Method for evaluating grouting water control effect of underground water-sealed cavern project

Abstract

The invention provides a method for evaluating the grouting water control effect of an underground water-sealed cave depot project. The method comprises the steps that firstly, grouting and geological parameters and hydrological monitoring data of a project area are obtained; establishing a three-dimensional seepage-grouting coupling model, and simulating the dynamic state of a seepage field by using a finite element analysis algorithm; then calculating the equivalent permeability change rate of the grouting affected zone based on a simulation result; constructing an evaluation index system, and performing quantitative evaluation by using a fuzzy comprehensive evaluation algorithm; and finally, generating a grouting parameter dynamic adjustment scheme according to an evaluation result. According to the method, scientific and accurate evaluation and engineering optimization of the grouting water control effect can be achieved, and the safety and economical efficiency of engineering are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy engineering, and more specifically, to a method for evaluating the grouting water control effect of an underground water-sealed cavern engineering. Background Art

[0002] As a safe and efficient underground oil and gas storage facility, underground water-sealed caverns play an important role in the field of energy storage. It uses the principle that the groundwater pressure is greater than the pressure of the storage medium in the cave, and relies on a stable groundwater level to form a natural water seal to prevent leakage of the storage medium. In the construction of underground water-sealed caverns, grouting is a key technology to control groundwater leakage and ensure the stability of the cavern. Traditional grouting technology is mostly designed and constructed based on experience and simple geological surveys, lacking accurate quantitative analysis. At present, the evaluation of grouting effects mainly relies on simple on-site observations, such as observing whether there is water seepage on the cave wall, and some conventional hydrogeological testing methods, such as pumping tests to indirectly infer the penetration of groundwater, but these methods are difficult to fully and accurately reflect the complex effects of grouting on the groundwater seepage field. The existing technical principles often only consider a single factor, and do not fully consider the mutual coupling between geological conditions, grouting parameters and groundwater seepage.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: it is impossible to accurately evaluate the water control effect of grouting, and it is difficult to optimize the grouting scheme according to different geological conditions and engineering requirements, resulting in waste of grouting resources or poor grouting effect, affecting the long-term stability and safety of the cavern, and limiting the high-quality construction and development of underground water-sealed cavern projects. Summary of the invention

[0004] The present invention provides a method for evaluating the water control effect of grouting in an underground water-sealed cavern engineering, comprising:

[0005] S1. Obtain grouting parameters, geological parameters and hydrological monitoring data of the project area;

[0006] S2. Establish a three-dimensional seepage-grouting coupling model and use finite element analysis algorithm to perform dynamic simulation of the seepage field;

[0007] S3. Calculate the equivalent permeability change rate of the grouting influence zone based on the simulation results;

[0008] S4. Construct an evaluation index system for grouting water control effect and apply fuzzy comprehensive evaluation algorithm for quantitative evaluation;

[0009] S5. Generate a dynamic adjustment plan for grouting parameters based on the evaluation results.

[0010] Furthermore, the finite element analysis algorithm in step S2 includes:

[0011] S21. Discretize the engineering area into tetrahedral unit grids;

[0012] S22. Establish the coupling control equation between Darcy's law and slurry diffusion equation;

[0013] S23. Use Newton-Raphson iteration method to solve nonlinear equations;

[0014] S24. Output the pore water pressure distribution and velocity vector field of each node.

[0015] Furthermore, the coupling control equation in step S22 is expressed as:

[0016]

[0017] Among them, K is the rock permeability tensor, μ is the slurry dynamic viscosity, P is the pore water pressure, Q is the grouting rate, z0 is the grouting hole position coordinate, S s is the water storage coefficient, and t is the time variable.

[0018] Furthermore, the calculation formula of the permeability tensor K is:

[0019] K=K 0 ·exp(α·C e )

[0020] In the formula, K 0 is the initial permeability, α is the grouting correction factor, C e is the slurry volume concentration, calculated by the following formula:

[0021] C e =(Q·t) / (π·R 2 ·n)

[0022] Among them, R is the slurry diffusion radius and n is the porosity of the rock mass.

[0023] Furthermore, the calculation method of the equivalent permeability change rate in step S3 includes:

[0024] S31. Define the permeability attenuation coefficient β = K initial / K final ;

[0025] S32. Calculate the permeability gradient change rate

[0026] S33. Establish a comprehensive evaluation index λ = β·exp(γ·Δt);

[0027] Among them, K initial is the permeability before grouting, K final is the permeability after grouting, v is the groundwater velocity vector, and Δt is the grouting duration.

[0028] Furthermore, the calculation of Δt in step S33 adopts a dynamic time step algorithm:

[0029] Δt n +1=min(Δt max ,Δt n ·(ε tol / ε n ) 0.5 )

[0030] In the formula, Δt max is the maximum allowed step length, ε tol is the error tolerance, ε n is the calculated error at the current time step.

[0031] Furthermore, the fuzzy comprehensive evaluation algorithm in step S4 includes:

[0032] S41. Establish an evaluation factor set U=u1,u2,u3, where u1 is the permeability drop, u2 is the grouting curtain continuity, and u3 is the water level drop rate;

[0033] S42. Determine the weight vector W = [0.5, 0.3, 0.2];

[0034] S43. Construct membership function matrix R = [μ ij ]3×5;

[0035] S44. Calculate the comprehensive evaluation value B=W·R.

[0036] Furthermore, the membership function μ i The expression of j is:

[0037] μ i j(x)=1 / (1+|(xc i j) / σ i j| 2 )

[0038] Among them, c i j is the benchmark value of the jth level of the ith factor, σ i j is the membership bandwidth parameter.

[0039] Furthermore, the dynamic adjustment scheme in step S5 includes:

[0040] S51. Setting the grouting pressure adjustment threshold ΔP th =0.2P mzx ;

[0041] S52. When real-time monitoring pressure P act Satisfy |P act -Pset |>ΔP th When , the grouting parameters are re-optimized;

[0042] S53. Use sequential quadratic programming algorithm to solve the optimization model:

[0043] min f(X)=w1·Q 2 +w2·(PP t ) 2

[0044] stg(X)=λ≥λ min

[0045] In the formula, X = [Q, P] is the optimization variable, Q is the grouting flow rate, P is the grouting pressure, w1, w2 are weight coefficients, P t is the target pressure value, λ m in is the minimum control standard.

[0046] Furthermore, the weight coefficient adopts an adaptive adjustment strategy:

[0047]

[0048] Where η is the learning rate and E is the control error function, which is defined as:

[0049] E=0.5·(λ aCt -λ tar ) 2 +0.3·(QQ max ) 2

[0050] In the formula, λ act is the actual evaluation value, λ tar is the target evaluation value, Q max is the maximum allowable grouting volume.

[0051] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the evaluation method of the grouting water control effect of the underground water-sealed cavern engineering can improve the scientificity and accuracy of the evaluation. By acquiring various data of the engineering area, establishing a three-dimensional seepage-grouting coupling model and performing dynamic simulation, it is possible to comprehensively consider the influence of various factors on the grouting effect, and accurately calculate the equivalent permeability change rate of the grouting influence area. By using the constructed evaluation index system and fuzzy comprehensive evaluation algorithm, the grouting water control effect is quantitatively evaluated from multiple dimensions, providing comprehensive and accurate evaluation results for engineering personnel, and assisting them in more accurately grasping the actual results of grouting.

[0052] This method can play a key role in engineering optimization. The dynamic adjustment scheme of grouting parameters generated according to the evaluation results can adapt to the actual needs of the project in real time. By setting the grouting pressure adjustment threshold and triggering parameter re-optimization, solving the optimization model with the sequential quadratic programming algorithm, and optimizing the weight coefficient with the adaptive adjustment strategy, parameters such as grouting flow and pressure can be accurately adjusted. This can not only improve the effectiveness of grouting and reduce resource waste, but also enhance the stability and safety of underground water-sealed caverns, providing a strong guarantee for the long-term stable operation of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0054] Figure 1 A schematic flow chart of a method for evaluating the water control effect of grouting in an underground water-sealed cavern project provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0055] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0056] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0057] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0058] Reference below Figure 1 , Figure 1 The present invention provides a schematic flow chart of a method for evaluating the water control effect of grouting in underground water-sealed cavern engineering according to an embodiment of the present invention. Figure 1 As shown, a method 100 for evaluating the water control effect of grouting in an underground water-sealed cavern engineering includes:

[0059] S1. Obtain grouting parameters, geological parameters and hydrological monitoring data of the project area;

[0060] S2. Establish a three-dimensional seepage-grouting coupling model and use finite element analysis algorithm to perform dynamic simulation of the seepage field;

[0061] S3. Calculate the equivalent permeability change rate of the grouting influence zone based on the simulation results;

[0062] S4. Construct an evaluation index system for grouting water control effect and apply fuzzy comprehensive evaluation algorithm for quantitative evaluation;

[0063] S5. Generate a dynamic adjustment plan for grouting parameters based on the evaluation results.

[0064] It should be noted that obtaining the grouting parameters, geological parameters and hydrological monitoring data of the project area is the basic step of the entire evaluation method. Grouting parameters mainly refer to the various data involved in the grouting process, such as grouting rate, grouting pressure, grouting volume, etc. These parameters determine the specific implementation of grouting; geological parameters cover the rock mass characteristics related data of the project area, such as rock mass permeability, porosity, etc., reflecting the geological conditions of the project area; hydrological monitoring data refers to various types of information about groundwater in the area, such as groundwater level, water flow rate, etc. These data can reflect the dynamic situation of groundwater.

[0065] Specifically, in actual operation, the grouting rate can be initially set according to the engineering requirements and geological conditions. For example, in a relatively loose rock area, the grouting rate may be appropriately increased so that the slurry can quickly fill the pores. The rock permeability can be obtained through on-site pumping tests or laboratory tests. The permeability of different rock types varies greatly. For example, the permeability of sandstone and shale is significantly different. Groundwater level monitoring can be carried out by using a water level meter, which is installed at a specific location in the project area to regularly record water level changes.

[0066] Preferably, when acquiring data, for grouting parameters, a multi-sensor real-time monitoring system can be used to ensure the accuracy and timeliness of the data. In terms of obtaining geological parameters, in addition to conventional testing methods, geophysical detection methods such as geological radar can also be used to obtain more detailed geological structure information. For hydrological monitoring data, increasing the density of monitoring points, especially encrypting the arrangement of monitoring points in key areas around the cavern, can more comprehensively grasp the dynamic changes of groundwater.

[0067] In some embodiments, the finite element analysis algorithm in step S2 includes:

[0068] S21. Discretize the engineering area into tetrahedral unit grids;

[0069] S22. Establish the coupling control equation between Darcy's law and slurry diffusion equation;

[0070] S23. Use Newton-Raphson iteration method to solve nonlinear equations;

[0071] S24. Output the pore water pressure distribution and velocity vector field of each node.

[0072] It should be noted that establishing a three-dimensional seepage-grouting coupling model and using the finite element analysis algorithm to perform dynamic simulation of the seepage field is a key step in evaluating the grouting water control effect. The three-dimensional seepage-grouting coupling model is a mathematical model that comprehensively considers the groundwater seepage and the diffusion process of slurry in the rock mass. This model can simulate the interaction between the two. The finite element analysis algorithm is a numerical calculation method that decomposes complex engineering problems into multiple simple units for solution. The dynamic simulation of the seepage field uses the above model and algorithm to simulate the changes in groundwater seepage over time during the grouting process.

[0073] Specifically, when establishing a three-dimensional seepage-grouting coupling model, the boundary conditions of the model must be determined first, such as the head value or flow value on the boundary of a given model. For the mesh discretized into tetrahedral units, the appropriate mesh density can be selected according to the size and complexity of the engineering area. Generally, the mesh is finer in the key areas (such as near the grouting holes), and the mesh away from the grouting area can be relatively sparse. When establishing the coupling control equation between Darcy's law and the slurry diffusion equation, parameters such as the rock permeability tensor K, the slurry dynamic viscosity μ, and the grouting rate Q need to be accurately obtained. The rock permeability tensor K reflects the ability of the rock mass to allow fluid to pass through, which can be determined by field tests or empirical formulas; the slurry dynamic viscosity μ depends on the material properties of the slurry, and different types of slurries have different viscosities; the grouting rate Q is set according to the engineering design and the actual grouting situation. When using the Newton-Raphson iteration method to solve the nonlinear equations, it is necessary to set appropriate iteration initial values ​​and convergence criteria. For example, the initial value can be given based on experience or preliminary estimates, and the convergence criterion can be set as the difference between the results of two adjacent iterations is less than a certain minimum value (such as 10 -6 ).

[0074] More specifically, when discretizing into tetrahedral unit meshes, automatic mesh generation software such as ANSYSMeshing can be used, which can quickly generate high-quality tetrahedral unit meshes based on the geometry of the model and the parameters set by the user, thereby improving modeling efficiency. When establishing the coupled control equation, if it is difficult to obtain the rock permeability tensor K on site, reference can be made to existing data in areas with similar geological conditions, and corrections can be made in combination with geological survey data. For the Newton-Raphson iteration method, if the iterative process converges slowly, methods such as the pre-conditioned conjugate gradient method can be used to accelerate convergence, reduce calculation time, and improve solution efficiency.

[0075] In some embodiments, the coupling control equation in step S22 is expressed as:

[0076]

[0077] Among them, K is the rock permeability tensor, μ is the slurry dynamic viscosity, P is the pore water pressure, Q is the grouting rate, z0 is the grouting hole position coordinate, S s is the water storage coefficient, and t is the time variable.

[0078] It should be noted that the coupling control equation in step S22 is expressed as This is the core equation describing the interaction between groundwater seepage and slurry diffusion. is the Hamiltonian operator, which represents the derivative operation of spatial coordinates and is used to describe the changes of physical quantities in space; K is the rock permeability tensor, which reflects the anisotropic characteristics of the rock's permeability to fluids (here, groundwater and slurry), and the permeability in different directions may be different; μ is the slurry dynamic viscosity, which reflects the internal friction resistance when the slurry flows; P is the pore water pressure, which refers to the pressure of water in the pores of the rock; Q is the grouting rate, that is, the volume of slurry injected per unit time; x0 is the grouting hole position coordinate, which is used to determine the position of the grouting hole in space; S S is the water storage coefficient, which reflects the amount of water released or stored in unit volume of rock mass due to elastic compression and water expansion when the head changes by unit height; t is the time variable, which is used to describe the change of seepage and grouting process with time.

[0079] Specifically, when determining these parameters in practical applications, the rock permeability tensor K can be determined through on-site pumping tests and water pressure tests, or it can be calculated based on information such as lithology and pore structure obtained from geological surveys combined with empirical formulas. The dynamic viscosity μ of the slurry is usually determined by the composition and ratio of the slurry. For example, different types of slurries such as cement slurry and chemical slurry have large differences in viscosity, and accurate values ​​can generally be obtained through laboratory viscosity testing instruments. The grouting rate Q should be set based on a comprehensive consideration of the project progress requirements, the injectability of the rock mass, and the performance of the grouting equipment. For example, in rock masses with well-developed fractures and strong permeability, the grouting rate can be appropriately increased to ensure that the slurry can quickly diffuse and fill the fractures; in dense rock masses, the grouting rate needs to be appropriately reduced to prevent the slurry from diffusing too quickly and becoming difficult to control. Water storage coefficient S S It can be determined through field pumping tests combined with relevant hydrogeological calculation methods, or by referring to empirical data from areas with similar geological conditions.

[0080] Preferably, when calculating the coupled control equation, for the rock permeability tensor K, if the field test data is limited, the geostatistical method can be used to combine the geological data of the surrounding area for spatial interpolation to obtain a more accurate K value distribution. When determining the grouting rate Q, in addition to considering the above factors, the Q value can also be dynamically adjusted by real-time monitoring and feedback. For example, during the grouting process, by monitoring the changes in the pore water pressure P, if it is found that the pressure rises too fast, it may mean that the grouting rate is too high. At this time, the Q value can be appropriately reduced to ensure the safety and effectiveness of the grouting process. For the water storage coefficient S S If conditions permit, multiple groups of pumping tests of different scales can be carried out to improve S S The accuracy of the value.

[0081] In some embodiments, the calculation formula of the permeability tensor K is:

[0082] K=K 0 ·exp(α·C e )

[0083] In the formula, K 0 is the initial permeability, α is the grouting correction factor, C e is the slurry volume concentration, calculated by the following formula:

[0084] C e =(Q·t) / (π·R 2 ·n)

[0085] Among them, R is the slurry diffusion radius and n is the porosity of the rock mass.

[0086] It should be noted that the calculation formula of the permeability tensor K is given here: K = K 0 ·exp(α·C e ), and the slurry volume concentration C e Calculation method C e =(Q·t) / (π·R 2 ·n). Among them, K 0 is the initial permeability, which refers to the permeability of the rock mass before grouting, reflecting the original permeability of the rock mass; α is the grouting correction coefficient, which reflects the degree of influence of grouting on the permeability of the rock mass, and is a coefficient related to factors such as grouting materials and rock mass characteristics; C e is the slurry volume concentration, which indicates the volume ratio of slurry in the rock pores; Q is the grouting rate, i.e. the volume of slurry injected per unit time; t is the grouting time, which is the time from the start of grouting to the current moment; r is the slurry diffusion radius, which refers to the maximum radius range reached by the slurry diffusion in the rock mass; n is the rock porosity, which is the ratio of the rock pore volume to the total volume, reflecting the degree of development of the rock pores.

[0087] Specifically, when determining these parameters in actual engineering, the initial permeability K 0 It can be directly measured through on-site pumping tests and water pressure tests, or it can be estimated based on the lithology, pore structure and other information obtained from geological surveys combined with empirical formulas. The grouting correction factor α usually has no fixed value, and it needs to be determined based on a large amount of field test data or the experience of similar projects. For example, in different rock types (such as granite, sandstone, etc.), the α value will also vary due to their different mineral composition and pore structure. The determination of the slurry diffusion radius R is more complicated, and it is related to many factors such as grouting pressure, slurry viscosity, rock pore size and connectivity. It can generally be calculated by theoretical formulas, such as formulas based on spherical diffusion theory, or it can be measured through on-site grouting tests. The rock porosity n can be determined by laboratory analysis of collected core samples, or indirectly estimated by geophysical detection methods.

[0088] More specifically, when determining the grouting correction coefficient α, the method of multivariate regression analysis can be used. Collect grouting data under different geological conditions, different grouting materials and processes, take rock type, slurry type, grouting pressure, grouting time, etc. as independent variables, and take the change in permeability before and after grouting as the dependent variable. Through regression analysis, establish a quantitative relationship between α and these independent variables, so as to obtain a more accurate α value. For the slurry diffusion radius R, in addition to theoretical calculations and field tests, numerical simulation software such as COMSOL Multiphysics can also be used to simulate and calculate the actual geological parameters and grouting conditions to obtain an R value that is more in line with the actual situation. When measuring the rock porosity n, if it is difficult to obtain core samples, nuclear magnetic resonance logging technology can be used, which can quickly and accurately measure the porosity of the underground rock mass without removing the core.

[0089] In some embodiments, the method for calculating the equivalent permeability change rate in step S3 includes:

[0090] S31. Define the permeability attenuation coefficient β = K initial / K final ;

[0091] S32. Calculate the permeability gradient change rate

[0092] S33. Establish a comprehensive evaluation index λ = β·exp(γ·Δt);

[0093] Among them, K initial is the permeability before grouting, K final is the permeability after grouting, v is the groundwater velocity vector, and Δt is the grouting duration.

[0094] It should be noted that calculating the equivalent permeability change rate of the grouting influence zone is an important part of evaluating the grouting effect. The grouting influence zone refers to the area where the grouting operation affects the permeability of the rock mass. The equivalent permeability change rate is an indicator to measure the degree of permeability change in the area before and after grouting. By comparing the initial permeability before grouting and the permeability at a certain moment after grouting, it can intuitively reflect the change in the permeability of the rock mass caused by grouting, thereby judging the water control effect of grouting.

[0095] Specifically, when calculating the equivalent permeability change rate, we must first accurately determine the initial permeability K before grouting. 1 and the permeability after grouting K 2 . Initial permeability K 1 It can be estimated through the field pumping test, water pressure test or empirical formula mentioned above. Permeability after grouting K 2 It is necessary to combine the calculation formula of the permeability tensor K (K = K 0 ·exp(α·C e )) to calculate, the parameters involved such as initial permeability K 0 , grouting correction factor α, slurry volume concentration C e etc., must be accurately determined according to the corresponding method. For example, the slurry volume concentration C e The calculation (C) should be based on the grouting rate Q, grouting time t, slurry diffusion radius R and rock porosity n. e =(Q·t) / (π·R 2 n)). When determining the scope of the grouting influence zone, a comprehensive judgment can be made based on the on-site monitoring data, such as groundwater level changes, pore water pressure changes, etc., combined with the numerical simulation results.

[0096] Preferably, in order to more accurately determine the initial permeability K before grouting 1 , multiple test points can be arranged in the project area for pumping test, and then the data of each test point can be analyzed and processed by statistical methods to obtain a more representative initial permeability value. 2 The real-time monitoring data can be introduced to correct the simulation results. For example, the distributed optical fiber sensor is used to monitor the temperature and strain inside the rock mass in real time. This information is closely related to the slurry diffusion and the permeability change of the rock mass. By establishing a corresponding coupling model, the monitoring data is integrated into the permeability calculation to improve K. 2 Calculation accuracy. When determining the scope of the grouting influence zone, in addition to the changes in groundwater level and pore water pressure, geophysical detection methods such as geological radar and high-density electrical method can also be combined to more intuitively determine the slurry diffusion range, thereby accurately defining the grouting influence zone.

[0097] In some embodiments, the calculation of Δt in step S33 adopts a dynamic time step algorithm:

[0098] Δt n +1=min(Δt max ,Δt n ·(ε tol / ε n ) 0.5 )

[0099] In the formula, Δt max is the maximum allowed step length, ε tol is the error tolerance, ε n is the calculated error at the current time step.

[0100] It should be noted that constructing an evaluation index system and using a fuzzy comprehensive evaluation algorithm to quantitatively evaluate the grouting water control effect is a key means of comprehensively and scientifically evaluating the effectiveness of grouting. The evaluation index system covers multiple indicators, which reflect the effect of grouting water control from different dimensions. The fuzzy comprehensive evaluation algorithm is an effective method for dealing with fuzzy problems. Since there are many factors that are difficult to accurately define in the evaluation of grouting effects, such as the complex characteristics of the rock mass and the uncertainty of slurry diffusion, the use of this algorithm can more reasonably quantify the evaluation results.

[0101] Specifically, the indicators in the evaluation index system may include the rate of change of equivalent permeability in the grouting influence area, the change of groundwater level before and after grouting, the cost of grouting, the construction time, etc. The rate of change of equivalent permeability in the grouting influence area reflects the degree of change of the permeability of the rock mass caused by grouting, which can be calculated by the method described above. The change of groundwater level before and after grouting reflects the control effect of grouting on groundwater flow, and relevant data can be obtained through water level monitoring equipment. The cost of grouting includes the cost of slurry materials, the cost of equipment use, the cost of labor, etc., which can be obtained through financial accounting statistics. The construction time refers to the time spent from the start of grouting to the completion of the grouting operation, which can be determined through construction records. When using the fuzzy comprehensive evaluation algorithm, the weight of each indicator must be determined first. The determination of the weight can be carried out by expert scoring method, hierarchical analysis method, etc. For example, the expert scoring method is to invite experts in related fields to score according to the importance of each indicator, and then perform statistical analysis on the scores to obtain the weight. Next, we need to establish an evaluation set. For example, the evaluation set can be set as {excellent, good, medium, poor}. Finally, we calculate the membership of each indicator to each level in the evaluation set according to the membership function, and then obtain the comprehensive evaluation result.

[0102] Preferably, in addition to the above common indicators, some new indicators can be introduced when determining the evaluation index system, such as the strength of the slurry stone body, which reflects the bearing capacity and impermeability of the slurry after solidification in the rock mass. The data can be obtained by testing the strength of the slurry stone body samples in the laboratory. For the determination of the weights of each indicator, in addition to the expert scoring method and the hierarchical analysis method, the entropy weight method can also be combined. The entropy weight method determines the weight according to the degree of variation of the data of each indicator. Combining it with the subjective method can make the determination of the weight more objective and reasonable. When establishing the membership function, the fitting optimization can be performed according to the actual engineering data to improve the accuracy of the membership calculation. For example, by collecting a large amount of grouting effect data and evaluation results of similar projects, the membership function is optimized and adjusted using data mining technology.

[0103] In some embodiments, the fuzzy comprehensive evaluation algorithm in step S4 includes:

[0104] S41. Establish an evaluation factor set U=u1,u2,u3, where u1 is the permeability drop, u2 is the grouting curtain continuity, and u3 is the water level drop rate;

[0105] S42. Determine the weight vector W = [0.5, 0.3, 0.2];

[0106] S43. Construct membership function matrix R = [μ ij ]3×5;

[0107] S44. Calculate the comprehensive evaluation value B=W·R.

[0108] It should be noted that the dynamic adjustment plan for grouting parameters generated according to the evaluation results is to optimize the subsequent grouting operations according to the actual grouting effect to achieve better water control effect. Grouting parameters include grouting pressure, grouting rate, slurry concentration, etc. These parameters directly affect the diffusion and filling of slurry in the rock mass. The dynamic adjustment plan makes timely and reasonable changes to these parameters based on the real-time evaluation results to ensure that the grouting project can be flexibly adjusted according to the actual situation and improve the efficiency and quality of the project.

[0109] Specifically, when the evaluation results show that the grouting effect is not good, the grouting parameters need to be adjusted. For example, if the equivalent permeability change rate of the grouting influence zone does not reach the expected target, it means that the slurry may not fully fill the rock pores. At this time, it is possible to consider increasing the grouting pressure or grouting rate. Grouting pressure is the driving force for the slurry to flow in the rock mass. Increasing the pressure can allow the slurry to diffuse deeper into the rock cracks. Generally speaking, the setting of grouting pressure should be determined according to the strength of the rock mass and the development of cracks. In hard rock with fewer cracks, the pressure can be appropriately increased, but it cannot exceed the bearing capacity of the rock mass to avoid causing rock mass damage. The grouting rate determines the amount of slurry injected per unit time. Increasing the grouting rate can speed up the filling speed of the slurry, but too fast may cause uneven distribution of the slurry. The slurry concentration will also affect the grouting effect. A higher slurry concentration can increase the stone body strength of the slurry, but may reduce its fluidity. When determining the slurry concentration, factors such as the pore size and connectivity of the rock mass should be considered comprehensively.

[0110] Preferably, in order to generate dynamic adjustment schemes more accurately, a real-time feedback mechanism can be established. Sensors are used to monitor various parameters in the grouting process in real time, such as pressure sensors monitoring grouting pressure, flow sensors monitoring grouting rate, etc., and these data are transmitted to the control system in real time. The control system automatically determines whether the grouting parameters need to be adjusted and how to adjust them based on the preset evaluation criteria and real-time monitoring data. When adjusting the grouting pressure, in addition to manually controlling the grouting equipment, an intelligent pressure regulating device can also be used, which can automatically adjust according to the preset pressure range to improve the accuracy of pressure control. For the adjustment of slurry concentration, an automated slurry preparation system can be used to automatically proportion the slurry components according to the set concentration requirements to ensure the stability and accuracy of the slurry concentration. At the same time, numerical simulation technology can be combined to perform simulation calculations before adjusting the parameters to predict the adjusted grouting effect, so as to determine the adjustment plan more scientifically.

[0111] In some embodiments, the membership function μ i The expression of j is:

[0112] μ ij (x) = 1 / (1+|(xc ij ) / σ ij | 2 )

[0113] Among them, c ij is the benchmark value of the jth level of the ith factor, σ ij is the membership bandwidth parameter.

[0114] It should be noted that setting the grouting pressure adjustment threshold and triggering parameter re-optimization are important measures to ensure the effect and safety of grouting projects. The grouting pressure adjustment threshold is a pre-set grouting pressure limit value. When the actual grouting pressure reaches or exceeds this threshold, it means that the current grouting situation may be abnormal and the grouting parameters need to be re-optimized. Parameter re-optimization is to readjust and optimize the grouting pressure, grouting rate, slurry concentration and other parameters according to the current actual situation and evaluation results to ensure that the grouting project can achieve the expected water control effect, while avoiding damage to the rock mass or other safety problems caused by excessive pressure.

[0115] Specifically, the setting of the grouting pressure adjustment threshold needs to consider many factors comprehensively. First of all, the mechanical properties of the rock mass. Different types of rock mass have different compressive strengths. For example, the grouting pressures that hard granite and relatively soft sandstone can withstand are very different. Generally speaking, for rock mass with higher compressive strength, the adjustment threshold can be appropriately increased; while for rock mass with lower compressive strength, the threshold should be lowered. Secondly, the design requirements of the project and the expected grouting effect should also be considered. If the slurry needs to penetrate deeper into the rock mass, a higher grouting pressure may be required, but it must also be controlled within a safe range. In actual operation, the adjustment threshold can be preliminarily determined through field tests or by referring to the experience of similar projects. When the actual grouting pressure reaches the threshold, the parameter re-optimization process is triggered. During the re-optimization process, the causes of the abnormal pressure should be analyzed in combination with the previous evaluation results and real-time monitoring data. If the pressure rises due to poor slurry diffusion, it may be necessary to adjust the grouting rate or change the slurry concentration; if it is caused by changes in the rock mass structure, the entire grouting plan may need to be re-evaluated.

[0116] Preferably, in order to more accurately set the grouting pressure adjustment threshold, a method combining numerical simulation and field monitoring can be used. Use numerical simulation software to simulate the grouting process under different geological conditions and grouting parameters, analyze the changing law of grouting pressure, and obtain a theoretically reasonable threshold range. At the same time, multiple pressure monitoring points are arranged on site to obtain grouting pressure data in real time. Through the analysis and statistics of a large amount of monitoring data, the threshold is further corrected and optimized. When triggering parameter re-optimization, an intelligent decision-making system can be introduced. The system can automatically generate the optimal parameter adjustment plan based on preset rules and algorithms, combined with real-time monitoring data and evaluation results. In addition, a historical case database can be established. When encountering similar situations, the system can refer to the successful experience in historical cases to quickly and accurately re-optimize parameters.

[0117] In some embodiments, the dynamic adjustment scheme in step S5 includes:

[0118] S51. Setting the grouting pressure adjustment threshold ΔP th=0.2P mzx ;

[0119] S52. When real-time monitoring pressure P act Satisfy |P act -P set |>ΔP th When , the grouting parameters are re-optimized;

[0120] S53. Use sequential quadratic programming algorithm to solve the optimization model:

[0121] min f(X)=w1·Q 2 +w2·(PP t ) 2

[0122] stg(X)=λ≥λ min

[0123] In the formula, X = [Q, P] is the optimization variable, Q is the grouting flow rate, P is the grouting pressure, w1, w2 are weight coefficients, P t is the target pressure value, λ min The minimum control standard.

[0124] It should be noted that the sequential quadratic programming algorithm is used to solve the optimization model to adjust the grouting parameters in order to achieve the optimal state of various indicators while ensuring the grouting effect. The sequential quadratic programming algorithm is an efficient nonlinear optimization algorithm used to find the optimal solution of the objective function under certain constraints. The optimization model is a mathematical model established based on the goals of the grouting project (such as improving the grouting effect, reducing costs, etc.) and constraints (such as rock bearing capacity, equipment performance, etc.). By solving the optimization model through this algorithm, the optimal combination of grouting parameters can be obtained, thereby achieving precise control of the grouting process.

[0125] Specifically, when establishing an optimization model, the objective function can be set according to engineering requirements. For example, if the main focus is on the grouting effect, the equivalent permeability change rate of the grouting influence zone can be used as the objective function, hoping that it can reach the maximum; if the cost factor is considered at the same time, the grouting cost and the equivalent permeability change rate can be combined into a comprehensive objective function. Constraints include mechanical property constraints of the rock mass, such as the grouting pressure cannot exceed the compressive strength of the rock mass; equipment performance constraints, such as the grouting rate cannot exceed the maximum flow of the grouting equipment, etc. When using the sequential quadratic programming algorithm to solve, it is necessary to set the initial grouting parameter value, which can be determined based on experience or preliminary test data. The algorithm will continuously iterate and calculate according to the objective function and constraints, and gradually approach the optimal solution. During the iteration process, the gradient of the objective function and the Hessian matrix and other information will be calculated based on the current solution to adjust the search direction and step size for the next step.

[0126] Preferably, in order to improve the solution efficiency and accuracy of the sequential quadratic programming algorithm, an adaptive strategy can be used to adjust the initial parameter values. For example, according to different geological conditions and engineering requirements, the initial grouting parameters are dynamically adjusted to make the algorithm converge to the optimal solution faster. When establishing the optimization model, more practical factors can be introduced as constraints, such as the influence of groundwater flow rate and flow direction on slurry diffusion, so that the model is more in line with the actual situation. For the iterative process of the algorithm, parallel computing technology can be used to perform calculations in multiple search directions at the same time to speed up the solution. In addition, other optimization algorithms can be combined for hybrid solutions, such as genetic algorithms, etc., first using genetic algorithms for global search to find a roughly optimal area, and then using sequential quadratic programming algorithms to perform local fine search in the area to improve the accuracy of the solution.

[0127] In some embodiments, the weight coefficient adopts an adaptive adjustment strategy:

[0128]

[0129] Where η is the learning rate and E is the control error function, which is defined as:

[0130] E=0.5·(λ act -λ tar ) 2 +0.3·(QQ max ) 2

[0131] In the formula, λ act is the actual evaluation value, λ tar is the target evaluation value, Q max is the maximum allowable grouting volume.

[0132] It should be noted that the use of adaptive adjustment strategies to optimize the weight coefficients of evaluation indicators can make the evaluation results more in line with the actual grouting situation. The weight coefficients of evaluation indicators reflect the importance of each evaluation indicator in the overall evaluation. Since the importance of each evaluation indicator to the evaluation of grouting effect will change at different grouting stages and under different geological conditions, it is necessary to use adaptive adjustment strategies to dynamically optimize these weight coefficients. This can make the evaluation results more accurately reflect the actual effect of grouting, and thus provide a more scientific basis for the subsequent adjustment of grouting parameters.

[0133] Specifically, the adaptive adjustment strategy will adjust the weight coefficient according to the real-time monitoring data and the characteristics of different grouting stages. Real-time monitoring data includes information such as grouting pressure, grouting flow, groundwater level changes, and rock deformation. For example, in the early stage of grouting, grouting flow and pressure may be more critical to the evaluation of grouting effect. At this time, the weight coefficients of these two indicators can be appropriately increased; in the later stage of grouting, indicators such as groundwater level changes and rock deformation may better reflect the final effect of grouting, and the weights of these indicators need to be adjusted accordingly. When determining the initial weight coefficient, expert scoring method, hierarchical analysis method, etc. can be used. The expert scoring method is to invite experts in related fields to score the importance of each indicator based on their own experience, and then calculate the average value to obtain the initial weight; the hierarchical analysis method is to establish a hierarchical structure model, compare each indicator pairwise, construct a judgment matrix, and then calculate the weight coefficient. In the adjustment process, it is necessary to set adjustment rules. For example, when the monitoring value of a certain indicator fluctuates abnormally, the weight of the indicator is increased or decreased accordingly.

[0134] Preferably, in order to achieve adaptive adjustment more accurately, a machine learning algorithm can be used to establish a weight adjustment model. Take real-time monitoring data as input and weight coefficient as output, train the model through a large amount of historical data, so that the model can automatically learn the reasonable distribution of weights of each indicator under different data characteristics. In terms of data monitoring, increase the density and frequency of monitoring points to obtain richer and more accurate real-time data to improve the timeliness and accuracy of weight adjustment. At the same time, combined with numerical simulation technology, predict the changing trends of various indicators in different grouting stages, and pre-adjust the weight coefficient in advance so that the evaluation results can better adapt to the upcoming grouting situation. In addition, establish a feedback mechanism to revise and optimize the weight adjustment strategy according to the difference between the actual grouting effect and the evaluation results, and continuously improve the scientificity and reliability of the evaluation.

[0135] The above-mentioned embodiments of the present invention have the following beneficial effects: the underground water-sealed cavern engineering grouting water control effect evaluation method and related technical solutions can comprehensively and accurately evaluate the grouting effect. In the evaluation process, obtaining multiple types of data provides a rich basis for analysis. The three-dimensional seepage-grouting coupling model combined with the finite element analysis algorithm fully considers the complex relationship between geology, grouting and seepage, and can accurately simulate the dynamic changes of the seepage field. By calculating the equivalent permeability change rate and constructing an evaluation index system, the fuzzy comprehensive evaluation algorithm is used for quantitative evaluation, and the grouting effect is comprehensively considered from multiple dimensions, avoiding the limitations of a single indicator evaluation, and providing engineering personnel with more comprehensive and accurate grouting effect information.

[0136] This method and technical solution can also play a key role in optimizing engineering construction and ensuring engineering safety. The dynamic adjustment scheme of grouting parameters generated based on the evaluation results is very practical. By setting the grouting pressure adjustment threshold and using the sequential quadratic programming algorithm to solve the optimization model, the grouting parameters can be accurately adjusted according to the real-time monitoring situation. The strategy of adaptively adjusting the weight coefficient can make the optimization process more intelligent and reasonable, improve the effectiveness and pertinence of grouting, and reduce resource waste. Reasonable grouting parameters can enhance the stability and sealing of underground water-sealed caverns, reduce the risk of leakage, ensure the safety of storage media, and ensure the long-term stable operation of the project.

[0137] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0138] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A method for evaluating the water control effect of grouting in underground water-sealed cavern engineering, characterized in that: The following steps are involved: S1. Obtain grouting parameters, geological parameters and hydrological monitoring data of the project area; S2. Establish a three-dimensional seepage-grouting coupling model and use finite element analysis algorithm to perform dynamic simulation of the seepage field; S3. Calculate the equivalent permeability change rate of the grouting influence zone based on the simulation results; S4. Construct an evaluation index system for grouting water control effect and apply fuzzy comprehensive evaluation algorithm for quantitative evaluation; S5. Generate a dynamic adjustment plan for grouting parameters based on the evaluation results.

2. The method according to claim 1, characterized in that The finite element analysis algorithm in step S2 includes: S21. Discretize the engineering area into tetrahedral unit grids; S22. Establish the coupling control equation between Darcy's law and slurry diffusion equation; S23. Use Newton-Raphson iteration method to solve nonlinear equations; S24. Output the pore water pressure distribution and velocity vector field of each node.

3. The method according to claim 2, characterized in that The coupling control equation in step S22 is expressed as: Among them, K is the rock permeability tensor, μ is the slurry dynamic viscosity, P is the pore water pressure, Q is the grouting rate, x0 is the grouting hole position coordinate, S s is the water storage coefficient, and t is the time variable.

4. The method according to claim 3, characterized in that The calculation formula of the permeability tensor K is: K=K0·exp(α·C e ) Where K0 is the initial permeability, α is the grouting correction coefficient, C e is the slurry volume concentration, calculated by the following formula: C e =(Q·t) / (π·R 2 ·n) Among them, R is the slurry diffusion radius and n is the porosity of the rock mass.

5. The method according to claim 1, characterized in that The calculation method of the equivalent permeability change rate in step S3 includes: S31. Define the permeability attenuation coefficient β = K initial / K final ; S32. Calculate the permeability gradient change rate S33. Establish a comprehensive evaluation index λ = β·exp(γ·Δt); Among them, K initial is the permeability before grouting, K final is the permeability after grouting, v is the groundwater velocity vector, and Δt is the grouting duration.

6. The method according to claim 5, characterized in that The calculation of Δt in step S33 adopts a dynamic time step algorithm: Δt n +1=min(Δt max ,Δt n ·(e tol / e n ) 0.5 ) In the formula, Δt max is the maximum allowed step size, ε tol is the error tolerance, ε n is the calculated error at the current time step.

7. The method according to claim 1, characterized in that The fuzzy comprehensive evaluation algorithm in step S4 includes: S41. Establish an evaluation factor set U=u1,u2,u3, where u1 is the permeability drop, u2 is the grouting curtain continuity, and u3 is the water level drop rate; S42. Determine the weight vector W = [0.5, 0.3, 0.2]; S43. Construct membership function matrix R = [μ ij ]3×5; S44. Calculate the comprehensive evaluation value B=W·R.

8. The method according to claim 7, characterized in that The membership function μ ij The expression is: m ij (x)=1 / (1+|(xc ij ) / s ij | 2 ) Among them, c ij is the benchmark value of the jth level of the ith factor, σ ij is the membership bandwidth parameter.

9. The method according to claim 1, characterized in that: The dynamic adjustment scheme in step S5 includes: S51. Setting the grouting pressure adjustment threshold ΔP th =0.2P max ; S52. When real-time monitoring pressure P act Satisfy |P act -P set |>ΔP th When , the grouting parameters are re-optimized; S53. Use sequential quadratic programming algorithm to solve the optimization model: minf(X)=w1·Q 2 +w2·(PP t ) 2 s.t.g(X)=λ≥λ min In the formula, X = [Q, P] is the optimization variable, Q is the grouting flow rate, P is the grouting pressure, w1, w2 are weight coefficients, P t is the target pressure value, λ min The minimum control standard.

10. The method according to claim 9, characterized in that The weight coefficient adopts an adaptive adjustment strategy: Where η is the learning rate and E is the control error function, which is defined as: E=0.5·(λ act -l tar ) 2 +0.3·(QQ max ) 2 In the formula, λ act is the actual evaluation value, λ tar is the target evaluation value, Q max is the maximum allowable grouting volume.

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