Method for pouring concrete anti-seepage body through large dam foundation karst cave guide pipe

Abstract

The invention discloses a method for pouring a concrete anti-seepage body through a large dam foundation karst cave guide pipe, which comprises the following steps of: designing an optimal size parameter of a trapezoidal section of the concrete anti-seepage body, generating a numerical model, establishing a single concrete pouring guide pipe, namely a single guide pipe and a single hopper, and carrying out concrete accumulation numerical simulation. Flow parameters of concrete particles of the numerical model are determined, a plurality of concrete pouring guide pipes, namely a plurality of guide pipes and a plurality of hoppers, are established based on the data, concrete accumulation numerical simulation is carried out, an arrangement scheme of the concrete pouring guide pipes is determined, and a prediction model with the optimal performance is constructed; and inputting the flow parameters of the concrete particles of the numerical model into the prediction model with the optimal performance to obtain the mix proportion of the conduit pouring concrete. According to the method, the optimal trapezoidal section, the guide pipe arrangement parameters and the concrete mix proportion of the anti-seepage body are designed through various technical means, and the large dam foundation karst cave guide pipe pouring concrete anti-seepage body is effectively guided.

Description

Technical Field

[0001] The invention belongs to the technical field of design and construction of a large-scale dam foundation karst cave conduit for pouring concrete anti-seepage bodies, and specifically relates to a method for pouring a large-scale dam foundation karst cave conduit for pouring concrete anti-seepage bodies. Background Art

[0002] Due to the existence of karst geological phenomena, it will have a significant impact on the foundation treatment during the construction period of the reservoir dam and the success of water storage during the operation period, and even affect the success or failure of the entire project. The anti-seepage treatment methods for karst dam foundation projects are relatively complex, and there are not many successful cases of actual engineering applications. The commonly used method is still to use cement and fine stone concrete for filling and grouting. The difficulties in the anti-seepage treatment of karst caves are mainly manifested in two aspects: one is the difficulty in accurately exploring the location of karst caves and the distribution of underground space; the other is the difficulty in effectively blocking large karst caves. For the anti-seepage treatment of large dam foundation karst caves, the key to ensuring the anti-seepage effect lies in scientifically determining the number, spacing, grouting volume, grouting pressure, cement water-cement ratio and other construction parameters of the pouring conduit grouting, which is directly related to the reliability and economy of the anti-seepage treatment of dam foundation karst caves. In actual projects, at present, construction can only rely on the experience of engineers, which leads to the excessively extensive treatment of dam foundation karst caves, which is difficult to meet the needs of scientific and fine construction.

[0003] Therefore, it is necessary to combine the characteristics of dam foundation karst cave anti-seepage treatment, obtain the location, size and spatial distribution of underground karst caves through drilling 3D laser radar scanning, and use research methods such as particle flow numerical simulation method, indoor concrete falling accumulation test and cement mix ratio parameter test to determine the best economic section and optimal construction parameters of the anti-seepage body for karst cave dam foundation anti-seepage construction treatment, and construct karst cave dam foundation treatment methods and technologies. This has positive significance for improving the construction technology of embankment anti-seepage body in karst areas and ensuring the safe operation of dam projects. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for casting a concrete anti-seepage body in a large-scale dam foundation cave conduit, the purpose of which is to solve the problem that in actual engineering, construction can only rely on the experience of engineers, which leads to the treatment of the dam foundation cave being too extensive and difficult to meet the requirements of scientific and fine construction.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for pouring a concrete impermeable body through a large dam foundation karst cave conduit, comprising the following steps: Step S1: Scan the underground cave using a three-dimensional laser radar to obtain scanning data, and design the optimal size parameters of the trapezoidal section of the concrete impermeable body in the underground cave based on the existing geological survey results and the anti-sliding stability safety factor; Step S2: Generate a numerical model boundary wall, establish a single concrete pouring conduit, i.e., a single conduit and a single hopper, use a particle flow discrete element method to perform a concrete accumulation numerical simulation on the single conduit and the hopper, form a particle accumulation body, draw the contour characteristics of the particle accumulation body, determine the size parameters of the cross section of the particle accumulation body according to the contour characteristics, make the size parameters of the cross section of the particle accumulation body match the optimal size parameters, and determine the flow parameters of the concrete particles of the numerical model; Step S3: Based on the scanning data in step S1, the existing geological survey results and the flow parameters of the concrete particles in the numerical model in step S2, multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers, are established, and the steps of step S2 are repeated to perform numerical simulation on the multiple conduits and hoppers to obtain the size parameters of the cross section of the particle accumulation body, and by continuously changing the arrangement of the multiple conduits and hoppers, the size parameters of each single-width cross section of the particle accumulation body are obtained to match the optimal size parameters, and the arrangement scheme of the multiple conduits for concrete pouring is determined; Step S4: Based on the arrangement scheme of the concrete pouring multi-duct in step S3, a physical model test of the concrete accumulation process and a particle flow discrete element method numerical simulation are carried out to generate a data set, and the improved lizard optimization algorithm is embedded in the least squares support vector machine to build a prediction model, and the data set is input into the prediction model for learning to obtain the prediction model with the best performance; Step S5: Input the flow parameters of the concrete particles in the numerical model in step S2 into the prediction model with the best performance to obtain the mix ratio of the conduit casting concrete.

[0006] Furthermore, in step S1, the optimal size parameters of the trapezoidal section of the concrete impermeable body in the underground cave are designed, and the specific process is as follows: Step S11, designing a trapezoidal section of an underground karst cave concrete impermeable body, wherein the trapezoidal section of the concrete impermeable body satisfies: the anti-sliding stability safety factor of the trapezoidal section of the concrete impermeable body meets the design or specification requirements; under given design parameters and construction conditions, the trapezoidal section area of ​​the concrete impermeable body is the smallest; The anti-sliding stability safety factor is calculated according to the anti-shear formula and is expressed as: (1); Where, K represents the anti-sliding stability safety factor of the concrete impermeable body; ΣW and ΣP represent the normal component and tangential component of the total load on the impermeable body on the sliding plane respectively; A represents the cross-sectional area of ​​the sliding surface; represents the shear friction coefficient; c′ represents the shear cohesion; Step S12, considering partial safety, ΣW only considers the friction between the bottom of the concrete impermeable body and the rock mass at the bottom of the cave; ΣP only considers the horizontal hydrostatic pressure, and the horizontal hydrostatic pressure at the cave depth corresponding to the highest water level of the reservoir is selected as the sliding load on the front side of the concrete impermeable body. The water pressure behind the wall is set to zero, and the anti-sliding stability safety factor K of the concrete impermeable body is expressed as: (2); In the formula, G c Indicates the deadweight of the impermeable body; G w It represents the vertical hydrostatic pressure on the upper part of the slope on the water-facing side in front of the anti-seepage body; H represents the horizontal hydrostatic pressure; G c , G w and H are calculated as: (3); Where, l1 represents the length of the lower side of the trapezoidal section of the concrete impermeable body; l2 represents the length of the upper side of the trapezoidal section of the concrete impermeable body; h0 represents the highest water level of the reservoir; h1 represents the elevation of the cave roof; h2 represents the elevation of the cave floor; γ c Indicates the bulk density of concrete; γ w Indicates the specific gravity of water; h k Indicates the height of the cave; Indicates the width of the cave; Step S13: The anti-sliding stability safety factor K of the concrete anti-seepage body of the cross section is equal to the allowable anti-sliding stability safety factor , allowable anti-slip stability safety factor Determined according to design requirements or specifications, expressed as: (4); When the trapezoidal section of the concrete impermeable body is calculated as a single-width section, the above formula (3) is substituted into formula (4) to express it as: Q = f ′ [ 2 ( l 1 + l 2 ) h k c c + ( l 2 − l 1 ) h k c w ] + 4 c ′ l 2 2 ( 2 h 0 − h 1 − h 2 ) h k c w (5); At the same time, according to the geometric relationship of the trapezoidal section of the concrete impermeable body, the bottom angle α of the trapezoid is obtained, which is expressed as: α = arctan [ 1 2 h k ( l 2 − l 1 ) ] (6); In the formula, represents the inverse tangent function; Step S14, designing the optimal size parameters l1, l2 and α of the trapezoidal section of the concrete impermeable body in the underground cave according to the design requirements of the trapezoidal section of the concrete impermeable body in the underground cave.

[0007] Further, in step S2, the flow parameters of the concrete particles in the numerical model are determined, and the specific process is as follows: Step S21, generating a numerical model boundary wall, establishing a single concrete pouring conduit, i.e., a single conduit and a single hopper, wherein the upper port of the single conduit is connected to the lower port of the single hopper; Step S22, generating a circular bottom wall at the bottom of the single conduit; generating concrete particles just above the single hopper, and applying gravity to make the particles fall into the single hopper; generating a bedrock layer at the bottom of the numerical model to simulate the bedrock layer at the bottom of the cave; Step S23, using the particle flow discrete element method to carry out numerical simulation of concrete accumulation in a single conduit and a single hopper; the concrete is composed of two phases, coarse aggregate and cement mortar, and two types of particles with different properties are used to simulate the coarse aggregate particles and cement mortar particles respectively; the particle size and friction coefficient of the coarse aggregate particles and cement mortar particles are set; in order to characterize the flow characteristics of the concrete, the contact model between the coarse aggregate particles and the cement mortar particles is set to the Burgers model; the normal parameter and the tangential parameter of the Burgers model are set to be the same; Step S24, deleting the bottom wall at the bottom of the single conduit, and the concrete represented by the coarse aggregate particles and the cement mortar particles falls under the action of gravity to form a particle accumulation body; Step S25, wait until the particle accumulation reaches the height h of the cave. k Finally, the particle accumulation body is placed in a rectangular coordinate system, and the contour characteristics of the particle accumulation body are drawn. The size parameters of the cross section of the particle accumulation body, including the upper and lower side lengths and the bottom angle, are determined according to the contour characteristics. The size parameters of the cross section of the particle accumulation body are compared with the optimal size parameters. When there is a deviation between the two, the size parameters of the cross section of the particle accumulation body are adjusted until the two match, and the flow parameters of the concrete particles in the numerical model are determined.

[0008] Further, in step S3, the arrangement scheme of multiple conduits for pouring concrete is determined, and the specific process is as follows: Step S31, according to the scanning data in step S1, the existing geological survey results and the flow parameters of the concrete particles in the numerical model in step S2, the range of the numerical model is determined, and the boundary wall is generated, and multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers, are established; Step S32, repeating the above steps S21 to S24 to complete the initial setting of the numerical model, using the particle flow discrete element method to carry out the numerical simulation of multi-duct and multi-hopper concrete accumulation, and forming a particle accumulation body; Step S33, wait until the particle accumulation reaches the height h of the cave. kFinally, the particle accumulation body is placed in a rectangular coordinate system, and the contour characteristics of the particle accumulation body are drawn. The size parameters of each single-width cross-section of the particle accumulation body, including the upper and lower side lengths and the bottom angle, are determined according to the contour characteristics. By continuously changing the layout of multiple conduits and multiple hoppers, the size parameters of each single-width cross-section of the particle accumulation body are obtained and compared with the optimal size parameters. When there is a deviation between the two, the multi-conduit layout is adjusted until the size parameters of each single-width cross-section of the particle accumulation body match the optimal size parameters, and the layout plan of the multi-conduit for concrete pouring is determined.

[0009] Further, in step S4, a data set is generated, and the specific process is as follows: Assume that the normal parameter and tangential parameter of the Burgers model are the same; the parameters of the Burgers model are 4, namely, the Maxwell model stiffness K1, the Maxwell model stiffness viscosity C1, the Kelvin model stiffness K2 and the Kelvin model stiffness viscosity C2; Based on the arrangement scheme of multiple conduits for concrete pouring in step S3, physical model tests and particle flow discrete element method numerical simulations of the concrete accumulation process are carried out, and the size parameters of the concrete accumulation body under different mix ratios of the physical model test are recorded; the size parameters of the concrete accumulation body under different particle flow parameters of the particle flow discrete element method numerical simulation are recorded; a group of physical model tests and particle flow discrete element method numerical simulations with similar size parameters are set as a group to construct a data set, the particle flow parameters of the numerical model are set as input values, and the mix ratio parameters of the physical model test are set as output values.

[0010] Further, in step S4, a prediction model is constructed, and the specific process is as follows: Using mapping function Map the data set to a high-dimensional space, expressed as: (7); In the formula, s represents the output value; represents the weight vector; represents the mapping function; x represents the input value; b represents the bias; s and x, expressed as: S = [ S 1 ， S 2 ， S 3 ， ⋯ ， S n ] (8); x = [ K 1 , C 1 , K 2 , C 2 ] (9); In the formula, It represents the composition of the nth concrete in the mix parameters of the physical model; Based on the optimization principle, the least squares support vector machine is constructed according to formula (7). The objective function of the least squares support vector machine optimization problem is , expressed as; { minutes J ( w , x ) = 1 2 w T w + c ∑ i = 1 n x i 2 st S i [ w T f ( x i ) + h ] − 1 + x i = 0 (10); In the formula, and Represent the estimated deviation and penalty coefficient respectively; T represents transposition; represents the i-th estimated deviation; represents the constraints of the optimization problem; S i represents the output value of the i-th group; x i represents the i-th group of input values; represents the mapping function of the i-th group of output values; h represents the intercept; Based on the Lagrange equation of formula (10), it can be expressed as: (11); In the formula, represents the Lagrange equation; α i represents the Lagrange multiplier; According to the Moser condition, there exists a kernel function , expressed as: (12); According to the above formula (11) and formula (12), the classification decision function based on the least squares support vector machine is expressed as: f ( x ) = sign [ ∑ i = 1 n α i S i U ( x , x i ) + h ] (13); In the formula, represents a classification decision function; sign represents a mathematical function; The radial basis function is selected as the kernel function of the least squares support vector machine, which is expressed as: (14); In the formula, Represents the kernel function width; Represents the exponential function operator; The penalty coefficient γ and kernel function width σ 2 As two variables in the least squares support vector machine; the improved lizard optimization algorithm is used to search the least squares support vector machine and build a prediction model.

[0011] Furthermore, the improved optimization algorithm for frilled lizards includes three parts: population initialization, improved hunting strategy, and tree climbing strategy; Population initialization: The improved frill optimization algorithm first initializes a set of randomly distributed candidate solutions in the solution space. , expressed as: (15); In the formula, Represents a random value in the interval [0, 1]; and They represent the lower and upper bounds of the d-th decision variable respectively; Simulate and improve hunting strategies: simulate the movement of individual frilled lizards toward prey; introduce the idea of ​​multiple excellent gray wolves surrounding prey in the gray wolf optimization algorithm, add the influence of optimal frilled lizard individuals and suboptimal frilled lizard individuals on prey to the original hunting strategy; set the distance between individual frilled lizards and prey , expressed as: (16); Where v represents the coefficient vector; and denote the positions of prey and frilled lizard individuals, respectively; The distances between the optimal and suboptimal frilled lizard individuals and their prey after updating are expressed as: (17); (18); In the formula, and represents the distance between the optimal and suboptimal individuals and their prey; P1 and P2 represent the positions of the optimal and suboptimal individuals; v D1 and v D2 Both represent the coefficient vectors used in calculating the distance between the individual frilled lizard and the prey; X1 and X2 represent the positions of the optimal frilled lizard and the suboptimal frilled lizard, respectively; v X1 and v X2 All represent the coefficient vectors used in calculating the individual positions of frilled lizards; The position update method of the i-th frilled lizard after improving the hunting strategy , expressed as: (19); Simulate the strategy of climbing up the tree: Model the retreat of the frilled lizard individual to the top of the tree near its own position, and use formula (20) to calculate the position of each frilled lizard individual in the frilled lizard population; when the position of each frilled lizard individual changes the objective function value, use formula (21) to replace the position of the corresponding frilled lizard individual, expressed as: (20); (twenty one); In the formula, represents the position update method of the i-th frilled lizard individual after the upward tree climbing strategy; v x represents the coefficient vector used in the calculation of the position update of individual frilled lizards; It indicates the position of the frilled lizard individual after feeding following the tree climbing strategy; represents the objective function value of the i-th frilled lizard individual after the upward tree climbing strategy; t represents the current number of iterations; represents the objective function value of the i-th frilled lizard individual; represents the position of the i-th frilled lizard after eating.

[0012] Further, in step S4, the prediction model with the best performance is obtained, and the specific process is as follows: After standardizing the data set, randomly divide it into training set and test set; The training set is input into the prediction model for training and learning. The prediction model continuously iterates and calculates according to the established ratio; the number of iterations g and the accuracy error threshold F of the prediction model are set Z , input the test set into the prediction model for prediction, obtain the result error rate error, and determine whether the result error rate error is less than the accuracy error threshold F Z , when error<F Z , then stop training; when error ≥ F Z , then iterates until the number of iterations g is reached and the prediction model with the best performance is obtained.

[0013] Compared with the existing technology, the present invention has the following beneficial effects: the present invention effectively guides the casting of concrete anti-seepage bodies in large-scale dam foundation cave conduits through the optimal trapezoidal cross-section of the anti-seepage body and the conduit layout parameters, which can significantly improve the reliability and economy of dam foundation leakage treatment and effectively ensure the safe operation of the dam project. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the present invention.

[0015] Figure 2 It is a schematic diagram of the calculated cross section and applied load of the trapezoidal concrete impermeable body of the present invention. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below with reference to implementation cases and accompanying drawings.

[0017] like Figure 1 As shown, a method for pouring a concrete impermeable body in a large dam foundation cave conduit comprises the following steps: Step S1: Scan the underground cave using a three-dimensional laser radar to obtain scanning data, and design the optimal size parameters of the trapezoidal section of the concrete impermeable body in the underground cave based on the existing geological survey results and the anti-sliding stability safety factor; Step S2: Generate a numerical model boundary wall, establish a single concrete pouring conduit, i.e., a single conduit and a single hopper, use a particle flow discrete element method to perform a concrete accumulation numerical simulation on the single conduit and the hopper, form a particle accumulation body, draw the contour characteristics of the particle accumulation body, determine the size parameters of the cross section of the particle accumulation body according to the contour characteristics, make the size parameters of the cross section of the particle accumulation body match the optimal size parameters, and determine the flow parameters of the concrete particles of the numerical model; Step S3: Based on the scanning data in step S1, the existing geological survey results and the flow parameters of the concrete particles in the numerical model in step S2, multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers, are established, and the steps of step S2 are repeated to perform numerical simulation on the multiple conduits and hoppers to obtain the size parameters of the cross section of the particle accumulation body, and by continuously changing the arrangement of the multiple conduits and hoppers, the size parameters of each single-width cross section of the particle accumulation body are obtained to match the optimal size parameters, and the arrangement scheme of the multiple conduits for concrete pouring is determined; Step S4: Based on the arrangement scheme of the concrete pouring multi-duct in step S3, a physical model test of the concrete accumulation process and a particle flow discrete element method numerical simulation are carried out to generate a data set, and the improved lizard optimization algorithm is embedded in the least squares support vector machine to build a prediction model, and the data set is input into the prediction model for learning to obtain the prediction model with the best performance; Step S5: Input the flow parameters of the concrete particles in the numerical model in step S2 into the prediction model with the best performance to obtain the mix ratio of the conduit casting concrete.

[0018] like Figure 2 As shown, in step S1, the optimal size parameters of the trapezoidal section of the concrete impermeable body in the underground cave are designed, and the specific process is: Step S11, designing a trapezoidal section of an underground karst cave concrete impermeable body, wherein the trapezoidal section of the concrete impermeable body satisfies: the anti-sliding stability safety factor of the trapezoidal section of the concrete impermeable body meets the design or specification requirements; under given design parameters and construction conditions, the trapezoidal section area of ​​the concrete impermeable body is the smallest; The anti-sliding stability safety factor is calculated according to the anti-shear formula and is expressed as: (1); Where, K represents the anti-sliding stability safety factor of the concrete impermeable body; ΣW and ΣP represent the normal force and tangential force of the total load acting on the impermeable body on the sliding plane, respectively; A represents the cross-sectional area of ​​the sliding surface; f′ represents the shear friction coefficient; c′ represents the shear cohesion; Step S12, considering partial safety, ΣW only considers the friction between the bottom of the concrete impermeable body and the rock mass at the bottom of the cave; ΣP only considers the horizontal hydrostatic pressure, and the horizontal hydrostatic pressure at the cave depth corresponding to the highest water level of the reservoir is selected as the sliding load on the front side of the concrete impermeable body. The water pressure behind the wall is set to zero, and the anti-sliding stability safety factor K of the concrete impermeable body is expressed as: (2); In the formula, G c Indicates the deadweight of the impermeable body; G w It represents the vertical hydrostatic pressure on the upper part of the slope on the water-facing side in front of the anti-seepage body; H represents the horizontal hydrostatic pressure; G c , G w and H are calculated as: (3); Where, l1 represents the length of the lower side of the trapezoidal section of the concrete impermeable body; l2 represents the length of the upper side of the trapezoidal section of the concrete impermeable body; h0 represents the highest water level of the reservoir; h1 represents the elevation of the cave roof; h2 represents the elevation of the cave floor; γ c Indicates the bulk density of concrete; γ w Indicates the specific gravity of water; h k represents the height of the cave, The calculation results of Indicates the width of the cave; Step S13, in order to minimize the trapezoidal cross-sectional area of ​​the concrete impermeable body, the anti-sliding stability safety factor K of the cross-sectional concrete impermeable body should be equal to the allowable anti-sliding stability safety factor , allowable anti-slip stability safety factor It can be determined according to design requirements or specifications and expressed as: (4); When the trapezoidal section of the concrete impermeable body is calculated as a single-width section, the above formula (3) is substituted into formula (4) to express it as: Q = f ′ [ 2 ( l 1 + l 2 ) h k c c + ( l 2 − l 1 ) h k c w ] + 4 c ′ l 2 2 ( 2 h 0 − h 1 − h 2 ) h k c w (5); At the same time, according to the geometric relationship of the trapezoidal section of the concrete impermeable body, the bottom angle α of the trapezoid is obtained, which is expressed as: α = arctan [ 1 2 h k ( l 2 − l 1 ) ] (6); In the formula, represents the inverse tangent function; Step S14, designing the optimal size parameters l1, l2 and α of the trapezoidal section of the concrete impermeable body in the underground cave according to the design requirements of the trapezoidal section of the concrete impermeable body in the underground cave.

[0019] Wherein, in step S2, the flow parameters of the concrete particles of the numerical model are determined, and the specific process is as follows: Step S21, generating a numerical model boundary wall, establishing a single concrete pouring conduit, i.e., a single conduit and a single hopper, wherein the upper port of the single conduit is connected to the lower port of the single hopper; Step S22, generating a circular bottom wall at the bottom of the single conduit; generating concrete particles just above the single hopper, and applying gravity to make the particles fall into the single hopper; generating a bedrock layer at the bottom of the numerical model to simulate the bedrock layer at the bottom of the cave; Step S23, using the particle flow discrete element method to carry out numerical simulation of concrete accumulation in a single conduit and a single hopper; the concrete is composed of two phases, coarse aggregate and cement mortar, and two types of particles with different properties are used to simulate the coarse aggregate particles and cement mortar particles respectively; the particle size and friction coefficient of the coarse aggregate particles and cement mortar particles are set; in order to characterize the flow characteristics of the concrete, the contact model between the coarse aggregate particles and the cement mortar particles is set to the Burgers model; the normal parameter and the tangential parameter of the Burgers model are set to be the same; Step S24, deleting the bottom wall at the bottom of the single conduit, and the concrete represented by the coarse aggregate particles and the cement mortar particles falls under the action of gravity to form a particle accumulation body; Step S25, wait until the particle accumulation reaches the height h of the cave. k Finally, the particle accumulation body is placed in a rectangular coordinate system, and the contour characteristics of the particle accumulation body are drawn. The size parameters of the cross section of the particle accumulation body, including the upper and lower side lengths and the bottom angle, are determined according to the contour characteristics. The size parameters of the cross section of the particle accumulation body are compared with the optimal size parameters. When there is a deviation between the two, the size parameters of the cross section of the particle accumulation body are adjusted until the two match, and the flow parameters of the concrete particles in the numerical model are determined.

[0020] Among them, the arrangement scheme of multiple conduits for concrete pouring is determined in step S3, and the specific process is as follows: Step S31, in order to determine the optimal arrangement parameters of the multi-conduit, the arrangement form of the multi-conduit, the number of rows of drill holes, the row spacing and the hole spacing are taken as influencing factors, the arrangement modes of different combinations of the multi-conduit are designed, and the numerical simulation of the concrete accumulation process is carried out; Step S32, according to the scanning data in step S1, the existing geological survey results and the flow parameters of the concrete particles in the numerical model in step S2, the range of the numerical model is determined, and the boundary wall is generated, and multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers, are established; Step S33, repeating the above steps S21 to S24 to complete the initial setting of the numerical model, using the particle flow discrete element method to carry out the numerical simulation of multi-duct and multi-hopper concrete accumulation, and forming a particle accumulation body; Step S34, wait until the particle accumulation reaches the height h of the cave. kFinally, the particle accumulation body is placed in a rectangular coordinate system, and the contour characteristics of the particle accumulation body are drawn. The size parameters of each single-width cross-section of the particle accumulation body, including the upper and lower side lengths and the bottom angle, are determined according to the contour characteristics. By continuously changing the layout of multiple conduits and multiple hoppers, the size parameters of each single-width cross-section of the particle accumulation body are obtained and compared with the optimal size parameters. When there is a deviation between the two, the multi-conduit layout is adjusted until the size parameters of each single-width cross-section of the particle accumulation body match the optimal size parameters, and the layout plan of the multi-conduit for concrete pouring is determined.

[0021] The data set is generated in step S4, and the specific process is as follows: Assume that the normal parameter and tangential parameter of the Burgers model are the same; the parameters of the Burgers model are 4, namely, the Maxwell model stiffness K1, the Maxwell model stiffness viscosity C1, the Kelvin model stiffness K2 and the Kelvin model stiffness viscosity C2; Based on the arrangement scheme of multiple conduits for concrete pouring in step S3, physical model tests and particle flow discrete element method numerical simulations of the concrete accumulation process are carried out, and the size parameters of the concrete accumulation body under different mix ratios of the physical model test are recorded; the size parameters of the concrete accumulation body under different particle flow parameters of the particle flow discrete element method numerical simulation are recorded; a group of physical model tests and particle flow discrete element method numerical simulations with similar size parameters are set as a group to construct a data set, the particle flow parameters of the numerical model are set as input values, and the mix ratio parameters of the physical model test are set as output values.

[0022] Among them, the prediction model is constructed in step S4, and the specific process is: Using mapping function Map the data set to a high-dimensional space, expressed as: (7); In the formula, s represents the output value; w represents the weight vector; represents the mapping function; x represents the input value; b represents the bias; Among them, s and x are expressed as: S = [ S 1 ， S 2 ， S 3 ， ⋯ S n ] (8); x = [ K 1 , C 1 , K 2 , C 2 ] (9); In the formula, It represents the composition of the nth concrete in the mix parameters of the physical model; Based on the optimization principle, the least squares support vector machine is constructed according to formula (7). The objective function of the least squares support vector machine optimization problem is , expressed as; { minutes J ( w , x ) = 1 2 w T w + c ∑ i = 1 n x i 2 st S i [ w T f ( x i ) + h ] − 1 + x i = 0 (10); In the formula, and Represent the estimated deviation and penalty coefficient respectively; T represents transposition; represents the i-th estimated deviation; represents the constraints of the optimization problem; S i represents the output value of the i-th group; x i represents the i-th group of input values; represents the mapping function of the i-th group of output values; h represents the intercept; Based on the Lagrange equation of formula (10), it can be expressed as: (11); In the formula, represents the Lagrange equation; α i represents the Lagrange multiplier, which is a non-negative number; According to the Merce condition, there exists a kernel function , expressed as: (12); According to the above formula (11) and formula (12), the classification decision function based on the least squares support vector machine is expressed as: f ( x ) = sign [ ∑ i = 1 n α i S i U ( x , x i ) + h ] (13); In the formula, Represents a classification decision function; sign represents a mathematical function that returns a number; The radial basis function is selected as the kernel function of the least squares support vector machine, which is expressed as: (14); In the formula, Represents the kernel function width; Represents the exponential function operator; The penalty coefficient γ and kernel function width σ 2 As two variables in the least squares support vector machine; the improved lizard optimization algorithm is used to search the least squares support vector machine and construct a prediction model (BLSO-LSSVM model).

[0023] Among them, the improved frilled lizard optimization algorithm includes three parts: population initialization, improved hunting strategy and tree climbing strategy; Population initialization: The improved frill optimization algorithm first initializes a set of randomly distributed candidate solutions in the solution space. , expressed as: (15); In the formula, Represents a random value in the interval [0, 1]; and They represent the lower and upper bounds of the d-th decision variable respectively; Simulate and improve hunting strategies: For the standard hunting strategy, simulate the movement of frilled lizard individuals toward prey; introduce the idea of ​​multiple excellent gray wolves surrounding prey in the gray wolf optimization algorithm, add the influence of the optimal frilled lizard individuals and suboptimal frilled lizard individuals on prey to the original hunting strategy; set the distance between frilled lizard individuals and prey , expressed as: (16); Where v represents the coefficient vector; and denote the positions of prey and frilled lizard individuals, respectively; The distances between the optimal and suboptimal frilled lizard individuals and their prey after updating are expressed as: (17); (18); In the formula, and represents the distance between the optimal and suboptimal individuals and their prey; P1 and P2 represent the positions of the optimal and suboptimal individuals; v D1 and v D2 Both represent the coefficient vectors used in calculating the distance between the individual frilled lizard and the prey; X1 and X2 represent the positions of the optimal frilled lizard and the suboptimal frilled lizard, respectively; v X1 and v X2 All represent the coefficient vectors used in calculating the individual positions of frilled lizards; The position update method of the i-th frilled lizard after improving the hunting strategy , expressed as: (19); Simulate the tree climbing strategy: After eating, the individual fringed lizard will retreat to the top of the tree near its location; the fringed lizard individual retreating to the top of the tree near its location is modeled, and the position of each fringed lizard individual in the fringed lizard population is calculated using formula (20); when each fringed lizard individual changes the value of the objective function, the position of the corresponding fringed lizard individual is replaced using formula (21), expressed as: (20); (twenty one); In the formula, represents the position update method of the i-th frilled lizard individual after the upward tree climbing strategy; v x represents the coefficient vector used in the calculation of the position update of individual frilled lizards; It indicates the position of the frilled lizard individual after feeding following the tree climbing strategy; represents the objective function value of the i-th frilled lizard individual after the upward tree climbing strategy; t represents the current number of iterations; represents the objective function value of the i-th frilled lizard individual; represents the position of the i-th frilled lizard after eating.

[0024] The prediction model with the best performance is obtained in step S4, and the specific process is as follows: After standardizing the data set, randomly divide it into training set and test set; The training set is input into the prediction model for training and learning. The prediction model continuously iterates and calculates according to the established ratio; the number of iterations g and the accuracy error threshold F of the prediction model are set Z , input the test set into the prediction model for prediction, obtain the result error rate error, and determine whether the result error rate error is less than the accuracy error threshold F Z , when error<F Z , then stop training; when error ≥ F Z , then iterates until the number of iterations g is reached and the prediction model with the best performance is obtained.

[0025] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for pouring concrete impermeable body in a large dam foundation cave conduit, It is characterized in that The steps include: Step S1: Scan the underground cave using a three-dimensional laser radar to obtain scanning data, and design the optimal size parameters of the trapezoidal section of the concrete impermeable body in the underground cave based on the existing geological survey results and the anti-sliding stability safety factor; Step S2: Generate a numerical model boundary wall, establish a single concrete pouring conduit, i.e., a single conduit and a single hopper, use a particle flow discrete element method to perform a concrete accumulation numerical simulation on the single conduit and the hopper, form a particle accumulation body, draw the contour characteristics of the particle accumulation body, determine the size parameters of the cross section of the particle accumulation body according to the contour characteristics, make the size parameters of the cross section of the particle accumulation body match the optimal size parameters, and determine the flow parameters of the concrete particles of the numerical model; Step S3: Based on the scanning data in step S1, the existing geological survey results and the flow parameters of the concrete particles in the numerical model in step S2, multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers, are established, and the steps of step S2 are repeated to perform numerical simulation on the multiple conduits and hoppers to obtain the size parameters of the cross section of the particle accumulation body, and by continuously changing the arrangement of the multiple conduits and hoppers, the size parameters of each single-width cross section of the particle accumulation body are obtained to match the optimal size parameters, and the arrangement scheme of the multiple conduits for concrete pouring is determined; Step S4: Based on the arrangement scheme of the concrete pouring multi-duct in step S3, a physical model test of the concrete accumulation process and a particle flow discrete element method numerical simulation are carried out to generate a data set, and the improved lizard optimization algorithm is embedded in the least squares support vector machine to build a prediction model, and the data set is input into the prediction model for learning to obtain the prediction model with the best performance; Step S5: Input the flow parameters of the concrete particles in the numerical model in step S2 into the prediction model with the best performance to obtain the mix ratio of the conduit casting concrete.

2. A method for pouring a concrete impermeable body through a large dam foundation cave conduit according to claim 1, Features: In step S1, the optimal size parameters of the trapezoidal section of the concrete impermeable body in the underground cave are designed, and the specific process is as follows: Step S11, designing a trapezoidal section of an underground karst cave concrete impermeable body, wherein the trapezoidal section of the concrete impermeable body satisfies: the anti-sliding stability safety factor of the trapezoidal section of the concrete impermeable body meets the design or specification requirements; under given design parameters and construction conditions, the trapezoidal section area of ​​the concrete impermeable body is the smallest; The anti-sliding stability safety factor is calculated according to the anti-shear formula and is expressed as: （1）； Where, K represents the anti-sliding stability safety factor of the concrete impermeable body; ΣW and ΣP represent the normal component and tangential component of the total load on the impermeable body on the sliding plane respectively; A represents the cross-sectional area of ​​the sliding surface; represents the shear friction coefficient; c′ represents the shear cohesion; Step S12, considering partial safety, ΣW only considers the friction between the bottom of the concrete impermeable body and the rock mass at the bottom of the cave; ΣP only considers the horizontal hydrostatic pressure, and the horizontal hydrostatic pressure at the cave depth corresponding to the highest water level of the reservoir is selected as the sliding load on the front side of the concrete impermeable body. The water pressure behind the wall is set to zero, and the anti-sliding stability safety factor K of the concrete impermeable body is expressed as: （2）； In the formula, G c Indicates the deadweight of the impermeable body; G w It represents the vertical hydrostatic pressure on the upper part of the slope on the water-facing side in front of the anti-seepage body; H represents the horizontal hydrostatic pressure; G c , G w and H are calculated as: （3）； In the formula, l 1 Indicates the length of the lower side of the trapezoidal section of the concrete impermeable body; l 2 Indicates the length of the upper side of the trapezoidal section of the concrete impermeable body; h 0 Indicates the maximum water level of the reservoir; h 1 Indicates the elevation of the cave roof; h 2 represents the elevation of the cave floor; γ c Indicates the bulk density of concrete; γ w Indicates the specific gravity of water; h k Indicates the height of the cave; Indicates the width of the cave; Step S13: The anti-sliding stability safety factor K of the concrete anti-seepage body of the cross section is equal to the allowable anti-sliding stability safety factor , allowable anti-slip stability safety factor Determined according to design requirements or specifications, expressed as: （4）； When the trapezoidal section of the concrete impermeable body is calculated as a single-width section, the above formula (3) is substituted into formula (4) to express it as: （5）； At the same time, according to the geometric relationship of the trapezoidal section of the concrete impermeable body, the bottom angle α of the trapezoid is obtained, which is expressed as: （6）； In the formula, represents the inverse tangent function; Step S14, according to the design requirements of the trapezoidal section of the concrete impermeable body in the underground cave, design the optimal size parameters l of the trapezoidal section of the concrete impermeable body in the underground cave 1 , l 2 and α.

3. A method for pouring a concrete impermeable body through a large dam foundation karst cave conduit according to claim 2, Features: In step S2, the flow parameters of the concrete particles in the numerical model are determined. The specific process is as follows: Step S21, generating a numerical model boundary wall, establishing a single concrete pouring conduit, i.e., a single conduit and a single hopper, wherein the upper port of the single conduit is connected to the lower port of the single hopper; Step S22, generating a circular bottom wall at the bottom of the single conduit; generating concrete particles just above the single hopper, and applying gravity to make the particles fall into the single hopper; generating a bedrock layer at the bottom of the numerical model to simulate the bedrock layer at the bottom of the cave; Step S23, using the particle flow discrete element method to carry out numerical simulation of concrete accumulation in a single conduit and a single hopper; the concrete is composed of two phases, coarse aggregate and cement mortar, and two types of particles with different properties are used to simulate the coarse aggregate particles and cement mortar particles respectively; the particle size and friction coefficient of the coarse aggregate particles and cement mortar particles are set; in order to characterize the flow characteristics of the concrete, the contact model between the coarse aggregate particles and the cement mortar particles is set to the Burgers model; the normal parameter and the tangential parameter of the Burgers model are set to be the same; Step S24, deleting the bottom wall at the bottom of the single conduit, and the concrete represented by the coarse aggregate particles and the cement mortar particles falls under the action of gravity to form a particle accumulation body; Step S25, wait until the particle accumulation reaches the height h of the cave. k Finally, the particle accumulation body is placed in a rectangular coordinate system, and the contour characteristics of the particle accumulation body are drawn. The size parameters of the cross section of the particle accumulation body, including the upper and lower side lengths and the bottom angle, are determined according to the contour characteristics. The size parameters of the cross section of the particle accumulation body are compared with the optimal size parameters. When there is a deviation between the two, the size parameters of the cross section of the particle accumulation body are adjusted until the two match, and the flow parameters of the concrete particles in the numerical model are determined.

4. A method for pouring a concrete impermeable body through a large dam foundation karst cave conduit according to claim 3, Features: In step S3, the arrangement scheme of multiple conduits for concrete pouring is determined, and the specific process is as follows: Step S31, according to the scanning data in step S1, the existing geological survey results and the flow parameters of the concrete particles in the numerical model in step S2, the range of the numerical model is determined, and the boundary wall is generated, and multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers, are established; Step S32, repeating the above steps S21 to S24 to complete the initial setting of the numerical model, using the particle flow discrete element method to carry out the numerical simulation of multi-duct and multi-hopper concrete accumulation, and forming a particle accumulation body; Step S33, wait until the particle accumulation reaches the height h of the cave. k Finally, the particle accumulation body is placed in a rectangular coordinate system, and the contour characteristics of the particle accumulation body are drawn. The size parameters of each single-width cross-section of the particle accumulation body, including the upper and lower side lengths and the bottom angle, are determined according to the contour characteristics. By continuously changing the layout of multiple conduits and multiple hoppers, the size parameters of each single-width cross-section of the particle accumulation body are obtained and compared with the optimal size parameters. When there is a deviation between the two, the multi-conduit layout is adjusted until the size parameters of each single-width cross-section of the particle accumulation body match the optimal size parameters, and the layout plan of the multi-conduit for concrete pouring is determined.

5. A method for pouring a concrete impermeable body through a large dam foundation karst cave conduit according to claim 4, Features: In step S4, a data set is generated. The specific process is as follows: Assume that the normal and tangential parameters of the Burgers model are the same; the parameters of the Burgers model are 4, which are the Maxwell model stiffness K 1 、Maxwell model stiffness viscosity C 1 , Kelvin model stiffness K 2 and Kelvin model stiffness viscosity C 2 ; Based on the arrangement scheme of multiple conduits for concrete pouring in step S3, physical model tests and particle flow discrete element method numerical simulations of the concrete accumulation process are carried out, and the size parameters of the concrete accumulation body under different mix ratios of the physical model test are recorded; the size parameters of the concrete accumulation body under different particle flow parameters of the particle flow discrete element method numerical simulation are recorded; a group of physical model tests and particle flow discrete element method numerical simulations with similar size parameters are set as a group to construct a data set, the particle flow parameters of the numerical model are set as input values, and the mix ratio parameters of the physical model test are set as output values.

6. A method for pouring a concrete impermeable body through a large dam foundation karst cave conduit according to claim 5, Features: In step S4, a prediction model is constructed. The specific process is as follows: using a mapping function Map the data set to a high-dimensional space, expressed as: （7）； In the formula, s represents the output value; represents the weight vector; represents the mapping function; x represents the input value; b represents the bias; s and x, expressed as: （8）； （9）； In the formula, It represents the composition of the nth concrete in the mix parameters of the physical model; Based on the optimization principle, the least squares support vector machine is constructed according to formula (7). The objective function of the least squares support vector machine optimization problem is , expressed as; （10）； In the formula, and Represent the estimated deviation and penalty coefficient respectively; T represents transposition; represents the i-th estimated deviation; represents the constraints of the optimization problem; S i represents the output value of the i-th group; x i represents the i-th group of input values; represents the mapping function of the i-th group of output values; h represents the intercept; Based on the Lagrange equation of formula (10), it can be expressed as: （11）； In the formula, represents the Lagrange equation; α i represents the Lagrange multiplier; According to the Moser condition, there exists a kernel function , expressed as: （12）； According to the above formula (11) and formula (12), the classification decision function based on the least squares support vector machine is expressed as: （13）； In the formula, represents a classification decision function; sign represents a mathematical function; The radial basis function is selected as the kernel function of the least squares support vector machine, which is expressed as: （14）； In the formula, Represents the kernel function width; Represents the exponential function operator; The penalty coefficient γ and kernel function width σ 2 As two variables in the least squares support vector machine; the improved lizard optimization algorithm is used to search the least squares support vector machine and build a prediction model.

7. A method for pouring a concrete impermeable body through a large dam foundation karst cave conduit according to claim 6, Features: The improved frill lizard optimization algorithm includes three parts: population initialization, improved hunting strategy and tree climbing strategy; Population initialization: The improved frill optimization algorithm first initializes a set of randomly distributed candidate solutions in the solution space. , expressed as: （15）； In the formula, Represents a random value in the interval [0, 1]; and They represent the lower and upper bounds of the d-th decision variable respectively; Simulate and improve hunting strategies: simulate the movement of frilled lizards toward prey; introduce the idea of ​​multiple excellent gray wolves surrounding prey in the gray wolf optimization algorithm, and add the influence of optimal frilled lizards and suboptimal frilled lizards on prey to the original hunting strategy; Set the distance between individual frilled lizards and prey , expressed as: （16）； Where v represents the coefficient vector; and denote the positions of prey and frilled lizard individuals, respectively; The distances between the optimal and suboptimal frilled lizard individuals and their prey after updating are expressed as: （17）； （18）； In the formula, and Represent the distances between the optimal and suboptimal frilled lizard individuals and their prey; P 1 and P 2 Represent the positions of the best and second-best frilled lizard individuals, respectively; v D1 and v D2 Both represent the coefficient vector used in calculating the distance between the individual frilled lizard and its prey; X 1 and X 2 Represent the positions of the best and second-best frilled lizard individuals, respectively; v X1 and v X2 All represent the coefficient vectors used in calculating the individual positions of frilled lizards; The position update method of the i-th frilled lizard after improving the hunting strategy , expressed as: （19）； Simulate the strategy of climbing up the tree: Model the retreat of the frilled lizard individual to the top of the tree near its own position, and use formula (20) to calculate the position of each frilled lizard individual in the frilled lizard population; when the position of each frilled lizard individual changes the objective function value, use formula (21) to replace the position of the corresponding frilled lizard individual, expressed as: （20）； （21）； In the formula, represents the position update method of the i-th frilled lizard individual after the upward tree climbing strategy; v x represents the coefficient vector used in the calculation of the position update of individual frilled lizards; It indicates the position of the frilled lizard individual after feeding following the tree climbing strategy; represents the objective function value of the i-th frilled lizard individual after the upward tree climbing strategy; t represents the current number of iterations; represents the objective function value of the i-th frilled lizard individual; represents the position of the i-th frilled lizard after eating.

8. A method for pouring a concrete impermeable body through a large dam foundation karst cave conduit according to claim 7, Features: In step S4, the prediction model with the best performance is obtained. The specific process is as follows: After standardizing the data set, randomly divide it into training set and test set; The training set is input into the prediction model for training and learning. The prediction model continuously iterates and calculates according to the established ratio; the number of iterations g and the accuracy error threshold F of the prediction model are set Z , input the test set into the prediction model for prediction, obtain the result error rate error, and determine whether the result error rate error is less than the accuracy error threshold F Z , when error<F Z , then stop training; when error ≥ F Z , then iterates until the number of iterations g is reached and the prediction model with the best performance is obtained.

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