A method, device, equipment and storage medium for quantifying residual shear strength of joints

Abstract

The present application relates to the field of rock mechanics, and specifically provides a joint residual shear strength quantification method, device, equipment and storage medium. The method includes obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model, wherein the influencing factors include: compression and wear data of the micro-asperities of the rock to be predicted during the shear process. The mechanical model is constructed by analyzing the geometric and mechanical properties of different rock micro-asperities to obtain rock characteristic data; the influencing factors are input into the preset joint residual shear strength model to obtain the quantitative results of the residual shear strength of the rock to be predicted. Through this method, the effect of accurately predicting the joint residual shear strength can be achieved.

Description

Technical Field

[0001] The present application relates to the field of rock mechanics, and in particular to a method, device, equipment and storage medium for quantifying residual shear strength of joints. Background Art

[0002] In the field of rock mechanics, the study of the mechanical properties of rock joints has long been crucial for ensuring the stability and safety of various geotechnical engineering projects. In recent years, in-depth research into the mechanical properties of rock joints has continuously driven technological advancement and innovation in this field. Compared to the peak shear strength of joints, the study of residual shear strength is of greater practical significance. In engineering practice, rock joints often undergo long-term deformation and stress. When relative sliding occurs in the joints, their shear strength gradually decreases from peak strength to residual strength. At this point, the residual shear strength determines the ultimate stability state and potential failure modes of the joints, playing a crucial role in assessing the long-term stability and safety of geotechnical engineering projects. For example, in large-scale water conservancy projects, tunnel projects, and slope engineering, the residual shear strength of joints directly affects the durability and reliability of the engineering structures. Therefore, accurately predicting the residual shear strength of rock joints is of irreplaceable value for engineering design and risk assessment.

[0003] Currently, the existing residual shear strength formula is a widely used classical theory in the study of the residual shear strength of rock joints. However, this formula has certain limitations, and its predicted values ​​are generally too low. This can lead to misjudgments of joint stability in practical engineering applications, thus affecting the safety of the project. With the development of machine learning technology, the use of machine learning methods to predict the residual shear strength of rock joints has become a new research direction. Although machine learning methods can improve prediction accuracy to a certain extent, they require a large amount of data as support, lack clear physical meaning, and have poor model interpretability, failing to fundamentally reveal the mechanical mechanism of the residual shear strength of rock joints.

[0004] Therefore, how to accurately predict the residual shear strength of joints is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a method for quantifying the residual shear strength of joints. Through the technical solutions of the embodiments of the present application, the effect of accurately predicting the residual shear strength of joints can be achieved.

[0006] In the first aspect, an embodiment of the present application provides a method for quantifying the residual shear strength of joints, comprising obtaining, through a preset mechanical model, influencing factors of the residual shear strength of rock joints to be predicted, wherein the influencing factors include: compression and wear data of the micro-asperities of the rock to be predicted during the shear process, and the mechanical model is constructed by analyzing the geometric and mechanical properties of micro-asperities of different rocks to obtain rock characteristic data; the influencing factors are input into a preset joint residual shear strength model to obtain a quantitative result of the residual shear strength of the rock to be predicted, wherein the joint residual shear strength model is obtained by training a basic machine learning model with sampling data of multiple rocks, and the sampling data of multiple rocks are obtained through a three-dimensional laser scanner and experimental data, and the sampling data include the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process.

[0007] In the above-mentioned embodiments of the present application, a joint residual shear strength model was trained by considering the compression and wear effects of actual contact micro-asperities. This model fully accounts for the compression and wear phenomena that occur when rock joints are subjected to contact micro-asperities during actual stress. Through in-depth research on the compression and wear effects of micro-asperities, the model can more accurately describe the residual shear strength characteristics of rock joints, providing a more accurate and reliable theoretical basis for the design and analysis of geotechnical engineering, and achieving the effect of accurately predicting the residual shear strength of joints.

[0008] In some embodiments, before obtaining the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model, it also includes: obtaining sampling data of multiple rocks through a three-dimensional laser scanner and experimental data, wherein the sampling data includes the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process; training the basic machine learning model through the sampling data of multiple rocks to obtain a joint residual shear strength model.

[0009] In the above embodiments of the present application, the joint residual shear strength model is trained by sampling data, so that the joint residual shear strength model learns to consider the contact conditions, cutting conditions, wear conditions and compression conditions of multiple rock micro-asperities during the shear process to judge the residual shear strength of the rock, thereby improving the model detection accuracy.

[0010] In some embodiments, before obtaining the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model, it also includes: obtaining rock characteristic data by analyzing the geometric and mechanical properties of different rock micro-asperities; and constructing a mechanical model through the rock characteristic data.

[0011] In the above embodiments of the present application, by analyzing the geometric and mechanical properties of different rock asperities, rock characteristic data is obtained to construct a mechanical model, so that rock sampling data can be obtained accurately and quickly.

[0012] In some embodiments, before obtaining the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model, the method further includes: analyzing the point cloud data of the rock to be predicted to determine the mapping relationship between the residual dilatancy angle and the residual shear strength in the presence and absence of asperity compression and wear;

[0013] The influencing factors are input into the preset joint residual shear strength model to obtain the quantitative results of the residual shear strength of the rock to be predicted, including: analyzing the influencing factors and point cloud data through the joint residual shear strength model to determine the target residual shear dilation angle of the rock to be predicted with or without micro-asperity compression and wear; and determining the quantitative results of the residual shear strength corresponding to the target residual shear dilation angle through a mapping relationship.

[0014] In the above embodiments of the present application, by constructing in advance the mapping relationship between the residual dilatancy angle and the residual shear strength in the presence and absence of micro-asperity compression and wear, the quantitative results of the residual shear strength can be quickly matched to the influencing factors and point cloud data in the subsequent joint residual shear strength model analysis.

[0015] In a second aspect, an embodiment of the present application provides a device for quantifying residual shear strength of joints, comprising:

[0016] An acquisition module is used to obtain the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model, wherein the influencing factors include: compression and wear data of the micro-asperities of the rock to be predicted during the shear process. The mechanical model is constructed by analyzing the geometric and mechanical properties of different rock micro-asperities to obtain rock characteristic data;

[0017] The prediction module is used to input the influencing factors into the preset joint residual shear strength model to obtain the quantitative results of the residual shear strength of the rock to be predicted, wherein the joint residual shear strength model is obtained by training the basic machine learning model through the sampling data of multiple rocks. The sampling data of multiple rocks are obtained through a three-dimensional laser scanner and experimental data. The sampling data include the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process.

[0018] Optionally, the device further includes:

[0019] The training module is used to obtain sampling data of multiple rocks using a three-dimensional laser scanner and experimental data before the acquisition module obtains the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model, wherein the sampling data includes the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of the multiple rocks during the shear process;

[0020] The basic machine learning model is trained using sampling data from multiple rocks to obtain a joint residual shear strength model.

[0021] Optionally, the device further includes:

[0022] A construction module is used to obtain rock characteristic data by analyzing the geometric and mechanical properties of different rock asperities before obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model;

[0023] A mechanical model is constructed based on rock characteristic data.

[0024] Optionally, the device further includes:

[0025] A mapping module is used to analyze the point cloud data of the rock to be predicted before obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model, and to determine the mapping relationship between the residual dilatancy angle and the residual shear strength in the presence and absence of micro-asperity compression and wear;

[0026] The prediction module is specifically used to:

[0027] The influencing factors and point cloud data are analyzed by the joint residual shear strength model to determine the target residual dilatancy angle of the rock to be predicted with or without asperity compression and wear.

[0028] Through the mapping relationship, the quantitative result of residual shear strength corresponding to the target residual dilatancy angle is determined.

[0029] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect above are executed.

[0030] In a fourth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the method provided in the first aspect are executed.

[0031] Other features and advantages of the present application will be described in the subsequent description, and in part will become apparent from the description, or will be understood by practicing the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] 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.

[0033] Figure 1 A flowchart of a joint residual shear strength quantification method provided in an embodiment of the present application;

[0034] Figure 2 A schematic block diagram of a device for quantifying residual shear strength of joints provided in an embodiment of the present application;

[0035] Figure 3 This is a schematic block diagram of the structure of a joint residual shear strength quantification device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0037] 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.

[0038] First, some of the terms involved in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0039] Rock shear strength refers to the maximum shear stress a rock can withstand when it fails under shear load. Rock shear strength is categorized into direct shear strength and triaxial shear strength based on different testing methods. Direct shear strength is divided into shear strength and shear (friction) strength. Shear strength refers to the strength of a rock when it fails under shear, while shear (friction) strength refers to the shear strength of the rock after it has been sheared and subjected to a friction test.

[0040] This application is applied to the scenario of rock shear strength detection. The specific scenario is to train a joint residual shear strength model that takes into account the compression and wear conditions of rock joints caused by contact micro-asperities during actual stress. The accuracy of rock shear strength detection can be improved through this model.

[0041] In the field of rock mechanics, the study of the mechanical properties of rock joints has long been crucial for ensuring the stability and safety of various geotechnical engineering projects. In recent years, in-depth research into the mechanical properties of rock joints has continuously driven technological advancement and innovation in this field. Compared to the peak shear strength of joints, the study of residual shear strength is of greater practical significance. In engineering practice, rock joints often undergo long-term deformation and stress. When relative slip occurs in the joints, their shear strength gradually decreases from peak strength to a residual strength state. At this point, the residual shear strength determines the ultimate stability state and potential failure modes of the joints, playing a crucial role in assessing the long-term stability and safety of geotechnical engineering projects. For example, in large-scale water conservancy projects, tunnel projects, and slope engineering, the residual shear strength of joints directly affects the durability and reliability of the engineering structures. Therefore, accurately predicting the residual shear strength of rock joints is of irreplaceable value for engineering design and risk assessment. Currently, the existing residual shear strength formula is a widely used classical theory in the study of the residual shear strength of rock joints. However, this formula has certain limitations, and its predicted values ​​are generally too low. This can lead to misjudgments of joint stability in practical engineering applications, thus affecting project safety. With the development of machine learning technology, using machine learning methods to predict the residual shear strength of rock joints has become a new research direction. While machine learning methods can improve prediction accuracy to a certain extent, they require a large amount of data as support, lack clear physical meaning, and have poor model interpretability, failing to fundamentally reveal the mechanical mechanism of the residual shear strength of rock joints.

[0042] In the field of residual shear strength research of rock joints, current technical solutions have exposed a series of defects in practical applications. These deficiencies seriously limit the accurate understanding of the mechanical properties of rock joints and make it difficult to meet the needs of engineering practice. Specifically,

[0043] 1. Insufficient consideration of actual contact joint effects and physical processes.

[0044] Traditional residual shear strength models lack a comprehensive understanding of the actual contact joint interactions and physical processes. During the actual shear process in a joint, not all asperities interact simultaneously and uniformly; rather, some asperities come into contact and interact first. As shearing progresses, these contacting asperities undergo complex physical changes, such as wear and shearing, which in turn alter their mechanical properties. However, previous models have failed to account for this critical contact process and instead derived and calculated residual shear strength based solely on idealized assumptions, resulting in a significant disconnect between the models and actual conditions. Furthermore, while machine learning methods have improved prediction accuracy to some extent, they are essentially data-driven "black box" models lacking clear physical meaning. Machine learning models, by learning and analyzing large amounts of data, establish a mapping between input parameters and residual shear strength, but they fail to clearly reveal the mechanisms by which various factors influence residual shear strength. This makes it difficult for researchers and engineers to gain a deep understanding of the underlying mechanical behavior of rock joints. In practical applications, when faced with new engineering problems or changes in data distribution, the model's generalization and reliability are severely impacted, making it difficult to guarantee the accuracy and stability of the prediction results.

[0045] 2. Ignore the compression and wear effects of contact micro-asperities.

[0046] During the residual shear process of rock joints, the compression and wear of contact asperities are key physical processes that cannot be ignored and have a significant impact on the residual shear strength of the joints. However, most existing technical solutions fail to fully consider this factor. Under the action of shear force, the contact asperities not only undergo compression deformation, changing their shape and size, but also, as the shear displacement increases, the mutual friction and wear between the asperities continue to intensify, leading to significant changes in the roughness and mechanical properties of the joint surface. Because the existing models ignore these effects, they are unable to accurately describe the mechanical behavior of the joints in the residual shear stage, resulting in a large deviation between the predicted residual shear strength and the actual situation. This inaccurate prediction may lead to misassessment of the safety of the engineering structure in actual engineering, posing a huge risk to the long-term stable operation of the project.

[0047] To this end, this application obtains the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model, wherein the influencing factors include: the compression and wear data of the micro-asperities of the rock to be predicted during the shear process, and the mechanical model is constructed by analyzing the geometric and mechanical properties of different rock micro-asperities to obtain rock characteristic data; the influencing factors are input into the preset joint residual shear strength model to obtain the quantitative results of the residual shear strength of the rock to be predicted, wherein the joint residual shear strength model is obtained by training the basic machine learning model with the sampling data of multiple rocks, and the sampling data of multiple rocks are obtained through three-dimensional laser scanners and experimental data, and the sampling data include the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process. A joint residual shear strength model is trained by considering the compression and wear effects of the actual contact micro-asperities. This model fully considers the compression and wear phenomena that occur in the contact micro-asperities of rock joints during the actual stress process. Through in-depth research on the compression and wear effects of micro-asperities, the model can more accurately describe the residual shear strength characteristics of rock joints, provide a more accurate and reliable theoretical basis for the design and analysis of geotechnical engineering, and achieve the effect of accurately predicting the residual shear strength of joints.

[0048] In the embodiment of the present application, the executing entity may be a joint residual shear strength quantification device in a joint residual shear strength quantification system. In actual applications, the joint residual shear strength quantification device may be an electronic device such as a terminal device and a server, which is not limited here.

[0049] The following combination Figure 1 The method for quantifying the residual shear strength of joints in an embodiment of the present application is described in detail.

[0050] Please see Figure 1 , Figure 1 A flow chart of a joint residual shear strength quantification method provided in an embodiment of the present application is shown in FIG. Figure 1 The methods shown for quantifying the residual shear strength of joints include:

[0051] Step 110: Obtain the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model.

[0052] Influencing factors include compression and wear data of the rock's asperities during shearing. The mechanical model is constructed by analyzing the geometric and mechanical properties of different rock asperities to obtain rock characteristic data. Other influencing factors include the rock's sampling interval, joint surface roughness parameters, uniaxial tensile strength, uniaxial compressive strength, normal pressure, cohesion, internal friction angle, and joint size.

[0053] In some embodiments of the present application, before obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model, Figure 1 The method shown also includes: obtaining sampling data of multiple rocks through a three-dimensional laser scanner and experimental data, wherein the sampling data includes the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process; and training the basic machine learning model through the sampling data of multiple rocks to obtain a joint residual shear strength model.

[0054] In the above process, this application trains the joint residual shear strength model through sampling data, so that the joint residual shear strength model learns to consider the contact conditions, cutting conditions, wear conditions and compression conditions of multiple rock micro-asperities during the shear process to judge the residual shear strength of the rock, thereby improving the model detection accuracy.

[0055] Among them, the experimental data can be the sampling interval, uniaxial tensile strength, and normal pressure of multiple rocks obtained through the Brazilian splitting test; the uniaxial compressive strength of multiple rocks obtained through the uniaxial compression test; and the cohesion, internal friction angle and other data of multiple rocks obtained through the triaxial test, as well as the above-mentioned contact conditions, cutting conditions, wear conditions and compression conditions.

[0056] Optionally, sampling data of multiple rocks are obtained by using a three-dimensional laser scanner and experimental data, including: obtaining a three-dimensional topography point cloud dataset of the structural surfaces of multiple rocks by using a three-dimensional laser scanner.

[0057] In some embodiments of the present application, before obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model, Figure 1 The method shown also includes: obtaining rock characteristic data by analyzing the geometric and mechanical properties of different rock asperities; and constructing a mechanical model based on the rock characteristic data.

[0058] In the above process, the present application analyzes the geometric and mechanical properties of different rock micro-asperities to obtain rock characteristic data and construct a mechanical model, so as to obtain rock sampling data accurately and quickly.

[0059] Among them, mechanical characteristics include normal pressure, force source and specific size, etc. Geometric characteristics include the shape and size of the rock and other characteristics.

[0060] Optionally, the geometric and mechanical properties of different rock micro-asperities can be analyzed through multiple functions and neural network analysis, specifically including linear, polynomial and radial basis function (RBF), support vector regression (SVR) training basic models to obtain models for geometric and mechanical feature analysis.

[0061] In some embodiments of the present application, before obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model, Figure 1 The method further includes: analyzing point cloud data of the rock to be predicted to determine a mapping relationship between the residual dilatancy angle and the residual shear strength in the presence and absence of asperity compression and wear;

[0062] Among them, the mapping relationship can be constructed in advance based on a large amount of data.

[0063] Step 120: Input the influencing factors into the preset joint residual shear strength model to obtain a quantitative result of the residual shear strength of the rock to be predicted.

[0064] Among them, the joint residual shear strength model is obtained by training the basic machine learning model through sampling data of multiple rocks. The sampling data of multiple rocks are obtained through three-dimensional laser scanners and experimental data. The sampling data include the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process.

[0065] In some embodiments of the present application, the influencing factors are input into a preset joint residual shear strength model to obtain a quantitative result of the residual shear strength of the rock to be predicted, including: analyzing the influencing factors and point cloud data through the joint residual shear strength model to determine the target residual shear angle under the conditions of micro-asperity compression and wear of the rock to be predicted with or without; and determining the quantitative result of the residual shear strength corresponding to the target residual shear angle through a mapping relationship.

[0066] In the above process, the present application constructs in advance the mapping relationship between the residual dilatancy angle and the residual shear strength in the presence and absence of micro-asperity compression and wear, so that the quantitative results of the residual shear strength can be quickly matched with the influencing factors and point cloud data in the subsequent joint residual shear strength model analysis.

[0067] Optionally, before establishing the mapping relationship, it is necessary to determine the minimum asperity contact angle under a specific normal stress and establish a residual dilatancy angle that can reflect the degree of compression and wear of the joint surface.

[0068] Specifically: Based on the joint surface point cloud data obtained by scanning, a joint morphology distribution function is established. Through mechanical analysis and mechanical shear process analysis, the minimum micro-convex contact angle under a certain normal stress is obtained without considering the influence of normal stress on the compression and wear degree of the joint surface. This angle is also the residual dilatancy angle when the compression and wear degree of the joint surface are not considered. Based on the joint surface point cloud data obtained by scanning, a joint morphology distribution function is established. Through mechanical analysis and mechanical shear process analysis, the minimum micro-convex contact angle under a certain normal stress is obtained without considering the influence of normal stress on the compression and wear degree of the joint surface. This angle is also the residual dilatancy angle when the compression and wear degree of the joint surface are not considered. The shear strength of rock joints during the shear process conforms to the Mohr-Coulomb criterion. The relationship between its residual shear strength and residual dilatancy angle and the calculation of the final quantitative results of the total residual shear strength can be obtained by the following formula:

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] in, , , is the inclination angle of the joint surface microelement, the effective shear inclination , is the angle between the joint surface inclination and the shear direction, t is the shear direction vector, n is the element external normal vector, n0 is the external normal vector of the shear plane, n1 is the projection vector of the shear direction on the shear plane, The sum of the areas of all joint elements with inclination angles greater than 0 and the sum of the joint surface areas The ratio of is the fitting coefficient, A is the sum of all joint areas, and the effective dip angle is greater than The sum of all infinitesimal areas and joint surface areas and , actual contact area It can be approximately considered as the normal load acting on the joint surface Divided by the uniaxial compressive strength of the rock , is the normal stress, N is a natural number, when the apparent inclination angle of the convex body is less than When , the separation of the asperities does not contribute to the shear strength, so is the minimum angle of the actual contact joint asperity under a specific normal stress, is the residual dilatancy angle of the joint without considering the compression and wear effects; is the residual shear strength, i r The residual dilatancy angle of the joint considering the compression and wear effects of the actual contact asperities, is the basic friction angle. The above formula is used to test the shear strength of rock.

[0076] Optionally, as the shear displacement increases, the apparent inclination angle is greater than The wear degree of the micro-convex body increases continuously, and the apparent inclination angle gradually increases. Therefore, it can be preliminarily assumed that the residual dilatancy angle of the jointed rock after shearing is With the increase of normal stress, the compression and wear of jointed rock in the shear process become more obvious, and when the normal stress reaches When the joint surface is flattened, Using the residual dilatancy angle of the joint to represent it cannot fully and accurately reflect the compression and damage effects of the joint surface under different normal stresses.

[0077] In the above Figure 1 In the process shown, the present application obtains the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model, wherein the influencing factors include: the compression and wear data of the micro-asperities of the rock to be predicted during the shear process, and the mechanical model is constructed by analyzing the geometric and mechanical properties of different rock micro-asperities to obtain rock characteristic data; the influencing factors are input into the preset joint residual shear strength model to obtain the quantitative results of the residual shear strength of the rock to be predicted, wherein the joint residual shear strength model is obtained by training the basic machine learning model with the sampling data of multiple rocks, and the sampling data of multiple rocks are obtained through a three-dimensional laser scanner and experimental data. The sampling data includes the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process. A joint residual shear strength model is trained by considering the compression and wear effects of the actual contact micro-asperities. This model fully considers the compression and wear phenomena of the contact micro-asperities of the rock joints during the actual stress process. Through in-depth research on the compression and wear effects of micro-asperities, the model can more accurately describe the residual shear strength characteristics of rock joints, provide a more accurate and reliable theoretical basis for the design and analysis of geotechnical engineering, and achieve the effect of accurately predicting the residual shear strength of joints.

[0078] Previous article passed Figure 1 The method for quantifying the residual shear strength of joints is described below. Figure 2-Figure 3Describe the device for quantifying the residual shear strength of joints.

[0079] Please refer to Figure 2 , is a schematic block diagram of a joint residual shear strength quantification device 200 provided in an embodiment of the present application, and the joint residual shear strength quantification device 200 can be a module, program segment or code on an electronic device. The joint residual shear strength quantification device 200 is similar to the above Figure 1 The method embodiment corresponds to the embodiment that can be executed Figure 1 The various steps involved in the method embodiment and the specific functions of the joint residual shear strength quantification device 200 can be found in the description below. To avoid repetition, detailed description is appropriately omitted here.

[0080] Optionally, the joint residual shear strength quantification device 200 includes:

[0081] An acquisition module 210 is configured to obtain, using a preset mechanical model, influencing factors of the residual shear strength of the rock joints to be predicted, wherein the influencing factors include compression and wear data of the asperities of the rock to be predicted during the shear process. The mechanical model is constructed by analyzing the geometric and mechanical properties of different rock asperities to obtain rock characteristic data.

[0082] The prediction module 220 is used to input the influencing factors into a preset joint residual shear strength model to obtain a quantitative result of the residual shear strength of the rock to be predicted, wherein the joint residual shear strength model is obtained by training a basic machine learning model through sampling data of multiple rocks. The sampling data of multiple rocks are obtained through a three-dimensional laser scanner and experimental data. The sampling data include the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process.

[0083] Optionally, the device further includes:

[0084] The training module is used to obtain sampling data of multiple rocks through a three-dimensional laser scanner and experimental data before the module obtains the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model. The sampling data includes the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process; the basic machine learning model is trained through the sampling data of multiple rocks to obtain a joint residual shear strength model.

[0085] Optionally, the device further includes:

[0086] The construction module is used to obtain rock characteristic data by analyzing the geometric and mechanical properties of different rock micro-asperities before obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model; and to construct a mechanical model based on the rock characteristic data.

[0087] Optionally, the device further includes:

[0088] A mapping module is used to analyze the point cloud data of the rock to be predicted before obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model, and to determine the mapping relationship between the residual dilatancy angle and the residual shear strength in the presence and absence of micro-asperity compression and wear;

[0089] The prediction module is specifically used to:

[0090] The joint residual shear strength model is used to analyze the influencing factors and point cloud data to determine the target residual dilatancy angle of the rock to be predicted with or without asperity compression and wear. The quantitative residual shear strength result corresponding to the target residual dilatancy angle is determined through the mapping relationship.

[0091] Please refer to Figure 3 This is a schematic block diagram of a joint residual shear strength quantification device provided in an embodiment of the present application. The device may include a memory 310 and a processor 320. Optionally, the device may also include: a communication interface 330 and a communication bus 340. The device is similar to the above Figure 1 The method embodiment corresponds to the embodiment that can be executed Figure 1 The various steps involved in the method embodiment and the specific functions of the device can be found in the description below.

[0092] Specifically, the memory 310 is used to store computer-readable instructions.

[0093] Processor 320 is used to process the readable instructions stored in the memory and can execute Figure 1 The steps in the method.

[0094] The communication interface 330 is used for signaling or data communication with other node devices, for example, for communication with a server or terminal, or for communication with other device nodes, but the present invention is not limited thereto.

[0095] The communication bus 340 is used to realize direct connection and communication among the above components.

[0096] The communication interface 330 of the device in the embodiment of the present application is used to communicate signaling or data with other node devices. The memory 310 can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The memory 310 can also be at least one storage device located away from the aforementioned processor. The memory 310 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 320, the electronic device executes the above-mentioned instructions. Figure 1 The method process shown. The processor 320 can be used in the joint residual shear strength quantification device 200 and is used to perform the functions of the present application. For example, the above-mentioned processor 320 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, but the embodiments of the present application are not limited thereto.

[0097] The embodiment of the present application further provides a readable storage medium, wherein when the computer program is executed by a processor, Figure 1 The method process in the illustrated method embodiment is performed by the electronic device.

[0098] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method, and will not be described in detail here.

[0099] In summary, the embodiments of the present application provide a method, device, equipment and storage medium for quantifying the residual shear strength of joints. The method includes obtaining influencing factors of the residual shear strength of rock joints to be predicted through a preset mechanical model, wherein the influencing factors include: compression and wear data of the micro-asperities of the rock to be predicted during the shear process, and the mechanical model is constructed by analyzing the geometric and mechanical properties of different rock micro-asperities to obtain rock characteristic data; the influencing factors are input into the preset joint residual shear strength model to obtain the quantitative results of the residual shear strength of the rock to be predicted, wherein the joint residual shear strength model is obtained by training a basic machine learning model with sampling data of multiple rocks, and the sampling data of multiple rocks are obtained through a three-dimensional laser scanner and experimental data, and the sampling data include the contact conditions, cutting conditions, wear conditions and compression conditions of the micro-asperities of multiple rocks during the shear process, so as to achieve the effect of accurately predicting the residual shear strength of the joints.

[0100] 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.

[0101] 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.

[0102] 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 a computer 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 USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0103] 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.

[0104] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

[0105] 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 quantifying residual shear strength of joints, characterized in that: include: Obtaining the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model, wherein the influencing factors include: compression and wear data of the asperities of the rock to be predicted during the shear process, and the mechanical model is constructed by analyzing the geometric and mechanical properties of different rock asperities to obtain rock characteristic data; Inputting the influencing factors into a preset joint residual shear strength model to obtain a quantitative result of the residual shear strength of the rock to be predicted, wherein the joint residual shear strength model is obtained by training a basic machine learning model using sampling data of multiple rocks, the sampling data of the multiple rocks being acquired using a three-dimensional laser scanner and experimental data, and the sampling data including contact conditions, cutting conditions, wear conditions, and compression conditions of asperities of the multiple rocks during a shear process; Before obtaining the influencing factors of the residual shear strength of the rock joints to be predicted through a preset mechanical model, the method further includes: determining the minimum micro-asperity contact angle under normal stress and establishing a residual dilatancy angle, wherein the minimum micro-asperity contact angle is obtained by scanning the joint surface point cloud data of the rock to be predicted to establish a joint morphology distribution function, mechanical analysis and mechanical shear process analysis method, and the residual dilatancy angle is used to reflect the degree of compression and wear of the joint surface; by analyzing the point cloud data of the rock to be predicted, determining the mapping relationship between the residual dilatancy angle and the residual shear strength in the presence and absence of micro-asperity compression and wear; The method of inputting the influencing factors into a preset joint residual shear strength model to obtain a quantitative result of the residual shear strength of the rock to be predicted includes: analyzing the influencing factors and the point cloud data through the joint residual shear strength model to determine the target residual dilatancy angle of the rock to be predicted under the conditions of micro-asperity compression and wear with or without; and determining the quantitative result of the residual shear strength corresponding to the target residual dilatancy angle through the mapping relationship.

2. The method according to claim 1, characterized in that Before obtaining the influencing factors of the residual shear strength of the rock joints to be predicted through the preset mechanical model, the method further includes: Acquire sampling data of the plurality of rocks by using a three-dimensional laser scanner and experimental data, wherein the sampling data includes contact conditions, cutting conditions, wear conditions, and compression conditions of the asperities of the plurality of rocks during the shearing process; The basic machine learning model is trained using the sampling data of the multiple rocks to obtain the joint residual shear strength model.

3. The method according to claim 1 or 2, characterized in that Before obtaining the influencing factors of the residual shear strength of the rock joints to be predicted through the preset mechanical model, the method further includes: By analyzing the geometric and mechanical properties of different rock asperities, rock characteristic data are obtained; The mechanical model is constructed using the rock characteristic data.

4. A device for quantifying residual shear strength of joints, characterized in that: include: An acquisition module is used to obtain influencing factors of the residual shear strength of the rock joint to be predicted through a preset mechanical model, wherein the influencing factors include: compression and wear data of the micro-asperities of the rock to be predicted during the shear process, and the mechanical model is constructed by analyzing the geometric and mechanical properties of different rock micro-asperities to obtain rock characteristic data; a prediction module, configured to input the influencing factors into a preset joint residual shear strength model to obtain a quantitative result of the residual shear strength of the rock to be predicted, wherein the joint residual shear strength model is obtained by training a basic machine learning model using sampling data of multiple rocks, the sampling data of the multiple rocks being acquired using a three-dimensional laser scanner and experimental data, and the sampling data including contact conditions, cutting conditions, wear conditions, and compression conditions of asperities of the multiple rocks during a shear process; The device further comprises: a mapping module for determining a minimum asperity contact angle under normal stress and establishing a residual dilatancy angle, wherein the minimum asperity contact angle is obtained by scanning the joint surface point cloud data of the rock to be predicted to establish a joint morphology distribution function, mechanical analysis, and mechanical shear process analysis method, and the residual dilatancy angle is used to reflect the degree of joint surface compression and wear; and determining a mapping relationship between the residual dilatancy angle and the residual shear strength in the presence and absence of asperity compression and wear by analyzing the point cloud data of the rock to be predicted; The prediction module is specifically used for: The influencing factors and the point cloud data are analyzed through the joint residual shear strength model to determine the target residual dilatancy angle of the rock to be predicted with or without micro-asperity compression and wear; and the quantitative result of the residual shear strength corresponding to the target residual dilatancy angle is determined through the mapping relationship.

5. The device according to claim 4, characterized in that The device further comprises: a training module configured to obtain sampling data of the plurality of rocks using a three-dimensional laser scanner and experimental data before the acquisition module obtains the influencing factors of the residual shear strength of the rock joints to be predicted using a preset mechanical model, wherein the sampling data includes contact conditions, cutting conditions, wear conditions, and compression conditions of the asperities of the plurality of rocks during the shear process; The basic machine learning model is trained using the sampling data of the multiple rocks to obtain the joint residual shear strength model.

6. The device according to claim 4 or 5, characterized in that The device further comprises: A construction module is used for the acquisition module to obtain rock characteristic data by analyzing the geometric and mechanical properties of different rock asperities before obtaining the influencing factors of the residual shear strength of the rock joint to be predicted through the preset mechanical model; The mechanical model is constructed using the rock characteristic data.

7. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 3 are executed.

8. A computer-readable storage medium, characterized in that include: A computer program, when running on a computer, causes the computer to perform the method according to any one of claims 1 to 3.

Images