Slope safety coefficient and sliding surface range solving method, device and equipment

Abstract

The invention relates to a slope safety coefficient and sliding surface range solving method, device and equipment. Firstly, a slope simulation model is constructed; performing grid division to obtain a plurality of unit bodies; determining the cohesive force in each unit body, the distribution condition in the internal friction angle and the displacement value of the node corresponding to each unit body through a random field; based on an autocorrelation function between adjacent unit bodies in the two-dimensional random field, generating a group of cohesive force and internal friction angle data by adopting a matrix decomposition algorithm; calculating a plurality of groups of cohesive force and internal friction angle data according to the set sample number, solving the standard deviation and variance of the slope safety coefficient corresponding to the plurality of groups of cohesive force and internal friction angle data, and calculating the range of the slope safety coefficient according to the standard deviation and variance; and calculating a plurality of groups of clusters with a set sample number according to a clustering algorithm, and determining a sliding surface range according to the clusters. According to the invention, a reliable and accurate safety coefficient and a sliding surface range can be obtained.

Description

Technical Field

[0001] The invention relates to the technical field of slope stability analysis, and in particular to a method, device and equipment for solving slope safety factor and sliding surface range. Background Art

[0002] In the field of geotechnical engineering, the safety factor and sliding surface of the slope are important indicators for evaluating the long-term stability of the slope. Usually, in order to simplify the calculation and analysis process, the cohesion and internal friction angle of the slope rock and soil are set to a uniform fixed value. However, in actual engineering, the cohesion and internal friction angle of the slope rock and soil often have spatial differences. This spatial variability may be caused by the complexity of geological conditions, weathering, groundwater level changes and other environmental factors. Therefore, the traditional homogeneous assumption may not accurately reflect the actual condition of the slope in the safety analysis and sliding surface prediction of the actual slope.

[0003] Since cohesion and internal friction angle are important control parameters of slope stability, the unevenness of their spatial distribution has a direct impact on the stability analysis of the slope. Traditional methods usually ignore the spatial variability of these parameters when calculating the slope safety factor and determining the sliding surface, resulting in the calculation results being too conservative in some cases. This error will affect the reliability of engineering design and may lead to unnecessary reinforcement measures or unidentified potential instability risks, which has certain limitations in practical applications.

[0004] In addition, when solving the sliding surface of the slope, traditional methods often use simplified assumptions and models for analysis, and fail to fully consider the heterogeneous characteristics of the rock and soil and the complex geological conditions. This may not only lead to deviations in the prediction of the sliding surface position, but also affect the accuracy of the safety factor, thereby bringing uncertainty to the slope stability evaluation and engineering design. Especially in areas with complex geological conditions, the calculation results of traditional methods may be significantly different from the actual situation, limiting its application value in complex projects. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device and equipment for solving the slope safety factor and sliding surface range, which takes into account the spatial variability of cohesion and internal friction angle and can obtain reliable and accurate safety factor range and sliding surface range to address the above technical problems.

[0006] A method for solving slope safety factor and sliding surface range, the method comprising:

[0007] Perform simulation modeling according to the geometric shape of the slope to obtain a slope simulation model;

[0008] The slope simulation model is meshed to obtain a plurality of unit bodies; the distribution of the cohesion and the internal friction angle in each unit body and the displacement value of the corresponding node of each unit body are determined by generating a random field of the cohesion and the internal friction angle;

[0009] Constructing an autocorrelation function between adjacent unit cells in a two-dimensional random field; based on the autocorrelation function, using a matrix decomposition algorithm to generate a set of cohesion and internal friction angle data;

[0010] Calculate several groups of cohesion and internal friction angle data according to the set sample quantity, then solve the standard deviation and variance of the slope safety factor corresponding to the several groups of cohesion and internal friction angle data, and calculate the range of the slope safety factor according to the standard deviation and variance;

[0011] According to the displacement values ​​of the nodes corresponding to each unit body, a clustering algorithm is used to calculate a number of clusters with a set number of samples, and the sliding surface range is determined according to the clusters.

[0012] A device for calculating slope safety factor and sliding surface range, the device comprising:

[0013] A model building module is used to perform simulation modeling according to the geometric shape of the slope to obtain a slope simulation model;

[0014] Random field generation and data acquisition are used to mesh the slope simulation model to obtain a number of unit bodies; by generating random fields of cohesion and internal friction angle, the distribution of cohesion and internal friction angle in each unit body and the displacement value of the corresponding node of each unit body are determined;

[0015] A cohesion and internal friction angle data calculation module is used to construct an autocorrelation function between adjacent unit bodies in a two-dimensional random field; based on the autocorrelation function, a set of cohesion and internal friction angle data is generated using a matrix decomposition algorithm;

[0016] The range solving module of slope safety factor is used to calculate several groups of cohesion and internal friction angle data according to the set number of samples, and then solve the standard deviation and variance of the slope safety factor corresponding to the several groups of cohesion and internal friction angle data, and calculate the range of slope safety factor according to the standard deviation and variance;

[0017] The sliding surface solving module is used to calculate a number of clusters of set sample quantities using a clustering algorithm according to the displacement values ​​of the nodes corresponding to each unit body, and determine the sliding surface range according to the clusters.

[0018] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for solving the slope safety factor and sliding surface range when executing the computer program.

[0019] The above-mentioned method, device and equipment for solving the slope safety factor and sliding surface range first perform simulation modeling according to the geometric shape of the slope to obtain a slope simulation model; mesh the slope simulation model to obtain a number of unit bodies; determine the distribution of cohesion and internal friction angle in each unit body and the displacement value of the corresponding node of each unit body by generating a random field of cohesion and internal friction angle; then construct an autocorrelation function between adjacent unit bodies in the two-dimensional random field; based on the autocorrelation function, use a matrix decomposition algorithm to generate a set of cohesion and internal friction angle data; calculate several groups of cohesion and internal friction angle data according to a set number of samples, and then solve the standard deviation and variance of the slope safety factor corresponding to the several groups of cohesion and internal friction angle data, and calculate the range of the slope safety factor according to the standard deviation and variance; according to the displacement value of the corresponding node of each unit body, use a clustering algorithm to calculate several groups of clusters of a set number of samples, and determine the sliding surface range according to the clusters.

[0020] The present invention first simulates the changes of cohesion and internal friction angle in space by random field method to understand the specific distribution of cohesion and internal friction angle in different units; then obtains a set of cohesion and internal friction angle data by matrix decomposition algorithm, repeats the above steps to obtain several sets of cohesion and internal friction angle data with sufficient sample quantity, calculates the standard deviation and variance of the slope safety factor corresponding to several sets of cohesion and internal friction angle data, and solves the range of slope safety factor; in addition, obtains several sets of clusters with sufficient sample quantity by clustering algorithm, so as to determine the range of sliding surface. In this way, the uncertainty of cohesion and internal friction angle can be quantified, so as to better reflect the influence of the variability of cohesion and internal friction angle on slope stability, make the calculation result more in line with engineering practice, and finally obtain reliable and accurate safety factor and sliding surface range. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of a flow chart of a method for solving a slope safety factor and a sliding surface range in one embodiment;

[0023] Figure 2 is a schematic diagram of the geometric shape of a slope in one embodiment;

[0024] Figure 3 A schematic diagram of a slope simulation model in an embodiment;

[0025] Figure 4 FIG. 1 is a schematic diagram of the distribution of cohesion and internal friction angle in a slope simulation model in one embodiment, wherein: Figure 4 (a) is the distribution of cohesion, 4(b) is the distribution of internal friction angle;

[0026] Figure 5 A schematic diagram of a set of data distribution of cohesion and internal friction angle in one embodiment;

[0027] Figure 6 A schematic diagram of dividing a unit body with a larger slope displacement deformation and a unit body with a smaller slope displacement deformation in one embodiment;

[0028] Figure 7 A schematic diagram of a sliding surface obtained through a group of clusters in one embodiment;

[0029] Figure 8 A schematic diagram of a sliding surface range obtained by calculating a plurality of clusters in one embodiment;

[0030] Fig. 9 It is a structural block diagram of a device for solving slope safety factor and sliding surface range in one embodiment;

[0031] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment.

[0032] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] It should be noted that the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0035] The following will describe the implementation of the present invention in detail with reference to the accompanying drawings in the embodiment diagram of the present invention.

[0036] Example 1

[0037] This embodiment discloses a method for solving the safety factor of the slope and the range of the sliding surface. The random field method is used to simulate the changes in cohesion and internal friction angle in space to understand the specific distribution of cohesion and internal friction angle in different units; then a set of cohesion and internal friction angle data is obtained through a matrix decomposition algorithm, and after repeating the above steps to obtain a number of sets of cohesion and internal friction angle data with a sufficient number of samples, the standard deviation and variance of the slope safety factor corresponding to the several sets of cohesion and internal friction angle data are calculated to solve the range of the slope safety factor; in addition, a number of clusters with a sufficient number of samples are obtained through a clustering algorithm to determine the range of the sliding surface. In this way, the uncertainty and variability of cohesion and internal friction angle can be quantified, so as to better reflect the influence of the variability of cohesion and internal friction angle on the stability of the slope, so that the calculation results are more in line with engineering practice, and finally a reliable and accurate safety factor and sliding surface range are obtained.

[0038] like Figure 1 As shown, the method for solving the slope safety factor and sliding surface range in this embodiment includes the following steps:

[0039] Step 201 , performing simulation modeling according to the geometric shape of the slope to obtain a slope simulation model.

[0040] Specifically, the geometric shape of the slope is first determined, the relevant parameters are obtained, and then the corresponding slope simulation model is constructed using the FLAC3D numerical simulation calculation software.

[0041] Step 202, meshing the slope simulation model to obtain a plurality of unit bodies; by generating a random field of cohesion and internal friction angle, determining the distribution of cohesion and internal friction angle in each unit body, and the displacement value of the corresponding node of each unit body.

[0042] It can be understood that since cohesion and internal friction angle are not uniformly distributed and have spatial variability, random fields are useful for understanding and processing complex data with spatial or temporal correlation. Therefore, after meshing, by generating random fields of cohesion and internal friction angle, the distribution of cohesion and internal friction angle in each unit can be intuitively understood. At the same time, the relevant parameters of each unit in the slope simulation model are obtained, including but not limited to the unit ID number, unit coordinates, displacement values ​​of corresponding nodes, etc.

[0043] Step 203, constructing an autocorrelation function between adjacent unit cells in the two-dimensional random field; based on the autocorrelation function, using a matrix decomposition algorithm to generate a set of cohesion and internal friction angle data.

[0044] It can be understood that by constructing the autocorrelation function between adjacent unit bodies, the spatial correlation of cohesion and internal friction angle can be quantified, and the parameter change trend between adjacent unit bodies can be captured, making the model more consistent with actual geological conditions.

[0045] Then, a matrix decomposition algorithm is used to generate a set of cohesion and internal friction angle data, which maintain the spatial correlation under actual geological conditions at different locations, so that the generated parameter field is closer to the actual situation, avoiding the problem of ignoring the spatial correlation between parameters in traditional methods and causing unrealistic results. The matrix decomposition algorithm can use the Cholesky decomposition algorithm.

[0046] Step 204, according to the set number of samples, several groups of cohesion and internal friction angle data are calculated through the slope simulation model, and then the standard deviation and variance of the slope safety factor corresponding to the several groups of cohesion and internal friction angle data are solved, and the range of the slope safety factor is calculated according to the standard deviation and variance.

[0047] It can be understood that by calculating the data of cohesion and internal friction angle with a sufficient number of samples, the influence of the uncertainty of cohesion and internal friction angle data on slope stability can be quantified and the influence of errors can be reduced. Standard deviation and variance are mainly used to reflect the distribution of slope safety factor to infer the possibility of slope instability. The range of slope safety factor is calculated by multiple sets of standard deviations and variances. Through the range of slope safety factor, it can be reasonably set according to demand to maintain a certain safety margin to ensure that the slope design is still safe enough after considering various possibilities; or appropriately reduce the redundancy of safety factor, so as to optimize resource allocation and reduce engineering costs.

[0048] Step 205: According to the displacement values ​​of the nodes corresponding to each unit body, a clustering algorithm is used to calculate a number of clusters with a set number of samples, and the sliding surface range is determined according to the clusters.

[0049] It can be understood that cluster analysis can effectively identify units with similar displacement characteristics in the slope, and these units are often located on or near the sliding surface. Therefore, the clustering results can help accurately determine the range of the sliding surface. By calculating clusters with a sufficient number of samples, random errors can be effectively reduced, which helps to more accurately capture the sliding surface boundary and avoid incomplete or biased sliding surface identification due to insufficient sample numbers, thereby ensuring that the sliding surface range is more accurately determined. In addition, analyzing clusters with a sufficient number of samples can reflect the spatial variability of the sliding surface area to identify secondary sliding or local unstable areas that may exist in the sliding surface. The clustering algorithm can adopt algorithms such as K-means clustering algorithm, support vector machine, decision tree, and logistic regression. Preferably, the clustering algorithm adopts K-means clustering algorithm.

[0050] In the specific implementation process of step 203, for the plane slope, the autocorrelation function expression between adjacent unit cells in the two-dimensional random field is:

[0051]

[0052] In the formula, i and j represent the serial numbers of adjacent units; δ x and δ y Represents cohesion c and internal friction angle The correlation length of the random field in the x-direction and the y-direction.

[0053] Then, based on the autocorrelation function, a set of cohesion and internal friction angle data is generated by using a matrix decomposition algorithm. Specifically, the corresponding distance autocorrelation matrix is ​​constructed according to the autocorrelation function; the coefficient cross-correlation matrix is ​​constructed according to the mutual correlation coefficient of cohesion and internal friction angle; the autocorrelation matrix and the coefficient cross-correlation matrix are decomposed by a matrix decomposition algorithm to obtain the corresponding triangular matrix under decomposition; the parameter matrix of the unit cell is obtained by calculation according to the triangular matrix; and a set of cohesion and internal friction angle data is obtained through the parameter matrix.

[0054] Among them, the distance autocorrelation matrix expression is:

[0055]

[0056] In the formula, ρ ij Represents the autocorrelation function of adjacent unit cells, i, j = 1, 2; n represents the number of unit cells.

[0057] The coefficient cross-correlation matrix expression is:

[0058]

[0059] In the formula, represents the cohesion c and the internal friction angle The mutual correlation coefficient of The internal friction angle The correlation coefficient with cohesion c; and,

[0060] The autocorrelation matrix and the coefficient cross-correlation matrix are decomposed by a matrix decomposition algorithm to obtain a corresponding triangular matrix under decomposition, which is expressed as follows:

[0061]

[0062]

[0063] Where, L C and L R represents the decomposed lower triangular matrix; [·]Τ represents the vector transpose, where · represents a replaceable variable.

[0064] The parameter matrix expression of the unit cell is:

[0065]

[0066] Where Φ represents the standard normal distribution cumulative function; ξ k represents a standard normal distribution random matrix with dimension n×2; q=1,2 represents cohesion c and internal friction angle respectively

[0067] It can be understood that after obtaining the cohesion and internal friction angle data through the parameter matrix, the cohesion and internal friction angle in the unit body can be assigned in the slope simulation model to obtain the mechanical parameters of the rock and soil body.

[0068] In the specific implementation process of step 204, if the number of samples is set to 1000 groups, 1000 groups of cohesion and internal friction angle data are generated through steps 201 to 203, and then 1000 slope simulation models (models of the same size, cohesion c and internal friction angle) are calculated. The data distribution is different) and the corresponding 1000 groups of safety factors, as well as the standard deviation and variance of the 1000 groups of safety factors, finally the maximum value FoS of the slope safety factor range is obtained by using the 3σ principle max and minimum FoS min Specifically, the range of slope safety factor is calculated based on the standard deviation and variance, and the expression is:

[0069]

[0070] In the formula, FoS min Indicates the minimum value of slope safety factor; FoS max represents the maximum value of slope safety factor; μ represents the standard deviation; σ represents the variance.

[0071] According to the maximum FoS max and minimum FoS min Determine the slope safety factor range as FoS max ~FoS min .

[0072] In the specific implementation process of step 205, first determine a set of sliding surface calculation results under the slope simulation model. The specific calculation of the sliding surface is:

[0073] The coordinate values ​​and displacement values ​​of the corresponding nodes of the unit body are obtained, and then the K-means clustering algorithm is used to calculate the clusters. The clusters include the unit bodies with larger slope displacement deformation and the unit bodies with smaller slope displacement deformation. The interface between the unit bodies with larger slope displacement deformation and the unit bodies with smaller slope displacement deformation is determined as the sliding surface.

[0074] By using the above method, several clusters of a set number of samples are calculated, and the range of the sliding surface is determined according to the clusters. For example, if the number of samples is 1000 groups, the sliding surfaces corresponding to the 1000 clusters are calculated, and the 1000 groups of sliding surfaces are superimposed to obtain the range of the sliding surface. The method provided by the present invention can provide guidance for engineering stability assessment and engineering design.

[0075] In one of the embodiments, the method for solving the slope safety factor and sliding surface range proposed in the present invention is verified.

[0076] First, determine the geometry of the slope, such as Figure 2 As shown; the geometric parameters of the slope are obtained based on the geometric shape of the slope, and then the slope simulation model is constructed using FLAC3D numerical simulation calculation software. The slope simulation model is as follows Figure 3 Then, the built-in FISH programming language in FLAC3D is used to output Figure 3 The ID number, corresponding coordinates and displacement value of each unit in the slope simulation model shown.

[0077] Since the safety factor and sliding surface of the slope are mainly determined by the cohesion c of the rock and soil and the internal friction angle Determine, therefore, the cohesion c and the internal friction angle are obtained by Cholesky decomposition The random field parameters of . Figure 4 As shown, a set of cohesion c and internal friction angle is given The distribution of slope simulation model, in the process of calculating this group of parameters, δ x =40m,δ y =1m; The average value of the cohesion c is 30 KPa, and the variance is 0.2; the internal friction angle The mean is 13° and the variance is 0.2. Then the cohesion c and internal friction angle in the unit cell in the slope simulation model are calculated. Assign values ​​and obtain the mechanical parameters of rock and soil as shown in Table 1.

[0078] Table 1 Mechanical parameters of rock and soil

[0079] <![CDATA[Density (kg / m 3 )]]> Elastic modulus(Pa) Shear modulus(Pa) Tensile strength(Pa) 2500 8.33e6 3.48e6 0.03e6

[0080] Then, the slope safety factor and the distribution range of the slope sliding surface are determined according to the calculation results. Specifically, assuming that the number of samples is 1000, based on the slope simulation model, 1000 sets of cohesion and internal friction angle data are calculated by the above method to obtain 1000 slope simulation models (the model size is the same, the cohesion c and internal friction angle are the same). The data distribution is different), and then 1000 groups of safety factors corresponding to 1000 slope simulation models are obtained, such as Figure 5As shown in the figure, the slope safety factor and frequency distribution are given. Then the standard deviation μ and variance σ of the corresponding 1000 groups are calculated, which are μ = 0.78 and σ = 0.04 respectively. The minimum value of the slope safety factor range FoS is calculated using the 3σ principle. min With maximum FoS max They are 0.66 and 0.90 respectively, thus determining that the slope safety factor ranges from 0.66 to 0.90.

[0081] In order to obtain the range of the slope sliding surface, it is necessary to determine the sliding surface of each set of numerical simulation calculation results. The sliding surface is calculated as follows: first, the coordinate values ​​and displacement values ​​of the FLAC3D numerical simulation calculation nodes are derived, and the K-means clustering algorithm is used to divide the node displacement values ​​in FLAC3D into two clusters, one representing the unit body with larger displacement deformation of the slope, and the other representing the unit body with smaller displacement deformation of the slope. Figure 6 The nodes with larger displacement and the nodes with smaller displacement are shown. The nodes with smaller displacement are gray, while the nodes with smaller displacement are black. Figure 6 It can be seen that the junction of the point with smaller displacement and the point with larger displacement is the sliding surface of the slope.

[0082] Repeat the above method to obtain Figure 8 The superposition of 1000 sets of numerical simulation calculations of sliding surfaces is shown to obtain the random field slope sliding surface area.

[0083] Although this embodiment Figure 1 The steps in the process are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps can be executed in other orders. Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0084] Example 2

[0085] Based on the slope safety factor and sliding surface range method in Example 1, this embodiment discloses a slope safety factor and sliding surface range device, such as Fig. 9 As shown, the slope safety factor and sliding surface range device includes: a model building module 401, a random field generation and data acquisition 402, a cohesion and internal friction angle data calculation module 403, a slope safety factor range solution module 404 and a sliding surface solution module 405, wherein:

[0086] The model building module 401 is used to perform simulation modeling according to the geometric shape of the slope to obtain a slope simulation model;

[0087] Random field generation and data acquisition 402, for meshing the slope simulation model to obtain a plurality of unit bodies; by generating random fields of cohesion and internal friction angle, determining the distribution of cohesion and internal friction angle in each unit body, and the displacement value of the corresponding node of each unit body;

[0088] The cohesion and internal friction angle data calculation module 403 is used to construct an autocorrelation function between adjacent unit cells in a two-dimensional random field; based on the autocorrelation function, a set of cohesion and internal friction angle data is generated using a matrix decomposition algorithm;

[0089] The slope safety factor range solving module 404 is used to calculate a number of sets of cohesion and internal friction angle data according to a set number of samples, and then solve the standard deviation and variance of the slope safety factor corresponding to the number of sets of cohesion and internal friction angle data, and calculate the range of the slope safety factor according to the standard deviation and variance;

[0090] The sliding surface solving module 405 is used to calculate a number of clusters of a set number of samples using a clustering algorithm according to the displacement values ​​of the nodes corresponding to each unit body, and determine the sliding surface range according to the clusters.

[0091] In this embodiment, the specific working process and working principle of the model building module 401, the random field generation and data acquisition 402, the cohesion and internal friction angle data calculation module 403, the slope safety factor range solution module 404 and the sliding surface solution module 405 are the same as those in the method in Example 1, so they are not repeated in this embodiment. Each unit module can be implemented in whole or in part by software, hardware and a combination thereof, and each unit module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above unit modules.

[0092] Example 3

[0093] like Fig.10 The terminal device disclosed in this embodiment includes a transmitter, a receiver, a memory, and a processor. The transmitter is used to send instructions and data, the receiver is used to receive instructions and data, the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions stored in the memory to implement the method in the above-mentioned embodiment 1.

[0094] It should be noted that the above memory can be independent or integrated with the processor. When the memory is independently provided, the terminal device further includes a bus for connecting the memory and the processor.

[0095] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0096] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for calculating slope safety factor and sliding surface range, It is characterized in that The method comprises: Perform simulation modeling according to the geometric shape of the slope to obtain a slope simulation model; The slope simulation model is meshed to obtain a plurality of unit bodies; the distribution of the cohesion and the internal friction angle in each unit body and the displacement value of the corresponding node of each unit body are determined by generating a random field of the cohesion and the internal friction angle; Constructing an autocorrelation function between adjacent unit cells in a two-dimensional random field; based on the autocorrelation function, using a matrix decomposition algorithm to generate a set of cohesion and internal friction angle data; According to the set number of samples, a plurality of groups of cohesion and internal friction angle data are calculated by the slope simulation model, and then the standard deviation and variance of the slope safety factor corresponding to the plurality of groups of cohesion and internal friction angle data are solved, and the range of the slope safety factor is calculated according to the standard deviation and variance; According to the displacement values ​​of the nodes corresponding to each unit body, a clustering algorithm is used to calculate a number of clusters with a set number of samples, and the sliding surface range is determined according to the clusters.

2. The method for calculating the slope safety factor and the sliding surface range according to claim 1, It is characterized in that Based on the autocorrelation function, a set of cohesion and internal friction angle data is generated using a matrix decomposition algorithm, including: Constructing a corresponding distance autocorrelation matrix according to the autocorrelation function; constructing a coefficient cross-correlation matrix according to the cross-correlation coefficients of cohesion and internal friction angle; The autocorrelation matrix and the coefficient cross-correlation matrix are decomposed by a matrix decomposition algorithm to obtain a corresponding triangular matrix under decomposition; a parameter matrix of a unit cell is obtained by calculation according to the triangular matrix; and a set of cohesion and internal friction angle data is obtained through the parameter matrix.

3. The method for calculating the slope safety factor and sliding surface range according to claim 2, It is characterized in that The autocorrelation function expression is: In the formula, i and j represent the serial numbers of adjacent units; δ x and δ y Represents cohesion c and internal friction angle The correlation length of the random field in the x-direction and the y-direction.

4. The method for calculating the slope safety factor and sliding surface range according to claim 2 or 3, It is characterized in that The distance autocorrelation matrix expression is: In the formula, ρ ij Represents the autocorrelation function of adjacent unit cells, i, j = 1, 2; n represents the number of unit cells.

5. The method for calculating the slope safety factor and the sliding surface range according to claim 4, It is characterized in that The coefficient cross-correlation matrix expression is: In the formula, represents the cohesion c and the internal friction angle The mutual correlation coefficient of The internal friction angle The mutual correlation coefficient with cohesion c.

6. The method for calculating the slope safety factor and sliding surface range according to claim 5, It is characterized in that The autocorrelation matrix and the coefficient cross-correlation matrix are decomposed by a matrix decomposition algorithm to obtain a corresponding triangular matrix under decomposition, which is expressed as follows: Where, L C and L R represents the decomposed lower triangular matrix; [·]Τ represents the vector transpose, where · represents a replaceable variable.

7. The method for calculating the slope safety factor and sliding surface range according to claim 6, It is characterized in that The parameter matrix expression of the unit cell is: Where Φ represents the standard normal distribution cumulative function; ξ k represents a standard normal distribution random matrix with dimension n×2; q=1,2 represents cohesion c and internal friction angle respectively 8. The method for calculating the slope safety factor and sliding surface range according to any one of claims 1 to 3, It is characterized in that The range of slope safety factor is calculated based on the standard deviation and variance, and the expression is: Where, FoS min Indicates the minimum value of slope safety factor; FoS max represents the maximum value of slope safety factor; μ represents the standard deviation; σ represents the variance.

9. A device for calculating slope safety factor and sliding surface range, It is characterized in that The device comprises: A model building module is used to perform simulation modeling according to the geometric shape of the slope to obtain a slope simulation model; Random field generation and data acquisition are used to mesh the slope simulation model to obtain a number of unit bodies; by generating random fields of cohesion and internal friction angle, the distribution of cohesion and internal friction angle in each unit body and the displacement value of the corresponding node of each unit body are determined; A cohesion and internal friction angle data calculation module is used to construct an autocorrelation function between adjacent unit bodies in a two-dimensional random field; based on the autocorrelation function, a set of cohesion and internal friction angle data is generated using a matrix decomposition algorithm; The range solving module of slope safety factor is used to calculate several groups of cohesion and internal friction angle data according to the set number of samples, and then solve the standard deviation and variance of the slope safety factor corresponding to the several groups of cohesion and internal friction angle data, and calculate the range of slope safety factor according to the standard deviation and variance; The sliding surface solving module is used to calculate a number of clusters of set sample quantities using a clustering algorithm according to the displacement values ​​of the nodes corresponding to each unit body, and determine the sliding surface range according to the clusters.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program. It is characterized in that When the processor executes the computer program, the steps of the method for solving the slope safety factor and sliding surface range described in any one of claims 1 to 8 are implemented.

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