In-situ testing system and method for engineering properties of deep overburden

Abstract

This invention relates to the field of geotechnical engineering technology and provides an in-situ testing system and method for the engineering properties of deep overburden layers. The method includes the following steps: obtaining the gradation curve and relative density of overburden samples; controlling indoor testing equipment to complete large-scale triaxial compression tests, large-scale compression tests, permeability coefficient tests, and permeability deformation tests based on preset gradation envelopes and relative density levels; collecting stress-strain data, volumetric deformation data, compression curves, and seepage data; and establishing an empirical relationship model between conventional physical properties of the overburden layer and multiple engineering characteristic parameters, including linear strength indices, nonlinear strength indices, Duncan-Chang constitutive model parameters, and permeability coefficient. This establishes an empirical relationship between conventional physical properties such as gradation and relative density and complex engineering characteristic parameters such as strength, deformation, and permeability, avoiding numerous repetitive large-scale triaxial, compression, and permeability tests and significantly shortening the exploration cycle.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering technology, specifically to an in-situ testing system and method for engineering properties of deep overburden layers. Background Technology

[0002] Constructing earth-rock dams, sluice gates, and other water conservancy and hydropower projects directly on deep overburden layers offers significant advantages such as cost savings, shorter construction periods, and environmental friendliness. However, deep overburden layers typically exhibit characteristics such as discrete gradation, uneven density distribution, significant structural differences, complex stratification, and the presence of weak interlayers, leading to a series of engineering problems including dam foundation leakage, anti-sliding stability, uneven settlement, and sand liquefaction.

[0003] In existing technologies, determining the engineering characteristic parameters of overburden layers mainly relies on two approaches: one is to conduct extensive in-situ tests (such as load tests, pressuremeter tests, and wave velocity tests), but these are expensive, time-consuming, and limited by site conditions, making it difficult to directly obtain the computational model parameters required for numerical analysis; the other is to use laboratory tests (such as triaxial compression tests, compression tests, and permeability tests), but these tests are usually only conducted on reshaped or disturbed samples, failing to reflect the true impact of the in-situ structure of the overburden layer on its mechanical properties. For coarse-grained soils such as gravel, obtaining undisturbed samples is extremely difficult, and laboratory test results are often significantly lower than the actual in-situ mechanical properties, leading to conservative engineering designs or potential safety hazards. Therefore, there is a need to provide an in-situ testing system and method for the engineering characteristics of deep overburden layers to address these problems. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an in-situ testing system and method for the engineering characteristics of deep coating layers, so as to solve the problems existing in the above-mentioned background technology.

[0005] This invention is implemented as follows: an in-situ testing method for the engineering characteristics of deep overburden layers, the method comprising the following steps: Obtain the gradation curve and relative density of the capping layer sample, and control the indoor testing equipment to complete the large-scale triaxial compression test, large-scale compression test, permeability coefficient test and permeability deformation test according to the preset gradation envelope and relative density level; Stress-strain data, volumetric deformation data, compression curves, and seepage data were collected to establish an empirical relationship model between the conventional physical properties of the overburden and multiple engineering characteristic parameters, including linear strength indices, nonlinear strength indices, Duncan-Chang constitutive model parameters, and permeability coefficient. The pressure gauge is controlled to apply pressure at different levels, and the volume change of the pressure chamber is collected to generate a pressure-volume curve; the load test equipment is controlled to apply load at different levels, and the settlement of the bearing plate is collected to generate a pressure-settlement curve. The pressure-volume curve and pressure-settlement curve are imported into the finite element inversion analysis module. The constitutive model parameters output by the empirical relation model are used as initial values ​​to establish an axisymmetric finite element model. The sum of squared errors between the calculated displacement and the measured displacement is minimized through iterative optimization. The inversion constitutive model parameters that can reflect the in-situ structural characteristics of the overburden are output. The parameters of the inverted constitutive model are compared with the engineering characteristic parameters output by the empirical relational model to calculate the in-situ structure influence factor.

[0006] As a further aspect of the present invention, the step of establishing an empirical relationship model specifically includes: The mass percentage of particles larger than five millimeters and the mass percentage of particles smaller than five millimeters were obtained by particle analysis test, and the on-site relative density value was obtained by relative density test. Using the percentage of particles larger than five millimeters in diameter and the relative density at the site as independent variables, linear regression fitting was performed on the cohesion and internal friction angle in the linear strength index, and the reference internal friction angle and internal friction angle attenuation in the nonlinear strength index, to obtain the linear prediction formula for each strength index. Using the mass percentage of particles larger than five millimeters and the relative density at the site as independent variables, nonlinear fitting in the form of exponential product is performed on the baseline of tangent elastic modulus and the baseline of tangent bulk modulus in the Duncan-Chang constitutive model to obtain the prediction formula for deformation parameters. Using the on-site relative density value and the mass percentage of particles smaller than five millimeters as independent variables, the permeability coefficient is fitted with a semi-logarithmic or power function to obtain the prediction formula for the permeability coefficient. All prediction formulas are compiled into executable code, and the corresponding engineering characteristic parameters are automatically output after any set of physical property indicators are input.

[0007] As a further aspect of the present invention, the step of controlling the application of progressively increasing pressure by the field pressure gauge specifically includes: Send step-by-step pressurization commands to the control valve of the pressure gauge, maintain the pressure at each level for a preset stable duration, and receive the pressure value returned by the pressure sensor and the volume change returned by the volume sensor in real time. The collected pressure values ​​and volume changes are corrected by elastic membrane constraint force correction and instrument comprehensive deformation coefficient correction to generate corrected pressure-volume data points. Identify the in-situ horizontal earth pressure at the start of the straight line segment, the critical plastic pressure at the end of the straight line segment, and the ultimate pressure when the curve tends to be parallel to the vertical axis on the corrected pressure-volume curve. The pressure modulus and pressure shear modulus were calculated based on elasticity theory, and all pressure characteristic values ​​and moduli were stored in the in-situ test database.

[0008] As a further aspect of the present invention, the step of establishing an axisymmetric finite element model specifically includes: A two-dimensional axisymmetric grid is generated based on the initial radius of the pressure bypass and the length of the measuring chamber. The calculation region boundary is defined by extending the initial radius radially and axially by a preset multiple from the midpoint of the pressure bypass. A pressure sequence measured by pressuremeter tests is applied as a load boundary condition to the inner wall of the grid. Pressure boundary conditions calculated from the soil self-weight are applied to the top and right boundaries. Displacement constraints are applied to the bottom and borehole wall boundaries. Using the parameters of the Duncan-Zhang constitutive model as the initial values ​​for inversion, the radial displacement under each pressure level was calculated step by step using the finite element solver. Construct an objective function, perform iterative optimization until the objective function value is less than a preset threshold, and output the converged parameters as the parameters of the inverted constitutive model.

[0009] As a further aspect of the present invention, the step of calculating the in-situ structure influence factor specifically includes: Extract the baseline values ​​of tangent elastic modulus and tangent bulk modulus under indoor disturbance conditions, and extract the corresponding inverted baseline values ​​of tangent elastic modulus and tangent bulk modulus. The ratios of the inverted tangent elastic modulus baseline to the indoor tangent elastic modulus baseline, and the ratios of the inverted tangent bulk modulus baseline to the indoor tangent bulk modulus baseline are calculated respectively to obtain the elastic modulus influence factor and the bulk modulus influence factor. The two influencing factors are correlated with the preset cementation degree classification, moisture content classification, and density classification to generate an influencing factor lookup table. When new overburden physical properties are entered, the computer automatically recommends influence factors applicable to that layer based on a lookup table, which are then used to correct the indoor test parameters.

[0010] Another object of the present invention is to provide an in-situ testing system for the engineering properties of deep overburden layers, the system comprising: The indoor testing module is used to obtain the gradation curve and relative density of the cover layer sample. Based on the preset gradation envelope and relative density level, it controls the indoor testing equipment to complete large triaxial compression test, large compression test, permeability coefficient test and permeability deformation test. The empirical relationship module is used to collect stress-strain data, volumetric deformation data, compression curves and seepage data, and to establish an empirical relationship model between the conventional physical properties of the overburden and multiple engineering characteristic parameters, including linear strength indices, nonlinear strength indices, Duncan-Chang constitutive model parameters and permeability coefficients. The curve generation module is used to control the field pressure gauge to apply progressively increasing pressure, collect the volume change of the pressure chamber, and generate a pressure-volume curve; and to control the field load testing equipment to apply progressively increasing load, collect the settlement of the bearing plate, and generate a pressure-settlement curve. The finite element model module is used to import the pressure-volume curve and pressure-settlement curve into the finite element inversion analysis module. Using the constitutive model parameters output by the empirical relation model as initial values, an axisymmetric finite element model is established. Through iterative optimization, the sum of squared errors between the calculated displacement and the measured displacement is minimized, and the inversion constitutive model parameters that can reflect the in-situ structural characteristics of the overburden are output. The Influence Factor module is used to compare the parameters of the inverted constitutive model with the engineering characteristic parameters output by the empirical relational model to calculate the in-situ structure influence factor.

[0011] Compared with the prior art, the beneficial effects of the present invention are: By regressing data from automated large-scale indoor tests, an empirical relationship was established between conventional physical properties such as gradation and relative density and complex engineering characteristics such as strength, deformation, and permeability. In practical engineering, only simple particle size distribution and on-site relative density need to be measured to quickly predict a complete set of mechanical parameters, avoiding a large number of repetitive large-scale triaxial, compression, and permeability tests, and significantly shortening the exploration cycle.

[0012] The pressure-deformation curves from pressuremeter tests or load tests are automatically imported into the finite element inversion module. Using indoor parameters as initial values, the constitutive model parameters are directly output through iterative optimization. This accurately reflects the in-situ structural characteristics of the overburden layer and can be directly used for three-dimensional finite element analysis of dam stress-deformation, slope stability, and seepage.

[0013] The in-situ structural influence factor is calculated by comparing the parameters of the inverted constitutive model with the engineering characteristic parameters output by the empirical relational model. The influence factor is correlated with the degree of cementation, water-bearing state, and density classification of the strata, forming a queryable correction table. For new areas where in-situ inversion has not been performed, influence factors can be quickly recommended based on geological descriptions to correct indoor parameters, making the design parameters closer to the actual in-situ performance. Attached Figure Description

[0014] Figure 1 A flowchart for in-situ testing methods for engineering properties of thick overburden layers.

[0015] Figure 2 A flowchart for establishing an empirical relationship model in the in-situ testing method for the engineering characteristics of deep overburden layers.

[0016] Figure 3 A flowchart illustrating the process of applying progressively increasing pressure using a pressure gauge in an in-situ testing method for engineering properties of deep overburden layers.

[0017] Figure 4 A flowchart for establishing an axisymmetric finite element model in the in-situ testing method for the engineering characteristics of thick overburden layers.

[0018] Figure 5 A flowchart for calculating the in-situ structural influence factor in the in-situ testing method for the engineering characteristics of thick overburden layers.

[0019] Figure 6 The structural block diagram of the in-situ testing system for the engineering characteristics of thick overburden layers. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0022] like Figure 1 As shown, this embodiment of the invention provides an in-situ testing method for the engineering characteristics of a deep overburden layer, the method comprising the following steps: S100: Obtain the gradation curve and relative density of the cover layer sample. Based on the preset gradation envelope and relative density level, control the indoor testing equipment to complete the large-scale triaxial compression test, large-scale compression test, permeability coefficient test and permeability deformation test.

[0023] In this embodiment of the invention, the gradation curve of the overburden gravel is first obtained through particle analysis tests (such as sieve analysis), and the characteristic particle size and non-uniformity coefficient are calculated. The maximum and minimum dry densities are determined through relative density tests (vibration compaction method), and the on-site relative density is estimated by combining in-situ field tests. Then, based on the upper, average, and lower envelopes of the gradation curve and the selected relative density grades (such as 0.55, 0.68, and 0.80), the computer automatically sends control commands to equipment such as a large triaxial apparatus, consolidation apparatus, and permeameter to complete sample preparation, saturation, loading, and data acquisition.

[0024] S200 collects stress-strain data, volumetric deformation data, compression curves, and seepage data to establish an empirical relationship model between the conventional physical properties of the overburden and multiple engineering characteristic parameters, including linear strength indices, nonlinear strength indices, Duncan-Chang constitutive model parameters, and permeability coefficient.

[0025] In this embodiment of the invention, a large amount of data obtained from indoor tests is used to establish a quantitative relationship between physical property indicators and complex mechanical parameters through regression analysis. The collected data includes axial stress-strain and volumetric strain from triaxial tests, void ratio-pressure curves from compression tests, and head-flow rate data from permeability tests. After processing, strength parameters, constitutive parameters, and permeability coefficients are obtained, and then empirical formulas are formed through statistical fitting.

[0026] S300 controls the field pressure gauge to apply progressively increasing pressure, collects the volume change of the pressure chamber, and generates a pressure-volume curve; it also controls the field load testing equipment to apply progressively increasing load, collects the settlement of the bearing plate, and generates a pressure-settlement curve.

[0027] In this embodiment of the invention, the computer pressurizes the pressure gauge step by step through the control valve and records the volume change, while simultaneously controlling the jack of the load test to load step by step and recording the settlement, thus obtaining two original response curves.

[0028] S400, import the pressure-volume curve and pressure-settlement curve into the finite element inversion analysis module, use the constitutive model parameters output by the empirical relation model as initial values, establish an axisymmetric finite element model, minimize the sum of squared errors between the calculated displacement and the measured displacement through iterative optimization, and output inversion constitutive model parameters that can reflect the in-situ structural characteristics of the overburden layer.

[0029] In this embodiment of the invention, field test curves are combined with numerical simulations to deduce the true in-situ parameters. Using the pressure-volume curve from the pressuremeter test or the pressure-settlement curve from the load test as the target response, and the Duncan-Zhang parameters obtained from the indoor test as initial values, a finite element model consistent with the field geometric boundary is established. By repeatedly adjusting the constitutive parameters, the simulated displacement is made to approximate the measured displacement, and finally a set of parameters that amplify the in-situ structural effect is output.

[0030] S500 compares the parameters of the inverted constitutive model with the engineering characteristic parameters output by the empirical relational model to calculate the in-situ structure influence factor.

[0031] In this embodiment of the invention, the ratio of the inversion parameters to the indoor parameters on the basis of tangential elastic modulus and bulk modulus is calculated to obtain the influence factor, which is used for subsequent engineering correction.

[0032] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of establishing an empirical relationship model specifically includes: S201, the mass percentage of particles larger than five millimeters and the mass percentage of particles smaller than five millimeters are obtained by particle analysis test, and the on-site relative density value is obtained by relative density test; S202, using the percentage of particles larger than five millimeters in diameter and the relative density at the site as independent variables, linear regression fitting is performed on the cohesion and internal friction angle in the linear strength index, and the reference internal friction angle and internal friction angle attenuation in the nonlinear strength index, to obtain the linear prediction formula for each strength index. S203, using the percentage of particles larger than five millimeters in diameter and the relative density at the site as independent variables, nonlinear fitting of the tangent elastic modulus base and the tangent bulk modulus base in the Duncan-Chang constitutive model in the form of exponential product is performed to obtain the prediction formula for the deformation parameters. S204. Using the on-site relative density value and the mass percentage of particles smaller than five millimeters as independent variables, the permeability coefficient is fitted with a semi-logarithmic or power function to obtain the prediction formula for the permeability coefficient. S205 compiles all prediction formulas into executable code, and automatically outputs the corresponding engineering characteristic parameters after inputting any set of physical property indicators.

[0033] Specifically, using the mass percentage of particles larger than five millimeters and the on-site relative density as independent variables, nonlinear fitting in the form of exponential products is performed on the baseline of the tangent elastic modulus and the baseline of the tangent bulk modulus in the Duncan-Chang constitutive model to obtain the prediction formula for the deformation parameters. The specific steps are as follows: Step a: First, sort the tangential elastic modulus base and the tangential bulk modulus base in ascending order of the mass percentage of particles larger than five millimeters. Then, sort the tangential elastic modulus base and the tangential bulk modulus base in ascending order of the on-site relative density values ​​to obtain the ordered data sequence of the tangential elastic modulus base and the data sequence of the tangential bulk modulus base. Step b: For each data point in the ordered data sequence of tangential modulus of elasticity, calculate the logarithm of the percentage of particle mass with a particle size greater than five millimeters, the logarithm of the relative density, and the logarithm of the tangential modulus of elasticity, and use the least squares regression algorithm to obtain the first set of fitting coefficients for the tangential modulus of elasticity. Step c: Perform exponential operations on the first set of fitting coefficients of the tangent elastic modulus base to obtain the proportional coefficient; take the two power exponents obtained from the least squares regression fitting as the powers of the mass percentage of particles larger than five millimeters and the powers of the field relative density value, respectively, and construct the first exponential product prediction function in the form of 'proportional coefficient multiplied by the power of the mass percentage of particles larger than five millimeters, and then multiplied by the power of the field relative density value'. Step d: For each data point in the tangent bulk modulus baseline data sequence, repeat the calculation process of step b to obtain the second set of fitting coefficients for the tangent bulk modulus baseline. Step e: Repeat the construction process of step c for the second set of fitting coefficients of the tangent bulk modulus base to obtain the second exponential product prediction function; Step f: The tangent elastic modulus base value and the tangent bulk modulus base value are calculated using the first exponential product prediction function and the second exponential product prediction function, respectively, to obtain the predicted values ​​of the tangent elastic modulus base value and the tangent bulk modulus base value; the relative errors between the predicted values ​​of the tangent elastic modulus base value and the tangent bulk modulus base value and the corresponding measured values ​​are calculated to obtain the first relative error and the second relative error. Step g: The product of the mass percentage of particles larger than 5 mm in diameter and the on-site relative density value is used as the cross term. When the first relative error or the second relative error exceeds 10%, the cross term is used to perform nonlinear fitting on the first exponential product prediction function or the second exponential product prediction function. The nonlinear fitting is repeated until both the first relative error and the second relative error do not exceed 10%, and the corrected first exponential product prediction function and the corrected second exponential product prediction function are obtained. The corrected first exponential product prediction function and the corrected second exponential product prediction function are used as the prediction formula for the deformation parameters.

[0034] Furthermore, this step significantly improves the prediction accuracy of deformation parameters in the Duncan-Zhang model by fitting the product of two independent variables and correcting for cross-term errors, ensuring that the relative error is controlled within 10%, thus providing a reliable basis for the analysis of the mechanical properties of coarse-grained soil.

[0035] In this embodiment of the invention, the particle mass percentage is obtained; the maximum and minimum dry densities are read from the relative density test, and the on-site relative density is calculated using empirical formulas based on in-situ tests (such as the number of blows in a heavy dynamic penetrometer test). These indicators serve as independent variables for subsequent regressions. Then, the linear prediction formulas for each strength indicator are obtained through linear fitting; the prediction formulas for deformation parameters are obtained through exponential function fitting; and the prediction formulas for permeability coefficients are obtained through semi-logarithmic or power function fitting. Finally, all the above regression formulas are encapsulated into a function library or executable module.

[0036] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of controlling the application of stepwise pressure by the field pressure gauge specifically includes: S301 sends a step-by-step pressurization command to the control valve of the pressure gauge, maintains the pressure at each level for a preset stable duration, and receives the pressure value returned by the pressure sensor and the volume change returned by the volume sensor in real time. S302 performs elastic membrane constraint force correction and instrument comprehensive deformation coefficient correction on the collected pressure value and volume change, and generates corrected pressure-volume data points; S303 identifies the in-situ horizontal earth pressure at the start of the straight line segment, the critical plastic pressure at the end of the straight line segment, and the ultimate pressure when the curve tends to be parallel to the vertical axis on the corrected pressure-volume curve. S304 calculates the pressure modulus and pressure shear modulus based on elasticity theory, and stores all pressure characteristic values ​​and moduli in the in-situ test database.

[0037] Specifically, on the corrected pressure-volume curve, identify the in-situ horizontal earth pressure at the start of the straight line segment, the critical plastic pressure at the end of the straight line segment, and the ultimate pressure when the curve tends to be parallel to the vertical axis. The specific steps are as follows: The corrected pressure-volume data point sequence is obtained based on the corrected pressure-volume curve; the corrected pressure-volume data point sequence is arranged in ascending order of pressure value, and the ratio of volume change to pressure change between each two adjacent data points is calculated to obtain the ratio sequence. Starting from the first ratio in the ratio sequence, adjacent ratios are compared sequentially to obtain the relative fluctuation result; when the continuous relative fluctuation result is within ±3%, the pressure value corresponding to the starting point of the corresponding relative fluctuation interval is determined as the starting point of the straight line segment, and the in-situ horizontal earth pressure is obtained. Based on the ratio sequence, the average ratio is calculated. Starting from the in-situ horizontal earth pressure, the ratio sequence is traversed. When the ratio decreases by more than 15% relative to the average ratio, the corresponding pressure value is determined as the end point of the straight line segment, and the critical plastic pressure corresponding to the end point of the straight line segment is obtained. Starting from the critical pressure corresponding to the end of the straight line segment, traverse the corrected pressure-volume data point sequence and calculate the ratio of the volume change at each data point to the volume change at the previous point to obtain the increment ratio. When the increment ratio continues to increase and exceeds 1.5, the corresponding pressure value is determined as the starting point when the curve tends to be parallel to the vertical axis, so as to obtain the limit pressure when the curve tends to be parallel to the vertical axis.

[0038] Furthermore, by automatically identifying curve feature points through ratio fluctuations and increment ratios, objective and continuous determination of in-situ horizontal earth pressure, critical plastic pressure, and ultimate pressure is achieved, avoiding human reading errors and improving the accuracy and efficiency of pressuremeter test data processing.

[0039] In this embodiment of the invention, the computer sends a pressure setpoint (e.g., 50 kPa per level) to the electric pressure regulating valve of the pressure shunting instrument via a serial port or wireless module. Once the pressure is reached, it is maintained for 1 minute (a preset stabilization period). During this time, data from the pressure gauge and volume tube are read at a high sampling rate, recording the average pressure and average volume change at each pressure level. Because the elastic membrane of the pressure shunting instrument itself has stiffness, it consumes some pressure; simultaneously, pipeline deformation also affects the volume reading. The computer automatically calls the pre-calibrated elastic membrane constraint force curve and the instrument's comprehensive deformation coefficient to correct the data. Second-order derivative analysis is performed on the corrected P-ΔV curve: the pressure at the end of the initial flat section and into the linear section is the in-situ horizontal earth pressure; the endpoint of the linear section (where the slope begins to change) is the critical plastic pressure Pf; the pressure corresponding to the tangent slope at each point on the curve approaching infinity (or the volume increment reaching twice the initial volume) is the ultimate pressure P1. Within the straight segment of the P-ΔV curve, select the pressure increment ΔP and volume increment ΔV, and substitute them into the formula: Pressure modulus Em = 2(1+μ)(Vc+Vm)×(ΔP / ΔV), where μ is Poisson's ratio, Vc is the initial volume of the pressure bypass cavity, and Vm is the average volume increment; Shear modulus Gm = Em / (2(1+μ)). All calculation results and original curves are automatically stored in the database.

[0040] like Figure 4 As shown, in a preferred embodiment of the present invention, the steps for establishing an axisymmetric finite element model specifically include: S401, a two-dimensional axisymmetric grid is generated according to the initial radius of the pressure bypass and the length of the measuring cavity. The calculation region boundary is defined by extending the initial radius in both the radial and axial directions from the center of the pressure bypass as a preset multiple. S402, apply the pressure sequence measured by the pressuremeter test as the load boundary condition on the inner wall of the grid, apply the pressure boundary condition calculated by the self-weight of the soil layer on the top and right boundary, and apply the displacement constraint on the bottom and hole wall boundary. S403, using the Duncan-Chang constitutive model parameters as the initial values ​​for inversion, calls the finite element solver to calculate the radial displacement under each pressure level step by step; S404: Construct the objective function, perform iterative optimization until the objective function value is less than a preset threshold, and output the converged parameters as the parameters of the inverted constitutive model.

[0041] Specifically, a two-dimensional axisymmetric mesh is generated based on the initial radius of the pressure bypass device and the length of the measuring cavity. The computational region boundary is defined by extending a preset multiple of the initial radius radially and axially from the midpoint of the pressure bypass device. The specific steps are as follows: Extract the in-situ horizontal earth pressure at the starting point of the straight line segment and the critical plastic pressure at the ending point of the straight line segment from the corrected pressure-volume curve, and calculate the pressure difference between the in-situ horizontal earth pressure and the critical plastic pressure at the ending point of the straight line segment; multiply the pressure difference by the initial radius of the pressure gauge to obtain the estimated thickness of the plastic zone in the radial direction. Add the initial radius of the pressure bypass device to the estimated thickness of the plastic zone in the radial direction to obtain the total radial distance from the midpoint of the pressure bypass device to the outer boundary of the plastic zone; The total radial distance from the midpoint of the pressure bypass to the outer boundary of the plastic zone is compared with the radial distance obtained by multiplying the initial radius of the pressure bypass by a preset multiple, and the larger value is taken as the boundary distance of the radial calculation area. With the midpoint of the pressure bypass as the center, the axial additional extension length is added to the initial radius by a preset multiple to obtain the upper and lower boundary distances of the axial calculation area; The boundary distance of the radial computational region is taken as the radial range from the axis of symmetry to the outer boundary of the axisymmetric finite element model, and the upper and lower boundary distances of the axial computational region are taken as the axial range from the midpoint of the pressure gauge to the top and bottom boundaries of the axisymmetric finite element model, and the geometric computational region of the two-dimensional axisymmetric mesh is determined.

[0042] This step estimates the thickness of the plastic zone based on the characteristic pressure of the pressuremeter test and adaptively determines the radial and axial calculation boundaries of the two-dimensional axisymmetric mesh, which ensures simulation accuracy and avoids computational waste caused by an excessively large mesh range.

[0043] In this embodiment of the invention, a rectangular computational domain is generated by extending radially outward by 50 times the initial radius and axially upward and downward by 50 times the initial radius, with the center of the pressure sag as the origin. Quadrilateral or triangular elements are used for the mesh, which is refined near the borehole wall. The initial radius corresponds to the borehole wall position, and the length of the measuring cavity corresponds to the load range. The corrected pressure sequence is applied to the corresponding nodes on the borehole wall in chronological order. The overburden self-weight pressure is applied to the top boundary, the at-rest earth pressure is applied to the right boundary, the vertical displacement is fixed at the bottom boundary, and the normal displacement at the borehole wall is constrained except for radial compression. The Duncan-Zhang parameters from the laboratory test are used as initial values. For each pressure load level, a finite element solver is called to perform static calculations, extracting the calculated radial displacement values ​​at the borehole wall nodes corresponding to the pressure sag's measuring cavity. Then, an objective function is constructed. The objective function value is the sum of the squares of the differences between the measured and calculated radial displacements for all loading levels, divided by the number of loading levels. An iterative optimization algorithm is used to automatically adjust the tangent elastic modulus base, tangent bulk modulus base, failure ratio, and modulus exponent parameters. After iterative optimization, parameters that truly reflect the characteristics of the in-situ structure are obtained.

[0044] like Figure 5 As shown, in a preferred embodiment of the present invention, the step of calculating the in-situ structure influence factor specifically includes: S501, extract the baseline of tangent elastic modulus and tangent bulk modulus under indoor disturbance sample conditions, and extract the corresponding inverted baseline of tangent elastic modulus and tangent bulk modulus. S502, calculate the ratio of the base value of the inverted tangent elastic modulus to the base value of the indoor tangent elastic modulus, and the ratio of the base value of the inverted tangent bulk modulus to the base value of the indoor tangent bulk modulus, to obtain the elastic modulus influence factor and the bulk modulus influence factor. S503, perform correlation analysis between the two influencing factors and the preset cementation degree classification, moisture content classification, and density classification to generate an influencing factor lookup table; S504: When a new cover layer property index is entered, the computer automatically recommends an influence factor applicable to that layer based on a lookup table, which is used to correct the indoor test parameters.

[0045] Specifically, the two influencing factors are correlated with the preset cementation degree classification, moisture content classification, and density classification to generate an influencing factor lookup table. The specific steps are as follows: Each test point corresponding to the elastic modulus influence factor and the bulk modulus influence factor is used as a record point. The elastic modulus influence factor and the bulk modulus influence factor of the same record point are associated with the preset classification descriptions of cementation degree classification, moisture content classification, and density classification as a record to obtain the factor-classification correspondence. According to the classification of cementation degree, all factor-grading correspondences are categorized, and test point data with the same cementation degree are grouped into the same group to obtain the influencing factor data set. The data in the influencing factor dataset are divided according to different categories of water content status classification. Test point data with the same degree of cementation and the same water content status classification are grouped into the same subset to obtain the data subset. The data subsets are divided according to the density classification. Test point data with the same degree of cementation, the same moisture content and the same density classification are grouped into the same group. The numerical distribution range of all elastic modulus influencing factors and the numerical distribution range of all bulk modulus influencing factors in each group are statistically analyzed to obtain typical value ranges. The preset cementation degree classification, moisture content classification, and density classification are mapped to typical value ranges to obtain the classification-value mapping rule. The grading-value mapping rules are organized into a two-dimensional table to obtain the influence factor lookup table. The row index of the two-dimensional table represents the various categories of cementation degree grading, and the column index represents the combination of water content grading and density grading. Each cell of the table contains the typical value range of the influence factor corresponding to the cementation degree grading of the current row, the water content grading of the current column, and the density grading.

[0046] Furthermore, this step establishes a two-dimensional lookup table for the degree of cementation, moisture content, density, and typical value ranges of influencing factors through multi-level classification statistics, thereby achieving standardization of parameter selection and rapid retrieval, and avoiding human arbitrariness.

[0047] In this embodiment of the invention, the two calculated ratios are the elastic modulus influence factor and the bulk modulus influence factor, respectively. Then, based on the geological descriptions of multiple measuring points (e.g., degree of cementation: weak / medium / strong; water content: dry / wet / saturated; density: loose / medium-dense / dense), a statistical correlation is performed with the calculated influence factors to form a lookup table. For example, the elastic modulus influence factor corresponding to strong cementation + dry + dense is 6-8, and the elastic modulus influence factor corresponding to weak cementation + wet + medium-dense is 2-3. For new areas where no field pressuremeter tests have been conducted, the user only needs to input the geological description of the stratum (degree of cementation, water content, density), and the lookup table will automatically match the influence factors. Multiplying these factors by the indoor test parameters yields the corrected in-situ equivalent parameters.

[0048] like Figure 6 As shown, this embodiment of the invention also provides an in-situ testing system for the engineering characteristics of deep overburden layers, the system comprising: The indoor testing module 100 is used to obtain the gradation curve and relative density of the cover layer sample. Based on the preset gradation envelope and relative density level, it controls the indoor testing equipment to complete the large-scale triaxial compression test, large-scale compression test, permeability coefficient test and permeability deformation test. The empirical relationship module 200 is used to collect stress-strain data, volumetric deformation data, compression curves and seepage data, and to establish an empirical relationship model between the conventional physical properties of the overburden and multiple engineering characteristic parameters, including linear strength indices, nonlinear strength indices, Duncan-Chang constitutive model parameters and permeability coefficients. The curve generation module 300 is used to control the field pressure gauge to apply progressively increasing pressure, collect the volume change of the pressure gauge chamber, and generate a pressure-volume curve; and to control the field load testing equipment to apply progressively increasing load, collect the settlement of the bearing plate, and generate a pressure-settlement curve. The finite element model module 400 is used to import the pressure-volume curve and the pressure-settlement curve into the finite element inversion analysis module. Using the constitutive model parameters output by the empirical relation model as initial values, an axisymmetric finite element model is established. Through iterative optimization, the sum of squared errors between the calculated displacement and the measured displacement is minimized, and the inversion constitutive model parameters that can reflect the in-situ structural characteristics of the overburden are output. The Influence Factor Module 500 is used to compare the parameters of the inverted constitutive model with the engineering characteristic parameters output by the empirical relational model to calculate the in-situ structure influence factor.

[0049] In a preferred embodiment of the present invention, the empirical relationship module 200 includes: The information acquisition unit is used to obtain the mass percentage of particles larger than five millimeters and the mass percentage of particles smaller than five millimeters from particle analysis tests, and to obtain the on-site relative density value from relative density tests. The strength index unit is used to perform linear regression fitting on the cohesion and internal friction angle in the linear strength index and the reference internal friction angle and internal friction angle attenuation in the nonlinear strength index, respectively, with the mass percentage of particles larger than five millimeters in diameter and the on-site relative density as independent variables, to obtain the linear prediction formula for each strength index. The deformation parameter unit is used to perform exponential product-based nonlinear fitting of the tangent elastic modulus base and the tangent bulk modulus base in the Duncan-Chang constitutive model with the mass percentage of particles larger than five millimeters and the on-site relative density as independent variables, so as to obtain the prediction formula of the deformation parameter. The permeability coefficient unit is used to fit the permeability coefficient with the on-site relative density value and the mass percentage of particles smaller than five millimeters as independent variables, and obtain the prediction formula for the permeability coefficient. The formula compilation unit is used to compile all prediction formulas into executable code, and automatically outputs the corresponding engineering characteristic parameters after inputting any set of physical property indicators.

[0050] In a preferred embodiment of the present invention, the curve generation module 300 includes: The pressure boosting control unit is used to send step-by-step pressure boosting commands to the control valve of the pressure bypass device. Each pressure level is maintained for a preset stable duration, and the unit receives the pressure value returned by the pressure sensor and the volume change returned by the volume sensor in real time. The data correction unit is used to correct the elastic membrane constraint force and the instrument's overall deformation coefficient for the collected pressure values ​​and volume changes, and to generate corrected pressure-volume data points. The pressure identification unit is used to identify the in-situ horizontal earth pressure at the start of the straight line segment, the critical plastic pressure at the end of the straight line segment, and the ultimate pressure when the curve tends to be parallel to the vertical axis on the corrected pressure-volume curve. The modulus calculation unit is used to calculate the pressure modulus and pressure shear modulus based on elasticity theory, and to store all pressure characteristic values ​​and moduli into the in-situ test database.

[0051] In a preferred embodiment of the present invention, the finite element model module 400 includes: The region boundary element is used to generate a two-dimensional axisymmetric mesh according to the initial radius of the pressure bypass and the length of the measuring cavity. The initial radius is extended radially and axially by a preset multiple from the center of the pressure bypass as the boundary of the calculation region. Displacement constraint elements are used to apply a pressure sequence measured by pressuremeter tests as a load boundary condition to the inner wall of the grid, apply a pressure boundary condition calculated from the soil self-weight to the top and right boundaries, and apply displacement constraints to the bottom and borehole wall boundaries. Radial displacement element, used to use Duncan-Chang constitutive model parameters as initial inversion values, and call the finite element solver to calculate the radial displacement at each pressure level step by step; The model parameter unit is used to construct the objective function, perform iterative optimization until the objective function value is less than a preset threshold, and output the converged parameters as the parameters of the inverted constitutive model.

[0052] The above description only details the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0053] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. An in-situ testing method for the engineering properties of deep overburden layers, characterized in that, The method includes the following steps: Obtain the gradation curve and relative density of the capping layer sample, and control the indoor testing equipment to complete the large-scale triaxial compression test, large-scale compression test, permeability coefficient test and permeability deformation test according to the preset gradation envelope and relative density level; Stress-strain data, volumetric deformation data, compression curves, and seepage data were collected to establish an empirical relationship model between the conventional physical properties of the overburden and multiple engineering characteristic parameters, including linear strength indices, nonlinear strength indices, Duncan-Chang constitutive model parameters, and permeability coefficient. The pressure gauge is controlled to apply pressure at different levels, and the volume change of the pressure chamber is collected to generate a pressure-volume curve; the load test equipment is controlled to apply load at different levels, and the settlement of the bearing plate is collected to generate a pressure-settlement curve. The pressure-volume curve and pressure-settlement curve are imported into the finite element inversion analysis module. The constitutive model parameters output by the empirical relation model are used as initial values ​​to establish an axisymmetric finite element model. The sum of squared errors between the calculated displacement and the measured displacement is minimized through iterative optimization. The inversion constitutive model parameters that can reflect the in-situ structural characteristics of the overburden are output. The parameters of the inverted constitutive model are compared with the engineering characteristic parameters output by the empirical relational model to calculate the in-situ structure influence factor.

2. The in-situ testing method for the engineering characteristics of deep overburden layers according to claim 1, characterized in that, The steps to establish an empirical relationship model include: The mass percentage of particles larger than five millimeters and the mass percentage of particles smaller than five millimeters were obtained by particle analysis test, and the on-site relative density value was obtained by relative density test. Using the percentage of particles larger than five millimeters in diameter and the relative density at the site as independent variables, linear regression fitting was performed on the cohesion and internal friction angle in the linear strength index, and the reference internal friction angle and internal friction angle attenuation in the nonlinear strength index, to obtain the linear prediction formula for each strength index. Using the mass percentage of particles larger than five millimeters and the relative density at the site as independent variables, nonlinear fitting in the form of exponential product is performed on the baseline of tangent elastic modulus and the baseline of tangent bulk modulus in the Duncan-Chang constitutive model to obtain the prediction formula for deformation parameters. Using the on-site relative density value and the mass percentage of particles smaller than five millimeters as independent variables, the permeability coefficient is fitted with a semi-logarithmic or power function to obtain the prediction formula for the permeability coefficient. All prediction formulas are compiled into executable code, and the corresponding engineering characteristic parameters are automatically output after any set of physical property indicators are input.

3. The in-situ testing method for the engineering characteristics of deep overburden layers according to claim 2, characterized in that, Using the mass percentage of particles larger than five millimeters and the on-site relative density as independent variables, nonlinear fitting in the form of exponential product is performed on the baseline of tangent elastic modulus and baseline of tangent bulk modulus in the Duncan-Chang constitutive model to obtain the prediction formula for deformation parameters. The specific steps are as follows: Step a: First, sort the tangential elastic modulus base and the tangential bulk modulus base in ascending order of the mass percentage of particles larger than five millimeters. Then, sort the tangential elastic modulus base and the tangential bulk modulus base in ascending order of the on-site relative density values ​​to obtain the ordered data sequence of the tangential elastic modulus base and the data sequence of the tangential bulk modulus base. Step b: For each data point in the ordered data sequence of tangential modulus of elasticity, calculate the logarithm of the percentage of particle mass with a particle size greater than five millimeters, the logarithm of the relative density, and the logarithm of the tangential modulus of elasticity, and use the least squares regression algorithm to obtain the first set of fitting coefficients for the tangential modulus of elasticity. Step c: Perform an exponential operation on the first set of fitting coefficients of the tangent modulus base to obtain the proportional coefficient; Based on the proportionality coefficient, an exponential product prediction function is constructed with the mass percentage of particles larger than five millimeters in diameter and the on-site relative density as the exponents, so as to obtain the first exponential product prediction function. Step d: For each data point in the tangent bulk modulus baseline data sequence, repeat the calculation process of step b to obtain the second set of fitting coefficients for the tangent bulk modulus baseline. Step e: Repeat the construction process of step c for the second set of fitting coefficients of the tangent bulk modulus base to obtain the second exponential product prediction function; Step f: Using the first exponential product prediction function and the second exponential product prediction function, calculate the baseline of the tangential elastic modulus and the baseline of the tangential bulk modulus respectively to obtain the predicted values ​​of the baseline of the tangential elastic modulus and the predicted values ​​of the baseline of the tangential bulk modulus. Calculate the relative errors between the predicted base values ​​of tangential elastic modulus and tangential bulk modulus and the corresponding measured values ​​to obtain the first relative error and the second relative error; Step g: The product of the mass percentage of particles larger than five millimeters and the on-site relative density value is used as the cross term. When the first relative error or the second relative error exceeds the preset allowable threshold, the cross term is used to perform nonlinear fitting on the first exponential product prediction function or the second exponential product prediction function. The nonlinear fitting is repeated until the first relative error and the second relative error do not exceed the preset allowable threshold, and the corrected first exponential product prediction function and the corrected second exponential product prediction function are obtained. The corrected first exponential product prediction function and the corrected second exponential product prediction function are used as the prediction formula for the deformation parameters.

4. The in-situ testing method for the engineering characteristics of deep overburden layers according to claim 1, characterized in that, The steps for controlling the application of progressively increasing pressure by the field pressure gauge specifically include: Send step-by-step pressurization commands to the control valve of the pressure gauge, maintain the pressure at each level for a preset stable duration, and receive the pressure value returned by the pressure sensor and the volume change returned by the volume sensor in real time. The collected pressure values ​​and volume changes are corrected by elastic membrane constraint force correction and instrument comprehensive deformation coefficient correction to generate corrected pressure-volume data points. Identify the in-situ horizontal earth pressure at the start of the straight line segment, the critical plastic pressure at the end of the straight line segment, and the ultimate pressure when the curve tends to be parallel to the vertical axis on the corrected pressure-volume curve. The pressure modulus and pressure shear modulus were calculated based on elasticity theory, and all pressure characteristic values ​​and moduli were stored in the in-situ test database. The specific steps for identifying the in-situ horizontal earth pressure at the start of the straight line segment, the critical plastic pressure at the end of the straight line segment, and the ultimate pressure when the curve tends to be parallel to the vertical axis on the corrected pressure-volume curve are as follows: The corrected pressure-volume data point sequence is obtained based on the corrected pressure-volume curve; the corrected pressure-volume data point sequence is arranged in ascending order of pressure value, and the ratio of volume change to pressure change between each two adjacent data points is calculated to obtain the ratio sequence. Starting from the first ratio in the ratio sequence, adjacent ratios are compared sequentially to obtain the relative fluctuation result; when the continuous relative fluctuation result is within the preset stable range, the pressure value corresponding to the starting point of the corresponding relative fluctuation interval is determined as the starting point of the straight line segment to obtain the in-situ horizontal earth pressure. Based on the ratio sequence, the average ratio is calculated. Starting from the in-situ horizontal earth pressure, the ratio sequence is traversed. When the decrease of the ratio relative to the average ratio exceeds the preset deviation threshold, the corresponding pressure value is determined as the end point of the straight line segment, and the critical plastic pressure corresponding to the end point of the straight line segment is obtained. Starting from the critical pressure corresponding to the end of the straight line segment, the corrected pressure-volume data point sequence is traversed, and the ratio of the volume change at each data point to the volume change at the previous point is calculated to obtain the increment ratio. When the increment ratio continues to increase and exceeds the preset limit threshold, the corresponding pressure value is determined as the starting point when the curve tends to be parallel to the vertical axis, so as to obtain the limit pressure when the curve tends to be parallel to the vertical axis.

5. The in-situ testing method for the engineering characteristics of deep overburden layers according to claim 4, characterized in that, The steps for establishing an axisymmetric finite element model include: A two-dimensional axisymmetric grid is generated based on the initial radius of the pressure bypass and the length of the measuring chamber. The calculation region boundary is defined by extending the initial radius radially and axially by a preset multiple from the midpoint of the pressure bypass. A pressure sequence measured by pressuremeter tests is applied as a load boundary condition to the inner wall of the grid. Pressure boundary conditions calculated from the soil self-weight are applied to the top and right boundaries. Displacement constraints are applied to the bottom and borehole wall boundaries. Using the parameters of the Duncan-Zhang constitutive model as the initial values ​​for inversion, the radial displacement under each pressure level was calculated step by step using the finite element solver. Construct an objective function, perform iterative optimization until the objective function value is less than a preset threshold, and output the converged parameters as the parameters of the inverted constitutive model.

6. The in-situ testing method for the engineering characteristics of deep overburden layers according to claim 5, characterized in that, A two-dimensional axisymmetric mesh is generated based on the initial radius of the pressure bypass and the length of the measuring cavity. The computational region is defined by extending a preset multiple of the initial radius radially and axially from the midpoint of the pressure bypass as the center. The specific steps are as follows: Extract the in-situ horizontal earth pressure at the starting point of the straight line segment and the critical plastic pressure at the ending point of the straight line segment from the corrected pressure-volume curve, and calculate the pressure difference between the in-situ horizontal earth pressure and the critical plastic pressure at the ending point of the straight line segment; multiply the pressure difference by the initial radius of the pressure gauge to obtain the estimated thickness of the plastic zone in the radial direction. Add the initial radius of the pressure bypass device to the estimated thickness of the plastic zone in the radial direction to obtain the total radial distance from the midpoint of the pressure bypass device to the outer boundary of the plastic zone; The total radial distance from the midpoint of the pressure bypass to the outer boundary of the plastic zone is compared with the radial distance obtained by multiplying the initial radius of the pressure bypass by a preset multiple, and the larger value is taken as the boundary distance of the radial calculation area. With the midpoint of the pressure bypass as the center, the axial additional extension length is added to the initial radius by a preset multiple to obtain the upper and lower boundary distances of the axial calculation area; The boundary distance of the radial computational region is taken as the radial range from the axis of symmetry to the outer boundary of the axisymmetric finite element model, and the upper and lower boundary distances of the axial computational region are taken as the axial range from the midpoint of the pressure gauge to the top and bottom boundaries of the axisymmetric finite element model, and the geometric computational region of the two-dimensional axisymmetric mesh is determined.

7. The in-situ testing method for the engineering characteristics of deep overburden layers according to claim 6, characterized in that, The objective function value is the sum of the squares of the differences between the measured radial displacement and the calculated radial displacement for all loading stages, divided by the number of loading stages. An iterative optimization algorithm is used to automatically adjust the base parameters of tangential elastic modulus, tangential bulk modulus, failure ratio, and modulus exponent.

8. The in-situ testing method for the engineering characteristics of deep overburden layers according to claim 7, characterized in that, The steps for calculating the in-situ structure influence factor specifically include: Extract the baseline values ​​of tangent elastic modulus and tangent bulk modulus under indoor disturbance conditions, and extract the corresponding inverted baseline values ​​of tangent elastic modulus and tangent bulk modulus. The ratios of the inverted tangent elastic modulus baseline to the indoor tangent elastic modulus baseline, and the ratios of the inverted tangent bulk modulus baseline to the indoor tangent bulk modulus baseline are calculated respectively to obtain the elastic modulus influence factor and the bulk modulus influence factor. The two influencing factors are correlated with the preset cementation degree classification, moisture content classification, and density classification to generate an influencing factor lookup table. When new overburden physical properties are entered, the computer automatically recommends influence factors applicable to that layer based on a lookup table, which are then used to correct the indoor test parameters.

9. The in-situ testing method for the engineering characteristics of deep overburden layers according to claim 8, characterized in that, The two influencing factors are correlated with the preset cementation degree classification, moisture content classification, and density classification to generate an influencing factor lookup table. The specific steps are as follows: Each test point corresponding to the elastic modulus influence factor and the bulk modulus influence factor is used as a record point. The elastic modulus influence factor and the bulk modulus influence factor of the same record point are associated with the preset classification descriptions of cementation degree classification, moisture content classification, and density classification as a record to obtain the factor-classification correspondence. According to the classification of cementation degree, all factor-level correspondences are categorized, and test point data with the same cementation degree classification are grouped into the same group to obtain the impact factor data set. The data in the influencing factor dataset are divided according to different categories of water content status classification. Test point data with the same degree of cementation and the same water content status classification are grouped into the same subset to obtain the data subset. The data subsets are divided according to the density classification. Test point data with the same degree of cementation, the same moisture content and the same density classification are grouped into the same group. The numerical distribution range of all elastic modulus influencing factors and the numerical distribution range of all bulk modulus influencing factors in each group are statistically analyzed to obtain typical value ranges. The preset cementation degree classification, moisture content classification, and density classification are mapped to typical value ranges to obtain the classification-value mapping rule. The grading-value mapping rules are organized into a two-dimensional table to obtain the influence factor lookup table. The row index of the two-dimensional table represents the various categories of cementation degree grading, and the column index represents the combination of water content grading and density grading. Each cell of the table contains the typical value range of the influence factor corresponding to the cementation degree grading of the current row, the water content grading of the current column, and the density grading.

10. An in-situ testing system for the engineering characteristics of deep overburden layers, characterized in that, The system employs the in-situ testing method for the engineering characteristics of deep overburden layers as described in any one of claims 1 to 9, and the system comprises: The indoor testing module is used to obtain the gradation curve and relative density of the cover layer sample. Based on the preset gradation envelope and relative density level, it controls the indoor testing equipment to complete large triaxial compression test, large compression test, permeability coefficient test and permeability deformation test. The empirical relationship module is used to collect stress-strain data, volumetric deformation data, compression curves and seepage data, and to establish an empirical relationship model between the conventional physical properties of the overburden and multiple engineering characteristic parameters, including linear strength indices, nonlinear strength indices, Duncan-Chang constitutive model parameters and permeability coefficients. The curve generation module is used to control the field pressure gauge to apply progressively increasing pressure, collect the volume change of the pressure chamber, and generate a pressure-volume curve; and to control the field load testing equipment to apply progressively increasing load, collect the settlement of the bearing plate, and generate a pressure-settlement curve. The finite element model module is used to import the pressure-volume curve and pressure-settlement curve into the finite element inversion analysis module. Using the constitutive model parameters output by the empirical relation model as initial values, an axisymmetric finite element model is established. Through iterative optimization, the sum of squared errors between the calculated displacement and the measured displacement is minimized, and the inversion constitutive model parameters that can reflect the in-situ structural characteristics of the overburden are output. The Influence Factor module is used to compare the parameters of the inverted constitutive model with the engineering characteristic parameters output by the empirical relational model to calculate the in-situ structure influence factor.

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