Roadbed humidity calculation method considering random distribution of plant root systems

Abstract

The invention provides a roadbed humidity calculation method considering random distribution of plant root systems. The roadbed humidity calculation method comprises the following steps: S1, constructing a vegetation soil hydraulic characteristic model considering the root systems; s2, constructing a vegetation water absorption model and a water migration control equation; s3, constructing a vegetation root system random field; and S4, calculating the roadbed humidity field by using the vegetation root system random field constructed in the S3 and the vegetation water absorption model and the water migration control equation constructed in the S2. The problems that an existing roadbed humidity calculation method is low in accuracy and does not conform to actual plant root system distribution are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of geotechnical engineering, and particularly relates to a method for calculating the humidity of a roadbed considering the random distribution of plant roots. Background Technique

[0002] The distribution of the humidity field of the roadbed is the core factor determining the strength and stiffness of the roadbed structure. Since the roadbed is usually exposed to the atmospheric environment and affected by external factors such as rainfall, evaporation, and groundwater, moisture will gradually migrate from the roadbed surface to the inside, resulting in an increase in the roadbed humidity, which in turn affects the overall stability of the roadbed. Therefore, it is crucial to accurately obtain the distribution of the roadbed humidity field.

[0003] For reasons such as greening and beauty, and erosion prevention, a large number of vegetation are often planted on the roadbed slope for coverage. On the one hand, the growth of vegetation will absorb water through its own physiological functions to produce transpiration and reduce the soil moisture content. On the other hand, the growth of the roots will change the soil structure and thus cause changes in the soil hydraulic characteristics. In the existing humidity calculation methods, the influence of vegetation on the moisture migration of the roadbed is considered from two aspects: the control equation and the hydraulic characteristic model. (1) Control equation: Based on the saturated-unsaturated seepage equation, a vegetation water absorption term is added for description. However, the water absorption model uses the root shape of a certain specific plant, and the water absorption amount of each region is distributed by geometric generalization into shape functions such as triangle, uniformity, and exponential type. However, in the actual roadbed slope surface, there is no single index and the roots of each vegetation will also cross each other. The existing method is unreasonable. (2) Hydraulic characteristic model: The existing model can consider the influence of the roots. However, the root characteristics have a high degree of uncertainty. Therefore, according to the conventional qualitative analysis method, it is impossible to accurately characterize the influence of the roots on the soil hydraulic characteristics, which in turn affects the accuracy of the humidity calculation results. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for calculating the humidity of a roadbed considering the random distribution of plant roots to solve the problems of low accuracy and non-conformance to the actual plant root distribution in the existing roadbed humidity calculation methods.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is that a method for calculating the humidity of a roadbed considering the random distribution of plant roots includes the following steps:

[0006] S1: Construct a hydraulic characteristic model of vegetation soil considering roots;

[0007] S2: Construct a vegetation water absorption model and a moisture migration control equation;

[0008] S3: Construct a random field of vegetation roots;

[0009] S4: Calculate the subgrade moisture field by using the vegetation root system random field constructed in S3, the vegetation water absorption model constructed in S2, and the water migration control equation.

[0010] Further, the S1 includes:

[0011] Take the undisturbed soil containing roots and measure the volumetric water content θ under different matrix suctions h by using a pressure plate apparatus to construct the soil-water characteristic curve; m

[0012] Test the saturated hydraulic conductivity k of the soil under different root volume ratios R by using a saturated permeameter; v s ;

[0013] Construct a vegetation soil hydraulic model considering the influence of the proportion of plant roots, perform data fitting, and obtain the vegetation soil hydraulic property model and the permeability coefficient function; the calculation formulas of the vegetation soil hydraulic property model and the permeability coefficient function are as shown in Equations (1a) and (1b):

[0014]

[0015] where S is the effective saturation degree, θ r is the residual volumetric water content, θ s is the saturated volumetric water content, θ r 、θ s are obtained through the soil-water characteristic curve; m2, m3, m4 are fitting parameters, m1 is a conversion parameter, m1 = 1 - 1 / m2; e v is the porosity of the vegetation soil, e b is the porosity of the bare soil, e b = θ s / (1 - θ s ); k is the unsaturated hydraulic conductivity.

[0016] Further, the S2 includes:

[0017] S21: Distribute the transpiration effect to each position of the soil layer according to the proportion of the roots, and construct the water absorption model S(x, y) as shown in Equation (2):

[0018] S(x, y) = g(x, y)LT p (2)

[0019] where T p is the transpiration rate, L is the slope length, and x, y represent the horizontal and longitudinal coordinates of the root position;

[0020] g(x, y) is the root proportion function, which is expressed as:

[0021] ​​

[0022] Among them, D is the area domain of the vegetation layer, R v (x, y) is the spatial distribution function of the root volume ratio.

[0023] Furthermore, the said S2 further includes:

[0024] S22: Construct the control equation for water migration in vegetated soil, as shown in Equation (4):

[0025]

[0026] Among them, k represents the unsaturated hydraulic conductivity, h represents the pressure, and when h is negative, it represents the matrix suction, θ represents the soil water content, represents the change rate of soil water content with time, represents the change rate of pressure in the horizontal and vertical directions; represents the change rate of the unsaturated hydraulic conductivity in the vertical direction.

[0027] Furthermore, the said S3 includes:

[0028] S31: Express the spatial distribution function R v (x, y) of the root volume ratio as N random variables with coordinates (x i , y i ) in space: R v (x, y) = [R v1 (x1, y1)…R vi (x i , y i )…R vN (x N , y N )], where each random variable has the same distribution and the mean and variance are both μ, σ 2 ;

[0029] S32: Select the exponential type autocorrelation function Equation (5) to construct the autocorrelation model of the root volume ratio random field,

[0030]

[0031] In the formula, Δx, Δy represent the relative coordinates of any two random variables in the x and y directions in the discrete space, ρ(Δx, Δy) is the autocorrelation coefficient matrix between any two random variables, δ x , δ y are the autocorrelation distances of the vegetation root volume ratio in the x and y directions respectively, obtained from the measured data of the root volume ratio; σ 2 is the variance of the random variable.

[0032] Further, S3 further includes:

[0033] S33: Based on the root system volume ratio random field autocorrelation model, generate the autocorrelation coefficient matrix ρ(Δx, Δy), and decompose the autocorrelation coefficient matrix to obtain the lower triangular matrix L, as shown in Equation (6a). Subsequently, multiply the lower triangular matrix L by the random matrix U that has the same distribution as the random variable of R v (x, y) to obtain the vegetation root system volume ratio random field R v (x, y), as shown in Equation (6b):

[0034] LL T = ρ(Δx, Δy) (6a)

[0035] R v (x, y) = LU (6b)

[0036] where L T is the transpose of matrix L, and U and R v (x, y) have the same mean and variance.

[0037] Further, S4 includes:

[0038] S41: Generate the random field R v (x, y).

[0039] Further, S4 further includes:

[0040] S42: Regard the base course and the road base course as the same calculation area, select the calculation formula for the non-vegetation area in Equation (4), assign hydraulic parameters, and calculate the humidity field of this area;

[0041]

[0042] Regard the vegetation layer as a separate calculation area, select the calculation formula for the vegetation area in Equation (4), and at the same time use the water absorption model Equation (2), assign hydraulic parameters, and calculate the humidity field of this area;

[0043] S(x, y) = g(x, y)LT p (2)

[0044] where T p is the transpiration rate, L is the slope length, x and y represent the horizontal and longitudinal coordinates of the root system position; g(x, y) is the root system proportion function.

[0045] The beneficial effects of the present invention are

[0046] 1. Based on the root volume ratio weight, the present invention constructs a generalization function based on the entire root planting layer, distributes the transpiration amount according to this generalization function, and proposes a water absorption model that is not restricted by the distribution of the root structure of a single vegetation, breaking through the confinement that the actual situation such as the intersection of plant roots cannot be considered in the calculation of subgrade humidity, making the subgrade humidity calculation more in line with the actual situation.

[0047] 2. By constructing a random field of root volume ratio, the present invention considers the random distribution of plant roots, and realizes the influence of the randomness of plant roots on the humidity calculation result from two aspects of hydraulic characteristics and water absorption function, making the humidity calculation more in line with the actual situation and improving the accuracy of subgrade humidity calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 It is a schematic diagram of the geometric model of a typical subgrade structure.

[0050] Figure 2 It is a distribution diagram of the root volume ratio with depth.

[0051] Figure 3 It is an evolution diagram of the water content at Observation Point 1 over time.

[0052] Figure 4 It is an evolution diagram of the water content at Observation Point 2 over time.

[0053] Figure 5 It is a change diagram of the cross-section water content distribution over time without considering vegetation.

[0054] Figure 6 It is a change diagram of the cross-section water content distribution over time considering vegetation.

[0055] Figure 7 It is a change diagram of the cross-section water content distribution over time considering vegetation and its root randomness.

[0056] Figure 8 It is the probability distribution of the water content value at Observation Point 1 after 15 years.

[0057] Figure 9 It is the probability distribution of the water content value at Observation Point 2 after 15 years. DETAILED DESCRIPTION OF THE INVENTION

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0059] The complete steps of the embodiments of the present invention are as follows:

[0060] S1: Construct a vegetation soil hydraulic property model considering the root system.

[0061] Construct a vegetation soil hydraulic property model.

[0062] Take undisturbed soil containing roots, and use a pressure plate apparatus to measure the volumetric water content θ under different matrix suctions h m to construct a soil-water characteristic curve; use a saturated permeameter to test the saturated permeability coefficient k of the soil under different root volume ratios R v ; then use a vegetation soil hydraulic model considering the influence of the proportion of plant roots, such as formulas (1a) and (1b), for data fitting to obtain the vegetation soil hydraulic property model and the permeability coefficient function. Incorporate the vegetation soil hydraulic property model into the COMSOL calculation program. s In the formula, S is the effective saturation,

[0063]

[0064] where θ is the volumetric water content, θ is the residual volumetric water content, θ r is the saturated volumetric water content, θ s is obtained through the soil-water characteristic curve; h r is the matrix suction; m2, m3, m4 are fitting parameters, m1 is a conversion parameter, m1 = 1 - 1 / m2; the void ratio s e of the vegetation soil m is the void ratio of the bare soil, e e b = θ b / (1 - θ s ) s ;

[0065] R v is the root volume ratio; k s is the saturated permeability coefficient; k is the unsaturated permeability coefficient, and after fitting m1, m2, m3, m4, k is calculated according to formula (1b).

[0066] S2: Construct a vegetation water absorption model and a water migration control equation.

[0067] S21: The influence of root water absorption on subgrade water migration is manifested as transpiration. Based on the principle of water balance, the transpiration effect is distributed to each position in the soil layer according to the proportion weight of the roots, and the water absorption model S(x, y) can be constructed as follows in Equation (2):

[0068] S(x, y) = g(x, y)LT p (2)

[0069] In the formula, T p is the transpiration rate, L is the slope length, and x and y represent the horizontal and vertical coordinates of the root position.

[0070] g(x, y) is a generalized root proportion function used to distribute the transpiration rate in the vegetation soil layer area, expressed as:

[0071]

[0072] Among them, D is the area domain of the vegetation layer. R v (x, y) is the spatial distribution function of the root volume ratio.

[0073] S22: Based on the principle of water balance and Darcy's law, considering the plant water absorption as a mass source to account for the influence of vegetation water absorption on the basis of the saturated-unsaturated seepage control equation, the water migration control equation for vegetated soil is proposed, as shown in Equation (4)

[0074]

[0075] Among them, k represents the unsaturated permeability coefficient, h represents the pressure, and when h is negative, it represents the matrix suction h m , θ represents the soil water content, represents the change rate of soil water content with time, represents the change rate of pressure in the horizontal and vertical directions; represents the change rate of the unsaturated permeability coefficient in the vertical direction.

[0076] S3: Construct the vegetation root random field.

[0077] S31: Discretize the vegetation root volume ratio R v in the spatial position and represent it as a set of N random variables R i with coordinates (x i ) in space, R v (x, y) = [R v1 (x1, y1)…R vi (x i , y i )…R vN (x N , y N)], each random variable has the same distribution, and its mean and variance are both μ and σ 2 , and shows spatial correlation in space, that is, the correlation between random variables is only determined by the relative positions of the variables

[0078] S32: Based on the random field theory, the exponential autocorrelation function formula (5) is selected to describe the root volume ratio R v The relationship between the relative positions and correlations of random variables after discretization

[0079]

[0080] In the formula, Δx and Δy represent the relative coordinates of any two random variables in the x and y directions in the discrete space, ρ(Δx, Δy) is the autocorrelation matrix between any two random variables, and δ x , δ y are the autocorrelation distances of the vegetation root volume ratio R v in the x and y directions respectively, which are obtained through statistical analysis of the measured data of the root volume ratio, and σ is the variance

[0081] S33: Based on the above constructed root volume ratio R v random field autocorrelation model, through the matlab software, generate the root volume ratio R v random field autocorrelation coefficient matrix ρ(Δx, Δy) in the subgrade space, use the covariance matrix cholesky decomposition method to decompose the random field autocorrelation coefficient matrix ρ(Δx, Δy) to obtain the lower triangular matrix L, as shown in formula (6a), and then multiply the lower triangular matrix L by the random matrix U with the same distribution as the R v variable, U and R v have the same mean and variance, and obtain the simulated random field R v of the vegetation root volume ratio with spatial correlation v (x, y), as shown in formula (6b), to describe the random distribution of the vegetation roots and the situation of being spatially correlated

[0082] LL T =ρ(Δx, Δy)(6a)

[0083] R v (x, y)=LU(6b)

[0084] In the formula, L T is the transpose of the matrix L, and U is a set of random matrices with the same distribution as the vegetation root volume ratio R v variable

[0085] S4: Calculation of the subgrade moisture field distribution

[0086] Based on the typical subgrade structure, a geometric model is established. According to the root depth of plant growth, it is divided into a slope vegetation layer, a subgrade bare soil layer, and a base layer; the foundation is 1 m high and 23 m wide, the subgrade is 6 m high, the top width of the subgrade is 13.5 m, and the slope ratio is 1:1.5; the thickness of the vegetation soil layer is set to 0.5 m, and the structure is as Figure 1 shown.

[0087] Transpiration rate T p = 5 mm / d, and the calculation time is 15 years.

[0088] Set the observation point ①(19.25, 3) in the vegetation layer and the observation point ②(18.75, 3) in the subgrade body; take a cross-section perpendicular to the vegetation layer, and the coordinates of the two intersection points of the cross-section and the vegetation layer are (17.5, 4) and (17.65, 4.23), and the thickness of the cross-section is 0.5 m, as Figure 1 shown.

[0089] S41: Implementation of the root volume ratio random field in COMSOL. Import the R v (x, y) random field generated by Matlab into the COMSOL calculation software using the interpolation function method. Using the variable module, set the vegetation root volume ratio R v random function R v = μ + σ * R v (x, y), where μ is the mean value of the vegetation root volume ratio random variable in space, calculated from the measured root volume ratio, and σ is the standard deviation of the vegetation root volume ratio random variable.

[0090] S42: Set the calculation area. Set the base layer and the subgrade bare soil area as the same calculation area, and use the Richards saturated-unsaturated seepage control equation, that is, the calculation formula for the non-vegetation area in Equation (4), and assign the same hydraulic parameters. Set the vegetation layer as a separate calculation area. Based on the calculation formula for the vegetation area in Equation (4), write the water absorption model (2) into the calculation program in the form of a mass source to realize the seepage calculation considering the influence of vegetation.

[0091] The hydraulic model parameters are shown in Table 1.

[0092] Table 1 Hydraulic model parameters

[0093]

[0094] Data processing. Through the above calculation method, input the basic parameters for calculation, and use the software data export function to output the water content values of observation points ① and ② for analysis.

[0095] Comparative analysis of different calculation methods:

[0096] Three working conditions, namely not considering vegetation, qualitatively considering vegetation, and randomly considering vegetation, are set to explore the influence of the randomness of plant roots on the moisture content of the subgrade. When qualitatively considering vegetation, a situation where the root systems of vegetation are approximately uniformly distributed along the depth is selected. Specifically, the generalization function f(z) = 1 / L1 can be adopted, where f(z) represents the distribution function of the root systems along the depth z, and L1 represents the root length; when considering the randomness of the root systems, the root volume ratio in the entire vegetation area follows the same probability distribution. Taking a set cross-section as an example, the root volume ratio distribution is as Figure 2 shown.

[0097] The moisture evolution laws of the observation points and cross-sections under each method are as Figures 3 - 9 shown. Figure 3 , Figure 4 shows the evolution laws of the water content with time at the No. 1 and No. 2 observation points inside and outside the vegetation layer under different calculation methods. Figure 3 In , for the No. 1 observation point set inside the vegetation layer, the water content generally shows an increase with time and tends to be stable after about one year. When the influence of vegetation is not considered, the water content at equilibrium is around 0.273 cm 3 / cm 3 ; while when considering the influence of vegetation, that is, not considering the random field, the water content at equilibrium is around 0.254 cm 3 / cm 3 and shows a decrease. This is because after considering the role of the vegetation root systems, the root systems occupy some of the pores inside the soil mass; at the same time, there are obvious differences in the water content after considering the random field and the water content after considering the influence of vegetation (here, a single result under an arbitrary random field is used for analysis).

[0098] Figure 4 In , for the No. 2 observation point set inside the subgrade, the equilibrium water contents calculated by each method are different, but they are all around 0.273 cm 3 / cm 3 This is because the hydraulic characteristics of the subgrade soil have not changed, and the influence of the vegetation layer on the equilibrium water content is weak, that is, the distribution and water absorption capacity of the root systems in the vegetation layer have an insignificant overall impact on the inside of the subgrade; at the same time, the growth rate of the water content slows down after considering the influence of vegetation, which indicates that the vegetation absorbs water through the root systems, reduces the water content in the soil, and thus delays the rising speed of the water content, and also shows that the vegetation has a certain regulatory effect on the water dynamics.

[0099] As Figures 5 - 7 shown, to analyze the overall moisture migration and change in the vegetation soil layer, the evolution laws of the water content with time under different methods are described by selecting a cross-section. It can be seen that Figures 5 - 6 in , the water content is generally uniformly distributed along the depth direction; comparing the results of the two, when the influence of vegetation is not considered ( Figure 5), the moisture content of the soil layer is generally high; Figure 7 When considering the influence of the random distribution of vegetation and its roots, the moisture content shows non-uniform variation along the depth direction, and this variation becomes larger until it stabilizes over time. This is because Figure 2 After the random variation of the roots in, their hydraulic properties, namely the effective saturation S and the volumetric moisture content θ, have changed, and the moisture content that can be maintained under the same matrix suction is different.

[0100] To analyze the influence of the random distribution of roots on the overall moisture content of the subgrade after 15 years of operation, based on the Monte Carlo theory, 200 groups of random field data were generated for calculation and statistical analysis, as Figures 8 - 9 shown, Figure 8 In, for Observation Point 1, after 15 years, the distribution of the moisture content shows a normal distribution, with a mean of 0.2534 cm 3 / cm 3 , and the variation range is 0.240 cm 3 / cm 3 ~0.264 cm 3 / cm 3 . Figure 9 In, for Observation Point 2, the mean is 0.2729 cm 3 / cm 3 , and the variation range is 0.27265~0.2731 cm 3 / cm 3 ; By comparing the two observation points, it can be found that the variation range of the moisture content at Observation Point 1 in the vegetation layer is large and relatively dispersed, while the variation range of the moisture content at Observation Point 2 inside the subgrade body is small and mainly concentrated around the mean value. This is because Observation Point 1 is directly affected by the random distribution of roots, while Observation Point 2 is affected by the moisture state of the vegetation layer, and the randomness of the roots has a sharp reduction in its effect.

[0101] In summary, it shows that considering the influence of vegetation and the random distribution of roots is crucial for the distribution of the moisture field of the subgrade and should be reasonably considered in actual engineering.

[0102] Each embodiment in this specification is described in a related manner. For the same or similar parts between each embodiment, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment.

[0103] The above is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are all included in the protection scope of the present invention.

Claims

1. A method for calculating the moisture content of a roadbed considering the random distribution of plant roots, characterized in that, It includes the following steps: S1: Construct a vegetation soil hydraulic property model considering the root system; S2: Construct a vegetation water absorption model and a water migration control equation; S3: Construct a vegetation root system random field; S4: Use the vegetation root system random field constructed in S3 and the vegetation water absorption model and water migration control equation constructed in S2 to calculate the subgrade moisture field.

2. The method for calculating the moisture content of a roadbed considering the random distribution of plant roots according to claim 1, characterized in that, The S1 includes: Take the undisturbed soil containing roots and use a pressure plate apparatus to measure the volumetric water content θ under different matrix suctions h m and construct the soil-water characteristic curve; Testing the saturated hydraulic conductivity k of soils with different root volume ratios R using a saturation permeameter v s ;​ Construct a vegetation soil hydraulic model considering the influence of the proportion of plant roots, and perform data fitting to obtain the vegetation soil hydraulic property model and the permeability coefficient function; the calculation formulas of the vegetation soil hydraulic property model and the permeability coefficient function are respectively as shown in formulas (1a) and (1b): Among them, S is the effective saturation, θ r is the residual volume water content, θ s is the saturated volume water content, θ r 、θ s are obtained through the soil-water characteristic curve; m2, m3, m4 are fitting parameters, m1 is a conversion parameter, m1 = 1 - 1 / m2; e v is the porosity of the vegetated soil, e b is the porosity of the bare soil, e b =θ s / (1 - θ s ); k is the unsaturated hydraulic conductivity.

3. The method for calculating the moisture content of the roadbed considering the random distribution of plant roots according to claim 1, wherein The S2 includes: S21: Distribute the transpiration effect to each position of the soil layer according to the proportion of the root system, and construct the water absorption model S(x, y) as shown in formula (2): S(x,y) = g(x,y) LT p (2) where T p is the transpiration rate, L is the slope length, and x and y represent the horizontal and vertical coordinates of the root position; g(x, y) is the root proportion function, expressed as: where D is the area domain of the vegetation layer, and R v (x, y) is the spatial distribution function of the root volume ratio.

4. The method for calculating the subgrade moisture content considering the random distribution of plant roots according to claim 3, characterized in that, The S2 also includes: S22: Construct the vegetation soil water migration control equation, as shown in formula (4): Among them, k represents the unsaturated hydraulic conductivity, h represents the pressure, and when h is negative, it represents the matrix suction, and θ represents the water content of the soil. represents the change rate of the soil water content with time. represents the change rates of the pressure in the horizontal and vertical directions. represents the change rate of the unsaturated hydraulic conductivity in the vertical direction.

5. The method for calculating the moisture content of the roadbed considering the random distribution of plant roots according to claim 1, characterized in that, The S3 includes: S31: Represent the root volume ratio spatial distribution function R v (x, y) as N random variables with coordinates (x i , y i ) in space: R v (x, y) = [R v1 (x1, y1)…R vi (x i , y i )…R vN (x N , y N )], where each random variable has the same distribution and the mean and variance are both μ, σ 2 ; S32: Select the exponential autocorrelation function formula (5) to construct the autocorrelation model of the root volume ratio random field, where Δx and Δy represent the relative coordinates of any two random variables in the x and y directions in the discrete space, ρ(Δx, Δy) is the autocorrelation coefficient matrix between any two random variables, and δ x , δ y are the autocorrelation distances of the root volume ratio of vegetation in the x and y directions, respectively, obtained from the measured data of the root volume ratio; σ 2 is the variance of the random variable.

6. The method for calculating the moisture content of a roadbed considering the random distribution of plant roots according to claim 5, characterized in that The S3 also includes: S33: Based on the root volume ratio random field autocorrelation model, generate the autocorrelation coefficient matrix ρ(Δx,Δy), and decompose the autocorrelation coefficient matrix to obtain the lower triangular matrix L, as shown in Equation (6a). Subsequently, multiply the lower triangular matrix L by the random matrix U that has the same distribution as the random variable of R v (x,y) to obtain the vegetation root volume ratio random field R v (x,y), as shown in Equation (6b): LL T = ρ(Δx, Δy)(6a) R v (x,y) = LU(6b) where L T is the transpose of matrix L, and U and R v (x, y) have the same mean and variance.

7. The method for calculating the moisture content of a roadbed considering the random distribution of plant roots according to claim 1, characterized in that The S4 includes: S41: Generate the random field R v (x, y).

8. The method for calculating the subgrade humidity considering the random distribution of plant roots according to claim 7, characterized in that, The S4 also includes: S42: Regard the subbase and the base course as the same calculation area, select the calculation formula for the non-vegetation area in formula (4), assign the hydraulic parameters, and calculate the moisture field of this area; Regard the vegetation layer as a separate calculation area, select the calculation formula for the vegetation area in formula (4), and at the same time use the water absorption model formula (2), assign the hydraulic parameters, and calculate the moisture field of this area; S(x,y) = g(x,y) LT p (2) Among them, T p is the transpiration rate, L is the slope length, x and y represent the horizontal and vertical coordinates of the root position; g(x, y) is the root proportion function.

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