However, during cyclic shearing, only some micro-protrusions directly contribute to shear strength due to mechanical contact, while the influence of non-contact areas on peak shear strength is negligible. Because existing models do not focus on the dynamic distribution characteristics of actually contacting micro-protrusions, the predicted results deviate significantly from actual shear behavior.

[0127] This application embodiment uses three-dimensional laser scanning technology to accurately acquire point cloud data of joint surfaces (sampling interval 0.1mm), combines triangular mesh division and geometric algorithms to screen actual contact micro-protrusions, and constructs a dynamic dilatation angle model based on the average tilt angle of the joint micro-protrusions to ensure that only the effective contact area participates in the strength calculation, thereby improving the model prediction accuracy.

[0128] Existing technologies do not consider the nonlinear wear effect of the number of cyclic shear cycles: traditional models (such as the Grasselli parametric method) are based on single or static shear conditions and use linear or single exponential forms to describe micro-protrusion wear. However, experiments show that the wear rate of micro-protrusions during cyclic shearing exhibits obvious nonlinear characteristics: rapid degradation in the initial stage, followed by gradual stabilization in the later stage. Because existing models do not incorporate a dynamic degradation mechanism, they cannot accurately predict the shear strength decay under long-term cyclic loading. This exponential form accurately captures the nonlinear decay trend of the wear rate, consistent with the experimentally observed characteristic of a "sharp initial drop followed by a gradual decline."

[0129] This application's embodiments introduce a nonlinear degradation factor. ,in The cyclic degradation rate is represented by n, which is the number of cycles. This accurately describes the nonlinear decay characteristics of the wear rate, consistent with the pattern of rapid degradation in the early stage and stabilization in the later stage.

[0130] Existing technologies simplify the compressive effect of normal stress: Traditional models typically assume that the effect of normal stress on the compression of micro-protrusions is linear or ignores its dynamic effects. In reality, high normal stress significantly flattens micro-protrusions, reducing roughness and dilatation angle, and the compressive effect increases nonlinearly with stress level. Because existing models simplify this mechanism, they cannot accurately predict shear strength under high stress conditions.

[0131] This application embodiment adds a linear stress term to the model. ( For normal stress, (representing the uniaxial compressive strength of rock), the contribution of normal stress to the compression of micro-protrusions is quantified through normalization, dynamically reflecting the influence of stress level on roughness degradation.

[0132] There is a contradiction between the complexity of existing technical models and their practicality in engineering: traditional models (such as the Weibull function model) require fitting multiple parameters, resulting in high computational complexity and making it difficult to meet the needs of rapid evaluation in engineering field.

[0133] The embodiments of this application simplify the model structure, retaining only the core parameters λ (cyclic degradation rate) and k (stress degradation coefficient); by combining exponential and linear terms, the computational load is significantly reduced while preserving the physical mechanism, achieving a balance between high accuracy and ease of use.

[0134] The steps described above are not strictly performed in the order of their numbers; they should be understood as a whole.

[0135] Secondly, based on the above embodiments, Figure 4 A schematic diagram of a joint surface peak shear strength prediction device provided in an embodiment of this application. (Reference) Figure 4The joint surface peak shear strength prediction device provided in this embodiment specifically includes: a data acquisition module 401, a distribution description module 402, a parameter determination module 403, a shear determination module 404, and a model determination module 405.

[0136] The data acquisition module 401 is used to scan the original rock joint surface to obtain the original rock joint point cloud data; the distribution description module 402 is used to obtain the description function and describe the distribution of micro-protrusions in the original rock joint surface by combining the description function with the joint point cloud data; the parameter determination module 403 is used to determine the distribution function density and any normal stress; the shear determination module 404 is used to determine the average dip angle of the actually contacting joint micro-protrusions based on the initial peak dilatation angle of the initial state when no cyclic shearing has occurred, and to determine the peak dilatation angle during several cyclic shearings; the model determination module 405 is used to determine the peak shear strength prediction model of the joint surface based on the number of cyclic shearings.

[0137] As described above, the embodiments of this application scan the original rock joint surface to obtain the original rock joint point cloud data; obtain the describing function, and describe the distribution of micro-protrusions in the original rock joint surface by combining the describing function with the joint point cloud data; determine the distribution function density and any normal stress; determine the average dip angle of the actually contacting joint micro-protrusions based on the initial peak dilatation angle of the initial state before cyclic shearing, and determine the peak dilatation angle during several cyclic shearings; determine the peak shear strength prediction model of the joint surface based on the number of cyclic shearings; solve the problem of neglecting the influence of non-contact micro-elements in traditional models by calculating the average dip angle of the actually contacting micro-protrusions; introduce the number of cyclic shearings to accurately reflect the dynamic characteristics of rapid wear in the early stage of cyclic shearing and gradual smoothing in the later stage; introduce normal stress to reflect the roughness degradation and dilatation angle reduction caused by the flattening of micro-protrusions under high normal stress; the model calculation is simple, can achieve high-precision prediction, and is suitable for engineering applications.

[0138] The beneficial effects of this application are:

[0139] 1. Accurately screen actual contact micro-protrusions to improve model prediction accuracy.

[0140] Technical means: High-precision point cloud data of joint surface (sampling interval 0.1mm) is obtained by 3D laser scanning technology. Triangular mesh division and geometric algorithm are used to dynamically screen actual contact micro-protrusions. Shear dilatation angle model is constructed based on the average tilt angle of joint micro-protrusions.

[0141] Technical Effects: Traditional models suffer from prediction bias because they fail to distinguish between actually contacting micro-protrusions and non-contact areas. This application's embodiment employs a geometric screening mechanism, incorporating only micro-protrusions that effectively participate in shearing, directly reflecting the true contact behavior. For example, during cyclic shearing, the contribution of non-contacting micro-protrusions to peak shear strength is negligible, while this model avoids the "global statistical error" of traditional models by accurately calculating the inclination angle of actually contacting micro-protrusions. Experimental verification shows that this improvement enhances prediction accuracy by 15%-20%, especially under complex shear directions.

[0142] 2. The nonlinear degradation factor accurately characterizes the dynamic properties of cyclic wear.

[0143] Technical approach: Introduce an exponential degradation factor, where λ is the cyclic degradation rate and n is the number of cycles.

[0144] Technical Effects: Traditional models, based on linear or single exponential forms, cannot capture the nonlinear characteristics of rapid initial wear followed by stabilization under cyclic shear. The exponential form in this application dynamically adjusts the degradation rate through the parameter λ, closely matching the experimentally observed pattern of a "sharp initial drop followed by a gradual decline." For example, when n=10, the wear of micro-protrusions accounts for approximately 60% of the total, while the traditional model only predicts 40%; when n≥50, the wear rate approaches zero, and the model prediction error decreases from 20% in the traditional model to less than 5%. This improvement significantly enhances the reliability of shear strength prediction under long-term cyclic loading.

[0145] 3. Linear quantification of normal stress compression effect enhances adaptability to high-stress conditions.

[0146] Technical means: Add a normalized linear stress term (σ) to the model n (where σc is the normal stress and σc is the uniaxial compressive strength of the rock).

[0147] Technical Effects: Traditional models simplify the compressive effect of normal stress on micro-convexities, leading to prediction errors under high-stress conditions. This model dynamically quantifies the stress influence through linear terms, for example, when σ... n When / σc increases from 0.2 to 0.8, the degradation of the dilatation angle increases from 5% to 30%, which is more consistent with experimental data compared to the traditional model (which only predicts 15% degradation). This improvement makes the model applicable to high-stress environments such as deep rock engineering, and reduces the prediction error to within 8%.

[0148] 4. Balancing model simplification with engineering practicality

[0149] Technical approach: Only the core parameters λ (cyclic degradation rate) and k (stress degradation coefficient) are retained, and the calculation is simplified by combining exponential and linear terms.

[0150] Technical Benefits: Traditional complex models (such as the Weibull function) require fitting multiple parameters, resulting in time-consuming calculations and reliance on specialized software. The embodiments in this application reduce computation time by 70% through parameter simplification while maintaining prediction accuracy (error ≤10%). For example, in engineering settings, shear strength can be directly and quickly assessed by inputting λ and k (calibrated through a small number of experiments), meeting real-time decision-making needs. This improvement significantly enhances the model's applicability in engineering settings.

[0151] The joint surface peak shear strength prediction device provided in this application embodiment can be used to execute the joint surface peak shear strength prediction method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0152] Thirdly, this application also provides an electronic device that can integrate the joint surface peak shear strength prediction device provided in this application. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. (Reference) Figure 5 The electronic device includes: an input device 53, an output device 54, a memory 52, and one or more processors 51; the memory 52 is used to store one or more programs; when the one or more programs are executed by the one or more processors 51, the one or more processors 51 implement the joint surface peak shear strength prediction method provided in the above embodiments. The input device 53, output device 54, memory 52, and processors 51 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0153] The processor 51 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 52, thereby realizing the above-mentioned method for predicting the peak shear strength of joint surfaces.

[0154] The electronic device provided above can be used to execute the joint surface peak shear strength prediction method provided in the above embodiments, and has corresponding functions and beneficial effects.

[0155] Fourthly, embodiments of this application also provide a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the joint surface peak shear strength prediction method as described above, and can achieve the same beneficial effects.

[0156] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the joint surface peak shear strength prediction method as described above, but can also perform related operations in the joint surface peak shear strength prediction method provided in any embodiment of this application.

[0157] Fifthly, embodiments of this application also provide a computer program product. The methods described in the various embodiments of this application can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the various embodiments of this application are executed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, network equipment, user equipment, core network equipment, OAM (Open Application Model), or other programmable devices.

[0158] The computer program or instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions may be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; or an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both volatile and non-volatile types of storage media.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0160] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0161] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0163] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0164] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method of predicting peak shear strength of a jointed rock mass, characterized by, The method comprises the following steps: Scanning the original rock joint surface to obtain original rock joint point cloud data; Obtaining a description function and describing the distribution of micro-asperities in the original rock joint surface by combining the description function with the joint point cloud data; Determining the distribution function density and any normal stress; Determining the average value of the actual contact angle of the joint micro-asperities according to the initial peak dilatancy angle of the initial state when no cyclic shear occurs, and determining the peak dilatancy angle when a plurality of cyclic shears occur; Determining a peak shear strength prediction model of the joint surface according to the number of cyclic shears; The method for obtaining the description function and describing the distribution of micro-asperities in the original rock joint surface by combining the description function with the joint point cloud data comprises the following steps: Determining the corresponding relationship between the effective shear angle and the joint shear strength; Determining the geometric relationship between the triangular mesh micro-element of the joint surface and the shear direction by representing the joint surface morphology by a triangular mesh micro-element, and calculating the effective shear angle: ； wherein ; ; is effective shear inclination angle; is the inclination angle of the joint plane triangular microelement, is the angle between the joint plane inclination and the shear direction, t is the shear direction vector, N is the unit outer normal vector, is the outer normal vector of the shear plane, is the projection vector of the shear direction in the shear plane; Determining the relationship between the ratio of the total area of the micro-elements with the effective inclination angle greater than the effective shear inclination angle and the total area of the joint surface and the effective shear inclination angle: ； wherein, is the ratio of the sum of the areas of the effective dip angles of the joint topography that are greater than 0 to the sum of the areas of the joint surface; is the joint roughness parameter, which controls the shape of the dip angle distribution curve; is the core part of the cumulative distribution function; The greater the joint roughness parameter is, the higher the roughness of the joint surface is, and the greater the length of the vertex of the micro-asperity from the shear surface is.

2. The method of predicting peak shear strength of a joint face according to claim 1, wherein, The method for scanning the original rock joint surface to obtain original rock joint point cloud data comprises the following steps: Setting a sampling interval, and scanning the original rock joint surface by using a three-dimensional laser scanner to obtain original rock joint point cloud data represented by axis coordinates.

3. The method of predicting peak shear strength of a joint face according to claim 1, wherein, The method for determining the distribution function density comprises the following steps: For Taking the derivative, we obtain the density of the distribution function: ； The method for determining any normal stress comprises the following steps: Assuming that the minimum inclination angle of all the contact micro-asperities is the inclination angle of the micro-asperity yielding under a set load; From Obtaining the minimum inclination angle in all the contacting asperities: ； wherein, is the normal stress, i.e. the vertical stress applied on the joint surface; is the uniaxial compressive strength of the rock, which is a reference stress value; is the normalized normal stress; is the minimum inclination angle of the asperity yielding.

4. The method of predicting peak joint plane shear strength according to claim 3, wherein, The method for determining the average value of the actual contact angle of the joint micro-asperities according to the initial peak dilatancy angle of the initial state when no cyclic shear occurs comprises the following steps: ； wherein the average of the inclination angles of the actually contacted joint asperities is the initial peak dilatancy angle from 0° to 90°; is the average of the inclination angles of the joint asperities. Determining the peak dilatancy angle when a plurality of cyclic shears occur: ； wherein, is the nonlinear degradation factor due to cyclic shear; is the cyclic degradation rate; n is the number of cyclic shear; is the compression degradation factor due to normal stress; k is the stress degradation coefficient; is the normal stress; is the uniaxial compressive strength of rock; is the normalized normal stress.

5. The method of predicting peak joint plane shear strength according to claim 4, wherein, The method for determining a peak shear strength prediction model of the joint surface according to the number of cyclic shears comprises the following steps: ； wherein, is the shear strength of the joint surface after n shearings; is the basic friction angle.

6. A joint plane peak shear strength prediction device, characterized by, The method comprises the following steps: A data acquisition module is configured to scan the original rock joint surface to obtain original rock joint point cloud data; A distribution description module is configured to obtain a description function and describe the distribution of micro-asperities in the original rock joint surface by combining the description function with the joint point cloud data; A parameter determination module is configured to determine the distribution function density and any normal stress; A shear determination module is configured to determine the average value of the actual contact angle of the joint micro-asperities according to the initial peak dilatancy angle of the initial state when no cyclic shear occurs, and determine the peak dilatancy angle when a plurality of cyclic shears occur; A model determination module is configured to determine a peak shear strength prediction model of the joint surface according to the number of cyclic shears; The distribution description module is specifically configured to: Determine the corresponding relationship between the effective shear angle and the joint shear strength; Determine the geometric relationship between the triangular mesh micro-element of the joint surface and the shear direction by representing the joint surface morphology by a triangular mesh micro-element, and calculate the effective shear angle: ； wherein ; ; is effective shear inclination angle; is the inclination angle of the joint plane triangular microelement, is the angle between the joint plane inclination and the shear direction, t is the shear direction vector, N is the unit outer normal vector, is the outer normal vector of the shear plane, is the projection vector of the shear direction in the shear plane; Determine the relationship between the ratio of the total area of the micro-elements with the effective inclination angle greater than the effective shear inclination angle and the total area of the joint surface and the effective shear inclination angle: ； wherein, is the ratio of the sum of the areas of the effective dip angles of the joint topography that are greater than 0 to the sum of the areas of the joint surface; is the joint roughness parameter, which controls the shape of the dip angle distribution curve; is the core part of the cumulative distribution function; The greater the joint roughness parameter is, the higher the roughness of the joint surface is, and the greater the length of the vertex of the micro-asperity from the shear surface is.

7. An electronic device, comprising: The method comprises the following steps: A processor connected with a memory through a bus, the memory storing computer readable instructions for implementing the joint surface peak shear strength prediction method according to any one of claims 1-5 when the computer readable instructions are executed by the processor.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by a server to implement the joint surface peak shear strength prediction method according to any one of claims 1-5.

9. A computer program product, characterised in that, The computer program product comprises instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1-5.

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