A method, apparatus, and electronic device for predicting peak shear strength.

Abstract

This application provides a method, apparatus, and electronic device for predicting peak shear strength, used to predict the peak shear strength of rock joints with different hanging wall and footwall rock types. By using the compressive strength of the hanging wall and footwall rock, the equivalent uniaxial compressive strength of the joint is determined. Then, using a pre-trained peak shear strength prediction model, input parameters including the equivalent uniaxial compressive strength are input into the model to obtain the predicted peak shear strength, thus realizing the prediction of peak shear strength for rock joints with different hanging wall and footwall rock types.

Description

Technical Field

[0001] This application relates to the field of rock engineering technology, and more specifically, to a method, apparatus, and electronic device for predicting peak shear strength. Background Technology

[0002] When the joint surface is not rough enough and the normal pressure level for joint closure is low, the joint may undergo shear slippage under disturbance, leading to engineering instability and failure. Therefore, the shear behavior of rock joints has always been a research hotspot for scholars both domestically and internationally. The shear strength of rock mass joints is an important factor in measuring rock mass stability and reflecting shear behavior. Since Patton first introduced the undulation angle into the Mohr-Coulomb formula based on the dilatation effect and proposed the bilinear shear strength formula, scholars both domestically and internationally have successively proposed many formulas for the peak shear strength of joints under constant normal stress.

[0003] In related technologies, when the joint wall strengths on both sides of the rock mass contact surface are different, the estimated value of the peak shear strength prediction model is often smaller or larger than the experimental value, which directly affects the stability and safety of engineering design. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, and electronic device for predicting peak shear strength, so as to achieve the technical effect of improving the accuracy of joint peak shear strength prediction by taking into account the effects of different upper and lower joint strengths.

[0005] The first aspect of this application provides a method for predicting peak shear strength, the method being used to predict the peak shear strength of rock joints with different hanging wall and footwall rock types, the method comprising:

[0006] Obtain the compressive strength of the hanging wall rock and the compressive strength of the footwall rock;

[0007] Based on the compressive strength of the hanging wall rock and the compressive strength of the footwall rock, determine the equivalent uniaxial compressive strength;

[0008] Obtain the peak shear strength prediction model and input parameters. The input parameters include the normal stress of the joint surface, the basic friction angle, the three-dimensional morphology parameters of the joint surface, and the equivalent uniaxial compressive strength. The three-dimensional morphology parameters of the joint surface include the maximum effective inclination angle of the joint surface micro-element, the fitting coefficient of the preset formula, and the area ratio of the target joint surface micro-element.

[0009] The input parameters are input into the peak shear strength prediction model to obtain the predicted peak shear strength.

[0010] In the above implementation process, based on the compressive strength of the hanging wall and footwall rocks, the equivalent uniaxial compressive strength of the joint is determined. A pre-trained peak shear strength prediction model is then used, with input parameters including the equivalent uniaxial compressive strength fed into the model to obtain the predicted peak shear strength. This enables the prediction of peak shear strength for rock joints with different hanging wall and footwall rock types.

[0011] Further, determining the equivalent uniaxial compressive strength based on the compressive strength of the hanging wall rock and the compressive strength of the footwall rock includes:

[0012] Based on the compressive strength of the upper rock and the compressive strength of the lower rock, the wall strength coefficient and the target compressive strength are determined;

[0013] The target compressive strength is corrected using the wall strength coefficient to obtain the equivalent uniaxial compressive strength.

[0014] In the above implementation process, by taking into account the compressive strength of the hanging wall and footwall rocks, a representative wall strength coefficient is first calculated. Then, this coefficient is used to adjust the target compressive strength, and finally, an equivalent uniaxial compressive strength value that more accurately reflects the actual geological conditions is obtained.

[0015] Furthermore, the wall strength coefficient, the target compressive strength, and the equivalent uniaxial compressive strength satisfy the following relationship:

[0016] σ c-eq =σ c-soft (α σc ^0.14);

[0017] Where, σ c-eq σ is the equivalent uniaxial compressive strength. c-soft Let α be the target compressive strength. σc The wall strength coefficient is denoted as .

[0018] In the above implementation process, the equivalent uniaxial compressive strength is obtained through the above relationship.

[0019] Furthermore, before obtaining the peak shear strength prediction model and input parameters, the following steps are also included:

[0020] Based on the three-dimensional model of the joint surface, the three-dimensional morphological parameters of the joint surface are obtained. The three-dimensional model of the joint surface includes multiple joint surface micro-elements.

[0021] In the above implementation process, in the process of establishing the peak shear strength prediction model, the key morphological parameters are first extracted by analyzing the three-dimensional model of the joint surface. This preliminary step is the foundation of the entire prediction framework.

[0022] Further, obtaining the three-dimensional morphological parameters of the joint surface based on the three-dimensional model of the joint surface includes:

[0023] Obtain joint surface data;

[0024] Based on the joint surface data, joint point cloud data is obtained;

[0025] Based on the joint point cloud data, the plurality of joint surface micro-elements are obtained;

[0026] Based on the joint surface micro-elements, the three-dimensional morphological parameters of the joint surface are determined.

[0027] In the above implementation process, detailed information about the joint surfaces is first obtained. Using this data, the joint surfaces are converted into point clouds through 3D reconstruction technology. Based on the joint point cloud data, the joint surfaces are meshed, dividing them into multiple tiny joint surface elements. Based on these micro-elements, parameters describing the 3D structural properties of the joint surfaces are further analyzed and calculated. This series of steps enhances the depth of understanding of the physical properties of the joint surfaces, providing decision support for subsequent shear strength analysis.

[0028] Furthermore, the preset formula fitting coefficients include multiple fitting coefficients of the joint point cloud data in different shearing directions.

[0029] In the above implementation process, obtaining the fitting coefficients, especially the specific coefficients for different shear directions, can significantly improve the prediction accuracy of the prediction model for the peak shear strength of joint surfaces. These coefficients, as input parameters, enable the prediction model to better adapt to mechanical changes in complex geological structures, thereby improving prediction accuracy.

[0030] Furthermore, the peak shear strength prediction model satisfies the formula

[0031]

[0032] Where τ is the predicted peak shear strength, σ n The normal stress of the joint surface. The basic friction angle is... σ is the maximum effective dip angle of the joint surface micro-element. c-soft Let α be the target compressive strength. σc A0 is the wall strength coefficient, A0 is the area ratio of the target joint surface micro-element, and C is the fitting coefficient of the preset formula.

[0033] In the above implementation process, a specific expression for the peak shear strength prediction model is proposed, and the peak shear strength can be predicted through this expression.

[0034] A second aspect of this application provides an apparatus, the apparatus comprising:

[0035] The acquisition module is used to obtain the compressive strength of the hanging wall rock and the compressive strength of the footwall rock;

[0036] The calculation module is used to determine the equivalent uniaxial compressive strength based on the compressive strength of the hanging wall rock and the compressive strength of the footwall rock;

[0037] The acquisition module is also used to acquire the peak shear strength prediction model and input parameters. The input parameters include the normal stress of the joint surface, the basic friction angle, the three-dimensional morphology parameters of the joint surface, and the equivalent uniaxial compressive strength. The three-dimensional morphology parameters of the joint surface include the maximum effective inclination angle of the joint surface micro-element, the preset formula fitting coefficient, and the area ratio of the target joint surface micro-element.

[0038] The processing module is used to input the input parameters into the peak shear strength prediction model to obtain the predicted peak shear strength.

[0039] In the above implementation process, the acquisition module collects compressive strength data of the hanging wall and footwall rocks. Based on these two data sets, the calculation module calculates a comprehensive equivalent uniaxial compressive strength value to uniformly measure the overall compressive performance of the rock. The acquisition module further acquires the peak shear strength prediction model and its required input parameters, including the normal stress of the joint surface, the basic friction angle, the three-dimensional morphology parameters of the joint surface (such as the maximum effective dip angle, specific fitting coefficient, and the area ratio of the target micro-element), and the equivalent uniaxial compressive strength. Finally, the processing module inputs these input parameters into the prediction model, processes them through the model algorithm, and outputs the predicted peak shear strength. By integrating the mechanical properties of the hanging wall and footwall rocks and calculating the equivalent uniaxial compressive strength, a more comprehensive assessment of the overall stability of the rock mass is achieved. The introduction of the three-dimensional morphology parameters of the joint surface, especially considering the effective dip angle, the area ratio of the micro-element, and the specific fitting coefficient, greatly improves the refinement of the prediction model and can more accurately reflect the true mechanical behavior of the joint surface under complex geological conditions.

[0040] A third aspect of this application provides an electronic device, the electronic device comprising:

[0041] processor;

[0042] Memory used to store processor-executable instructions;

[0043] Wherein, when the processor invokes the executable instructions, it implements any of the methods described in the first aspect.

[0044] A fourth aspect of this application provides a computer program product, the computer program product including a computer program, which, when executed by a processor, implements any of the methods described in the first aspect. Attached Figure Description

[0045] 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.

[0046] Figure 1 A flowchart illustrating a method for predicting peak shear strength provided in an embodiment of this application;

[0047] Figure 2 This is a schematic diagram illustrating the process of obtaining joint surfaces in natural rocks, as provided in an embodiment of this application.

[0048] Figure 3 A schematic diagram illustrating the geometric relationship between the triangular micro-element and the shearing direction provided in an embodiment of this application;

[0049] Figure 4 A flowchart illustrating another method for predicting peak shear strength provided in this application embodiment;

[0050] Figure 5 A schematic diagram of the structure of an apparatus provided in an embodiment of this application;

[0051] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0052] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0053] 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.

[0054] In related technologies, shear formulas are proposed based on the premise that the joint walls of the upper and lower plates have the same properties. However, in some engineering projects, the joint wall strengths on both sides of the rock mass contact surface are different. When dealing with the stability of joints with different wall strengths, the common practice is to use the mechanical parameters of the side with lower wall strength as the calculation parameters for the joint. However, both walls of the joint have a certain influence on the shear strength. Therefore, for soft-hard joints, the relevant strength formulas have certain limitations. When the parameter wall strength is taken as the uniaxial compressive strength of the softer or harder joint side, the estimated value is often smaller or larger than the experimental value, which directly affects the stability and safety of engineering design.

[0055] To address any of the problems mentioned above, embodiments of this application provide a method for predicting peak shear strength, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a method for predicting peak shear strength provided in an embodiment of this application.

[0056] In this embodiment, the method is used to predict the peak shear strength of rock joints with different hanging wall and footwall rock types. The method includes:

[0057] Step S10: Obtain the compressive strength of the hanging wall rock and the compressive strength of the footwall rock;

[0058] It should be noted that the executing entity in this embodiment can be a computer or other devices with data processing capabilities. When the executing entity is a computer, the compressive strength data of the hanging wall rock and footwall rock can be automatically collected from the testing equipment through computer control system software. This embodiment and the following embodiments predict peak shear strength based on rock joints with different hanging wall rock types, and there are no restrictions on the hanging wall rock types. As shown in Table 1, Table 1 shows four combinations of coupled joints:

[0059] Table 1

[0060]

[0061]

[0062] Among them, the rocks on the footwall of the joints in Table 1 are all red sandstone, which are consistent with the mechanical properties of the rocks on the hanging wall of Assembly 1.

[0063] It should be understood that joints, also known as fissures, are small fracture structures in rock masses where the rock blocks on either side do not experience significant displacement after fracturing under stress. Joints are a very common structural geological phenomenon, or cracks in rocks. The compressive strength of the hanging wall refers to the maximum vertical pressure that the rock strata above the joint surface (i.e., the upper part of the structure) can withstand in a uniaxial compression test until the rock fails. This value reflects the hanging wall's ability to resist compressive loads. Correspondingly, the compressive strength of the footwall refers to the maximum vertical pressure that the rock strata below the joint surface (i.e., the lower part of the structure) can withstand under the same test conditions until failure. It is also a key indicator for measuring the mechanical properties of rocks and is crucial for understanding the bearing capacity and stability of the underlying rock strata.

[0064] Step S20: Determine the equivalent uniaxial compressive strength based on the compressive strength of the hanging wall rock and the compressive strength of the footwall rock;

[0065] It should be understood that the equivalent uniaxial compressive strength is intended to reflect the overall mechanical behavior of jointed rock mass under a combined stress state, rather than just the property of a single part. Therefore, determining the equivalent uniaxial compressive strength is not simply an arithmetic average of the compressive strengths of the hanging wall and footwall or a direct application of the values.

[0066] For example, the equivalent uniaxial compressive strength can be determined using one or a combination of the following methods:

[0067] Geomechanical model: It uses rock mechanics theory (such as the Mohr-Coulomb criterion, Hoek-Brown criterion, etc.) combined with the geometric and mechanical properties of joints (such as joint spacing, angle, roughness, and filling conditions) to evaluate them.

[0068] Numerical simulation: Using numerical simulation techniques such as finite element analysis and discrete element method, the response of rock mass under actual stress state is simulated, taking into account the complexity of rock mass structure.

[0069] Empirical formulas or statistical associations: Based on previous research or project experience, published association models or empirical formulas are applied.

[0070] In-situ testing: The strength characteristics of rock mass are directly evaluated through in-situ tests, such as plate load tests and in-situ direct shear tests.

[0071] Step S30: Obtain the peak shear strength prediction model and input parameters. The input parameters include the normal stress of the joint surface, the basic friction angle, the three-dimensional morphology parameters of the joint surface, and the equivalent uniaxial compressive strength. The three-dimensional morphology parameters of the joint surface include the maximum effective dip angle of the joint surface micro-element, the preset formula fitting coefficient, and the area ratio of the target joint surface micro-element.

[0072] It should be noted that the peak shear strength prediction model is used to predict the peak shear strength of a joint based on the geometric features and mechanical properties of the joint surface. This embodiment and the following embodiments do not limit the type of peak shear strength prediction model. The peak shear strength prediction model can be the Mohr-Coulomb model, the Barton model, or a more advanced model, such as the JRC-JCS model (a model that jointly considers joint roughness and dilatation), etc.

[0073] Normal stress on a joint surface: Stress acting perpendicular to the joint surface.

[0074] Basic friction angle: The inherent frictional characteristics of a joint surface without external conditions, which can be estimated through direct shear tests in the laboratory or relevant empirical formulas. As mentioned in Table 1 above, "Basic friction angles of upper and lower plate joints".

[0075] Three-dimensional morphological parameters of joint surfaces:

[0076] The maximum effective tilt angle refers to the largest or most representative tilt angle among all important micro-elements (such as protrusions and depressions on the joint surface) that may affect shear strength or stability when considering the microstructural characteristics of the joint surface. This angle reflects the microstructural feature of the joint surface that is most detrimental to stability in the shear or stress direction.

[0077] Preset formula fitting coefficients: This embodiment and the following embodiments do not limit the preset formula fitting coefficients. The formula fitting coefficients for each shear direction reflect the degree of fit between the point cloud data (i.e., the directional distribution of joints) and the theoretical model or expected distribution in that specific direction. The formula fitting coefficients can, to some extent, quantify the concentration or tendency of the point cloud in that direction. The magnitude and sign of the value may represent the degree of fit or the deviation in direction. Therefore, if n different shear directions are analyzed, theoretically n different formula fitting coefficients will be obtained. These coefficients, when combined, can provide a comprehensive description of the angular distribution characteristics of the point cloud data in three-dimensional space. By calculating the formula fitting coefficients for each shear direction, the distribution pattern or concentration trend of the point cloud data in each direction can be comprehensively evaluated and quantified.

[0078] The area ratio of target joint surface micro-elements refers to the proportion of joint surface micro-elements with specific geometric features (such as a specific dip angle range) to the total area, reflecting the distribution characteristics of joint surface roughness. For example, if the target joint surface is a micro-element with a dip angle greater than 0, the area ratio of the target joint surface micro-element is the ratio of the sum of the areas of all joint micro-elements with dip angles greater than 0 to the sum of the joint surface areas.

[0079] Step S40: Input the input parameters into the peak shear strength prediction model to obtain the predicted peak shear strength.

[0080] Understandably, the normal stress, basic friction angle, three-dimensional morphology parameters of the joint surface (including the maximum effective dip angle, the fitting coefficient of the preset formula, and the area ratio of the target joint surface micro-elements), and equivalent uniaxial compressive strength are input into a pre-trained peak shear strength prediction model. The model calculates the maximum shear force that the joint surface can withstand under specific conditions, i.e., the predicted peak shear strength, based on a specific combination of these parameters and their inherent mathematical or physical relationships. When the execution entity is a computer, the peak shear strength prediction model can be built into computer software or a specialized numerical analysis program. This means that the peak shear strength prediction model is implemented in a programmed form. Users can input relevant geological parameters (such as normal stress, friction angle, joint surface morphology parameters, etc.) through the software interface, and the software automatically executes the calculation process and outputs the predicted peak shear strength result. Peak shear strength prediction is automatically completed through computer software or specialized numerical analysis programs.

[0081] In this embodiment, firstly, compressive strength data of the hanging wall and footwall rocks are collected. These data are then used to determine a comprehensive equivalent uniaxial compressive strength value to reflect the combined influence of the hanging wall and footwall rocks on the overall mechanical properties of the joint. Secondly, a model for predicting the peak shear strength of the rock joint is established, and necessary input parameters are collected, including the normal stress of the joint surface, the basic friction angle, and detailed three-dimensional morphological features of the joint surface. Finally, all the above parameters are input into the prediction model, and the peak shear strength of the rock joint under the expected shear conditions is calculated through the model. This method, by combining the mechanical properties of the hanging wall and footwall rocks, provides a more comprehensive perspective for evaluating the shear strength of the joint, helping to more accurately reflect the overall stability and mechanical behavior of rocks under complex geological conditions. Furthermore, the introduction of three-dimensional morphological parameters (i.e., roughness parameters) of the joint surface, particularly the effective dip angle, fitting coefficient, and area ratio, allows the prediction model to more meticulously characterize the microstructural features of the joint surface, improving the accuracy and practicality of the prediction results.

[0082] Based on any of the above embodiments, step S20 may specifically include steps S21-S22:

[0083] Step S21: Based on the compressive strength of the upper rock and the compressive strength of the lower rock, determine the wall strength coefficient and the target compressive strength;

[0084] It should be noted that the wall strength coefficient is used to quantify the difference in mechanical properties between the two sides of a joint. By comparing the compressive strength of the hanging wall and the footwall, the compressive strength of the side with lower joint wall strength and the side with higher joint wall strength are determined. For example, the wall strength coefficient is positively correlated with the compressive strength of the side with lower joint wall strength, and negatively correlated with the compressive strength of the side with higher joint wall strength. As an example, the wall strength coefficient can be calculated using Equation 1:

[0085] α σc =σ c-hard / σ c-soft Formula 1;

[0086] Where, α σc σ is the wall strength coefficient. c-hard σ is the compressive strength of the side with lower joint wall strength; c-soft This represents the compressive strength of the side with greater joint wall strength. Of course, the formula for calculating the wall strength coefficient is not limited to Equation 1; it only requires that the wall strength coefficient has a positive correlation with the compressive strength of the side with less joint wall strength and a negative correlation with the compressive strength of the side with greater joint wall strength.

[0087] It should be understood that the target compressive strength can be the compressive strength of the side with lower joint wall strength or the compressive strength of the side with higher joint wall strength. This embodiment and the following embodiments use the compressive strength of the side with higher joint wall strength as an example to illustrate the target compressive strength.

[0088] Step S22: Correct the target compressive strength using the wall strength coefficient to obtain the equivalent uniaxial compressive strength.

[0089] Understandably, by using a determined wall strength coefficient to correct the target compressive strength, an equivalent uniaxial compressive strength that better reflects the actual situation is calculated. This process reflects the comprehensive adjustment of the properties of the upper and lower wall rocks to the overall mechanical response of the joints, ensuring that the predicted shear strength more accurately reflects the actual geological conditions.

[0090] In this embodiment, based on the compressive strength values ​​of the hanging wall and footwall rocks, a specific calculation method is used to determine the wall strength coefficient. The target compressive strength is then determined by comparing the compressive strengths of the hanging wall and footwall rocks. This target compressive strength is then corrected based on the wall strength coefficient to obtain an equivalent uniaxial compressive strength that more closely reflects actual conditions. By introducing the wall strength coefficient and the equivalent uniaxial compressive strength, the differences in rock type and strength between the hanging wall and footwall can be considered more precisely, improving the accuracy of joint compressive strength prediction.

[0091] Based on any of the above embodiments, the wall strength coefficient, the target compressive strength, and the equivalent uniaxial compressive strength satisfy the following relationship:

[0092] σ c-eq =σ c-soft (α σc ^0.14);

[0093] Where, σ c-eq σ is the equivalent uniaxial compressive strength. c-soft Let α be the target compressive strength. σc The wall strength coefficient is denoted as .

[0094] It should be noted that the target compressive strength σ c-so ft represents the compressive strength of the side with lower joint wall strength. Using the above formula, the target compressive strength can be corrected based on the wall strength coefficient to obtain the equivalent uniaxial compressive strength.

[0095] In this embodiment, the target compressive strength is corrected by using the relationship between the wall strength coefficient, the target compressive strength, and the equivalent uniaxial compressive strength, resulting in an equivalent uniaxial compressive strength that is closer to the actual situation, which helps to improve the accuracy of joint compressive strength prediction.

[0096] Based on any of the above embodiments, before obtaining the peak shear strength prediction model and input parameters, the method further includes:

[0097] Based on the three-dimensional model of the joint surface, the three-dimensional morphological parameters of the joint surface are obtained. The three-dimensional model of the joint surface includes multiple joint surface micro-elements.

[0098] It should be noted that the first step is to obtain the three-dimensional morphological data of the natural joints, and then construct a three-dimensional model of the joint surface. Taking granite as an example, the joint morphology surface of the granite is obtained through the Brazilian splitting test. A three-dimensional laser scanner is used to scan the joint surface, with a sampling interval of 0.2 mm, to obtain joint point cloud data represented by X, Y, and Z axis coordinates. A three-dimensional model of the joint surface is then constructed based on the joint surface point cloud data. Figure 2 As shown, Figure 2 This is a schematic diagram of the process of obtaining natural rock joint surfaces provided in the embodiments of this application. The diagram shows the process of obtaining natural rock joint surfaces using a 200*100*100mm granite block as an example. Specifically, it includes step ① determining the cutting line, step ② performing a Brazilian splitting test along the cutting line on the granite block to obtain the cut rock block, step ③ scanning the joint surface using a three-dimensional laser scanner, and step ④ setting the sampling interval to 0.2mm to obtain the original rock joint point cloud data represented by the X, Y, and Z axis coordinates, and finally generating a three-dimensional model of the joint surface.

[0099] Based on direct shear tests of rock joints, it is evident that the effective shear angle is well correlated with the joint shear strength. The joint morphology can be approximated using triangular mesh elements, such as... Figure 3 As shown, Figure 3 Figure 1 is a schematic diagram illustrating the geometric relationship between a triangular micro-element and the shear direction provided in an embodiment of this application. Figure (a) is a reconstructed joint topography diagram, Figure (b) is a magnified view of a local area, and Figure (c) is a diagram of the triangular micro-element. Figure (a) shows a three-dimensional model of the joint surface, constructed from point cloud data. Line segments represent shear directions, and boxes mark specific micro-element regions. Figure (b) is a magnified view of the micro-element within the box in Figure (a), showing local details of the micro-element, including its surface texture and shape. Figure (c) is an abstract representation of Figures (a) and (b), a two-dimensional schematic diagram used to explain the relationship between the micro-element and the shear direction. In the figure, triangle ABC represents a micro-element of the joint surface, located within a rectangular frame, which represents the shear plane. In Figure (c), n is the element's outward normal vector, n0 is the shear plane's outward normal vector, perpendicular to the shear plane and pointing outward, n1 is the projection vector of the shear direction onto the shear plane, C is the formula fitting coefficient, α is the angle between the joint surface dip and the shear direction, θ is the dip angle of the joint surface element, θ* is the effective shear dip angle of the element, and t is the shear direction vector. Figure 3 The geometric relationships shown can be used to calculate the stress on the infinitesimal element in the shear direction, and then analyze the shear strength of the joint surface.

[0100] The effective shear tilt angle refers to the effective average angle formed between the microstructural units (such as rough surfaces, protrusions, and depressions) that actually participate in shear sliding on the joint surface during the shearing process and the direction of shear stress.

[0101] Effective shear tilt angle θ * The calculation formula is shown in Formula 2 below:

[0102] tanθ * = -tanθcosα Equation 2;

[0103] In the formula: θ is the dip angle of the joint surface element, α is the angle between the joint surface dip and the shear direction, t is the shear direction vector, n is the element's external normal vector, n0 is the external normal vector of the shear plane, and n1 is the projection vector of the shear direction onto the shear plane.

[0104] Based on the three-dimensional model of the joint surface, the three-dimensional morphological parameters of a specific shear direction under different sampling intervals are finally obtained: the maximum effective tilt angle of the joint surface micro-element, the fitting coefficient of the preset formula, and the area ratio of the target joint surface micro-element.

[0105] It should be understood that the relationship between actual contact joint roughness and peak shear strength is as follows: Based on variable angle shear tests, the functional relationship between the effective apparent dip angle and the potential contact area ratio of joint micro-protrusions is known, and a three-dimensional roughness parameter that can reflect the shear direction and effective shear area is proposed. It is also pointed out that only joint micro-elements on the shear-facing side (effective apparent dip angle greater than 0°) can contribute to shear strength, and for the first time, the three-dimensional morphology parameters of rock are linked to the shear strength of the joint surface.

[0106] The effective dip angle of the topographic surface is greater than θ * The formula for calculating the ratio of the area of ​​each infinitesimal element to the total area is shown below.

[0107] Formula 3:

[0108]

[0109] In the formula: A0 is the ratio of the total area of ​​all joint elements with a dip angle greater than 0 to the total area of ​​the joint surface, θ * max The maximum effective dip angle of the joint micro-element is given by equation 3. The formula fitting coefficient C for each point cloud data in each shear direction can be calculated using equation 3, which describes the angular distribution of the micro-element. Through fitting, the roughness parameters of all joints are obtained: the maximum effective dip angle of the joint surface micro-element, the preset formula fitting coefficient, and the area ratio of the target joint surface micro-element.

[0110] In this embodiment, point cloud data of the joint surface can be collected using scanning technology. Through data processing and 3D reconstruction techniques, a 3D model of the joint surface is created. Based on this model, the joint surface is divided into multiple micro-units (micro-elements), each representing a local region on the joint surface. This facilitates detailed analysis of the geometric features and mechanical properties of the joint surface. Each micro-element is then analyzed to extract a series of parameters reflecting its morphological characteristics, including the maximum effective dip angle, the area ratio of the micro-element, and the fitting coefficient of a preset formula. By subdividing the joint surface into micro-elements and extracting detailed morphological parameters, the complexity of the joint surface can be captured and quantified more accurately, improving the accuracy of predicting rock mechanical behavior.

[0111] Based on any of the above embodiments, obtaining the three-dimensional morphology parameters of the joint surface according to the three-dimensional model of the joint surface includes, for example: Figure 4 Steps S301-S304 are shown below:

[0112] Step S301: Obtain joint surface data;

[0113] It should be noted that the joint surface data is used to further generate joint point cloud data. This embodiment does not limit the joint surface data. For example, the joint surface data may include the following elements:

[0114] 1. Geometric features:

[0115] Direction: The direction of the projection of the joint surface onto the horizontal plane, usually measured and recorded as the azimuth using a compass.

[0116] Inclination: The direction of maximum inclination of the joint surface relative to the horizontal plane.

[0117] Dip angle: The angle between the joint surface and the horizontal plane.

[0118] Length, width, and height: the physical dimensions of a joint, reflecting its size.

[0119] Spacing: The average distance between adjacent joints.

[0120] 2. Mechanical properties:

[0121] Uniaxial compressive strength: The maximum pressure a material can withstand when subjected to compressive force in only one direction until failure occurs.

[0122] Uniaxial tensile strength: The maximum tensile force a rock can withstand before failure under a tensile load in a single direction. Compared to compressive strength, the tensile strength of most rocks is much lower.

[0123] Coefficient of friction: describes the resistance to sliding between two surfaces of a joint.

[0124] Roughness: The degree of unevenness of the joint surface, which affects its mechanical behavior.

[0125] 3. Spatial distribution:

[0126] Distribution pattern: The spatial arrangement of joints, such as random, ordered or clustered distribution.

[0127] Density: The number or length of joints per unit volume or area.

[0128] 4. Microstructure:

[0129] Surface features: microstructure of joint surfaces, crack filling, etc.

[0130] Roughness parameter: A quantitative index of the microscopic irregularities of the joint surface obtained through three-dimensional scanning.

[0131] 5. Joint types and causes:

[0132] Types: such as tension joints, shear joints, etc., distinguished according to their formation mechanism.

[0133] Analysis of formation: geological history and mechanical conditions that led to the formation of joints.

[0134] 6. Other environmental factors:

[0135] Properties of the filling material: Whether there is filling material in the joint and its composition, hardness, etc.

[0136] Hydrological characteristics: The impact of joints on groundwater flow, including permeability.

[0137] Step S302: Obtain joint point cloud data based on the joint surface data;

[0138] It should be noted that when only geometric feature data of the joint surface (such as strike, dip, dip angle, etc.) are available, joint point cloud data can be generated through the following steps:

[0139] (1) Define the geometric model of the joint surface:

[0140] Principal joint surface modeling: First, the basic orientation and plane equations of the joint surface are defined using strike, dip, and dip angle. For each principal joint surface, a series of planar points can be created based on its geometric characteristics. For example, a range of parameter values ​​(such as distances in the dip and strike directions) can be set, and then points are uniformly sampled on the joint surface based on these parameters.

[0141] (2) Consider the scale and variation of joints:

[0142] Applications of length, width, and height: Adjust the sampling range and density according to the physical dimensions of the joint. For example, if the joint is long, more sampling points can be added in that direction; if the joint width varies, different sampling intervals can be set in the width direction.

[0143] Spacing and distribution pattern: When simulating multiple joints, these joint surfaces are located and distributed in three-dimensional space according to the joint spacing and distribution pattern (such as random, ordered or clustered).

[0144] (3) Introducing mechanical properties and microstructure information:

[0145] Roughness and Surface Features: While directly generating point clouds with microstructures is complex, a certain degree of random perturbation can be introduced into the point cloud to simulate roughness. For example, based on given roughness parameters, a small offset can be added to the normal direction of each point to reflect unevenness.

[0146] Influence of mechanical properties: Although mechanical properties (such as compressive strength and tensile strength) do not directly affect the direct generation of point clouds, they can be used to guide the adjustment of point density in certain areas of the point cloud, such as increasing the density of points in stress concentration areas to reflect potential deformation or fracture trends.

[0147] (4) Spatial distribution and environmental factors:

[0148] Spatial distribution pattern: The distribution pattern of joints is used to determine the layout of joint surfaces in the point cloud. For example, if the joints exhibit a clustered distribution, clustering algorithms can be used to simulate this distribution characteristic.

[0149] Environmental factors integration: Although the properties of infill materials and hydrological characteristics do not directly affect the geometric structure of point clouds, points in specific areas can be marked or classified after point clouds are generated to distinguish jointed areas containing different infill materials or joint features that affect flow characteristics.

[0150] In addition, point clouds can be generated and processed using Python programming, combined with libraries such as NumPy, SciPy, and Open3D.

[0151] Step S303: Obtain the plurality of joint surface micro-elements based on the joint point cloud data;

[0152] Preferably, the point cloud data is first loaded using an appropriate library, such as PCL (PointCloud Library) or other libraries that support point cloud operations in Python. Next, the point cloud data is projected onto a two-dimensional plane, and then a mesh is created on this plane. Subsequently, a mesh is created to cover the projected point cloud data; this can be done by calculating the minimum and maximum boundaries of the point cloud and the required mesh size. Then, each cell in the mesh is traversed, checking if it contains any point cloud data; if so, this cell is treated as a micro-element. Finally, the generated micro-elements are visualized using matplotlib or other suitable tools.

[0153] Step S304: Determine the three-dimensional morphology parameters of the joint surface based on the joint surface micro-elements.

[0154] Understandably, analyzing joint surface micro-elements yields the three-dimensional morphological parameters of the joint surface (these parameters include the maximum effective dip angle of the joint surface micro-elements, the fitting coefficient of the preset formula, and the area ratio of the target joint surface micro-elements; the area ratio of the target joint surface micro-elements is taken as the ratio of the total area of ​​joint surface micro-elements with an effective dip angle greater than 0 to the total area of ​​the joint surface). Specifically:

[0155] Calculating the effective dip angle of a joint surface element: The effective dip angle typically refers to the angle of the joint surface relative to a reference plane (such as a horizontal plane, ground plane, or shear plane). For each element, it can be calculated by the angle between its normal vector and the normal vector of the reference plane.

[0156] Determine the maximum effective tilt angle: Traverse all infinitesimal elements, find the infinitesimal element with the maximum effective tilt angle, and record the tilt angle value.

[0157] Determine the preset formula fitting coefficients: Based on the relationship between the effective dip angle distribution and area of ​​the joint surface micro-element, select an appropriate mathematical model to describe the characteristic relationship of the joint surface micro-element; perform data preprocessing, such as checking the integrity of the data, eliminating outliers, and ensuring data quality; use statistical software or programming languages ​​(such as Python's scipy.optimize.curve_fit function) to perform nonlinear least squares fitting, find the best formula fitting parameters, and minimize the sum of squared residuals between the model and the data.

[0158] To calculate the ratio of the total area of ​​joint surface micro-elements with an effective dip angle greater than 0 to the total area of ​​all joint surfaces: For each micro-element, calculate its area based on its dimensions; select micro-elements with an effective dip angle greater than 0 and calculate the total area of ​​these micro-elements; calculate the total area of ​​all joint surface micro-elements; divide the total area of ​​micro-elements with an effective dip angle greater than 0 by the total area to obtain the desired ratio.

[0159] In addition, Python code can be used to complete the steps of determining the three-dimensional morphological parameters of joint surfaces based on joint surface micro-elements.

[0160] In this embodiment, joint surface data is acquired, including basic information such as the joint's strike, dip, and dip angle, as well as features such as roughness and spacing. The joint surface data is converted into high-density point cloud data. Point cloud data accurately reflects the spatial morphology of the joint surface, including irregular contours and fine structures. The joint point cloud is further subdivided using specific algorithms (such as 3D meshing, voxelization, or triangulation) to divide the joint surface into multiple micro-units. Each micro-unit represents a local region on the joint surface and contains its own geometric properties (such as normal vector, area, curvature, etc.). Finally, based on the detailed information of the joint surface micro-units, the 3D morphological parameters of the joint surface are determined through calculation and analysis. Statistical analysis based on a large amount of micro-data makes the determined 3D morphological parameters more reliable, which is beneficial for constructing a more accurate peak shear strength prediction model.

[0161] Based on any of the above embodiments, the preset formula fitting coefficients include multiple fitting coefficients of the joint point cloud data in different shearing directions.

[0162] Understandably, when calculating the fitting coefficient for point cloud data across different shearing directions, this coefficient is typically calculated for each specific shearing direction. This means that if multiple different shearing directions are considered, then there will be multiple different fitting coefficients.

[0163] In this embodiment, the joint point cloud data is fitted in different shear directions to obtain multiple fitting coefficients, which is helpful for understanding the behavior of joint surfaces under different stress states. This is very important for predicting the mechanical properties of rocks.

[0164] Based on any of the above embodiments, the peak shear strength prediction model satisfies the formula

[0165]

[0166] Where τ is the predicted peak shear strength, σ n The normal stress of the joint surface. The basic friction angle is... σ is the maximum effective dip angle of the joint surface micro-element. c-so ft is the target compressive strength, α σc A0 is the wall strength coefficient, A0 is the area ratio of the target joint surface micro-element, and C is the fitting coefficient of the preset formula.

[0167] It should be noted that in related technologies, based on the aforementioned three-dimensional morphology parameters of the joint surface, i.e., roughness indices, a rock joint peak shear strength model considering the actual three-dimensional roughness of the contact joints has been proposed. Among these, the uniaxial compressive strength of the joint is a crucial parameter. However, for situations similar to this embodiment with different wall and footwall rock types, the uniaxial compressive strength of the joint's two sidewalls differs. Therefore, the rock joint peak shear strength model considering the actual three-dimensional roughness of the contact joints has certain limitations. The following analysis focuses on the peak shear strength of rock joints with different wall and footwall rock types:

[0168] First, using the equivalent uniaxial compressive strength σ c-eq To calculate the peak shear strength of the joint, and σ c-eq The compressive strength σ between the sides with greater joint wall strength c-soft The compressive strength σ of the side with lower joint wall strength c- Between hard and soft. Because the wear on the lower side joints is more pronounced, the lower side joints have a lower effect on the joint's equivalent uniaxial compressive strength σ. c-eq The contribution is greater. Therefore, the wall strength σ on the joint side with lower strength is considered more significant. c-soft Based on this, establish the relationship between the wall strength coefficient α and the wall strength coefficient α. σc The modified relation function f(α) σc The equivalent wall strength of soft-hard joints is determined by equation 4 below:

[0169] σ c-eq =f(α) σc )σ c-soft Equation 4;

[0170] Where, σ c-eq For the equivalent uniaxial compressive strength, α σc σ is the wall strength coefficient. c-softThis refers to the compressive strength of the side with greater joint wall strength.

[0171] Meanwhile, consider the correction factor function for the equivalent wall strength of actual contact joints:

[0172] For the shear problem of rock joints under normal stress, the actual contact area can be approximated as the normal load N acting on the joint surface divided by the uniaxial compressive strength σ of the rock. c See equation 5 below:

[0173]

[0174] Among them, A c Where N is the actual contact area, A is the normal load, and σ is the nominal area (total area) of the joint surface. n This is the normal stress. When the equivalent uniaxial compressive strength σ is taken... c-eq When calculating the peak shear strength, the contact range of the joint during the shear process can be assumed as shown in Equation 6 below:

[0175]

[0176] Among them, A c-eq To calculate the peak shear strength using the equivalent uniaxial compressive strength, the contact range of the joint during the shear process is given, where N is the normal load and σ is the tensile strength. c-eq For the equivalent uniaxial compressive strength, A is the nominal area (total area) of the joint surface, σ n This is the normal stress.

[0177] Equivalent uniaxial compressive strength σ c-eq With α σc and σ c-soft Related to further determining σ c-eq With α σc and σ c-so The quantitative relationship of ft is obtained by fitting the rock joint peak shear strength model that considers the three-dimensional roughness of the actual contact joints to the four types of joint combinations in Table 1. c-eq The values ​​are 32, 35.84, 38.72, and 40.32 MPa, respectively (see Table 2). Table 2 compares the peak shear strength calculated using the equivalent wall strength obtained through optimal fitting with the experimental results.

[0178] Table 2

[0179]

[0180] The power function can be used to measure σ very well. c-eq With α σc and σ c-soft The relationship between these three factors, therefore, the correction coefficient function for the joint equivalent wall strength satisfies the following equation 7:

[0181] σ c-eq =σ c-soft (α σc Equation 7 (^0.14);

[0182] Where, σ c-eq For the equivalent uniaxial compressive strength, σ c-soft α represents the compressive strength of the side with greater joint wall strength. σc This is the wall strength coefficient.

[0183] Based on this, the rock joint peak shear strength model that considers the three-dimensional roughness of actual contact joints can be improved to obtain the peak shear strength prediction model proposed in this embodiment:

[0184]

[0185] Where τ is the predicted peak shear strength, σ n The normal stress of the joint surface. The basic friction angle is... σ is the maximum effective dip angle of the joint surface micro-element. c-soft Let α be the target compressive strength. σc A0 is the wall strength coefficient, A0 is the area ratio of the target joint surface micro-element, and C is the fitting coefficient of the preset formula.

[0186] In this embodiment, a peak shear strength prediction model applicable to joints of different hanging wall and footwall rock types is proposed. This model can focus on the strength of joints in different hanging wall and footwall rock types, thereby improving the accuracy of joint peak shear strength prediction.

[0187] like Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of a device 500 provided in an embodiment of this application. The device includes:

[0188] Module 501 is used to obtain the compressive strength of the hanging wall rock and the compressive strength of the footwall rock;

[0189] Calculation module 502 is used to determine the equivalent uniaxial compressive strength based on the compressive strength of the upper rock and the compressive strength of the lower rock;

[0190] The acquisition module 501 is also used to acquire the peak shear strength prediction model and input parameters. The input parameters include the normal stress of the joint surface, the basic friction angle, the three-dimensional morphology parameters of the joint surface, and the equivalent uniaxial compressive strength. The three-dimensional morphology parameters of the joint surface include the maximum effective inclination angle of the joint surface micro-element, the preset formula fitting coefficient, and the area ratio of the target joint surface micro-element.

[0191] The processing module 503 is used to input the input parameters into the peak shear strength prediction model to obtain the predicted peak shear strength.

[0192] Optionally, the above-mentioned device further includes:

[0193] The first calculation module is used to determine the wall strength coefficient and the target compressive strength based on the compressive strength of the upper rock and the compressive strength of the lower rock.

[0194] The second calculation module is used to correct the target compressive strength using the wall strength coefficient to obtain the equivalent uniaxial compressive strength.

[0195] Optionally, the above-mentioned device further includes:

[0196] The first processing module is used to obtain the three-dimensional morphology parameters of the joint surface based on the three-dimensional model of the joint surface, wherein the three-dimensional model of the joint surface includes multiple joint surface micro-elements.

[0197] Optionally, the above-mentioned device further includes:

[0198] The first acquisition module is used to acquire joint surface data;

[0199] The second processing module is used to obtain joint point cloud data based on the joint surface data;

[0200] The third processing module is used to obtain the plurality of joint surface micro-elements based on the joint point cloud data;

[0201] The third calculation module is used to determine the three-dimensional morphology parameters of the joint surface based on the joint surface micro-elements.

[0202] The apparatus provided in this application determines the equivalent uniaxial compressive strength of a joint by measuring the compressive strength of the hanging wall rock and the footwall rock. It then uses a pre-trained peak shear strength prediction model to input parameters, including the equivalent uniaxial compressive strength, into the model to obtain the predicted peak shear strength. This enables the prediction of peak shear strength for rock joints with different hanging wall and footwall rock types.

[0203] Based on the methods described in any of the above embodiments, this application also provides, as follows: Figure 6 The diagram shows the structure of an electronic device. Figure 6 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the methods described in any of the above embodiments.

[0204] Based on the methods described in any of the above embodiments, this application also provides a computer program product, which includes one or more computer programs or instructions. The computer program or instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. When executed by a processor, the computer program implements the methods described in any of the above embodiments.

[0205] 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.

[0206] 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.

[0207] 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 a computer 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.

[0208] 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.

[0209] 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.

[0210] 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, characterized by, The method is used for predicting peak shear strength of rock joints with different upper and lower disc rock types, and the method comprises: obtaining upper disc rock compressive strength and lower disc rock compressive strength; determining equivalent uniaxial compressive strength based on the upper disc rock compressive strength and the lower disc rock compressive strength; the determination of the equivalent uniaxial compressive strength based on the upper disc rock compressive strength and the lower disc rock compressive strength comprises: determining a wall surface strength coefficient and a target compressive strength based on the upper disc rock compressive strength and the lower disc rock compressive strength; and correcting the target compressive strength by using the wall surface strength coefficient to obtain the equivalent uniaxial compressive strength; obtaining a peak shear strength prediction model and input parameters, the input parameters comprising normal stress of a joint surface, basic friction angle, three-dimensional topographic parameters of the joint surface, and the equivalent uniaxial compressive strength, the three-dimensional topographic parameters of the joint surface comprising maximum value of effective dip angle of a joint surface microelement, preset formula fitting coefficient, and area proportion of a target joint surface microelement; inputting the input parameters into the peak shear strength prediction model to obtain predicted peak shear strength.

2. The method of claim 1, wherein, The wall surface strength coefficient, the target compressive strength, and the equivalent uniaxial compressive strength satisfy the following relationship: ； wherein, is the equivalent uniaxial compressive strength, is the target compressive strength, is the wall surface strength coefficient.

3. The method of claim 1, wherein, Before the peak shear strength prediction model and the input parameters are obtained, the method further comprises: obtaining the three-dimensional topographic parameters of the joint surface from a three-dimensional joint surface model, the three-dimensional joint surface model comprising a plurality of joint surface microelements.

4. The method of claim 3, wherein, The obtaining of the three-dimensional topographic parameters of the joint surface from the three-dimensional joint surface model comprises: obtaining joint surface data; obtaining joint point cloud data from the joint surface data; obtaining the plurality of joint surface microelements from the joint point cloud data; determining the three-dimensional topographic parameters of the joint surface based on the joint surface microelements.

5. The method of claim 4, wherein, The preset formula fitting coefficient comprises a plurality of fitting coefficients of the joint point cloud data in different shear directions.

6. The method of any one of claims 1-5, wherein, The peak shear strength prediction model satisfies the formula ； wherein τ is the predicted peak shear strength, is the normal stress of the joint surface, is the basic friction angle, is the maximum value of the effective inclination angle of the joint surface element, is the target compressive strength, is the wall surface strength coefficient, is the area ratio of the target joint surface element, and C is the preset formula fitting coefficient.

7. An apparatus, comprising: The device comprises: an obtaining module configured to obtain upper disc rock compressive strength and lower disc rock compressive strength; a calculating module configured to determine equivalent uniaxial compressive strength based on the upper disc rock compressive strength and the lower disc rock compressive strength; the obtaining module is further configured to obtain a peak shear strength prediction model and input parameters, the input parameters comprising normal stress of a joint surface, basic friction angle, three-dimensional topographic parameters of the joint surface, and the equivalent uniaxial compressive strength, the three-dimensional topographic parameters of the joint surface comprising maximum value of effective dip angle of a joint surface microelement, preset formula fitting coefficient, and area proportion of a target joint surface microelement; a processing module configured to input the input parameters into the peak shear strength prediction model to obtain predicted peak shear strength; a first calculating module configured to determine a wall surface strength coefficient and a target compressive strength based on the upper disc rock compressive strength and the lower disc rock compressive strength; a second calculating module configured to correct the target compressive strength by using the wall surface strength coefficient to obtain the equivalent uniaxial compressive strength.

8. An electronic device, comprising: The electronic device comprises: a processor; a memory for storing processor-executable instructions; The processor realizes the method of any one of claims 1-6 when invoking the executable instructions.

9. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program realizes the method of any one of claims 1-6 when executed by a processor.

Images