Slope stability analysis method based on probabilistic coordinated occurrence and spatial difference reduction

Abstract

The present application discloses a slope stability analysis method based on probabilistic coordinated occurrence and spatial differential reduction, which relates to the field of slope stability analysis. The method includes: obtaining on-site acoustic wave velocity distribution data and obtaining a certain parameter distribution based on the Bayesian-sinusoidal incremental model; then inverting the acoustic wave velocity distribution based on the Hoek-Brown criterion and the nonlinear intensity criterion linearization method; determining the spatial distribution of the remaining parameters at different depths through a mapping method to achieve coordinated coupling occurrence of elastic modulus, cohesion and internal friction angle; using multi-physics field simulation software to obtain the reduction coefficient conditions corresponding to different reduction depths, perform spatial differential reduction on the spatial variability distribution of the slope, and complete the slope stability analysis. The method in the present application can take into account the nonlinear failure characteristics of rock, accurately describe the rock strength distribution, and obtain the true spatial variability distribution characteristics and perform more accurate slope stability analysis, thereby improving the safety of artificial slope design.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of slope stability occurrence analysis, and in particular to a slope stability analysis method based on probabilistic coordinated occurrence and spatial difference reduction. Background Art

[0002] Slope stability refers to the stability of rock and soil under certain conditions of slope height and angle. Based on their origin, slopes are categorized as natural slopes and artificial slopes, the latter further divided into excavation slopes and dam slopes. Based on their material composition, slopes are divided into three types: rock slopes, soil slopes, and rock-soil composite slopes. Based on their stability, they are categorized as stable slopes, unstable slopes, and slopes in a state of ultimate equilibrium. Unstable natural slopes and artificial slopes with excessively large designed slope angles often slide or collapse under the influence of rock and soil gravity, water pressure, vibration, and other external forces. Large-scale slope rock and soil failure can cause traffic disruptions, building collapses, river blockages, and reservoir siltation, resulting in significant loss of life and property. The purpose of slope stability research is to predict the time, scale, and severity of slope instability, so that preventive measures can be taken in advance to mitigate geological hazards and ensure the safety and cost-effectiveness of artificial slope design.

[0003] However, actual slopes are extremely complex in terms of shape and material distribution. Studies have shown that the spatial distribution characteristics of these slope shapes, materials, and structures can significantly affect the results of slope stability analysis, leading to irrational assessments and preventive measures for slope instability, and even causing unnecessary losses and casualties. Therefore, how to accurately improve the results of slope stability analysis, thereby improving the safety of artificial slope designs and reducing unnecessary losses and casualties, has become a pressing technical challenge. Currently, there is no technical solution to this problem, nor is there a slope stability data analysis method based on the Hoek-Brown criterion. Summary of the Invention

[0004] The purpose of this application is to solve at least one of the above technical deficiencies.

[0005] On the one hand, an embodiment of the present application provides a slope stability analysis method based on probabilistic coordinated occurrence and spatial difference reduction, the method comprising:

[0006] Acquiring geometric shape data of the slope and on-site acoustic wave velocity distribution data, and processing the on-site acoustic wave velocity distribution data based on a Bayesian-sine increment model to obtain first distribution data of a first rock mass mechanical parameter;

[0007] According to the field acoustic wave velocity distribution data and the Hoek-Brown criterion, a first mapping relationship between the first rock mass mechanical parameter and the acoustic wave velocity and a second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity are obtained;

[0008] Determining target acoustic wave velocity distribution data corresponding to the first distribution data according to the first mapping relationship, and determining distribution data of a second rock mass mechanical parameter according to the second mapping relationship and the target acoustic wave velocity distribution data;

[0009] The first distribution data, the distribution data of the second rock mass mechanical parameter, and the geometric shape data are processed using multi-physics field simulation software to obtain the spatial variability distribution of the slope;

[0010] Obtain the reduction coefficient conditions corresponding to different reduction depths, and reduce the spatial variability distribution of the slope based on the reduction coefficient conditions to obtain the spatial variability distribution of the target slope;

[0011] The stability analysis is carried out according to the spatial variability distribution of the target slope to obtain the slope stability analysis results.

[0012] Optionally, the first rock mass mechanics parameter is cohesion, the second rock mass mechanics parameter includes an internal friction angle, the first mapping relationship is a mapping relationship between cohesion and acoustic wave velocity, and the second mapping relationship includes a mapping relationship between the internal friction angle and acoustic wave velocity;

[0013] According to the field acoustic wave velocity distribution data and the Hoek-Brown criterion, a first mapping relationship between the first rock mass mechanical parameter and the acoustic wave velocity and a second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity are obtained, including:

[0014] Determine the rock material parameters of the Hoek-Brown criterion based on the on-site acoustic wave velocity distribution data;

[0015] According to the determined rock material parameters, the nonlinear strength criterion based on the Hoek-Brown criterion is used to map out the second distribution data of cohesion and the distribution data of the internal friction angle;

[0016] A first mapping relationship is obtained based on the second distribution data of cohesion and the on-site acoustic wave velocity distribution data, and a mapping relationship between the internal friction angle and the acoustic wave velocity is obtained based on the distribution data of the internal friction angle and the on-site acoustic wave velocity distribution data.

[0017] Optionally, the first mapping relationship is expressed as:

[0018]

[0019] in, c Refers to cohesion, C p′ Refers to the target sound wave speed;

[0020] The mapping relationship between the internal friction angle and the acoustic wave velocity is expressed as:

[0021]

[0022] in, Angle of internal friction, C p′ Refers to the target sound wave speed.

[0023] Optionally, the second rock mass mechanical parameter further includes an elastic modulus, and the second mapping relationship further includes a mapping relationship between the elastic modulus and the acoustic wave velocity. The second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity is obtained based on on-site acoustic wave velocity distribution data and the Hoek-Brown criterion, further including:

[0024] Obtain the corresponding relationship between elastic modulus and disturbance factor in Hoek-Brown criterion;

[0025] The corresponding relationship between the elastic modulus and the disturbance factor is processed according to the on-site acoustic wave velocity distribution data to obtain the mapping relationship between the elastic modulus and the acoustic wave velocity;

[0026] Among them, the mapping relationship between elastic modulus and acoustic wave velocity is expressed as:

[0027]

[0028] in, represents the elastic modulus of intact rock, is the elastic modulus, C p′ Refers to the target sound wave speed.

[0029] Optionally, the multi-physics simulation software is COMSOL, and the multi-physics simulation software is used to process the first distribution data, the distribution data of the second rock mass mechanical parameter, and the geometric shape data to obtain the slope spatial variability distribution, including:

[0030] Importing the first distribution data, the distribution data of the internal friction angle, and the distribution data of the elastic modulus into COMSOL;

[0031] In COMSOL, the distribution interpolation of elastic modulus, cohesion and internal friction angle is set respectively to form the coupled distribution of elastic modulus, cohesion and internal friction angle;

[0032] The geometric shape data is modeled by the parameterized curve surface method to obtain the slope geometric model;

[0033] Based on the parameterized curve surface method, the slope geometric model is combined with the coupled distribution of elastic modulus, cohesion and internal friction angle to obtain the spatial variability distribution of the slope.

[0034] Optionally, obtain the reduction coefficient conditions corresponding to different reduction depths, including:

[0035] Get the discount range and set discount conditions;

[0036] The set reduction range and the set reduction condition are introduced into the set interpolation function, and the target reduction range is obtained by combining the spatial coordinate system;

[0037] Obtaining a set reduction coefficient, and obtaining target reduction parameters corresponding to different depths of the reduction range based on the target reduction range and the set reduction coefficient;

[0038] The target interpolation function representing the coordinates of the slope geometric model is obtained, and the target reduction parameter is combined with the target interpolation function to obtain the reduction coefficient conditions corresponding to different reduction depths.

[0039] Optionally, the spatial variability distribution of the slope is reduced based on the reduction coefficient condition to obtain the target spatial variability distribution of the slope, including:

[0040] Determine the spatial distribution variables of slope spatial variability;

[0041] The spatial variability distribution of the target slope is obtained by performing reduction processing according to the spatial occurrence variables and reduction coefficient conditions.

[0042] Optionally, the reduction coefficient conditions corresponding to different reduction depths are expressed by the following formula:

[0043]

[0044] in, 、 and is the coordinate of the slope geometric model, F 1 refers to the initial value of the reduction factor, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range;

[0045] The spatial variability distribution of the target slope is obtained by reducing the spatial occurrence variables and the reduction coefficient conditions through the following formula:

[0046]

[0047] in, and Within the reduction range Coordinate minimum and maximum values, and Within the reduction range Coordinate minimum and maximum values, Refers to the maximum value of the reduction parameter, F 1 refers to the initial value of the reduction factor, Refers to the reduction parameter value, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range.

[0048] Optionally, obtain the slope geometry data, including:

[0049] Obtain slope elevation data of the slope;

[0050] The slope elevation data is processed by equivalent cross section and interpolation to obtain geometric shape data.

[0051] On the other hand, an embodiment of the present application provides a slope stability analysis device based on probabilistic coordinated occurrence and spatial difference reduction, which is applied to occurrence analysis of slope stability, including:

[0052] a data acquisition module for acquiring geometric shape data of the slope and on-site acoustic wave velocity distribution data, and performing data processing on the on-site acoustic wave velocity distribution data based on a Bayesian-sine increment model to obtain first distribution data of a first rock mass mechanical parameter;

[0053] a data mapping module for obtaining a first mapping relationship between a first rock mass mechanical parameter and acoustic wave velocity, and a second mapping relationship between a second rock mass mechanical parameter and acoustic wave velocity, based on field acoustic wave velocity distribution data and the Hoek-Brown criterion;

[0054] a data determination module, configured to determine target acoustic wave velocity distribution data corresponding to the first distribution data according to the first mapping relationship, and to determine distribution data of a second rock mass mechanical parameter according to the second mapping relationship and the target acoustic wave velocity distribution data;

[0055] a data processing module for processing the first distribution data, the distribution data of the second rock mass mechanical parameter, and the geometric shape data using multi-physics field simulation software to obtain a slope spatial variability distribution;

[0056] The data reduction module is used to obtain the reduction coefficient conditions corresponding to different reduction depths, and reduce the spatial variability distribution of the slope based on the reduction coefficient conditions to obtain the spatial variability distribution of the target slope;

[0057] The data analysis module is used to perform stability analysis based on the spatial variability distribution of the target slope and obtain the slope stability analysis results.

[0058] In another aspect, an embodiment of the present application provides an electronic device, including a processor and a memory:

[0059] The memory is configured to store machine-readable instructions, which, when executed by the processor, enable the processor to perform any one of the slope stability analysis methods based on probabilistic coordinated occurrence and spatial difference reduction.

[0060] The beneficial effects of the technical solutions provided in the embodiments of the present application include at least:

[0061] This application aims to reasonably simulate the spatial variability of strength parameters of actual complex slopes, proposes to map the spatial variability distribution of elastic modulus, cohesion and internal friction angle at different depths and in different regions under the profile of complex slopes based on the Hoek-Brown criterion, and realizes the coordinated coupling distribution of elastic modulus, cohesion and internal friction angle by combining the Hoek-Brown criterion, combining the Bayesian-sine increment model, parameter inversion and mapping methods. Since the spatial variability distribution is realized based on the Hoek-Brown criterion in this application, it is possible to better understand the influence of parameters on rock deformation, consider the nonlinear failure characteristics of rocks and rock masses, and more accurately describe the rock strength distribution under complex stress states encountered in actual engineering. It provides a feasible method and theoretical basis for the spatial variability and parameter coupling distribution of strength parameters of complex slopes, and can simulate more realistic mechanical properties, obtain reasonable and accurate spatial variability distribution characteristics and slope stability data analysis results, thereby improving the safety of artificial slope design and reducing unnecessary losses and casualties.

[0062] Furthermore, while this application acquires basic rock mass mechanical parameters based on traditional geological exploration, it also incorporates acoustic wave extraction technology, overcoming the existing limitations of existing technologies, which often fail to fully reflect the mechanical properties of rock masses. Furthermore, the proposed method considers the reduction ranges of different materials and incorporates a spatially differential parameter reduction method into slope stability analysis, enabling more accurate stability analysis. This method provides a feasible method and theoretical basis for spatially differential reduction, offering valuable reference suggestions and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0064] Figure 1 A schematic flow chart of a slope stability analysis method based on probabilistic coordinated occurrence and spatial difference reduction provided in an embodiment of the present application;

[0065] Figure 2aA 2D simple slope schematic diagram provided in an embodiment of the present application;

[0066] Figure 2b A schematic diagram of a 2D complex slope provided in an embodiment of the present application;

[0067] Figure 2c A schematic diagram of a 2D multi-step slope provided in an embodiment of the present application;

[0068] Figure 2d Schematic diagram of a 2D complex multi-step slope provided in an embodiment of the present application;

[0069] Figure 2e A schematic diagram of a 3D multi-step slope provided in an embodiment of the present application;

[0070] Figure 2f A schematic diagram of a 3D complex slope provided in an embodiment of the present application;

[0071] Figure 3 A schematic diagram of the mapping relationship between cohesion and acoustic wave velocity provided in an embodiment of the present application;

[0072] Figure 4 A schematic diagram of the mapping relationship between the internal friction angle and the acoustic wave velocity provided in an embodiment of the present application;

[0073] Figure 5 A schematic diagram of the mapping relationship between elastic modulus and acoustic wave velocity provided in an embodiment of the present application;

[0074] Figure 6 A schematic diagram of the coordinated coupling distribution of 2D simple slope cohesion and internal friction angle provided in an embodiment of the present application;

[0075] Figure 7 Schematic diagram of the coordinated coupling distribution of the cohesion and internal friction angle of a 2D complex slope provided in an embodiment of the present application;

[0076] Figure 8 Schematic diagram of the coordinated coupling distribution of 2D multi-step slope cohesion and internal friction angle provided in an embodiment of the present application;

[0077] Figure 9 Schematic diagram of the coordinated coupling distribution of cohesion and internal friction angle of a 2D complex multi-step slope provided in an embodiment of the present application;

[0078] Figure 10 Schematic diagram of the coordinated coupling distribution of 3D multi-step slope cohesion and internal friction angle provided in an embodiment of the present application;

[0079] Figure 11 Schematic diagram of the coordinated coupling distribution of cohesion and internal friction angle of a 3D complex slope provided in an embodiment of the present application;

[0080] Figure 12 A schematic diagram of the reduction area division provided in an embodiment of the present application;

[0081] Figure 13 A schematic diagram of the change in reduction coefficient at different depths provided in an embodiment of the present application;

[0082] Figure 14 Provided in the embodiments of this application F Schematic diagram of slope material distribution when = 1.5;

[0083] Figure 15 Provided in the embodiments of this application F Schematic diagram of slope material distribution when = 2;

[0084] Figure 16 Provided in the embodiments of this application F Schematic diagram of slope material distribution when = 1;

[0085] Figure 17 Provided in the embodiments of this application F Schematic diagram of slope material distribution when = 1.2;

[0086] Figure 18 A schematic diagram of the structure of a slope stability analysis device based on probabilistic coordinated occurrence and spatial difference reduction provided in an embodiment of the present application;

[0087] Figure 19 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0088] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present invention.

[0089] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0090] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0091] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0092] Specifically, such as Figure 1 As shown, the method may include:

[0093] Step S101 : obtaining geometric shape data of the slope and on-site acoustic wave velocity distribution data, and processing the on-site acoustic wave velocity distribution data based on a Bayesian-sine increment model to obtain first distribution data of a first rock mass mechanical parameter.

[0094] Optionally, the on-site acoustic wave velocity refers to the acoustic wave distribution characteristics of the slope at different depths and different areas obtained by conducting an acoustic wave test experiment on the slope site. The first rock mass mechanics parameter can be the cohesion of the slope. At this time, the first distribution data of the first rock mass mechanics parameter (i.e., cohesion) can be a probability distribution function of the depth and area of ​​cohesion determined by combining the Bayesian-sine incremental model, and then a specific function is written with the help of Python, and the assumed depth is derived. h and cohesion c The data of the column is used as the first distribution data.

[0095] In this application, while basic rock mechanical parameters are obtained based on traditional geological exploration, acoustic wave extraction technology is also added to overcome the defect of existing technology that it cannot fully reflect the mechanical properties of the rock mass. Rock mechanical parameters can be obtained more accurately through acoustic wave testing technology.

[0096] In an optional embodiment of the present application, obtaining the geometric shape data of the slope includes:

[0097] Obtain slope elevation data of the slope;

[0098] The slope elevation data is processed by equivalent cross section and interpolation to obtain geometric shape data.

[0099] Optionally, this application can obtain slope elevation data through satellite positioning, actual elevation measurement, drone photography measurement, infrared ranging and GIS technology, and then use equivalent cross-section and interpolation processing methods to determine the 2D and 3D geometric shapes of the slope, as shown in Figure 2 (including Figures 2a to 2f ) shows the commonly occurring 2D and 3D slope shapes.

[0100] Step S102 : obtaining a first mapping relationship between a first rock mass mechanical parameter and acoustic wave velocity, and a second mapping relationship between a second rock mass mechanical parameter and acoustic wave velocity according to the field acoustic wave velocity distribution data and the Hoek-Brown criterion.

[0101] In an optional embodiment of the present application, the first rock mass mechanics parameter is cohesion, the second rock mass mechanics parameter includes an internal friction angle, the first mapping relationship is a mapping relationship between cohesion and acoustic wave velocity, and the second mapping relationship includes a mapping relationship between the internal friction angle and acoustic wave velocity;

[0102] According to the field acoustic wave velocity distribution data and the Hoek-Brown criterion, a first mapping relationship between the first rock mass mechanical parameter and the acoustic wave velocity and a second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity are obtained, including:

[0103] Determine the rock material parameters of the Hoek-Brown criterion based on the on-site acoustic wave velocity distribution data;

[0104] According to the determined rock material parameters, the nonlinear strength criterion based on the Hoek-Brown criterion is used to map out the second distribution data of cohesion and the distribution data of the internal friction angle;

[0105] A first mapping relationship is obtained based on the second distribution data of cohesion and the on-site acoustic wave velocity distribution data, and a mapping relationship between the internal friction angle and the acoustic wave velocity is obtained based on the distribution data of the internal friction angle and the on-site acoustic wave velocity distribution data.

[0106] Optionally, in the present application, the first rock mechanics parameter may be the cohesion mentioned above, and the second rock mechanics parameter may include the internal friction angle. In this case, the first mapping relationship is the mapping relationship between cohesion and acoustic wave velocity, and the second mapping relationship includes the mapping relationship between the internal friction angle and acoustic wave velocity.

[0107] The first mapping relationship can be determined based on the Hoek-Brown criterion. In this case, the rock material parameters of the Hoek-Brown criterion can be determined based on the acquired field acoustic wave velocity distribution data. Then, based on the determined rock material parameters, the nonlinear strength criterion of the Hoek-Brown criterion is used to linearly map the second distribution data of cohesion. The second distribution data refers to the cohesion at different depths and regions. The cohesion at different depths and regions linearized based on the nonlinear strength criterion of the Hoek-Brown criterion is determined using the following formula:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] in ，m b 、 s 、 a is the rock material parameter involved in the Heok-Brown criterion, C p and C p′ They refer to the acoustic wave velocity of the rock mass without disturbance and the acoustic wave velocity of the rock mass after disturbance, and C p′ is the target value of the final mapping (i.e., the target sound wave speed).

[0114] Furthermore, after determining the second distribution data of the cohesion, the second distribution data can be mapped with the acquired on-site radio wave velocity distribution data to obtain a first mapping relationship, and the first mapping relationship is expressed as:

[0115]

[0116] in, c Refers to cohesion, C p′ Refers to the target sound wave velocity. It can be understood that in this application, the first mapping relationship corresponds to The value of is 0.9993, that is Furthermore, after obtaining the first mapping relationship, the cohesion of the acoustic wave velocity (1-3) km / s can be calculated by the first mapping relationship. The correlation between the cohesion and the acoustic wave velocity is as follows: Figure 3 shown.

[0117] Optionally, the mapping relationship between the internal friction angle and the acoustic wave velocity can also be determined using the Hoek-Brown criterion. In this case, the rock material parameters of the Hoek-Brown criterion are first determined based on the acquired on-site acoustic wave velocity distribution data. Then, based on the determined rock material parameters, the nonlinear strength criterion of the Hoek-Brown criterion is used to linearly map the distribution data of the internal friction angle. The distribution data of the internal friction angle refers to the internal friction angle at different depths and regions. The internal friction angle at different depths and regions linearly mapped based on the nonlinear strength criterion of the Hoek-Brown criterion is determined using the following formula:

[0118]

[0119] in, , ,γ represents the bulk density of rock mass, Hs is the height of the relevant slope, mb, s, a It is the rock material parameter involved in the Heok-Brown criterion.

[0120] Furthermore, after determining the distribution data of the internal friction angle based on the Hoek-Brown criterion, the distribution data of the internal friction angle can be mapped with the acquired on-site sound wave velocity distribution data to obtain the mapping relationship between the internal friction angle and the sound wave velocity. The mapping relationship between the internal friction angle and the sound wave velocity can be expressed as:

[0121]

[0122] in, Angle of internal friction, C p′ Refers to the target acoustic wave velocity. It is understood that in this application, the mapping relationship between the internal friction angle and the acoustic wave velocity corresponds to The value of is 0.9995, that is .

[0123] Furthermore, after obtaining the mapping relationship between the internal friction angle and the acoustic wave velocity, the internal friction angle of the acoustic wave velocity (1-3) km / s can be calculated by the mapping relationship between the internal friction angle and the acoustic wave velocity. The correlation between the internal friction angle and the acoustic wave velocity is as follows: Figure 4 shown.

[0124] In an optional embodiment of the present application, the second rock mass mechanical parameter further includes an elastic modulus, and the second mapping relationship further includes a mapping relationship between the elastic modulus and the acoustic wave velocity. The second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity is obtained based on the on-site acoustic wave velocity distribution data and the Hoek-Brown criterion, and further includes:

[0125] Obtain the corresponding relationship between elastic modulus and disturbance factor in Hoek-Brown criterion;

[0126] The corresponding relationship between the elastic modulus and the disturbance factor is processed according to the on-site acoustic wave velocity distribution data to obtain the mapping relationship between the elastic modulus and the acoustic wave velocity.

[0127] Optionally, the second rock mass mechanical parameter may also include an elastic modulus. In this case, the second mapping relationship may also include a mapping relationship between the elastic modulus and the acoustic wave velocity. For the mapping relationship between the elastic modulus and the acoustic wave velocity, the corresponding relationship between the elastic modulus and the disturbance factor in the Hoek-Brown criterion may be obtained. Then, the corresponding relationship between the elastic modulus and the disturbance factor may be processed based on the acquired on-site acoustic wave velocity distribution data to obtain a second mapping relationship, i.e., a mapping relationship between the elastic modulus and the acoustic wave velocity. The relationship between the elastic modulus Em and the disturbance factor D in the Hoek-Brown strength criterion may be expressed as:

[0128]

[0129] When D=0, it means the rock mass is not disturbed and the elastic modulus is Em and disturbance factor D The relationship between them can be expressed as:

[0130]

[0131] Furthermore, the relationship between the elastic modulus of disturbed and undisturbed rock masses can be expressed as:

[0132]

[0133] And through a lot of engineering experience data, we know that the elastic modulus Em and rock mass evaluation indicators RMR 89 The relationship can be expressed as:

[0134]

[0135] Among them, in this formula It represents the elastic modulus of intact rock in GPa.

[0136] Furthermore, for the above formula GSI ,because GSI and rock mass evaluation indicators RMR 89 The relationship can be expressed as:

[0137]

[0138] Therefore, the final mapping relationship between elastic modulus and sound wave velocity is:

[0139]

[0140] in, represents the elastic modulus of intact rock, is the elastic modulus, Cp′ Refers to the target sound wave speed.

[0141] Correspondingly, the correlation between the elastic modulus and the acoustic wave velocity (1-3) km / s can be calculated through the mapping relationship between the elastic modulus and the acoustic wave velocity, which can be specifically calculated as follows: Figure 5 shown.

[0142] Step S103 : determining target acoustic wave velocity distribution data corresponding to the first distribution data according to the first mapping relationship, and determining distribution data of a second rock mass mechanical parameter according to the second mapping relationship and the target acoustic wave velocity distribution data.

[0143] In practical applications, after obtaining the mapping relationship between cohesion and acoustic wave velocity (i.e., the first mapping relationship), the first distribution data of cohesion (i.e., the distribution data of cohesion obtained based on the Bayesian-sine increasing model) can be substituted into the first mapping relationship to reversely obtain the target acoustic wave velocity distribution data. Furthermore, the reversed target acoustic wave velocity distribution data is substituted into the obtained mapping relationship between elastic modulus and acoustic wave velocity and the mapping relationship between internal friction angle and acoustic wave velocity, respectively, to obtain the elastic modulus and internal friction angle at different depths and regions.

[0144] Step S104 : using multi-physics field simulation software to process the first distribution data, the distribution data of the second rock mass mechanical parameter, and the geometric shape data to obtain the slope spatial variability distribution.

[0145] Optionally, in order to better understand the stability of the slope, the distribution data of cohesion, the distribution data of internal friction angle and the distribution data of elastic modulus can be processed in combination with the geometric shape data based on multi-physics field simulation software to obtain the spatial variability distribution of the slope.

[0146] In an optional embodiment of the present application, the multi-physics field simulation software is COMSOL, and the first distribution data, the distribution data of the second rock mass mechanical parameter, and the geometric shape data are processed using the multi-physics field simulation software to obtain the slope spatial variability distribution, including:

[0147] Importing the first distribution data, the distribution data of the internal friction angle, and the distribution data of the elastic modulus into COMSOL;

[0148] In COMSOL, the distribution interpolation of elastic modulus, cohesion and internal friction angle is set respectively to form the coupled distribution of elastic modulus, cohesion and internal friction angle;

[0149] The geometric shape data is modeled by the parameterized curve surface method to obtain the slope geometric model;

[0150] Based on the parameterized curve surface method, the slope geometric model is combined with the coupled distribution of elastic modulus, cohesion and internal friction angle to obtain the spatial variability distribution of the slope.

[0151] Optionally, the multi-physics simulation software can be COMSOL software, whose full name is COMSOL Multiphysics. It is an advanced multi-physics simulation software that is widely used in scientific research and engineering computing. It can simulate and simulate various physical phenomena, especially multi-physics coupling problems. The core advantage of COMSOL software is that it can solve partial differential equations (PDEs), which are suitable for mathematical models that describe various physical phenomena. COMSOL software provides a wealth of physical application modes, covering a variety of physical fields such as fluid flow, heat conduction, structural mechanics, and electromagnetic analysis. Users can quickly build models and flexibly define the material properties, source terms, and boundary conditions of the model. Based on the finite element method, COMSOL software realizes the simulation of real physical phenomena by solving partial differential equations of single or multiple fields.

[0152] In an embodiment of the present application, Python programming can be used to export the distribution data of the elastic modulus, the distribution data of the cohesion, and the distribution data of the internal friction angle space, and then the distribution data of the elastic modulus, the distribution data of the cohesion, and the distribution data of the internal friction angle space can be imported into COMSOL. Furthermore, the distribution interpolation of the elastic modulus, cohesion, and internal friction angle is set in COMSOL respectively, thereby forming a coupled distribution of the elastic modulus, cohesion, and internal friction angle of the slope.

[0153] Accordingly, the previously acquired slope geometry data can be modeled through parameterized curve surfaces to obtain a slope geometry model, such as slope models of different shapes including 2D and 3D. The slope geometry model is then combined with the coupled characteristics of elastic modulus, cohesion and internal friction angle through parameterized curve surface drawing to obtain the spatial variability distribution of the slope, thus realizing the spatial variability distribution of elastic modulus, cohesion and internal friction angle of complex shapes. For example, taking cohesion and internal friction angle as an example, the following can be finally achieved: Figures 6 to 11 The coordinated coupled distribution of spatial cohesion and internal friction angle is shown.

[0154] It is worth noting that, in the embodiments of the present application, COMSOL software is used for multi-physics simulation processing, but this does not mean that only COMSOL software can be used for processing, that is, the technical means for performing multi-physics simulation processing are not limited to COMSOL. Those skilled in the art, relying on their common technical knowledge in the field, can use a variety of multi-physics simulation software similar to COMSOL as tools for processing, exemplified by: finite element analysis software ANSYS, finite element analysis software Abaqus, simulation tool Altair, Dassault Systemes for 3D simulation and material testing, MSC Software for structural analysis and fatigue analysis, etc., which will not be described in detail due to space limitations.

[0155] Step S105 , obtaining reduction coefficient conditions corresponding to different reduction depths, and reducing the spatial variability distribution of the slope based on the reduction coefficient conditions to obtain the target spatial variability distribution of the slope.

[0156] Step S106: performing stability analysis based on the spatial variability distribution of the target slope to obtain a slope stability analysis result.

[0157] Optionally, after obtaining the slope spatial variability distribution, the obtained slope spatial variability distribution can be reduced according to the reduction coefficient conditions corresponding to different reduction depths to obtain the target slope spatial variability distribution. Finally, a stability analysis is performed based on the target slope spatial variability distribution to obtain the slope stability analysis results. This process takes into account the reduction range of different materials, allowing for more accurate stability analysis and providing a feasible method and theoretical basis for spatial variability reduction, which has certain reference suggestions and practical significance.

[0158] It is worth noting that the slope stability data analysis method provided by steps S101 to S106 can be used not only for spatial variability analysis of cohesion, internal friction angle, and elastic modulus, but can also be used for other rock mechanics parameters. For example, other rock mechanics parameters may include shear modulus, tensile strength, compressive strength, porosity, density, fracture toughness, etc. Without expending any creative effort, those skilled in the art can make non-substantial modifications to the slope stability analysis method provided by steps S101 to S106 and perform similar stability analysis on the aforementioned other rock mechanics parameters based on the technical means of steps S101 to S106.

[0159] In an optional embodiment of the present application, obtaining the reduction coefficient conditions corresponding to different reduction depths includes:

[0160] Get the discount range and set discount conditions;

[0161] The set reduction range and the set reduction condition are introduced into the set interpolation function, and the target reduction range is obtained by combining the spatial coordinate system;

[0162] Obtaining a set reduction coefficient, and obtaining target reduction parameters corresponding to different depths of the reduction range based on the target reduction range and the set reduction coefficient;

[0163] The target interpolation function representing the coordinates of the slope geometric model is obtained, and the target reduction parameter is combined with the target interpolation function to obtain the reduction coefficient conditions corresponding to different reduction depths.

[0164] Optionally, in order to achieve local coordinated reduction of slopes with spatial differences, the coordinates of the slope geometric model can be represented by defining an interpolation function, which is x,y Characterization z The interpolation function of the coordinates (i.e. the target interpolation function) can be expressed as:

[0165]

[0166] Among them, int refers to the interpolation function, Z max Refers to the surface of the slope geometric model z Coordinate values, x, y Refers to space x and y Coordinate value.

[0167] In order to characterize the differences in reduction coefficients in different geometric spaces, we can define the variables of the reduction coefficients, specify the reduction range and reduction conditions, and introduce the interpolation function and combine the spatial coordinate system to obtain the target reduction range. The target reduction range can be expressed as:

[0168]

[0169] In this formula, x min and x max Refers to the reduction range x Coordinate minimum and maximum values, similarly y min and y max Refers to the reduction range y Coordinate minimum and maximum values, W Refers to the parameters that need to be reduced. F Refers to the reduction coefficient. At this time, it can be divided into Figure 12 The area of ​​local reduction is shown.

[0170] Furthermore, considering that the reduction coefficients at different depths are also different, it is assumed that the reduction coefficients FThe change with depth satisfies the linear change, and the reduction coefficient at different depths (i.e., the set reduction coefficient) can be expressed as:

[0171]

[0172] in, Refers to the initial value of the reduction coefficient, which is usually 1. m Refers to the interval step of each reduction, for example, 0.02, Refers to the maximum depth within the reduction range, h Refers to the current depth, is the reduction factor after reduction.

[0173] Accordingly, based on the target reduction range and the set reduction coefficient, the target reduction parameters corresponding to different depths of the reduction range are obtained as follows:

[0174]

[0175] in, x min and x max Refers to the reduction range x Coordinate minimum and maximum values, similarly y min and y max Refers to the reduction range y Coordinate minimum and maximum values, Refers to the initial value of the reduction factor, m Refers to the interval step of each reduction Refers to the maximum depth within the reduction range, h Refers to the current depth.

[0176] Accordingly, the obtained target reduction parameter can be combined with the obtained target interpolation function to obtain the reduction coefficient conditions corresponding to different reduction depths. Specifically, the reduction coefficient conditions corresponding to different reduction depths can be expressed by the following formula:

[0177]

[0178] in, 、 and is the coordinate of the slope geometric model, F 1 refers to the initial value of the reduction factor, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range.

[0179] For example, when Take 10m, m=0.02 and reduction factor F =1.1, the reduction factor F The changing curves at different depths are as follows Figure 13 As shown in the figure, the reduction range of cohesion and internal friction angle is as follows Figure 14 and Figure 15 As shown, by setting conditions, the material properties and reduction range of different regions can be controlled. Figure 14 It can be seen that F =1.5, the cohesion of the slope is 0.8e5Pa, and the arc value of the internal friction angle is 0.37. At this time, the reduction range can be clearly seen. F =2, by Figure 14 It can be seen that the cohesion of the slope is 0.6e5Pa and the radian value of the internal friction angle is 0.28.

[0180] In an optional embodiment of the present application, the spatial variability distribution of the slope is reduced based on the reduction coefficient condition to obtain the target spatial variability distribution of the slope, including:

[0181] Determine the spatial distribution variables of slope spatial variability;

[0182] The spatial variability distribution of the target slope is obtained by performing reduction processing according to the spatial occurrence variables and reduction coefficient conditions.

[0183] Optionally, to achieve a coordinated reduction of the spatial variability of the material distribution, it is necessary to define the spatial distribution variables of cohesion and internal friction angle (i.e., define parameter variables) before defining the reduction variables. The definition of parameter variables is expressed as:

[0184]

[0185] in, and Within the reduction range Coordinate minimum and maximum values, and Within the reduction range Coordinate minimum and maximum values, Refers to the maximum value of the reduction parameter.

[0186] Furthermore, the spatial variability distribution of the target slope can be obtained by performing a reduction process based on the spatial endowment variables and the reduction coefficient conditions. This is done by combining the expression of the defined parameter variables with the formulas of the reduction coefficient conditions corresponding to different reduction depths to complete the reduction process. Specifically, it can be expressed as:

[0187]

[0188] in, and Within the reduction range Coordinate minimum and maximum values, and Within the reduction range Coordinate minimum and maximum values, Refers to the maximum value of the reduction parameter, F 1 refers to the initial value of the reduction factor, Refers to the reduction parameter value, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range. For example, F = 1, the distribution of slope cohesion and internal friction angle is as follows: Figure 15 As shown, when F =1.2, the distribution of slope cohesion and internal friction angle is as follows Figure 16 shown.

[0189] In order to better understand the method provided by this application, Figure 17 This method is explained again. In this application, slope elevation data is first obtained through satellite positioning, actual elevation measurement, drone photography measurement, infrared ranging, and GIS technology. Then, the slope geometry data (i.e., the slope in the figure) is determined using methods such as equivalent cross-section and interpolation processing. Then, based on the probability distribution method combined with the Bayesian-sinusoidal incremental model, the cohesion distribution data at different depths and regions are obtained (i.e., the theoretical combination part in the figure). The cohesion is mapped out by combining the Hoek-Brown criterion and the nonlinear intensity criterion linearization method through field tests to obtain the sound wave velocity. Then, based on this cohesion, the sound wave velocity at different depths is inferred. Correspondingly, the spatial distribution of elastic modulus and internal friction angle at different depths is linearly mapped by inversely inferring the acoustic wave velocity and using the nonlinear intensity criterion (i.e., the elastic modulus and internal friction angle corresponding to different depths and regions of the slope in the figure. Secondly, the calculated values ​​are imported into COMSOL to write interpolation, establish variables and set conditions under complex profiles. Through parameterized curve surface drawing and polygon drawing methods (i.e., writing interpolation and establishing variables, bringing in interpolation and setting conditions in COMSOL in the figure), the spatial variability of cohesion and internal friction angle at different depths and different regions under complex slope profiles is finally completed in a coordinated coupling manner (i.e., the coordinated coupling manner of shear strength of complex side holes is completed in the figure). Then, different reduction coefficients are considered for different reduction depths, and the parameter space difference reduction method is completed by combining COMSOL interpolation and conditional functions, and finally the slope stability analysis of six commonly used slope types is obtained.

[0190] This application aims to reasonably simulate the spatial variability of strength parameters of actual complex slopes, and proposes to map the spatial variability distribution of elastic modulus, cohesion and internal friction angle at different depths and in different areas under the profile of complex slopes based on the Hoek-Brown criterion. The coordinated coupling distribution of elastic modulus, cohesion and internal friction angle is achieved by combining the Hoek-Brown criterion, the Bayesian-sinusoidal increment model, parameter inversion and mapping, and the parameter spatial difference reduction method is combined with the slope stability analysis of 2D and 3D slopes. This can better understand the influence of parameters on rock deformation, consider the nonlinear failure characteristics of rocks and rock masses, and more accurately describe the strength of rock masses under complex stress states encountered in actual engineering projects. It provides a feasible method and theoretical basis for the spatial variability of strength parameters, parameter coupling distribution and spatial difference reduction of complex slopes, which has certain reference value and practical significance.

[0191] The embodiment of the present application provides a slope stability analysis device based on probabilistic coordinated occurrence and spatial difference reduction, which is applied to occurrence analysis of slope stability, such as Figure 18 As shown, the apparatus 180 may include: a data acquisition module 1801, a data mapping module 1802, a data determination module 1803, a data processing module 1804, a data reduction module 1805 and a data analysis module 1806, wherein:

[0192] a data acquisition module for acquiring geometric shape data of the slope and on-site acoustic wave velocity distribution data, and performing data processing on the on-site acoustic wave velocity distribution data based on a Bayesian-sine increment model to obtain first distribution data of a first rock mass mechanical parameter;

[0193] a data mapping module for obtaining a first mapping relationship between a first rock mass mechanical parameter and acoustic wave velocity, and a second mapping relationship between a second rock mass mechanical parameter and acoustic wave velocity, based on field acoustic wave velocity distribution data and the Hoek-Brown criterion;

[0194] a data determination module, configured to determine target acoustic wave velocity distribution data corresponding to the first distribution data according to the first mapping relationship, and to determine distribution data of a second rock mass mechanical parameter according to the second mapping relationship and the target acoustic wave velocity distribution data;

[0195] a data processing module for processing the first distribution data, the distribution data of the second rock mass mechanical parameter, and the geometric shape data using multi-physics field simulation software to obtain a slope spatial variability distribution;

[0196] The data reduction module is used to obtain the reduction coefficient conditions corresponding to different reduction depths, and reduce the spatial variability distribution of the slope based on the reduction coefficient conditions to obtain the spatial variability distribution of the target slope;

[0197] The data analysis module is used to perform stability analysis based on the spatial variability distribution of the target slope and obtain the slope stability analysis results.

[0198] Optionally, the first rock mass mechanics parameter is cohesion, the second rock mass mechanics parameter includes an internal friction angle, the first mapping relationship is a mapping relationship between cohesion and acoustic wave velocity, and the second mapping relationship includes a mapping relationship between the internal friction angle and acoustic wave velocity;

[0199] The data mapping module is specifically used to obtain a first mapping relationship between a first rock mass mechanical parameter and acoustic wave velocity, and a second mapping relationship between a second rock mass mechanical parameter and acoustic wave velocity based on the field acoustic wave velocity distribution data and the Hoek-Brown criterion:

[0200] Determine the rock material parameters of the Hoek-Brown criterion based on the on-site acoustic wave velocity distribution data;

[0201] According to the determined rock material parameters, the nonlinear strength criterion based on the Hoek-Brown criterion is used to map out the second distribution data of cohesion and the distribution data of the internal friction angle;

[0202] A first mapping relationship is obtained based on the second distribution data of cohesion and the on-site acoustic wave velocity distribution data, and a mapping relationship between the internal friction angle and the acoustic wave velocity is obtained based on the distribution data of the internal friction angle and the on-site acoustic wave velocity distribution data.

[0203] Optionally, the first mapping relationship is expressed as:

[0204]

[0205] in, c Refers to cohesion, C p′ Refers to the target sound wave speed;

[0206] The mapping relationship between the internal friction angle and the acoustic wave velocity is expressed as:

[0207]

[0208] in, Angle of internal friction, C p′ Refers to the target sound wave speed.

[0209] Optionally, the second rock mass mechanical parameter further includes an elastic modulus, and the second mapping relationship further includes a mapping relationship between the elastic modulus and the acoustic wave velocity. When the data mapping module obtains the second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity based on the on-site acoustic wave velocity distribution data and the Hoek-Brown criterion, it is further configured to:

[0210] Obtain the corresponding relationship between elastic modulus and disturbance factor in Hoek-Brown criterion;

[0211] The corresponding relationship between the elastic modulus and the disturbance factor is processed according to the on-site acoustic wave velocity distribution data to obtain the mapping relationship between the elastic modulus and the acoustic wave velocity;

[0212] Among them, the mapping relationship between elastic modulus and acoustic wave velocity is expressed as:

[0213]

[0214] in, represents the elastic modulus of intact rock, is the elastic modulus, C p′ Refers to the target sound wave speed.

[0215] Optionally, the multi-physics simulation software is COMSOL. When the data processing module processes the first distribution data, the distribution data of the second rock mass mechanical parameter, and the geometric shape data using the multi-physics simulation software to obtain the slope spatial variability distribution, it is specifically used to:

[0216] Importing the first distribution data, the distribution data of the internal friction angle, and the distribution data of the elastic modulus into COMSOL;

[0217] In COMSOL, the distribution interpolation of elastic modulus, cohesion and internal friction angle is set respectively to form the coupled distribution of elastic modulus, cohesion and internal friction angle;

[0218] The geometric shape data is modeled by the parameterized curve surface method to obtain the slope geometric model;

[0219] Based on the parameterized curve surface method, the slope geometric model is combined with the coupled distribution of elastic modulus, cohesion and internal friction angle to obtain the spatial variability distribution of the slope.

[0220] Optionally, when the data reduction module obtains the reduction coefficient conditions corresponding to different reduction depths, it is specifically used to:

[0221] Get the discount range and set discount conditions;

[0222] The set reduction range and the set reduction condition are introduced into the set interpolation function, and the target reduction range is obtained by combining the spatial coordinate system;

[0223] Obtaining a set reduction coefficient, and obtaining target reduction parameters corresponding to different depths of the reduction range based on the target reduction range and the set reduction coefficient;

[0224] The target interpolation function representing the coordinates of the slope geometric model is obtained, and the target reduction parameter is combined with the target interpolation function to obtain the reduction coefficient conditions corresponding to different reduction depths.

[0225] Optionally, when the data reduction module reduces the spatial variability distribution of the slope based on the reduction coefficient condition to obtain the spatial variability distribution of the target slope, it is specifically used to:

[0226] Determine the spatial distribution variables of slope spatial variability;

[0227] The spatial variability distribution of the target slope is obtained by performing reduction processing according to the spatial occurrence variables and reduction coefficient conditions.

[0228] Optionally, the reduction coefficient conditions corresponding to different reduction depths are expressed by the following formula:

[0229]

[0230] in, 、 and is the coordinate of the slope geometric model, F 1 refers to the initial value of the reduction factor, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range;

[0231] The spatial variability distribution of the target slope is obtained by reducing the spatial occurrence variables and the reduction coefficient conditions through the following formula:

[0232]

[0233] in, and Within the reduction range Coordinate minimum and maximum values, and Within the reduction range Coordinate minimum and maximum values, Refers to the maximum value of the reduction parameter, F 1 refers to the initial value of the reduction factor, Refers to the reduction parameter value, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range.

[0234] Optionally, when acquiring the geometric shape data of the slope, the data acquisition module is specifically used to:

[0235] Obtain slope elevation data of the slope;

[0236] The slope elevation data is processed by equivalent cross section and interpolation to obtain geometric shape data.

[0237] The slope stability analysis device based on probabilistic coordinated occurrence and spatial difference reduction of this embodiment can execute the slope stability analysis method based on probabilistic coordinated occurrence and spatial difference reduction shown in the embodiment of this application. Its implementation principle is similar and will not be repeated here.

[0238] An embodiment of the present application provides an electronic device, and the electronic device in the embodiment of the present application includes: a processor; and a memory, the memory being configured to store machine-readable instructions, which, when executed by the processor, causes the processor to execute a slope stability analysis method based on probabilistic coordinated endowment and spatial difference reduction.

[0239] The present application embodiment provides an electronic device, such as Figure 19 As shown, Figure 19 The electronic device 2000 shown includes a processor 2001 and a memory 2003. The processor 2001 and the memory 2003 are connected, for example, via a bus 2002. Optionally, the electronic device 2000 may further include a transceiver 2004. It should be noted that in actual applications, the number of transceivers 2004 is not limited to one, and the structure of the electronic device 2000 does not constitute a limitation on the embodiments of the present application.

[0240] Processor 2001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 2001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0241] The bus 2002 may include a path for transmitting information between the above components. The bus 2002 may be a PCI bus or an EISA bus, etc. The bus 2002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 19 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0242] The memory 2003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0243] The memory 2003 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 2001. The processor 2001 is used to execute the application code stored in the memory 2003 to implement Figure 18 The illustrated embodiment provides an operation of a slope stability analysis device based on probabilistic coordinated occurrence and spatial difference reduction.

[0244] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0245] The above descriptions are only partial embodiments of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A slope stability analysis method based on probabilistic coordinated occurrence and spatial difference reduction, applied to occurrence analysis of slope stability, characterized by: include: Acquiring geometric shape data of the slope and on-site acoustic wave velocity distribution data, and processing the on-site acoustic wave velocity distribution data based on a Bayesian-sine increment model to obtain first distribution data of a first rock mass mechanical parameter; Obtaining a first mapping relationship between a first rock mass mechanical parameter and acoustic wave velocity, and a second mapping relationship between a second rock mass mechanical parameter and acoustic wave velocity, according to the field acoustic wave velocity distribution data and the Hoek-Brown criterion; determining target acoustic wave velocity distribution data corresponding to the first distribution data according to the first mapping relationship, and determining distribution data of the second rock mass mechanical parameter according to the second mapping relationship and the target acoustic wave velocity distribution data; Processing the first distribution data, the second rock mass mechanical parameter distribution data, and the geometric shape data using multi-physics field simulation software to obtain a slope spatial variability distribution; Obtaining reduction coefficient conditions corresponding to different reduction depths, and reducing the spatial variability distribution of the slope based on the reduction coefficient conditions to obtain the spatial variability distribution of the target slope; Performing stability analysis based on the spatial variability distribution of the target slope to obtain a slope stability analysis result; The reduction coefficient conditions corresponding to the different reduction depths are expressed by the following formula: ； in, 、 and is the coordinate of the slope geometric model, F 1 refers to the initial value of the reduction factor, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range; The spatial variability distribution of the target slope is obtained by reducing the spatial endowment variables and the reduction coefficient conditions through the following formula: ； in, and Within the reduction range Coordinate minimum and maximum values, and Within the reduction range Coordinate minimum and maximum values, Refers to the maximum value of the reduction parameter, F 1 refers to the initial value of the reduction factor, Refers to the reduction parameter value, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range.

2. The method according to claim 1, characterized in that The first rock mass mechanics parameter is cohesion, the second rock mass mechanics parameter includes an internal friction angle, the first mapping relationship is a mapping relationship between cohesion and acoustic wave velocity, and the second mapping relationship includes a mapping relationship between the internal friction angle and acoustic wave velocity; The method of obtaining a first mapping relationship between a first rock mass mechanical parameter and acoustic wave velocity and a second mapping relationship between a second rock mass mechanical parameter and acoustic wave velocity based on the on-site acoustic wave velocity distribution data and the Hoek-Brown criterion includes: Determining rock material parameters of the Hoek-Brown criterion based on the on-site acoustic wave velocity distribution data; According to the determined rock material parameters, second distribution data of cohesion and distribution data of internal friction angle are mapped based on a nonlinear strength criterion of the Hoek-Brown criterion; The first mapping relationship is obtained based on the second distribution data of the cohesion and the on-site acoustic wave velocity distribution data, and the mapping relationship between the internal friction angle and the acoustic wave velocity is obtained based on the distribution data of the internal friction angle and the on-site acoustic wave velocity distribution data.

3. The method according to claim 2, characterized in that The first mapping relationship is expressed as: ； in, c Refers to cohesion, C p′ Refers to the target sound wave speed; The mapping relationship between the internal friction angle and the acoustic wave velocity is expressed as: ； in, Angle of internal friction, C p′ Refers to the target sound wave speed.

4. The method according to claim 1, wherein The second rock mass mechanical parameter further includes an elastic modulus, and the second mapping relationship further includes a mapping relationship between the elastic modulus and the acoustic wave velocity. The second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity is obtained based on the on-site acoustic wave velocity distribution data and the Hoek-Brown criterion, further including: Obtain the corresponding relationship between elastic modulus and disturbance factor in Hoek-Brown criterion; Performing data processing on the correspondence between the elastic modulus and the disturbance factor according to the on-site acoustic wave velocity distribution data to obtain a mapping relationship between the elastic modulus and the acoustic wave velocity; The mapping relationship between the elastic modulus and the acoustic wave velocity is expressed as: ； in, represents the elastic modulus of intact rock, is the elastic modulus, C p′ Refers to the target sound wave speed.

5. The method according to claim 1, characterized in that The multi-physics field simulation software is COMSOL, and the multi-physics field simulation software is used to process the first distribution data, the distribution data of the second rock mass mechanical parameter, and the geometric shape data to obtain the slope spatial variability distribution, including: Importing the first distribution data, the distribution data of the internal friction angle, and the distribution data of the elastic modulus into the COMSOL; In the COMSOL, the distribution interpolation of the elastic modulus, cohesion and internal friction angle is set respectively to form a coupled distribution of the elastic modulus, cohesion and internal friction angle; Modeling the geometric shape data using a parameterized curve surface method to obtain a slope geometric model; Based on the parameterized curve surface method, the slope geometric model is combined with the coupled distribution of the elastic modulus, cohesion and internal friction angle to obtain the spatial variability distribution of the slope.

6. The method according to claim 5, characterized in that The conditions for obtaining the reduction coefficients corresponding to different reduction depths include: Get the discount range and set discount conditions; Introducing the set reduction range and the set reduction condition into a set interpolation function, and combining with a spatial coordinate system to obtain a target reduction range; Obtaining a set reduction coefficient, and obtaining target reduction parameters corresponding to different depths of the reduction range based on the target reduction range and the set reduction coefficient; A target interpolation function representing the coordinates of the slope geometric model is obtained, and the target reduction parameter is combined with the target interpolation function to obtain the reduction coefficient conditions corresponding to the different reduction depths.

7. The method according to claim 6, characterized in that The reducing the slope spatial variability distribution based on the reduction coefficient condition to obtain the target slope spatial variability distribution includes: Determining the spatial distribution variables of the slope spatial variability distribution; A reduction process is performed according to the spatial endowment variables and the reduction coefficient conditions to obtain the spatial variability distribution of the target slope.

8. The method according to claim 1, characterized in that The step of obtaining the geometric shape data of the slope includes: Obtaining slope elevation data of the slope; The slope elevation data is processed by equivalent cross section and interpolation to obtain the geometric shape data.

9. A slope stability analysis device based on probabilistic coordinated occurrence and spatial difference reduction, applied to occurrence analysis of slope stability, characterized in that: include: a data acquisition module, configured to acquire geometric shape data of the slope and on-site acoustic wave velocity distribution data, and process the on-site acoustic wave velocity distribution data based on a Bayesian-sine increment model to obtain first distribution data of a first rock mass mechanical parameter; a data mapping module, configured to obtain a first mapping relationship between a first rock mass mechanical parameter and acoustic wave velocity, and a second mapping relationship between a second rock mass mechanical parameter and acoustic wave velocity, based on the field acoustic wave velocity distribution data and the Hoek-Brown criterion; a data determination module, configured to determine target acoustic wave velocity distribution data corresponding to the first distribution data based on the first mapping relationship, and to determine distribution data of the second rock mass mechanical parameter based on the second mapping relationship and the target acoustic wave velocity distribution data; a data processing module, configured to process the first distribution data, the second rock mass mechanical parameter distribution data, and the geometric shape data using multi-physics field simulation software to obtain a slope spatial variability distribution; a data reduction module, configured to obtain reduction coefficient conditions corresponding to different reduction depths, and reduce the spatial variability distribution of the slope based on the reduction coefficient conditions to obtain the spatial variability distribution of the target slope; A data analysis module is used to perform stability analysis based on the spatial variability distribution of the target slope to obtain a slope stability analysis result; The reduction coefficient conditions corresponding to the different reduction depths are expressed by the following formula: ； in, 、 and is the coordinate of the slope geometric model, F 1 refers to the initial value of the reduction factor, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range; The spatial variability distribution of the target slope is obtained by reducing the spatial endowment variables and the reduction coefficient conditions through the following formula: ； in, and Within the reduction range Coordinate minimum and maximum values, and Within the reduction range Coordinate minimum and maximum values, Refers to the maximum value of the reduction parameter, F 1 refers to the initial value of the reduction factor, Refers to the reduction parameter value, Refers to the interval step of each reduction, Refers to the maximum depth within the reduction range, Refers to the depth within the reduction range.

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