Methods and systems for calculating channel leakage applicable to layered soil and lining conditions

Abstract

This invention discloses a method and system for calculating channel leakage under layered soil and lining conditions, comprising: collecting basic channel cross-sectional parameters, soil parameters, and parameters of different lining schemes; constructing a numerical model of channel leakage loss under unlined conditions; using the numerical model of channel leakage loss to simulate the channel leakage process and leakage volume, generating a dataset; constructing a multiple linear regression model of channel leakage volume and verifying it, using the verified model as the channel leakage calculation model under unlined conditions; constructing a calculation model for the seepage reduction coefficient of different lining schemes based on the changes in channel leakage volume with and without lining; combining the channel leakage calculation model with each seepage reduction coefficient calculation model to obtain a calculation model for calculating channel leakage volume under different lining schemes. This invention calculates channel leakage volume based on channel cross-sectional parameters, soil parameters, and lining parameters, improving the accuracy of calculating leakage loss in lined channels.

Description

Technical Field

[0001] This invention belongs to the technical field of agricultural irrigation water, specifically relating to a method and system for calculating channel leakage under layered soil and lining conditions. Background Technology

[0002] Canals are a key component of the water distribution process in irrigation districts. The vast majority (over 90%) of water loss during the journey from the source to the crops is due to canal seepage, and excessive seepage is a major cause of low agricultural water use efficiency. Lining canals with low-permeability materials can significantly reduce seepage, prevent erosion and siltation, and lower management costs, making it a widely used seepage prevention method both domestically and internationally. Due to the ease of construction and good seepage prevention effect of concrete and geomembranes, composite linings consisting of concrete, geomembranes, or both are the most widely used in practice. As more and more canals adopt canal lining for seepage prevention, accurately calculating the seepage of lined canals is crucial for assessing irrigation water use efficiency, evaluating the benefits of lining projects, developing accurate irrigation plans, managing irrigation districts, improving water resource utilization efficiency, and promoting sustainable water use.

[0003] Currently, methods for calculating canal leakage include analytical methods, empirical formulas, and numerical methods. However, the permeability coefficient of actual canal bed soil varies significantly in space. In analytical solution derivation, the assumed boundary and initial conditions are difficult to satisfy, and the final parameter calculation process is more complex than with general empirical formulas. Therefore, analytical solutions are rarely used in practice, especially for calculating leakage in layered soils and lined canals. Empirical formulas can quickly and easily calculate the leakage rate of lined canals; however, the lining parameters in these formulas are either constant values ​​that vary with the lining material or are given a broad range. The calculations do not consider the weakening of the lining effect over time or the changes in leakage rate due to cracks and voids. Therefore, empirical formulas can introduce significant errors when calculating the leakage rate of lined canals. Furthermore, existing empirical formulas for calculating earthen canals also fail to consider the influence of layered soils, making them difficult to apply directly in practice. Numerical calculation methods can be well adapted to solving channel leakage problems under various boundary conditions. By simulating the changes in channel leakage in time and space, as well as the response of groundwater and soil moisture content to channel leakage, this method requires a large number of iterations, resulting in low computational efficiency and the possibility of non-convergence.

[0004] Accurate evaluation of channel leakage loss in applications such as irrigation district design, canal system water use efficiency assessment, and water rights transfer in canal lining requires more precise assessment of channel leakage under conditions of actual stratified soil, lining form, and the presence of cracks and pores in the irrigation district. It is necessary to construct a method for calculating earthen canal leakage under stratified soil conditions and establish a mathematical relationship between lining permeability parameters and the degree of canal damage, and to develop a more efficient and accurate method for calculating channel leakage. Summary of the Invention

[0005] One objective of this invention is to address the shortcomings of existing technologies by providing a method for calculating channel leakage under layered soil and lining conditions. This method calculates channel leakage based on channel cross-sectional parameters, layered soil parameters of the channel bed, and lining parameters, thereby effectively ensuring the accuracy of leakage loss calculation for lined channels.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for calculating channel leakage applicable to layered soil and lining conditions includes the following steps:

[0008] Step 1: Collect basic channel cross-sectional parameters, soil parameters, and parameters for different lining schemes;

[0009] Step 2: Based on the channel cross-sectional parameters and soil parameters collected in Step 1, construct a numerical model of channel leakage loss under unlined conditions.

[0010] Step 3: Using the numerical model of channel seepage loss obtained in Step 2, simulate the channel seepage process and channel seepage amount under different channel cross-sectional parameters, soil parameters and boundary conditions, and generate a dataset based on this.

[0011] Step 4: Using channel cross-section parameters and soil parameters as independent variables, construct a multiple linear regression model for channel leakage based on a portion of the dataset, and use the remaining data to test the multiple linear regression model. The test result serves as the channel leakage calculation model under unlined conditions.

[0012] Step 5: Construct a calculation model for the seepage reduction coefficient of different lining schemes based on the changes in seepage volume with and without lining.

[0013] Step 6: Combine the channel leakage calculation model from Step 4 with the seepage reduction coefficient calculation model from Step 5 to obtain the calculation model for calculating channel leakage under different lining schemes.

[0014] Furthermore, the channel cross-sectional parameters include the channel bottom width, channel water depth, and channel slope coefficient, while the soil parameters include the saturated permeability coefficient and moisture characteristic curve data of each soil layer.

[0015] Furthermore, the lining scheme includes concrete lining and geomembrane lining. The parameters for concrete lining include the permeability coefficient of concrete material, crack width, crack length and location, while the parameters for geomembrane lining include the permeability coefficient of geomembrane, the location and area of ​​pores.

[0016] Furthermore, the numerical model for channel leakage loss constructed in step 2 is as follows:

[0017] ;

[0018] In the formula, θ This refers to the soil volumetric water content. t For time; z is the vertical Z-axis coordinate of the soil, with the ground as the 0 point and upward as the positive direction; x is the horizontal X-axis coordinate of the soil, which is perpendicular to the direction of the channel, with the channel axis as the 0 point; K ( h ) represents the unsaturated hydraulic conductivity of the soil, which is a function of the soil water potential h and is determined based on the soil moisture characteristic curve; h Soil water potential.

[0019] Furthermore, the multiple linear regression model for channel leakage constructed in step 4 is as follows:

[0020] ;

[0021] In the formula: It is the seepage rate of earthen canals; and These are empirical parameters; t Infiltration time, i c It is the stable leakage rate of the channel; f 1 、f 2 and f 3 represents three multivariate linear functions; b It is the width of the channel bottom; m It is the channel slope coefficient; h The channel is deep; K s It represents the saturated permeability coefficient of each soil layer; the subscript n indicates that there are a total of n layers.

[0022] Furthermore, in step 5, the calculation model for the reduction factor of the concrete lining is as follows:

[0023] ;

[0024] In the formula: β c This is the reduction factor for concrete lining; K slThe equivalent permeability coefficient of the concrete lining; K soil It is the permeability coefficient of the canal soil; B It is the width of the water surface in the cross-section of the channel; C It is a dimensionless coefficient, according to B / h and slope coefficient m Interpolate the value. h Soil water potential; δ It refers to the thickness of the lining; K sc The permeability coefficient of the concrete lining material; l c It is the length of the crack; subscript i Representing the i A crack; C k It is the permeability coefficient contribution rate of the fracture, representing the increase in the equivalent permeability coefficient caused by a fracture of unit length; a It is a double logarithmic curve C k With crack width d f The slope of a straight upward line; d f0 and d f These are the actual width and the equivalent width of the crack, respectively. C kmax It is with d f Increase C k The maximum value; d fs yes C k No longer follow d f Increased critical crack width value; p f It is an equivalent multiple of the crack width area.

[0025] Furthermore, the calculation model for the reduction factor of the geomembrane lining in step 5 is as follows:

[0026] ;

[0027] In the formula: β g This is the reduction factor for geomembrane lining; D g The degree of damage to the geomembrane lining is dimensionless. β g，min This is the reduction factor when the geomembrane lining is free of holes; K soil It is the permeability coefficient of the canal soil. Let T be the permeability coefficient of the geomembrane, and T be the thickness of the geomembrane. B It is the width of the water surface in the cross-section of the channel; C It is a dimensionless coefficient, according to B / h and slope coefficient m Interpolate the value. h Soil water potential; a d and a g These are the area of ​​the holes in the underwater geomembrane lining and the area of ​​the geomembrane, respectively. a de It is the equivalent area of ​​the holes in the underwater geomembrane lining; p g It is an equivalent multiple of the hole area.

[0028] Another objective of this invention is to provide a method for determining a lining scheme, which involves determining the design target for channel leakage, continuously adjusting the parameters of the calculation model for channel leakage under different lining schemes and simulating channel leakage, and outputting the lining scheme and its corresponding parameters when the design target is achieved.

[0029] The present invention also provides a system for implementing the above-described method for calculating channel leakage under layered soil and lining conditions, comprising:

[0030] The data collection module is used to collect parameters of the basic channel cross-section, soil parameters, and parameters of different lining schemes;

[0031] The numerical model building module is used to build a numerical model of channel leakage loss under unlined conditions based on the channel cross-section parameters and soil parameters collected by the data collection module.

[0032] The dataset generation module is used to simulate the channel seepage loss numerical model obtained by the numerical model building module, simulate the channel seepage process and channel seepage amount under different channel cross-sectional parameters, soil parameters and boundary conditions, and generate a dataset based on this.

[0033] The model building module is used to construct a multiple linear regression model of channel leakage using channel cross-section parameters and soil parameters as independent variables. It generates a portion of the dataset from the dataset generation module and uses the remaining data for testing. It also constructs a model to calculate the corresponding leakage reduction coefficient based on the changes in leakage of channels with and without lining.

[0034] The channel leakage calculation module is used to combine the channel leakage calculation model under unlined conditions obtained from the model building module with the seepage reduction coefficient calculation model for different lining schemes to obtain a calculation model for calculating channel leakage under different lining schemes, and calculate the channel leakage for each lining scheme based on the obtained model.

[0035] Furthermore, it also includes a lining scheme determination module, which continuously adjusts the parameters of the channel leakage calculation model for different lining schemes according to the design target of channel leakage, and inputs them into the channel leakage calculation module to simulate the channel leakage. When the design target is achieved, the lining scheme and its corresponding parameters are output.

[0036] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention calculates the seepage rate of earthen canals based on the channel cross-sectional parameters and soil parameters of each layer of the canal bed. The physical meaning of the parameters is clear, and it is applicable to various types of layered soil channels. The soil transport formula constructed through numerical simulation has a theoretical basis and is applicable to different conditions. The reduction coefficients for concrete and geomembrane lining no longer rely solely on empirical values, but have a clear quantitative relationship with the permeability coefficient of the lining material and the characteristics of damage (cracks and pores). This method can be applied to various irrigation areas and various soil and lining conditions, providing a more accurate and efficient way to calculate and evaluate channel seepage. In addition, this invention can also generate channel parameters and lining schemes that meet the corresponding channel seepage loss targets, providing more intuitive technical support for actual channel design and channel seepage prevention. Construction and lining scheme management based on channel parameters can effectively ensure the seepage prevention effect of the channel. Attached Figure Description

[0037] Figure 1 is a flowchart of a channel leakage calculation method applicable to layered soil and lining conditions provided by an embodiment of the present invention;

[0038] Figure 2 is a diagram illustrating the boundary conditions during numerical simulation according to an embodiment of the present invention;

[0039] Figure 3 is a comparison of the steady seepage rates predicted by the multiple linear regression models for large and small water depths in the embodiments of the present invention.

[0040] Figure 4 shows the change in channel leakage over time under the condition that the equivalent permeability coefficient of the concrete lining increases year by year in the embodiment of the present invention.

[0041] Figure 5 shows the change in channel leakage over time under the condition that the pore density of the geomembrane lining increases year by year according to the embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0044] The present invention will be further described below with reference to specific embodiments, but these are not intended to limit the scope of the invention.

[0045] like Figure 1 As shown, this invention discloses a method for calculating channel leakage applicable to layered soil and lining conditions, comprising the following steps:

[0046] Step 1: Collect basic channel cross-sectional parameters, soil parameters, and parameters for different lining schemes;

[0047] In this embodiment, the test area is located in an irrigation area of ​​the Hetao Irrigation District in Inner Mongolia. The channel cross-sectional parameters include channel water depth, channel bottom width, and channel slope coefficient. The soil under the channel consists of three layers, and examples of soil data for each layer are shown in Table 1.

[0048] Table 1. Main parameters of soil layers in typical canals

[0049]

[0050] The lining scheme includes concrete lining and geomembrane lining. The parameters for concrete lining include the permeability coefficient of concrete material, crack width, crack length and location. The parameters for geomembrane lining include the permeability coefficient of geomembrane, the location and area of ​​pores.

[0051] Step 2. Based on the channel cross-sectional parameters and soil parameters collected in Step 1, construct a numerical model of channel leakage loss under unlined conditions;

[0052] Under lining conditions, channel seepage loss depends on the movement of water in the soil. Therefore, constructing a soil water movement model is equivalent to constructing a numerical model for channel seepage loss under unlined conditions. Specifically, the soil water movement model is as follows:

[0053] ;

[0054] In the formula, θ Soil volumetric water content, dimensionless; t For time, d; zis the vertical Z-axis coordinate of the soil, with the ground as 0 and upward as the positive direction, in cm; x is the horizontal X-axis coordinate of the soil (perpendicular to the channel direction), with the channel axis as 0, in cm; K ( h ) represents the unsaturated hydraulic conductivity of the soil, which is a function of the soil water potential h and is determined based on the soil moisture characteristic curve; h Soil water potential.

[0055] Step 3. Use the numerical model of channel seepage loss from Step 2 to simulate the channel seepage process and seepage volume under different channel cross-sections, channel water depths, soil parameters and boundary conditions, and generate a dataset based on this.

[0056] Figure 2 A simplified diagram of the channel simulation boundary conditions is provided. The channel cross-section is assumed to be axisymmetric, with AI as the axis of symmetry, dividing the channel symmetrically into two parts. Since the channel is a temporary test area, water needs to be injected to a certain level before the hydrostatic test. Therefore, sides AB and BC can be set as variable-depth boundaries; sides CD, DE, EF, and FG are connected to the atmosphere, and their boundary conditions are set as atmospheric boundaries. Based on the symmetry of the seepage domain, side AI is set as a zero-flux boundary. During the simulation, the groundwater depth in the study area is approximately 2.2–2.4 m. Since vertical exchange of groundwater within the simulation domain is minimal, vertical exchange below 10 m can be ignored. Therefore, the bottom boundary of the simulation domain is set to 10 meters below the surface as a zero-flux boundary. When the width of the lateral simulation domain is 15 times the width of the channel bottom, the influence of the lateral boundary on channel leakage can be eliminated; therefore, GH is also set as a zero-flux boundary. Under the above boundary conditions, this embodiment uses a channel seepage loss numerical model to simulate the channel seepage process and channel seepage amount under different channel cross-sections, channel water depths, and soil parameters, obtaining a total of 787 sets of data, which are then organized into a dataset.

[0057] Step 4: Using channel cross-section parameters and soil parameters as independent variables, construct a multiple linear regression model for channel leakage based on a portion of the dataset, and use the remaining data to test the multiple linear regression model. The test result serves as the channel leakage calculation model under unlined conditions.

[0058] Based on a portion of the dataset from step 3 (generally about 70%, but can be adjusted according to specific needs), using channel cross-sectional parameters (channel water depth, channel bottom width, channel slope coefficient) and soil parameters as independent variables, a multiple linear regression model for the seepage volume of earthen canals is constructed as follows:

[0059] ;

[0060] In the formula: Ss It is the seepage rate of the earthen canal, in meters. 3 / (s·m); k and These are empirical parameters; i c It is the stable leakage rate of the channel, m 3 / (s·m); f 1 、f 2 and f 3 represents three multivariate linear functions; b is the channel bottom width, m; m It is the slope coefficient of the channel, in m / m; h It refers to the depth of the channel, in meters (m). K s It is the saturated permeability coefficient of each soil layer, in m / s; the subscript n indicates that there are a total of n layers.

[0061] Then, the remaining data in the dataset (usually about 30%) is used to test the constructed multiple linear regression model. Once the accuracy is achieved, it can be used as a method for calculating channel leakage under unlined conditions.

[0062] In this embodiment, some scenario cases used to construct the multiple linear regression model are shown in Table 2, totaling 625 groups; scenario cases used to validate the multiple linear regression model are shown in Table 3, totaling 162 groups. Ultimately, the validated multiple linear regression model in this embodiment consists of two parts, one applicable to large water depths (above 1m) and the other to small water depths (below 1m).

[0063] The formula applicable to deep water is as follows:

[0064] ;

[0065] In the formula: i For channel leakage rate, cm 3 / s; t Infiltration time, in seconds; i c To stabilize the infiltration rate, cm 3 / s; l The length of the channel is in meters (m). h The water depth is in meters (m). b The width of the base is in meters (m). m This refers to the slope coefficient; k s1 The saturated hydraulic conductivity of the soil layer from 0 to 60 cm is expressed in m / d. k s2 The saturated hydraulic conductivity of the soil layer from 60 to 160 cm is given in m / d. k s3 The saturated hydraulic conductivity of the soil layer from 160 to 300 cm is given in m / d. ks The saturated hydraulic conductivity of the channel soil is expressed in m / d.

[0066] The formula applicable to shallow water depths is as follows:

[0067]

[0068] In the formula: i For channel leakage rate, cm 3 / s; t Infiltration time, in seconds; i c To stabilize the infiltration rate, cm 3 / s; l The length of the channel is in meters (m). h The water depth is in meters (m). b The width of the base is in meters (m). m This refers to the slope coefficient; k s1 The saturated hydraulic conductivity of the soil layer from 0 to 60 cm is expressed in m / d. k s2 The saturated hydraulic conductivity of the soil layer from 60 to 160 cm is given in m / d. k s3 The saturated hydraulic conductivity of the soil layer from 160 to 300 cm is given in m / d. k s The saturated hydraulic conductivity of the channel soil is expressed in m / d.

[0069] The results predicted by the two formulas are shown below. Figure 3 .

[0070] Table 2 Simulation scenarios for constructing multi-factor models

[0071]

[0072] Table 3 Simulation scenarios for validating the multi-factor model

[0073]

[0074] Note: Combinations B and C in the table are the verification combinations under the two conditions of large water depth and small water depth, respectively.

[0075] Step 5: Construct a calculation model for the seepage reduction coefficient of different lining schemes based on the changes in seepage volume with and without lining.

[0076] The leakage rate of a lined channel is equal to the lining reduction factor multiplied by the leakage rate of the channel without lining. Based on this premise, and combining the cross-sectional parameters of the basic channel, soil parameters, and lining parameters (including the permeability coefficient, crack width, crack length, and crack location of the concrete lining), and the permeability coefficient, pore location, and size of the geomembrane lining, a calculation model for the leakage reduction factor of the channel lining is constructed. The calculation model for the leakage reduction factor of the concrete lining is as follows:

[0077] ;

[0078] In the formula: β c This is the reduction factor for concrete lining, dimensionless; K sl is the equivalent permeability coefficient of the concrete lining, in m / s; K soil It is the permeability coefficient of the canal soil, in m / s; B It is the width of the water surface in the cross-section of the channel, in meters; C It is a dimensionless coefficient, according to B / h and slope coefficient m Interpolate the value; δ This refers to the lining thickness, in meters (m). When calculating leakage in small channels, the effect of soil capillary action on leakage also needs to be considered; in this case, the parameters... C according to B / h v and slope coefficient m Interpolation, h v It equals the channel water depth plus half of the maximum capillary rise height. K sc Let be the permeability coefficient of the concrete lining material, in m / s; l c The length of the crack is in meters (m); subscript i Representing the i A crack; C k It is the permeability coefficient contribution rate of the fracture, representing the increase in equivalent permeability coefficient per unit length caused by the fracture, 1 / s; a It is a double logarithmic curve C k With crack width d f The slope of a straight upward line, [-]; d f0 and d f These are the actual width and equivalent width of the crack, respectively, in mm; C kmax It is with df Increase C k The maximum value is 1 / s; d fs yes C k No longer follow d f Increased critical crack width value, mm; p f It is an equivalent multiple of the crack width area;

[0079] The calculation model for the reduction factor of geomembrane lining is as follows:

[0080] ;

[0081] In the formula: β g , which is the reduction factor for geomembrane lining, dimensionless; D g The degree of damage to the geomembrane lining is dimensionless. β g，min , is the reduction factor when the geomembrane lining is free of pores, dimensionless; Where is the permeability coefficient of the geomembrane, T is the thickness of the geomembrane, and the meanings of the other symbols are the same as those in the formula for calculating the reduction coefficient of concrete lining. a d and a g These are the area of ​​the holes in the underwater geomembrane lining and the area of ​​the geomembrane, respectively, in meters. 2 ; a de It is the equivalent area (m²) of the pores in the underwater geomembrane lining. 2 ; p g It is an equivalent multiple of the hole area.

[0082] The equivalent multiples of crack width area in the reduction factor of concrete lining and the equivalent multiples of pore density in the reduction factor of geomembrane lining were calibrated by combining measured data. When experimental data is insufficient, the equivalent multiple of crack width area cannot be calibrated easily; in such cases, the equivalent permeability coefficient of concrete lining can be directly calibrated based on measured leakage data. In this embodiment, the equivalent permeability coefficient of concrete lining was calibrated using measured test data from a concrete-lined channel. The channel was lined with concrete, with a thickness of 0.1 m, a bottom width of 1.2 m, and a slope coefficient of 1.5. The permeability coefficients of the three soil layers were 0.31, 0.31, and 0.66 m / d, respectively. Before and after lining, at a water depth of 0.82 m, the leakage rate of the channel was 0.196 cm⁻¹. 2 / s and 0.096 cm 2 / s, with a corresponding lining reduction factor of 0.49. In the formula of step 6, the water surface width B is 3.66 m, and the canal bed soil permeability coefficient is... K soil The value is 0.31 m / d, and the coefficient C is 2.35, therefore the calibrated value is... Ks l 2.05×10 -7 m / s.

[0083] In this embodiment, the equivalent multiple of pore density is determined using measured test data from a geomembrane-lined channel. The specific parameter values ​​for this channel are as follows: the measured reduction factor for the geomembrane lining is 0.89; β g，min This is the reduction factor when the geomembrane lining is non-porous, since the permeability coefficient is less than 1.0 × 10⁻⁶. -10 m / s, with a value of 0.001; actual measured a d and a g They are 48 mm 2 and 1.91×10 6 mm, therefore the rate was determined p g It is 100.

[0084] Step 6: Combine the channel leakage calculation model from Step 4 with the seepage reduction coefficient calculation model from Step 5 to obtain the calculation model for calculating channel leakage under different lining schemes.

[0085] Multiplying the channel leakage calculation model from step 4 with the seepage reduction coefficient calculation model from step 5 yields the calculation model for calculating channel leakage under different lining schemes. For an unlined channel with a bottom width of 1.2 m, a slope coefficient of 1.5, and permeability coefficients of the three soil layers of 0.31, 0.31, and 0.66 m / d respectively, the calculated seepage rate of the soil channel at a water depth of 1 m is 0.196 cm³. 2 / s. Based on the results of step 5, the equivalent permeability coefficient of the concrete lining at 1.0×10⁻⁶ can be calculated respectively. -10 ~ 1.0×10 -6 Leakage rate under conditions of m / s variation increasing by one order of magnitude every 2.5 years (see results). Figure 4 (And with the use of geomembrane lining, the actual density of pores increases by 3.713 × 10⁻⁶ per year.) -7 Channel leakage rate under the given conditions (see results) Figure 5 ).

[0086] This embodiment also provides a method for determining the lining scheme. First, the design target for channel leakage is determined. The parameters of the calculation model for channel leakage under different lining schemes are continuously adjusted, and channel leakage is simulated. When the design target is achieved, the lining scheme and its corresponding parameters are output. In this example, the design target is to ensure that the leakage rate of the lined channel is less than 50% of that of the earthen channel for the first 10 years. Among the two lining schemes, since the leakage rates of concrete lining and geomembrane lining are 66% and 28% of the earthen channel leakage rate in the 10th year, respectively, the geomembrane lining scheme is selected.

[0087] The present invention also provides a system for implementing the above-described method for calculating channel leakage under layered soil and lining conditions, comprising:

[0088] The data collection module is used to collect parameters of the basic channel cross-section, soil parameters, and parameters of different lining schemes;

[0089] The numerical model building module is used to build a numerical model of channel leakage loss under unlined conditions based on the channel cross-section parameters and soil parameters collected by the data collection module.

[0090] The dataset generation module is used to simulate the channel seepage loss numerical model obtained by the numerical model building module, simulate the channel seepage process and channel seepage amount under different channel cross-sectional parameters, soil parameters and boundary conditions, and generate a dataset based on this.

[0091] The model building module is used to construct a multiple linear regression model of channel leakage using channel cross-section parameters and soil parameters as independent variables. It generates a portion of the dataset from the dataset generation module and uses the remaining data for testing. It also constructs a model to calculate the corresponding leakage reduction coefficient based on the changes in leakage of channels with and without lining.

[0092] The channel leakage calculation module is used to combine the channel leakage calculation model under unlined conditions obtained from the model building module with the seepage reduction coefficient calculation model for different lining schemes to obtain a calculation model for calculating channel leakage under different lining schemes, and calculate the channel leakage for each lining scheme based on the obtained model.

[0093] In another embodiment, the system for calculating the leakage of lined channels further includes a lining scheme determination module, which continuously adjusts the parameters of the calculation model for the leakage of different lining schemes according to the design target of the channel leakage, and inputs them into the channel leakage calculation module to simulate the channel leakage. When the design target is achieved, the lining scheme and its corresponding parameters are output.

[0094] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and scope of protection of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the content of this specification should be included within the scope of protection of the present invention.

Claims

1. A method for calculating channel leakage applicable to layered soil and lining conditions, characterized in that, Includes the following steps: Step 1: Collect basic channel cross-sectional parameters, soil parameters, and parameters for different lining schemes; Step 2: Based on the channel cross-sectional parameters and soil parameters collected in Step 1, construct a numerical model of channel leakage loss under unlined conditions. Step 3: Using the numerical model of channel seepage loss obtained in Step 2, simulate the channel seepage process and channel seepage amount under different channel cross-sectional parameters, soil parameters and boundary conditions, and generate a dataset based on this. Step 4: Using channel cross-section parameters and soil parameters as independent variables, construct a multiple linear regression model for channel leakage based on a portion of the dataset, and use the remaining data to test the multiple linear regression model. The test result serves as the channel leakage calculation model under unlined conditions. Step 5: Construct a calculation model for the seepage reduction coefficient of different lining schemes based on the changes in seepage volume with and without lining. Step 6: Combine the channel leakage calculation model from Step 4 with the seepage reduction coefficient calculation model from Step 5 to obtain the calculation model for calculating channel leakage under different lining schemes. The multiple linear regression model for channel leakage constructed in step 4 is as follows: ； In the formula: It is the seepage rate of earthen canals; and These are empirical parameters; t Infiltration time, i c It is the stable leakage rate of the channel; f 1 、f 2 and f 3 represents three multivariate linear functions; b It is the width of the channel bottom; m It is the channel slope coefficient; h The channel is deep; K s It represents the saturated permeability coefficient of each soil layer; the subscript n indicates that there are a total of n layers.

2. The method for calculating channel leakage under layered soil and lining conditions according to claim 1, characterized in that, In step 1, the channel cross-sectional parameters include the channel bottom width, channel water depth, and channel slope coefficient, while the soil parameters include the saturated permeability coefficient and moisture characteristic curves of each soil layer.

3. The method for calculating channel leakage under layered soil and lining conditions according to claim 1, characterized in that, The lining scheme includes concrete lining and geomembrane lining. The parameters for concrete lining include the permeability coefficient of concrete material, crack width, crack length and location. The parameters for geomembrane lining include the permeability coefficient of geomembrane, the location and area of ​​pores.

4. The method for calculating channel leakage under layered soil and lining conditions according to claim 2, characterized in that, The numerical model for channel leakage loss constructed in step 2 is as follows: ； In the formula, θ This refers to the soil volumetric water content. t For time; z It is the vertical Z-axis coordinate of the soil, with the ground as the 0 point and upward as the positive direction; x It is the X-axis coordinate of the soil in the horizontal direction, that is, perpendicular to the direction of the channel, with the channel axis as the 0 point; K ( h ) represents the unsaturated hydraulic conductivity of the soil, which is a function of the soil water potential h and is determined based on the soil moisture characteristic curve; h Soil water potential.

5. The method for calculating channel leakage under layered soil and lining conditions according to claim 3, characterized in that, In step 5, the calculation model for the reduction factor of concrete lining is as follows: ； In the formula: β c This is the reduction factor for concrete lining; K sl The equivalent permeability coefficient of the concrete lining; K soil It is the permeability coefficient of the canal soil; B It is the width of the water surface in the cross-section of the channel; C It is a dimensionless coefficient, according to B / h and slope coefficient m Interpolate the value. h Soil water potential; δ It refers to the thickness of the lining; K sc The permeability coefficient of the concrete lining material; l c It is the length of the crack; subscript i Representing the i A crack; C k It is the permeability coefficient contribution rate of the fracture, representing the increase in the equivalent permeability coefficient caused by a fracture of unit length; a It is a double logarithmic curve C k With crack width d f The slope of a straight upward line; d f0 and d f These are the actual width and the equivalent width of the crack, respectively. C kmax It is with d f Increase C k The maximum value; d fs yes C k No longer follow d f Increased critical crack width value; p f It is an equivalent multiple of the crack width area.

6. The method for calculating channel leakage under layered soil and lining conditions according to claim 3, characterized in that, The calculation model for the reduction factor of geomembrane lining in step 5 is as follows: ； In the formula: β g This is the reduction factor for geomembrane lining; D g The degree of damage to the geomembrane lining is dimensionless. β g，min This is the reduction factor when the geomembrane lining is free of holes; K soil It is the permeability coefficient of the canal soil. Let T be the permeability coefficient of the geomembrane, and T be the thickness of the geomembrane. B It is the width of the water surface in the cross-section of the channel; C It is a dimensionless coefficient, according to B / h and slope coefficient m Interpolate the value. h Soil water potential; a d and a g These are the area of ​​the holes in the underwater geomembrane lining and the area of ​​the geomembrane, respectively. a de It is the equivalent area of ​​the holes in the underwater geomembrane lining; p g It is an equivalent multiple of the hole area.

7. A method for determining a lining scheme, characterized in that, The design target for channel leakage is determined, and the parameters of the calculation model for channel leakage under different lining schemes as described in any one of claims 1-6 are continuously adjusted and the channel leakage is simulated. When the design target is achieved, the lining scheme and its corresponding parameters are output.

8. A system for implementing the channel leakage calculation method applicable to layered soil and lining conditions as described in any one of claims 1-6, characterized in that, include: The data collection module is used to collect parameters of the basic channel cross-section, soil parameters, and parameters of different lining schemes; The numerical model building module is used to build a numerical model of channel leakage loss under unlined conditions based on the channel cross-section parameters and soil parameters collected by the data collection module. The dataset generation module is used to simulate the channel seepage loss numerical model obtained by the numerical model building module, simulate the channel seepage process and channel seepage amount under different channel cross-sectional parameters, soil parameters and boundary conditions, and generate a dataset based on this. The model building module is used to construct a multiple linear regression model of channel leakage using channel cross-section parameters and soil parameters as independent variables. It generates a portion of the dataset from the dataset generation module and uses the remaining data for testing. It also constructs a model to calculate the corresponding leakage reduction coefficient based on the changes in leakage of channels with and without lining. The channel leakage calculation module is used to combine the channel leakage calculation model under unlined conditions obtained from the model building module with the seepage reduction coefficient calculation model for different lining schemes to obtain a calculation model for calculating channel leakage under different lining schemes, and calculate the channel leakage for each lining scheme based on the obtained model.

9. The system for calculating channel leakage under layered soil and lining conditions according to claim 8, characterized in that, It also includes a lining scheme determination module, which continuously adjusts the parameters of the channel leakage calculation model for different lining schemes according to the design target of channel leakage, and inputs them into the channel leakage calculation module to simulate the channel leakage. When the design target is achieved, the lining scheme and its corresponding parameters are output.

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