Method for determining slope stability based on void ratio and liquid index

Abstract

The invention discloses a method for determining slope stability based on a void ratio and a liquid index. The method comprises the following steps: 1, sampling a slope soil layer; 2, measuring key parameters of soil layer samples and constructing a soil layer sample database; 3, measuring the shear strength of the soil layer sample; 4, determining the stability of the slope corresponding to the soil layer sample by utilizing finite element analysis; 5, verifying the stability of the slope; 6, establishing and training a slope stability prediction model; and 7, determining the actual slope stability based on the void ratio and the liquidity index. The slope stability is evaluated through the moisture content, the void ratio and the liquidity index of the soil, the void ratio directly affects the density and water permeability of the soil, the liquidity index reflects the capability of converting the soil from a plastic state to a flowing state, the slope stability is evaluated more accurately, the safety of slope engineering is further improved, and the slope engineering cost is reduced. Potential geological disaster risks are reduced, and theoretical basis and technical support are provided for practical engineering.

Description

Technical Field

[0001] The invention belongs to the technical field of slope stability determination, and in particular relates to a method for determining slope stability based on void ratio and liquid index. Background Art

[0002] Slope stability analysis is of great significance in civil engineering, geological engineering and environmental engineering, especially in mountainous areas and rainy areas, where geological disasters such as slope landslides and collapses occur frequently, directly threatening people's lives and property. Traditional slope stability assessment methods mainly rely on the physical and mechanical properties of soil, such as internal friction angle, cohesion, etc. However, in actual engineering, the mechanical properties of soil are affected by multiple factors such as moisture and temperature, making it difficult to accurately reflect the true state of the soil, and the slope stability changes unpredictably. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for determining slope stability based on porosity and liquid index in view of the deficiencies in the above-mentioned prior art. The slope stability is evaluated by the moisture content, porosity and liquid index of the soil. The porosity directly affects the density and permeability of the soil. The liquid index reflects the ability of the soil to change from a plastic state to a fluid state. The slope stability is evaluated more accurately, thereby improving the safety of slope engineering and reducing the potential risk of geological disasters. The method provides a theoretical basis and technical support for actual engineering and is convenient for promotion and use.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for determining slope stability based on porosity ratio and liquid index, characterized in that the method comprises the following steps:

[0005] Step 1: Sampling of soil layers on slopes: Select a variety of representative slope locations, determine multiple sampling points at the upper, middle and lower positions of the slope, set the sampling depth according to the soil layer structure, and sample soil layers at different depths;

[0006] Step 2: Determine the key parameters of soil samples and build a soil sample database: the key parameters of the soil samples include moisture content, porosity, liquid index and slope gradient, and organize the measurement results into a soil sample database;

[0007] Step 3, measuring the shear strength of the soil layer sample: the shear strength of the soil layer sample includes the cohesion and internal friction angle of the soil layer sample;

[0008] Step 4: Use finite element analysis to determine the stability of the slope corresponding to the soil layer sample;

[0009] Step 5: Verification of slope stability: Calculate the stability coefficient by using the limit equilibrium Swedish strip method, Bishop method, and Morgenstern-Price method to verify the slope stability analysis results in step 4;

[0010] Step 6: Establish and train a slope stability prediction model: Establish a slope stability prediction model, which is a neural network model, with moisture content, porosity, liquid index, slope gradient and confining pressure as input layer nodes of the neural network model, and stability coefficient as output layer node of the neural network model, and train the neural network model;

[0011] Step 7. Determine the stability of the actual slope based on the porosity and liquid index: collect the moisture content, porosity and liquid index of the soil sample at a position on the actual slope, measure the slope gradient of the actual slope, survey the confining pressure of the soil sample at a position on the actual slope, input the actually obtained moisture content, porosity, liquid index, slope gradient and confining pressure data into the trained neural network model, output the stability coefficient, and determine the stability of the actual slope based on the stability coefficient.

[0012] The above-mentioned method for determining slope stability based on porosity ratio and liquid index is characterized in that: in step one, the sampling depth starts from 0.5m, and sampling is performed every 0.5m.

[0013] The above-mentioned method for determining slope stability based on porosity and liquid index is characterized in that: in step three, after setting the confining pressure, the shear strength of the soil layer sample is determined by the consolidation undrained test method, and the soil layer sample is sheared until it is destroyed or reaches the expected value to cause axial failure; during the test, if a peak value appears in the graph, the peak point is selected. If there is no obvious peak value, the ordinate value when the axial strain reaches 15% is used as the stress failure standard. According to the Mohr-Coulomb strength theory, the Mohr circle under different confining pressure conditions is drawn, and a straight line is drawn by connecting the tangent points of each circle. The coordinates of the intersection of the strength envelope and the vertical axis are the cohesion, and the angle formed with the horizontal axis is the internal friction angle.

[0014] The above-mentioned method for determining slope stability based on void ratio and liquid index is characterized in that: in step 4, the process of determining the stability of the slope corresponding to the soil layer sample by finite element analysis includes the following steps:

[0015] Step 401, using finite element analysis software ABAQUS to establish a two-dimensional homogeneous slope model in part module 1;

[0016] Step 402, set the material, section properties, elastic model parameters and Poisson's ratio, set the friction angle, dilatancy angle and reduction coefficient that vary with the field in the mechanical parameter plasticity interface, and set the cohesion that varies with the field in the Cohension option;

[0017] Step 403, assemble components and establish corresponding instances;

[0018] Step 404, define the analysis cloth, select the static analysis step in the step module, store the matrix as asymmetric, and set the initial increment step to 0.1. Set the reduce static analysis step in the same way;

[0019] Step 405, the load option defines the load and the limit size displacement boundary conditions, applies the gravity load in the load module, and constrains the displacements in the two directions on both sides of the horizontal and the bottom of the model to simulate the effect of the gravity load;

[0020] Step 406, select the face splitting operation, assign the mesh control attributes and set its size, execute the seed command to adjust the density of the mesh, and then perform the mesh splitting work;

[0021] Step 407: Add the control field variable changes, submit the job1 job file, and wait for the result;

[0022] Step 408, enter the visualization post-processing module, and view the slope stability analysis results as needed. The slope stability analysis results include slope stability, slope critical stability and slope instability.

[0023] The above-mentioned method for determining slope stability based on porosity and liquid index is characterized in that: in step 2, a combined measuring instrument is used to measure the liquid index of the soil layer sample.

[0024] The above-mentioned method for determining slope stability based on porosity ratio and liquid index is characterized in that: in step six, the neural network model includes a BP neural network model.

[0025] Compared with the prior art, the present invention has the following advantages:

[0026] 1. The present invention determines the key parameters of soil samples and constructs a soil sample database. The slope stability is evaluated by the moisture content, porosity and liquid index of the soil. The porosity directly affects the density and permeability of the soil. The liquid index reflects the ability of the soil to change from a plastic state to a flow state, which can more accurately evaluate the slope stability.

[0027] 2. The present invention uses finite element analysis to determine the stability of the slope corresponding to the soil layer sample and calculates the stability coefficient through the limit equilibrium Swedish strip method, Bishop method, and Morgenstern-Price method to verify the slope stability analysis result in step 4. It is reliable and stable and has good use effect.

[0028] 3. The method of the present invention has simple steps. By establishing and training a slope stability prediction model, it is convenient to predict the stability of the actual slope. The actually obtained moisture content, porosity, liquid index, slope gradient and confining pressure data are input into the trained neural network model, and the stability coefficient is output. The stability of the actual slope is determined based on the stability coefficient, which reduces the potential risk of geological disasters, provides a theoretical basis and technical support for actual engineering, and is easy to promote and use.

[0029] In summary, the present invention evaluates slope stability through soil moisture content, porosity and liquid index. The porosity directly affects soil density and water permeability. The liquid index reflects the ability of soil to change from a plastic state to a fluid state. It can more accurately evaluate slope stability, thereby improving the safety of slope engineering and reducing potential geological disaster risks. It provides a theoretical basis and technical support for practical engineering and is easy to promote and use.

[0030] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The figure is a flowchart of the method of the present invention.

[0032] Figure 2a Graph showing the stress-strain relationship under different levels of confining pressure when the moisture content of the soil sample in this embodiment is 5%.

[0033] Figure 2b 1 is a stress-strain relationship curve diagram under different levels of confining pressure when the moisture content of the soil sample in this embodiment is 10%.

[0034] Figure 2c 1 is a stress-strain relationship curve diagram under different levels of confining pressure when the moisture content of the soil sample in this embodiment is 15%.

[0035] Figure 2d 1 is a stress-strain relationship curve diagram under different levels of confining pressure when the moisture content of the soil sample in this embodiment is 20%.

[0036] Figure 2e 1 is a stress-strain relationship curve diagram under different levels of confining pressure when the moisture content of the soil sample in this embodiment is 25%.

[0037] Figure 2fIt is a graph of shear fracture failure modes of soil samples under different levels of confining pressure when the moisture content of the soil samples in this embodiment is 5%-25%.

[0038] Figure 3a This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.6, the moisture content of the soil sample is 15%, and the liquid index is less than 0 in this embodiment.

[0039] Figure 3b This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.6, the moisture content of the soil sample is 17%, and the liquid index is equal to 0 in this embodiment.

[0040] Figure 3c This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.6, the moisture content of the soil sample is 20%, and the liquid index is 0.25 in this embodiment.

[0041] Figure 3d This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.6, the moisture content of the soil sample is 27.1%, and the liquid index is 0.75 in this embodiment.

[0042] Figure 3e This is a curve diagram of the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.6, the moisture content of the soil sample is 30.4%, and the liquid index is 1.0 in this embodiment.

[0043] Figure 3f This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.6, the moisture content of the soil sample is 31.72%, and the liquid index is 1.1 in this embodiment.

[0044] Figure 4a This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.5, the moisture content of the soil sample is 15%, and the liquid index is less than 0 in this embodiment.

[0045] Figure 4b This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.5, the moisture content of the soil sample is 17%, and the liquid index is equal to 0 in this embodiment.

[0046] Figure 4c This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.5, the moisture content of the soil sample is 20%, and the liquid index is 0.25 in this embodiment.

[0047] Figure 4d This is a curve diagram showing the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.5, the moisture content of the soil sample is 27.1%, and the liquid index is 0.75 in this embodiment.

[0048] Figure 4eThis is a curve diagram of the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.5, the moisture content of the soil sample is 30.4%, and the liquid index is 1.0 in this embodiment.

[0049] Figure 4f This is a curve diagram of the relationship between the deviator stress and the confining pressure when the porosity ratio is 0.5, the moisture content of the soil sample is 31.72%, and the liquid index is 1.1 in this embodiment.

[0050] Figure 5a This is a Mohr circle effect diagram when the porosity ratio is 0.7 and the liquid index is less than 0 in this embodiment.

[0051] Figure 5b This is a Mohr circle effect diagram when the porosity ratio is 0.7 and the liquid index is equal to 0 in this embodiment.

[0052] Figure 5c This is a Mohr circle effect diagram when the porosity ratio is 0.7 and the liquid index is 0.25 in this embodiment.

[0053] Figure 5d This is a Mohr circle effect diagram when the porosity ratio is 0.7 and the liquid index is 0.75 in this embodiment.

[0054] Figure 5e This is a Mohr circle effect diagram when the porosity ratio is 0.7 and the liquid index is 1.0 in this embodiment.

[0055] Figure 5f This is a Mohr circle effect diagram when the porosity ratio is 0.7 and the liquid index is 1.1 in this embodiment.

[0056] Figure 6a This is a comparison diagram of the stability coefficients after verification by the finite element analysis and the limit equilibrium Swedish strip method under the slope ratio of 1:1.5 in this embodiment.

[0057] Figure 6b This is a comparison diagram of the stability coefficients after verification by the finite element analysis and the limit equilibrium Swedish strip method under the slope ratio of 1:1.75 in this embodiment.

[0058] Figure 6c This is a comparison diagram of the stability coefficients after verification by the finite element analysis and the limit equilibrium Swedish strip method under the slope ratio of 1:2 in this embodiment.

[0059] Figure 6d This is a comparison diagram of the stability coefficients after verification by the finite element analysis and the limit equilibrium Swedish strip method when the void ratio is 0.5 under the slope ratio of 1:1.5 in this embodiment.

[0060] Figure 6e This is a comparison diagram of the stability coefficients after verification by the finite element analysis and the limit equilibrium Swedish strip method when the void ratio is 0.6 under the slope ratio of 1:1.5 in this embodiment.

[0061] Figure 6f This is a comparison diagram of the stability coefficients after verification by the finite element analysis and the limit equilibrium Swedish strip method when the void ratio is 0.7 under the slope ratio of 1:1.5 in this embodiment. DETAILED DESCRIPTION

[0062] like Figures 1 to 6f As shown, a method for determining slope stability based on void ratio and liquid index of the present invention comprises the following steps:

[0063] Step 1: Sampling of soil layers on slopes: Select a variety of representative slope locations, determine multiple sampling points at the upper, middle and lower positions of the slope, set the sampling depth according to the soil layer structure, and sample soil layers at different depths;

[0064] Step 2: Determine the key parameters of soil samples and build a soil sample database: the key parameters of the soil samples include moisture content, porosity, liquid index and slope gradient, and organize the measurement results into a soil sample database;

[0065] Step 3, measuring the shear strength of the soil layer sample: the shear strength of the soil layer sample includes the cohesion and internal friction angle of the soil layer sample;

[0066] Step 4: Use finite element analysis to determine the stability of the slope corresponding to the soil layer sample;

[0067] Step 5: Verification of slope stability: Calculate the stability coefficient by using the limit equilibrium Swedish strip method, Bishop method, and Morgenstern-Price method to verify the slope stability analysis results in step 4;

[0068] In the actual verification process, when the soil moisture content reaches the plastic limit state, the strength is significantly reduced, and slope failure is most likely to occur at this time. This phenomenon reveals the inherent nature of soil consistency and strength changes. The data of the two are compared and the difference is not large, indicating that the simulation effect is applicable. The larger the liquid index, the smaller the error between the two data. For soil samples with the same liquid index, we can increase the stability of the slope by reducing the porosity. This method is most obvious around the optimal soil moisture content, such as Figure 6d to Figure 6f As shown in the figure. When the void ratio is 0.7, the water content reaches the hard plastic state, the slope safety factor FS <1.3, and the slope is already unstable. When the void ratio is reduced to 0.6, the slope safety factor increases again, indicating that the size of the pores inside the soil has an important influence on the shear strength. Comparing different slope ratios, the same liquid index and void ratio, the gentler the slope, the higher the slope stability coefficient, and the coefficient changes most significantly, as shown in the figure. Figures 6a to 6cAs shown in the figure, if the site conditions allow, we should first consider slope reduction to increase its stability. Comparison of the two shows that the method of determining slope stability based on void ratio and liquid index is feasible and verified to be correct.

[0069] In actual implementation, the measured porosity and liquid index of the soil layer sample are used to fit the fitting function of cohesion on the porosity and liquid index, and the fitting function of the internal friction angle on the porosity and liquid index. In this embodiment, the fitting function of cohesion on the porosity and liquid index, and the fitting function of the internal friction angle on the porosity and liquid index are fitted. Where c is the cohesion of the soil sample, is the internal friction angle, e0 is the porosity ratio, I L is the liquid index. In step 5, when When the slope stability analysis results in step 4 are verified, the stability coefficient is calculated by the limit equilibrium Swedish strip method.

[0070] It should be noted that the following Tables 1 and 2 show the slope safety factor tables for different void ratios and liquid indexes with three slope ratios (1:1, 1:1.5 and 1:2) calculated by the Morgenstern-Price method and the Bishop method.

[0071] Table 1

[0072]

[0073] Table 2

[0074]

[0075] Step 6: Establish and train a slope stability prediction model: Establish a slope stability prediction model, which is a neural network model, with moisture content, porosity, liquid index, slope gradient and confining pressure as input layer nodes of the neural network model, and stability coefficient as output layer node of the neural network model, and train the neural network model;

[0076] Step 7. Determine the stability of the actual slope based on the porosity and liquid index: collect the moisture content, porosity and liquid index of the soil sample at a position on the actual slope, measure the slope gradient of the actual slope, survey the confining pressure of the soil sample at a position on the actual slope, input the actually obtained moisture content, porosity, liquid index, slope gradient and confining pressure data into the trained neural network model, output the stability coefficient, and determine the stability of the actual slope based on the stability coefficient.

[0077] In this embodiment, in step 1, the sampling depth starts from 0.5 m, and sampling is performed every 0.5 m.

[0078] In this embodiment, in step three, after setting the confining pressure, the shear strength of the soil layer sample is determined by the consolidation undrained test method, and the soil layer sample is sheared until it is destroyed or reaches the expected value to cause axial failure; during the test, if a peak value appears in the graph, the peak point is selected. If there is no obvious peak value, the ordinate value when the axial strain reaches 15% is used as the stress failure standard. According to the Mohr-Coulomb strength theory, the Mohr circle under different confining pressure conditions is drawn, and a straight line is drawn by connecting the tangent points of each circle. The coordinates of the intersection of the strength envelope and the vertical axis are the cohesion, and the angle formed with the horizontal axis is the internal friction angle.

[0079] In this embodiment, according to the stress-strain curve, we can draw the Mohr circle. Through this Mohr circle, we can further calculate two important mechanical parameters of the soil: cohesion c and internal friction angle φ, which are crucial for understanding and describing the strength and deformation characteristics of loess, such as Figure 5a to Figure 5f shown.

[0080] In this embodiment, in step 4, the process of determining the stability of the slope corresponding to the soil layer sample by finite element analysis includes the following steps:

[0081] Step 401, using finite element analysis software ABAQUS to establish a two-dimensional homogeneous slope model in part module 1;

[0082] Step 402, set the material, section properties, elastic model parameters and Poisson's ratio, set the friction angle, dilatancy angle and reduction coefficient that vary with the field in the mechanical parameter plasticity interface, and set the cohesion that varies with the field in the Cohension option;

[0083] Step 403, assemble components and establish corresponding instances;

[0084] Step 404, define the analysis cloth, select the static analysis step in the step module, store the matrix as asymmetric, and set the initial increment step to 0.1. Set the reduce static analysis step in the same way;

[0085] Step 405, the load option defines the load and the limit size displacement boundary conditions, applies the gravity load in the load module, and constrains the displacements in the two directions on both sides of the horizontal and the bottom of the model to simulate the effect of the gravity load;

[0086] Step 406, select the face splitting operation, assign the mesh control attributes and set its size, execute the seed command to adjust the density of the mesh, and then perform the mesh splitting work;

[0087] Step 407: Add the control field variable changes, submit the job1 job file, and wait for the result;

[0088] Step 408, enter the visualization post-processing module, and view the slope stability analysis results as needed. The slope stability analysis results include slope stability, slope critical stability and slope instability.

[0089] In this embodiment, in step 2, a combined measuring instrument is used to measure the liquid index of the soil layer sample.

[0090] In this embodiment, in step six, the neural network model includes a BP neural network model.

[0091] In this embodiment, the stress-strain relationship curves of soil samples with a moisture content of 5%-25% under different levels of confining pressure show that when the soil is at a low moisture content, peak points appear under confining pressure. This is because when the moisture content is low, the adsorbed water film between soil particles is thinner, and the attraction between water molecules is stronger, resulting in higher cohesion of the soil, which enables the soil to resist higher stresses during shearing until it reaches a peak value, but after a period of time, it transitions to residual strength and manifests as a strain softening feature. When the moisture content is 10% and 15%, the σ3=100_300kPa confining pressure curve is a strain softening feature. When the confining pressure σ3 reaches 400kPa, the deviation stress of the sample decreases within 5% of the axial strain, showing strong strain hardening. When the moisture content is 20% and 25% (high moisture content), it can be seen that under the low confining pressure of σ3 = 100 kPa, the stress-strain curve has no obvious change and is still in a softening characteristic. Then, under the confining pressure of σ3 = 200_400 kPa, the soil sample transitions from a softening characteristic to a hardening characteristic, but the inflection point time is delayed, and the degree of strain hardening increases, such as Figure 2a to Figure 2f shown.

[0092] In this embodiment, by comparing the same liquid index and different porosity ratios, it can be found that the relationship between deviator stress and confining pressure is: under the condition that the porosity ratio and water content are kept consistent, the deviator stress of the soil shows an increasing trend with the increase of the confining pressure, and this increasing trend is more obvious when the horizontal confining pressure is relatively large. The reason for this phenomenon is that the increase in confining pressure strengthens the contact force between soil particles, thereby improving the shear resistance of the soil, and ultimately leading to an increase in deviator stress. The influence of water content on the failure stress point: the failure stress point of the soil with the same porosity ratio will decrease with the increase of the liquid index, especially in the range close to the optimal water content, the change of the failure stress point tends to be gentle and not very significant. During the entire shear process, the stress-strain curve does not show an obvious peak value. This is mainly because, under the action of vertical pressure, the soil can re-form a new structural strength; when the confining pressure remains unchanged and the vertical pressure continues to increase, the distance between the soil particles will gradually expand until the soil is destroyed. Influence of porosity on stress-strain curve: Under the same water content, the stress-strain curve shows significant changes due to different porosity, especially under high confining pressure. As the porosity decreases, the failure point of deviatoric stress gradually increases. This is because the dry density of the soil sample increases, which reduces the pore space in the sample and the volume accordingly, resulting in an increase in deviatoric stress. The above analysis fully demonstrates that the three factors of confining pressure, water content and porosity have a profound impact on the mechanical properties of soil, such as Figures 3a to 4f As shown, in engineering applications, the reasonable selection and control of these parameters are of great significance to improving the stability and bearing capacity of soil.

[0093] When the present invention is used, key parameters of soil samples are measured and a soil sample database is constructed. The slope stability is evaluated by the moisture content, void ratio and liquid index of the soil. The void ratio directly affects the density and water permeability of the soil. The liquid index reflects the ability of the soil to change from a plastic state to a fluid state, and the slope stability is evaluated more accurately. The stability of the slope corresponding to the soil sample is determined by finite element analysis, and the stability coefficient is calculated by the limit equilibrium Swedish strip method, the Bishop method and the Morgenstern-Price method, and the slope stability analysis result in step 4 is verified. The slope stability prediction model is established and trained to facilitate the prediction of the actual slope stability. The actually obtained moisture content, void ratio, liquid index, slope gradient and confining pressure data are input into the trained neural network model, and the stability coefficient is output. The stability of the actual slope is determined based on the stability coefficient, and the potential risk of geological disasters is reduced, which provides a theoretical basis and technical support for actual engineering.

[0094] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for determining slope stability based on void ratio and liquid index, characterized in that: The method comprises the following steps: Step 1: Sampling of soil layers on slopes: Select a variety of representative slope locations, determine multiple sampling points at the upper, middle and lower positions of the slope, and set the sampling depth according to the soil layer structure to sample soil samples at different depths; Step 2: Determine the key parameters of soil samples and build a soil sample database: the key parameters of the soil samples include moisture content, porosity, liquid index and slope gradient, and organize the measurement results into a soil sample database; Step 3, measuring the shear strength of the soil layer sample: the shear strength of the soil layer sample includes the cohesion and internal friction angle of the soil layer sample; Step 4: Use finite element analysis to determine the stability of the slope corresponding to the soil layer sample; Step 5: Verification of slope stability: Calculate the stability coefficient by using the limit equilibrium Swedish strip method, Bishop method, and Morgenstern-Price method to verify the slope stability analysis results in step 4; Step 6: Establish and train a slope stability prediction model: Establish a slope stability prediction model, which is a neural network model, with moisture content, porosity, liquid index, slope gradient and confining pressure as input layer nodes of the neural network model, and stability coefficient as output layer node of the neural network model, and train the neural network model; Step 7. Determine the stability of the actual slope based on the porosity and liquid index: collect the moisture content, porosity and liquid index of the soil sample at a position on the actual slope, measure the slope gradient of the actual slope, survey the confining pressure of the soil sample at a position on the actual slope, input the actually obtained moisture content, porosity, liquid index, slope gradient and confining pressure data into the trained neural network model, output the stability coefficient, and determine the stability of the actual slope based on the stability coefficient.

2. A method for determining slope stability based on void ratio and liquid index according to claim 1, characterized in that: In step 1, the sampling depth starts from 0.5 m and sampling is performed every 0.5 m.

3. A method for determining slope stability based on void ratio and liquid index according to claim 1, characterized in that: In step three, after setting the confining pressure, the shear strength of the soil layer sample is determined by the consolidation undrained test method, and the soil layer sample is sheared until failure or axial failure occurs when the expected value is reached; during the test, if a peak value appears in the graph, the peak point is selected. If there is no obvious peak value, the ordinate value when the axial strain reaches 15% is used as the stress failure standard. According to the Mohr-Coulomb strength theory, the Mohr circle under different confining pressure conditions is drawn, and a straight line is drawn by connecting the tangent points of each circle. The coordinates of the intersection of the strength envelope and the vertical axis are the cohesion, and the angle formed with the horizontal axis is the internal friction angle.

4. A method for determining slope stability based on void ratio and liquid index according to claim 1, characterized in that: In step 4, the process of determining the stability of the slope corresponding to the soil layer sample using finite element analysis includes the following steps: Step 401, using finite element analysis software ABAQUS to establish a two-dimensional homogeneous slope model in part module 1; Step 402, set the material, section properties, elastic model parameters and Poisson's ratio, set the friction angle, dilatancy angle and reduction coefficient that vary with the field in the mechanical parameter plasticity interface, and set the cohesion that varies with the field in the Cohension option; Step 403, assemble components and establish corresponding instances; Step 404, define the analysis cloth, select the static analysis step in the step module, store the matrix as asymmetric, and set the initial increment step to 0.

1. Set the reduce static analysis step in the same way; Step 405, the load option defines the load and the limit size displacement boundary conditions, applies the gravity load in the load module, and constrains the displacements in the two directions on both sides of the horizontal and the bottom of the model to simulate the effect of the gravity load; Step 406, select the face splitting operation, assign the mesh control attributes and set its size, execute the seed command to adjust the density of the mesh, and then perform the mesh splitting work; Step 407: Add the control field variable changes, submit the job1 job file, and wait for the result; Step 408, enter the visualization post-processing module, and view the slope stability analysis results as needed. The slope stability analysis results include slope stability, slope critical stability and slope instability.

5. A method for determining slope stability based on void ratio and liquid index according to claim 1, characterized in that: In step 2, a combined measuring instrument is used to measure the liquid index of the soil layer sample.

6. A method for determining slope stability based on void ratio and liquid index according to claim 1, characterized in that: In step six, the neural network model includes a BP neural network model.

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