A method for calibrating a sand liquefaction discrimination criterion, a sand liquefaction discrimination method, a device, equipment and a medium

Abstract

This invention relates to the field of data analysis technology, specifically to a method for calibrating a liquefaction criterion for sand, a sand liquefaction discrimination method, apparatus, equipment, and medium. The method includes: classifying sand samples according to their inhomogeneity coefficient and curvature coefficient to obtain particle size distribution types; for each type, analyzing the measured fine particle content and initial void ratio to obtain an influence weighting function and statistical values ​​of fine particle content; preparing samples with different fine particle contents and initial void ratios, conducting cyclic triaxial tests under different cyclic stress ratios and equivalent cycle numbers, and recording the number of liquefaction cycles; correcting the initial void ratio using the influence weighting function and statistical values ​​to obtain an equivalent initial void ratio; fitting a sand liquefaction discrimination criterion based on the equivalent initial void ratio, cyclic stress ratio, number of liquefaction cycles, and equivalent cycle number; and performing sand liquefaction discrimination based on the sand liquefaction discrimination criterion, thereby improving the reliability of liquefaction risk assessment.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a method for calibrating a standard for judging sand liquefaction, a method for judging sand liquefaction, an apparatus, equipment, and a medium. Background Technology

[0002] Soil liquefaction is a phenomenon in which saturated sand loses its shear strength under seismic loading, which can lead to serious disasters such as foundation failure and structural damage. Accurately assessing the risk of soil liquefaction is a key issue in geotechnical earthquake engineering.

[0003] Existing methods for assessing sand liquefaction typically use the measured initial void ratio as the density index when establishing the criteria, without fully considering the impact of fine particle content on the initial void ratio of sand with different gradations. Since variations in fine particle content alter the initial compaction state of sand, and this effect differs significantly across different gradations, existing criteria fail to accurately reflect the true liquefaction resistance of sand, thus reducing the reliability of liquefaction risk assessments. Summary of the Invention

[0004] The purpose of this invention is to provide a method for determining the liquefaction criteria of sand and soil, a method, apparatus, equipment, and medium for determining sand and soil liquefaction, thereby solving the problems in the prior art.

[0005] This invention is achieved through the following technical solution:

[0006] In a first aspect, embodiments of the present invention provide a method for calibrating a standard for judging sand liquefaction, comprising:

[0007] Based on the collected multiple sand samples and the coefficient of non-uniformity and curvature of each sand sample, the multiple sand samples are classified to obtain at least one particle size distribution type.

[0008] For each particle size distribution type, the measured values ​​of fine particle content and initial porosity of all samples within that particle size distribution type are analyzed to obtain the influence weight function of the measured fine particle content on the initial porosity and the statistical value of fine particle content. The influence weight function characterizes the change in initial porosity caused by a change in the measured value of fine particle content per unit.

[0009] For the aforementioned particle size distribution type, multiple samples with different fine particle contents and initial void ratios were prepared, and cyclic triaxial tests were conducted under different cyclic stress ratios and different equivalent cycle numbers. The number of cycles at which each sample reached liquefaction was recorded. Liquefaction was defined as the pore water pressure ratio reaching a preset critical value, and the equivalent cycle number was determined based on preset different vibration levels.

[0010] The initial porosity of each sample is corrected based on the influence weighting function and the statistical value of fine particle content to obtain an equivalent initial porosity.

[0011] Based on the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number, and equivalent cycle number of each sample, the liquefaction discrimination criteria for sandy soil were fitted.

[0012] Preferably, the step of classifying the multiple sand samples based on the collected samples and the coefficient of uniformity and curvature of each sand sample to obtain at least one particle size distribution type includes:

[0013] Based on the particle size distribution curve of each sand sample, obtain the particle size corresponding to the cumulative mass percentage on the particle size distribution curve reaching the first preset percentage, the second preset percentage, and the third preset percentage, respectively, wherein the first preset percentage is less than the second preset percentage, and the second preset percentage is less than the third preset percentage;

[0014] The non-uniformity coefficient is obtained based on the particle size reaching the third preset percentage and the particle size reaching the first preset percentage.

[0015] The curvature coefficient is obtained based on the particle size that reaches the second preset percentage and the particle size that reaches the first preset percentage and the third preset percentage.

[0016] Based on the non-uniformity coefficient and curvature coefficient of each sand sample, the multiple sand samples are classified to obtain at least one particle size distribution type.

[0017] Preferably, the step of analyzing the measured fine particle content and initial void ratio of all samples within the particle size distribution type to obtain the weighting function of the influence of the measured fine particle content on the initial void ratio and the statistical value of fine particle content includes:

[0018] Based on the measured values ​​of fine particle content and initial porosity of all samples within the particle size distribution type, a univariate linear regression analysis was performed with the measured value of fine particle content as the independent variable and the initial porosity as the dependent variable to obtain the regression slope and coefficient of determination.

[0019] The influence weighting function is determined based on the regression slope and the coefficient of determination;

[0020] The average of the measured fine particle content values ​​of all samples within the particle size distribution type is taken as the statistical value of fine particle content.

[0021] Preferably, determining the influence weight function based on the regression slope and the coefficient of determination includes:

[0022] When the determination coefficient is less than the preset threshold, the sample is divided into multiple sub-intervals according to the measured value of fine particle content in each sample within the particle size distribution type. A univariate linear regression analysis is performed in each sub-interval to obtain the sub-interval regression slope.

[0023] The influence weight function is constructed in piecewise form using the regression slope of each sub-interval.

[0024] When the determination coefficient is greater than or equal to a preset threshold, the regression slope is used as the influence weight function.

[0025] Preferably, the step of correcting the initial porosity of each sample based on the influence weighting function and the statistical value of fine particle content to obtain an equivalent initial porosity includes:

[0026] For each sample, the measured value of fine particle content is compared with the statistical value of fine particle content, and the difference between the actual value of fine particle content and the statistical value of fine particle content is calculated to obtain the fine particle content deviation.

[0027] Multiply the deviation in fine particle content by the influence weighting function to obtain the influence amount;

[0028] The equivalent initial porosity is obtained by subtracting the influence amount from the initial porosity of each sample.

[0029] Preferably, the step of fitting the sand liquefaction discrimination criterion based on the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number, and equivalent cycle number of each sample includes:

[0030] Based on the number of liquefaction cycles and the cyclic stress ratio of each sample, a power function relationship between the cyclic stress ratio and the number of liquefaction cycles is established.

[0031] The cyclic stress ratio of each specimen is normalized to the normalized cyclic stress ratio under the reference equivalent number of cycles based on the power function relationship.

[0032] Based on the normalized cyclic stress ratio and equivalent initial porosity of each sample, a first function relationship is obtained by fitting the critical cyclic stress ratio as the equivalent initial porosity decreases exponentially.

[0033] Based on the difference between the equivalent number of cycles and the reference equivalent number of cycles for each sample, and the relative change of the critical cyclic stress ratio under different equivalent number of cycles, the power exponent parameter of the equivalent number of cycles is fitted.

[0034] By incorporating the power exponent parameter into the first functional relationship, a second functional relationship is obtained in which the critical cyclic stress ratio decreases exponentially with the equivalent initial porosity ratio and decreases exponentially with the number of equivalent cycles.

[0035] The second functional relationship is used as the criterion for judging sand liquefaction.

[0036] Secondly, embodiments of the present invention provide a method for judging sand liquefaction, which uses the influence weight function and sand liquefaction judgment criteria obtained by the method of the first aspect to judge the liquefaction risk of the sand sample to be tested, including:

[0037] The fine particle content, initial void ratio, and particle size distribution of the sand sample to be tested were determined.

[0038] The particle size distribution is used to determine the gradation type of the sand sample to be tested;

[0039] The initial porosity is corrected according to the gradation type and the influence weight function to obtain an equivalent initial porosity.

[0040] Calculate the actual cyclic stress ratio based on the seismic motion parameters of the site where the sand sample to be tested is located;

[0041] Based on the gradation type, the equivalent initial void ratio, and the magnitude in the seismic motion parameters, the critical cyclic stress ratio is calculated using the sand liquefaction discrimination criterion.

[0042] The actual cyclic stress ratio is compared with the critical cyclic stress ratio, and the liquefaction risk level is output based on the comparison result.

[0043] Thirdly, embodiments of the present invention provide a calibration device for determining the liquefaction criteria of sandy soil, comprising:

[0044] The classification module is used to classify the multiple sand samples based on the collected samples and the coefficient of non-uniformity and curvature of each sand sample, so as to obtain at least one particle size distribution type.

[0045] The analysis module is used to analyze the measured values ​​of fine particle content and initial porosity of all samples within each particle gradation type for each particle gradation type, and to obtain the influence weight function of the measured value of fine particle content on the initial porosity and the statistical value of fine particle content. The influence weight function characterizes the change in initial porosity caused by a unit change in the measured value of fine particle content.

[0046] The test module is used to prepare multiple samples with different fine particle contents and initial void ratios for the particle size distribution type, and to conduct cyclic triaxial tests under different cyclic stress ratios and different equivalent cycle numbers, and record the number of cycles when each sample reaches liquefaction; wherein, liquefaction is defined as the pore water pressure ratio reaching a preset critical value, and the equivalent cycle number is determined according to preset different vibration levels;

[0047] The correction module is used to correct the initial porosity of each sample according to the influence weight function and the fine particle content statistics to obtain an equivalent initial porosity.

[0048] The fitting module is used to fit the liquefaction criteria of sand based on the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number and equivalent cycle number of each sample.

[0049] Fourthly, embodiments of the present invention provide an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method of the first aspect described above.

[0050] Fifthly, embodiments of the present invention provide a storage medium storing computer program instructions thereon, which, when executed by a processor, implement the method of the first aspect described above.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0052] By classifying sand samples according to particle size distribution and establishing a weighting function for the influence of fine particle content on the initial void ratio for each gradation type, the interference of differences in fine particle filling behavior between different gradation types on the initial compaction assessment can be eliminated. This ensures that the equivalent initial void ratio only reflects the compaction state of the sand skeleton, thus providing accurate basic parameters for establishing a unified liquefaction discrimination standard.

[0053] The influence weight function is determined by univariate linear regression and piecewise function form, and the initial porosity of each sample in the cyclic triaxial test is corrected by the weight function. This can transform the initial porosity of samples with different fine particle contents to the equivalent initial porosity of the same reference fine particle content level. This makes it unnecessary to use fine particle content as an independent variable in subsequent fitting, which simplifies the mathematical form of the discrimination criterion and improves the fitting accuracy.

[0054] By simultaneously setting different cyclic stress ratios and different equivalent number of cycles (corresponding to different magnitudes) in cyclic triaxial tests, the power function relationship between the number of liquefaction cycles and the cyclic stress ratio, as well as the influence of the equivalent number of cycles on the critical cyclic stress ratio, can be obtained in a single test sequence. This avoids systematic errors caused by batch testing and ensures the consistency of data under different magnitude conditions.

[0055] A three-step fitting strategy is adopted, which first establishes a power function relationship between the cyclic stress ratio and the number of liquefaction cycles, then normalizes the data of each sample to the reference equivalent number of cycles for exponential fitting, and finally introduces the power exponent parameter of the equivalent number of cycles. This strategy can gradually separate the influence of multiple factors in the experimental data and determine the function parameters one by one, reducing the convergence difficulty of multi-parameter nonlinear regression and improving the stability and interpretability of the discrimination standard formula.

[0056] The final criterion for judging sand liquefaction is expressed in a closed form where the critical cyclic stress ratio decreases exponentially with the equivalent initial void ratio and decreases by a power function with the number of equivalent cycles. Only two parameters, the site's equivalent initial void ratio and the earthquake magnitude, need to be input to calculate the critical cyclic stress ratio. This makes it easy to compile calculation tables or embed them into engineering software, significantly reducing the calculation cost of liquefaction risk assessment. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0058] Figure 1 A schematic flowchart illustrating the calibration method for the sand liquefaction discrimination standard provided by the present invention;

[0059] Figure 2 A schematic flowchart of the sand liquefaction discrimination method provided by the present invention;

[0060] Figure 3 A schematic diagram of the structure of the calibration device for the sand liquefaction discrimination standard provided by the present invention;

[0061] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0063] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0064] It should be noted that all actions involving the acquisition of signals, information, or data in this invention are carried out in compliance with the relevant data protection laws and regulations of the locality and with authorization from the owner of the relevant device.

[0065] Example 1

[0066] Please see Figure 1 This invention provides a method for calibrating the criteria for judging sand liquefaction, comprising:

[0067] S1. Based on the collected multiple sand samples and the non-uniformity coefficient and curvature coefficient of each sand sample, classify the multiple sand samples to obtain at least one particle size distribution type.

[0068] The uniformity coefficient is calculated as the ratio of the particle size distribution curve at which the cumulative mass percentage reaches 60% to that at 10%. The curvature coefficient is calculated as the square of the particle size distribution curve at which the cumulative mass percentage reaches 30%, divided by the product of the particle size distribution curve at which the cumulative mass percentage reaches 10% and that at 60%. Particle size distribution type is a category defined based on the numerical range of the uniformity coefficient and curvature coefficient, as well as the shape of the particle size distribution curve, including well-graded, uniform, and discontinuous gradation types. Sieve analysis was performed on each sand sample to obtain the particle size distribution curve. The uniformity coefficient and curvature coefficient were calculated, and each sample was assigned to the corresponding gradation type based on the values ​​of these two coefficients and the curve shape. The samples were separated by gradation type to facilitate the subsequent establishment of influence weighting functions and liquefaction criteria for each gradation type.

[0069] In some embodiments, S1, based on the collected multiple sand samples and the non-uniformity coefficient and curvature coefficient of each sand sample, the multiple sand samples are classified to obtain at least one particle size distribution type, including:

[0070] Based on the particle size distribution curve of each sand sample, obtain the particle size corresponding to the cumulative mass percentage on the particle size distribution curve reaching the first preset percentage, the second preset percentage, and the third preset percentage, respectively, wherein the first preset percentage is less than the second preset percentage, and the second preset percentage is less than the third preset percentage;

[0071] The particle size distribution curve is obtained through standard sieve analysis, plotting particle size on the x-axis and cumulative mass percentage on the y-axis. Each sand sample corresponds to one curve. The cumulative mass percentage refers to the percentage of the total mass of particles smaller than a certain size, accumulated from the smallest particle size, out of the total sample mass. The first, second, and third preset percentages are three pre-defined cumulative mass percentage values, satisfying the condition that the first preset percentage is less than the second preset percentage, and the second preset percentage is less than the third preset percentage. Specifically, the first preset percentage can be 10%, the second preset percentage can be 30%, and the third preset percentage can be 60%. The particle size values ​​corresponding to these three percentages are read from the particle size distribution curve and recorded as the first particle size, the second particle size, and the third particle size, respectively. The first particle size represents 10% of the particles in the sample having a particle size smaller than this value, the second particle size represents 30% of the particles having a particle size smaller than this value, and the third particle size represents 60% of the particles having a particle size smaller than this value.

[0072] The non-uniformity coefficient is obtained based on the particle size reaching the third preset percentage and the particle size reaching the first preset percentage.

[0073] The uniformity coefficient is the ratio obtained by dividing the third particle size by the first particle size. The uniformity coefficient reflects the uniformity of the particle size distribution of sand. The larger the ratio, the wider the particle size range and the higher the degree of mixing of particles of different sizes.

[0074] The curvature coefficient is obtained based on the particle size that reaches the second preset percentage and the particle size that reaches the first preset percentage and the third preset percentage.

[0075] The curvature coefficient is the ratio obtained by dividing the square of the second particle size by the product of the first and third particle sizes. The curvature coefficient reflects the continuity of the particle size distribution. When the ratio is between 1 and 3, it indicates continuous gradation. When it is less than 1 or greater than 3, it indicates discontinuous gradation or the presence of missing particle sizes.

[0076] Based on the non-uniformity coefficient and curvature coefficient of each sand sample, the multiple sand samples are classified to obtain at least one particle size distribution type.

[0077] The particle size distribution types include well-distributed, uniform, and discontinuous types. A sample is classified as well-distributed if the non-uniformity coefficient is greater than or equal to 5 and the curvature coefficient is between 1 and 3. A sample is classified as uniformly distributed if the non-uniformity coefficient is less than 5 and the particle size distribution curve is continuous without plateaus. A sample is classified as discontinuously distributed if the particle size distribution curve has obvious horizontal segments or a missing particle size range. After classifying all samples, each sample is assigned a particle size distribution type label, resulting in datasets of at least one particle size distribution type. Each dataset contains only samples of the same particle size distribution type. This classification makes it possible to subsequently establish influence weighting functions and liquefaction criteria for each particle size distribution type, avoiding interference caused by differences in particle filling mechanisms between different particle size distribution types.

[0078] S2. For each particle size distribution type, analyze the measured values ​​of fine particle content and initial porosity of all samples within the particle size distribution type to obtain the influence weight function of the measured value of fine particle content on the initial porosity and the statistical value of fine particle content. The influence weight function characterizes the change in initial porosity caused by the change in unit fine particle content.

[0079] The measured fine particle content is the percentage of particles smaller than 0.075 mm in diameter, determined by sieving, out of the total sample mass. The initial void ratio is the ratio of pore volume to solid particle volume calculated by combining the sample density and moisture content determined by the drying method with the soil particle specific gravity. The influence weighting function is the regression slope obtained from a univariate linear regression analysis with the measured fine particle content as the independent variable and the initial void ratio as the dependent variable. This slope represents the average increase or decrease in the initial void ratio when the measured fine particle content increases by one percentage point. The fine particle content statistical value is the arithmetic mean of the measured fine particle content values ​​of all samples within this gradation type. Through regression analysis, the influence of changes in fine particle content on the initial compaction state of this type of sandy soil can be quantitatively described, providing a basis for subsequent elimination of fine particle content interference.

[0080] In some embodiments, S2 involves analyzing the measured fine particle content and initial porosity of all samples within the particle size distribution type to obtain a weighting function for the influence of the measured fine particle content on the initial porosity and statistical values ​​of the fine particle content, including:

[0081] Based on the measured values ​​of fine particle content and initial porosity of all samples within the particle size distribution type, a univariate linear regression analysis was performed with the measured value of fine particle content as the independent variable and the initial porosity as the dependent variable to obtain the regression slope and coefficient of determination.

[0082] The measured value of fine particle content is recorded as follows: The unit is %, which represents the percentage of the total sample mass containing particles smaller than 0.075 mm, determined by sieving. The initial porosity is denoted as . The results were obtained through the drying method and the specific gravity bottle method: ,in For pore volume, Let be the volume of the solid particles. The univariate linear regression model assumes... and satisfy ,in The intercept is... Let be the regression slope. Calculated using the least squares method. :

[0083] ;

[0084] in, This represents the total number of samples within this gradation type. For the first one sample , and For all samples and The arithmetic mean of the coefficients. The coefficient of determination is denoted as... The calculation formula is:

[0085] ;

[0086] in, For the first The initial porosity regression prediction value for each sample. The range of values ​​is The larger the value, the stronger the regression model's ability to interpret the data. The initial porosity regression prediction value is calculated by substituting the measured value of fine particle content of each sample into the established univariate linear regression equation.

[0087] The influence weighting function is determined based on the regression slope and the coefficient of determination;

[0088] The influence weight function is denoted as This is used to characterize the change in initial void ratio caused by a change in unit fine particle content. The determination rule is as follows: When... When the value is greater than or equal to a preset threshold (which can be 0.6), take... That is, a constant weighting function; when When the value is less than the preset threshold, a piecewise weighting function is used.

[0089] The average of the measured fine particle content values ​​of all samples within the particle size distribution type is taken as the statistical value of fine particle content.

[0090] Among them, the statistical value of fine particle content is recorded as The calculation formula is:

[0091] ;

[0092] This value serves as a reference level for the fine particle content of this type of sandy soil, and is used as a benchmark point to determine the deviation of the fine particle content in subsequent correction formulas.

[0093] In some embodiments, determining the influence weight function based on the regression slope and the coefficient of determination includes:

[0094] When the determination coefficient is less than the preset threshold, the sample is divided into multiple sub-intervals according to the measured value of fine particle content in each sample within the particle size distribution type. A univariate linear regression analysis is performed in each sub-interval to obtain the sub-interval regression slope.

[0095] Specifically, let the preset threshold be... (It can be taken as 0.6). When At that time, The range of values ​​is divided into Sub-intervals ( ), the boundary is defined according to The distribution is determined, for example, by using boundary values. and Divided into three sub-intervals: , , For each subinterval ( Let the subinterval contain For each sample, perform univariate linear regression to obtain the sub-interval regression slope:

[0096] ;

[0097] in and Samples within this sub-interval and The arithmetic mean.

[0098] The influence weight function is constructed in piecewise form using the regression slope of each sub-interval.

[0099] The piecewise influence weight function is expressed as follows:

[0100] ;

[0101] That is, based on the input Given the given subinterval, return the corresponding subinterval regression slope.

[0102] When the determination coefficient is greater than or equal to a preset threshold, the regression slope is used as the influence weight function.

[0103] Specifically, when At that time, take ;

[0104] in This represents the slope obtained from the overall univariate linear regression, where the weighting function is a constant.

[0105] S3. For the particle size distribution type, prepare multiple samples with different fine particle contents and initial void ratios, and conduct cyclic triaxial tests under different cyclic stress ratios and different equivalent cycle numbers, and record the number of cycles when each sample reaches liquefaction; wherein, liquefaction is defined as the pore water pressure ratio reaching a preset critical value, and the equivalent cycle number is determined according to preset different vibration levels;

[0106] The cyclic stress ratio is the ratio of the cyclic shear stress amplitude applied in a cyclic triaxial test to the initial effective consolidation pressure, used to characterize the intensity level of the dynamic load. The equivalent cycle number is a value representing the effective number of cycles within the earthquake duration, calculated based on the earthquake magnitude using empirical formulas or tables. The cyclic triaxial test is a test method that applies an axial cyclic load to a saturated sand sample after consolidation under a set consolidation pressure. Liquefaction is defined as the state in which the ratio of pore water pressure to the initial effective consolidation pressure reaches a preset critical value during the test. For each particle size distribution type, multiple samples with different fine particle contents and different initial void ratios are artificially prepared. Each sample undergoes a cyclic triaxial test at multiple different cyclic stress ratio levels, with corresponding equivalent cycle numbers set as test conditions according to different preset earthquake magnitudes. Under each test condition, the number of cycles from the start of cyclic loading to the first liquefaction state of the sample is recorded. Through this multivariate cross-test design, liquefaction response data covering different fine particle contents, different densities, different dynamic load intensities, and different earthquake magnitudes can be obtained.

[0107] S4. Correct the initial porosity of each sample according to the influence weighting function and the statistical value of fine particle content to obtain the equivalent initial porosity.

[0108] The equivalent initial porosity ratio is obtained by subtracting the porosity change caused by the deviation of fine particle content from the reference level from the measured initial porosity ratio of the sample. It is used to characterize the skeleton compactness after eliminating the interference of fine particle content. The specific correction operation is as follows: first, calculate the difference between the measured value and the statistical value of fine particle content for each sample; multiply this difference by the influence weighting function to obtain the influence amount; then subtract this influence amount from the initial porosity ratio of the sample. Through this correction, samples with different fine particle contents can be considered to have similar skeleton compactness when they have the same equivalent initial porosity ratio, thereby avoiding the interference of fine particle content differences on liquefaction resistance assessment in subsequent fitting.

[0109] In some embodiments, S4, the initial porosity of each sample is corrected according to the influence weighting function and the statistical value of fine particle content to obtain an equivalent initial porosity, including:

[0110] For each sample, the measured value of fine particle content is compared with the statistical value of fine particle content, and the difference between the actual value of fine particle content and the statistical value of fine particle content is calculated to obtain the fine particle content deviation.

[0111] For each sample, the difference between the measured value and the statistical value of the fine particle content is calculated and denoted as . This difference indicates whether the fine particle content of the sample is higher or lower than the reference level for this type of sand, and to what extent it is higher. For example, if... , ,but This indicates that the fine particulate matter content is 2 percentage points higher than the reference level; if ,but This indicates a decrease of 2 percentage points.

[0112] Multiply the deviation in fine particle content by the influence weighting function to obtain the influence amount;

[0113] For each sample, the fine particle content was determined based on the measured value. For the interval in question (if a piecewise function is used), select the corresponding weight value to account for the fine particle content deviation. Multiply by the weight value to obtain the influence amount, denoted as . This influence quantity represents the change in initial void ratio caused by the deviation of the fine particle content of the sample from the reference level. For example, if... (Good gradation) ,but This indicates that the higher fine particle content increased the initial porosity by 0.016 compared to the reference level; if (uniform gradation and) ), ,but This indicates that the lower fine particle content resulted in an initial porosity 0.036 higher than the reference level, meaning it was more porous.

[0114] The equivalent initial porosity is obtained by subtracting the influence amount from the initial porosity of each sample.

[0115] The measured initial void ratio is denoted as . The equivalent initial void ratio is denoted as... The calculation formula is:

[0116] ;

[0117] This formula subtracts the contribution from the measured initial void ratio due to the deviation of fine particle content from the reference level, obtaining an equivalent initial void ratio that only reflects the density of the sand skeleton. For example, if , ,but ;like , ,but With this correction, samples with different fine particle contents, if having the same... If the particle content is similar, then their skeletal density is considered to be the same, and therefore their liquefaction resistance should also be similar. This correction laid the data foundation for the subsequent establishment of a unified liquefaction discrimination standard, avoiding the interference of differences in fine particle content on liquefaction resistance assessment.

[0118] S5. Based on the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number and equivalent cycle number of each sample, the liquefaction discrimination criterion of sand is obtained by fitting.

[0119] Fitting is a process of determining the parameters in a functional relationship using mathematical regression, with the equivalent initial void ratio and equivalent cycle number as independent variables and the critical cyclic stress ratio as the dependent variable. The liquefaction criterion for sand uses the equivalent initial void ratio and equivalent cycle number as inputs and outputs as a functional relationship for the critical cyclic stress ratio, or a set of parameters for that functional relationship. In the fitting process, liquefaction cycle number data measured for all samples under different cyclic stress ratios and equivalent cycle numbers are used to establish a mathematical expression for the critical cyclic stress ratio as a function of the equivalent initial void ratio and equivalent cycle number through multivariate nonlinear regression. This expression is the calibrated liquefaction criterion for sand. In practical applications, only the equivalent initial void ratio of the site and the equivalent cycle number corresponding to the earthquake magnitude need to be input to calculate the critical cyclic stress ratio, which can then be compared with the actual cyclic stress ratio of the site to determine the liquefaction risk.

[0120] In some embodiments, S5, based on the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number, and equivalent cycle number of each sample, a liquefaction discrimination criterion for sand is fitted, including:

[0121] Based on the number of liquefaction cycles and the cyclic stress ratio of each sample, a power function relationship between the cyclic stress ratio and the number of liquefaction cycles is established.

[0122] For each sample (i.e., in a fixed...) (Below), multiple cyclic stress ratios were tested. The cyclic triaxial test recorded a corresponding number of liquefaction cycles for each test. This group When data points are plotted in a log-log coordinate system, they can show a linear relationship, indicating that... and The relationship between them satisfies an power function: ,in and These are undetermined coefficients. The coefficients for each sample are obtained by regressing the data points for each sample using the least squares method. and Specifically, take the logarithm of both sides of the equation: ,by As the independent variable, Perform linear regression on the dependent variable to obtain the slope. and intercept Thus determine and .

[0123] The cyclic stress ratio of each specimen is normalized to the normalized cyclic stress ratio under the reference equivalent number of cycles based on the power function relationship.

[0124] Wherein, the reference equivalent loop count is a pre-defined constant, denoted as We can take 15 (corresponding to a magnitude of 7.5). Substituting the reference equivalent cycle number into the above power function relationship, we calculate the cyclic stress ratio of the specimen at the reference equivalent cycle number, which is called the normalized cyclic stress ratio, denoted as . :

[0125] ;

[0126] If the sample is placed The cyclic stress ratio that causes liquefaction under constant amplitude cyclic loading. Through this normalization process, discrete data obtained from different specimens under different test conditions are converted to a unified reference load number, thus allowing for comparison of their liquefaction resistance.

[0127] Based on the normalized cyclic stress ratio and equivalent initial porosity of each sample, a first function relationship is obtained by fitting the critical cyclic stress ratio as the equivalent initial porosity decreases exponentially.

[0128] For all samples, each sample corresponds to an equivalent initial void ratio. and a normalized cyclic stress ratio These data points When plotted in a coordinate system, it can be presented as Follow The trend of increasing and decreasing can be described by an exponential function: ,in and These are the parameters to be fitted. Taking the logarithm yields... ,by As the independent variable, Performing linear regression on the dependent variable yields the following results: and Thus determine This relationship indicates that the critical cyclic stress ratio increases with the increase of skeleton density (i.e., The decrease in the index and the increase in the exponent are consistent with the basic laws of soil mechanics.

[0129] Based on the difference between the equivalent number of cycles and the reference equivalent number of cycles for each sample, and the relative change of the critical cyclic stress ratio under different equivalent number of cycles, the power exponent parameter of the equivalent number of cycles is fitted.

[0130] Among them, the equivalent number of loops Depending on the magnitude, different samples may have different characteristics. The critical cyclic stress ratio of each specimen. (i.e., the actual use in the experiment) of Divide by the value obtained from the first functional relation To obtain the ratio Observations revealed that, and The relationship between them satisfies an power function: ,in The parameter is the power exponent. Taking the logarithm yields... ,by As the independent variable, Perform linear regression on the dependent variable to obtain the slope. Thus determine This parameter reflects the degree to which the equivalent number of cycles affects the liquefaction capacity.

[0131] By incorporating the power exponent parameter into the first functional relationship, a second functional relationship is obtained in which the critical cyclic stress ratio decreases exponentially with the equivalent initial porosity ratio and decreases exponentially with the number of equivalent cycles.

[0132] Among them, the power exponent parameter Substituting the first functional relation, we get the complete expression:

[0133] ;

[0134] make The expression then simplifies to:

[0135] ;

[0136] This formula represents the second functional relationship: the critical cyclic stress ratio decreases exponentially with the equivalent initial porosity ratio, and decreases exponentially with the equivalent number of cycles.

[0137] The second functional relationship is used as the criterion for judging sand liquefaction.

[0138] Among them, the liquefaction criterion for sand is the second functional relationship mentioned above. In practical engineering applications, for any site to be tested, the fine particle content of the sand to be tested is first obtained based on on-site drilling and testing. and initial porosity Calculate based on gradation type and pre-stored influence weight function Then, calculate the equivalent number of cycles based on the earthquake magnitude at the site. Substituting the values ​​into the discrimination criteria yields the critical cyclic stress ratio. Finally, the ratio of the actual cyclic stress calculated based on the seismic motion parameters is compared. In comparison, if If the liquefaction risk is high, it will be classified as high risk; otherwise, it will be classified as low risk. This criterion will be stored as a mathematical formula for use in subsequent liquefaction risk assessment procedures.

[0139] The following will further illustrate this embodiment through specific examples.

[0140] Using saturated standard sand with a relative density of 60% as the object, its liquefaction discrimination criteria were determined according to the above method.

[0141] I. Sample Collection and Classification

[0142] Forty sets of standard sand samples were collected, and sieving tests were performed on each sample to obtain particle size distribution curves. The particle sizes corresponding to cumulative mass percentages of 10%, 30%, and 60% on the particle size distribution curves of each sample were recorded as follows: , , .

[0143] Calculate the coefficient of heterogeneity for each sample. and curvature coefficient .

[0144] Typical gradation data for standard sand are as follows: , , Calculated , .because and Furthermore, the particle size distribution curves are continuous without plateaus; therefore, all 40 sample groups are classified as well-graded, denoted as [missing information]. .

[0145] II. Determining the Influencing Factors of the Weighting Function and Fine Particle Content Statistics

[0146] The fine particulate matter content was measured in 40 samples. (mass percentage of particles <0.075mm) and initial porosity Partial data is as follows:

[0147] Calculate all samples The arithmetic mean of the two values ​​is used to obtain the statistical value of fine particle content. .

[0148] by For independent variable, A univariate linear regression was performed on the dependent variable, and the regression equation is:

[0149] ;

[0150] The regression slope was calculated using the least squares method.

[0151] ;

[0152] Substituting the data, we can calculate... Coefficient of determination .

[0153] because Therefore, this affects the weight function taking the value of a constant:

[0154] .

[0155] III. Sample preparation and cyclic triaxial testing

[0156] For well-graded products, 15 different fine particle contents were artificially prepared. and initial porosity Combined samples. Values: ; They were controlled at around 0.58, 0.62, and 0.66 respectively.

[0157] Each specimen at effective consolidation pressure Under a frequency of 1 Hz, different cyclic stress ratios were tested. Cyclic triaxial test. The values ​​are 0.15, 0.20, and 0.25.

[0158] Considering three earthquake magnitudes simultaneously: , , The corresponding equivalent number of iterations are respectively calculated according to the empirical formula. Calculate or look up a table to get , , In each The experiment was conducted under the corresponding conditions.

[0159] Record the liquefaction rate (pore water pressure ratio) under each test condition. ) Number of loops required Partial data is as follows:

[0160] IV. Obtaining the equivalent initial void ratio by correcting the initial void ratio

[0161] For each sample, calculate the equivalent initial void ratio:

[0162] ;

[0163] in , .

[0164] Taking sample A as an example: , .calculate (Unit: percentage points). Impact .but .

[0165] Taking sample B as an example: , . Impact . .

[0166] Taking sample C as an example: , . Impact . .

[0167] After correction, samples with different fine particle contents were converted to the equivalent void ratio at the same reference level, eliminating the interference of fine particle content on the initial density.

[0168] V. Fitting the criteria for judging sand liquefaction

[0169] 1. Establish and power function relationship

[0170] For each specimen (fixed) and (There are multiple) Data points. Taking sample B as an example, the data points are: , , Assume an exponential function relationship:

[0171] ;

[0172] Take the logarithm:

[0173] ;

[0174] Linear regression was performed on three points of sample B to obtain... , ,Right now .therefore:

[0175] ;

[0176] Repeat the above regression for all samples to obtain the coefficients for each sample. and .

[0177] 2. Normalize to the reference equivalent loop count

[0178] Take the reference equivalent loop count For each specimen, calculate the normalized critical cyclic stress ratio:

[0179] ;

[0180] Taking sample B as an example: .

[0181] calculate

[0182] .but .

[0183] 3. Fit the first functional relationship ( and (exponential relationship)

[0184] All samples The data points are fitted using the following model:

[0185] ;

[0186] Take the logarithm:

[0187] ;

[0188] Perform linear regression on all data to obtain , Therefore The first functional relationship is:

[0189] .

[0190] 4. Fitting power exponent parameters

[0191] Utilize different The experimental data below. For the same In different Below, critical cyclic stress ratio (even though of They are different. Define the ratio:

[0192] ;

[0193] With a certain Taking the sample as an example, the following was measured:

[0194] when hour, ;

[0195] when hour, ;

[0196] when hour, ;

[0197] calculate:

[0198] .

[0199] Then the ratio:

[0200] ;

[0201] ;

[0202] ;

[0203] Simultaneous calculation : , , Assume an exponential function relationship:

[0204] ;

[0205] Take the logarithm:

[0206] ;

[0207] by As the independent variable, Performing linear regression on the dependent variable yields the following results: .

[0208] 5. Obtain complete discrimination criteria

[0209] Power parameter Introducing the first functional relationship:

[0210] ;

[0211] Substitution , , , ,have to:

[0212] ;

[0213] Sorted as:

[0214] ;

[0215] calculate .therefore:

[0216] ;

[0217] This expression is the liquefaction criterion for well-graded standard sand (relative density 60%).

[0218] VI. Verification

[0219] The back-judgment was performed using five independent groups of samples that were not involved in the fitting. For example, a certain test sample earthquake magnitude (correspond ),calculate Calculated , , Actual site calculations ,because The samples were determined to be at high risk of liquefaction, consistent with the results of the field tests. All five groups of independent samples were correctly identified, and calibration was completed.

[0220] Example 2

[0221] Please see Figure 2 This invention provides a method for judging sand liquefaction, which uses the influence weight function and sand liquefaction judgment criteria obtained by the method of Example 1 to judge the liquefaction risk of the sand sample to be tested, including:

[0222] S21. Determine the fine particle content, initial void ratio, and particle size distribution of the sand sample to be tested;

[0223] In this process, following the same test procedures as in the calibration method, the sand sample to be tested was subjected to sieve analysis, density test, and moisture content test to obtain the fine particle content. Initial porosity The particle size distribution curve provides basic data for subsequent determination of gradation type and correction of equivalent initial void ratio.

[0224] S22. Determine the gradation type of the sand sample to be tested based on the particle size distribution;

[0225] The non-uniformity coefficient is calculated based on the particle size distribution curve. and curvature coefficient And observe the shape of the curve. When and It is judged as good gradation at the time; when Furthermore, a continuous curve without plateaus is considered a uniform gradation; a curve with obvious horizontal segments or missing particles within a certain size range is considered a discontinuous gradation. Output gradation type number. .

[0226] S23. Based on the gradation type and the influence weight function, the initial porosity is corrected to obtain an equivalent initial porosity.

[0227] Among them, the type of gradation is read from the pre-stored database. Corresponding influence weight function and the statistical value of fine particulate matter content Based on the sample to be tested Value, if For piecewise functions, select the weight values ​​within the corresponding interval. The equivalent initial void ratio is calculated using the following formula:

[0228] ;

[0229] This step eliminates the interference of fine particle content deviating from the reference level on the initial void ratio, resulting in a result that reflects only the density of the skeleton. .

[0230] S24. Calculate the actual cyclic stress ratio based on the seismic parameters of the site where the sand sample to be tested is located;

[0231] Among them, ground motion parameters include peak ground acceleration. (unit ), magnitude With sample burial depth (unit Calculate the actual cyclic stress ratio using the following procedure. :

[0232] ;

[0233] in, and The depth reduction factor is calculated based on the unit weight of the soil layer and the groundwater level. ( This step uses the Seed simplification method to estimate the equivalent cyclic shear stress level generated by the earthquake at the depth to be measured.

[0234] S25. Based on the gradation type, the equivalent initial void ratio, and the magnitude in the seismic motion parameters, calculate the critical cyclic stress ratio using the sand liquefaction discrimination criterion.

[0235] This involves reading the gradation type from a pre-stored database. Corresponding calibration parameters , , First, based on the magnitude... Calculate the equivalent number of loops Empirical formulas can be used. Or look it up in a table. Then... and Substitute into the discrimination criterion formula:

[0236] ;

[0237] Obtain the critical cyclic stress ratio This refers to the maximum cyclic stress ratio that the sand can resist liquefaction under the current equivalent initial void ratio and earthquake magnitude.

[0238] S26. Compare the actual cyclic stress ratio with the critical cyclic stress ratio, and output the liquefaction risk level based on the comparison result.

[0239] The risk level is output according to the following rules:

[0240] like Output low risk;

[0241] like Output of medium risk;

[0242] like This poses a high risk.

[0243] The output results can be directly used for engineering decisions. This discrimination method reuses all the parameters and models established by the calibration method, eliminating the need for re-testing or refitting. It can quickly complete the site liquefaction risk classification using only a small number of geotechnical parameters and seismic motion parameters.

[0244] Example 3

[0245] Please see Figure 3 This invention provides a calibration device for determining the liquefaction criteria of sandy soil, comprising:

[0246] The classification module is used to classify the multiple sand samples based on the collected samples and the coefficient of non-uniformity and curvature of each sand sample, so as to obtain at least one particle size distribution type.

[0247] The analysis module is used to analyze the measured values ​​of fine particle content and initial porosity of all samples within each particle gradation type for each particle gradation type, and to obtain the influence weight function of the measured value of fine particle content on the initial porosity and the statistical value of fine particle content. The influence weight function characterizes the change in initial porosity caused by a unit change in the measured value of fine particle content.

[0248] The test module is used to prepare multiple samples with different fine particle contents and initial void ratios for the particle size distribution type, and to conduct cyclic triaxial tests under different cyclic stress ratios and different equivalent cycle numbers, and record the number of cycles when each sample reaches liquefaction; wherein, liquefaction is defined as the pore water pressure ratio reaching a preset critical value, and the equivalent cycle number is determined according to preset different vibration levels;

[0249] The correction module is used to correct the initial porosity of each sample according to the influence weight function and the fine particle content statistics to obtain an equivalent initial porosity.

[0250] The fitting module is used to fit the liquefaction criteria of sand based on the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number and equivalent cycle number of each sample.

[0251] It should be noted that each module and unit in the calibration device for the sand liquefaction discrimination standard in this embodiment corresponds one-to-one with each step in the calibration method for the sand liquefaction discrimination standard in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned calibration method for the sand liquefaction discrimination standard, and will not be repeated here.

[0252] Example 4

[0253] Please see Figure 4This embodiment provides an electronic device, including at least one processor 401 and a memory 402. Optionally, the device further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0254] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0255] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0256] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0257] The memory may include high-speed memory (Random Access Memory, RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0258] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0259] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0260] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0261] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0262] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0263] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0264] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0265] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0266] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0267] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0268] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calibrating a criterion for identifying sand liquefaction, characterized by, include: Based on the collected multiple sand samples and the coefficient of non-uniformity and curvature of each sand sample, the multiple sand samples are classified to obtain at least one particle size distribution type. For each particle size distribution type, the measured values ​​of fine particle content and initial porosity of all samples within that particle size distribution type are analyzed to obtain the influence weight function of the measured fine particle content on the initial porosity and the statistical value of fine particle content. The influence weight function characterizes the change in initial porosity caused by a change in the measured value of fine particle content per unit. For the aforementioned particle size distribution type, multiple samples with different fine particle contents and initial void ratios were prepared, and cyclic triaxial tests were conducted under different cyclic stress ratios and different equivalent cycle numbers. The number of cycles at which each sample reached liquefaction was recorded. Liquefaction was defined as the pore water pressure ratio reaching a preset critical value, and the equivalent cycle number was determined based on preset different vibration levels. The initial porosity of each sample is corrected based on the influence weighting function and the statistical value of fine particle content to obtain an equivalent initial porosity. Based on the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number, and equivalent cycle number of each sample, the liquefaction discrimination criteria for sandy soil were fitted.

2. The method of claim 1, wherein The process involves classifying multiple sand samples based on their collected data, as well as the uniformity coefficient and curvature coefficient of each sample, to obtain at least one particle size distribution type, including: Based on the particle size distribution curve of each sand sample, obtain the particle size corresponding to the cumulative mass percentage on the particle size distribution curve reaching the first preset percentage, the second preset percentage, and the third preset percentage, respectively, wherein the first preset percentage is less than the second preset percentage, and the second preset percentage is less than the third preset percentage; The non-uniformity coefficient is obtained based on the particle size reaching the third preset percentage and the particle size reaching the first preset percentage. The curvature coefficient is obtained based on the particle size that reaches the second preset percentage and the particle size that reaches the first preset percentage and the third preset percentage. Based on the non-uniformity coefficient and curvature coefficient of each sand sample, the multiple sand samples are classified to obtain at least one particle size distribution type.

3. The method of claim 1, wherein The analysis, based on the measured fine particle content and initial void ratio of all samples within the particle size distribution type, yields the weighting function of the influence of the measured fine particle content on the initial void ratio and the statistical value of the fine particle content, including: Based on the measured values ​​of fine particle content and initial porosity of all samples within the particle size distribution type, a univariate linear regression analysis was performed with the measured value of fine particle content as the independent variable and the initial porosity as the dependent variable to obtain the regression slope and coefficient of determination. The influence weighting function is determined based on the regression slope and the coefficient of determination; The average of the measured fine particle content values ​​of all samples within the particle size distribution type is taken as the statistical value of fine particle content.

4. The method of claim 3, wherein The step of determining the influence weight function based on the regression slope and the coefficient of determination includes: When the determination coefficient is less than the preset threshold, the sample is divided into multiple sub-intervals according to the measured value of fine particle content in each sample within the particle size distribution type. A univariate linear regression analysis is performed in each sub-interval to obtain the sub-interval regression slope. The influence weight function is constructed in piecewise form using the regression slope of each sub-interval. When the determination coefficient is greater than or equal to a preset threshold, the regression slope is used as the influence weight function.

5. The method of claim 1, wherein The step of correcting the initial porosity of each sample based on the influence weighting function and the statistical value of fine particle content to obtain an equivalent initial porosity includes: For each sample, the measured value of fine particle content is compared with the statistical value of fine particle content, and the difference between the actual value of fine particle content and the statistical value of fine particle content is calculated to obtain the fine particle content deviation. Multiply the deviation in fine particle content by the influence weighting function to obtain the influence amount; The equivalent initial porosity is obtained by subtracting the influence amount from the initial porosity of each sample.

6. The method of claim 1, wherein The liquefaction criteria for sandy soil are obtained by fitting the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number, and equivalent cycle number for each sample, including: Based on the number of liquefaction cycles and the cyclic stress ratio of each sample, a power function relationship between the cyclic stress ratio and the number of liquefaction cycles is established. The cyclic stress ratio of each specimen is normalized to the normalized cyclic stress ratio under the reference equivalent number of cycles based on the power function relationship. Based on the normalized cyclic stress ratio and equivalent initial porosity of each sample, a first function relationship is obtained by fitting the critical cyclic stress ratio as the equivalent initial porosity decreases exponentially. Based on the difference between the equivalent number of cycles and the reference equivalent number of cycles for each sample, and the relative change of the critical cyclic stress ratio under different equivalent number of cycles, the power exponent parameter of the equivalent number of cycles is fitted. By incorporating the power exponent parameter into the first functional relationship, a second functional relationship is obtained in which the critical cyclic stress ratio decreases exponentially with the equivalent initial porosity ratio and decreases exponentially with the number of equivalent cycles. The second functional relationship is used as the criterion for judging sand liquefaction.

7. A method for determining the liquefaction of sandy soil, characterized in that, Using the influence weight function and sand liquefaction discrimination criterion obtained by the method described in any one of claims 1 to 6, the liquefaction risk of the sand sample to be tested is judged, including: The fine particle content, initial void ratio, and particle size distribution of the sand sample to be tested were determined. The particle size distribution is used to determine the gradation type of the sand sample to be tested; The initial porosity is corrected according to the gradation type and the influence weight function to obtain an equivalent initial porosity. Calculate the actual cyclic stress ratio based on the seismic motion parameters of the site where the sand sample to be tested is located; Based on the gradation type, the equivalent initial void ratio, and the magnitude in the seismic motion parameters, the critical cyclic stress ratio is calculated using the sand liquefaction discrimination criterion. The actual cyclic stress ratio is compared with the critical cyclic stress ratio, and the liquefaction risk level is output based on the comparison result.

8. A calibration device for a sand liquefaction discrimination criterion, characterized by include: The classification module is used to classify the multiple sand samples based on the collected samples and the coefficient of non-uniformity and curvature of each sand sample, so as to obtain at least one particle size distribution type. The analysis module is used to analyze the measured values ​​of fine particle content and initial porosity of all samples within each particle gradation type for each particle gradation type, and to obtain the influence weight function of the measured value of fine particle content on the initial porosity and the statistical value of fine particle content. The influence weight function characterizes the change in initial porosity caused by a unit change in the measured value of fine particle content. The test module is used to prepare multiple samples with different fine particle contents and initial void ratios for the particle size distribution type, and to conduct cyclic triaxial tests under different cyclic stress ratios and different equivalent cycle numbers, and record the number of cycles when each sample reaches liquefaction; wherein, liquefaction is defined as the pore water pressure ratio reaching a preset critical value, and the equivalent cycle number is determined according to preset different vibration levels; The correction module is used to correct the initial porosity of each sample according to the influence weight function and the fine particle content statistics to obtain an equivalent initial porosity. The fitting module is used to fit the liquefaction criteria of sand based on the equivalent initial void ratio, cyclic stress ratio, liquefaction cycle number and equivalent cycle number of each sample.

9. An electronic device, comprising: include: At least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium having stored thereon computer program instructions, wherein, The method as described in any one of claims 1-7 is implemented when the computer program instructions are executed by the processor.

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