Numerical simulation method suitable for stability analysis of earth-rock mixture slope

Abstract

The invention relates to a numerical simulation method suitable for soil-rock aggregate slope stability analysis, and belongs to the technical field of soil-rock aggregate slope stability analysis. On the basis of a meshless particle method, rock in any shape is randomly generated by adopting a MonteCarlo random sampling principle, a particle generator of a point containment test algorithm is provided, a geometric model of the earth-rock aggregate slope is analyzed, the model is converted into particles carrying physical quantities, and an SPH numerical model of the earth-rock aggregate slope is constructed; and carrying out stability analysis on the SPH numerical model of the earth-rock mixture slope by adopting an SPH method, and generating a slope accumulative plastic strain cloud picture and a speed and displacement analysis report of a slope monitoring point. The method provided by the invention not only improves the precision of modeling and numerical simulation analysis, but also has very important engineering practical significance for accurately and efficiently predicting the landslide of the earth-rock mixture slope and the behavior thereof after damage.

Description

Technical Field

[0001] The invention relates to a numerical simulation method suitable for soil-rock mixture slope stability analysis, belonging to the technical field of soil-rock mixture slope stability analysis. Background Art

[0002] The soil-rock mixture is composed of blocks of stone with certain engineering scale and high strength, fine-grained soil and pores. Due to the unique physical and mechanical properties of the slope material, it is difficult to evaluate the stability of the soil-rock mixture slope and describe the failure form using traditional methods. Therefore, in-depth analysis of the characteristics of soil-rock interaction under deformation conditions and revealing the instability mechanism of soil-rock mixture slopes are of great engineering practical significance for accurately and efficiently predicting the landslide of soil-rock mixture slopes and their post-failure behavior.

[0003] So far, the research on soil-rock mixture has made great progress, and the main means of research are indoor and outdoor tests and numerical simulation. In terms of indoor and outdoor tests, a large number of experiments have revealed the macroscopic mechanical properties and microscopic failure mechanisms of soil-rock mixtures that are widely present in nature, and analyzed the stability of soil-rock mixture slopes. However, large-scale experimental studies on soil-rock mixtures are costly and time-consuming, and due to research difficulties and limitations such as experimental errors and strong randomness of results, it is difficult to fully reveal the deformation and failure mechanism of soil-rock mixtures. In terms of numerical simulation, researchers have used different numerical methods to explore the mechanical behavior of soil-rock mixtures from many aspects. The research on finite element method and finite difference method mainly focuses on the small deformation stage of slope instability, because the mesh distortion in the large deformation stage may lead to inaccurate or even interrupted numerical solutions; on the other hand, the discrete element method is difficult to perform large-scale calculations and analysis due to its large amount of calculations. Summary of the invention

[0004] In order to carry out numerical tests on soil-rock mixture slopes more comprehensively, the present invention proposes a numerical simulation method suitable for stability analysis of soil-rock mixture slopes, that is, using smoothed particle hydrodynamics (SPH) to study the stability of soil-rock mixture slopes, based on the meshless particle method, using the Monte Carlo random sampling principle to randomly generate rocks of arbitrary shapes, and proposing a particle generator of a point inclusion test algorithm, by analyzing the geometric model of the soil-rock mixture slope, converting the model into particles carrying physical quantities, and constructing the SPH numerical model of the soil-rock mixture slope; using the SPH method to analyze the stability of the SPH numerical model of the soil-rock mixture slope, generating the slope cumulative plastic strain cloud map, the velocity and displacement analysis report of the slope monitoring point. The method of the present invention not only improves the accuracy of modeling and numerical simulation analysis, but also has very important engineering practical significance for accurately and efficiently predicting the landslide of soil-rock mixture slopes and their post-destruction behaviors.

[0005] A numerical simulation method suitable for soil-rock mixture slope stability analysis, the specific steps are as follows: S1. Based on the geometric parameters of the soil-rock mixture slope, the rock content in the soil-rock mixture, and the rock particle size gradation curve, the Monte Carlo random sampling principle is used in MATLAB software to generate rock blocks of any shape matching the rock particle size; S2. Latin hypercube sampling method is used for stratified sampling. According to the actual gradation ratio of the rock, the entire particle size distribution cumulative probability curve is decomposed into different particle size intervals, where the probability of each particle size interval corresponds to the area percentage of rocks with different particle sizes under two-dimensional conditions; S3. randomly placing the generated rock blocks uniformly distributed in each particle size range into the model area of ​​the soil-rock mixture slope; S4. After all the generated rock blocks are placed, a soil-rock mixture slope model is generated, and each rock coordinate point is sorted and stored in a counterclockwise order; S5. Use the point inclusion test algorithm to analyze the soil-rock mixture slope model, convert the model into material particles carrying physical quantities, and construct the SPH numerical model of the soil-rock mixture slope; the carried physical quantities include position, velocity, mass and pressure; S6. According to the material parameters of the soil-rock mixture, the SPH method is used to perform stability analysis on the SPH numerical model of the soil-rock mixture slope, and generate the slope cumulative plastic strain cloud map, the velocity and displacement analysis report of the slope monitoring points.

[0006] The side length or diameter of the rock block is: ; in represents the side length or diameter of the rock mass, Indicates the height of the slope.

[0007] The specific process of S3. rock block placement is as follows: rock blocks are placed in the model area of ​​the soil-rock mixture slope in order from large to small according to the rock particle size; the rock blocks do not overlap with the boundary of the model area of ​​the soil-rock mixture slope where they are placed, and adjacent rock blocks do not interfere with each other; the rock blocks placed in the model area of ​​the soil-rock mixture slope meet the total area of ​​the rock grading.

[0008] S5. using the point inclusion test algorithm to analyze the soil-rock mixture slope model, converting the model into material particles carrying physical quantities, and constructing the soil-rock mixture slope SPH numerical model, specifically including: S51. Setting the initial particle spacing; S52. Analytical soil-rock mixture slope model; S53. defining a Cartesian node grid covering the initial particle spacing required for the model, and then creating particles only at the nodes required to draw the model; S54. After generating the preprocessed initial particle distribution, the geometric model of the soil-rock mixture slope is converted into material particles carrying physical quantities, and the SPH numerical model of the soil-rock mixture slope is established.

[0009] S6. The SPH method is used to perform stability analysis on the SPH numerical model of soil-rock mixture slope, specifically including: The Drucker-Prager model is used to study the evolution of shear bands in soil-rock mixture slopes. The final form of the Drucker-Prager model is: ; ; ; ; In the formula, , , represents the Cartesian components x, y and z; is the total stress tensor; is the stress component in the xz direction; is the rotation rate tensor in the yx direction; is the stress component in the zy direction; is the rotation rate tensor in the xz direction; K and G represent the elastic bulk modulus and shear modulus, respectively; is the deviatoric shear strain rate tensor; is the sum of the three normal strain rate components; is the Dirac function; is the plastic strain operator; is the expansion factor; is the deviatoric stress tensor; is the total strain rate tensor; is the yield function; is the first invariant of the stress tensor; is the second invariant of the deviatoric stress tensor; , is the Drucker-Prager constant.

[0010] The beneficial effects of the present invention are: (1) The present invention adopts the Monte Carlo random sampling principle to randomly generate rocks of arbitrary shapes, and establishes the corresponding structural model according to the statistical regularity of the structural composition of the soil-rock mixture at the macroscopic level, which is more suitable for conducting mechanistic research; (2) The present invention proposes a particle generator for a point inclusion test algorithm. The particle generation method is easy to implement, and the generated particle configuration is isotropic and has the best interpolation properties. (3) The present invention is based on the meshless particle method, which not only improves the accuracy of modeling and numerical simulation analysis, but also has very important engineering practical significance for accurately and efficiently predicting soil-rock mixture slope landslides and their post-destruction behaviors. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 The soil-rock mixture slope model with different rock contents in Example 1; Figure 3 The SPH model of soil-rock mixture with different rock contents in Example 1; Figure 4 is the cumulative plastic strain cloud diagram of the soil-rock mixture slope in Example 1; Figure 5 The velocity and displacement evolution of three points near the surface of the soil-rock mixture slope in Example 1: Figure 5 (a) is the velocity of the particle, Figure 5 (b) is the displacement of the particle; Figure 6 The soil-rock mixture slope model with different rock contents in Example 2; Figure 7 The SPH model of soil-rock mixture with different rock contents in Example 2; Figure 8 is the cumulative plastic strain cloud diagram of the soil-rock mixture slope in Example 2; Fig. 9 The velocity and displacement evolution of three points near the surface of the soil-rock mixture slope in Example 2: Fig. 9 (a) The velocity of the particle, Fig. 9 (b) Displacement of particles. DETAILED DESCRIPTION

[0012] The present invention is further described in detail below in conjunction with specific implementation modes, but the protection scope of the present invention is not limited to the described contents.

[0013] Summary of the Invention A numerical simulation method for soil-rock mixture slope stability analysis (see Figure 1 ), the specific steps are as follows: S1. According to the geometric parameters of the soil-rock mixture slope, the rock content in the soil-rock mixture and the rock particle size gradation curve, the Monte Carlo random sampling principle is used in MATLAB software to generate rock blocks of any shape matching the rock particle size; the initial shape of the rock block includes polygons such as triangles, quadrilaterals, and pentagons, and the shape of the rock block is adjusted by adjusting the shape parameters (polygon sides and deflection angle); the side length or diameter of the rock block is: ; in represents the side length or diameter of the rock mass, Indicates the height of the slope; S2. The Latin hypercube sampling method is used to perform stratified sampling on the soil-rock mixture slope. According to the actual gradation ratio of the rock, the entire particle size distribution cumulative probability curve is decomposed into different particle size intervals, where the probability of each particle size interval corresponds to the area percentage of rocks with different particle sizes under two-dimensional conditions, and the area percentage represents the rock content in the soil-rock mixture; S3. The generated rock blocks are randomly placed into the model area of ​​the soil-rock mixture slope according to the uniform distribution of each particle size interval; the specific process of the rock block placement is as follows: the rock blocks are placed into the model area of ​​the soil-rock mixture slope in order from large to small according to the rock particle size; the rock blocks do not overlap with the boundary of the model area of ​​the soil-rock mixture slope where they are placed, and adjacent rock blocks do not interfere with each other; the rock blocks placed in the model area of ​​the soil-rock mixture slope meet the total area of ​​the rock gradation; S4. After all the generated rock blocks are placed, a soil-rock mixture slope model is generated, and each rock coordinate point is sorted and stored in a counterclockwise order; S5. Use the point inclusion test algorithm to analyze the soil-rock mixture slope model, convert the model into material particles carrying physical quantities, and construct the SPH numerical model of the soil-rock mixture slope; the carried physical quantities include position, velocity, mass and pressure; specifically include: S51. Setting the initial particle spacing; S52. Analytical soil-rock mixture slope model; S53. defining a Cartesian node grid covering the initial particle spacing required for the model, and then creating particles only at the nodes required to draw the model; S54. After generating the preprocessed initial particle distribution, the geometric model of the soil-rock mixture slope is converted into material particles carrying physical quantities, and the SPH numerical model of the soil-rock mixture slope is established; S6. According to the material parameters of the soil-rock mixture, the SPH method is used to perform stability analysis on the SPH numerical model of the soil-rock mixture slope, and generate the slope cumulative plastic strain cloud map, the velocity and displacement analysis report of the slope monitoring point; The SPH method is used to perform stability analysis on the SPH numerical model of soil-rock mixture slope, specifically including: The Drucker-Prager model is used to study the evolution of shear bands in soil-rock mixture slopes. The final form of the Drucker-Prager model is: ; ; ; ; In the formula, , , represents the Cartesian components x, y and z; is the total stress tensor; is the stress component in the xz direction; is the rotation rate tensor in the yx direction; is the stress component in the zy direction; is the rotation rate tensor in the xz direction; K and G represent the elastic bulk modulus and shear modulus, respectively; is the deviatoric shear strain rate tensor; is the sum of the three normal strain rate components; is the Dirac function; is the plastic strain operator; is the expansion factor; is the deviatoric stress tensor; is the total strain rate tensor; is the yield function; is the first invariant of the stress tensor; is the second invariant of the deviatoric stress tensor; , is the Drucker-Prager constant.

[0014] Example 1: A numerical simulation method for soil-rock mixture slope stability analysis (see Figure 1 ), the specific steps are as follows: S1. According to the geometric parameters of the soil-rock mixture slope (slope height 29m, slope foot 1:1) and the rock content and rock particle size gradation curve inside the soil-rock mixture, five slope geometric models with different rock contents (0%, 20%, 30%, 40%, 50%) were established. The Monte Carlo random sampling principle was used in MATLAB software to generate rock blocks of any shape matching the rock particle size. The initial shape of the rock block includes polygons such as triangles, quadrilaterals, and pentagons. The shape of the rock block can be adjusted by adjusting the shape parameters (number of polygon sides and deflection angle). The side length or diameter of the rock block is: ; in represents the side length or diameter of the rock mass, Indicates the height of the slope; S2. The Latin hypercube sampling method is used to perform stratified sampling on the soil-rock mixture slope. According to the actual gradation ratio of the rock, the entire particle size distribution cumulative probability curve is decomposed into different particle size intervals, where the probability of each particle size interval corresponds to the area percentage of rocks with different particle sizes under two-dimensional conditions, and the area percentage represents the rock content in the soil-rock mixture; S3. The generated rock blocks are randomly placed into the model area of ​​the soil-rock mixture slope according to the uniform distribution of each particle size interval; the specific process of the rock block placement is as follows: the rock blocks are placed into the model area of ​​the soil-rock mixture slope in order from large to small according to the rock particle size; the rock blocks do not overlap with the boundary of the model area of ​​the soil-rock mixture slope where they are placed, and adjacent rock blocks do not interfere with each other; the rock blocks placed in the model area of ​​the soil-rock mixture slope meet the total area of ​​the rock gradation; S4. After all the generated rock blocks are placed, five soil-rock mixture slope models with different rock contents (0%, 20%, 30%, 40% and 50%) are generated (see Figure 2 ), and store each rock coordinate point in counterclockwise order; S5. The point inclusion test algorithm was used to analyze the soil-rock mixture slope models with five different rock contents (0%, 20%, 30%, 40% and 50%), and the models were converted into material particles carrying physical quantities to construct the SPH numerical model of the soil-rock mixture slope (see Figure 3 ); the physical quantities carried include position, speed, mass and pressure; specifically include: S51. Set the initial particle spacing to 0.4m; S52. Analytical soil-rock mixture slope model; S53. defining a Cartesian node grid covering the initial particle spacing required for the model, and then creating particles only at the nodes required to draw the model; S54. After generating the preprocessed initial particle distribution, the geometric model of the soil-rock mixture slope is converted into material particles carrying physical quantities, and the SPH numerical model of the soil-rock mixture slope is established; S6. According to the material parameters of the soil-rock mixture (see Table 1), the SPH method is used to perform stability analysis on the SPH numerical model of the soil-rock mixture slope, and generate the slope cumulative plastic strain cloud map, the velocity and displacement analysis report of the slope monitoring point; Table 1 Material parameters of soil-rock mixture ; The SPH method is used to perform stability analysis on the SPH numerical model of soil-rock mixture slope, specifically including: The Drucker-Prager model with non-associated plastic flow law is used to study the shear band evolution of soil-rock mixture slope. The final form of the Drucker-Prager model is: ; ; ; ; In the formula, , , represents the Cartesian components x, y and z; is the total stress tensor; is the stress component in the xz direction; is the rotation rate tensor in the yx direction; is the stress component in the zy direction; is the rotation rate tensor in the xz direction; K and G represent the elastic bulk modulus and shear modulus, respectively; is the deviatoric shear strain rate tensor; is the sum of the three normal strain rate components; is the Dirac function; is the plastic strain operator; is the expansion factor; is the deviatoric stress tensor; is the total strain rate tensor; is the yield function; is the first invariant of the stress tensor; is the second invariant of the deviatoric stress tensor; , is the Drucker-Prager constant; The condensed plastic strain of soil-rock mixture slopes with different rock contents is shown in Figure 4 It can be observed that the presence of rock will affect the position of the sliding surface and the volume of the landslide. Overall, the rock will play a certain role in stabilizing the slope. The shear zone of the soil-rock mixture slope with a rock content of 0% is arc-shaped. With the increase of rock content, the formation law of the plastic zone of the soil medium inside the soil-rock mixture slope has changed greatly, which is manifested as uneven distribution of the plastic zone and the phenomenon that the plastic zone is truncated by rock. Compared with the pure soil slope, the sliding surface of the soil-rock mixture slope will gradually change from a single, smooth arc-shaped sliding surface to multiple tortuous, narrow, rock-surrounding sliding surfaces, and connect with the wider main sliding surface. The velocity and displacement evolution of three points near the surface of the soil-rock mixture slope with different rock contents is shown in Figure 5 ,Depend on Figure 5 (a) It can be seen that the landslide velocity of the soil-rock mixture slope can be divided into three different stages. (1) Sliding acceleration stage: In this stage, the landslide surfaces gradually connect with each other, and the sliding body accelerates along the sliding surface. (2) Sliding rapid deceleration stage: In this stage, the sliding body has slid a certain distance along the shear cut, and the front end of the sliding body is close to the accumulation area. This will cause resistance to the sliding of the rear, resulting in rapid deceleration. (3) Accumulation stabilization stage: Finally, the rocks and soil blocks in front of the sliding body form a large amount of accumulation near the deposition area, further hindering the sliding. The landslide gradually decelerates and eventually stabilizes in the accumulation area. However, due to the presence of rocks, the landslide velocity may fluctuate. Figure 5 (b) It can be seen that the displacement of each point on the slope increases with time. Points 1 and 2 located in the middle and upper part of the slope have larger displacements, while point 3 has smaller displacement. The displacement curves of each point increase rapidly at first and then gradually stabilize, indicating that the soil-rock mixed slope has displacement due to instability failure, but secondary equilibrium occurs after the displacement, and the slope tends to be stable.

[0015] Example 2: A numerical simulation method for soil-rock mixture slope stability analysis (see Figure 1 ), the specific steps are as follows: S1. According to the geometric parameters of the soil-rock mixture slope (slope height 6m, slope foot 1:1) and the rock content and rock particle size gradation curve inside the soil-rock mixture, five slope geometric models with different rock contents (0%, 15%, 25%, 35%, 45%) were established, and the Monte Carlo random sampling principle was used in MATLAB software to generate rock blocks of any shape matching the rock particle size; the initial shape of the rock block includes polygons such as triangles, quadrilaterals, and pentagons, and the shape of the rock block is adjusted by adjusting the shape parameters (number of polygon sides and deflection angle); the side length or diameter of the rock block is: ; in represents the side length or diameter of the rock mass, Indicates the height of the slope; S2. The Latin hypercube sampling method is used to perform stratified sampling on the soil-rock mixture slope. According to the actual gradation ratio of the rock, the entire particle size distribution cumulative probability curve is decomposed into different particle size intervals, where the probability of each particle size interval corresponds to the area percentage of rocks with different particle sizes under two-dimensional conditions, and the area percentage represents the rock content in the soil-rock mixture; S3. The generated rock blocks are randomly placed into the model area of ​​the soil-rock mixture slope according to the uniform distribution of each particle size interval; the specific process of the rock block placement is as follows: the rock blocks are placed into the model area of ​​the soil-rock mixture slope in order from large to small according to the rock particle size; the rock blocks do not overlap with the boundary of the model area of ​​the soil-rock mixture slope where they are placed, and adjacent rock blocks do not interfere with each other; the rock blocks placed in the model area of ​​the soil-rock mixture slope meet the total area of ​​the rock gradation; S4. After all the generated rock blocks are placed, five soil-rock mixture slope models with different rock contents (0%, 15%, 25%, 35%, and 45%) are generated (see Figure 6 ), and store each rock coordinate point in counterclockwise order; S5. The point inclusion test algorithm was used to analyze the soil-rock mixture slope models with five different rock contents (0%, 15%, 25%, 35%, and 45%), and the models were converted into material particles carrying physical quantities to construct the SPH numerical model of the soil-rock mixture slope (see Figure 7 ); the physical quantities carried include position, speed, mass and pressure; specifically include: S51. Set the initial particle spacing to 0.1m; S52. Analytical soil-rock mixture slope model; S53. defining a Cartesian node grid covering the initial particle spacing required for the model, and then creating particles only at the nodes required to draw the model; S54. After generating the preprocessed initial particle distribution, the geometric model of the soil-rock mixture slope is converted into material particles carrying physical quantities, and the SPH numerical model of the soil-rock mixture slope is established; S6. According to the material parameters of the soil-rock mixture (see Table 2), the SPH method is used to perform stability analysis on the SPH numerical model of the soil-rock mixture slope, and generate the slope cumulative plastic strain cloud map, the velocity and displacement analysis report of the slope monitoring point; Table 2 Material parameters of soil-rock mixture ; The SPH method is used to perform stability analysis on the SPH numerical model of soil-rock mixture slope, specifically including: The Drucker-Prager model with non-associated plastic flow law is used to study the shear band evolution of soil-rock mixture slope. The final form of the Drucker-Prager model is: ; ; ; ; In the formula, , , represents the Cartesian components x, y and z; is the total stress tensor; is the stress component in the xz direction; is the rotation rate tensor in the yx direction; is the stress component in the zy direction; is the rotation rate tensor in the xz direction; K and G represent the elastic bulk modulus and shear modulus, respectively; is the deviatoric shear strain rate tensor; is the sum of the three normal strain rate components; is the Dirac function; is the plastic strain operator; is the expansion factor; is the deviatoric stress tensor; is the total strain rate tensor; is the yield function; is the first invariant of the stress tensor; is the second invariant of the deviatoric stress tensor; , is the Drucker-Prager constant; The condensed plastic strain of soil-rock mixture slopes with different rock contents is shown in Figure 8 It can be observed that the distribution of plastic zones of soil-rock mixture slope and pure soil slope is different. The shear zone of pure soil slope is circular, continuous and smooth, extending from the foot to the top of the slope, while the plastic zone of soil-rock mixture slope is interwoven, with a central shear zone extending to the top of the slope and the rest of the shear zones interwoven with it, showing obvious rock rolling characteristics; The velocity and displacement evolution of three points near the surface of the soil-rock mixture slope with different rock contents is shown in Fig. 9 ,Depend on Fig. 9 (a) It can be seen that the landslide velocity of the soil-rock mixture slope can be divided into three different stages. (1) Sliding acceleration stage: In this stage, the landslide surfaces gradually connect with each other, and the sliding body accelerates along the sliding surface. (2) Sliding rapid deceleration stage: In this stage, the sliding body has slid a certain distance along the shear cut, and the front end of the sliding body is close to the accumulation area. This will cause resistance to the sliding of the rear, resulting in rapid deceleration. (3) Accumulation stabilization stage: Finally, the rocks and soil blocks in front of the sliding body form a large amount of accumulation near the deposition area, further hindering the sliding. The landslide gradually decelerates and eventually stabilizes in the accumulation area. However, due to the presence of rocks, the landslide velocity may fluctuate. As Fig. 9As shown in (b), it can be seen that under the action of self-weight stress, the soil-rock mixture slope has a large displacement and deforms outward from the slope. The displacement first increases, then decreases, and finally remains unchanged. From the displacement and deformation analysis, the maximum displacement and deformation of the landslide body appear in the middle and upper part of the landslide body. Points 1 and 2 have good proximity conditions, so the sliding displacement and deformation are large, and the sliding displacement along the bedrock surface of the slope is relatively small.

[0016] The specific implementation modes of the present invention are described in detail above, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A numerical simulation method suitable for soil-rock mixture slope stability analysis, characterized in that: The specific steps are as follows: S1. Based on the geometric parameters of the soil-rock mixture slope, the rock content in the soil-rock mixture, and the rock particle size gradation curve, the Monte Carlo random sampling principle is used in MATLAB software to generate rock blocks of any shape matching the rock particle size; S2. Latin hypercube sampling method is used for stratified sampling. According to the actual gradation ratio of the rock, the entire particle size distribution cumulative probability curve is decomposed into different particle size intervals, where the probability of each particle size interval corresponds to the area percentage of rocks with different particle sizes under two-dimensional conditions; S3. randomly placing the generated rock blocks uniformly distributed in each particle size range into the model area of ​​the soil-rock mixture slope; S4. After all the generated rock blocks are placed, a soil-rock mixture slope model is generated, and each rock coordinate point is sorted and stored in a counterclockwise order; S5. Use the point inclusion test algorithm to analyze the soil-rock mixture slope model, convert the model into material particles carrying physical quantities, and construct the SPH numerical model of the soil-rock mixture slope; the carried physical quantities include position, velocity, mass and pressure; S6. According to the material parameters of the soil-rock mixture, the SPH method is used to perform stability analysis on the SPH numerical model of the soil-rock mixture slope, and generate the slope cumulative plastic strain cloud map, the velocity and displacement analysis report of the slope monitoring points.

2. The numerical simulation method for soil-rock mixture slope stability analysis according to claim 1 is characterized in that: S1. The side length or diameter of the rock block is: ； in represents the side length or diameter of the rock mass, Indicates the height of the slope.

3. The numerical simulation method for soil-rock mixture slope stability analysis according to claim 1 is characterized in that: S3. Specific process of rock block placement: rock blocks are placed in the model area of ​​the soil-rock mixture slope in order from large to small according to the rock particle size; the rock blocks do not overlap with the boundary of the model area of ​​the soil-rock mixture slope, and adjacent rock blocks do not interfere with each other; the rock blocks placed in the model area of ​​the soil-rock mixture slope meet the total area of ​​the rock grading.

4. The numerical simulation method for soil-rock mixture slope stability analysis according to claim 1 is characterized in that: S5. The soil-rock mixture slope model is analyzed by using the point inclusion test algorithm, the model is converted into material particles carrying physical quantities, and the SPH numerical model of the soil-rock mixture slope is constructed, including: S51. Setting the initial particle spacing; S52. Analytical soil-rock mixture slope model; S53. defining a Cartesian node grid covering the initial particle spacing required for the model, and then creating particles only at the nodes required to draw the model; S54. After generating the preprocessed initial particle distribution, the geometric model of the soil-rock mixture slope is converted into material particles carrying physical quantities, and the SPH numerical model of the soil-rock mixture slope is established.

5. The numerical simulation method for soil-rock mixture slope stability analysis according to claim 1 is characterized in that: S6. The SPH method is used to analyze the stability of the SPH numerical model of the soil-rock mixture slope, including: The Drucker-Prager model is used to study the evolution of shear bands in soil-rock mixture slopes. The final form of the Drucker-Prager model is: ； ； ； ； In the formula, , , represents the Cartesian components x, y and z; is the total stress tensor; is the stress component in the xz direction; is the rotation rate tensor in the yx direction; is the stress component in the zy direction; is the rotation rate tensor in the xz direction; K and G represent the elastic bulk modulus and shear modulus, respectively; is the deviatoric shear strain rate tensor; is the sum of the three normal strain rate components; is the Dirac function; is the plastic strain operator; is the expansion factor; is the deviatoric stress tensor; is the total strain rate tensor; is the yield function; is the first invariant of the stress tensor; is the second invariant of the deviatoric stress tensor; , is the Drucker-Prager constant.

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