Construction method and deformation prediction method and equipment for constitutive model of playground soil under underground erosion

Abstract

The application discloses a kind of playground soil body constitutive model construction method under the action of latent corrosion, deformation prediction method and equipment, the model construction method steps include: step S01.Obtain the distribution data of latent corrosion rate along the length of sample obtained by carrying out latent corrosion test on sample soil body under different influence factors;Step S02. The distribution data of latent corrosion rate along the length of sample is fitted into a straight line, and the uneven state parameter of fine particle of soil body after latent corrosion is defined;Step S03. Establish the first quantitative relationship model between uneven state parameter and each influence factor;Step S04. Establish the second quantitative relationship model between the critical state parameter of soil body after latent corrosion and uneven state parameter;Step S05. According to the second quantitative relationship model, the constitutive model of soil body after latent corrosion is constructed based on lower yield surface cambridge model.The application can accurately describe the mechanical properties of soil body after latent corrosion, and improve the accuracy of deformation prediction.

Description

Methods and equipment for constructing constitutive models and predicting deformation of playground soil under erosion. Technical Field

[0001] This invention relates to the field of geotechnical engineering technology, and in particular to a method for constructing a constitutive model of playground soil under erosion, a method for predicting deformation, and equipment. Background Technology

[0002] After rain, fine particles on the surface of playgrounds are often lost with runoff. This infiltration of rainwater triggers internal erosion (fine particle loss), leading to abrupt changes in soil gradation, structural damage, and stiffness degradation, ultimately causing playground deformation. Erode is the process by which fine particles (such as silt and clay) in the soil are gradually carried away, migrated, and eventually lost from the pores of the coarse-grained skeleton by the seepage hydraulic gradient. Soil and rock constitutive models are used to describe the mechanical properties of soil. However, existing models typically focus primarily on changes in parameters such as soil friction angle, critical state line, and void ratio caused by erosion, without explicitly considering the aforementioned "particle loss" effect. This results in predictions that significantly deviate from actual field measurements of deformation and cracking.

[0003] Specifically, for the problem of soil erosion, existing technologies typically modify the constitutive model by analyzing changes in parameters such as soil friction angle, critical state line, and void ratio caused by erosion, thereby describing the mechanical properties of the soil after erosion. While this method can reflect the changes in soil mechanical properties caused by erosion to some extent, it is essentially a spatial homogenization approach. It assumes that the fine particle content of the soil remains constant after erosion, neglecting the influence of the uneven distribution of fine particles. This leads to an inaccurate description of the fine particle distribution after erosion, thus affecting the accuracy of deformation prediction. Summary of the Invention

[0004] The technical problem to be solved by the present invention is as follows: In view of the above-mentioned problems existing in the prior art, the present invention provides a method for constructing a constitutive model of playground soil under erosion, a method for predicting deformation, and a device that can take into account the influence of the uneven distribution of fine particles in the soil after erosion, accurately describe the mechanical properties of the soil after erosion, and thus improve the accuracy of deformation prediction.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0006] A method for constructing a constitutive model of playground soil under erosion, comprising the following steps:

[0007] Step S101. Obtain the distribution data of the erosion rate along the length of the sample soil obtained by conducting erosion tests on the sample soil under different influencing factors, including the initial fine particle content, hydraulic gradient, erosion angle and confining pressure.

[0008] Step S102. Fit the distribution data of the erosion rate along the sample length into a straight line, obtain the slope of the fitted straight line and define it as the non-uniformity state parameter of the fine particles in the soil after erosion, so as to characterize the degree of non-uniform distribution of the fine particle content in the soil after erosion.

[0009] Step S103. Based on the test results of the erosion test, establish a first quantitative relationship model between the non-uniform state parameters and each of the influencing factors;

[0010] Step S104. Based on the critical state parameters obtained from triaxial experiments on soil with non-uniform state parameters, establish a second quantitative relationship model between the critical state parameters of the soil with non-uniform state parameters and the non-uniform state parameters. The critical state parameters of the soil with non-uniform state parameters include the critical stress ratio.

[0011] Step S105. Based on the Cambridge model of the lower yield surface, according to the second quantitative relationship model between the critical state parameters and the non-uniform state parameters of the eroded soil, a constitutive model of the eroded soil with the function of dynamically characterizing the fine particle loss process and the influence of non-uniform distribution is constructed to predict the mechanical properties of the eroded soil. The critical state parameters of the eroded soil are determined according to the non-uniform state parameters, and the non-uniform state parameters are determined according to the first quantitative relationship model.

[0012] Furthermore, in step S103, the first quantitative relationship model is a fitting function between the non-uniform state parameters and each influencing factor. , where C s Here, FC0 represents the initial fine particle content, i represents the hydraulic gradient, β represents the erosion angle, and p represents the confining pressure.

[0013] Furthermore, in step S104, the critical state parameters of the eroded soil also include the critical state porosity and the critical state compressibility index, and the second quantitative relationship model includes the critical stress ratio M and the critical state porosity. and critical compressibility index With the non-uniform state parameter C respectively s Quantitative relationship between them: .

[0014] Further, in step S104, the critical stress ratio of the soil is obtained based on the stress path of the soil after the triaxial drained test, and the critical compression index of the soil is obtained by simulating the stress-strain change trend of the soil after the triaxial drained and undrained tests. When the soil reaches the critical state, the critical state void ratio is calculated.

[0015] Further, in step S105, the Cambridge model of the lower yield surface is modified to describe the mechanical properties of the soil after erosion, taking into account the change in the critical stress ratio M after erosion. This forms a modified lower yield surface model. The modified lower yield surface model is then used to construct a constitutive model of the eroded soil that considers the non-uniform distribution of fine particles. The modified lower yield surface model is as follows:

[0016]

[0017] Where p is the average effective stress, p0 is the reference stress, and q is the deviatoric stress. Let R be the strain of the plastic body, R be the similarity ratio, and D be the material constant. M is the critical stress ratio. , and These are the compression index and the expansion index, respectively. The porosity is taken as a reference stress.

[0018] Furthermore, step S103 also includes establishing a soil fine particle content decay model based on the fine particle content data measured at different time points in the erosion test, in order to describe the fine particle loss characteristics after erosion and predict the change in fine particle content after erosion. The soil fine particle content decay model is obtained by fitting the data of soil fine particle changes over time and various influencing factors after erosion.

[0019] Further, step S101 includes:

[0020] Unstable fine particles under erosion are mixed according to a specified fine particle content and then compacted in layers to prepare a soil sample.

[0021] Different influencing factors were applied to the prepared soil samples, and erosion tests were conducted on each sample.

[0022] The soil sample was uniformly divided into multiple layers. Based on the test results of the erosion test, the erosion rate of fine particles in different layers was calculated to obtain the distribution data of the erosion rate of fine particles along the length of the sample.

[0023] A triaxial testing device for constructing a constitutive model of playground soil under the aforementioned erosion action includes an integrated triaxial system, an erosion system, and a fine particle collection system. The triaxial system includes a triaxial chamber, an axial loading system, and a confining pressure control system. The soil sample is arranged in the triaxial chamber, which provides a closed space for the erosion test and the triaxial test. The axial loading system is connected to the sample via a loading piston to simulate confining pressure and apply axial load. The confining pressure control system controls the magnitude of the confining pressure in the triaxial chamber. The fine particle collection system collects the eroded fine particles and water flow, and measures the mass of the eroded fine particles. The erosion system includes a top pressure control system located at the top of the inlet tank and a bottom pressure control system located at the bottom of the sample to control the hydraulic gradient by adjusting the pressure difference between the inlet tank and the bottom of the sample.

[0024] A deformation prediction method utilizing the aforementioned constitutive model construction method for playground soil under erosion includes the following steps:

[0025] Step 201: Model initialization: Establish a finite element model of the soil region to be analyzed, and set boundary conditions and initial material parameters. The soil constitutive relationship adopts the constructed post-erosion soil constitutive model. At the initial moment, seepage-stress coupling calculation is performed based on the initial material parameters to obtain the initial deformation, stress distribution and hydraulic gradient distribution data.

[0026] Step 202: State Update: Calculate the non-uniform state parameters using the first quantitative relationship model based on the currently calculated hydraulic gradient distribution;

[0027] Step 203: Parameter update: Calculate the critical state parameters of the eroded soil using the second quantitative relationship model based on the currently calculated non-uniform state parameters;

[0028] Step 204. Iterative calculation: Update the constitutive model of the eroded soil according to the critical state parameters of the eroded soil obtained by the current calculation, and use the updated constitutive model of the eroded soil to perform seepage-stress coupling calculation to obtain updated deformation, stress distribution and hydraulic gradient distribution data. Return to step S202 until the iteration stop condition is reached, and output the final predicted soil stress-strain relationship attenuation distribution data.

[0029] A computer device includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method described above.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention defines the non-uniformity state parameters of fine particles in soil after erosion by utilizing the distribution data of erosion rate along the sample length obtained from erosion tests under different influencing factors. It can make full use of the variation law of non-uniformity state parameters under the influence of different factors to accurately describe the distribution state of fine particles after erosion. At the same time, based on the first quantitative relationship model between the non-uniformity state parameters of erosion tests and various influencing factors, a second quantitative relationship model is established between the non-uniform distribution of fine particles under erosion and the critical state parameters of soil. Furthermore, based on the Cambridge model of the lower yield surface, a constitutive model of soil after erosion is constructed that can explicitly track the loss of fine particles and couple the gradation equation. This model can fully consider the non-uniform distribution characteristics of fine particles after erosion to accurately describe the mechanical properties of soil after erosion and improve the accuracy of deformation prediction. Attached Figure Description

[0031] Figure 1 is a schematic diagram of the implementation process of the constitutive model construction method of the playground soil under the action of erosion in this embodiment.

[0032] Figure 2 is a schematic diagram of the principle of the sample preparation method in a specific application embodiment of the present invention.

[0033] Figure 3 is a schematic diagram illustrating the principle of defining the non-uniform state parameters of fine particles after erosion in this embodiment.

[0034] Figure 4 shows the stress path diagram obtained in a specific application embodiment and a schematic diagram of the principle for calculating the critical pressure ratio.

[0035] Figure 5 is a schematic diagram of the structure of the penetrating triaxial test equipment used in this embodiment.

[0036] Figure 6 is a schematic diagram of the lower yield surface and the normal yield surface.

[0037] Figure 7 is a schematic diagram of the yield surface variation principle considering the change of M. Detailed Implementation

[0038] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0039] Solving the problem of soil erosion can be mainly divided into three key steps: ① revealing the changes in the loose state of the soil after erosion; ② quantitatively characterizing the changes in the particle size distribution of the soil after erosion; ③ elucidating the changes in the mechanical properties of the soil after erosion. Existing constitutive models typically focus on achieving steps ① and ③, neglecting the non-uniform distribution of fine particles within the soil space after erosion, which significantly affects the peak strength, residual strength, and deformation characteristics of the soil. For example, with the same fine particle content, the peak stress and residual stress of soil with a non-uniformly distributed fine particle distribution after erosion are both lower than those of soil with a uniformly distributed fine particle distribution. Therefore, using existing constitutive models to calculate the mechanical properties of eroded soil in practical engineering may result in predictions that are significantly inconsistent with actual field measurements of deformation and cracking.

[0040] This invention considers the microscopic mechanism of water-fine particle fluid-structure interaction under burrowing and the migration and distribution patterns of fine particles. By fitting the burrowing rate distribution data along the sample length obtained from burrowing tests under different influencing factors, a fitted straight line is obtained. The slope of the fitted straight line defines the non-uniformity parameters of fine particles in the burrowed soil, quantitatively describing the degree of non-uniform distribution. This fully utilizes the variation patterns of non-uniformity parameters under different influencing factors to accurately describe the distribution state of fine particles after burrowing. Simultaneously, based on burrowing tests, a first quantitative relationship model is established between the non-uniformity parameters and various influencing factors, and a second quantitative relationship model is established between the non-uniform distribution of fine particles under burrowing and the critical state parameters of the soil. Finally, based on the Cambridge model of the lower yield surface, a constitutive model of the burrowed soil is constructed that can explicitly track the loss of fine particles and couple the gradation equation. This model can fully consider the characteristics of the non-uniform distribution of fine particles after burrowing and accurately describe the mechanical properties of the burrowed soil (changes in soil strength and deformation characteristics under stress), laying a theoretical foundation for numerical simulation of the stability of buildings (structures) after burrowing and improving the accuracy of deformation prediction.

[0041] As shown in Figure 1, the steps of the constitutive model construction method for the playground soil under erosion in this embodiment include:

[0042] Step S101. Obtain the distribution data of the erosion rate along the length of the soil sample obtained by conducting erosion tests on the soil sample under different influencing factors, including the initial fine particle content, hydraulic gradient, erosion angle and confining pressure.

[0043] As an optional implementation, step S101 includes:

[0044] Unstable fine particles under erosion are mixed according to a specified fine particle content and then compacted in layers to prepare a soil sample.

[0045] Different influencing factors were applied to the prepared soil samples, and erosion tests were conducted on each sample.

[0046] The soil sample was uniformly divided into multiple layers. Based on the test results of the erosion test, the erosion rate of fine particles in different layers was calculated to obtain the distribution data of the erosion rate of fine particles along the length of the sample.

[0047] Specifically, taking the loss of fine-particle soil in a playground after rain as an example, to facilitate the study of the impact of erosion on the migration and stability of fine particles in the soil under laboratory conditions, a mixed sample of No. 3 quartz sand (coarse particles) and No. 8 quartz sand (fine particles), which are internally unstable under erosion, was selected. This sample is similar to the soil being tested. Preferably, the fine particles can be dyed blue for easy observation, and samples with fine particle contents of 15%, 25%, and 35% were prepared. These samples were then layered and compacted sequentially to prepare standard soil samples with a length × width × height of 60 mm × 60 mm × 100 mm. The preparation height and set moisture content of each layer are shown in Figure 2. The samples were then saturated and consolidated.

[0048] During the experiment, a high-magnification electron microscope can be used to conduct a detailed study of the migration law of fine particles. Considering the actual engineering conditions of the soil, a systematic erosion test was conducted with different initial fine particle contents (FC0: 15%, 25%, 35%), hydraulic gradients (i: 0.25, 0.30, 0.35), erosion angles (angle β between the erosion direction and the horizontal direction: 90°, 120°, 150°), and confining pressures (p: 50kPa, 100kPa, 200kPa). After erosion, the samples were layered (e.g., the samples were evenly divided into 6 layers) and dried. Based on the test results, the erosion rate of fine particles in different soil layers and the overall erosion rate of the samples were calculated. The particle size distribution curve and the distribution line of the erosion rate of fine particles in each soil layer along the length of the sample can be obtained.

[0049] Furthermore, the experimental results can reveal the variation patterns of fine particle content. Fine particles can be tracked using PIV (Particle Image Velocimetry) technology, and their migration patterns under different influencing factors can be compared and analyzed. Simultaneously, X-ray and CT scanning techniques can be used to analyze the variation patterns of fine particles in the soil, such as their filling or bearing functions, as well as the changes in the soil skeleton composed of coarse particles. Changes in the soil skeleton can be directly used to explain changes in soil mechanical properties. Furthermore, using the above patterns and changes in the soil skeleton, the variation of fine particle content after erosion can be predicted.

[0050] Step S102. Fit the distribution data of the erosion rate along the sample length into a straight line, obtain the slope of the fitted straight line and define it as the non-uniformity state parameter of the fine particles in the soil after erosion, so as to characterize the degree of non-uniform distribution of the fine particle content in the soil after erosion.

[0051] Under the action of burrowing erosion, the loss of fine particles is not uniformly distributed within the soil space. The soil erosion rate ΔFC changes linearly along the sample length L (i.e., the layer number), meaning that burrowing erosion causes the content of fine particles inside the soil to change linearly with the sample length, as shown in Figure 3. The two fitted lines in the figure correspond to the relationship between the burrowing rate and the sample length under different seepage rates at A and B, respectively. In this embodiment, the straight line obtained by fitting the burrowing rate distribution data along the sample length obtained from the burrowing microscopic test is used to define the state parameter used to describe the non-uniform distribution of fine particles, i.e., the non-uniform state parameter, by the slope of the fitted line. This allows full utilization of the burrowing test to obtain the variation law of the non-uniform state parameter under the influence of different factors, and realizes a quantitative description of the non-uniform distribution characteristics of fine particles in the soil after burrowing erosion.

[0052] Specifically, based on the experimental data obtained in step S101, the erosion rate of fine particles in different soil layers (the sample is uniformly divided into 6 layers) and the overall erosion rate of the sample can be calculated. Then, a fitted line of the erosion rate of fine particles along the sample length can be plotted. This fitted line represents the distribution of the erosion rate ΔFC as a function of the sample length L. The slope of this fitted line is defined as the non-uniformity parameter C of the fine particles in the soil after erosion. s This is to reflect the uneven distribution of fine particle content. C s The larger the value of C, the more uneven the distribution of fine particles in the eroded soil; when C s When the value approaches 0, it indicates that the content of fine particles in each layer of the soil after erosion tends to be consistent.

[0053] Step S103. Based on the fine particle content data obtained from the erosion test at different time points, establish a first quantitative relationship model between the non-uniform state parameters and various influencing factors.

[0054] This embodiment utilizes the influence of various factors such as initial fine particle content, hydraulic gradient, erosion angle, and confining pressure on the migration of fine particles to construct a first quantitative relationship model between the non-uniform state parameters and various influencing factors, enabling a comprehensive study of the influencing factors on the migration of fine particles in soil under erosion.

[0055] As an optional implementation, the first quantitative relationship model is a fitting function between the inhomogeneous state parameters and various influencing factors, which can be expressed as follows: , where C s Here, FC0 represents the initial fine particle content, i represents the hydraulic gradient, β represents the erosion angle, and p represents the confining pressure.

[0056] Furthermore, a soil fine-particle content decay model can be constructed to predict changes in fine-particle content after erosion. Specifically, this decay model can be obtained by fitting data on the changes in soil fine-particle content over time and various influencing factors after erosion, and can be expressed as: FC represents the fine particle content, and t represents time.

[0057] Specifically, by analyzing the results of microscopic tests on subsurface erosion, fitting functions can be established between the non-uniform state parameters and factors such as initial fine particle content, hydraulic gradient, subsurface erosion angle, and confining pressure. Considering the effects of initial fine particle content, hydraulic gradient, erosion angle, and confining pressure, a sample fine particle content decay function is established: The above model can be used to determine the quantitative relationship between non-uniformity parameters, soil fine particle content decay function, and different influencing factors.

[0058] Step S104. Based on the critical state parameters obtained from triaxial experiments on soil with non-uniform state parameters after erosion, establish a second quantitative relationship model between the critical state parameters and non-uniform state parameters of the soil after erosion. The critical state parameters of the soil after erosion include the critical stress ratio, which is the ratio of the deviatoric stress (q) to the average effective stress (p) of the soil under the critical state, i.e., M = q / p.

[0059] This embodiment constructs a quantitative relationship between the critical state parameters and the non-uniform state parameters of the soil, which can take into account the influence of the non-uniform distribution of fine particles on the critical state line after erosion. As a result, the constitutive model constructed using this model can accurately describe the mechanical properties of the soil after erosion.

[0060] As an optional implementation, the critical state parameters of the soil after erosion may also include the critical state void ratio and the critical state compressibility index. The critical state void ratio represents the void ratio corresponding to the soil volume remaining constant under undrained shear or ceasing to change volume under drained shear (neither shrinking nor dilatating). The critical state compressibility index represents the slope of the critical state line in e-lnp space, used to quantitatively describe the rate at which the void ratio e decreases as the soil moves along the critical state line. Further, the second quantitative relationship model includes the critical stress ratio M and the critical state void ratio. and critical compressibility index With the non-uniform state parameter C respectively s The quantitative relationship between them can be expressed as: Utilizing the critical state porosity and critical compressibility index It can further reflect the properties of the soil, thereby improving the accuracy of predictions.

[0061] As an optional implementation, the critical stress ratio of the soil can be obtained from the stress path of the soil after the triaxial drained test, and the critical compressibility index of the soil can be obtained by simulating the stress-strain change trend of the soil after the triaxial drained and undrained tests. When the soil reaches the critical state, the critical state void ratio is calculated. The stress path diagram obtained in a specific application embodiment is shown in Figure 4. By connecting the endpoints of the final average stress and deviatoric stress under different confining pressures in the stress path diagram, the slope of the line is obtained as the critical stress ratio. The erosion test and the triaxial test can be implemented using the test equipment shown in Figure 5.

[0062] This embodiment compares and analyzes the mechanical properties of soils with uniform and non-uniform fine particle distribution before and after erosion, and establishes a quantitative relationship between the critical state parameters (critical stress ratio, critical state void ratio, and critical state compression index) and non-uniform state parameters of the soil after erosion. This can lay the foundation for constructing a constitutive model of the soil after erosion.

[0063] Step S105. Based on the Cambridge model of the lower yield surface, according to the second quantitative relationship model between the critical state parameters and the non-uniform state parameters of the eroded soil, a constitutive model of the eroded soil with dynamic characterization of the fine particle loss process and the influence of non-uniform distribution is constructed to predict the mechanical properties of the eroded soil. The critical state parameters of the eroded soil are determined based on the non-uniform state parameters, which are determined based on the first quantitative relationship model.

[0064] This embodiment constructs a constitutive model of eroded soil based on the Cambridge model of the lower yield surface and the quantitative relationship between the critical state parameters and the non-uniform state parameters of the soil. It can accurately predict the mechanical properties of eroded soil and solve the problem that traditional prediction of the stability of buildings (structures) after erosion ignores the non-uniform distribution of fine particles. Thus, it can realize the numerical simulation and prediction of the stability of buildings (structures) after erosion.

[0065] Compared to state-dependent constitutive models, the Cambridge model of the lower yield surface uses only physical parameters and employs the correlated flow rule, making it easier to apply in practice. Because the deviatoric stress of the soil decreases after erosion, the critical stress ratio also decreases, causing changes in the normal yield surface and the lower yield surface. Furthermore, erosion causes the loss of fine particles, fundamentally altering the soil composition and leading to the expansion or contraction of the normal yield surface, as shown in Figures 6 and 7. This embodiment compares and analyzes the mechanical properties of soils with uniform and non-uniform fine particle distributions before and after erosion. It uses the Cambridge model of the lower yield surface and modifies it to construct a new constitutive model, which can accurately describe the mechanical properties of the soil after erosion.

[0066] As a preferred implementation, taking the critical stress ratio as the critical state parameter as an example, the Cambridge model of the lower yield surface is modified to describe the mechanical properties of the soil after erosion, considering the change in the critical stress ratio M after erosion. This results in a modified lower yield surface model. The modified lower yield surface model is then used to construct a constitutive model of the eroded soil that considers the non-uniform distribution of fine particles. The modified lower yield surface model is as follows:

[0067] ;

[0068] Where p is the average effective stress, p0 is the reference stress, and q is the deviatoric stress. Let R be the strain of the plastic body, R be the similarity ratio, and D be the material constant. M is the critical stress ratio. , and These are the compression index and the expansion index, respectively. The porosity is taken as a reference stress.

[0069] Furthermore, a critical state porosity ratio can be introduced. and critical compressibility index Critical state parameters are used to more accurately reflect the critical state of the soil.

[0070] This embodiment is based on the Cambridge model of the lower yield surface. By studying the evolution law of fine particle distribution inside the soil under burrowing, and determining the non-uniform state parameters that quantitatively describe the fine particle distribution after burrowing, a constitutive model of the burrowed soil is constructed based on the quantitative relationship between the critical state parameters (critical stress ratio, critical state void ratio, and critical state compression index, etc.) and the non-uniform state parameters. This model can accurately predict the mechanical properties of the burrowed soil, and thus accurately predict the deformation state of the soil under burrowing.

[0071] This embodiment further provides a triaxial testing device for the construction method of the constitutive model of the playground soil under the above-mentioned erosion action, so as to realize the triaxial test and erosion test of the soil after erosion. As shown in Figure 5, the triaxial testing device integrates a triaxial system, an erosion system and a fine particle collection system to realize the fusion of the triaxial system and the erosion system, so that the triaxial test and the erosion test can be realized simultaneously using a set of devices. The triaxial system includes a triaxial chamber, an axial loading system, and a confining pressure control system. The soil sample is placed in the triaxial chamber, which provides a closed space for the erosion test and the triaxial test. By filling the chamber with liquid and applying pressure, a uniform lateral pressure is applied to the sample wrapped in a rubber membrane. The pressure is measured by a pressure measuring element. The axial loading system is connected to the sample through a loading piston to simulate confining pressure and apply axial load. Linear displacement sensors and radial displacement sensors measure the axial and radial deformation of the sample during the test, respectively. The confining pressure control system controls the confining pressure in the triaxial chamber. The fine particle collection system is located at the bottom of the sample to collect the eroded fine particles and water flow, and to measure the mass of the eroded fine particles. The fine particle collection system mainly includes a miniature weighing sensor, a weighing tray, a funnel, an electric pneumatic regulator, and a fine particle collection trough. It can not only collect the eroded fine particles and water flow, but also simultaneously measure the mass of the eroded fine particles. The erosion system includes a top pressure control system located at the top of the inlet tank and a bottom pressure control system located at the bottom of the sample. By adjusting the pressure difference between the inlet tank (i.e., the inlet container) and the bottom of the sample, the hydraulic gradient can be controlled, making the permeation force on the sample more uniform. In the process of erosion test and triaxial test, the changes in the mechanical properties of the soil after erosion under different non-uniform state parameters can be analyzed, and the quantitative relationship between the critical state parameters and non-uniform state parameters of the soil can be accurately constructed.

[0072] Traditional subsurface erosion systems control the hydraulic gradient through water flow velocity, which is inherently fluctuating. This embodiment improves upon the traditional subsurface erosion system by configuring a differential pressure control system consisting of a top pressure control system and a bottom pressure control system. By adjusting the pressure difference between the inlet tank and the bottom of the sample, the hydraulic gradient is accurately controlled. Compared to traditional head difference control methods, this effectively avoids the impact of uneven distribution of seepage force caused by changes in water flow velocity within the sample, thereby improving the accuracy of constitutive model construction.

[0073] Specifically, before the experiment, the test apparatus was first filled with distilled water. After 10 minutes, the airtightness of the apparatus was determined by checking for air bubbles in all pipes. The sample was an unstable quartz sand mixture under erosion, prepared as shown in Figure 2, with a height × diameter of 150 mm × 75 mm, and the mass of the sample was weighed. The sample was saturated by setting a pressure difference of 10 kPa between the confining pressure and the back pressure. When the saturation reached 95%, the back pressure was increased to match the confining pressure. Then, the pressure of the water tank at the top of the sample was increased, and the erosion test was started. After the erosion test, triaxial consolidated drained and undrained tests were conducted to obtain the stress-strain relationship of the eroded soil. At the same time, triaxial consolidated drained and undrained tests were conducted on the un-eroded soil to compare and analyze the changes in the mechanical properties of the soil before and after erosion. This law can be used to verify the performance of the constructed constitutive model. Finally, the eroded sample was layered and dried, and the particle size distribution curve and the distribution line of the fine particle erosion rate of each soil layer along the sample length were plotted. The non-uniform state parameters can be obtained by fitting this distribution line. Furthermore, by analyzing the stress path of the soil after triaxial drainage tests, the critical stress ratio of the soil can be obtained, and a quantitative relationship between the critical state parameters and the inhomogeneous state parameters of the soil can be established. .

[0074] This embodiment further provides a deformation prediction method using the above-mentioned constitutive model construction method for playground soil under erosion, the steps of which include:

[0075] Step 201: Model initialization: Establish a finite element model of the soil region to be analyzed, and set boundary conditions and initial material parameters. The soil constitutive relationship adopts the constructed post-emergence soil constitutive model. At the initial moment, seepage-stress coupling calculation is performed based on the initial material parameters to obtain the initial deformation, stress distribution and hydraulic gradient distribution data.

[0076] Step 202: State Update: Calculate the non-uniform state parameters using the first quantitative relationship model based on the currently calculated hydraulic gradient distribution;

[0077] Step 203: Parameter Update: Calculate the critical state parameters of the soil after erosion using the second quantitative relationship model based on the currently calculated non-uniform state parameters;

[0078] Step 204. Iterative Calculation: Update the constitutive model of the eroded soil based on the critical state parameters of the eroded soil obtained from the current calculation. Use the updated constitutive model of the eroded soil to perform seepage-stress coupling calculations to obtain updated deformation, stress distribution and hydraulic gradient distribution data. Return to step S202 until the iteration stop condition is met, and output the final predicted soil stress-strain relationship attenuation distribution data.

[0079] This embodiment integrates seepage-deformation calculations with the erosion process through the above steps, and iteratively achieves soil deformation prediction.

[0080] Specifically, this embodiment first meshes the model, defines boundary conditions and material parameters, and introduces a constitutive model modified under erosion: Characterizing the constitutive relationship of the soil; at the initial time, i.e., t=0, without considering the influence of burrowing, seepage-deformation calculations are performed directly in steady condition to calculate deformation and hydraulic head pressure. For example, this can be calculated using the weak form equations of soil-water mixtures and pore water. Then, the calculated hydraulic head pressure distribution, stress distribution, and hydraulic gradient parameters are iteratively calculated. Non-uniformity parameters are calculated at each time step. In practice, the change in fine particle content can also be calculated simultaneously. This change in fine particle content is affected by factors such as the initial fine particle content, hydraulic gradient, burrowing angle, and confining pressure. Furthermore, based on the non-uniform state parameters, the critical state parameters of the soil after erosion are calculated. The model is updated using the calculated parameters to update the deformation, stress distribution, and hydraulic gradient distribution data. The above steps are repeated to calculate the seepage-deformation and erosion process until the erosion ends, and the attenuation distribution data of soil stress-strain relationship under fine particle loss state are obtained, that is, the trend of soil mechanical strength decrease after erosion, so as to achieve deformation prediction.

[0081] This embodiment further provides a computer device, including a processor and a memory, wherein the memory is used to store a computer program and the processor is used to execute the computer program to perform the method as described above.

[0082] It is understood that the method described in this embodiment can be executed by a single device, such as a computer or server, or it can be applied to a distributed scenario where multiple devices cooperate to complete the task. In a distributed scenario, one of the multiple devices may execute only one or more steps of the method described in this embodiment, and the multiple devices interact to complete the method. The processor can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the method described in this embodiment. The memory can be implemented using read-only memory (ROM), random access memory (RAM), static storage devices, and dynamic storage devices. The memory can store the operating system and other applications. When the method described in this embodiment is implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0083] Those skilled in the art will understand that the above embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0084] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for constructing a constitutive model of a playground soil under erosion, characterized in that, The steps include: Step S101. Obtaining the distribution data of the erosion rate along the sample length obtained from the erosion test on the soil sample under different influencing factors, including the initial fine particle content, hydraulic gradient, erosion angle, and confining pressure; Step S102. Fitting the distribution data of the erosion rate along the sample length into a straight line, obtaining the slope of the fitted straight line and defining it as the non-uniformity state parameter of the fine particles in the eroded soil to characterize the degree of non-uniformity of the fine particle content in the eroded soil; Step S103. Establishing a first quantitative relationship model between the non-uniformity state parameter and each of the influencing factors based on the test results of the erosion test; Step S104. Establishing a critical state parameter of the eroded soil and the non-uniformity state parameter based on the critical state parameter obtained from the triaxial test on the eroded soil with non-uniformity state parameter. A second quantitative relationship model between uniform state parameters is established, wherein the critical state parameters of the eroded soil include the critical stress ratio; Step S105. Based on the lower yield surface Cambridge model, a constitutive model of the eroded soil with dynamic characterization of fine particle loss process and the influence of non-uniform distribution is constructed according to the second quantitative relationship model between the critical state parameters and the non-uniform state parameters to predict the mechanical properties of the eroded soil, wherein the critical state parameters of the eroded soil are determined according to the non-uniform state parameters, and the non-uniform state parameters are determined according to the first quantitative relationship model; In step S104, the critical state parameters of the eroded soil also include the critical state void ratio and the critical state compressibility index, and the second quantitative relationship model includes the critical stress ratio M and the critical state void ratio. and critical compressibility index With the non-uniform state parameter C respectively s Quantitative relationship between them: In step S105, the Cambridge model of the lower yield surface is modified to describe the mechanical properties of the soil after erosion, taking into account the change in the critical stress ratio M after erosion. This forms a modified lower yield surface model. The modified lower yield surface model is then used to construct a constitutive model of the eroded soil that considers the non-uniform distribution of fine particles. The modified lower yield surface model is as follows: Where p is the average effective stress, p0 is the reference stress, and q is the deviatoric stress. Let R be the strain of the plastic body, R be the similarity ratio, and D be the material constant. M is the critical stress ratio. ， and These are the compression index and the expansion index, respectively. The porosity is taken as a reference stress.

2. The method for constructing a constitutive model of playground soil under erosion as described in claim 1, characterized in that, In step S103, the first quantitative relationship model is a fitting function between the non-uniform state parameters and each influencing factor. , where C s Here, FC0 represents the initial fine particle content, i represents the hydraulic gradient, β represents the erosion angle, and p represents the confining pressure.

3. The method for constructing a constitutive model of playground soil under erosion as described in claim 1, characterized in that, In step S104, the critical stress ratio of the soil is obtained based on the stress path of the soil after the triaxial drained test, and the critical compression index of the soil is obtained by simulating the stress-strain change trend of the soil after the triaxial drained and undrained tests. When the soil reaches the critical state, the critical state void ratio is calculated.

4. The method for constructing a constitutive model of playground soil under erosion according to any one of claims 1 to 3, characterized in that, Step S103 also includes establishing a soil fine particle content decay model based on the fine particle content data measured at different time points in the erosion test, in order to describe the characteristics of fine particle loss after erosion and predict the change of fine particle content after erosion. The soil fine particle content decay model is obtained by fitting the data of soil fine particle changes over time and various influencing factors after erosion.

5. The method for constructing a constitutive model of playground soil under erosion according to any one of claims 1 to 3, characterized in that, Step S101 includes: mixing fine particles that are unstable under the action of erosion according to a specified fine particle content and compacting them in layers to prepare a sample soil; applying different influencing factors to the prepared sample soil and conducting erosion tests on each sample soil; dividing the sample soil into multiple layers evenly, calculating the erosion rate of fine particles in different layers according to the test results of the erosion test, and obtaining the distribution data of the erosion rate of fine particles along the length of the sample.

6. A triaxial test apparatus for constructing a constitutive model of playground soil under erosion as described in any one of claims 1 to 5, characterized in that, The system includes an integrated triaxial system, a burrowing system, and a fine particle collection system. The triaxial system comprises a triaxial chamber, an axial loading system, and a confining pressure control system. The soil sample is placed in the triaxial chamber, which provides a closed space for both the burrowing and triaxial tests. The axial loading system is connected to the sample via a loading piston to simulate confining pressure and apply axial loads. The confining pressure control system controls the magnitude of the confining pressure within the triaxial chamber. The fine particle collection system collects the burrowed fine particles and water flow, and measures the mass of the burrowed fine particles. The burrowing system includes a top pressure control system located at the top of the inlet tank and a bottom pressure control system located at the bottom of the sample to control the hydraulic gradient by adjusting the pressure difference between the inlet tank and the bottom of the sample.

7. A deformation prediction method using the constitutive model construction method for playground soil under erosion as described in any one of claims 1 to 5, characterized in that, The steps include: Step 201: Model initialization: Establish a finite element model of the soil region to be analyzed, and set boundary conditions and initial material parameters. The soil constitutive relationship adopts the constructed post-erosion soil constitutive model. At the initial moment, seepage-stress coupling calculation is performed based on the initial material parameters to obtain the initial deformation, stress distribution, and hydraulic gradient distribution data; Step 202: State update: Calculate the non-uniform state parameters using the first quantitative relationship model based on the currently calculated hydraulic gradient distribution; Step 203: Parameter update: Calculate the critical state parameters of the post-erosion soil using the second quantitative relationship model based on the currently calculated non-uniform state parameters; Step 204: Iterative calculation: Update the post-erosion soil constitutive model based on the currently calculated critical state parameters of the post-erosion soil, and perform seepage-stress coupling calculation using the updated post-erosion soil constitutive model to obtain updated deformation, stress distribution, and hydraulic gradient distribution data. Return to step S202 until the iteration stop condition is reached, and output the final predicted soil stress-strain relationship attenuation distribution data.

8. A computer device comprising a processor and a memory, the memory being used to store computer programs, characterized in that, The processor is used to execute the computer program to perform the method as described in any one of claims 1 to 5.

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