Slope stability probability assessment method and device

Abstract

The invention relates to the technical field of slope monitoring, and discloses a slope stability probability assessment method and device, and the method comprises the steps: carrying out the sensitivity analysis processing of the rock and soil parameters of a slope, and obtaining m sensitive relation curves; slope change mean values corresponding to the m sensitive relation curves are calculated and sorted, and k random rock-soil body parameters are determined; constructing n groups of samples containing k random rock-soil body parameters; performing finite element simulation on the n groups of samples to obtain n slope stability coefficient simulation results; analyzing the sample group proportion frequencies corresponding to the stable and unstable states of the slope; and calculating the slope stability level failure probability and reliability corresponding to the sample group by adopting a Monte Carlo method. According to the method, the random finite element probability evaluation method is adopted, the physical and mechanical parameters of the rock-soil body are regarded as random variables, the stability state of the slope under different random parameter combinations is analyzed through finite element numerical simulation, the actual stability condition of the slope can be reflected more truly, and the evaluation accuracy is improved.

Description

Technical Field

[0001] The invention relates to the technical field of slope monitoring, and in particular to a slope stability probability assessment method and device. Background Art

[0002] In the vast field of geotechnical engineering, slope stability assessment plays an indispensable role in ensuring engineering safety. However, the currently widely used slope stability assessment methods, although providing guidance for engineering practice to a certain extent, generally rely on deterministic analysis methods, which presuppose that the physical and mechanical properties of rock and soil are constant and ignore the inherent variability and uncertainty of these parameters in actual complex geological environments.

[0003] Although this simplified processing strategy is convenient for calculation and analysis, it is difficult to fully capture the true response characteristics of the slope system under different environmental conditions and loading states. In actual engineering, the physical and mechanical parameters of rock and soil (such as strength, deformation modulus, etc.) are often affected by many factors, such as groundwater level fluctuations, weathering, stress history and temperature changes, which leads to significant temporal and spatial variability of these parameters.

[0004] Therefore, relying solely on deterministic analysis may not be able to fully characterize the stability of the slope under different working conditions, which may lead to misjudgment of the potential risks of the slope. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention proposes a slope stability probabilistic assessment method and device to solve the above technical problems.

[0006] In a first aspect, a slope stability probabilistic assessment method is provided, comprising:

[0007] The sensitivity analysis of the geotechnical parameters of the slope is carried out to obtain m sensitivity relationship curves, which are used to characterize the relationship between the slope stability coefficient and the evolution of the geotechnical parameters.

[0008] Calculate the mean values ​​of slope changes corresponding to m sensitive relationship curves and sort them to determine k random rock and soil parameters;

[0009] A random number generator is used to construct n groups of samples containing k random rock and soil parameters;

[0010] Input n groups of samples into the numerical model for finite element simulation, and obtain n simulation results of slope stability coefficients;

[0011] According to the defined critical value of stability coefficient, the frequency of sample groups corresponding to the stable and unstable states of the slope is analyzed;

[0012] The Monte Carlo method is used to calculate the horizontal failure probability and reliability of the slope stability corresponding to the sample group.

[0013] Furthermore, the sensitivity analysis of the rock and soil parameters of the slope is performed to obtain m sensitivity relationship curves, including:

[0014] Obtain the rock and soil parameter set of the slope from the engineering statistical data;

[0015] The variables of the geotechnical parameter set are controlled according to the control variable method combined with the strip method, and m sensitive relationship curves of the stability coefficient changing with the variables are obtained.

[0016] Furthermore, the calculation of the mean values ​​of slope changes corresponding to the m sensitive relationship curves and sorting them to determine k random rock and soil parameters includes:

[0017] Calculating the corresponding slope change values ​​of the m sensitive relationship curves and taking the average to obtain the average of the m slope values;

[0018] Sorting the mean values ​​of the m slope values ​​to obtain a list of mean values ​​sorted from largest to smallest;

[0019] The rock and soil parameters corresponding to the first k values ​​in the mean value list are determined as random parameter variables.

[0020] Furthermore, a random number generator is used to construct n groups of samples containing k random rock and soil parameters, including:

[0021] Samples are drawn from k predefined probability distribution functions according to a random number generator to obtain n groups of random parameter samples.

[0022] Furthermore, the n groups of samples are input into the numerical model for finite element simulation to obtain n corresponding slope stability coefficient simulation results, including:

[0023] Extract finite element models;

[0024] Inputting each group of random parameter samples into a finite element model to perform ground stress balance calculation, and obtaining a finite element model after ground stress balance;

[0025] The strength reduction calculation is performed on the finite element model after the ground stress is balanced to obtain n slope stability coefficient simulation results.

[0026] Furthermore, the steps of constructing the finite element model include:

[0027] Obtain slope terrain data and construct a three-dimensional geometric model of the slope;

[0028] Discretizing the three-dimensional geometric model of the slope according to a finite element network partitioning strategy to obtain a plurality of calculation units;

[0029] A finite element model of the slope is constructed according to the plurality of calculation units.

[0030] Furthermore, the analysis of the frequency of sample groups corresponding to the stable and unstable states of the slope according to the defined critical value of the stability coefficient includes:

[0031] The simulation results of slope stability coefficient under n sample groups are sorted and counted to obtain statistical data;

[0032] Delineate critical values ​​for slope stability and instability;

[0033] Calculate the frequency of reaching the corresponding stable state and the probability of accounting for the total sample.

[0034] Furthermore, the method of calculating the failure probability and reliability of the slope stability level corresponding to the sample group by using the Monte Carlo method includes:

[0035] Fitting the statistical data according to the probability distribution function to construct a probability distribution model;

[0036] The failure probability and reliability of the slope stability level corresponding to different rock and soil parameter sample combinations are calculated based on the Monte Carlo method combined with the probability distribution model.

[0037] In a second aspect, a slope stability probability assessment device is provided, comprising:

[0038] The analysis and processing module is used to perform sensitivity analysis on the rock and soil parameters of the slope to obtain m sensitive relationship curves;

[0039] A determination module is used to calculate and sort the mean values ​​of slope changes corresponding to m sensitive relationship curves, and determine k random rock and soil parameters;

[0040] A construction module, for constructing n groups of samples including k random rock and soil parameters using a random number generator;

[0041] A simulation module is used to input n groups of samples into the numerical model for finite element simulation to obtain n simulation results of slope stability coefficients;

[0042] An analysis module is used to analyze the frequency of sample groups corresponding to the stable and unstable states of the slope according to the defined critical value of the stability coefficient;

[0043] The calculation module is used to calculate the horizontal failure probability and reliability of the slope stability corresponding to the sample group using the Monte Carlo method.

[0044] Furthermore, a slope stability probability assessment device includes a processor and a memory storing program instructions, wherein the processor is used to execute a slope stability probability assessment method as described in any one of the above when running the program instructions.

[0045] The invention adopting the above technical solution has the following advantages:

[0046] The present invention uses a finite element-based slope stability probabilistic assessment method, which regards the physical and mechanical parameters of the rock mass as random variables, and uses random finite element analysis to simulate the stability state of the slope under different random parameter combinations, which can more realistically reflect the actual stability of the slope and improve the accuracy of the assessment. By calculating the probability distribution of slope stability, the failure probability and reliability of the slope at different stability levels can be comprehensively assessed, providing more abundant risk assessment information for engineering decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the specific implementation of the present invention, the following will briefly introduce the drawings required for use in the specific implementation. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0048] Figure 1 A flow chart of a slope stability probability assessment method of the present invention;

[0049] Figure 2 A curve diagram showing the sensitive relationship between rock and soil parameters and slope stability coefficient in a slope stability probability assessment method of the present invention;

[0050] Figure 3 A finite element model diagram of a slope in a slope stability probabilistic assessment method of the present invention;

[0051] Figure 4 A schematic diagram of inputting rock and soil parameters in the form of random variables in a slope stability probabilistic assessment method of the present invention;

[0052] Figure 5 It is a schematic diagram of the assembly of components in a slope stability probabilistic assessment method of the present invention;

[0053] Figure 6 A schematic diagram of determining the analysis step in a slope stability probabilistic assessment method of the present invention;

[0054] Figure 7 A schematic diagram of interaction settings in a slope stability probabilistic assessment method of the present invention;

[0055] Figure 8 A schematic diagram of load application in a slope stability probabilistic assessment method of the present invention;

[0056] Fig. 9 A schematic diagram of boundary condition settings in a slope stability probabilistic assessment method of the present invention;

[0057] Fig.10 A schematic diagram of a predefined field setting in a slope stability probabilistic assessment method of the present invention;

[0058] Fig.11 A schematic diagram of grid division in a slope stability probabilistic assessment method of the present invention;

[0059] Fig.12 A schematic diagram of a stability coefficient variation curve in a slope stability probability assessment method of the present invention;

[0060] Fig.13 A schematic diagram of normal distribution statistics in a slope stability probability assessment method of the present invention;

[0061] Fig.14 A schematic diagram of reliability index and failure probability in a slope stability probabilistic assessment method of the present invention;

[0062] Fig.15 The invention provides a flow chart of a slope stability probability assessment device. DETAILED DESCRIPTION

[0063] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0064] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should be the common meanings understood by technicians in the field to which the present invention belongs. The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to implement the embodiments of the present disclosure described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. Unless otherwise specified, the term "multiple" means two or more. In the embodiments of the present disclosure, the character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B. The term "and / or" is a description of the association relationship of objects, indicating that there can be three relationships. For example, A and / or B means: A or B, or, A and B. The term "corresponding" can refer to an association relationship or a binding relationship, and A and B corresponding to each other means that there is an association relationship or a binding relationship between A and B.

[0065] like Figure 1 to Figure 15As shown, a slope stability probability assessment method of the present invention comprises:

[0066] Step S01, performing sensitivity analysis on the geotechnical parameters of the slope to obtain m sensitivity relationship curves, where the sensitivity relationship curves are used to characterize the relationship between the slope stability coefficient and the evolution of the geotechnical parameters;

[0067] Step S02, calculating the mean values ​​of slope changes corresponding to m sensitive relationship curves and sorting them, and determining k random rock and soil parameters;

[0068] Step S03, constructing n groups of samples containing k random rock and soil parameters using a random number generator;

[0069] Step S04, inputting n groups of samples into the numerical model for finite element simulation to obtain n simulation results of slope stability coefficients;

[0070] Step S05, analyzing the frequency of sample groups corresponding to the stable and unstable states of the slope according to the defined critical value of the stability coefficient;

[0071] Step S06: Calculate the horizontal failure probability and reliability of the slope stability corresponding to the sample group using the Monte Carlo method.

[0072] Specifically, the finite element-based slope stability probabilistic assessment method treats the physical and mechanical parameters of the rock mass as random variables, and uses random finite element analysis to simulate the stability state of the slope under different random parameter combinations, which can more realistically reflect the actual stability of the slope and improve the accuracy of the assessment. By calculating the probability distribution of slope stability, the failure probability and reliability of the slope at different stability levels can be comprehensively assessed, providing more abundant risk assessment information for engineering decision-making.

[0073] In some embodiments, sensitivity analysis is performed on the rock and soil parameters of the slope to obtain m sensitivity relationship curves, including:

[0074] Obtain the rock and soil parameter set of the slope from the engineering statistical data;

[0075] The variables of the geotechnical parameter set are controlled according to the control variable method combined with the strip method, and m sensitive relationship curves of the stability coefficient changing with the variables are obtained.

[0076] Specifically, sensitivity analysis: Combined with engineering statistical data, the sensitivity relationship between geotechnical parameters and slope stability is analyzed for rock, soil, binary medium and other types of slopes. The control variables are combined with the strip analysis to draw the curve of slope stability coefficient changing with the development of different geotechnical parameters, such as Figure 2As shown (a, b, c, d, e, f, g...represent different rock and soil parameters, such as cohesion, friction angle, Young's modulus, Poisson's ratio, specific gravity, shear dilation angle, porosity, density, etc.).

[0077] In some embodiments, calculating and sorting the mean values ​​of slope changes corresponding to m sensitive relationship curves to determine k random rock and soil parameters includes:

[0078] Calculating the corresponding slope change values ​​of the m sensitive relationship curves and taking the average to obtain the average of the m slope values;

[0079] Sorting the mean values ​​of the m slope values ​​to obtain a list of mean values ​​sorted from largest to smallest;

[0080] The rock and soil parameters corresponding to the first k values ​​in the mean value list are determined as random parameter variables.

[0081] Specifically, determine the random variable: calculate the slope change values ​​of all sensitive relationship curves (such as Figure 2 Medium α 1 , α 2 , α 3 The mean of the slope change values ​​is calculated, and the rock and soil parameters corresponding to the curve with the maximum mean value are recorded as the ones with the strongest sensitivity to the slope stability coefficient. The mean values ​​of the slope change values ​​of the curves are sorted from large to small, and finally the rock and soil parameters corresponding to the first k in the sorting are determined as random variables.

[0082] In some embodiments, a random number generator is used to construct n sets of samples containing k random geotechnical parameters, including:

[0083] Samples are drawn from k predefined probability distribution functions according to a random number generator to obtain n groups of random parameter samples;

[0084] In some embodiments, n groups of samples are input into a numerical model for finite element simulation to obtain n corresponding slope stability coefficient simulation results, including:

[0085] Extract finite element models;

[0086] Inputting each group of random parameter samples into a finite element model to perform ground stress balance calculation, and obtaining a finite element model after ground stress balance;

[0087] The strength reduction calculation is performed on the finite element model after the ground stress is balanced to obtain n slope stability coefficient simulation results.

[0088] In some embodiments, the step of constructing the finite element model includes:

[0089] Obtain slope terrain data and construct a three-dimensional geometric model of the slope;

[0090] Discretize the 3D geometric model of the slope according to the finite element network division strategy to obtain multiple calculation units;

[0091] Construct a finite element model of the slope based on multiple calculation units.

[0092] Specifically, for model establishment and parameter setting: First, obtain the slope terrain data using drone aerial photography, satellite imagery, or topographic maps; then use CAD, GIS, or professional geotechnical engineering software to construct a 3D geometric model according to the actual terrain and geometric dimensions of the slope; after that, based on the geometric model, select a suitable finite element mesh division strategy (such as triangles, quadrilaterals, etc.) to discretize the slope into multiple units, as Figure 3 shown.

[0093] In the finite element model, set the determined random variables as the random input parameters of the finite element model, define the probability distribution function for each parameter, define each parameter as a variable that can take continuous values. Let the variable X take values in the interval (a, b), and assume that its distribution function F(x) is a monotonically increasing function, and is differentiable in -∞ < x < +∞ and its derivative F’(x) is continuous in this interval. Then the probability that the variable X falls within the interval (x, x + Δx) is:

[0094] P(x ≤ X ≤ x + Δx) = F(x + Δx) - F(x)

[0095] The derivative F’(x) corresponding to F(x) is the probability distribution density function f(x):

[0096]

[0097] By reverse deduction, F(x) is:

[0098]

[0099] The probability that the variable X falls within a certain interval (x 1 , x 2 ) is:

[0100]

[0101] Its distribution density function is:

[0102]

[0103] In the property section of the numerical model, define the distribution area of rock and soil layers and set the boundary conditions of the slope (including fixed boundaries, free boundaries) according to the actual situation of the slope, and pre-define fields and loads, etc.

[0104] Stochastic finite element analysis and simulation

[0105] 1) Random parameter sampling:

[0106] The random number X generated by the random number generator is used to perform a large number of sampling in the distribution function F(x) to ensure that the number of samples is large enough to cover the main range of variation of the random variable and improve the accuracy of the evaluation results. The random number generator code is:

[0107]

[0108] 2) Random finite element simulation:

[0109] The generated random numbers are input into the constructed finite element model. According to the simulation process of "component assembly-analysis step determination-interaction setting-load application-meshing-job submission", the ground stress balance is first performed, and then the stability analysis is performed using the strength reduction method.

[0110] ① In the geostress balance step: the rock and soil parameters use random variable values ​​generated by a random number generator; the analysis step determination includes the initial analysis step and the load analysis step, the initial analysis step is used for the transition of the analysis step, and the load analysis step is used to apply the load; the interaction is used to set different contact types, such as the contact between the support or reinforcement measures and the slope, the coupling effect within the reinforcement measures, etc.; the load application includes load application, boundary condition setting and predefined field setting; the load application includes the self-weight of the slope or the application of the load, etc., the boundary conditions include free boundaries (boundaries that can move freely) and fixed boundaries (boundaries with constraints such as displacement, rotation angle, and velocity), and the predefined field setting is used to calculate the geostress state of the slope after the load is applied in the load analysis step (i.e., the geostress predefined field); meshing is to divide the finite element model into regular or irregular discrete units according to certain shapes, sizes and analysis types; job submission is used for the calculation and analysis of the task and the output of post-processing results.

[0111] ② After completing the in-situ stress balance of the model, the strength reduction calculation of the model is performed. The steps are as follows: the rock and soil parameters adopt the values ​​after the strength reduction of the random variables; a reduction calculation analysis step is added to the analysis step of the existing model (used to perform strength reduction on the input random rock and soil parameters, and the slope stability coefficient is calculated by continuously reducing the rock and soil strength value until the slope reaches the critical failure state); the interaction remains unchanged; a strength reduction predefined field is added after the in-situ stress predefined field in the load application step, and the original calculation-type in-situ stress predefined field is changed to an import-type. The data adopts the incremental step and analysis step of the model after the in-situ stress balance, and the strength reduction predefined field is used to set the amplitude range of the strength reduction; the grid division remains unchanged, and finally the job is submitted and the post-processing results are output.

[0112] ③ When repeating finite element simulation of multiple sets of random variable rock and soil parameters, select the same node in the post-processing module to output FV (field) and U1 (horizontal displacement) data through ODB field variables, use the combine function to associate the two sets of data and draw the relationship curve between horizontal displacement and field number. The inflection point of the node horizontal displacement is used as the instability judgment standard of the slope. The field number corresponding to the inflection point is the slope stability coefficient. Repeating multiple sets of finite element simulation can obtain multiple slope stability coefficient values, that is, a single set of rock and soil random variable values ​​corresponds to a single slope stability coefficient.

[0113] In some embodiments, analyzing the frequency of sample groups corresponding to the stable and unstable states of the slope according to the defined critical value of the stability coefficient includes:

[0114] The simulation results of slope stability coefficient under n sample groups are sorted and counted to obtain statistical data;

[0115] Delineate critical values ​​for slope stability and instability;

[0116] Calculate the frequency of reaching the corresponding stable state and the probability of accounting for the total sample.

[0117] In some embodiments, the Monte Carlo method is used to calculate the failure probability and reliability of the slope stability level corresponding to the sample group, including:

[0118] Fitting the statistical data according to the probability distribution function to construct a probability distribution model;

[0119] The Monte Carlo method combined with the probability distribution model is used to calculate the failure probability and reliability of the slope stability level corresponding to different rock and soil parameter sample combinations.

[0120] Specifically, probability assessment and result analysis:

[0121] The single set of rock and soil parameters and the corresponding slope stability coefficients were sorted and counted, and the stability coefficient of 1.0 was set as the critical value of the slope stability state, that is, the stability coefficient less than 1.0 is unstable, and greater than or equal to 1.0 is stable. The number of samples in stable and unstable states was counted respectively, and the frequency of the corresponding stable state and the probability of accounting for the total sample were calculated. Finally, according to the statistical analysis results, the probability distribution function of slope stability (such as normal distribution, Beta distribution, etc.) was fitted and the distribution diagram was drawn. The Monte Carlo method was used to calculate the failure probability and reliability of the slope stability level corresponding to different rock and soil parameter sample combinations, providing technical guidance for the reinforcement of unstable slopes.

[0122] In some other embodiments, a slope stability probability assessment device is provided, comprising:

[0123] The analysis and processing module is used to perform sensitivity analysis on the rock and soil parameters of the slope to obtain m sensitive relationship curves;

[0124] A determination module is used to calculate and sort the mean values ​​of slope changes corresponding to m sensitive relationship curves, and determine k random rock and soil parameters;

[0125] A construction module, for constructing n groups of samples including k random rock and soil parameters using a random number generator;

[0126] A simulation module is used to input n groups of samples into the numerical model for finite element simulation to obtain n simulation results of slope stability coefficients;

[0127] An analysis module is used to analyze the frequency of sample groups corresponding to the stable and unstable states of the slope according to the defined critical value of the stability coefficient;

[0128] The calculation module is used to calculate the horizontal failure probability and reliability of the slope stability corresponding to the sample group using the Monte Carlo method.

[0129] In some embodiments, a slope stability probability assessment device includes a processor and a memory storing program instructions, and the processor is used to execute a slope stability probability assessment method as described in any of the above items when running the program instructions.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A slope stability probabilistic assessment method, characterized in that: include: The sensitivity analysis of the geotechnical parameters of the slope is carried out to obtain m sensitivity relationship curves, which are used to characterize the relationship between the slope stability coefficient and the evolution of the geotechnical parameters. Calculate the mean values ​​of slope changes corresponding to m sensitive relationship curves and sort them to determine k random rock and soil parameters; A random number generator is used to construct n groups of samples containing k random rock and soil parameters; Input n groups of samples into the numerical model for finite element simulation, and obtain n simulation results of slope stability coefficients; According to the defined critical value of stability coefficient, the frequency of sample groups corresponding to the stable and unstable states of the slope is analyzed; The Monte Carlo method is used to calculate the horizontal failure probability and reliability of the slope stability corresponding to the sample group.

2. A slope stability probabilistic assessment method according to claim 1, characterized in that: The sensitivity analysis of the rock and soil parameters of the slope is performed to obtain m sensitivity relationship curves, including: Obtain the rock and soil parameter set of the slope from the engineering statistical data; The variables of the geotechnical parameter set are controlled according to the control variable method combined with the strip method, and m sensitive relationship curves of the stability coefficient changing with the variables are obtained.

3. A slope stability probabilistic assessment method according to claim 1, characterized in that: The calculating and sorting of the mean values ​​of the slope changes corresponding to the m sensitive relationship curves to determine the k random rock and soil parameters includes: Calculating the corresponding slope change values ​​of the m sensitive relationship curves and taking the average to obtain the average of the m slope values; Sorting the mean values ​​of the m slope values ​​to obtain a list of mean values ​​sorted from largest to smallest; The rock and soil parameters corresponding to the first k values ​​in the mean value list are determined as random parameter variables.

4. A slope stability probabilistic assessment method according to claim 1, characterized in that: A random number generator is used to construct n groups of samples containing k random geotechnical parameters, including: Samples are drawn from k predefined probability distribution functions according to a random number generator to obtain n groups of random parameter samples.

5. A slope stability probabilistic assessment method according to claim 1, characterized in that: The n groups of samples are input into the numerical model for finite element simulation to obtain n corresponding slope stability coefficient simulation results, including: Extract finite element models; Inputting each group of random parameter samples into a finite element model to perform ground stress balance calculation, and obtaining a finite element model after ground stress balance; The strength reduction calculation is performed on the finite element model after the ground stress is balanced to obtain n slope stability coefficient simulation results.

6. A slope stability probabilistic assessment method according to claim 5, characterized in that: The steps of constructing the finite element model include: Obtain slope terrain data and construct a three-dimensional geometric model of the slope; Discretizing the three-dimensional geometric model of the slope according to a finite element network partitioning strategy to obtain a plurality of calculation units; A finite element model of the slope is constructed according to the plurality of calculation units.

7. A slope stability probabilistic assessment method according to claim 1, characterized in that: The analysis of the frequency of sample groups corresponding to the stable and unstable states of the slope according to the defined critical value of the stability coefficient includes: The simulation results of slope stability coefficient under n sample groups are sorted and counted to obtain statistical data; Delineate critical values ​​for slope stability and instability; Calculate the frequency of reaching the corresponding stable state and the probability of accounting for the total sample.

8. A slope stability probabilistic assessment method according to claim 1, characterized in that: The Monte Carlo method is used to calculate the failure probability and reliability of the slope stability level corresponding to the sample group, including: Fitting the statistical data according to the probability distribution function to construct a probability distribution model; The Monte Carlo method combined with the probability distribution model is used to calculate the failure probability and reliability of the slope stability level corresponding to different rock and soil parameter sample combinations.

9. A slope stability probability assessment device, characterized in that: include: The analysis and processing module is used to perform sensitivity analysis on the rock and soil parameters of the slope to obtain m sensitive relationship curves; A determination module is used to calculate and sort the mean values ​​of slope changes corresponding to m sensitive relationship curves, and determine k random rock and soil parameters; A construction module, for constructing n groups of samples including k random rock and soil parameters using a random number generator; A simulation module is used to input n groups of samples into the numerical model for finite element simulation to obtain n simulation results of slope stability coefficients; An analysis module is used to analyze the frequency of sample groups corresponding to the stable and unstable states of the slope according to the defined critical value of the stability coefficient; The calculation module is used to calculate the horizontal failure probability and reliability of the slope stability corresponding to the sample group using the Monte Carlo method.

10. A slope stability probability assessment device, comprising a processor and a memory storing program instructions, characterized in that: The processor is used to execute a slope stability probabilistic assessment method as described in any one of claims 1 to 9 when running the program instructions.

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