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

Abstract

The embodiment of the invention provides a joint surface peak shear strength prediction method, device and equipment, a medium and a program product, and the method comprises the steps: scanning a protolith joint surface, and obtaining protolith joint point cloud data; acquiring a description function, and describing the distribution of the micro-convex bodies in the primary rock joint surface by combining the description function with the joint point cloud data; determining distribution function density and any normal stress; according to the initial peak shear and dilatancy angle in the initial state when cyclic shearing does not occur, the average value of the dip angles of the joint micro-convex bodies in actual contact is determined, and the peak shear and dilatancy angle during multiple times of cyclic shearing is determined; determining a peak shear strength prediction model of the joint surface according to the number of cyclic shear times; model calculation is simple, high-precision prediction can be achieved only through a small number of parameters, and the method is suitable for engineering application.

Description

Technical Field

[0001] The present application relates to the field of joint surface quantification technology, and specifically to a method, device, equipment, medium and program product for predicting the peak shear strength of joint surfaces. Background Art

[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 dilatancy during shearing, thereby 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 loads in that they cause the joint surfaces to undergo multiple repeated shear events, not just a single shear event. This multiple cyclic shearing can significantly alter the mechanical behavior of the joint surface, leading to continuous wear of the asperities and roughness degradation, thereby reducing the peak shear strength. Therefore, studying the shear strength of structural surfaces under cyclic loading is of great significance to accurately predict their performance under complex working conditions and ensure engineering safety. However, traditional prediction models are insufficiently applicable to cyclic shear loads, failing to comprehensively consider the nonlinear degradation with the number of cycles, the effects of actual contact asperities, nonlinear wear characteristics, and the compressive effects of normal stress. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, device, equipment, medium and program product for predicting the peak shear strength of joint surfaces, so as to solve the problem that the existing traditional prediction model is insufficiently applicable under cyclic shear loads and fails to comprehensively consider the nonlinear degradation of the number of cycles, the effect of actual contact micro-asperities, nonlinear wear characteristics and the compressive effect of normal stress.

[0004] In a first aspect, an embodiment of the present application provides a method for predicting peak shear strength of a joint, comprising: Scan the original rock joint surface to obtain the original rock joint point cloud data; Obtaining a description function, and using the description function in combination with joint point cloud data to describe the distribution of asperities in the original rock joint surface; Determine the distribution function density and any normal stress; According to the initial peak dilatancy angle of the initial state when no cyclic shear occurs, the average value of the inclination angle of the actual contact joint micro-asperities is determined, and the peak dilatancy angle after several cyclic shears is determined; According to the number of cyclic shearing cycles, the peak shear strength prediction model of the joint surface is determined.

[0005] In the above implementation process, the embodiment of the present application scans the original rock joint surface to obtain the original rock joint point cloud data; obtains the description function, and describes the distribution of micro-asperities in the original rock joint surface by combining the description function with the joint point cloud data; determines the distribution function density and any normal stress; determines the average value of the inclination angle of the actual contact joint micro-asperities based on the initial peak shear angle of the initial state when no cyclic shear occurs, and determines the peak shear angle during several cyclic shears; determines the peak shear strength prediction model of the joint surface based on the number of cyclic shears; solves the problem that the traditional model ignores the influence of non-contact elements by calculating the average value of the inclination angle of the joint micro-asperities of the actual contact micro-asperities; introduces the number of cyclic shears, which can accurately reflect the dynamic characteristics of cyclic shear that the wear is faster in the early stage and tends to be flat in the later stage; introduces the normal stress, which can reflect the roughness degradation and shear dilation angle reduction caused by the flattening of the micro-asperities under high normal stress; the model calculation is simple, high-precision prediction can be achieved, and it is suitable for engineering applications.

[0006] Furthermore, the scanning of the original rock joint surface to obtain the original rock joint point cloud data includes: The sampling interval is set, and the original rock joint surface is scanned using a three-dimensional laser scanner to obtain the original rock joint point cloud data represented by axis coordinates.

[0007] In the above implementation process, the point cloud data of the joint surface of the original rock is obtained so as to subsequently determine various parameters and models of the joint surface.

[0008] Furthermore, the obtaining of the description function and describing the distribution of asperities in the original rock joint surface by combining the description function with the joint point cloud data include: Determine the corresponding relationship between effective shear angle and joint shear strength; The 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 inclination is calculated: ; in, ; ;for effective shear inclination; is the inclination angle of the triangular element of the joint surface, is the angle between the joint surface dip and the shear direction, t is the shear direction vector, N is the element external normal vector, is the normal vector outside the clipping plane, is the projection vector of the shear direction on the shear plane; Determine the relationship between the ratio of the sum of the area of ​​the micro-elements whose effective inclination angle of the joint surface is greater than the effective shear inclination angle to the sum of the joint surface area and the effective shear inclination angle: ; in, It is the ratio of the total area of ​​the joint topography surface with an effective inclination angle greater than 0 to the total area of ​​the joint surface; is the joint roughness parameter, which controls the shape of the inclination distribution curve; It is the core part of the cumulative distribution function; The larger the joint roughness parameter, the higher the joint surface roughness, and the longer the distance between the vertex of the micro-asperity and the shear surface.

[0009] In the above implementation process, triangular meshing and geometric algorithms are combined to screen the actual contact asperities to ensure that only the effective contact areas are involved in the strength calculation, thereby improving the subsequent model prediction accuracy.

[0010] Furthermore, determining the distribution function density includes: right Taking the derivative, we get the distribution function density: ; The determining of any normal stress comprises: Assume that the minimum inclination angle among all contact asperities is the inclination angle of the asperity that yields under the set load; Depend on Get the minimum inclination angle among all contact asperities: ; in, is the normal stress, i.e. the vertical stress applied to the joint surface; is the uniaxial compressive strength of rock, which is a reference stress value; is the normalized normal stress.

[0011] In the above implementation process, the compressive effect of the normal stress is accurately described by the linear stress term, reflecting the roughness degradation and the reduction of the shear dilatancy angle caused by the flattening of the micro-asperities under high normal stress.

[0012] Furthermore, the method of determining the average value of the inclination angle of the actual contact joint asperities according to the initial peak dilatancy angle in the initial state when no cyclic shear occurs includes: ; The average inclination angle of the actual contact joint micro-asperities is the initial peak dilatancy angle from Integral to 90°; Determine the peak dilatancy angle for several shear cycles: ; in, is the nonlinear degradation factor caused by cyclic shear; is the cyclic degradation rate; m is the number of cyclic shearing; is the compression degradation factor caused by normal stress; k is the stress degradation coefficient; is the normal stress; is the uniaxial compressive strength of rock; is the normalized normal stress.

[0013] In the above implementation process, the average inclination angle of the actual contact asperities is taken into account, which makes up for the deficiency of previous models that do not fully reflect the real contact behavior.

[0014] Furthermore, the peak shear strength prediction model of the joint surface is determined according to the number of cyclic shearing cycles, including: ; in, is the shear strength of the joint surface after n shearing times; is the basic friction angle.

[0015] In the above implementation process, the model calculation is simple and only a small number of parameters are required to achieve high-precision prediction, which is suitable for engineering applications.

[0016] In a second aspect, an embodiment of the present application provides a device for predicting peak shear strength of a joint, comprising: The data acquisition module is used to scan the original rock joint surface and obtain the original rock joint point cloud data; The distribution description module is used to obtain the 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 for determining the distribution function density and any normal stress; a shear determination module, for determining an average value of the inclination angles of the actual contact joint asperities based on an initial peak shear dilatancy angle in an initial state when no cyclic shear occurs, and determining a peak shear dilatancy angle after several cyclic shears; The model determination module is used to determine the peak shear strength prediction model of the joint surface according to the number of cyclic shearing cycles.

[0017] In a third aspect, an embodiment of the present application provides an electronic device, including: A processor, a memory and a bus, wherein the processor is connected to the memory via the bus, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, they are used to implement the method for predicting the peak shear strength of a joint surface as described above.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a server, the method for predicting the peak shear strength of a joint surface as described above is implemented.

[0019] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed by a computer, the computer implements the method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A schematic flow chart of a method for predicting peak shear strength of a joint provided in an embodiment of the present application; Figure 2 A digital model diagram of a joint surface for predicting peak shear strength of a joint surface provided in an embodiment of the present application; Figure 3 A schematic diagram of a triangular element of a joint surface for predicting the peak shear strength of a joint surface provided in an embodiment of the present application; Figure 4 Schematic diagram of the structure of a device for predicting peak shear strength of a joint provided in an embodiment of the present application; Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0023] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0024] In the field of geotechnical engineering, the shear strength of structural surfaces is a key factor affecting engineering stability. The roughness of the joint surface directly determines the shear dilatancy effect during its shear process, thereby affecting the peak shear strength. However, in actual engineering, structural surfaces are often subjected to cyclic shear loads, such as earthquakes, blasting vibrations, or mechanical loading. These cyclic loads are different from single static shears. They cause the joint surface to undergo multiple repeated shears, not just one-time shears. This multiple cyclic shear will significantly change the mechanical behavior of the joint surface, leading to continuous wear and roughness degradation of the micro-asperities, thereby reducing the peak shear strength. Therefore, it is of great significance to study the shear strength of structural surfaces under cyclic loads in order to accurately predict their performance under complex working conditions and ensure engineering safety.

[0025] Furthermore, in actual engineering, not all micro-elements on a joint surface participate in contact and shear. During shear, only some micro-asperities (i.e., those in actual contact) directly bear the shear force, while non-contact micro-elements contribute only a limited amount to the shear strength. Therefore, it is crucial to consider the distribution and behavior of actual contact micro-asperities to more realistically reflect the shear properties of the joint surface. Ignoring the role of actual contact micro-asperities can cause the prediction model to deviate from reality, reducing prediction accuracy.

[0026] Furthermore, asperity wear during cyclic shear exhibits nonlinear characteristics. Initially, asperity wear occurs rapidly, resulting in a significant decrease in roughness. As the number of cycles increases, the wear rate gradually slows, and the roughness stabilizes. This nonlinear wear directly affects the dilatancy angle and shear strength. Traditional models (such as the Barton model or the Grasselli model), which are mostly based on static conditions, fail to fully describe this dynamic degradation process, resulting in inaccurate predictions. Therefore, it is necessary to introduce a nonlinear wear mechanism to accurately capture the impact of cyclic shear on joint roughness.

[0027] At the same time, the compressive effect of normal stress is also an important factor that cannot be ignored. During cyclic shear, normal stress flattens asperities on the joint surface, reducing their height and roughness, further weakening the dilatancy effect and shear strength. Especially under high normal stress, the compressive deformation of asperities is more pronounced, and the degradation of roughness is more pronounced. Traditional models often simplify the compressive effect of normal stress and fail to accurately reflect its impact on asperity wear and shear strength, leading to prediction errors.

[0028] In summary, conventional prediction models are insufficiently applicable under cyclic shear loading, failing to comprehensively account for nonlinear degradation with the number of cycles, the effects of actual contact asperities, nonlinear wear characteristics, and the compressive effects of normal stress. Therefore, a peak shear strength prediction method that comprehensively considers these factors is urgently needed to improve the reliability and safety of engineering designs under complex working conditions.

[0029] The embodiment of the present application proposes a method for predicting the peak shear strength of joint surfaces to solve the above problems.

[0030] Please see Figure 1 , Figure 1 A schematic flow chart of a method for predicting peak shear strength of a joint provided in an embodiment of the present application. The method for predicting peak shear strength of a joint includes: 100. Scan the original rock joint surface to obtain the original rock joint point cloud data.

[0031] Optionally, set the sampling interval and use a 3D laser scanner 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 Figure 2 ; Thus, it is possible to obtain the point cloud data of the joint surface of the original rock, so as to subsequently determine the various parameters and models of the joint surface.

[0032] It can be understood that the sampling interval refers to the horizontal or vertical distance between two adjacent scanning points during the scanning process of the scanner, which directly affects the density and accuracy of the point cloud data. The sampling interval can be set according to needs.

[0033] 200. Obtain a description function, and describe the distribution of asperities in the original rock joint surface by combining the description function with the joint point cloud data.

[0034] Specifically, determine the corresponding relationship 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 elements of the joint surface and the shear direction is determined, and the effective shear inclination is calculated: ; in, ; ;for effective shear inclination; is the inclination angle of the triangular element of the joint surface, is the angle between the joint surface dip and the shear direction, t is the shear direction vector, N is the element external normal vector, is the normal vector outside the clipping plane, is the projection vector of the shear direction on the shear plane.

[0035] Determine the relationship between the ratio of the sum of the area of ​​the micro-elements whose effective inclination angle of the joint surface is greater than the effective shear inclination angle to the sum of the joint surface area and the effective shear inclination angle: ; in, It is the ratio of the total area of ​​the joint topography surface with an effective inclination angle greater than 0 to the total area of ​​the joint surface; is the joint roughness parameter, which controls the shape of the inclination distribution curve; It is the core part of the cumulative distribution function; The larger the joint roughness parameter, the higher the joint surface roughness, the longer the distance between the vertex of the micro-asperity and the shear surface, that is, the steeper the inclination distribution of the micro-asperity. The joint roughness parameter is usually determined by fitting experimental data.

[0036] The relationship between the effective shear dip and the ratio of the sum of the asperity areas whose effective dip angle is greater than the effective shear dip to the sum of the joint surface areas, using a double exponential form, effectively captures the nonlinear distribution of the joint surface's apparent dip angle. Compared with traditional linear or single exponential models, this function more accurately describes the gradual change in asperity dip angle from low to high, consistent with the geometric characteristics of the actual joint surface. By introducing a joint roughness parameter, the distribution function can reflect the differences in joint surface roughness. The magnitude of the joint roughness parameter directly affects the steepness of the distribution curve, making the model applicable to a wide range of joint surfaces, from smooth to highly rough. This distribution function can adapt to joint surface characteristics under complex working conditions, such as varying shear directions and roughness levels. Compared with traditional models (such as the Grasselli model), this distribution function provides greater flexibility in describing the distribution of asperity elements in the shear direction, making it suitable for dynamic loading scenarios such as cyclic shear.

[0037] The obtained point cloud data is used in combination with step 200 to obtain the three-dimensional morphological parameters of a specific shear direction under different sampling intervals: the joint roughness parameter.

[0038] Therefore, the actual contact asperities are screened by combining triangular meshing with geometric algorithms to ensure that only effective contact areas participate in the strength calculation, thereby improving the accuracy of subsequent model predictions.

[0039] 300. Determine the distribution function density and any normal stress.

[0040] As you can understand, the above formula is differentiated to further determine the distribution function density. The distribution function density more precisely reflects the density of joint asperities at different apparent inclination angles, which is crucial for accurately characterizing the microscopic characteristics of joint surfaces. Subsequent analysis will help to more precisely understand the distribution of actual contact asperities, thereby improving the accuracy of predictions of peak shear strength of joint surfaces.

[0041] Specifically, determining the distribution function density includes: right Taking the derivative, we get the distribution function density: .

[0042] It should be noted that the physical significance of the distribution function density lies in its precise display of the rate of change of the apparent inclination angle distribution of joint asperities. In practical rock engineering applications, by analyzing this distribution function density, we can clearly understand the density of asperity distribution at different apparent inclination angles. For example, in certain areas, the distribution function density is relatively high, indicating that the asperity distribution near this apparent inclination angle is relatively dense; while in areas with a lower distribution function density, the asperity distribution is relatively sparse. This refined analysis of asperity distribution provides a key theoretical basis for the subsequent accurate determination of the average inclination angle of actual contact joint asperities and the construction of a more accurate prediction model for the peak shear strength of joint surfaces.

[0043] Specifically, determining any normal stress includes: Understandably, for a fully coupled joint with no filler, when subjected to a normal load, a small number of contacting asperities within the joint will undergo elastic deformation, with yielding occurring in the initial contacting asperities. As the normal stress continues to increase, more asperities come into contact and yield, and this process continues until the area of ​​contacting asperities is large enough to withstand the given normal load. Therefore, it can be assumed that the minimum inclination angle among all contacting asperities is the inclination of the asperity that just yields under a given load.

[0044] Depend on Get the minimum inclination angle among all contact asperities: ; in, is the normal stress, i.e. the vertical stress applied to the joint surface; is the uniaxial compressive strength of rock, which is a reference stress value; is the normalized normal stress.

[0045] More specifically, the normal stress is the vertical stress applied to the joint surface, which affects the compression and shear behavior of the micro-asperities; the higher the normal stress, the easier it is for the micro-asperities to be flattened, resulting in a decrease in roughness and shear angle.

[0046] Therefore, the compressive effect of the normal stress is accurately described by the linear stress term, reflecting the roughness degradation and the reduction of the dilatancy angle caused by the flattening of the micro-asperities under high normal stress.

[0047] The average value of the inclination angle of the actual contact joint micro-asperities is determined based on the initial peak shear dilatancy angle of the initial state when no cyclic shear occurs, and the peak shear dilatancy angle during several cyclic shears is determined.

[0048] Specifically, determining the average value of the inclination angle of the actual contact joint asperities according to the initial peak dilatancy angle in the initial state when no cyclic shear occurs includes: ; The average inclination angle of the actual contact joint micro-asperities is the initial peak dilatancy angle from Integral to 90°; Determine the peak dilatancy angle for several shear cycles: ; in, is the nonlinear degradation factor caused by cyclic shear; is the cyclic degradation rate; m is the number of cyclic shearing; is the compression degradation factor caused by normal stress; k is the stress degradation coefficient; is the normal stress; is the uniaxial compressive strength of rock; is the normalized normal stress.

[0049] More specifically, the peak dilatancy angle is the equivalent inclination angle contributed by the dilatancy effect caused by roughness during the shear process of the joint surface, which directly affects the peak shear strength. increase and degenerate.

[0050] Initial peak dilatancy angle (unit: degrees): This represents the initial state when no cyclic shear occurs (i.e., the dilatancy angle when n = 0 and normal stress degradation is not considered). It is usually determined by the average initial actual contact asperity inclination angle and reflects the original roughness characteristics of the joint surface.

[0051] Nonlinear degradation factor due to cyclic shear (unitless): exponential form The wear and roughness degradation of joint asperities during cyclic shearing are described. The exponential form reflects the nonlinear characteristics of wear: the degradation is faster in the early stage and tends to be gentle in the later stage.

[0052] Cyclic degradation rate (unit: dimensionless, depends on the definition of n): controls the speed at which the asperity roughness degrades with the number of cycles; the larger the cyclic degradation rate, the faster the degradation. It is usually determined by fitting a cyclic shear test and reflects the wear resistance and shear conditions of the joint material.

[0053] Cyclic shear number (unit: dimensionless, integer): represents the number of cyclic shear cycles experienced by the joint surface, such as repeated shear caused by earthquakes, blasting vibrations, or mechanical loading; an increase in the number of cyclic shear cycles will lead to asperity wear and reduce the roughness and shear dilatancy angle.

[0054] Normal stress-induced compression degradation factor (unitless): This term describes the compressive and shearing effects of normal stress on the asperity. As the normal stress increases, the asperity is flattened and the dilatancy angle decreases. A linear form approximates this physical process.

[0055] Stress degradation coefficient (unit: dimensionless): reflects the degree of influence of normal stress on the degradation of asperity roughness; the larger the stress degradation coefficient, the stronger the inhibitory effect of normal stress on the shear dilatancy angle. It is determined through experimental fitting and is usually related to the rock material and joint properties.

[0056] Normal stress (unit: MPa or Pa): The vertical stress applied to the joint surface affects the compression and shear behavior of the micro-asperities. The higher the normal stress, the easier it is for the micro-asperities to be flattened, resulting in a decrease in roughness and shear angle.

[0057] Uniaxial compressive strength of rock (unit: MPa or Pa): It is a reference stress value used to normalize normal stress. .

[0058] Normalized normal stress (unitless): by Normalizing the normal stress facilitates comparison of the effects of different stress levels on asperity degradation.

[0059] Therefore, the embodiment of the present application is simple to calculate and easy to apply in engineering: the formula only contains two main parameters (λ and k), which greatly reduces the difficulty of fitting and calculation compared to the original complex form (involving Weibull function and multiple parameters). and the linear stress term It is convenient for quick calculation and suitable for rapid evaluation at engineering sites.

[0060] The embodiment of the present application retains the core physical mechanism: fully considers the nonlinear degradation of the number of cycles: through the exponential term , describes the nonlinear wear effect of micro-asperities in cyclic shear; the exponential form can well capture the trend of wear rate gradually slowing down with the increase of cycle number, which is consistent with the physical law of actual joint roughness degradation. The compressive effect of normal stress is fully considered: through the linear term , reflecting the compression and shearing effect of normal stress on the micro-asperity; as the normal stress increases, the micro-asperity is flattened and the peak shear angle decreases. The linear form can effectively approximate this effect within a reasonable range.

[0061] The embodiments of this application are highly applicable: The simplified formula is applicable to most cyclic shear scenarios, and the parameters λ and k can be fitted using a small amount of experimental data, reducing experimental costs. The model has a small prediction error (typically within 5%-10%) for moderate cycles and normal stress ranges, making it suitable for preliminary engineering design.

[0062] Two factors are fully considered in the embodiments of this application: 1. Nonlinear degradation of the number of cycles: Exponential form The nonlinear process of asperity wear is described. In the early stages of the cycle, wear is rapid (decreasing significantly); as the number of cycles increases, the wear rate slows down (in line with the physical law that asperities gradually smooth out). This is consistent with experimental observations: the surface roughness of the joint decreases rapidly in the early cycles and stabilizes in the later stages. 2. Compressive effect of normal stress: linear term It reflects the compressive effect of normal stress on the micro-asperities. Higher normal stress will flatten the micro-asperities, reducing the equivalent inclination angle and peak dilatancy angle. This simple but effective term captures the direct effect of stress on roughness degradation and is applicable to most engineering stress ranges.

[0063] 500. Determine the peak shear strength prediction model of the joint surface based on the number of cyclic shearing cycles.

[0064] Specifically, the peak shear strength prediction model of the joint surface is determined according to the number of cyclic shearing cycles, including: ; in, is the shear strength of the joint surface after n shearing times; is the basic friction angle, obtained from the tilt test, in degrees.

[0065] Therefore, the model is simple to calculate and only requires a small number of parameters to achieve high-precision predictions, making it suitable for engineering applications.

[0066] As described above, the embodiment of the present application scans the original rock joint surface to obtain the original rock joint point cloud data; obtains the description function, and describes the distribution of micro-asperities in the original rock joint surface through the description function combined with the joint point cloud data; determines the distribution function density and any normal stress; determines the average value of the inclination angle of the actual contact joint micro-asperities based on the initial peak shear angle of the initial state when no cyclic shear occurs, and determines the peak shear angle during several cyclic shears; determines the peak shear strength prediction model of the joint surface based on the number of cyclic shears; solves the problem that the traditional model ignores the influence of non-contact elements by calculating the average value of the inclination angle of the joint micro-asperities of the actual contact micro-asperities; introduces the number of cyclic shears, which can accurately reflect the dynamic characteristics of cyclic shear that the wear is faster in the early stage and tends to be flat in the later stage; introduces the normal stress, which can reflect the degradation of roughness and the reduction of shear angle caused by the flattening of micro-asperities under high normal stress; the model calculation is simple, high-precision prediction can be achieved, and it is suitable for engineering applications.

[0067] Existing technologies ignore the dynamic screening mechanism of actual contact asperities: Traditional models (such as the Barton model and the Grasselli model) typically evaluate the shear strength of joint surfaces based on the statistical characteristics of the overall joint morphology, without distinguishing between asperities actually participating in shear and non-contact areas. However, during cyclic shear, only some asperities directly contribute to shear strength due to mechanical contact, and the contribution of non-contact areas to peak shear strength is negligible. Existing models fail to focus on the dynamic distribution of actual contact asperities, leading to significant deviations between predicted results and actual shear behavior.

[0068] The embodiment of the present application uses three-dimensional laser scanning technology to accurately obtain joint surface point cloud data (sampling spacing 0.1mm), combines triangular mesh division and geometric algorithm to screen actual contact micro-asperities, and constructs a dynamic shear dilation angle model based on the average value of the inclination angle of its joint micro-asperities, ensuring that only the effective contact area participates in the strength calculation, thereby improving the model prediction accuracy.

[0069] Existing technologies fail to account for the nonlinear wear effects of the number of shear cycles: Traditional models (such as the Grasselli parameterization method) are based on single or static shear conditions and use linear or single exponential forms to describe asperity wear. However, experiments have shown that the asperity wear rate exhibits distinct nonlinear characteristics during cyclic shear: rapid degradation initially and gradual stabilization later. Existing models, lacking a dynamic degradation mechanism, are unable to 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 "steep initial decline and gradual stabilization" characteristic.

[0070] The embodiment of this application introduces a nonlinear degradation factor ,in is the cyclic degradation rate, n is the number of cycles, which accurately describes the nonlinear attenuation characteristics of the wear rate, which is consistent with the law of rapid degradation in the early stage and stabilization in the later stage.

[0071] Existing technologies simplify the compressive effect of normal stress: Traditional models often assume that the effect of normal stress on asperity compression is linear or ignore its dynamic effects. In reality, high normal stress significantly flattens asperities, reducing roughness and dilatancy, and the compressive effect increases nonlinearly with stress level. Existing models, due to this simplification, cannot accurately predict shear strength under high-stress conditions.

[0072] In the embodiment of the present application, a linear stress term is added to the model ( is the normal stress, is the uniaxial compressive strength of rock), the contribution of normal stress to asperity compression is quantified through normalization, dynamically reflecting the effect of stress level on roughness degradation.

[0073] The complexity of existing technical models is in conflict with engineering practicality: traditional models (such as the Weibull function model) require fitting multiple parameters, have high computational complexity, and are difficult to meet the needs of rapid on-site assessment in engineering projects.

[0074] The embodiment of the present application simplifies the model structure, retaining only the core parameters λ (cyclic degradation rate) and k (stress degradation coefficient); through the combination of exponential terms and linear terms, the amount of calculation is greatly reduced while retaining the physical mechanism, achieving a balance between high precision and ease of use.

[0075] The above steps are not to be performed in a strict order as described in the numbers, but should be understood as an overall solution.

[0076] In the second aspect, based on the above embodiments, Figure 4 This is a schematic diagram of the structure of a device for predicting peak shear strength of a joint provided in an embodiment of the present application. Figure 4 The device for predicting the peak shear strength of a joint 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 .

[0077] Among them, the data acquisition module 401 is used to scan the original rock joint surface and 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-asperities 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 inclination angle of the actual contact joint micro-asperities based on the initial peak shear angle of the initial state when no cyclic shear occurs, and determine the peak shear angle during several cyclic shears; the model determination module 405 is used to determine the peak shear strength prediction model of the joint surface according to the number of cyclic shears.

[0078] As described above, the embodiment of the present application scans the original rock joint surface to obtain the original rock joint point cloud data; obtains the description function, and describes the distribution of micro-asperities in the original rock joint surface through the description function combined with the joint point cloud data; determines the distribution function density and any normal stress; determines the average value of the inclination angle of the actual contact joint micro-asperities based on the initial peak shear angle of the initial state when no cyclic shear occurs, and determines the peak shear angle during several cyclic shears; determines the peak shear strength prediction model of the joint surface based on the number of cyclic shears; solves the problem that the traditional model ignores the influence of non-contact elements by calculating the average value of the inclination angle of the joint micro-asperities of the actual contact micro-asperities; introduces the number of cyclic shears, which can accurately reflect the dynamic characteristics of cyclic shear that the wear is faster in the early stage and tends to be flat in the later stage; introduces the normal stress, which can reflect the degradation of roughness and the reduction of shear angle caused by the flattening of micro-asperities under high normal stress; the model calculation is simple, high-precision prediction can be achieved, and it is suitable for engineering applications.

[0079] Beneficial effects of this application: 1. Accurately screen actual contact asperities to improve model prediction accuracy.

[0080] Technical means: High-precision point cloud data of the joint surface is obtained through three-dimensional laser scanning technology (sampling interval 0.1mm), and the actual contact micro-asperities are dynamically screened by combining triangular mesh division and geometric algorithms. The shear dilation angle model is constructed based on the average inclination angle of the joint micro-asperities.

[0081] Technical effect: The traditional model does not distinguish between actual contact micro-asperities and non-contact areas, resulting in prediction deviations. The embodiment of the present application uses a geometric screening mechanism to only include micro-asperities that effectively participate in shear, directly reflecting the actual contact behavior. For example, in the cyclic shear process, the contribution of non-contact micro-asperities to the peak shear strength is negligible, and this model avoids the "global statistical error" of the traditional model by accurately calculating the inclination angle of the actual contact micro-asperities. Experimental verification shows that this improvement improves the prediction accuracy by 15%-20%, especially under complex shear directions.

[0082] 2. The nonlinear degradation factor accurately characterizes the dynamic characteristics of cyclic wear.

[0083] Technical means: Introduce an exponential degradation factor, where λ is the cyclic degradation rate and n is the number of cycles.

[0084] Technical effect: The traditional model is based on a linear or single exponential form, which cannot capture the nonlinear characteristics of cyclic shear, with rapid wear in the early stage and stabilization in the later stage. The exponential form of the embodiment of the present application dynamically adjusts the degradation rate through the parameter λ, which is highly consistent with the "steep drop in the early stage and gentle in the later stage" law observed in the experiment. For example, when n=10, the wear amount of micro-asperities accounts for about 60% of the total, while the traditional model only predicts 40%; when n≥50, the wear rate approaches zero, and the model prediction error is reduced from 20% of the traditional model to less than 5%. This improvement significantly improves the reliability of shear strength prediction under long-term cyclic loading.

[0085] 3. Linear quantification of normal stress compression effect to enhance adaptability to high stress conditions Technical means: Add normalized linear stress term (σ n is the normal stress, σc is the uniaxial compressive strength of rock).

[0086] Technical effect: The traditional model simplifies the compression effect of normal stress on the micro-asperity, which leads to prediction deviation under high stress conditions. This model dynamically quantifies the stress effect through linear terms, such as when σ nWhen / σc increases from 0.2 to 0.8, the degradation of the dilatancy angle increases from 5% to 30%, which is more consistent with the experimental data than the traditional model (which only predicts 15% degradation). This improvement makes the model suitable for high-stress environments such as deep rock mass engineering, reducing the prediction error to within 8%.

[0087] 4. Balance between model simplification and engineering practicality Technical means: Only the core parameters λ (cyclic degradation rate) and k (stress degradation coefficient) are retained, and the calculation is simplified by combining exponential terms with linear terms.

[0088] Technical Effect: Traditional complex models (such as the Weibull function) require fitting multiple parameters, are time-consuming, and rely on specialized software. This embodiment of the present application reduces calculation time by 70% through parameter simplification while maintaining prediction accuracy (error ≤ 10%). For example, at the construction site, by inputting λ and k (calibrated through a small number of experiments), shear strength can be directly and quickly assessed, meeting the needs of real-time decision-making. This improvement significantly enhances the model's applicability in construction sites.

[0089] The device for predicting the peak shear strength of joints provided in the embodiments of the present application can be used to execute the method for predicting the peak shear strength of joints provided in the above embodiments, and has corresponding functions and beneficial effects.

[0090] In a third aspect, an embodiment of the present application further provides an electronic device that can integrate the joint peak shear strength prediction device provided in an embodiment of the present application. Figure 5 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. 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 41, the one or more processors 51 implement the method for predicting the peak shear strength of a joint surface as provided in the above embodiment. The input device 53, the output device 54, the memory 52, and the processor 51 can be connected by a bus or other means. Figure 5 The bus connection is taken as an example.

[0091] The processor 51 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 52, that is, realizes the above-mentioned joint surface peak shear strength prediction method.

[0092] The electronic device provided above can be used to execute the method for predicting the peak shear strength of joints provided in the above embodiment, and has corresponding functions and beneficial effects.

[0093] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the joint surface peak shear strength prediction method as described above, and can achieve the same beneficial effects.

[0094] Of course, the storage medium containing computer-executable instructions provided in an embodiment of the present application is not limited to the method for predicting the peak shear strength of joints as described above, and can also execute related operations in the method for predicting the peak shear strength of joints provided in any embodiment of the present application.

[0095] In a fifth aspect, the embodiments of the present application further provide a computer program product. The methods described in the various embodiments of the present application can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part 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 the present application are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, a core network device, an OAM (Open Application Model) or other programmable device.

[0096] The computer program or instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless method. The computer-readable storage medium may be any available medium that can be accessed by a computer 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, a hard disk, or a magnetic tape; an optical medium, such as a digital video disk; 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.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0098] In addition, the functional modules in each embodiment of the present 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.

[0099] If the functions are implemented in the form of software function 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 the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0100] The foregoing is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures.

[0101] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0102] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

Claims

1. A method for predicting peak shear strength of joints, characterized in that: include: Scan the original rock joint surface to obtain the original rock joint point cloud data; Obtaining a description function, and using the description function in combination with joint point cloud data to describe the distribution of asperities in the original rock joint surface; Determine the distribution function density and any normal stress; According to the initial peak dilatancy angle of the initial state when no cyclic shear occurs, the average value of the inclination angle of the actual contact joint micro-asperities is determined, and the peak dilatancy angle after several cyclic shears is determined; According to the number of cyclic shearing cycles, the peak shear strength prediction model of the joint surface is determined.

2. The method for predicting peak shear strength of joints according to claim 1, wherein: Scanning the original rock joint surface to obtain original rock joint point cloud data includes: The sampling interval is set, and the original rock joint surface is scanned using a three-dimensional laser scanner to obtain the original rock joint point cloud data represented by axis coordinates.

3. The method for predicting peak shear strength of joints according to claim 1, wherein: The obtaining of the description function and describing the distribution of asperities in the original rock joint surface by combining the description function with the joint point cloud data includes: Determine the corresponding relationship between effective shear angle and joint shear strength; The 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 inclination is calculated: ； in, ; ;for effective shear inclination; is the inclination angle of the triangular element of the joint surface, is the angle between the joint surface dip and the shear direction, t is the shear direction vector, N is the element external normal vector, is the normal vector outside the clipping plane, is the projection vector of the shear direction on the shear plane; Determine the relationship between the ratio of the sum of the area of ​​the micro-elements whose effective inclination angle of the joint surface is greater than the effective shear inclination angle to the sum of the joint surface area and the effective shear inclination angle: ； in, It is the ratio of the total area of ​​the joint topography surface with an effective inclination angle greater than 0 to the total area of ​​the joint surface; is the joint roughness parameter, which controls the shape of the inclination distribution curve; It is the core part of the cumulative distribution function; The larger the joint roughness parameter, the higher the joint surface roughness, and the longer the distance between the vertex of the micro-asperity and the shear surface.

4. The method for predicting joint peak shear strength according to claim 3, wherein: Determining the distribution function density includes: right Taking the derivative, we get the distribution function density: ； The determining of any normal stress comprises: Assume that the minimum inclination angle among all contact asperities is the inclination angle of the asperity that yields under the set load; Depend on Get the minimum inclination angle among all contacting asperities: ； in, is the normal stress, i.e. the vertical stress applied to the joint surface; is the uniaxial compressive strength of rock, which is a reference stress value; is the normalized normal stress.

5. The method for predicting peak shear strength of joints according to claim 4, wherein: The method of determining the average inclination angle of the actual contact joint asperities according to the initial peak dilatancy angle in the initial state when no cyclic shear occurs comprises: ； The average inclination angle of the actual contact joint micro-asperities is the initial peak dilatancy angle from Integral to 90°; Determine the peak dilatancy angle for several shear cycles: ； in, is the nonlinear degradation factor caused by cyclic shear; is the cyclic degradation rate; m is the number of cyclic shearing; is the compression degradation factor caused by normal stress; k is the stress degradation coefficient; is the normal stress; is the uniaxial compressive strength of rock; is the normalized normal stress.

6. The method for predicting peak shear strength of joints according to claim 5, wherein: The peak shear strength prediction model of the joint surface is determined according to the number of cyclic shearing cycles, including: ； in, is the shear strength of the joint surface after n shearing times; is the basic friction angle.

7. A device for predicting peak shear strength of joints, characterized in that: include: The data acquisition module is used to scan the original rock joint surface and obtain the original rock joint point cloud data; The distribution description module is used to obtain the 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 for determining the distribution function density and any normal stress; a shear determination module, for determining an average value of the inclination angles of the actual contact joint asperities based on an initial peak shear dilatancy angle in an initial state when no cyclic shear occurs, and determining a peak shear dilatancy angle after several cyclic shears; The model determination module is used to determine the peak shear strength prediction model of the joint surface according to the number of cyclic shearing cycles.

8. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the processor is connected to the memory via the bus, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, they are used to implement the method for predicting the peak shear strength of a joint as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a server, implements the method for predicting the peak shear strength of a joint surface according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises instructions which, when executed by a computer, cause the computer to implement the method according to any one of claims 1 to 6.

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