A method and system for rapid estimation of hydraulic characteristics of unsaturated soil based on hierarchical Bayesian

Abstract

The invention provides a method and system for rapidly estimating hydraulic characteristics of unsaturated soil based on hierarchical Bayesian. The specific steps are as follows: obtaining the location of a tested site and soil properties; performing a tensiometer method detection at the location of the tested site to obtain a first database consisting of matrix suction and effective saturation data pairs; using the location of the tested site and the soil properties as indexes, retrieving n sites with similar soil properties to the tested site in a soil-water characteristic curve database of unsaturated soil, wherein the n sites correspond to n groups of matrix suction and effective saturation data pairs to form a second database; constructing a model for rapidly estimating hydraulic characteristics of unsaturated soil based on hierarchical Bayesian; inputting data from the first database and the second database into the model for rapidly estimating hydraulic characteristics of unsaturated soil based on hierarchical Bayesian to obtain a soil-water characteristic curve representing the hydraulic characteristics of unsaturated soil at the tested site, thereby reducing the cost of detecting the soil-water characteristic curve and improving work efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of unsaturated soil research, and in particular to a method and system for quickly estimating the hydraulic characteristics of unsaturated soil based on hierarchical Bayesian. Background Art

[0002] The hydraulic properties of unsaturated soil can be used for geological disaster prediction and research, the flow of water in saturated soil, and the determination of shear strength and permeability coefficient of unsaturated soil. Therefore, it is of great significance to study the hydraulic properties of unsaturated soil. The hydraulic properties of unsaturated soil are usually expressed by soil-water characteristic curve, which is a curve composed of matrix suction and effective saturation data. There are many traditional methods for measuring matrix suction and effective saturation data, including tensiometer technology, axis translation technology, polyethylene glycol solution method, vapor pressure method, contact filter paper technology, etc. Although various methods have achieved good results in their existing applicable scope, they are difficult to be quickly applied in actual engineering practice due to the very harsh experimental conditions and long experimental test cycle.

[0003] Therefore, the existing methods for measuring matrix suction and effective saturation have the following deficiencies:

[0004] (1) Direct measurement of matrix suction and effective saturation is expensive, requires harsh conditions, is time-consuming, and has low operating efficiency;

[0005] (2) The experimental results measured cannot accurately represent the actual matrix suction and effective saturation of the site due to the influence of various factors. Summary of the invention

[0006] In order to further improve the existing measurement technology, the present invention provides a method and system for rapid estimation of the hydraulic characteristics of unsaturated soil based on hierarchical Bayesian. The hierarchical Bayesian method is used to couple existing data information. Combined with the small sample area data measured by the tensiometer method, the matrix suction and effective saturation of the small sample area can be accurately estimated, which reduces the cost of measuring matrix suction and effective saturation, shortens the project cycle, improves work efficiency, and can obtain soil-water characteristic curves representing the full range.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian, the specific steps are as follows:

[0008] S1 obtains the location and soil properties of the site being tested;

[0009] S2 performs a tensiometer test at the location of the tested site to obtain a first database consisting of matrix suction and effective saturation data pairs;

[0010] S3 uses the location and soil properties of the tested site as indexes, retrieves n sites with similar soil properties to the tested site in the soil-water characteristic curve database of unsaturated soil, and the n sites correspond to n sets of data pairs of matrix suction and effective saturation, forming a second database;

[0011] S4 constructs a rapid estimation model of unsaturated soil hydraulic characteristics based on hierarchical Bayesian, including database joint module, model selection module and hierarchical Bayesian coupling module;

[0012] S5 inputs the data of the first database and the second database into a rapid estimation model of hydraulic properties of unsaturated soil based on hierarchical Bayesian, so as to perform rapid estimation of hydraulic properties of unsaturated soil in the measured site.

[0013] Further, in S2, 1 to 3 representative data points in the measured site are selected for measurement using the tensiometer method to obtain a matrix suction and effective saturation data pair to form a first database containing a set of data pairs. ;

[0014] in, Expressed as the soil matrix suction, The soil matrix suction is The effective saturation at , and They represent the directly measured volumetric moisture content, saturated volumetric moisture content and residual volumetric moisture content respectively.

[0015] Furthermore, in S2, the matrix suction and effective saturation data pairs of soils with similar properties to the soil of the tested site are obtained from the soil-water characteristic curve database of unsaturated soils, and the soil-water characteristic curve is fitted separately to obtain the matrix suction values ​​near the inflection points on the soil-water characteristic curve, and the tensiometer method is used to detect the matrix suction values.

[0016] Further, in S3, the database combining module is used to combine the first database and the second database to obtain a specific database containing n+1 sets of data;

[0017] The model selection module is used to determine the unsaturated soil-water characteristic curve fitting model of the tested site according to the soil properties to obtain model parameters;

[0018] The hierarchical Bayesian coupling module is used to perform hierarchical Bayesian coupling calculation on a specific database containing n+1 groups of data to determine model parameters, and to quantify uncertainty intervals to obtain a rapid estimation model for hydraulic properties of unsaturated soil based on hierarchical Bayesian.

[0019] Furthermore, in S3, at least five data pairs of matrix suction and effective saturation were obtained for each site.

[0020] Furthermore, the hierarchical Bayesian coupling conditions in the hierarchical Bayesian coupling module include:

[0021] The prior distribution and hyperparameter distribution of each model follow the same distribution;

[0022] The logarithm of the model parameter is set as the prior distribution, and the parameter of the prior distribution is set as the hyperparameter. In addition, the hyperparameter is determined and The distribution of corresponds to the hyperparameter prior distribution;

[0023] Construct likelihood function based on soil-water characteristic curve fitting model and data points;

[0024] The posterior sample mean of the model parameters of the first database is selected as the model parameter.

[0025] Furthermore, the uncertainty intervals were quantified to obtain 95% confidence intervals.

[0026] The present invention provides a system for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian, comprising:

[0027] The data acquisition module is used to obtain the location of the tested site and the soil properties, and perform tensiometer detection at the location of the tested site to obtain a first database consisting of matrix suction and effective saturation data pairs; using the location of the tested site and the soil properties as indexes, retrieve n sites with similar soil properties to the tested site in the soil-water characteristic curve database of unsaturated soil, and the n sites correspond to n groups of matrix suction and effective saturation data pairs to form a second database;

[0028] Model building module, used to build a fast estimation model of unsaturated soil hydraulic properties based on hierarchical Bayesian;

[0029] The unsaturated soil hydraulic characteristic rapid estimation module is used to input the data of the first database and the second database into the unsaturated soil hydraulic characteristic rapid estimation model based on hierarchical Bayesian to perform rapid estimation of the unsaturated soil hydraulic characteristics of the measured site.

[0030] The present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0031] The present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0032] Compared with the prior art, the present invention has at least the following beneficial effects:

[0033] The present invention proposes a method for quickly estimating the hydraulic characteristics of unsaturated soil based on hierarchical Bayes, constructs a model for quickly estimating the hydraulic characteristics of unsaturated soil based on hierarchical Bayes, utilizes the hierarchical Bayes method to couple existing data information, combines the matrix suction and effective saturation data measured by the tensiometer method, can accurately estimate the real matrix suction and effective saturation data of the measured site, and can also accurately realize the coupling of matrix suction and effective saturation data in a single site, quantify the uncertainty range of matrix suction and effective saturation data, characterize the reliability of experimental results by analyzing the quantified uncertainty interval, reasonably express the reliability of scientific experimental results, and overcome the influence of uncertainty factors on experimental results in the experimental measurement process, the whole process operation steps are simple, the cost of experimental measurement is greatly saved, the engineering time is shortened, the work efficiency is improved, and great help is provided for the application of engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Flow chart of the rapid estimation method of soil-water characteristic curve of unsaturated soil based on hierarchical Bayes;

[0035] Figure 2 Comparison chart of small sample test data of the tested site (f) and test data of other similar sites;

[0036] Figure 3 Under small sample conditions ( Figure 2 -f) Hierarchical Bayesian estimation results of soil-water characteristic curve. DETAILED DESCRIPTION

[0037] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0038] The present invention provides a method for quickly estimating the hydraulic characteristics of unsaturated soil based on hierarchical Bayesian, and the specific steps are as follows:

[0039] (1) Search for literature and engineering record data, use the unsaturated soil properties and soil sample locations as indexes, and combine the data of matrix suction and effective saturation of unsaturated soil to establish a soil-water characteristic curve database for unsaturated soil. ; Or you can directly use existing database data such as UNSODA;

[0040] (2) Determine the corresponding properties of the soil in the test site, such as soil type and geological sedimentation history, through on-site and indoor testing experiments;

[0041] (3) Search for multiple site data with similar soil properties to the site being tested in the database. Each site data forms a data pair. Randomly select one of the site data pairs for fitting to obtain the soil-water characteristic curve of the soil sample. Determine the matrix suction value near the inflection point of the curve as the control quantity to achieve representative small sample data measurement;

[0042] Specifically, by using the tensiometer method, a direct measurement method of the soil-water characteristic curve, 1-3 relatively representative data points in the measured site are measured to obtain the matrix suction and effective saturation data pairs to form a first database containing 1 set of data. ;

[0043] in, Expressed as the soil matrix suction, The soil matrix suction is The effective saturation at , and They represent the directly measured volumetric moisture content, saturated volumetric moisture content and residual volumetric moisture content respectively.

[0044] Using the unsaturated soil properties and soil sample locations of the tested site as indexes, Retrieve n groups of data corresponding to n sites with similar soil properties to the tested site, obtain at least 5 groups of data pairs of matrix suction and effective saturation for each site, and construct a second database containing n groups of data;

[0045] (4) Constructing a rapid estimation model for the hydraulic properties of unsaturated soils based on hierarchical Bayesian

[0046] The hierarchical Bayesian-based rapid estimation model for hydraulic properties of unsaturated soil can utilize the hierarchical Bayesian method to couple the soil-water characteristic curve information of similar soil properties in the engineering site, and combine the small sample data points obtained by the tensiometer method of the measured site to achieve rapid measurement of the soil-water characteristic curve of the unsaturated soil of the measured site; the parameter posteriori obtained by the hierarchical Bayesian coupling can be analyzed to quantify the uncertainty range of the soil-water characteristic curve and reasonably express the reliability of the scientific experimental results;

[0047] Specifically, it includes database union module, model selection module, and hierarchical Bayesian coupling module;

[0048] (4.1) In the database union module, the second database is combined with the first database to establish a specific database containing n+1 sets of data ,in, represents the number of sites with similar soil properties, Indicates the number of data points for each site. In this process, it is necessary to ensure that there are enough data points for sites with similar soil properties, and the number of sites with similar properties should also be large;

[0049] (4.2) In the model selection module, according to the soil properties of the tested site (particle size, soil sample type, etc.), select the unsaturated soil-water characteristic curve fitting model of the tested site from the Brooks-Corey (BC), Van Genuchten (VG), Fredlund and Xing (FX) models, and determine the model parameters. , is the number of model parameters;

[0050] (4.3) In the hierarchical Bayesian coupling module, the specific database obtained in step (4.1) The data in the medium uses hierarchical Bayesian coupling to set the prior distribution and hyperparameter distribution of each site to obey the same distribution to ensure that the soil samples in each tested site are both similar and different. The specific steps of prior setting include:

[0051] (4.3.1) Since the model parameters The actual value of is generally a positive number, so in the process of setting the hierarchical Bayesian prior distribution, the logarithm of the parameter is used. Make settings;

[0052] (4.3.2) Determine the prior distribution. Considering that the logarithm of the model parameters can be positive or negative, the prior distribution follows a normal distribution. In order to reflect the hierarchical Bayesian approach, the parameters of the prior distribution are set as hyperparameters. Corresponding to the prior distribution, determine the hyperparameters. and The distribution of , ensuring that the range of model parameters covers the range of actual parameters, is explained by the following formula (1):

[0053] (1)

[0054] (4.4) The specific calculation of the entire processor processing process is realized with the help of PyMC tools, and the model parameters The prior settings are the same as those described above. In addition, the likelihood function is set according to the data points and the soil-water characteristic curve model. Finally, the exponential power of the prior distribution with base e is taken. According to the default NUTS algorithm of PyMC, each model parameter generate A sample from the posterior distribution.

[0055] Arrange the posterior distribution samples to obtain the posterior mean, median, standard deviation, and the point with the largest probability density to characterize the posterior distribution, select the mean of the posterior distribution samples corresponding to the site in the first database as the model parameter, and then obtain the soil-water characteristic curve of the small sample tested site;

[0056] (4.5) The hierarchical Bayesian theorem used in steps 4.3 to 4.4 is used in the database data. , coupling parameters , hyperparameters and In case, It can be expressed as shown in the following formula (2):

[0057] (2)

[0058] (4.6) Quantify the uncertainty interval, calculate the 95% confidence interval, quantify the uncertainty range of the soil-water characteristic curve, and reasonably express the reliability of the scientific experimental results. The specific steps are as follows:

[0059] (4.6.1) Extraction parameters The posterior distribution samples,combine the posterior sampling results of all parameters to obtain different,curves.,At a specific matrix suction, there will be different saturation values,taking the 2.5% and 97.5% values;

[0060] (4.6.2) Repeat step (9.1) for different matrix suction values, connect all the numerical points corresponding to 2.5% and 97.5%, and you can get a 95% confidence interval;

[0061] (4.6.3) This range can be used to characterize the impact of uncertainty factors in the experimental process, making the data distribution range more scientific and more convincing for the coupling of small sample areas in hierarchical Bayesian.

[0062] (4.7) Substitute the optimal values ​​obtained from the posterior distribution analysis of the model parameters into the model to draw the soil-water characteristic curve of the entire measured site, and combine it with the 95% confidence interval to obtain the complete soil-water characteristic curve graph of the measured site. Output the drawn graph and the model expression of the measured site to get the final result.

[0063] The present invention provides a system for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian, comprising:

[0064] The data acquisition module is used to obtain the location of the tested site and the soil properties, and perform tensiometer detection at the location of the tested site to obtain a first database consisting of matrix suction and effective saturation data pairs; using the location of the tested site and the soil properties as indexes, retrieve n sites with similar soil properties to the tested site in the soil-water characteristic curve database of unsaturated soil, and the n sites correspond to n groups of matrix suction and effective saturation data pairs to form a second database;

[0065] Model building module, used to build a fast estimation model of unsaturated soil hydraulic properties based on hierarchical Bayesian;

[0066] The unsaturated soil hydraulic characteristic rapid estimation module is used to input the data of the first database and the second database into the unsaturated soil hydraulic characteristic rapid estimation model based on hierarchical Bayesian to perform rapid estimation of the unsaturated soil hydraulic characteristics of the measured site.

[0067] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method for rapid estimation of hydraulic characteristics of unsaturated soil based on hierarchical Bayes are implemented.

[0068] The computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0069] The terminal device can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server.

[0070] The terminal device may include, but is not limited to, a processor and a memory.

[0071] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0072] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0073] If the module / unit integrated in the terminal device is implemented in the form of 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 present invention implements all or part of the process in the above method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc.

[0074] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0075] Example

[0076] This embodiment discloses a method for quickly estimating the hydraulic characteristics of unsaturated soil based on hierarchical Bayesian. The method flow is as follows: Figure 1 As shown, the specific implementation includes the following steps:

[0077] (1) The cases in the embodiments are derived from the UNSODA database, which contains 715 soil samples and 7931 data points. These data are from indoor tests of 12 soil types under dry conditions. The soil types are classified according to the Soil Conservation Service classification scheme of the United States Department of Agriculture.

[0078] (2) The sandy soil in Lakeland, USA was obtained from the UNSODA database as the soil sample of the test site. The code of the soil sample in UNSODA is 1075;

[0079] (3) Two representative data points were selected from the 1075 soil samples in the UNSODA database as the measurement points, thus obtaining the first database ,in, It is the soil matrix suction. The soil matrix is The effective saturation at

[0080] In the UNSODA database, five sets of soil-water characteristic curve data points with similar properties to the soil in 1075 were retrieved. The soils in the five sites were coded 1070, 1071, 1072, 1073, and 1074 in the UNSODA database. Each site included at least five data pairs to construct the second database.

[0081] (4) The data from the first and second databases are input into the rapid estimation model of unsaturated soil hydraulic characteristics based on hierarchical Bayesian. The specific data processing process is as follows:

[0082] In the database combination module, in the second database composed of soil sample codes 1070, 1071, 1072, 1073, and 1074 and the first database composed of soil sample code 1075, each soil sample data corresponds to a group of data, which together constitute a specific database containing 6 groups of data. ,in, represents the number of sites with similar soil properties, Indicates the number of data points for each site. In this process, it is necessary to ensure that there are enough data points for sites with similar soil properties, and the number of sites should be large. The comparison of data points of different sites in the database is as follows: Figure 2 As shown, Figure 2 (f) is the estimated model, (a)-(e) are site model data points with similar properties to (f);

[0083] (5) In the model selection module, according to the soil properties, this embodiment selects the Fredlund and Xing (FX) model and determines the model parameters , the FX model is converted into the relationship between matrix suction and effective saturation as shown in the following formula (3);

[0084] (3)

[0085] (6) In the hierarchical Bayesian coupling module, when using hierarchical Bayesian coupling for data in a specific database, the prior distribution and hyperparameter distribution of each model are set to obey the same distribution to ensure that the soil samples in each site are both similar and different. The specific steps of the prior include:

[0086] (6.1) Since the model parameters The actual value of is generally positive, so in the hierarchical Bayesian setting process, the logarithm of the model parameter is used Make settings;

[0087] (6.2) Determine the prior distribution. Considering that the logarithmic value of the model parameter can be positive or negative, the prior distribution obeys the normal distribution. In order to reflect the hierarchical Bayesian, the model parameters in the distribution are set as hyperparameters, which can be explained by the following formulas (4) to (6):

[0088] (4)

[0089] (5)

[0090] (6)

[0091] (6.3) Determine hyperparameters , , and , , The distribution of , corresponds to the prior distribution, ensuring that the range of model-generated parameters covers the range of actual parameters;

[0092] (7) Set the likelihood function. The specific calculation is implemented with the help of PyMC tools. Take the exponential power of the prior distribution with e as the base. According to the default NUTS algorithm of PyMC, each model parameter Generate 5000 samples of posterior distribution. Arrange the posterior distribution samples to obtain the posterior mean, median, standard deviation, and the point with the largest probability density to characterize the posterior distribution. Select the posterior sample mean as the coupled soil-water characteristic curve model parameter. The posterior sample arrangement results are shown in Table 1:

[0093] Table 1. Sample summary of posterior distribution of parameters in hierarchical Bayesian model

[0094]

[0095] (8) Quantify the uncertainty interval and calculate the 95% confidence interval to quantify the uncertainty range of the soil-water characteristic curve and reasonably express the reliability of the scientific experimental results. The specific steps are as follows:

[0096] (8.1) Extracting model parameters The posterior distribution samples were analyzed with a 95% confidence interval. The posterior sampling results of all model parameters were combined to obtain different curves. At a specific matrix suction, there would be different saturation values. The 2.5% and 97.5% values ​​were taken to obtain a 95% confidence interval.

[0097] (8.2) Repeat step (8.1) for different matrix suction values, connect all the numerical points corresponding to 2.5% and 97.5%, and you can get a 95% confidence interval;

[0098] (8.3) This range can be used to characterize the impact of uncertainty factors in the experimental process, making the data distribution range more scientific and more convincing for the hierarchical Bayesian coupling of small sample sites.

[0099] (9) At the output end of the unsaturated soil hydraulic characteristics rapid estimation model based on hierarchical Bayesian, the soil-water characteristic curve of the measured site and the quantitative result diagram of the uncertainty interval can be obtained. The coupled soil-water characteristic curve and 95% confidence interval of the small sample site in this implementation case are shown as follows: Figure 3 shown.

[0100] The present invention provides a method and system for rapidly estimating hydraulic characteristics of unsaturated soil based on hierarchical Bayesian, and the specific steps are as follows: obtaining the location of a tested site and soil properties; performing a tensiometer method test at the location of the tested site to obtain a first database consisting of matrix suction and effective saturation data pairs; using the location of the tested site and the soil properties as indexes, retrieving n sites with similar soil properties to the tested site in a soil-water characteristic curve database of unsaturated soil, wherein the n sites correspond to n groups of matrix suction and effective saturation data pairs, forming a second database; constructing a model for rapidly estimating hydraulic characteristics of unsaturated soil based on hierarchical Bayesian; inputting data from the first database and the second database into the model for rapidly estimating hydraulic characteristics of unsaturated soil based on hierarchical Bayesian, performing statistical analysis on posterior samples, and obtaining a soil-water characteristic curve that characterizes the hydraulic characteristics of unsaturated soil at the tested site, thereby reducing the cost of detecting the soil-water characteristic curve and improving work efficiency.

Claims

1. A method for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian, characterized in that: The specific steps are as follows: S1 obtains the location and soil properties of the site being tested; S2 performs a tensiometer test at the location of the tested site to obtain a first database consisting of matrix suction and effective saturation data pairs; S3 uses the location and soil properties of the tested site as indexes, retrieves n sites with similar soil properties to the tested site from the soil-water characteristic curve database of unsaturated soil, and the n sites correspond to n sets of data pairs of matrix suction and effective saturation, forming a second database; S4 constructs a rapid estimation model of unsaturated soil hydraulic characteristics based on hierarchical Bayesian, including database joint module, model selection module and hierarchical Bayesian coupling module; S5 inputs the data of the first database and the second database into a rapid estimation model of hydraulic properties of unsaturated soil based on hierarchical Bayesian to perform rapid estimation of hydraulic properties of unsaturated soil at the site under test; In S4, the database combining module is used to combine the first database and the second database to obtain a specific database containing n+1 sets of data; The model selection module is used to determine the unsaturated soil-water characteristic curve fitting model of the tested site according to the soil properties to obtain model parameters; The hierarchical Bayesian coupling module is used to perform hierarchical Bayesian coupling calculation on a specific database containing n+1 groups of data to determine model parameters, and to quantify uncertainty intervals to obtain a rapid estimation model for hydraulic properties of unsaturated soil based on hierarchical Bayesian.

2. The method for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian approach according to claim 1 is characterized in that: In S2, 1 to 3 representative data points in the measured site are selected and measured using the tensiometer method to obtain matrix suction and effective saturation data pairs to form a first database containing 1 set of data pairs. ; in, Expressed as the soil matrix suction, The soil matrix suction is The effective saturation at , and They represent the directly measured volumetric moisture content, saturated volumetric moisture content and residual volumetric moisture content respectively.

3. The method for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian approach according to claim 2 is characterized in that: In S2, the matrix suction and effective saturation data pairs of multiple site soils with similar properties to the soil of the tested site are obtained from the soil-water characteristic curve database of unsaturated soil, one group is randomly selected and fitted separately to obtain the soil-water characteristic curve, and the matrix suction values ​​near the inflection points on the soil-water characteristic curve are selected for tensiometer detection.

4. The method for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian approach according to claim 1 is characterized in that: In S3, at least five data pairs of matrix suction and effective saturation were obtained for each site.

5. The method for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian according to claim 1 is characterized in that: The hierarchical Bayesian coupling conditions in the hierarchical Bayesian coupling module include: The prior distribution and hyperparameter distribution of each model follow the same distribution; The logarithm of the model parameter is set as the prior distribution, and the parameter of the prior distribution is set as the hyperparameter. In addition, the hyperparameter is determined and The distribution of corresponds to the hyperparameter prior distribution; Construct likelihood function based on soil-water characteristic curve fitting model and data points; The posterior sample mean of the model parameters of the first database is selected as the model parameter.

6. The method for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian according to claim 1 is characterized in that: Uncertainty intervals were quantified to obtain 95% confidence intervals.

7. A system for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian, characterized in that: Running a method for rapid estimation of hydraulic properties of unsaturated soil based on hierarchical Bayesian as described in any one of claims 1 to 6 comprises: The data acquisition module is used to obtain the location of the tested site and the soil properties, and perform tensiometer detection at the location of the tested site to obtain a first database consisting of matrix suction and effective saturation data pairs; using the location of the tested site and the soil properties as indexes, retrieve n sites with similar soil properties to the tested site in the soil-water characteristic curve database of unsaturated soil, and the n sites correspond to n groups of matrix suction and effective saturation data pairs to form a second database; Model building module, used to build a fast estimation model of unsaturated soil hydraulic properties based on hierarchical Bayesian; The unsaturated soil hydraulic characteristic rapid estimation module is used to input the data of the first database and the second database into the unsaturated soil hydraulic characteristic rapid estimation model based on hierarchical Bayesian to perform rapid estimation of the unsaturated soil hydraulic characteristics of the measured site.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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