Method and device for measuring permeability coefficient of in-situ soil body, computer equipment and readable storage medium

Abstract

The invention relates to the field of exploration, and particularly discloses a method and device for measuring the permeability coefficient of an in-situ soil body, computer equipment and a readable storage medium, and the method comprises the following steps: obtaining a plurality of soil samples with preset burial depths, and obtaining the initial void ratio of each soil sample; carrying out a consolidation test on the soil samples to obtain the compression amount, the total compression amount and the test value of the consolidation coefficient of the sampling point of each soil sample under a plurality of preset consolidation pressures per minute; based on the initial void ratio, the preset burial depth, the preset consolidation pressure, the compression amount per minute, the total compression amount and the consolidation coefficient test value of the sampling point of each soil sample, a permeability coefficient prediction model is obtained by combining a fitting algorithm; and obtaining the burial depth and the corresponding pressure of the to-be-predicted soil body, and calculating the permeability coefficient of the to-be-predicted soil body based on the permeability coefficient prediction model. According to the method provided by the invention, sampling is carried out in soil layers with different burial depths, so that the real state of the soil body in the in-situ stratum can be restored, and the reliability and accuracy of a prediction result are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of surveying, in particular to a method, device, computer device and readable storage medium for measuring in-situ soil permeability coefficient. BACKGROUND

[0002] Darcy's law is an experimental law reflecting the seepage law of water in rock and soil pores, which is obtained by a large number of experiments by a French hydraulics scientist Darcy in 1852-1855, and its expression is Q=kAi, wherein Q is flow rate, k is permeability coefficient, A is water permeation area, and i is hydraulic gradient. The permeability coefficient k is an important parameter for characterizing the permeability of soil, and measuring the permeability coefficient k becomes one of the important indoor soil test steps before the design and construction of geotechnical engineering.

[0003] At present, the measurement methods of the permeability coefficient k mainly include laboratory measurement and field measurement. The laboratory measurement methods include constant head method and variable head method seepage test, and the field measurement methods mainly include well pumping test and well injection test. However, the laboratory measurement method has the following problems: firstly, the in-situ soil sample needs to be transported to the laboratory for test, and in this process, the physical parameters such as stress state and pore ratio of the sample have changed compared with the undisturbed soil, which affects the representativeness of the results; secondly, the measured permeability coefficient can only reflect the local characteristics at the sampling point, and since the pore ratio changes with the depth in the natural deposition process of the soil layer, the laboratory results are difficult to truly reflect the permeability characteristics of the entire field soil; in addition, the permeability coefficient obtained by the laboratory seepage test is a certain value, and the permeability coefficient of the actual soil layer is always in dynamic change due to the effect of additional stress in the loading and unloading process, so this method cannot reflect the change behavior of the permeability coefficient under the stress path. On the other hand, the field measurement also has some deficiencies: firstly, it is difficult to obtain the permeability coefficients of the soil at different positions and depths, and the test results usually reflect the average permeability characteristics of the soil layer; secondly, the in-situ test has a large scale, a long period, needs the cooperation of multiple parties, and has a high test cost. SUMMARY

[0004] The purpose of the present application is to overcome the above problems existing in the existing permeability coefficient k measurement method, and to provide a method, device, computer device and readable storage medium for measuring in-situ soil permeability coefficient. The method provided by the present application realizes the fine characterization of the characteristics by obtaining experiments on soil layers at different depths, and does not need to rely on a special permeameter, which significantly reduces the demand for experimental equipment.

[0005] In order to achieve the above purpose, the first aspect of the present application provides a method for measuring in-situ soil permeability coefficient, comprising:

[0006] Multiple soil samples with preset burial depths are obtained, and the initial void ratio of each soil sample is obtained, wherein the multiple preset burial depths are different.

[0007] Consolidation tests were conducted on the soil samples to obtain the compression per minute, total compression, and consolidation coefficient of each soil sample under multiple preset consolidation pressures.

[0008] Based on the initial void ratio, preset burial depth, preset consolidation pressure, corresponding compression per minute, total compression, and experimental value of consolidation coefficient at sampling points of each soil sample, a permeability coefficient prediction model is obtained by combining a fitting algorithm.

[0009] Obtain the burial depth and corresponding pressure of the soil to be predicted, and calculate the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model.

[0010] The method provided by this invention samples soil layers at different burial depths. Soil samples at different depths reflect the effective overburden pressure and stress history experienced by the soil in situ, thereby restoring the true state of the soil in the in-situ strata and significantly improving the reliability and accuracy of the prediction results. Furthermore, by conducting consolidation tests on soil samples at different depths, the method obtains the compression per minute, total compression, and consolidation coefficient test values ​​at sampling points under different consolidation pressures. All of these parameters are then fitted to obtain a permeability coefficient prediction model. This allows the permeability coefficient to be calculated from the model at any given time, provided that any burial depth and the pressure experienced by the soil at that depth are input. This provides continuous, complete, and accurate data support for seepage analysis of the entire geological profile, greatly facilitating engineering design.

[0011] Preferably, the step of obtaining the permeability coefficient prediction model based on the initial void ratio, preset burial depth, preset consolidation pressure, corresponding compression per minute, total compression, and the experimental value of the consolidation coefficient at the sampling point for each soil sample, combined with a fitting algorithm, includes:

[0012] Based on the initial void ratio and the corresponding preset burial depth of each soil sample, a first model is obtained by combining a fitting algorithm;

[0013] Based on the total compression of each soil sample under multiple preset consolidation pressures and the experimental values ​​of the consolidation coefficient at the sampling points, a second model is obtained by combining a fitting algorithm.

[0014] Based on the first model and the corresponding total compression, the updated porosity of each soil sample is obtained;

[0015] Based on the preset burial depth, preset consolidation pressure, and updated void ratio corresponding to all the soil samples, a third model is obtained by combining a fitting algorithm.

[0016] Based on the second and third models, a permeability coefficient prediction model is obtained.

[0017] Using the above approach, a model was established to show the distribution of initial porosity with burial depth, the model relationship between compression per minute under preset consolidation pressure, total compression and consolidation coefficient, and the model relationship between porosity and burial depth and consolidation pressure. The complex problem was gradually decomposed into three stages, reducing the modeling difficulty. The final permeability coefficient prediction model integrates multi-dimensional experimental data such as initial porosity, burial depth, consolidation pressure, compression, and consolidation coefficient, ensuring comprehensive model input information and improving the reliability of prediction results.

[0018] Preferably, the step of obtaining the second model based on the total compression of each soil sample under multiple preset consolidation pressures and the experimental values ​​of the consolidation coefficient at the sampling points, combined with a fitting algorithm, includes:

[0019] Based on the total compression of the soil sample under the preset consolidation pressure, the test value of the consolidation coefficient of the corresponding sampling point is calculated using the square root of time method or the logarithm of time method.

[0020] The second model is obtained based on the preset burial depth, preset consolidation pressure, and experimental values ​​of the consolidation coefficient at the sampling points for all the soil samples, combined with a fitting algorithm.

[0021] There are no special requirements for the fitting algorithm, as long as a continuous and differentiable function can be obtained. For example, a bivariate polynomial or nonlinear semi-empirical method can be used to fit and obtain the second model, which can be expressed as C. v = g(z,p), where C v denoted as the consolidation coefficient, p as the preset consolidation pressure, and z as the preset burial depth.

[0022] The above scheme breaks through the limitation of traditional methods that only obtain discrete consolidation coefficient values, and establishes a continuous consolidation coefficient prediction model. Moreover, this model simultaneously considers the two influencing factors of pressure and depth, which can more accurately reflect the real change law of the consolidation properties of in-situ soil. The consolidation coefficient calculation model obtained from this can calculate the consolidation coefficient under any stress state and burial depth, providing a data foundation for the entire stratum analysis.

[0023] Preferably, the permeability coefficient prediction model is as follows: Where, k x Let γ be the permeability coefficient of the soil to be predicted. w Let g(z,p) be the specific weight of water, g(z,p) be a function of the second model, h(z,p) be a function of the third model, h′(z,p) be the derivative function of the third model, z be the burial depth, and p be the pressure.

[0024] Obtain the burial depth z of the soil to be predictedx and the corresponding pressure p x The steps for calculating the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model include:

[0025] z x and p x The consolidation coefficient of the soil to be predicted is obtained by substituting it into the function of the second model, the void ratio of the soil to be predicted is obtained by substituting it into the function of the third model, the derivative function value is obtained by substituting it into the derivative function of the function of the third model, and the consolidation coefficient, void ratio and derivative function value are substituted into the permeability coefficient prediction model to obtain the permeability coefficient of the soil to be predicted.

[0026] In the above scheme, users only need to input the burial depth and pressure. Inputting these two values ​​into the second model will yield the consolidation coefficient of the soil to be predicted. Inputting these two values ​​into the third model will yield the void ratio of the soil to be predicted. Inputting these two values ​​into the derivative function of the third model will yield the specific value corresponding to the derivative function. Inputting all of them into the permeability coefficient prediction function will quickly yield the permeability coefficient that accurately corresponds to the stress state, greatly improving the efficiency and accuracy of engineering design.

[0027] Preferably, when the soil to be predicted is engineering soil, its corresponding pressure p x This method calculates the pressure corresponding to the predicted burial depth of the soil under distributed or concentrated loads using analytical solutions or numerical simulations. Engineering soil generally refers to soil containing tunnels or foundation pits. This approach calculates the additional stress generated by tunnel or foundation pit excavation and support using analytical solutions or numerical simulations, ensuring that the final permeability coefficient accurately reflects the dynamic impact of engineering construction on the soil structure.

[0028] Preferably, when the soil to be predicted is natural soil, its corresponding pressure p x p is obtained by calculating using the following formula: x =γ'×z x Where γ' is the buoyant unit weight of the natural soil, z x The depth of the soil to be predicted is given. For natural soil masses undisturbed by engineering projects, the effective overburden pressure experienced by the soil mass under natural sedimentary conditions is directly calculated as the corresponding pressure. This effectively ensures that the initial benchmark conditions for permeability coefficient prediction are highly accurate and reliable, thus improving the reliability of the prediction.

[0029] Preferably, the step of obtaining the updated porosity of each soil sample based on the first model and the corresponding total compression includes:

[0030] The updated porosity is calculated using the following formula:

[0031] e(z)'=[f(z)(H0-s)-s] / H0, where e(z)' is the updated void ratio, H0 is the height of the sample during the consolidation test, s is the total compression corresponding to the preset consolidation pressure, f(z) is a function of the first model, and z is the preset burial depth.

[0032] The above scheme, based on the basic definition of porosity, calculates the total compression obtained through direct measurement. The results are accurate and reliable, providing a high-quality data foundation for subsequent modeling.

[0033] A second aspect of the present invention provides an apparatus for predicting the permeability coefficient of in-situ soil, wherein the apparatus comprises:

[0034] The first module is used to acquire multiple soil samples at preset burial depths and obtain the initial void ratio of each soil sample, wherein the multiple preset burial depths are different.

[0035] The second module is used to obtain the compression amount per minute, total compression amount, and consolidation coefficient test value of each soil sample under multiple preset consolidation pressures based on consolidation tests on the soil samples.

[0036] The third module is used to obtain a permeability coefficient prediction model based on the initial void ratio, preset burial depth, preset consolidation pressure, corresponding compression per minute, total compression, and the experimental value of the consolidation coefficient at the sampling point of each soil sample, combined with a fitting algorithm.

[0037] The fourth module is used to obtain the burial depth and corresponding pressure of the soil to be predicted, and to calculate the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model.

[0038] A third aspect of the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect of the present invention.

[0039] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect of the present invention.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] (1) By sampling soil layers at different burial depths, the true state of the soil in the in-situ strata is restored, and the permeability characteristics are refined, which significantly improves the reliability and accuracy of the prediction results.

[0042] (2) It eliminates the dependence on dedicated permeameters and significantly reduces the requirements for experimental equipment;

[0043] (3) The parameters are more accurately determined, and the compression coefficient can be accurately calculated by the tangent slope instead of the secant slope that can only be approximated in the current project, and then the more accurate consolidation coefficient and permeability coefficient can be obtained.

[0044] (4) A model was established to show the distribution law of initial void ratio with burial depth, the model relationship between compression per minute under preset consolidation pressure, total compression and consolidation coefficient, and the model relationship between void ratio with burial depth and consolidation pressure. Based on the above model relationships, the final permeability coefficient prediction model was constructed, so that as long as any burial depth and the pressure on the soil at that depth are input at any time, the corresponding permeability coefficient can be calculated by the model. This provides continuous and complete data support for seepage analysis of the entire stratum profile, which greatly facilitates engineering design.

[0045] (5) The permeability coefficient calculation formula proposed in this patent innovatively establishes a real-time correlation between the permeability coefficient and the porosity and pressure, breaking through the limitations of traditional formulas that do not fully consider the actual change process of the permeability coefficient. It possesses strong numerical calculation adaptability and engineering practicality. The formula is concise and has a clear physical meaning, requiring no additional complex correction coefficients or empirical parameters. It can be directly embedded as a subroutine or user-defined function into the calculation programs of mainstream numerical software such as finite element analysis and finite difference analysis (e.g., FLAC3D, COMSOL, ABAQUS), without requiring significant adjustments to the existing program architecture. In practical applications, porosity and pressure, as conventional monitoring parameters in numerical simulation, can update the permeability coefficient in real time through the calculation process, achieving dynamic coupling simulation of the seepage field with the stress field and deformation field. This significantly improves the accuracy and efficiency of seepage calculations under complex geological conditions (e.g., high-stress, large-deformation strata). Meanwhile, the formula's versatility makes it applicable to seepage analysis of various media such as soil and rock, providing a more realistic permeability coefficient calculation scheme for numerical simulation in fields such as water conservancy engineering, underground engineering, and environmental engineering. It effectively reduces the complexity of numerical modeling and parameter calibration costs, and has broad engineering application prospects and promotional value. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without any inventive effort. In the drawings:

[0047] Figure 1 A flowchart illustrating the method for measuring the permeability coefficient of in-situ soil;

[0048] Figure 2This is a fitted curve of the initial porosity in a specific embodiment;

[0049] Figure 3 This is a fitted curve of the initial porosity as a function of burial depth in a specific embodiment;

[0050] Figure 4 This is the function surface of the second model obtained by fitting in a specific embodiment;

[0051] Figure 5 This is a fitted curve of the updated porosity obtained in a specific embodiment;

[0052] Figure 6 This is the function surface of the third model obtained by fitting in a specific embodiment;

[0053] Figure 7 Here is a permeability coefficient function curve obtained by fitting in a specific embodiment;

[0054] Figure 8 This is a function surface for the permeability coefficient obtained by fitting in a specific embodiment;

[0055] Figure 9 A schematic diagram of a device for predicting the permeability coefficient of in-situ soil. Detailed Implementation

[0056] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0057] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0058] Existing methods for measuring the permeability coefficient k are mainly divided into two categories: laboratory testing and field testing. Laboratory testing requires transporting soil samples from the original situ to the laboratory for testing. During this process, the stress state, void ratio, and other physical parameters of the tested samples have changed compared to the undisturbed soil, affecting the representativeness of the results. Secondly, the measured permeability coefficient only reflects the local characteristics at the sampling point. Because the void ratio of soil layers changes with depth during natural deposition, laboratory results cannot accurately reflect the permeability characteristics of the entire soil mass. Field testing, on the other hand, is large-scale, time-consuming, requires multi-party cooperation, and is costly. Furthermore, the final results are difficult to obtain permeability coefficients at different locations and depths; the test results typically reflect the average permeability characteristics of the soil layer.

[0059] In response to the above issues, such as Figure 1 As shown, in one embodiment, a method for measuring the permeability coefficient of in-situ soil is provided, the method comprising:

[0060] S101, Obtain multiple soil samples at preset burial depths and obtain the initial void ratio of each soil sample, wherein the depths of the multiple preset burial depths are different;

[0061] S102, a consolidation test is performed on the soil sample to obtain the compression amount per minute, total compression amount, and consolidation coefficient test value of each soil sample under multiple preset consolidation pressures.

[0062] S103, based on the initial void ratio, preset burial depth, preset consolidation pressure, corresponding compression per minute, total compression, and experimental value of consolidation coefficient at sampling points of each soil sample, a permeability coefficient prediction model is obtained by combining a fitting algorithm.

[0063] S104, Obtain the burial depth and corresponding pressure of the soil to be predicted, and calculate the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model.

[0064] This application does not specify any particular method for determining the initial porosity e0; any method commonly used in the field is acceptable. For example, the drying method can be used for determination, and the specific operation is as follows:

[0065] First, obtain the soil particle density ρ of the soil sample. s Given the initial density ρ0 and initial moisture content w0, calculate the dry density ρ based on the initial density ρ0 and initial moisture content w0. d =ρ0 / (1+w0), finally obtaining the initial void ratio e0 = (ρ s / ρ d ) - 1. Among them, the soil particle density ρ s The initial density ρ0 is generally determined using the hydrometer method, or by consulting empirical values ​​in the specifications based on the soil type. This initial density ρ0 is measured for the entire specimen used in the consolidation test, and its initial total mass (M) is also measured.t The initial volume V0 (calculated using the inner diameter and height of the ring cutter) is then used to calculate the initial density ρ0.

[0066] The pre-set consolidation pressure in this application adopts a graded loading method. Generally, an initial loading pressure of approximately 12.5 kPa can be selected, and it can be gradually increased in increments of not less than 12.5 kPa until the maximum load that may occur in the project is covered. It should be noted that the initial value of the consolidation pressure and the loading step size can be set according to the specific engineering conditions, soil parameters and limitations of the testing equipment, and this application does not limit its fixed values.

[0067] Before conducting the consolidation test, the obtained soil sample needs to be prepared to obtain the consolidation test sample, and then the consolidation test is carried out on the consolidation test sample.

[0068] When sampling, multiple points are taken at intervals, such as 5 or 10 points, in the soil layer depth and above the area of ​​interest in the project. A sampler is used for sampling, and the sample taken is generally cylindrical. The depth of the soil sample is the midpoint of the cylinder's height. There are no specific requirements for the height and diameter of the soil sample taken; these vary depending on the sampler's specifications, but they must be greater than the height and diameter of the cutter ring. Typically, the cutter ring height and diameter are 20mm and 80mm, respectively.

[0069] In one embodiment, the step of obtaining a permeability coefficient prediction model based on the initial void ratio, preset burial depth, preset consolidation pressure, corresponding compression per minute, total compression, and experimental consolidation coefficient values ​​of each soil sample, combined with a fitting algorithm, includes:

[0070] Based on the initial void ratio and the corresponding preset burial depth of each soil sample, a first model is obtained by combining a fitting algorithm. The function of the first model is e0 = f(z), where e0 is the initial void ratio, z is the preset burial depth, and f(z) is the function of the initial void ratio changing with the burial depth.

[0071] Based on the total compression of each soil sample under multiple preset consolidation pressures and the experimental values ​​of the consolidation coefficient at the sampling points, a second model is obtained using a fitting algorithm. The specific function of the second model is C. v = g(z,p), where C v denoted as the consolidation coefficient, p as the preset consolidation pressure, and z as the preset burial depth. The second model is a function of the consolidation coefficient as a function of burial depth and consolidation pressure.

[0072] Based on the first model and the corresponding total compression, the updated porosity of each soil sample is obtained;

[0073] Based on the preset burial depth, preset consolidation pressure, and corresponding updated void ratio of all the soil samples, a third model is obtained by combining a fitting algorithm. The third model is specifically e = h(z,p), which is the function of void ratio as a function of burial depth and consolidation pressure.

[0074] Based on the second and third models, a permeability coefficient prediction model is obtained.

[0075] The third model is a model relating porosity to burial depth and pressure. In this application, the fitting algorithm is used to ensure that the porosity in the third model is a continuous and differentiable function related to burial depth and pressure. The fitting algorithm is implemented using numerical software, including Origin, MATLAB, and Python. Specifically, the fitting algorithm can be a standard numerical optimization (such as the least squares method), and the benchmark function can be a bivariate quadratic polynomial or a nonlinear regression model based on physical experience.

[0076] Specifically, when fitting a model using a nonlinear regression model based on physical experience, such as a semi-empirical model consisting of exponential, logarithmic, and hyperbolic function terms, standard nonlinear least squares algorithms can be called (e.g., MATLAB's lsqcurvefit, Python's SciPy's curve_fit, Origin's NLFit module, R's nls(), etc.). The software will automatically iteratively optimize the model parameters based on the initial input values, minimizing the sum of squared residuals, thereby obtaining an analytical, smooth, and differentiable parametric function. This type of method is a mature nonlinear regression technique, and can be repeatedly implemented using any numerical tool.

[0077] Specifically, the coefficients are solved using bivariate polynomial regression. When fitting with a bivariate quadratic or higher-order polynomial, the following function can be directly constructed:

[0078] C v (or e) = A0 + A1z + A2p + A3z 2 +A4p 2 +A5zp, where C v Let be the consolidation coefficient (in the second model obtained through fitting), and e be the void ratio (in the third model obtained through fitting). Of course, the function representing the initial void ratio as a function of burial depth in the first function model can also be obtained in this way. All coefficients A0 to A5 are then solved using the linear least squares method. This step can be completed using MATLAB's `regress` function, Python's `numpy.linalg.lstsq` function, Origin's polynomial fitting module, or Excel's data analysis plugin. Since polynomial fitting ultimately transforms into solving a system of linear equations, its calculation process is simple, reliable, and completely reproducible.

[0079] By using any of the above methods, a continuously differentiable function form that varies with burial depth z and pressure p can ultimately be obtained.

[0080] In some embodiments, the step of fitting a second model based on the total compression of each soil sample under multiple preset consolidation pressures and the experimental values ​​of the consolidation coefficient at the sampling points includes:

[0081] Based on the total compression of the soil sample under the preset consolidation pressure, the test value of the consolidation coefficient of the corresponding sampling point is calculated using the square root of time or the logarithm of time method.

[0082] Based on the preset burial depth, the preset consolidation pressure, and the test value of the consolidation coefficient at the sampling point corresponding to all the soil samples, the second model is obtained by using a fitting algorithm.

[0083] The fitted model yielded two models: the first model, which represents the variation of initial void ratio and burial depth, can be expressed as e0 = f(z), and the second model, which represents the variation of consolidation coefficient with burial depth and pressure, can be expressed as C. v = g(z,p), where C v is the consolidation coefficient, p is the preset consolidation pressure (in kPa), and z is the preset burial depth (in m).

[0084] The formula for calculating the experimental value of the consolidation coefficient of the sampling points obtained by the square root of time method is as follows: Where t 90 It is the time required for the degree of consolidation to reach 90%, and H is 1 / 4 of the sum of the thickness of the soil sample before and after consolidation.

[0085] The formula for calculating the experimental value of the consolidation coefficient of the sampling points obtained by the logarithmic time method is as follows:

[0086] Among them, t 50 The definition is the time required for the degree of consolidation to reach 50%, and H is 1 / 4 of the sum of the thickness of the soil sample before and after consolidation.

[0087] In this application, H = (H0 + H0 - s) / 4 = H0 / 2 - s / 4, where H0 is the height of the sample during the consolidation test, i.e., the ring cutter height, and s is the total compression under the preset pressure.

[0088] The degree of consolidation refers to the percentage of compression that reaches the final total compression s. For example, a 50% degree of consolidation means that the current compression has reached 50% of the final compression s, and the time t that has elapsed is denoted as t_s. 50 .

[0089] In some implementations, the step of obtaining the updated porosity of each soil sample based on the first model and the corresponding total compression includes:

[0090] The updated porosity is calculated using the following formula:

[0091] e(z)'=[f(z)(H0-s)-s] / H0, where e(z)' is the updated void ratio, H0 is the height of the sample during the consolidation test, i.e., the ring cutter height, which is generally 20mm, s is the total compression (in mm) corresponding to the preset consolidation pressure, f(z) is a function of the first model, and z is the preset burial depth.

[0092] From the basic formula of soil compression test, e = e0 - (1 + e0) × s / H0, we can know that... By replacing e0 with e0=f(z), we can obtain the updated porosity ratio e(z)'=[f(z)(H0-s)-s] / H0.

[0093] In some implementations, the step of obtaining the third model based on the preset burial depth, the preset consolidation pressure, and the corresponding updated void ratio for all the soil samples, combined with a fitting algorithm, includes:

[0094] The preset burial depth z, the preset consolidation pressure p, and the corresponding updated porosity e(z)' data points are plotted on a curve. Then, using data analysis software such as Origin, a fitting algorithm is used to obtain the third model e=h(z,p). This third model is a function of porosity as a function of burial depth and pressure, and is a continuous and differentiable function.

[0095] In some implementations, the permeability prediction model is: Where, k x Let γ be the permeability coefficient of the soil to be predicted. w Let g(z,p) be the specific weight of water, g(z,p) be a function of the second model, h(z,p) be a function of the third model, h′(z,p) be the derivative function of the third model, z be the burial depth, and p be the pressure.

[0096] Obtain the burial depth z of the soil to be predicted x and the corresponding pressure p x The steps for calculating the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model include:

[0097] z x and p xThe consolidation coefficient of the soil to be predicted is obtained by substituting it into the function of the second model, the void ratio of the soil to be predicted is obtained by substituting it into the function of the third model, the derivative function value is obtained by substituting it into the derivative function of the function of the third model, and the consolidation coefficient, void ratio and derivative function value are substituted into the permeability coefficient prediction model to obtain the permeability coefficient of the soil to be predicted.

[0098] Predicted permeability coefficient k x The formula is obtained based on Terzaghi's definition of the one-dimensional consolidation coefficient. In engineering, 'a' is often defined as the compressibility coefficient of soil at any given depth, obtained by taking a soil sample and conducting a compression test (in which the pre-set pressure 'p' varies). The curve of void ratio 'e' versus 'p' (a univariate function) is then obtained, along with the secant slope of 'p' in the range of [100-200] kPa. This secant slope is used as the compressibility coefficient 'a' across the entire burial depth range. However, this method neglects different burial depths. Clearly, calculating the consolidation coefficient using a fixed secant slope under constantly changing consolidation pressure is inaccurate. Therefore, this invention replaces the absolute value of the secant slope with a more accurate tangent slope, namely the void ratio 'e'. x Slope of p The value of a changes continuously with the consolidation pressure p.

[0099] By simply rearranging the above formula, we can obtain the result. Also, e = h(z,p), C v Substituting g(z,p) into the above equation yields the result.

[0100] Based on the above function model, for any soil body whose permeability coefficient needs to be predicted, as long as the burial depth and corresponding pressure of the soil body are obtained, its permeability coefficient can be quickly calculated using the above formula, which is very convenient and fast. Furthermore, this invention uses the tangent slope instead of the secant slope, which can only be approximated in current engineering, to accurately calculate the compression coefficient, thereby obtaining a more accurate permeability coefficient prediction value, greatly improving the accuracy of the prediction.

[0101] When the soil to be predicted is engineering soil (such as foundations, tunnels, pit foundations, etc. under localized surcharge), the corresponding pressure p x The specific process for calculating the pressure corresponding to the predicted burial depth of the soil under the action of distributed or concentrated loads using analytical solutions or numerical simulation methods is as follows:

[0102] Analytical Solution Method: For working conditions that satisfy the assumptions of classical elasticity, existing theoretical models can be used to calculate the additional stress inside the soil. For example, in infinitely homogeneous and isotropic soil, the additional stress at a specified burial depth can be calculated using Boussinesq point load solution, Mindlin embedded load solution, or relevant linear elasticity theory results. The "Standard for Geotechnical Testing Methods" (GB / T 50123-2019) and the "Code for Design of Foundations" (GB 50007-2011) also provide analytical methods for calculating the additional stress of foundations under several typical load conditions. This invention allows for the selection of an applicable analytical solution model based on specific engineering conditions to estimate the additional stress value of the soil to be predicted. This is an existing technology in the field and will not be elaborated further here.

[0103] Numerical simulation method: For complex engineering scenarios that do not meet classical analytical assumptions (such as tunnels, deep foundation pits, layered soil, and irregular structures), the additional stress of the soil to be predicted at a specific burial depth can be obtained through numerical calculation methods. This method can use the finite element method (FEM), finite difference method (FDM), or other existing mature foundation and underground structure analysis tools, such as PLAXIS, ABAQUS, MIDAS GTS NX, FLAC, and other commercial software, to perform modeling and calculation according to relevant industry standards (such as the "Code for Geotechnical Investigation" GB 50021-2020 and the "Technical Code for Waterproofing of Underground Engineering" GB 50108-2008), and obtain the additional stress field at the corresponding depth under loading conditions. This invention does not limit the numerical calculation platform used, as long as reliable additional stress values ​​are obtained. This is an existing technology in the field and will not be elaborated further here.

[0104] When the soil to be predicted is natural soil, the corresponding pressure p x p is obtained by calculating using the following formula: x =γ'×z x Where γ' is the buoyant unit weight of the natural soil (unit: kN / m³). 3 ), z x The depth of the soil to be predicted.

[0105] In one specific embodiment, a method for measuring the permeability coefficient of in-situ soil is provided, the method comprising:

[0106] S201: Obtain multiple soil samples at a preset burial depth z, and obtain the initial void ratio e0 of each soil sample, wherein the depths of the multiple preset burial depths z are different.

[0107] In this step, considering the layout of site buildings and equipment, as well as the difficulty of sampling, a soil sampler is used to take samples at any location. Given that the void ratio and compression curve are different at different depths of the foundation, to obtain the soil permeability coefficient within the area of ​​interest (e.g., above the diaphragm wall burial depth of the foundation pit), 10 samples need to be taken at different depths above the burial depth of the area of ​​interest, and their respective burial depths z (in meters) are recorded. Their initial void ratios e0 are then measured. The final results in this example are shown in Table 1. The initial void ratio e0 is determined using the drying method, which is a conventional prior art in this field and will not be elaborated upon here.

[0108] Table 1. Calculation data of initial void ratio e0 of soil at different burial depths.

[0109]

[0110] Based on the data in Table 1, a fitting algorithm was used to obtain the model of the initial porosity e0 as a function of burial depth, i.e., the first model e0 = f(z). In this example, a nonlinear semi-empirical formula fitting was specifically used (calling the standard nonlinear least squares algorithm), and finally e0 = 0.9r was obtained. -0.08z +0.4, where r is the natural base e, to distinguish the natural base from the porosity ratio. The curve of this model is shown below. Figure 2 As shown.

[0111] S202, a consolidation test is conducted on the soil samples to obtain the compression rate d per minute, the total compression rate s, and the test value of the consolidation coefficient C at the sampling point for each soil sample under multiple preset consolidation pressures p. v0 The specific steps are as follows:

[0112] S2021, using a geotextile ring cutter to cut samples (sample height 20mm), specimens are prepared and placed in a consolidation apparatus for testing. After pressure is applied, the vertical deformation of the specimen is measured using a dial indicator. Deformation stability is defined as a deformation not exceeding 0.005mm per hour. During the test, the consolidation pressure p is increased incrementally, starting at 12.5kPa and applied at intervals of no less than 12.5kPa, up to the maximum possible load in the project, such as 800kPa. The compression d per minute under each pressure level is measured, as well as the vertical deformation s of the specimen when stability is achieved at each pressure level.

[0113] S022, using the well-known square root of time method or logarithm of time method, based on the total compression s under each pressure level, the experimental value c of the consolidation coefficient at the sampling point corresponding to each pressure level is obtained. v0 The specific results are shown in Table 2-9.

[0114] Table 2. Calculation examples of consolidation coefficient test values ​​for soil samples at different burial depths under a consolidation pressure of 12.5 kPa.

[0115]

[0116] Table 3. Calculation examples of consolidation coefficient test values ​​for soil samples at different burial depths under a consolidation pressure of 25 kPa.

[0117]

[0118]

[0119] Table 4. Calculation examples of consolidation coefficient test values ​​for soil samples at different burial depths under a consolidation pressure of 37.5 kPa.

[0120] Table 5. Calculation examples of consolidation coefficient test values ​​for soil samples at different burial depths under a consolidation pressure of 50 kPa.

[0121]

[0122] Table 6. Calculation examples of consolidation coefficient test values ​​for soil samples at different burial depths under a consolidation pressure of 100 kPa.

[0123]

[0124] Table 7. Calculation examples of consolidation coefficient test values ​​for soil samples at different burial depths under a consolidation pressure of 200 kPa.

[0125]

[0126] Table 8. Calculation examples of consolidation coefficient test values ​​for soil samples at different burial depths under a consolidation pressure of 400 kPa.

[0127]

[0128] Table 9. Calculation examples of consolidation coefficient test values ​​for soil samples at different burial depths under a consolidation pressure of 800 kPa.

[0129]

[0130]

[0131] The burial depth z, the preset consolidation pressure p, and the experimental value of the consolidation coefficient at the sampling point c are used. v0 The data points were plotted on a curve, and the second model C was obtained by fitting the curve using Origin's NLFit (NonlinearCurveFit) module (a semi-empirical nonlinear least squares algorithm). v =g(z,p), the second model is a function of the consolidation coefficient as a function of consolidation pressure and burial depth. In this example, the second model C v(z,p)=0.3r -0.15z r -0.003p Where r is the natural base, its fitted curve is as follows: Figure 3 As shown, the function surface is as follows Figure 4 As shown.

[0132] S2023, based on the first model and the corresponding total compression, the updated porosity of each soil sample is obtained. Specifically, the updated porosity is calculated according to the following formula:

[0133] e(z)'=[f(z)(H0-s)-s] / H0, where e(z)' is the updated void ratio, H0 is the height of the sample during the consolidation test, i.e., the ring cutter height (20mm in this example), s is the total compression under the preset consolidation pressure (in mm), and f(z) is a function of the first model.

[0134] The updated porosity e(z)' for each load level in this example is shown in Table 10-19.

[0135] Table 10. Example data of updated void ratio e(z)' of soil at a depth of 2.5m under different consolidation pressures.

[0136]

[0137] Table 11. Example data of updated void ratio e(z)' of soil at a depth of 5m under different consolidation pressures.

[0138]

[0139] Table 12. Example data of updated void ratio e(z)' of soil at a depth of 7.5m under different consolidation pressures.

[0140]

[0141]

[0142] Table 13. Example data of updated void ratio e(z)' of soil at a depth of 10m under different consolidation pressures.

[0143]

[0144] Table 14. Example data of updated void ratio e(z)' of soil at a depth of 12.5m under different consolidation pressures.

[0145] Table 15. Example data of updated void ratio e(z)' of soil at a depth of 15m under different consolidation pressures.

[0146]

[0147] Table 16. Example data of updated void ratio e(z)' of soil at a depth of 17.5m under different consolidation pressures.

[0148] Table 17. Example data of updated void ratio e(z)' of soil at a depth of 20m under different consolidation pressures.

[0149]

[0150] Table 18. Example data of updated void ratio e(z)' of soil at a depth of 22.5m under different consolidation pressures.

[0151]

[0152] Table 19. Example data of updated void ratio e(z)' of soil at a depth of 25m under different consolidation pressures.

[0153]

[0154] The univariate function curves obtained by fitting the updated void ratio of soil at different depths are shown below. Figure 5 As shown.

[0155] S2024, Based on the preset burial depth, the preset consolidation pressure, and the corresponding updated porosity, a third model is obtained using a fitting algorithm. The specific steps are as follows:

[0156] All preset burial depths z, the corresponding preset consolidation pressure p, and the corresponding updated porosity e(z)' data points are plotted on a curve. A third model e = h(z,p) is obtained by fitting the data using Origin's NLFit module. In this example, based on the above data, the fitted third model is specifically e(z,p) = 0.9r. -0.08z +0.4-log 10 (1+p / 50)*[0.1+0.05tanh(0.1z)], where r is the natural base, and its function surface is as follows: Figure 6 As shown.

[0157] S203, the permeability coefficient prediction model is obtained based on the second model and the third model, and the specific steps are as follows:

[0158] Permeability coefficient k x The formula is derived from Terzaghi's definition of the one-dimensional consolidation coefficient. The functions of the second and third models, along with the derivative of the third model, are substituted into the permeability coefficient definition formula. The tangent slope is used instead of the traditional secant slope, resulting in the final permeability coefficient prediction model. Where, k x γ is the permeability coefficient (in m / s) of the soil to be predicted. w The specific weight of water (unit: kN / m³) 3 g(z,p) is a function of the second model used to calculate the consolidation coefficient of the soil to be predicted, h(z,p) is a function of the third model used to calculate the void ratio of the soil to be predicted, h′(z,p) is the derivative function of the third model, z is the burial depth, and p is the pressure.

[0159] Based on the above data, the final fitted curve of permeability coefficient as a function of burial depth and pressure in this example is shown below. Figure 7 As shown, its function surface is as follows Figure 8 As shown.

[0160] S204, For the soil mass to be predicted, obtain the burial depth z of the soil mass to be predicted. x and the corresponding pressure p x Substitute into the second model C v (z,p)=0.3r -0.15z r -0.003p Obtain the corresponding consolidation coefficient value and substitute it into the third model e(z,p)=0.9r -0.08z +0.4-log 10 The corresponding void ratio is obtained by calculating (1+p / 50)*[0.1+0.05tanh(0.1z)]. The third model e(z,p) = 0.9r -0.08z +0.4-log 10 Differentiating (1+p / 50)*[0.1+0.05tanh(0.1z)], we get z x and p x Substitute these values ​​into the derived function to calculate the derivative value, and then substitute these values ​​into k. x In the calculation formula, the permeability coefficient k of the soil to be predicted is calculated. x .

[0161] In some embodiments, such as Figure 9 As shown, an apparatus for predicting the in-situ soil permeability coefficient is also provided, wherein the apparatus includes:

[0162] The first module 301 is used to acquire multiple soil samples at preset burial depths and obtain the initial void ratio of each soil sample, wherein the depths of the multiple preset burial depths are different.

[0163] The second module 302 is used to obtain the compression amount per minute, total compression amount, and consolidation coefficient test value of each soil sample under multiple preset consolidation pressures based on the consolidation test of the soil sample.

[0164] The third module 303 is used to obtain a permeability coefficient prediction model based on the initial void ratio, preset burial depth, preset consolidation pressure, compression per minute, total compression and the experimental value of the consolidation coefficient at the sampling point of each soil sample, combined with a fitting algorithm.

[0165] The fourth module 304 is used to obtain the burial depth and corresponding pressure of the soil to be predicted, and to calculate the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model.

[0166] In some embodiments, the device further includes a permeability coefficient prediction module, which is specifically used for: obtaining a first model based on the initial void ratio and the corresponding preset burial depth of each soil sample, combined with a fitting algorithm; obtaining a second model based on the total compression of each soil sample under multiple preset consolidation pressures and the experimental value of the consolidation coefficient at the sampling point, combined with a fitting algorithm; obtaining an updated void ratio for each soil sample based on the first model and the corresponding total compression; obtaining a third model based on the preset burial depth, the preset consolidation pressure, and the corresponding updated void ratio of all soil samples, combined with a fitting algorithm; and obtaining a permeability coefficient prediction model based on the second model and the third model.

[0167] In some embodiments, the device further includes a second model acquisition module, which is specifically used to: calculate the corresponding sampling point consolidation coefficient test value based on the total compression of the soil sample under a preset consolidation pressure using the square root of time method or the logarithm of time method; and obtain the second model based on the preset burial depth, the corresponding preset consolidation pressure, and the corresponding sampling point consolidation coefficient test value of all the soil samples, combined with a fitting algorithm.

[0168] In some embodiments, the device further includes a porosity update module, which is specifically used to: calculate the updated porosity according to the following formula: e(z)'=[f(z)(H0-s)-s] / H0, where e(z)' is the updated porosity, H0 is the height of the sample during the consolidation test, s is the total compression corresponding to the preset consolidation pressure, f(z) is a function of the first model, and z is the preset burial depth.

[0169] In some embodiments, the permeability coefficient prediction module is further configured to: obtain the burial depth z of the soil to be predicted. x and the corresponding pressure p x Based on the second model, the consolidation coefficient of the soil to be predicted is obtained; based on the third model, the void ratio of the soil to be predicted is obtained; and based on the function of the third model, its derivative function is obtained. The predicted permeability coefficient is then calculated using the following formula: Where, k x Let γ be the permeability coefficient of the soil to be predicted. wLet g(z,p) be the specific weight of water, g(z,p) be a function of the second model, h(z,p) be a function of the third model, h′(z,p) be the derivative function of the third model, z be the burial depth, and p be the pressure; Let z be the specific weight of water, g(z,p) be a function of the second model, h′(z,p) be a function of the third ... z be a burial depth, and x and p x The consolidation coefficient of the soil to be predicted is obtained by substituting it into the function of the second model, the void ratio of the soil to be predicted is obtained by substituting it into the function of the third model, the derivative function value is obtained by substituting it into the derivative function of the function of the third model, and the consolidation coefficient, void ratio and derivative function value are substituted into the permeability coefficient prediction model to obtain the permeability coefficient of the soil to be predicted.

[0170] In some embodiments, the apparatus further includes a pressure update module, which is specifically used to: based on the soil to be predicted being engineering soil, its corresponding pressure p x To calculate the pressure corresponding to the predicted burial depth of the soil under the action of distributed or concentrated loads using analytical solutions or numerical simulation methods for the additional stress inside the soil.

[0171] In some embodiments, the pressure update module is further configured to: based on the soil to be predicted being natural soil, its corresponding pressure p x p is obtained by calculating using the following formula: x =γ'×z x Where γ' is the buoyant unit weight of the natural soil, z x The depth of the soil to be predicted.

[0172] It should be understood that, for the foregoing method embodiments, although the steps in the flowcharts are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the method embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0173] The contents not described in detail in this specification are existing technologies known to those skilled in the art, and will not be elaborated upon here.

[0174] In some embodiments, a computer device is also provided, including a memory and a processor, the memory storing a computer program, wherein the processor executes the computer program to implement the steps in the various method embodiments.

[0175] In some embodiments, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps in the above method embodiments.

[0176] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0178] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device 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, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0180] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0181] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0182] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0183] It should be noted that, in the embodiments of this application, certain existing solutions in the industry, such as software, components, and models, may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions. All methods described in this application are entirely independently developed executable software algorithms. All software algorithms are implemented using general-purpose high-level languages, such as C++ and Python. The development environments, such as Visual Studio Community Edition and PyCharm Community Edition, are free and publicly available software, and do not involve software licensing or other intellectual property issues.

[0184] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0185] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0186] In this article, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0187] The terms "first" and "second" used herein are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permissible. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0188] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for measuring the permeability coefficient of in-situ soil, characterized in that, The method includes: Multiple soil samples with preset burial depths are obtained, and the initial void ratio of each soil sample is obtained, wherein the multiple preset burial depths are different. Consolidation tests were conducted on the soil samples to obtain the compression per minute, total compression, and consolidation coefficient of each soil sample under multiple preset consolidation pressures. Based on the initial void ratio, preset burial depth, preset consolidation pressure, corresponding compression per minute, total compression, and experimental value of consolidation coefficient at sampling points of each soil sample, a permeability coefficient prediction model is obtained by combining a fitting algorithm. Obtain the burial depth and corresponding pressure of the soil to be predicted, and calculate the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model.

2. The method according to claim 1, wherein, The steps for obtaining the permeability coefficient prediction model based on the initial void ratio, preset burial depth, preset consolidation pressure, corresponding compression rate per minute, total compression, and experimental consolidation coefficient values ​​at sampling points for each soil sample, combined with a fitting algorithm, include: Based on the initial void ratio and the corresponding preset burial depth of each soil sample, a first model is obtained by combining a fitting algorithm; Based on the total compression of each soil sample under multiple preset consolidation pressures and the experimental values ​​of the consolidation coefficient at the sampling points, a second model is obtained by combining a fitting algorithm. Based on the first model and the corresponding total compression, the updated porosity of each soil sample is obtained; Based on the preset burial depth, preset consolidation pressure, and updated void ratio corresponding to all the soil samples, a third model is obtained by combining a fitting algorithm. Based on the second and third models, a permeability coefficient prediction model is obtained.

3. The method according to claim 2, wherein, The step of obtaining the second model based on the total compression of each soil sample under multiple preset consolidation pressures and the experimental values ​​of the consolidation coefficient at the sampling points, combined with a fitting algorithm, includes: Based on the total compression of the soil sample under the preset consolidation pressure, the test value of the consolidation coefficient of the corresponding sampling point is calculated using the square root of time method or the logarithm of time method. The second model is obtained based on the preset burial depth, preset consolidation pressure, and experimental values ​​of the consolidation coefficient of the sampling points corresponding to all the soil samples, combined with a fitting algorithm.

4. The method according to any one of claims 1-3, wherein, The permeability coefficient prediction model is as follows: Where, k x Let γ be the permeability coefficient of the soil to be predicted. w Let g(z,p) be the specific weight of water, g(z,p) be a function of the second model, h(z,p) be a function of the third model, h′(z,p) be the derivative function of the third model, z be the burial depth, and p be the pressure. Obtain the burial depth z of the soil to be predicted x and the corresponding pressure p x The steps for calculating the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model include: z x and p x The consolidation coefficient of the soil to be predicted is obtained by substituting it into the function of the second model, the void ratio of the soil to be predicted is obtained by substituting it into the function of the third model, the derivative function value is obtained by substituting it into the derivative function of the function of the third model, and the consolidation coefficient, void ratio and derivative function value are substituted into the permeability coefficient prediction model to obtain the permeability coefficient of the soil to be predicted.

5. The method according to claim 4, wherein, Based on the fact that the soil to be predicted is engineering soil, the corresponding pressure p x To calculate the pressure p corresponding to the predicted burial depth of the soil under the action of distributed or concentrated loads using analytical solutions or numerical simulation methods for the additional stress inside the soil body, new .

6. The method according to claim 4, wherein, Based on the assumption that the soil to be predicted is natural soil, the corresponding pressure p x p is obtained by calculating using the following formula: x =γ ' ×z x , where γ ' The buoyant weight of natural soil, z x The depth of the soil to be predicted.

7. The method according to claim 4, wherein, The step of obtaining the updated porosity of each soil sample based on the first model and the corresponding total compression includes: The updated porosity is calculated using the following formula: e(z) ' = [f(z)(H0-s)-s] / H0, where e(z) ' For the updated porosity, H0 is the height of the sample during the consolidation test, s is the total compression corresponding to the preset consolidation pressure, f(z) is a function of the first model, and z is the preset burial depth.

8. A device for predicting the permeability coefficient of in-situ soil, characterized in that, The device includes: The first module is used to acquire multiple soil samples at preset burial depths and obtain the initial void ratio of each soil sample, wherein the multiple preset burial depths are different. The second module is used to obtain the compression amount per minute, total compression amount, and consolidation coefficient test value of each soil sample under multiple preset consolidation pressures based on consolidation tests on the soil samples. The third module is used to obtain a permeability coefficient prediction model based on the initial void ratio, preset burial depth, preset consolidation pressure, corresponding compression per minute, total compression, and the experimental value of the consolidation coefficient at the sampling point of each soil sample, combined with a fitting algorithm. The fourth module is used to obtain the burial depth and corresponding pressure of the soil to be predicted, and to calculate the permeability coefficient of the soil to be predicted based on the permeability coefficient prediction model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

Images