A segmented fitting dynamic triaxial test data processing method, system and device

Abstract

The application discloses a segmented dynamic triaxial test data processing method, system and equipment, and relates to the technical field of data processing; original dynamic triaxial test data is acquired, and dynamic shear strain is converted into logarithmic domain independent variables; based on the distribution characteristics of the logarithmic domain independent variables and normalized shear modulus, a segmentation point is determined and data segments are divided; a modulus fitting function is constructed by performing polynomial fitting on each data segment, and a damping ratio fitting function is constructed by performing single polynomial fitting on the damping ratio data; continuity constraint conditions are applied at the segmentation points, polynomial coefficients are solved based on the least square method; and the logarithmic domain independent variables are restored to original dynamic shear strain, thereby obtaining the fitting relationship between the normalized shear modulus, the damping ratio and the dynamic shear strain. The application eliminates the order of magnitude difference of the dynamic shear strain through logarithmic conversion, avoids the high-order fitting ill-conditioned problem by adopting segmented polynomial fitting, and guarantees the smooth continuity of the curve by using continuity constraints, thereby effectively improving the fitting accuracy and reliability of the wide strain domain dynamic triaxial data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically discloses a segmented fitting dynamic triaxial test data processing method, system and equipment. Background Technology

[0002] In seismic design and dynamic analysis of geotechnical engineering, the dynamic shear modulus and damping ratio of soil are core parameters reflecting the dynamic characteristics of soil, directly affecting the accuracy of the dynamic response calculation results of engineering structures. Dynamic triaxial testing is the standard test method for determining the dynamic characteristic parameters of soil in the laboratory. By simulating seismic loads, normalized shear modulus (G / Gmax) and damping ratio (λ) data under different dynamic shear strains can be measured. However, due to limitations such as test loading conditions, time costs, and sample preparation processes, dynamic triaxial testing can only obtain a limited number of discrete data points. In contrast, actual engineering calculations often require continuous dynamic parameters corresponding to arbitrary dynamic shear strains. Therefore, it is necessary to mathematically fit the discrete test data to obtain continuous and smooth G / Gmax-γ and λ-γ relationship curves.

[0003] Existing dynamic triaxial test data fitting techniques mainly employ two types of methods: empirical models and single polynomial fitting. When the dynamic shear strain γ is at 10... -4 ~10 -2 Within a narrow range, the industry often uses hyperbolic models to fit the G / Gmax-γ and λ-γ relationships. For different soil types in different regions, some have proposed empirical formulas for the dynamic modulus and damping ratio of soft soil in Shanghai, while others have used the Davidenkov model to fit the test curves of various soils in Nanjing and neighboring areas. Some experts have also used logarithmic models to fit the relationship between the damping ratio and shear strain of silty soil in the Yellow River Delta, and have given suggested ranges for the normalized shear modulus and damping ratio of sand as a function of shear strain.

[0004] However, existing technologies have significant drawbacks: firstly, when the dynamic shear strain extends to 10... -6 ~10 -2First, when the data spans a wide range, the hyperbolic model, Davidenkov model, and HD model all fail to match the curve shape, resulting in significant fitting deviations. Second, the empirical formulas proposed by different scholars are greatly affected by regional soil types and test conditions, leading to significant differences in curve shape and numerical values. Direct use of these formulas results in unacceptable dispersion in engineering calculations. Third, the dynamic properties of soil are influenced by multiple factors, including dynamic strain level, consolidation stress, consolidation stress ratio, void ratio, initial shear stress, particle size, gradation characteristics, and test equipment. General empirical formulas are difficult to adapt to specific site soil samples. Fourth, dynamic triaxial test data are discrete and finite, and a single model cannot accurately match the measured data. Fifth, when directly using high-order polynomials to fit the wide-range data, the normal equations exhibit severe ill-conditioning due to the large differences in the order of dynamic shear strain. The higher the order, the more severe the ill-conditioning, resulting in unstable solution results and oscillations, distortions, and abnormal inflection points in the fitted curves, failing to reflect the true dynamic properties of the soil.

[0005] Currently, research on dynamic triaxial test data processing based on the least squares method combined with logarithmic transformation and piecewise polynomial fitting is limited. A standardized data processing method that adapts to a wide strain range, achieves high fitting accuracy, produces smooth and continuous curves, and provides stable and reliable solutions has yet to be developed, making it difficult to meet the stringent requirements for the accuracy and reliability of soil dynamic characteristic parameters in major geotechnical engineering projects. Therefore, this paper proposes a piecewise fitting method and system for processing dynamic triaxial test data to address the problems of low fitting accuracy, non-smooth curves, ill-conditioned equation systems, and poor versatility in existing technologies. This method has significant engineering application value and academic research significance. Summary of the Invention

[0006] To overcome the above-mentioned defects, this invention provides a segmented fitting method, system and equipment for processing dynamic triaxial test data, which achieves stable, high-precision and smooth fitting of dynamic triaxial test data, and provides reliable soil dynamic characteristic parameters for geotechnical engineering dynamic calculations.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a segmented fitting method for processing dynamic triaxial test data, the method comprising: Multiple sets of raw data from dynamic triaxial tests were obtained, including dynamic shear strain, normalized shear modulus, and damping ratio. The dynamic shear strain is converted into a logarithmic domain independent variable; based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus, the segmentation point is determined, and the data of the logarithmic domain independent variable and the normalized shear modulus are divided into two data segments according to the segmentation point; For each data segment, a polynomial fit is performed on the normalized dynamic shear modulus to construct the corresponding modulus fitting function; a single polynomial fit is performed on the logarithmic domain independent variable and damping ratio data for all data segments to construct the damping ratio fitting function. Establish continuity constraints at the segmentation points, and solve for the polynomial coefficients of the modulus fitting function and the damping ratio fitting function for each data segment under the continuity constraints. The logarithmic domain independent variable is reduced to the original dynamic shear strain, and the fitting relationship with the normalized shear modulus and damping ratio is obtained.

[0009] Optionally, converting the dynamic shear strain into a logarithmic domain independent variable includes: The logarithmic domain independent variable is obtained by taking the logarithm of the dynamic shear strain with a preset base; wherein the preset base is determined according to the range of differences in the order of magnitude of the dynamic shear strain.

[0010] Optionally, determining the segmentation points based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus includes: Analyze the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus, identify the curvature change positions of the data, and determine the curvature change positions as segmentation points; The identification of the curvature change locations in the data includes: Calculate the rate of change of slope between adjacent data points; The location where the rate of change of slope exceeds a preset threshold is determined as the curvature change location.

[0011] Optionally, dividing the data of the logarithmic domain independent variable and the normalized shear modulus into two data segments according to the segmentation point includes: Using the segmentation point as the boundary, the logarithmic domain independent variable and normalized shear modulus data are divided into a first data segment and a second data segment.

[0012] Optionally, the step of performing polynomial fitting on the normalized dynamic shear modulus for each data segment to construct a corresponding modulus fitting function includes: performing polynomial fitting on the normalized dynamic shear modulus of the first data segment to establish a first modulus fitting function; the degree of polynomial fitting on the normalized dynamic shear modulus of the first data segment is determined based on the number of data points in the first data segment; performing polynomial fitting on the normalized dynamic shear modulus of the second data segment to establish a second modulus fitting function; the degree of polynomial fitting on the normalized dynamic shear modulus of the second data segment is determined based on the number of data points in the second data segment. The number of times polynomial fitting is performed on the first and second data segments is different.

[0013] Optionally, establishing continuity constraints at the segmentation points includes: Establish a function value continuity constraint to ensure that the function values ​​of the modulus fitting function of adjacent data segments are equal at the segmentation points; Establish a first-order derivative continuity constraint so that the first-order derivatives of the adjacent modulus fitting functions are equal at the segmentation points.

[0014] Optionally, the step of solving for the polynomial coefficients of the modulus fitting function and the damping ratio fitting function for each data segment under continuity constraints includes: Based on the least squares method, and according to the constraints of continuous function values ​​and continuous first derivatives, the correlation between the polynomial coefficients of the first data segment and the polynomial coefficients of the second data segment is established. Substituting the aforementioned correlation into the least squares estimator expression, a normal system of equations is constructed regarding the polynomial coefficients of the second data segment; Solve the normal system of equations to obtain the polynomial coefficients of the second data segment; Based on the aforementioned correlation, the polynomial coefficients of the first data segment are calculated from the polynomial coefficients of the second data segment; Furthermore, using the logarithmic domain independent variable of all data segments as the independent variable and the damping ratio as the dependent variable, a system of equations is constructed based on the least squares method, and the polynomial coefficients of the damping ratio fitting function are obtained by solving the equations.

[0015] Optionally, the step of restoring the logarithmic domain independent variable to the original dynamic shear strain and obtaining the fitting relationship with the normalized shear modulus and damping ratio includes: performing an inverse exponential transformation on the logarithmic domain independent variable to restore the independent variables of the modulus fitting function and the damping ratio fitting function to the original dynamic shear strain. Using the restored dynamic shear strain as the abscissa and the normalized shear modulus output by the modulus fitting function as the ordinate, a relationship curve between the normalized shear modulus and the dynamic shear strain is generated. Using the restored dynamic shear strain as the abscissa and the damping ratio output by the damping ratio fitting function as the ordinate, a curve relating the damping ratio and the dynamic shear strain is generated.

[0016] Secondly, the present invention provides a segmented fitting dynamic triaxial test data processing system, comprising: The acquisition module is used to acquire multiple sets of raw data from the dynamic triaxial test, including dynamic shear strain, normalized shear modulus, and damping ratio. The processing module is used to convert the dynamic shear strain into a logarithmic domain independent variable; determine the segmentation point based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus; and divide the data of the logarithmic domain independent variable and the normalized shear modulus into two data segments according to the segmentation point. The data fitting module is used to perform polynomial fitting on the normalized dynamic shear modulus for each data segment to construct the corresponding modulus fitting function; and to perform single polynomial fitting on the logarithmic domain independent variables and damping ratio data for all data segments to construct the damping ratio fitting function. The constraint calculation module is used to establish continuity constraints at the segmentation points and solve for the polynomial coefficients of the modulus fitting function and the damping ratio fitting function of each data segment under the continuity constraints. The fitting relationship determination module is used to restore the logarithmic domain independent variable to the original dynamic shear strain and obtain the fitting relationship with the normalized shear modulus and damping ratio.

[0017] Thirdly, the present invention provides an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method described in any one of the first aspects.

[0018] Compared with the closest prior art, the beneficial effects of the present invention are as follows: This invention proposes a segmented fitting method, system, and equipment for processing dynamic triaxial test data. By performing logarithmic transformation on the dynamic shear strain, the difference in the order of magnitude of variables is reduced, fundamentally solving the ill-conditioned problem of the normal equation system when fitting strain over a wide range, avoiding oscillations and distortions in the solution results, and ensuring the stability and reliability of the polynomial coefficient solution.

[0019] This invention segments data based on its distribution characteristics and uses low-order polynomials of different degrees to fit different data segments. This avoids the oscillation problem of high-order polynomial fitting and overcomes the insufficient accuracy of low-order polynomial fitting, effectively improving the fitting accuracy. Furthermore, by applying continuous constraints on function values ​​and first derivatives at the segmentation points, the piecewise fitting curves are ensured to be smooth and continuous overall, without discontinuities or sharp angles, conforming to the gradual change law of soil dynamic characteristics, and the fitted curves more closely match the actual soil dynamic characteristics.

[0020] This invention can directly fit measured data without relying on empirical formulas, and is not limited by soil type, test conditions, or site characteristics. It is adaptable to dynamic triaxial data processing of various soil types, such as sand, silt, and cohesive soil, and has a wide range of applications. By combining piecewise fitting with continuity constraints, the fitted curve closely matches the experimental data, with small mean square error and significantly improved fitting accuracy. The entire process can be automated through a numerical calculation program, which is simple to operate and highly efficient. It can directly output the dynamic parameters corresponding to any dynamic shear strain, providing reliable parameter support for seismic design and foundation dynamic calculation in geotechnical engineering. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0022] Figure 1 This is a flowchart of a segmented fitting dynamic triaxial test data processing method provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of a segmented fitting dynamic triaxial test data processing system provided in an embodiment of the present invention; Figure 3 This is an internal structure diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore merely examples, and should not be construed as limiting the scope of protection of the present invention.

[0024] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0025] This invention provides a segmented fitting method, system, and equipment for processing dynamic triaxial test data. It is applicable to fitting curves relating dynamic shear modulus, damping ratio, and dynamic shear strain to various soil types, including sand, silt, and cohesive soil. It can be applied to engineering fields such as seismic design in geotechnical engineering, stability evaluation of offshore oil platform foundations, dynamic response analysis of underground structures, and dynamic settlement calculation for rail transit. The embodiments of this invention are described below with reference to the accompanying drawings.

[0026] Example 1: As Figure 1 As shown, Embodiment 1 of the present invention provides a segmented fitting method for processing dynamic triaxial test data. This method specifically includes the following steps: S101 acquires multiple sets of raw data from the dynamic triaxial test, including dynamic shear strain, normalized shear modulus, and damping ratio; S102 converts the dynamic shear strain into a logarithmic domain independent variable; determines the segmentation point based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus, and divides the data of the logarithmic domain independent variable and the normalized shear modulus into two data segments according to the segmentation point; S103 performs polynomial fitting on the normalized dynamic shear modulus for each data segment to construct the corresponding modulus fitting function; and performs single polynomial fitting on the logarithmic domain independent variable and damping ratio data for all data segments to construct the damping ratio fitting function. S104 establishes continuity constraints at the segmentation points, and solves for the polynomial coefficients of the modulus fitting function and the damping ratio fitting function of each data segment under the continuity constraints. S105 restores the logarithmic domain independent variable to the original dynamic shear strain, and obtains the fitting relationship with the normalized shear modulus and damping ratio.

[0027] In step S102 above, converting the dynamic shear strain into a logarithmic domain independent variable includes: taking the logarithm of the dynamic shear strain with a preset base to obtain a logarithmic domain independent variable; wherein, the preset base is determined according to the range of differences in the order of magnitude of the dynamic shear strain.

[0028] Further, in step S102, determining the segmentation point based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus includes: analyzing the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus, identifying the curvature change position of the data, and determining the curvature change position as the segmentation point; wherein, identifying the curvature change position in the data includes: calculating the slope change rate between adjacent data points; and determining the position where the slope change rate exceeds a preset threshold as the curvature change position.

[0029] Further, in step S102, dividing the data of logarithmic domain independent variable and normalized shear modulus into two data segments according to the segmentation point includes: dividing the data of logarithmic domain independent variable and normalized shear modulus into a first data segment and a second data segment with the segmentation point as the boundary.

[0030] In one embodiment, a sandy silt soil sample with a burial depth of 3.2m was selected from an engineering site as the test soil sample. Eleven sets of raw data were obtained through dynamic triaxial tests, including dynamic shear strain γ, normalized shear modulus G / G max The damping ratio λ and the data are shown in Table 1: Table 1. Relationship between dynamic shear strain, normalized shear modulus, and damping ratio.

[0031] in, , For the density of soil, The shear wave velocity of the soil.

[0032] Taking the logarithm to base 10 of the dynamic shear strain value yields the logarithmic domain independent variable. The transformation results are shown in the table above. Analysis of the independent variable in the number field. With G / G max Based on the data distribution characteristics, calculate the rate of change of slope between adjacent data points, and determine t. m =-4.349 (corresponding to the 3rd group of data) is the segmentation point, dividing the data into the first data segment (groups 1-3) and the second data segment (groups 3-11). In dynamic triaxial tests, the dynamic shear strain γ typically ranges from 10. 6 Up to 10 2 The difference in magnitude is as large as four orders of magnitude, and directly performing polynomial fitting would lead to severe ill-conditioning of the normal equation system. In this embodiment, by taking a logarithmic transformation of the dynamic shear strain t=lgγ, the wide-domain strain value is converted into a uniformly distributed logarithmic domain independent variable t, which effectively reduces the difference in magnitude between variables, fundamentally eliminates the ill-conditioning problem of the equation system, and ensures the stability of the solution.

[0033] Further, in step S103, the step of performing polynomial fitting on the normalized dynamic shear modulus for each data segment to construct the corresponding modulus fitting function includes: performing polynomial fitting on the normalized dynamic shear modulus of the first data segment to establish a first modulus fitting function; the number of times the polynomial fitting of the normalized dynamic shear modulus on the first data segment is determined based on the number of data points in the first data segment; performing polynomial fitting on the normalized dynamic shear modulus of the second data segment to establish a second modulus fitting function; the number of times the polynomial fitting of the normalized dynamic shear modulus on the second data segment is determined based on the number of data points in the second data segment. The number of times polynomial fitting is performed on the first and second data segments is different.

[0034] In one embodiment, a quadratic polynomial fitting is used to fit the first data segment, and the first modulus fitting function is constructed using the following formula: 2 , , All of these are the coefficients of the quadratic polynomial in the first data segment, obtained by solving using the least squares method and the continuation constraint conditions; A cubic polynomial is used to fit the second data segment, and the second modulus fitting function is constructed using the following formula: Right now ; In the formula, , , , All are the coefficients of the cubic polynomial in the second data segment, obtained by solving the least squares method and the continuity constraint; y represents the normalized shear modulus.

[0035] Furthermore, a fourth-order polynomial is used to fit all t and λ data to construct a damping ratio fitting function: ; In the formula, , , , , All are fourth-order polynomial damping ratio fitting functions.

[0036] In one embodiment, the above method further includes: Calculate the mean square error between the piecewise fitting function and the experimental data; The fitting accuracy of the piecewise fitting function is evaluated based on the mean square error. When the fitting accuracy does not meet the preset requirements, the position of the segmentation point is adjusted or the number of times the polynomial fitting function is adjusted, and the steps of establishing the polynomial fitting function and solving the polynomial coefficients are repeated until the fitting accuracy meets the preset requirements.

[0037] Further, in step S104, establishing continuity constraints at the segmentation point includes: establishing function value continuity constraints so that the function values ​​of the modulus fitting functions of adjacent data segments are equal at the segmentation point; and establishing first derivative continuity constraints so that the first derivatives of the adjacent modulus fitting functions are equal at the segmentation point.

[0038] Further, in step S104, the step of solving for the polynomial coefficients of the modulus fitting function and the damping ratio fitting function of each data segment under the continuity constraint includes: establishing a correlation between the polynomial coefficients of the first data segment and the polynomial coefficients of the second data segment based on the least squares method and the constraints of continuous function values ​​and continuous first derivatives; substituting the correlation into the least squares estimator expression to construct a normal equation system about the polynomial coefficients of the second data segment; solving the normal equation system to obtain the polynomial coefficients of the second data segment; calculating the polynomial coefficients of the first data segment from the polynomial coefficients of the second data segment based on the correlation; and constructing an equation system based on the least squares method with the logarithmic domain independent variable of all data segments as the independent variable and the damping ratio as the dependent variable to obtain the polynomial coefficients of the damping ratio fitting function.

[0039] In one embodiment, at the segmentation point t m Establish a function value continuity constraint at -4.349. ; and first derivative continuity constraint Derivation of the correlation relationships of the coefficients: , , ; Among them, a0, a1, a2 are related to b0, b1, b2, b3.

[0040] Substituting the correlation into the least squares estimator expression, a system of normal equations for b0, b1, b2, and b3 is constructed. The equations are then solved using Matlab programming to obtain the coefficients of the damping ratio fitting function.

[0041] In step S105 above, the step of restoring the logarithmic domain independent variable to the original dynamic shear strain and obtaining the fitting relationship with the normalized shear modulus and damping ratio includes: performing an inverse exponential transformation on the logarithmic domain independent variable to restore the independent variables of the modulus fitting function and the damping ratio fitting function to the original dynamic shear strain. Using the restored dynamic shear strain as the abscissa and the normalized shear modulus output by the modulus fitting function as the ordinate, a relationship curve between the normalized shear modulus and the dynamic shear strain is generated. Using the restored dynamic shear strain as the abscissa and the damping ratio output by the damping ratio fitting function as the ordinate, a curve relating the damping ratio and the dynamic shear strain is generated.

[0042] In one embodiment, an inverse exponential transformation is performed on the logarithmic domain independent variable t: 10 t ; Restore to the original dynamic shear strain. Plot γ as the abscissa, G / G max Using γ as the ordinate, a normalized shear modulus-dynamic shear strain relationship curve is generated; using γ as the abscissa and λ as the ordinate, a damping ratio-dynamic shear strain relationship curve is generated.

[0043] Example 2: Based on the same technical concept, Example 2 of this invention also provides an aero-engine surge diagnostic system based on spatiotemporal feature fusion, such as... Figure 2 As shown, it includes: an acquisition module 210, a processing module 220, a data fitting module 230, a constraint calculation module 240, and a fitting relationship determination module 250, wherein: The acquisition module 210 is used to acquire multiple sets of raw data from the dynamic triaxial test, including dynamic shear strain, normalized shear modulus and damping ratio. Processing module 220 is used to convert the dynamic shear strain into a logarithmic domain independent variable; determine the segmentation point based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus; and divide the data of the logarithmic domain independent variable and the normalized shear modulus into two data segments according to the segmentation point. The data fitting module 230 is used to perform polynomial fitting on the normalized dynamic shear modulus for each data segment to construct the corresponding modulus fitting function; and to perform single polynomial fitting on the logarithmic domain independent variable and damping ratio data of all data segments to construct the damping ratio fitting function. The constraint calculation module 240 is used to establish continuity constraints at the segmentation points and solve the polynomial coefficients of the modulus fitting function and the damping ratio fitting function of each data segment under the continuity constraints. The fitting relationship determination module 250 is used to restore the logarithmic domain independent variable to the original dynamic shear strain and obtain the fitting relationship with the normalized shear modulus and damping ratio.

[0044] Example 3: In one embodiment, Example 3 of the present invention also provides an electronic device; the electronic device may be a terminal, and its internal structure diagram may be as follows. Figure 3 As shown. The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements the segmented fitting dynamic triaxial test data processing method described in any one of steps S101 to S105. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0045] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0046] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A segmented fitting method for processing dynamic triaxial test data, characterized in that, The method includes: Multiple sets of raw data from dynamic triaxial tests were obtained, including dynamic shear strain, normalized shear modulus, and damping ratio. The dynamic shear strain is converted into a logarithmic domain independent variable; based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus, the segmentation point is determined, and the data of the logarithmic domain independent variable and the normalized shear modulus are divided into two data segments according to the segmentation point; For each data segment, a polynomial fit is performed on the normalized dynamic shear modulus to construct the corresponding modulus fitting function; a single polynomial fit is performed on the logarithmic domain independent variable and damping ratio data for all data segments to construct the damping ratio fitting function. Establish continuity constraints at the segmentation points, and solve for the polynomial coefficients of the modulus fitting function and the damping ratio fitting function for each data segment under the continuity constraints. The logarithmic domain independent variable is reduced to the original dynamic shear strain, and the fitting relationship with the normalized shear modulus and damping ratio is obtained.

2. The method according to claim 1, characterized in that, The step of converting the dynamic shear strain into a logarithmic domain independent variable includes: The logarithmic domain independent variable is obtained by taking the logarithm of the dynamic shear strain with a preset base; wherein the preset base is determined according to the range of differences in the order of magnitude of the dynamic shear strain.

3. The method according to claim 1, characterized in that, The determination of segmentation points based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus includes: Analyze the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus, identify the curvature change positions of the data, and determine the curvature change positions as segmentation points; The identification of the curvature change locations in the data includes: Calculate the rate of change of slope between adjacent data points; The location where the rate of change of slope exceeds a preset threshold is determined as the curvature change location.

4. The method according to claim 1, characterized in that, The step of dividing the data of the logarithmic domain independent variable and the normalized shear modulus into two data segments based on the segmentation point includes: Using the segmentation point as the boundary, the logarithmic domain independent variable and normalized shear modulus data are divided into a first data segment and a second data segment.

5. The method according to claim 1, characterized in that, The step of performing polynomial fitting on the normalized dynamic shear modulus for each data segment to construct a corresponding modulus fitting function includes: performing polynomial fitting on the normalized dynamic shear modulus of the first data segment to establish a first modulus fitting function; the degree of polynomial fitting on the normalized dynamic shear modulus of the first data segment is determined based on the number of data points in the first data segment; performing polynomial fitting on the normalized dynamic shear modulus of the second data segment to establish a second modulus fitting function; the degree of polynomial fitting on the normalized dynamic shear modulus of the second data segment is determined based on the number of data points in the second data segment. The number of times polynomial fitting is performed on the first and second data segments is different.

6. The method according to claim 1, characterized in that, The establishment of continuity constraints at the segmentation points includes: Establish a function value continuity constraint to ensure that the function values ​​of the modulus fitting function of adjacent data segments are equal at the segmentation points; Establish a first-order derivative continuity constraint so that the first-order derivatives of the modulus fitting functions of the adjacent data segments are equal at the segmentation points.

7. The method according to claim 6, characterized in that, The polynomial coefficients of the modulus fitting function and the damping ratio fitting function for each data segment, obtained under the continuity constraint, include: Based on the least squares method, and according to the constraints of continuous function values ​​and continuous first derivatives, the correlation between the polynomial coefficients of the first data segment and the polynomial coefficients of the second data segment is established. Substituting the aforementioned correlation into the least squares estimator expression, a normal system of equations is constructed regarding the polynomial coefficients of the second data segment; Solve the normal system of equations to obtain the polynomial coefficients of the second data segment; Based on the aforementioned correlation, the polynomial coefficients of the first data segment are calculated from the polynomial coefficients of the second data segment; Furthermore, using the logarithmic domain independent variable of all data segments as the independent variable and the damping ratio as the dependent variable, a system of equations is constructed based on the least squares method, and the polynomial coefficients of the damping ratio fitting function are obtained by solving the equations.

8. The method according to claim 1, characterized in that, The step of restoring the logarithmic domain independent variable to the original dynamic shear strain and obtaining the fitting relationship with the normalized shear modulus and damping ratio includes: performing an inverse exponential transformation on the logarithmic domain independent variable to restore the independent variables of the modulus fitting function and the damping ratio fitting function to the original dynamic shear strain. Using the restored dynamic shear strain as the abscissa and the normalized shear modulus output by the modulus fitting function as the ordinate, a relationship curve between the normalized shear modulus and the dynamic shear strain is generated. Using the restored dynamic shear strain as the abscissa and the damping ratio output by the damping ratio fitting function as the ordinate, a curve relating the damping ratio and the dynamic shear strain is generated.

9. A segmented fitting dynamic triaxial test data processing system, characterized in that, include: The acquisition module is used to acquire multiple sets of raw data from the dynamic triaxial test, including dynamic shear strain, normalized shear modulus, and damping ratio. The processing module is used to convert the dynamic shear strain into a logarithmic domain independent variable; determine the segmentation point based on the data distribution characteristics of the logarithmic domain independent variable and the normalized shear modulus; and divide the data of the logarithmic domain independent variable and the normalized shear modulus into two data segments according to the segmentation point. The data fitting module is used to perform polynomial fitting on the normalized dynamic shear modulus for each data segment and construct the corresponding modulus fitting function. A single polynomial is used to fit the logarithmic domain independent variable and the damping ratio data for all data segments to construct the damping ratio fitting function. The constraint calculation module is used to establish continuity constraints at the segmentation points and solve for the polynomial coefficients of the modulus fitting function and the damping ratio fitting function of each data segment under the continuity constraints. The fitting relationship determination module is used to restore the logarithmic domain independent variable to the original dynamic shear strain and obtain the fitting relationship with the normalized shear modulus and damping ratio.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-8.

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