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

Abstract

The invention discloses a slope stability analysis method based on probability coordinated occurrence and spatial difference reduction, and relates to the field of slope stability analysis. The method comprises the following steps: acquiring field sound wave velocity distribution data, and obtaining a certain parameter distribution based on a Bayesian-sine incremental model; sound wave velocity distribution is inversed based on a Hoek-Brown criterion and a nonlinear intensity criterion linearization method; determining parameter space distribution of other different depths through a mapping method, and realizing coordinated coupling occurrence of the elastic modulus, the cohesive force and the internal friction angle; and obtaining reduction coefficient conditions corresponding to different reduction depths by utilizing multi-physics field simulation software, and carrying out spatial difference reduction on the slope spatial variability distribution to finish slope stability analysis. According to the method, the nonlinear failure characteristics of the rock can be considered, the strength distribution of the rock mass can be accurately described, the real spatial variability distribution characteristics can be obtained, more accurate slope stability analysis can be carried out, and the safety of artificial slope design can be improved.

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 slope rock and soil under certain slope height and slope angle conditions. According to the cause of formation, slopes are divided into two categories: natural slopes and artificial slopes, the latter of which are further divided into excavation slopes and dam slopes. According to the material composition, slopes are divided into three types: rock slopes, soil slopes, and rock-soil composite slopes. According to the degree of stability, they are divided into stable slopes, unstable slopes, and limit equilibrium slopes. Unstable natural slopes and artificial slopes with excessively large slope angles often slide or collapse under the action of rock and soil gravity, water pressure, vibration and other external forces. Large-scale slope rock and soil damage can cause traffic interruption, building collapse, river blockage, reservoir siltation, and cause huge losses to people's lives and property. The purpose of studying slope stability is to predict the time, scale, and degree of damage of slope instability, take preventive measures in advance, reduce geological disasters, and make the design of artificial slopes safe and economical.

[0003] However, the actual slopes are extremely complex in shape and material distribution, and studies have shown that the spatial distribution characteristics of these slope shapes, materials and structures will greatly affect the results of slope stability analysis, leading to unreasonable 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 design and reducing unnecessary losses and casualties has become a technical problem that needs to be solved urgently. At present, there is no technical solution that can solve the above technical problems, and there is no 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-mentioned 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 performing data processing on the on-site acoustic wave velocity distribution data based on a Bayesian-sine incremental 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] Determine target acoustic wave velocity distribution data corresponding to the first distribution data according to the first mapping relationship, and determine 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 by 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, and the slope stability analysis results are obtained.

[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 second distribution data of cohesion and the distribution data of internal friction angle are mapped out based on the nonlinear strength criterion of Hoek-Brown criterion;

[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] c=25.219C p ' 2 -17.48C p ′+1.139

[0019] Where c refers to cohesion, Cp′ refers to the target sound wave velocity;

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

[0021]

[0022] in, The internal friction angle, Cp′ refers to the target sound wave velocity.

[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. According to the on-site acoustic wave velocity distribution data and the Hoek-Brown criterion, the second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity is obtained, and further includes:

[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 sound wave velocity is expressed as:

[0027]

[0028] Among them, E i represents the elastic modulus of intact rock, E m is the elastic modulus, and Cp′ refers to the target sound wave velocity.

[0029] Optionally, 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 by the multi-physics field simulation software 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 existence 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 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 of the coordinates representing 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 spatial variability distribution of the target slope, including:

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

[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] Among them, x, y and z are the coordinates of the slope geometry model, F1 refers to the initial value of the reduction factor, m refers to the interval step of each reduction, and h max refers to the maximum depth within the reduction range, and h refers to the depth within the reduction range;

[0045] The spatial occurrence variables and reduction coefficient conditions are reduced by the following formula to obtain the spatial variability distribution of the target slope:

[0046] if((x min <x<x max )*(y min <y<y max ),W′ / (F1+mm / h max ·h),W max )

[0047] Among them, x min and x max Refers to the minimum and maximum x-coordinate values ​​within the reduction range, and y min and max Refers to the minimum and maximum y coordinates within the reduction range, Wmax refers to the maximum value of the reduction parameter, F1 refers to the initial value of the reduction coefficient, W′ refers to the reduction parameter value, m refers to the interval step of each reduction, h max refers to the maximum depth within the reduction range, and h refers to the depth within the reduction range.

[0048] Optionally, obtain the geometric shape data of the slope, 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, 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, including:

[0052] A data acquisition module is used to acquire geometric shape data of the slope and on-site acoustic wave velocity distribution data, and to process the on-site acoustic wave velocity distribution data based on a Bayesian-sine incremental model to obtain first distribution data of a first rock mass mechanical parameter;

[0053] A data mapping module, used 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 according to field acoustic wave velocity distribution data and the Hoek-Brown criterion;

[0054] A data determination module, used 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 is used to process 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 the spatial variability distribution of the slope;

[0056] A data reduction module is used to obtain the reduction coefficient conditions corresponding to different reduction depths, and to 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] On the other hand, 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 brought by the technical solution provided in the embodiments of the present application include at least:

[0061] The present 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 of elastic modulus, cohesion and internal friction angle by combining the Hoek-Brown criterion, combining the Bayesian-sine increment model, parameter inversion and mapping, etc. Since the spatial variability distribution is realized based on the Hoek-Brown criterion in the present application, the influence of parameters on rock deformation can be better understood, the nonlinear failure characteristics of rocks and rock masses can be considered, and the strength distribution of rock masses under complex stress states encountered in actual engineering can be more accurately described, providing a feasible method and theoretical basis for the spatial variability of strength parameters of complex slopes and the coupling of parameters, and at the same time, more realistic mechanical properties can be simulated, and reasonable and accurate spatial variability distribution characteristics and slope stability data analysis results can be obtained, thereby improving the safety of artificial slope design and reducing unnecessary losses and casualties.

[0062] In addition, in this application, when basic rock mass mechanical parameters are obtained based on traditional geological exploration, acoustic wave extraction technology is added to overcome the defect of the existing technology that it cannot fully reflect the mechanical properties of the rock mass. At the same time, the reduction range of different materials is taken into account, and the parameter space difference reduction method is combined with the slope stability analysis, so that the stability analysis can be carried out more accurately, providing a feasible method and theoretical basis for spatial difference reduction, which has certain 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 drawings required for use in the description of the embodiments will be briefly introduced below. 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 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 A 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 the elastic modulus and the 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 A 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 A schematic diagram of the coordinated coupling distribution of 2D multi-order slope cohesion and internal friction angle provided in an embodiment of the present application;

[0077] Fig. 9 A schematic diagram of the coordinated coupling distribution of cohesion and internal friction angle of a 2D complex multi-order slope provided in an embodiment of the present application;

[0078] Fig.10 A schematic diagram of the coordinated coupling distribution of 3D multi-stage slope cohesion and internal friction angle provided in an embodiment of the present application;

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

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

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

[0082] Fig.14 A schematic diagram of slope material distribution when F=1.5 provided in an embodiment of the present application;

[0083] Fig.15 A schematic diagram of slope material distribution when F=2 provided in an embodiment of the present application;

[0084] Fig.16 A schematic diagram of slope material distribution when F=1 provided in an embodiment of the present application;

[0085] Fig.17 A schematic diagram of slope material distribution when F=1.2 provided in an embodiment of the present application;

[0086] Fig.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] Fig.19 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0088] The embodiments of the present application are described in detail below, and examples of the embodiments 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 cannot be interpreted as limiting the present invention.

[0089] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include 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 an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

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

[0091] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. 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, Figure 1 As shown, the method may include:

[0093] Step S101, obtaining geometric shape data of the slope and field acoustic wave velocity distribution data, and processing the field acoustic wave velocity distribution data based on a Bayesian-sine incremental 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 in 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 data with depth h and cohesion c as columns are exported 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 the existing technology that it cannot fully reflect the mechanical properties of the rock mass. The 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, the present 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 Figure 2a to Figure 2f ) shows the commonly seen 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 mechanical parameter is cohesion, the second rock mass mechanical 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 second distribution data of cohesion and the distribution data of internal friction angle are mapped out based on the nonlinear strength criterion of Hoek-Brown criterion;

[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 mechanical parameter may be the cohesion mentioned above, and the second rock mechanical 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. At this time, 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 is linearly mapped based on the nonlinear strength criterion of the Hoek-Brown criterion and is determined by the following formula:

[0108]

[0109] C p ′=(7-ln(a-1 / 2)) / 68

[0110] K = exp(-13.64(C p -C p ′) / 36)

[0111] Among them, m b , s, and a are the rock material parameters involved in the Heok-Brown criterion, Cp and Cp′ refer to the acoustic wave velocity of the rock mass without disturbance and the acoustic wave velocity of the rock mass after disturbance, respectively, and Cp′ is the target value of the final mapping (i.e., the target acoustic wave velocity).

[0112] Further, after determining the second distribution data of cohesion, the second distribution data can be mapped with the acquired on-site radiation wave velocity distribution data to obtain a first mapping relationship, and the first mapping relationship is expressed as:

[0113] c=25.219C p ' 2 -17.48C p ′+1.139

[0114] Wherein, c refers to cohesion, Cp′ refers to the target acoustic wave velocity, and it can be understood that in this application, the first mapping relationship corresponds to R 2 The value of is 0.9993, that is, R 2 =0.9993. Further, 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.

[0115] Optionally, the mapping relationship between the internal friction angle and the acoustic wave velocity can also be determined by 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, and then the nonlinear strength criterion of the Hoek-Brown criterion is used to linearly map the distribution data of the internal friction angle based on the determined rock material parameters. The distribution data of the internal friction angle refers to the internal friction angle at different depths and regions. Among them, the internal friction angle at different depths and regions is linearly mapped based on the nonlinear strength criterion of the Hoek-Brown criterion and is determined by the following formula:

[0116]

[0117] in, γ represents the bulk density of the rock mass, Hs is the height of the relevant slope, and mb, s, and a are the rock material parameters involved in the Heok-Brown criterion.

[0118] Furthermore, after the distribution data of the internal friction angle is determined 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 a mapping relationship between the internal friction angle and the sound wave velocity, and the mapping relationship between the internal friction angle and the sound wave velocity can be expressed as:

[0119]

[0120] in, The internal friction angle, Cp′ refers to the target acoustic wave velocity. It can be understood that in this application, the mapping relationship between the internal friction angle and the acoustic wave velocity corresponds to R 2 The value of is 0.9995, that is, R 2 =0.9995.

[0121] Further, 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 through 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.

[0122] 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. According to the field acoustic wave velocity distribution data and the Hoek-Brown criterion, the second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity is obtained, and further includes:

[0123] Obtain the corresponding relationship between elastic modulus and disturbance factor in Hoek-Brown criterion;

[0124] 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.

[0125] 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 the second mapping relationship, i.e., the 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:

[0126]

[0127] When D = 0, it means that the rock mass is not disturbed. The relationship between the elastic modulus Em and the disturbance factor D can be expressed as:

[0128]

[0129] Furthermore, the relationship between the elastic modulus of the rock mass disturbed and the rock mass undisturbed can be expressed as:

[0130]

[0131] And through a lot of engineering experience data, we know that the elastic modulus Em and rock mass evaluation index RMR 89 The relationship can be expressed as:

[0132]

[0133] Among them, E i It represents the elastic modulus of intact rock in GPa.

[0134] Furthermore, for the GSI in the above formula, since GSI and rock mass evaluation index RMR 89 The relationship can be expressed as:

[0135] GSI=RMR 89 -5

[0136] Therefore, the final mapping relationship between elastic modulus and sound wave velocity is:

[0137]

[0138] Among them, E i represents the elastic modulus of intact rock, E m is the elastic modulus, and Cp′ refers to the target sound wave velocity.

[0139] 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. Specifically, Figure 5 shown.

[0140] 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.

[0141] 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 obtain the target acoustic wave velocity distribution data. Further, the target acoustic wave velocity distribution data obtained by reverse calculation are respectively 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 to obtain the elastic modulus and internal friction angle at different depths and regions.

[0142] 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 spatial variability distribution of the slope.

[0143] 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 based on multi-physics field simulation software in combination with the geometric shape data to obtain the spatial variability distribution of the slope.

[0144] 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 by the multi-physics field simulation software to obtain the slope spatial variability distribution, including:

[0145] Importing the first distribution data, the distribution data of the internal friction angle, and the distribution data of the elastic modulus into COMSOL;

[0146] 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;

[0147] The geometric shape data is modeled by the parameterized curve surface method to obtain the slope geometric model;

[0148] Based on the parameterized curve surface method, the slope geometric model is combined with the coupled existence of elastic modulus, cohesion and internal friction angle to obtain the spatial variability distribution of the slope.

[0149] Optionally, the multi-physics simulation software can be COMSOL software, which is called 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. COMSOL software is based on the finite element method and simulates real physical phenomena by solving partial differential equations of single or multiple fields.

[0150] In the 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 are imported into COMSOL. Further, the distribution interpolation of the elastic modulus, cohesion, and internal friction angle is set in COMSOL respectively, so as to form the coupled distribution of the elastic modulus, cohesion, and internal friction angle of the slope.

[0151] Correspondingly, the previously acquired slope geometry data can be modeled through parameterized curves and surfaces to obtain a slope geometry model, such as a slope model of different shapes, such as 2D and 3D. Then, the slope geometry model is combined with the coupled existence of elastic modulus, cohesion and internal friction angle through parameterized curve and surface drawing to obtain the spatial variability distribution of the slope, and realize 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 realized: Figures 6 to 11 The coordinated coupled distribution of spatial cohesion and internal friction angle is shown.

[0152] It is worth noting that COMSOL software is used for multi-physics simulation processing in the embodiment of the present application, but it does not mean that only COMSOL software can be used for processing, that is to say, the technical means for multi-physics simulation processing are not limited to COMSOL. Those skilled in the art can use a variety of multi-physics simulation software similar to COMSOL as tools for processing by virtue of their common technical knowledge in the field, such as: 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.

[0153] 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 spatial variability distribution of the target slope.

[0154] Step S106, performing stability analysis according to the spatial variability distribution of the target slope to obtain a slope stability analysis result.

[0155] Optionally, after obtaining the spatial variability distribution of the slope, the obtained spatial variability distribution of the slope can be reduced according to the reduction coefficient conditions corresponding to different reduction depths to obtain the spatial variability distribution of the target slope, and finally stability analysis is performed based on the spatial variability distribution of the target slope to obtain the slope stability analysis result. In this process, the reduction range of different materials is taken into account, so that the stability analysis can be performed more accurately, and a feasible method and theoretical basis can be provided for spatial difference reduction, which has certain reference suggestions and practical significance.

[0156] It is worth noting that the slope stability data analysis method provided by steps S101 to S106 can be used not only for the spatial variability analysis of cohesion, internal friction angle and elastic modulus, but also for other rock mass mechanical parameters, illustratively: other rock mass mechanical parameters may include shear modulus, tensile strength, compressive strength, porosity, density, fracture toughness, etc. Those skilled in the art can make non-substantial modifications to the slope stability analysis method provided by steps S101 to S106 without creative labor, and can perform similar stability analysis on the above other rock mass mechanical parameters based on the technical means of steps S101 to S106.

[0157] In an optional embodiment of the present application, obtaining reduction coefficient conditions corresponding to different reduction depths includes:

[0158] Get the discount range and discount conditions.

[0159] 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;

[0160] 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;

[0161] The target interpolation function of the coordinates representing 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.

[0162] Optionally, in order to achieve local coordinated reduction of slopes with spatial differences, an interpolation function can be defined to represent the coordinates of the slope geometric model. The interpolation function is an interpolation function in which x and y represent the z coordinate (i.e., a target interpolation function), which can be expressed as:

[0163] Z max =int(x,y)

[0164] Among them, int refers to the interpolation function, Z max It refers to the z coordinate value on the surface of the slope geometric model, and x and y refer to the x and y coordinate values ​​in space.

[0165] 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, where the target reduction range can be expressed as:

[0166] if((x min <x<x max )*(y min <y<y max ),W / F,W)

[0167] In this formula, x min and x max Refers to the minimum and maximum x-coordinate values ​​of the reduction range, and the same applies to y min and max refers to the minimum and maximum y-coordinate values ​​of the reduction range, W refers to the parameter to be reduced, and F refers to the reduction coefficient. Fig.12 The area of ​​local reduction is shown.

[0168] Furthermore, considering that the reduction coefficients at different depths are also different, assuming that the reduction coefficient F changes linearly with depth, the reduction coefficients at different depths (i.e., the set reduction coefficients) can be expressed as:

[0169]

[0170] Among them, F1 refers to the initial value of the reduction coefficient, which is generally 1, m refers to the interval step of each reduction, such as 0.02, h max refers to the maximum depth within the reduction range, h refers to the current depth, is the reduction factor after reduction.

[0171] Correspondingly, 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:

[0172] if((x min <x<x max )*(y min <y<ymax ),W / (F1+mm / h max ·h),W)

[0173] Among them, x min and x max Refers to the minimum and maximum x-coordinate values ​​of the reduction range, and the same applies to y min and max refers to the minimum and maximum y-coordinate values ​​of the reduction range, F1 refers to the initial value of the reduction coefficient, and m refers to the interval step length h of each reduction max refers to the maximum depth within the reduction range, and h refers to the current depth.

[0174] 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:

[0175]

[0176] Among them, x, y and z are the coordinates of the slope geometry model, F1 refers to the initial value of the reduction factor, m refers to the interval step of each reduction, and h max refers to the maximum depth within the reduction range, and h refers to the depth within the reduction range.

[0177] For example, when h max When 10m, m=0.02 and reduction factor F=1.1, the variation curve of reduction factor F at different depths is as follows: Fig.13 As shown in the figure, the reduction range of cohesion and internal friction angle is as follows Fig.14 and Fig.15 As shown in the figure, the material properties and reduction range of different regions can be controlled by setting conditions. Fig.14 It can be seen that when 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. When F = 2, Fig.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.

[0178] 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 spatial variability distribution of the target slope, including:

[0179] Determine the spatial occurrence variables of the slope spatial variability distribution;

[0180] The spatial variability distribution of the target slope is obtained by performing reduction processing according to the spatial occurrence variables and reduction coefficient conditions.

[0181] Optionally, to achieve coordinated reduction of the spatial variability distribution of materials, it is necessary to define the spatial occurrence 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:

[0182] if((x min <x<x max )*(y min <y<y max ),int2(int1(x,y)-z),W max )

[0183] Among them, x min and x max Refers to the minimum and maximum x-coordinate values ​​within the reduction range, and y min and max Refers to the minimum and maximum y coordinates within the reduction range, W max Refers to the maximum value of the reduction parameter.

[0184] Furthermore, the reduction processing can be performed according to the spatial occurrence variables and the reduction coefficient conditions to obtain the spatial variability distribution of the target slope, that is, the expression of the defined parameter variables and the formula of the reduction coefficient conditions corresponding to different reduction depths are combined to complete the reduction processing, which can be specifically expressed as:

[0185] if((x min <x<x max )*(y min <y<y max ),W′ / (F1+nm / h max ·h),W max )

[0186] Among them, x min and x max Refers to the minimum and maximum x-coordinate values ​​within the reduction range, and y min and max Refers to the minimum and maximum y coordinates within the reduction range, W max refers to the maximum value of the reduction parameter, F1 refers to the initial value of the reduction coefficient, W′ refers to the reduction parameter value, m refers to the interval step of each reduction, h max refers to the maximum depth within the reduction range, and h refers to the depth within the reduction range. For example, when F = 1, the distribution of the cohesion and internal friction angle of the slope is as follows: Fig.15 As shown in Figure 2, when F = 1.2, the distribution of the cohesion and internal friction angle of the slope is as follows: Fig.16 shown.

[0187] In order to better understand the method provided by this application, Fig.17The method is explained again. In the present application, the slope elevation data is first obtained by satellite positioning, actual elevation measurement, drone photography measurement, infrared ranging and GIS technology, and then the slope geometry data (i.e. the slope in the figure) is determined by using methods such as equivalent cross section and interpolation processing, and then the cohesion distribution data of different depths and regions (i.e. the theoretical combination part in the figure) is obtained based on the probability endowment method combined with the Bayesian-sine incremental model, and the cohesion is mapped out by the acoustic wave velocity obtained by field tests in combination with the Hoek-Brown criterion and the nonlinear intensity criterion linearization method, and then the acoustic wave velocity at different depths is inferred based on the cohesion. 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 of shear strength of complex side holes is completed in the figure). Then, different reduction depths are considered to have different reduction coefficients, and the parameter space difference reduction method is completed in combination with COMSOL interpolation and conditional functions, and finally the slope stability analysis of six commonly used slope types is obtained.

[0188] 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 different areas under the profile of complex slopes based on the Hoek-Brown criterion. The coordinated coupling of elastic modulus, cohesion and internal friction angle is achieved by combining the Hoek-Brown criterion, the Bayesian-sine increment model, parameter inversion and mapping, and other methods. The method is combined with the parameter spatial difference reduction method and applied to 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 of complex slopes, parameter coupling and spatial difference reduction, which has certain reference value and practical significance.

[0189] The embodiment of the present application provides a slope stability analysis device based on probability coordinated occurrence and spatial difference reduction, which is applied to the occurrence analysis of slope stability, such as Fig.18As shown, the device 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:

[0190] A data acquisition module is used to acquire geometric shape data of the slope and on-site acoustic wave velocity distribution data, and to process the on-site acoustic wave velocity distribution data based on a Bayesian-sine incremental model to obtain first distribution data of a first rock mass mechanical parameter;

[0191] A data mapping module, used 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 according to field acoustic wave velocity distribution data and the Hoek-Brown criterion;

[0192] A data determination module, used 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;

[0193] A data processing module is used to process 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 the spatial variability distribution of the slope;

[0194] A data reduction module is used to obtain the reduction coefficient conditions corresponding to different reduction depths, and to reduce the spatial variability distribution of the slope based on the reduction coefficient conditions to obtain the spatial variability distribution of the target slope;

[0195] 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.

[0196] 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;

[0197] 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 according to the field acoustic wave velocity distribution data and the Hoek-Brown criterion:

[0198] Determine the rock material parameters of the Hoek-Brown criterion based on the on-site acoustic wave velocity distribution data;

[0199] According to the determined rock material parameters, the second distribution data of cohesion and the distribution data of internal friction angle are mapped out based on the nonlinear strength criterion of Hoek-Brown criterion;

[0200] 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.

[0201] Optionally, the first mapping relationship is expressed as:

[0202] c=25.219C p ' 2 -17.48C p ′+1.139

[0203] Where c refers to cohesion, Cp′ refers to the target sound wave velocity;

[0204] The mapping relationship between the internal friction angle and the acoustic wave velocity is expressed as:

[0205]

[0206] in, The internal friction angle, Cp′ refers to the target sound wave velocity.

[0207] Optionally, the second rock mass mechanics 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 mechanics parameter and the acoustic wave velocity according to the on-site acoustic wave velocity distribution data and the Hoek-Brown criterion, it is further used to:

[0208] Obtain the corresponding relationship between elastic modulus and disturbance factor in Hoek-Brown criterion;

[0209] 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;

[0210] Among them, the mapping relationship between elastic modulus and sound wave velocity is expressed as:

[0211]

[0212] Among them, E i represents the elastic modulus of intact rock, E m is the elastic modulus, and Cp′ refers to the target sound wave velocity.

[0213] Optionally, the multi-physics field simulation software is COMSOL, and the data processing module uses the 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, specifically for:

[0214] Importing the first distribution data, the distribution data of the internal friction angle, and the distribution data of the elastic modulus into COMSOL;

[0215] 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;

[0216] The geometric shape data is modeled by the parameterized curve surface method to obtain the slope geometric model;

[0217] Based on the parameterized curve surface method, the slope geometric model is combined with the coupled existence of elastic modulus, cohesion and internal friction angle to obtain the spatial variability distribution of the slope.

[0218] Optionally, when the data reduction module obtains the reduction coefficient conditions corresponding to different reduction depths, it is specifically used for:

[0219] Get the discount range and discount conditions.

[0220] 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;

[0221] 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;

[0222] The target interpolation function of the coordinates representing 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.

[0223] 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:

[0224] Determine the spatial occurrence variables of the slope spatial variability distribution;

[0225] The spatial variability distribution of the target slope is obtained by performing reduction processing according to the spatial occurrence variables and reduction coefficient conditions.

[0226] Optionally, the reduction coefficient conditions corresponding to different reduction depths are expressed by the following formula:

[0227]

[0228] Among them, x, y and z are the coordinates of the slope geometry model, F1 refers to the initial value of the reduction factor, m refers to the interval step of each reduction, and h max refers to the maximum depth within the reduction range, and h refers to the depth within the reduction range;

[0229] The spatial occurrence variables and reduction coefficient conditions are reduced by the following formula to obtain the spatial variability distribution of the target slope:

[0230] if((x min <x<x max )·(y min <y<y max ),W′ / (F1+mm / h max ·h),W max )

[0231] Among them, x min and x max Refers to the minimum and maximum x-coordinate values ​​within the reduction range, and y min and max Refers to the minimum and maximum y coordinates within the reduction range, W max refers to the maximum value of the reduction parameter, F1 refers to the initial value of the reduction coefficient, W′ refers to the reduction parameter value, m refers to the interval step of each reduction, h max refers to the maximum depth within the reduction range, and h refers to the depth within the reduction range.

[0232] Optionally, when acquiring the geometric shape data of the slope, the data acquisition module is specifically used to:

[0233] Obtain slope elevation data of the slope;

[0234] The slope elevation data is processed by equivalent cross section and interpolation to obtain geometric shape data.

[0235] 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. The implementation principle is similar and will not be repeated here.

[0236] 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 is configured to store machine-readable instructions, and when the instructions are executed by the processor, the processor executes a slope stability analysis method based on probabilistic coordinated occurrence and spatial difference reduction.

[0237] The present application embodiment provides an electronic device, such as Fig.19 As shown, Fig.19The electronic device 2000 shown includes: a processor 2001 and a memory 2003. The processor 2001 and the memory 2003 are connected, such as through a bus 2002. Optionally, the electronic device 2000 may also include a transceiver 2004. It should be noted that in actual applications, the transceiver 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.

[0238] 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 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, etc.

[0239] The bus 2002 may include a path to transmit 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, Fig.19 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0240] 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 disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0241] 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 Fig.18 The illustrated embodiment provides the operation of the slope stability analysis device based on probabilistic coordinated occurrence and spatial differential reduction.

[0242] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence 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 a part of the steps in the flowchart 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 part of the sub-steps or stages of other steps.

[0243] The above descriptions are only some embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as 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 in that: include: 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 incremental model to obtain first distribution data of a first rock mass mechanical parameter; According to the field acoustic wave velocity distribution data and the Hoek-Brown criterion, 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 are obtained; Determine target acoustic wave velocity distribution data corresponding to the first distribution data according to the first mapping relationship, and determine 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 distribution data of the rock mass mechanical parameters 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; A stability analysis is performed based on the spatial variability distribution of the target slope to obtain a slope stability analysis result.

2. The method according to claim 1, characterized in that The first rock mass mechanical parameter is cohesion, the second rock mass mechanical 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 according to the on-site acoustic wave velocity distribution data and the Hoek-Brown criterion includes: Determining the rock material parameters of the Hoek-Brown criterion according to 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 out based on the nonlinear strength criterion of the Hoek-Brown criterion; The first mapping relationship is obtained according to 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 according to 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: c=25.219C p ′2 -17.48C p ′ +1.139 Where c refers to cohesion, Cp′ refers to the target sound wave velocity; The mapping relationship between the internal friction angle and the acoustic wave velocity is expressed as: in, The internal friction angle, Cp′ refers to the target sound wave velocity.

4. The method according to claim 1, characterized in that: The second rock mass mechanical parameter further includes an elastic modulus, the second mapping relationship further includes a mapping relationship between the elastic modulus and the acoustic wave velocity, and the second mapping relationship between the second rock mass mechanical parameter and the acoustic wave velocity is obtained according to 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; According to the on-site acoustic wave velocity distribution data, data processing is performed on the corresponding relationship between the elastic modulus and the disturbance factor 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: Among them, E i represents the elastic modulus of intact rock, E m is the elastic modulus, and Cp′ refers to the target sound wave velocity.

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, the cohesion and the internal friction angle is set respectively to form the coupled distribution of the elastic modulus, the cohesion and the internal friction angle; Modeling the geometric shape data by 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 existence 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 obtaining of reduction coefficient conditions corresponding to different reduction depths includes: Get the discount range and discount conditions; Introducing the set reduction range and the set reduction condition into a set interpolation function, and obtaining a target reduction range in combination with a spatial coordinate system; Acquire a set reduction coefficient, and obtain 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 occurrence variables and the reduction coefficient conditions to obtain the spatial variability distribution of the target slope.

8. The method according to claim 7, characterized in that The reduction coefficient conditions corresponding to the different reduction depths are expressed by the following formula: Among them, x, y and z are the coordinates of the slope geometry model, F1 refers to the initial value of the reduction factor, m refers to the interval step of each reduction, and h max refers to the maximum depth within the reduction range, and h refers to the depth within the reduction range; The spatial occurrence variables and the reduction coefficient conditions are reduced by the following formula to obtain the spatial variability distribution of the target slope: Among them, x min and x max Refers to the minimum and maximum x-coordinate values ​​within the reduction range, and y min and max Refers to the minimum and maximum y coordinates within the reduction range, W max refers to the maximum value of the reduction parameter, F1 refers to the initial value of the reduction coefficient, W′ refers to the reduction parameter value, m refers to the interval step of each reduction, h max refers to the maximum depth within the reduction range, and h refers to the depth within the reduction range.

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

10. A slope stability analysis device based on probability coordinated occurrence and spatial difference reduction, applied to occurrence analysis of slope stability, characterized in that: include: A data acquisition module, used to acquire geometric shape data of the slope and on-site acoustic wave velocity distribution data, and to process the on-site acoustic wave velocity distribution data based on a Bayesian-sine incremental model to obtain first distribution data of a first rock mass mechanical parameter; A data mapping module, used 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 according to 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 according to the first mapping relationship, and to determine distribution data of the second rock mass mechanical parameter according to the second mapping relationship and the target acoustic wave velocity distribution data; A data processing module, used for processing the first distribution data, the second distribution data of the rock mass mechanical parameters and the geometric shape data by using multi-physics field simulation software to obtain the spatial variability distribution of the slope; A data reduction module, used for 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; The data analysis module is used to perform stability analysis according to the spatial variability distribution of the target slope to obtain slope stability analysis results.

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