A method for pouring concrete impermeable body in large dam foundation karst cave conduit

Abstract

The present invention discloses a method for casting a concrete impermeable body through a conduit in a large-scale dam foundation karst cave, comprising the following steps: designing the optimal size parameters of the trapezoidal section of the concrete impermeable body, generating a numerical model, establishing a single concrete casting conduit, i.e., a single conduit and a single hopper, and carrying out a numerical simulation of concrete accumulation, determining the flow parameters of concrete particles in the numerical model, based on the above data, establishing multiple concrete casting conduits, i.e., multiple conduits and multiple hoppers, and carrying out a numerical simulation of concrete accumulation, determining the layout scheme of multiple conduits for concrete casting, constructing a prediction model with optimal performance, inputting the flow parameters of concrete particles in the numerical model into the prediction model with optimal performance, and obtaining the mix ratio of conduit casting concrete. The present invention designs the optimal trapezoidal section, conduit layout parameters, and concrete mix ratio of the impermeable body through a variety of technical means, and effectively guides the casting of concrete impermeable bodies through conduits in large-scale dam foundation karst caves.

Description

Technical Field

[0001] The invention belongs to the technical field of the design and construction of concrete impervious bodies cast by conduits in large dam foundations of water conservancy projects, and specifically relates to a method for casting concrete impervious bodies by conduits in large dam foundations with karst caves. 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 reservoir dams 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 in actual engineering applications. The currently commonly used method is still to carry out filling grouting with cement and fine aggregate concrete. The difficulties in anti-seepage treatment of karst caves are mainly manifested in two aspects: one is the difficulty in accurately detecting the location of karst caves and the underground space distribution; the other is the difficulty in effectively sealing 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 construction parameters such as the number, spacing, grouting volume, grouting pressure, and water-cement ratio of the casting conduits for grouting, which is directly related to the reliability and economy of the anti-seepage treatment of dam foundation karst caves. In actual engineering, at present, construction often still relies only on the experience of engineers, which leads to overly rough treatment of dam foundation karst caves and is difficult to meet the needs of scientific and precise construction.

[0003] Therefore, it is necessary to combine the characteristics of anti-seepage treatment of dam foundation karst caves, obtain the location, size, and spatial distribution of underground karst caves through three-dimensional laser radar scanning of boreholes, and use research methods such as particle flow numerical simulation methods, indoor concrete falling and stacking tests, and cement mix ratio parameter tests to determine the optimal economic section and optimal construction parameters of the impervious body for anti-seepage construction treatment of karst cave dam foundations, and construct a method and technology for treating karst cave dam foundations. It has positive significance for improving the construction technology of impervious bodies of dikes in karst areas and ensuring the operation safety of dam projects. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the invention provides a method for casting a concrete impervious body by conduits in a large dam foundation with karst caves, and its purpose is to solve the problem that in actual engineering, construction often still relies only on the experience of engineers at present, which leads to overly rough treatment of dam foundation karst caves and is difficult to meet the requirements of scientific and precise construction.

[0005] To achieve the above purpose, the invention provides the following technical solutions: A method for casting a concrete impervious body by conduits in a large dam foundation with karst caves, including the following steps:

[0006] Step S1: Use three-dimensional laser radar to scan the underground karst cave to obtain scan data, and based on the comprehensive existing geological exploration results and the anti-slide stability safety factor, design the optimal size parameters of the trapezoidal section of the concrete impervious body in the underground karst cave;

[0007] Step S2: Generate the boundary wall of the numerical model, establish a single concrete pouring conduit, i.e., a single conduit and a single hopper, conduct a numerical simulation of concrete accumulation for the single conduit and hopper using the particle flow discrete element method to 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 in the numerical model;

[0008] Step S3: Based on the scanning data in Step S1, the existing geological exploration results, and the flow parameters of the concrete particles in the numerical model in Step S2, establish multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers, repeat the steps of Step S2, conduct a numerical simulation of the multiple conduits and hoppers, obtain the size parameters of the cross-section of the particle accumulation body, and by continuously changing the layout of the multiple conduits and hoppers, obtain that the size parameters of each unit-width cross-section of the particle accumulation body match the optimal size parameters, and determine the layout scheme of the multiple concrete pouring conduits;

[0009] Step S4: Based on the layout scheme of the multiple concrete pouring conduits in Step S3, conduct a physical model test of the concrete accumulation process and a numerical simulation using the particle flow discrete element method to generate a data set, embed the improved umbrella lizard optimization algorithm into the least squares support vector machine, construct a prediction model, input the data set into the prediction model for learning, and obtain the prediction model with the optimal performance;

[0010] Step S5: Input the flow parameters of the concrete particles in the numerical model in Step S2 into the prediction model with the optimal performance to obtain the mix proportion of the concrete poured by the conduit.

[0011] Furthermore, the specific process of designing the optimal size parameters of the trapezoidal section of the concrete impervious body in the underground karst cave in Step S1 is as follows:

[0012] Step S11, design the trapezoidal section of the concrete impervious body in the underground karst cave, and the trapezoidal section of the concrete impervious body satisfies: the anti-sliding stability safety factor of the trapezoidal section of the concrete impervious body meets the design or specification requirements; under the given design parameters and construction conditions, the trapezoidal section area of the concrete impervious body is the smallest;

[0013] The anti-sliding stability safety factor is calculated according to the anti-shearing formula and is expressed as:

[0014] (1);

[0015] In the formula, K represents the anti-sliding stability safety factor of the concrete impervious body; ΣW and ΣP respectively represent the normal component force and tangential component force of all loads on the impervious body to the sliding plane; A represents the cross-sectional area of the sliding surface; represents the anti-shearing friction coefficient; c′ represents the anti-shearing cohesion;

[0016] Step S12, considering safety factors, ΣW only considers the frictional force between the bottom of the concrete impervious body and the rock mass at the bottom of the karst cave; ΣP only considers the horizontal hydrostatic pressure, and selects the horizontal hydrostatic pressure at the position corresponding to the depth of the karst cave at the highest reservoir water level as the sliding load on the front water-facing side of the concrete impervious body, and the water pressure behind the wall is set to zero. Then, the anti-sliding stability safety factor K of the concrete impervious body is expressed as:

[0017] (2);

[0018] In the formula, G c represents the self-weight of the impervious body; G w represents the vertical hydrostatic pressure on the upper part of the front water-facing slope of the impervious body; H represents the horizontal hydrostatic pressure;

[0019] G c 、G w and H are calculated and expressed as:

[0020] (3);

[0021] In the formula, l 1 represents the lower side length of the trapezoidal section of the concrete impervious body; l 2 represents the upper side length of the trapezoidal section of the concrete impervious body; h 0 represents the highest reservoir water level; h 1 represents the elevation of the karst cave roof; h 2 represents the elevation of the karst cave floor; γ c represents the unit weight of concrete; γ w represents the unit weight of water; h k represents the height of the karst cave; represents the width of the karst cave;

[0022] Step S13, the anti-sliding stability safety factor K of the concrete impervious body in the section is equal to the allowable anti-sliding stability safety factor , and the allowable anti-sliding stability safety factor is determined according to the design requirements or specifications and is expressed as:

[0023] (4);

[0024] When calculating the trapezoidal section of the concrete impervious body as a single-width section, substitute the above formula (3) into formula (4) and express it as:

[0025] Q = f ′ [ 2 ( l 1 + l 2 ) h k γ c + ( l 2 − l 1 ) h k γ w ] + 4 c ′ l 2 2 ( 2 h 0 − h 1 − h 2 ) h k γ w (5);

[0026] At the same time, according to the geometric relationship of the trapezoidal section of the concrete impervious body, obtain the bottom angle α of the trapezoid and express it as:

[0027] α = arctan [ 1 2 h k ( l 2 − l 1 ) ] (6);

[0028] In the formula, represents the arctangent function;

[0029] Step S14, according to the design requirements of the trapezoidal cross-section of the concrete impervious body in the underground karst cave, design the optimal size parameters l 1 , l 2 and α.

[0030] Further, in step S2, determine the flow parameters of the concrete particles in the numerical model. The specific process is as follows:

[0031] Step S21, generate the boundary wall of the numerical model, establish a single concrete pouring conduit, that is, a single conduit and a single hopper. The upper port of the single conduit is connected to the lower port of the single hopper;

[0032] Step S22, generate a circular bottom wall at the bottom of the single conduit; generate concrete particles directly above the single hopper and apply gravity to make the particles fall into the single hopper; generate a bedrock layer at the bottom of the numerical model to simulate the bedrock layer at the bottom of the karst cave;

[0033] Step S23, use the particle flow discrete element method to carry out the numerical simulation of the concrete accumulation of the single conduit and the single hopper; the concrete is composed of coarse aggregate and cement mortar. Two types of particles with different properties are used to simulate the coarse aggregate particles and the cement mortar particles respectively; set the particle sizes and friction coefficients of the coarse aggregate particles and the cement mortar particles; to characterize the flow characteristics of the concrete, the contact models between the coarse aggregate particles and the cement mortar particles are both set as the Burgers model; assume that the normal parameters and tangential parameters of the Burgers model are the same;

[0034] Step S24, delete the bottom wall at the bottom of the single conduit, and the concrete characterized by the coarse aggregate particles and the cement mortar particles falls under the action of gravity and forms a particle accumulation body;

[0035] Step S25, after the height of the particle accumulation body reaches the height h k of the karst cave, place the particle accumulation body in a rectangular coordinate system, draw the contour characteristics of the particle accumulation body, determine the size parameters of the cross-section of the particle accumulation body, including the upper and lower side lengths and the bottom angles, and compare the size parameters of the cross-section of the particle accumulation body with the optimal size parameters. When there is a deviation between the two, adjust the size parameters of the cross-section of the particle accumulation body until the two match, and determine the flow parameters of the concrete particles in the numerical model.

[0036] Further, in step S3, determine the layout scheme of the multiple concrete pouring conduits. The specific process is as follows:

[0037] Step S31: Determine the numerical model range based on the scanning data, existing geological exploration results in Step S1, and the flow parameters of concrete particles in the numerical model in Step S2. Generate a boundary wall and establish multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers.

[0038] Step S32: Repeat the above Steps S21 - S24 to complete the initial setting of the numerical model. Use the particle flow discrete element method to carry out the numerical simulation of concrete accumulation with multiple conduits and multiple hoppers, and form a particle accumulation body.

[0039] Step S33: After the height of the particle accumulation body reaches the height h of the karst cave k , place the particle accumulation body in a rectangular coordinate system, draw the contour characteristics of the particle accumulation body, determine the size parameters of each unit-width cross-section of the particle accumulation body, including the upper and lower side lengths and the bottom angle. By continuously changing the layout of the multiple conduits and multiple hoppers, compare the size parameters of each unit-width cross-section of the particle accumulation body with the optimal size parameters. When there is a deviation between the two, adjust the layout of the multiple conduits until the size parameters of each unit-width cross-section of the particle accumulation body match the optimal size parameters, and determine the layout scheme of the multiple concrete pouring conduits.

[0040] Furthermore, in Step S4, the process of generating the data set is as follows:

[0041] 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 K 1 , the Maxwell model stiffness viscosity C 1 , the Kelvin model stiffness K 2 and the Kelvin model stiffness viscosity C 2 ;

[0042] Based on the layout scheme of the multiple concrete pouring conduits in Step S3, carry out the physical model test of the concrete accumulation process and the numerical simulation by the particle flow discrete element method. Record the size parameters of the concrete accumulation body under different mix ratios in the physical model test; record the size parameters of the concrete accumulation body under different particle flow parameters in the numerical simulation by the particle flow discrete element method; set a group of physical model tests and numerical simulations by the particle flow discrete element method with similar size parameters as a group to construct a data set, set the particle flow parameters of the numerical model as input values, and set the mix ratio parameters of the physical model test as output values.

[0043] Furthermore, in Step S4, the process of constructing the prediction model is as follows:

[0044] Use the mapping function to map the data set to a high-dimensional space, expressed as:

[0045] (7);

[0046] 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;

[0047] s and x are expressed as:

[0048] S = [ S 1 ， S 2 ， S 3 ， ⋯ ， S n ] (8);

[0049] x = [ K 1 , C 1 , K 2 , C 2 ] (9);

[0050] In the formula, represents the composition of the nth type of concrete among the mix proportion parameters of the physical model;

[0051] Based on the optimization principle, a 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;

[0052] { min J ( w , ξ ) = 1 2 w T w + γ ∑ i = 1 n ξ i 2 s.t. S i [ w T φ ( x i ) + h ] − 1 + ξ i = 0 (10);

[0053] In the formula, and respectively represent the estimation deviation and the penalty coefficient; T represents the transpose; represents the ith estimation deviation; represents the constraint condition of the optimization problem; S i represents the ith group of output values; x i represents the ith group of input values; represents the mapping function of the ith group of output values; h represents the intercept;

[0054] The Lagrangian equation based on formula (10) is expressed as:

[0055] (11);

[0056] In the formula, represents the Lagrangian equation; α i represents the Lagrange multiplier;

[0057] From the Mercer condition, there exists a kernel function , which is expressed as:

[0058] (12);

[0059] According to the above formula (11) and the above formula (12), the classification decision function based on the least squares support vector machine is expressed as:

[0060] f ( x ) = sign [ ∑ i = 1 n α i S i U ( x , x i ) + h ] (13);

[0061] In the formula, represents the classification decision function; sign represents the mathematical function;

[0062] Select the radial basis function as the kernel function of the least squares support vector machine, expressed as:

[0063] (14);

[0064] In the formula, represents the kernel function width; represents the exponential function operation symbol;

[0065] Take the penalty coefficient γ and the kernel function width σ 2 as two variables in the least squares support vector machine; use the improved frill-necked lizard optimization algorithm to search for the least squares support vector machine and construct a prediction model.

[0066] Furthermore, the improved frill-necked lizard optimization algorithm includes three parts: population initialization, improved hunting strategy, and climbing-up-the-tree strategy;

[0067] Population initialization: The improved frill-necked lizard optimization algorithm first initializes a group of randomly distributed candidate solutions in the solution space , expressed as:

[0068] (15);

[0069] In the formula, represents a random value in the interval [0, 1]; and represent the lower bound and the upper bound of the d-th decision variable respectively;

[0070] Simulate the improved hunting strategy: Simulate the movement of the frill-necked lizard individual towards the prey; introduce the idea of multiple excellent gray wolves surrounding the prey in the gray wolf optimization algorithm, and add the influence of the optimal frill-necked lizard individual and the sub-optimal frill-necked lizard individual on the prey to the original hunting strategy; set the distance between the frill-necked lizard individual and the prey , expressed as:

[0071] (16);

[0072] In the formula, v represents the coefficient vector; and represent the positions of the prey and the frill-necked lizard individual respectively;

[0073] The distances between the updated optimal frill-necked lizard individual and the sub-optimal frill-necked lizard individual and the prey, expressed as:

[0074] (17);

[0075] (18);

[0076] Wherein, and respectively represent the distances between the optimal frilled lizard individual and the sub - optimal frilled lizard individual and the prey; P 1 and P 2 respectively represent the positions of the optimal frilled lizard individual and the sub - optimal frilled lizard individual; v D1 and v D2 both represent the coefficient vectors used when calculating the distance between the frilled lizard individual and the prey; X 1 and X 2 respectively represent the positions of the optimal frilled lizard individual and the sub - optimal frilled lizard individual; v X1 and v X2 both represent the coefficient vectors used when calculating the position of the frilled lizard individual;

[0077] The update method of the position of the i - th frilled lizard individual after improving the hunting strategy , is expressed as:

[0078] (19);

[0079] Simulate the strategy of climbing up the tree: Model the situation where the frilled lizard individual retreats 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, then use formula (21) to replace the position of the corresponding frilled lizard individual, which is expressed as:

[0080] (20);

[0081] (21);

[0082] Wherein, represents the update method of the position of the i - th frilled lizard individual after the climbing - up - tree strategy; v x represents the coefficient vector used when calculating the position update of the frilled lizard individual; represents the position of the frilled lizard individual after eating after the climbing - up - tree strategy; represents the objective function value of the frilled lizard individual after the climbing - up - tree strategy; t represents the current iteration number; represents the objective function value of the i - th frilled lizard individual; represents the position of the i - th frilled lizard individual after eating.

[0083] Furthermore, in step S4, to obtain the prediction model with the optimal performance, the specific process is as follows:

[0084] After standardizing the data set, randomly divide it into a training set and a test set;

[0085] Input the training set into the prediction model for training and learning. The prediction model continuously iterates and calculates according to a predetermined ratio; set the number of iterations g and the precision error threshold F of the prediction model Z , input the test set into the prediction model for prediction to obtain the result error rate error, and determine whether the result error rate error is less than the precision error threshold F Z , when error < F Z , then stop the training; when error ≥ F Z , then keep iterating until the number of iterations g is reached to obtain the prediction model with the optimal performance.

[0086] Compared with the existing technologies, the present invention has the following beneficial effects: Through the optimal trapezoidal section of the anti-seepage body and the duct layout parameters, the present invention effectively guides the casting of concrete anti-seepage bodies in conduits of large dam foundation karst caves, can significantly improve the reliability and economy of dam foundation leakage treatment, and effectively ensure the safe operation of dam projects. Brief Description of the Drawings

[0087] Figure 1 is the flow chart of the present invention.

[0088] Figure 2 is the schematic diagram of the calculation section and acting loads of the trapezoidal concrete anti-seepage body of the present invention. Detailed Embodiment Modes

[0089] The present invention will be further described in detail below in conjunction with the embodiment cases and the drawings.

[0090] As Figure 1 shown, a method for casting a concrete anti-seepage body in a conduit of a large dam foundation karst cave includes the following steps:

[0091] Step S1: Use 3D lidar to scan the underground karst cave to obtain scan data. Based on the comprehensive existing geological exploration results and the anti-sliding stability safety factor, design the optimal dimension parameters of the trapezoidal section of the concrete anti-seepage body in the underground karst cave;

[0092] Step S2: Generate the boundary wall of the numerical model, establish a single concrete casting conduit, i.e., a single conduit and a single hopper, use the particle flow discrete element method to carry out numerical simulation of concrete accumulation for the single conduit and the hopper to form a particle accumulation body, draw the contour characteristics of the particle accumulation body, and determine the dimension parameters of the cross-section of the particle accumulation body according to the contour characteristics, so that the dimension parameters of the cross-section of the particle accumulation body match the optimal dimension parameters, and determine the flow parameters of the concrete particles in the numerical model;

[0093] Step S3: Based on the scanning data in Step S1, the existing geological exploration 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. Repeat the steps in Step S2 to conduct numerical simulations on the multiple conduits and hoppers, obtain the size parameters of the cross-section of the particle accumulation body, and by continuously changing the layout of the multiple conduits and hoppers, make the size parameters of each unit-width cross-section of the particle accumulation body match the optimal size parameters, and determine the layout plan of the multiple concrete pouring conduits;

[0094] Step S4: Based on the layout plan of the multiple concrete pouring conduits in Step S3, conduct physical model tests on the concrete accumulation process and numerical simulations using the particle flow discrete element method to generate a data set. Embed the improved umbrella lizard optimization algorithm into the least squares support vector machine to construct a prediction model, input the data set into the prediction model for learning, and obtain the prediction model with the optimal performance;

[0095] Step S5: Input the flow parameters of the concrete particles in the numerical model in Step S2 into the prediction model with the optimal performance to obtain the mix proportion of the concrete poured by the conduits.

[0096] As Figure 2 shown, among them, the specific process of designing the optimal size parameters of the trapezoidal section of the concrete impervious body in the underground karst cave in Step S1 is as follows:

[0097] Step S11, design the trapezoidal section of the concrete impervious body in the underground karst cave, and the trapezoidal section of the concrete impervious body satisfies: the anti-sliding stability safety factor of the trapezoidal section of the concrete impervious body meets the design or specification requirements; under the given design parameters and construction conditions, the trapezoidal section area of the concrete impervious body is the smallest;

[0098] The anti-sliding stability safety factor is calculated according to the anti-shearing formula and is expressed as:

[0099] (1);

[0100] In the formula, K represents the anti-sliding stability safety factor of the concrete impervious body; ΣW and ΣP respectively represent the normal component force and tangential component force of all the loads acting on the impervious body on the sliding plane; A represents the cross-sectional area of the sliding surface; f′ represents the anti-shearing friction coefficient; c′ represents the anti-shearing cohesion;

[0101] Step S12, considering on the safe side, ΣW only considers the frictional force between the bottom of the concrete impervious body and the rock mass at the bottom of the karst cave; ΣP only considers the horizontal hydrostatic pressure, selects the horizontal hydrostatic pressure at the position of the karst cave depth corresponding to the highest reservoir water storage level as the sliding load on the front water-facing side of the concrete impervious body, and sets the water pressure behind the wall to zero. Then, the anti-sliding stability safety factor K of the concrete impervious body is expressed as:

[0102] (2);

[0103] Wherein, G c represents the self-weight of the impervious body; G w represents the vertical hydrostatic pressure on the upper part of the slope on the water-facing side in front of the impervious body; H represents the horizontal hydrostatic pressure;

[0104] G c and G w and H are calculated and expressed as:

[0105] (3);

[0106] Wherein, l 1 represents the lower side length of the trapezoidal section of the concrete impervious body; l 2 represents the upper side length of the trapezoidal section of the concrete impervious body; h 0 represents the highest water storage level of the reservoir; h 1 represents the elevation of the karst cave roof; h 2 represents the elevation of the karst cave floor; γ c represents the unit weight of concrete; γ w represents the unit weight of water; h k represents the height of the karst cave, which is the calculation result of ; represents the width of the karst cave;

[0107] Step S13, to minimize the trapezoidal section area of the concrete impervious body, the anti-sliding stability safety factor K of the concrete impervious body in the section should be equal to the allowable anti-sliding stability safety factor , and the allowable anti-sliding stability safety factor can be determined according to the design requirements or specifications and is expressed as:

[0108] (4);

[0109] When calculating the trapezoidal section of the concrete impervious body as a unit-width section, substituting the above formula (3) into formula (4), it is expressed as:

[0110] Q = f ′ [ 2 ( l 1 + l 2 ) h k γ c + ( l 2 − l 1 ) h k γ w ] + 4 c ′ l 2 2 ( 2 h 0 − h 1 − h 2 ) h k γ w (5);

[0111] Meanwhile, according to the geometric relationship of the trapezoidal section of the concrete impervious body, the bottom angle α of the trapezoid is obtained and expressed as:

[0112] α = arctan [ 1 2 h k ( l 2 − l 1 ) ] (6);

[0113] Wherein, represents the arctangent function;

[0114] Step S14: According to the design requirements of the trapezoidal section of the concrete impervious body in the underground karst cave, design the optimal size parameters \(l\), \(l\) and \(\alpha\) of the trapezoidal section of the concrete impervious body in the underground karst cave. 1 、l 2 and \(\alpha\).

[0115] Among them, the process of determining the flow parameters of concrete particles in the numerical model in Step S2 is as follows:

[0116] Step S21: Generate the boundary wall of the numerical model, establish a single concrete pouring conduit, i.e., a single conduit and a single hopper. The upper port of the single conduit is connected to the lower port of the single hopper.

[0117] Step S22: Generate a circular bottom wall at the bottom of the single conduit; generate concrete particles directly above the single hopper and apply gravity to make the particles fall into the single hopper; generate a bedrock layer at the bottom of the numerical model to simulate the bedrock layer at the bottom of the karst cave.

[0118] Step S23: Carry out the numerical simulation of the concrete accumulation of the single conduit and the single hopper by using the particle flow discrete element method; the concrete is composed of coarse aggregate and cement mortar, and two types of particles with different properties are used to simulate the coarse aggregate particles and the cement mortar particles respectively; set the particle sizes and friction coefficients of the coarse aggregate particles and the cement mortar particles; to characterize the flow characteristics of the concrete, the contact models between the coarse aggregate particles and the cement mortar particles are both set as the Burgers model; assume that the normal parameters and tangential parameters of the Burgers model are the same.

[0119] Step S24: Delete the bottom wall at the bottom of the single conduit, and the concrete characterized by the coarse aggregate particles and the cement mortar particles falls under the action of gravity and forms a particle accumulation body.

[0120] Step S25: After the height of the particle accumulation body reaches the height \(h\) of the karst cave, place the particle accumulation body in a rectangular coordinate system, draw the contour characteristics of the particle accumulation body, determine the size parameters of the cross-section of the particle accumulation body, including the upper and lower side lengths and the bottom angles, and compare the size parameters of the cross-section of the particle accumulation body with the optimal size parameters. When there is a deviation between the two, adjust the size parameters of the cross-section of the particle accumulation body until the two match, and determine the flow parameters of the concrete particles in the numerical model. k

[0121] Among them, the process of determining the layout scheme of multiple concrete pouring conduits in Step S3 is as follows:

[0122] Step S31: To determine the optimal layout parameters of multiple conduits, take the layout form of multiple conduits, the number of rows of drill holes, the row spacing and the hole spacing as influencing factors, design the layout methods of different combinations of multiple conduits, and carry out the numerical simulation of the concrete accumulation process.

[0123] Step S32: Determine the numerical model range based on the scanning data, existing geological exploration results in Step S1, and the flow parameters of concrete particles in the numerical model in S2, generate a boundary wall, and establish multiple concrete pouring conduits, i.e., multiple conduits and multiple hoppers.

[0124] Step S33: Repeat the above Steps S21 to S24 to complete the initial setting of the numerical model, conduct a numerical simulation of concrete accumulation with multiple conduits and multiple hoppers using the particle flow discrete element method, and form a particle accumulation body.

[0125] Step S34: After the height of the particle accumulation body reaches the height h of the karst cave k , place the particle accumulation body in a rectangular coordinate system, draw the contour characteristics of the particle accumulation body, determine the size parameters of each unit-width cross-section of the particle accumulation body, including the upper and lower side lengths and the bottom angle. By continuously changing the layout of the multiple conduits and multiple hoppers, compare the size parameters of each unit-width cross-section of the particle accumulation body with the optimal size parameters. When there is a deviation between the two, adjust the layout of the multiple conduits until the size parameters of each unit-width cross-section of the particle accumulation body match the optimal size parameters, and determine the layout plan of the multiple concrete pouring conduits.

[0126] Among them, in Step S4, the process of generating the data set is as follows:

[0127] Assume that the normal parameter and tangential parameter of the Burgers model are the same; there are 4 parameters for the Burgers model, namely the Maxwell model stiffness K 1 , the Maxwell model stiffness viscosity C 1 , the Kelvin model stiffness K 2 and the Kelvin model stiffness viscosity C 2 ;

[0128] Based on the layout plan of the multiple concrete pouring conduits in Step S3, conduct a physical model test of the concrete accumulation process and a numerical simulation using the particle flow discrete element method, record the size parameters of the concrete accumulation body under different mix ratios in the physical model test; record the size parameters of the concrete accumulation body under different particle flow parameters in the numerical simulation using the particle flow discrete element method; set a group of physical model tests and numerical simulations using the particle flow discrete element method with similar size parameters as a group to construct a data set, set the particle flow parameters of the numerical model as input values, and set the mix ratio parameters of the physical model test as output values.

[0129] Among them, in Step S4, the process of constructing the prediction model is as follows:

[0130] Use the mapping function to map the data set to a high-dimensional space, which is expressed as:

[0131] (7);

[0132] 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;

[0133] Among them, s and x are expressed as:

[0134] S = [ S 1 ， S 2 ， S 3 ， ⋯ S n ] (8);

[0135] x = [ K 1 , C 1 , K 2 , C 2 ] (9);

[0136] In the formula, represents the composition of the nth type of concrete among the mix proportion parameters of the physical model;

[0137] Based on the optimization principle, a least squares support vector machine is constructed according to formula (7), and the objective function of the least squares support vector machine optimization problem , is expressed as;

[0138] { min J ( w , ξ ) = 1 2 w T w + γ ∑ i = 1 n ξ i 2 s.t. S i [ w T φ ( x i ) + h ] − 1 + ξ i = 0 (10);

[0139] In the formula, and respectively represent the estimation deviation and the penalty coefficient; T represents the transpose; represents the ith estimation deviation; represents the constraint condition of the optimization problem; S i represents the ith group of output values; x i represents the ith group of input values; represents the mapping function of the ith group of output values; h represents the intercept;

[0140] The Lagrangian equation based on formula (10) is expressed as:

[0141] (11);

[0142] In the formula, represents the Lagrangian equation; α i represents the Lagrange multiplier and is a non - negative number;

[0143] According to the Mercer condition, there exists a kernel function , which is expressed as:

[0144] (12);

[0145] According to the above formula (11) and the above formula (12), the classification decision function based on the least squares support vector machine is expressed as:

[0146] f ( x ) = sign [ ∑ i = 1 n α i S i U ( x , x i ) + h ] (13);

[0147] Wherein, represents the classification decision function; sign represents a mathematical function used to return a number;

[0148] The radial basis function is selected as the kernel function of the least squares support vector machine, expressed as:

[0149] (14);

[0150] Wherein, represents the kernel function width; represents the exponential function operation symbol;

[0151] The penalty coefficient γ and the kernel function width σ 2 are used as two variables in the least squares support vector machine; the improved frill-necked lizard optimization algorithm is used to search for the least squares support vector machine to construct a prediction model (BLSO-LSSVM model).

[0152] Among them, the improved frill-necked lizard optimization algorithm includes three parts: population initialization, improved hunting strategy, and upward tree climbing strategy;

[0153] Population initialization: The improved frill-necked lizard optimization algorithm first initializes a group of randomly distributed candidate solutions in the solution space , expressed as:

[0154] (15);

[0155] Wherein, represents a random value in the interval [0, 1]; and respectively represent the lower bound and upper bound of the d-th decision variable;

[0156] Simulating the improved hunting strategy: In the standard hunting strategy, the movement of the frill-necked lizard individual towards the prey is simulated; the idea of multiple excellent grey wolves surrounding the prey in the grey wolf optimization algorithm is introduced, and the influence of the optimal frill-necked lizard individual and the sub-optimal frill-necked lizard individual on the prey is added to the original hunting strategy; the distance between the frill-necked lizard individual and the prey , expressed as:

[0157] (16);

[0158] Wherein, v represents the coefficient vector; and respectively represent the positions of the prey and the frill-necked lizard individual;

[0159] The updated distances between the optimal frill-necked lizard individual and the sub-optimal frill-necked lizard individual and the prey are expressed as:

[0160] (17);

[0161] (18);

[0162] In the formula, and respectively represent the distances between the optimal frilled lizard individual and the sub - optimal frilled lizard individual and the prey; P 1 and P 2 respectively represent the positions of the optimal frilled lizard individual and the sub - optimal frilled lizard individual; v D1 and v D2 both represent the coefficient vectors used when calculating the distance between the frilled lizard individual and the prey; X 1 and X 2 respectively represent the positions of the optimal frilled lizard individual and the sub - optimal frilled lizard individual; v X1 and v X2 both represent the coefficient vectors used when calculating the position of the frilled lizard individual;

[0163] The update method of the position of the i - th frilled lizard individual after improving the hunting strategy , is expressed as:

[0164] (19);

[0165] Simulate the strategy of climbing up the tree: After eating, the frilled lizard individual will retreat to the top of the tree near its position; Model the situation where the frilled lizard individual retreats 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 each frilled lizard individual changes the objective function value, then use formula (21) to replace the position of the corresponding frilled lizard individual, which is expressed as:

[0166] (20);

[0167] (21);

[0168] In the formula, represents the update method of the position of the i - th frilled lizard individual after the climbing - up - the - tree strategy; v x represents the coefficient vector used when calculating the update of the frilled lizard individual's position; represents the position of the frilled lizard individual after eating in the climbing - up - the - tree strategy; represents the objective function value of the i - th frilled lizard individual after the climbing - up - the - tree strategy; t represents the current iteration number; represents the objective function value of the i - th frilled lizard individual; represents the position of the i - th frilled lizard individual after eating.

[0169] Among them, in step S4, obtaining the prediction model with the optimal performance, the specific process is:

[0170] After normalizing the data set, it is randomly divided into a training set and a test set;

[0171] The training set is input into the prediction model for training and learning, and the prediction model continuously iterates and calculates according to a predetermined ratio; Set the number of iterations g and the precision error threshold F of the prediction model Z , the test set is input into the prediction model for prediction to obtain the result error rate error, and it is judged whether the result error rate error is less than the precision error threshold F Z , when error < F Z , then stop training; when error ≥ F Z , then keep iterating until the number of iterations g is reached to obtain the prediction model with the optimal performance.

[0172] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for pouring a concrete impermeable body through a large dam foundation cave conduit, 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. The method for pouring a concrete impermeable body through a large dam foundation karst cave conduit according to claim 1, characterized in that: 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: （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, 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.

3. The method for pouring concrete impermeable body through a large dam foundation karst cave conduit according to claim 2, characterized in that: 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. The method for pouring concrete impermeable body through a large dam foundation karst cave conduit according to claim 3, characterized in that: 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. The method for pouring concrete impermeable body through a large dam foundation karst cave conduit according to claim 4, characterized in that: In step S4, a data set is generated. 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.

6. The method for pouring concrete impermeable body through a large dam foundation karst cave conduit according to claim 5, characterized in that: 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 minJ 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 symbol; The penalty coefficient γ and kernel function width σ 2 As two variables in the least squares support vector machine; the improved umbrella 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.

7. The method for pouring concrete impermeable body through a large dam foundation karst cave conduit according to claim 6, characterized in that: 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 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 mth 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 mth 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 mth frilled lizard individual after the upward tree climbing strategy; t represents the current number of iterations; represents the objective function value of the mth frilled lizard individual; Represents the position of the mth frilled lizard individual after eating.

8. The method for pouring concrete impermeable body through a large dam foundation karst cave conduit according to claim 7, characterized in that: 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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