Tunnel surrounding rock grouting control method and system
By optimizing grouting parameters through multi-dimensional real-time monitoring and multi-field coupling equations, the problem of uncertain grout diffusion paths in tunnel engineering was solved, achieving precise control of grout diffusion paths and uniformity and stability of grouting effects.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING CONSTRUCTION ENGINEERING GROUP CO LTD
- Filing Date
- 2025-08-06
- Publication Date
- 2026-07-03
AI Technical Summary
In existing tunnel engineering, the diffusion behavior of grout in the surrounding rock is non-uniform, resulting in uncertain grout diffusion paths and ranges, making precise control difficult.
By constructing a dynamic three-dimensional geological model through multi-dimensional real-time monitoring, and combining genetic algorithms and multi-field coupling equations, grouting parameters are optimized to generate the optimal grouting scheme, thereby achieving accurate prediction and control of grout diffusion path.
It enables precise prediction and control of the grout diffusion path, avoids the blind injection of grout, and improves the uniformity and stability of the grouting effect.
Smart Images
Figure CN120946402B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of tunnel engineering technology, and more specifically, to a method and system for controlling grouting in tunnel surrounding rock. Background Technology
[0002] In existing tunnel engineering technologies, grouting control in surrounding rock is generally controlled through experience, lacking effective observation and quantification methods. In actual engineering scenarios, the actual diffusion behavior of grout in fractured rock masses often exhibits non-uniform characteristics. Specifically, grout may excessively accumulate in some areas of the rock mass, leading to localized grout enrichment; while in other areas, insufficient grout filling may create voids. This non-uniform diffusion characteristic further increases the uncertainty of the grout diffusion path and range, resulting in difficulties in precisely controlling grout diffusion.
[0003] There is an urgent need for a method and system for controlling grouting in tunnel surrounding rock, which would solve the problem of the difficulty in accurately controlling grout diffusion. Summary of the Invention
[0004] The purpose of this invention is to provide a method and system for controlling grouting in tunnel surrounding rock, in order to improve the above-mentioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:
[0005] In a first aspect, this application provides a method for controlling grouting in tunnel surrounding rock, including:
[0006] Obtain grouting parameters, including grouting pressure and grout viscosity;
[0007] Multidimensional real-time monitoring of surrounding rock fissures was conducted, and a dynamic three-dimensional geological model was constructed using the data from the multidimensional real-time monitoring.
[0008] Based on the three-dimensional geological model, the slurry diffusion path is predicted, and an initial grouting scheme is generated based on the predicted slurry diffusion path.
[0009] The fractal dimension is optimized using a genetic algorithm, and the grouting parameters are dynamically corrected based on the optimized fractal dimension to obtain the grout diffusion results.
[0010] Based on the slurry diffusion results, a multi-field coupling equation is established, and the initial grouting scheme is optimized through the multi-field coupling equation to obtain the optimal grouting scheme;
[0011] Dynamic pressure compensation and grouting control are performed according to the optimal grouting scheme.
[0012] Secondly, this application also provides a tunnel surrounding rock grouting control device, comprising:
[0013] The acquisition module is used to acquire grouting parameters, including grouting pressure and grout viscosity;
[0014] The module is used for multi-dimensional real-time monitoring of surrounding rock fractures and for building a dynamic three-dimensional geological model based on the data from the multi-dimensional real-time monitoring.
[0015] The prediction module is used to predict the grout diffusion path based on the three-dimensional geological model and generate an initial grouting plan based on the predicted grout diffusion path.
[0016] The optimization module is used to optimize the fractal dimension according to the genetic algorithm, and to dynamically correct the grouting parameters through the optimized fractal dimension to obtain the grout diffusion result;
[0017] A module is established to establish multi-field coupling equations based on the slurry diffusion results, and to optimize the initial grouting scheme through the multi-field coupling equations to obtain the optimal grouting scheme;
[0018] The compensation module is used to perform dynamic pressure compensation and grouting control according to the optimal grouting scheme.
[0019] The beneficial effects of this invention are as follows:
[0020] This invention constructs a dynamic three-dimensional geological model using multi-dimensional real-time monitoring data. The three-dimensional geological model dynamically adjusts based on updates from the multi-dimensional real-time monitoring, thereby accurately predicting the diffusion path of grout in the surrounding rock and avoiding blind grout injection. Furthermore, by predicting the grout diffusion path using the dynamic three-dimensional geological model, the grouting path and grouting parameters are planned based on the actual fracture structure and geological conditions of the surrounding rock. A genetic algorithm is used to find the optimal fractal dimension parameter, accurately describing the distribution characteristics of the fractures. A multi-field coupling equation is established, considering the interaction between multiple factors such as the mechanical field and flow field of the surrounding rock during the grouting process, thereby optimizing the initial grouting scheme. By comprehensively considering the influence of various factors on the grouting effect, the optimal grouting scheme is obtained. In summary, this invention solves the problem of difficult precise control of grout diffusion.
[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description
[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 This is a schematic diagram of the tunnel surrounding rock grouting control method described in an embodiment of the present invention;
[0024] Figure 2 This is a schematic diagram of the tunnel surrounding rock grouting control equipment described in an embodiment of the present invention.
[0025] The markings in the diagram are: 800, tunnel surrounding rock grouting control equipment; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0028] Example 1:
[0029] This embodiment provides a method for controlling grouting in tunnel surrounding rock.
[0030] See Figure 1 The figure shows that the method includes steps S1 to S6, including:
[0031] S1: Obtain grouting parameters, including grouting pressure and grout viscosity;
[0032] S2: Multi-dimensional real-time monitoring of surrounding rock fissures, and construction of a dynamic three-dimensional geological model based on the data from multi-dimensional real-time monitoring;
[0033] To clarify the specific method for obtaining the three-dimensional geological model, step S2 includes S21 to S24, specifically:
[0034] S21: Real-time monitoring of surrounding rock fissures using microseismic and ground-penetrating radar, and construction of a three-dimensional fissure model based on real-time monitoring data;
[0035] In this step, the surrounding rock fissures are monitored in real time using microseismic monitoring technology and ground-penetrating radar technology. The microseismic monitoring technology is used to capture the dynamic changes of the fissures, and the ground-penetrating radar technology is used to obtain the spatial distribution information of the fissures.
[0036] S22: Monitor the porosity distribution of the surrounding rock and construct a digital twin of porosity using the porosity distribution data;
[0037] In this step, the porosity distribution of the surrounding rock is monitored using porosity monitoring technology. A digital twin of the porosity is constructed using the monitoring data, and this digital twin is used to reflect the distribution characteristics of the porosity of the surrounding rock in real time.
[0038] Preferably, the porosity monitoring technology includes resistivity method and ultrasonic method.
[0039] S23: The location of the slurry front is obtained by distributed monitoring of the stress and strain state of the surrounding rock using fiber optic grating sensors;
[0040] In this step, the stress and strain state of the surrounding rock is monitored in a distributed manner using fiber Bragg grating sensors. The fiber Bragg grating sensors acquire real-time stress and strain changes within the surrounding rock, and the location of the grout front is inferred from the stress and strain change data.
[0041] S24: Based on the porosity distribution characteristics in the porosity digital twin and the position of the slurry front and the parameters of the three-dimensional fracture model, the three-dimensional geological model is optimized and adjusted to obtain the three-dimensional geological model.
[0042] In this step, based on the porosity distribution characteristics in the porosity digital twin, the location of the slurry front, and the parameters of the three-dimensional fracture model, the three-dimensional fracture model is optimized and adjusted to obtain a more accurate three-dimensional geological model. This model is then dynamically adjusted according to the updates from multi-dimensional real-time monitoring, thereby accurately predicting the diffusion path of the slurry in the surrounding rock and avoiding the blind injection of slurry.
[0043] S3: Based on the three-dimensional geological model, predict the slurry diffusion path and generate an initial grouting scheme based on the predicted slurry diffusion path;
[0044] To clarify the specific method for obtaining the initial grouting scheme, step S3 includes S31 to S33, specifically:
[0045] S31: Based on the three-dimensional geological model, the geological characteristics of the surrounding rock and the grouting environment are learned through deep reinforcement learning algorithm, and the grouting parameters are optimized by combining quantum genetic algorithm to generate a grouting strategy;
[0046] In this step, the deep reinforcement learning algorithm learns from the surrounding rock geological features and grouting environment in the three-dimensional geological model. The interaction between the deep reinforcement learning algorithm and the grouting environment dynamically adjusts the strategy to maximize cumulative rewards; the quantum genetic algorithm accelerates the search process and improves optimization efficiency through the probabilistic characteristics of qubits.
[0047] Preferably, the geological features of the surrounding rock include fracture distribution and porosity, and the grouting environment includes grouting pressure and grout viscosity.
[0048] S32: Input the grouting strategy into a multiphysics coupled virtual simulation system to predict grout diffusion, and adjust the grouting parameters based on the prediction results to obtain an optimized grouting strategy;
[0049] In this step, the virtual simulation system with multi-physics coupling considers the coupling effects of multiple physical fields such as fluid flow, solid mechanics, and heat conduction;
[0050] The multiphysics coupling equation is as follows:
[0051]
[0052] In equation (1) above, P is the grouting pressure, η is the grout viscosity, σ is the stress tensor, ρ is the density, g is the gravitational acceleration, T is the temperature, p is the pressure, and c is the specific heat capacity. The symbol represents the partial derivative, t represents time, and k represents thermal conductivity.
[0053] S33: Simulate the grout diffusion path based on the optimized grouting strategy, and generate an initial grouting scheme through the simulated grout diffusion path.
[0054] In this step, the grout diffusion path is simulated based on the optimized grouting strategy to generate a reasonable initial grouting scheme, thereby improving the grouting effect.
[0055] S4: The fractal dimension is optimized using a genetic algorithm, and the grouting parameters are dynamically corrected using the optimized fractal dimension to obtain the grout diffusion result;
[0056] To clarify the specific method for obtaining the slurry diffusion results, step S4 includes S41 to S44, specifically:
[0057] S41: Adjust the parameters in the fractal dimension according to the genetic algorithm, and learn the fractal characteristics of the surrounding rock fractures by combining the deep learning algorithm to obtain the fractal model of the surrounding rock fractures;
[0058] In this step, the fractal dimension is an important parameter used to describe the complexity of the crack. The optimal fractal dimension parameter is found through the genetic algorithm to accurately describe the distribution characteristics of the crack.
[0059] To clarify the specific method for obtaining the fractal model of the surrounding rock fracture, step S41 includes S411 to S413, specifically:
[0060] S411: Calculate the mean square error between the fractal dimension and the fractal dimension threshold using the genetic algorithm to obtain the error evaluation index;
[0061] In this step, the mean square error is a commonly used indicator to measure the difference between the predicted value and the true value.
[0062] The mean square error expression is as follows:
[0063]
[0064] In equation (2) above, MSE is the mean squared error, N is the sample size, and D is the mean squared error. i Let D be the fractal dimension of the i-th sample. threshold This is the preset fractal dimension threshold.
[0065] S412: Optimize the parameters of the genetic algorithm based on the error evaluation index. During the optimization process, iterate the population until the preset convergence condition is met, and output the optimized parameter combination.
[0066] S413: Based on the optimized parameter combination, calculate the fractal characteristics of the surrounding rock fractures using the fractal dimension, and construct a fractal model of the surrounding rock fractures using the fractal characteristics;
[0067] In this step, the fractal dimension of the surrounding rock fractures is recalculated based on the optimized parameter combination, and the fractal features of the surrounding rock fractures are extracted based on the fractal dimension.
[0068] The expression for the fractal dimension of the surrounding rock fracture is:
[0069]
[0070] In equation (3) above, D represents the fractal dimension of the surrounding rock fracture, lim is the limit operator, N(∈) is the number of boxes required to cover the fracture network, and ∈ is the side length of the box.
[0071] S42: Based on the fractal model of the surrounding rock fracture, the diffusion range of the grout is predicted.
[0072] In this step, based on the fractal model of the surrounding rock fractures and combined with the principles of fluid mechanics and geomechanics, the diffusion process of the slurry is predicted, thereby simulating the flow of the slurry in the fractures.
[0073] S43: The error value is obtained by comparing the slurry diffusion range with the preset slurry diffusion threshold based on the fitness function of the genetic algorithm;
[0074] In this step, the error value is calculated using the fitness function to quantify the deviation between the prediction result and the target, providing a basis for the dynamic correction of grouting parameters and solving the problem of lack of quantitative evaluation indicators in traditional methods.
[0075] S44: Based on the error value, the grouting parameters are dynamically corrected to obtain the grout diffusion result.
[0076] S5: Based on the slurry diffusion results, establish a multi-field coupling equation, optimize the initial grouting scheme through the multi-field coupling equation, and obtain the optimal grouting scheme;
[0077] To clarify the specific method for obtaining the optimal grouting scheme, step S5 includes S51 to S54, specifically:
[0078] S51: Determine the interaction relationship of the coupled physical fields based on the slurry diffusion results, and establish multi-field coupling equations;
[0079] In this step, the interaction between physical fields such as fluid flow, solid mechanics, and heat conduction is analyzed based on the grout diffusion results, and multi-field coupling equations are established. These multi-field coupling equations are used to describe the grout diffusion process in the surrounding rock and the mechanical and thermal response of the surrounding rock; this solves the problem of poor grouting effect caused by considering only a single physical field (such as fluid flow) in traditional grouting design, and improves the uniformity and stability of grouting.
[0080] S52: Solve the multi-field coupling equations using the finite element method discretization to obtain the multi-field distribution results;
[0081] In this step, the finite element method accurately solves the multi-field coupling equations, obtains the distribution results of each physical field, and solves the problem that the multi-field coupling equations are difficult to solve directly.
[0082] The distribution results of each physical field are multi-field distribution results.
[0083] S53: Input the initial grouting scheme into the multi-field coupling equation and solve it to obtain the initial grouting result;
[0084] In this step, the initial grouting results include the grout diffusion range, surrounding rock stress distribution, and temperature distribution. By simulating the initial grouting scheme, the grouting effect can be preliminarily evaluated.
[0085] S54: Adjust the grouting parameters and optimize the grouting points based on the initial grouting results and the multi-field distribution results to form an optimized grouting scheme;
[0086] In this step, the uniformity of grout diffusion, the mechanical response of the surrounding rock, and the thermal response are analyzed based on the initial grouting results and multi-field distribution results. Grouting parameters are adjusted and the location of grouting points is optimized based on the analysis results to form an optimized grouting scheme.
[0087] S55: Input the optimized grouting scheme into the multi-field coupling equation for simulation and verification to obtain the optimal grouting scheme.
[0088] In this step, the optimized grouting scheme is input into the multi-field coupling equation and simulated and verified using the finite element method to obtain the verification results. Based on the verification results, the grouting scheme is further adjusted until the design requirements are met, thus obtaining the optimal grouting scheme.
[0089] Preferably, the verification results include slurry diffusion range, surrounding rock stress distribution, temperature distribution, etc.
[0090] S6: Perform dynamic pressure compensation and grouting control according to the optimal grouting scheme.
[0091] To clarify the specific process of grouting control, step S6 includes S61 to S63, specifically:
[0092] S61: Calculate the deviation between the target grouting pressure and the grouting pressure of the optimal grouting scheme to obtain the pressure deviation value;
[0093] In this step, the pressure deviation value is used to quantify the difference between the actual grouting pressure and the target pressure.
[0094] S62: Dynamically adjust the grouting pump pressure based on the pressure compensation algorithm and the pressure deviation value to obtain the adjusted pump pressure;
[0095] In this step, the pressure compensation algorithm adjusts the output pressure of the grouting pump to reduce the deviation based on the magnitude and direction of the deviation value.
[0096] S63: Establish a real-time feedback mechanism based on the adjusted pump pressure and the real-time monitoring data, and control the grouting through the real-time feedback results.
[0097] In this step, the real-time feedback results dynamically adjust the grouting parameters according to the actual grouting situation, thereby achieving precise grouting control.
[0098] Example 2:
[0099] This embodiment provides a tunnel surrounding rock grouting control device, the device comprising:
[0100] The acquisition module is used to acquire grouting parameters, including grouting pressure and grout viscosity;
[0101] The module is used for multi-dimensional real-time monitoring of surrounding rock fractures and for building a dynamic three-dimensional geological model based on the data from the multi-dimensional real-time monitoring.
[0102] To clarify the specific process of building modules, the following are included:
[0103] The first building unit is used to monitor the surrounding rock fissures in real time using microseismic and ground-penetrating radar, and to build a three-dimensional fissure model using real-time monitoring data.
[0104] The second building unit is used to monitor the porosity distribution of the surrounding rock and to construct a porosity digital twin based on the porosity distribution data.
[0105] The monitoring unit is used to perform distributed monitoring of the stress and strain state of the surrounding rock based on fiber optic grating sensors to obtain the location of the slurry front.
[0106] An optimization unit is used to optimize and adjust the porosity distribution characteristics in the porosity digital twin and the parameters of the slurry front position and the three-dimensional fracture model to obtain a three-dimensional geological model.
[0107] The prediction module is used to predict the grout diffusion path based on the three-dimensional geological model and generate an initial grouting plan based on the predicted grout diffusion path.
[0108] To clarify the specific process of the prediction module, the following are included:
[0109] The learning unit is used to learn the geological characteristics of the surrounding rock and the grouting environment based on the three-dimensional geological model through a deep reinforcement learning algorithm, and to optimize the grouting parameters by combining a quantum genetic algorithm to generate a grouting strategy.
[0110] The first prediction unit is used to input the grouting strategy into a multi-physics coupled virtual simulation system to predict grout diffusion, and adjust the grouting parameters based on the prediction results to obtain an optimized grouting strategy.
[0111] The simulation unit is used to simulate the grout diffusion path based on the optimized grouting strategy, and to generate an initial grouting scheme through the simulated grout diffusion path.
[0112] The optimization module is used to optimize the fractal dimension according to the genetic algorithm, and to dynamically correct the grouting parameters through the optimized fractal dimension to obtain the grout diffusion result;
[0113] To clarify the specific process and methods for optimizing the module, the following are included:
[0114] The adjustment unit is used to adjust the parameters in the fractal dimension according to the genetic algorithm, and to learn the fractal characteristics of the surrounding rock fractures by combining the deep learning algorithm to obtain the fractal model of the surrounding rock fractures.
[0115] The second prediction unit is used to predict the diffusion of grout based on the fractal model of the surrounding rock fracture, and to obtain the diffusion range of grout.
[0116] The calculation unit is used to compare and calculate the slurry diffusion range and the preset slurry diffusion threshold according to the fitness function of the genetic algorithm, and obtain the error value;
[0117] The correction unit is used to dynamically correct the grouting parameters based on the error value to obtain the grout diffusion result.
[0118] A module is established to establish multi-field coupling equations based on the slurry diffusion results, and to optimize the initial grouting scheme through the multi-field coupling equations to obtain the optimal grouting scheme;
[0119] The compensation module is used to perform dynamic pressure compensation and grouting control according to the optimal grouting scheme.
[0120] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.
[0121] Example 3:
[0122] Corresponding to the above method embodiments, this embodiment also provides a tunnel surrounding rock grouting control device. The tunnel surrounding rock grouting control device described below and the tunnel surrounding rock grouting control method described above can be referred to in correspondence.
[0123] Figure 2 This is a block diagram illustrating a tunnel surrounding rock grouting control device 800 according to an exemplary embodiment. Figure 2 As shown, the tunnel surrounding rock grouting control device 800 may include: a processor 801 and a memory 802. The tunnel surrounding rock grouting control device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.
[0124] The processor 801 controls the overall operation of the tunnel surrounding rock grouting control device 800 to complete all or part of the steps in the aforementioned tunnel surrounding rock grouting control method. The memory 802 stores various types of data to support the operation of the tunnel surrounding rock grouting control device 800. This data may include, for example, instructions for any application or method operating on the tunnel surrounding rock grouting control device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the tunnel surrounding rock grouting control device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.
[0125] In an exemplary embodiment, the tunnel surrounding rock grouting control device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the tunnel surrounding rock grouting control method described above.
[0126] Example 4:
[0127] Corresponding to the above method embodiments, this embodiment also provides a medium. The medium described below can be referred to in conjunction with the tunnel surrounding rock grouting control method described above.
[0128] A medium storing a computer program, which, when executed by a processor, implements the steps of the tunnel surrounding rock grouting control method described in the above method embodiments.
[0129] The medium can specifically be any medium capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.
[0130] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
[0131] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A tunnel surrounding rock grouting control method, characterized in that, include: Obtain grouting parameters, including grouting pressure and grout viscosity; Multidimensional real-time monitoring of surrounding rock fissures was conducted, and a dynamic three-dimensional geological model was constructed using the data from the multidimensional real-time monitoring. Based on the three-dimensional geological model, the slurry diffusion path is predicted, and an initial grouting scheme is generated based on the predicted slurry diffusion path. The fractal dimension is optimized using a genetic algorithm, and the grouting parameters are dynamically corrected based on the optimized fractal dimension to obtain the grout diffusion results. Based on the slurry diffusion results, a multi-field coupling equation is established, and the initial grouting scheme is optimized through the multi-field coupling equation to obtain the optimal grouting scheme; The specific method for obtaining the optimal grouting scheme includes: The interaction relationship of the coupled physical fields was determined based on the slurry diffusion results, and multi-field coupling equations were established. The multi-field coupling equations are solved by discretizing the finite element method to obtain the multi-field distribution results; The initial grouting scheme is input into the multi-field coupling equation and solved to obtain the initial grouting result; Based on the initial grouting results and the multi-field distribution results, the grouting parameters are adjusted and the grouting points are optimized to form an optimized grouting scheme; The optimized grouting scheme was input into the multi-field coupling equation for simulation and verification, and the optimal grouting scheme was obtained. Dynamic pressure compensation and grouting control are performed according to the optimal grouting scheme.
2. The tunnel surrounding rock grouting control method according to claim 1, characterized in that, based on the three-dimensional geological model, grout diffusion path prediction is performed, and an initial grouting scheme is generated through the predicted grout diffusion path, including: Based on the aforementioned three-dimensional geological model, the geological characteristics of the surrounding rock and the grouting environment are learned through a deep reinforcement learning algorithm, and the grouting parameters are optimized by combining a quantum genetic algorithm to generate a grouting strategy. The grouting strategy is input into a multiphysics coupled virtual simulation system to predict grout diffusion. The grouting parameters are then adjusted based on the prediction results to obtain an optimized grouting strategy. Based on the optimized grouting strategy, the grout diffusion path is simulated, and an initial grouting scheme is generated through the simulated grout diffusion path.
3. The tunnel surrounding rock grouting control method according to claim 1, characterized in that multi-dimensional real-time monitoring of surrounding rock fissures is performed, and a dynamic three-dimensional geological model is constructed using the data from the multi-dimensional real-time monitoring, including: Microseismic and ground-penetrating radar were used to monitor the surrounding rock fissures in real time, and a three-dimensional fissure model was constructed using the real-time monitoring data. Porosity distribution monitoring of surrounding rock is conducted, and a digital twin of porosity is constructed using the porosity distribution data; The location of the slurry front is obtained by distributed monitoring of the stress and strain state of the surrounding rock using fiber optic grating sensors. Based on the porosity distribution characteristics in the porosity digital twin and the location of the slurry front and the parameters of the three-dimensional fracture model, a three-dimensional geological model is obtained through optimization and adjustment.
4. The tunnel surrounding rock grouting control method according to claim 1, characterized in that, the fractal dimension is optimized according to a genetic algorithm, and the grouting parameters are dynamically corrected through the optimized fractal dimension to obtain the grout diffusion result, including: The parameters in the fractal dimension are adjusted according to the genetic algorithm, and the fractal characteristics of the surrounding rock fractures are learned by combining the deep learning algorithm to obtain the fractal model of the surrounding rock fractures. Based on the fractal model of the surrounding rock fracture, the diffusion range of the grout is predicted. The error value is obtained by comparing the slurry diffusion range with the preset slurry diffusion threshold based on the fitness function of the genetic algorithm. The grouting parameters are dynamically corrected based on the error value to obtain the grout diffusion result.
5. The tunnel surrounding rock grouting control method according to claim 4, characterized in that, based on real-time monitoring data, the parameters in the fractal dimension are adjusted according to the genetic algorithm, and the fractal characteristics of the surrounding rock fractures are learned by combining a deep learning algorithm, to obtain a fractal model of the surrounding rock fractures, including: The mean square error between the fractal dimension and the fractal dimension threshold is calculated using the genetic algorithm to obtain an error evaluation index. The parameters of the genetic algorithm are optimized based on the error evaluation index. During the optimization process, the population is iterated until the preset convergence condition is met, and the optimized parameter combination is output. Based on the optimized parameter combination, the fractal characteristics of the surrounding rock fractures are calculated using the fractal dimension, and a fractal model of the surrounding rock fractures is constructed using the fractal characteristics.
6. The tunnel surrounding rock grouting control method according to claim 1, characterized in that, dynamic pressure compensation and grouting control are performed according to the optimal grouting scheme, including: The deviation between the target grouting pressure and the grouting pressure of the optimal grouting scheme is calculated to obtain the pressure deviation value. The grouting pump pressure is dynamically adjusted based on the pressure compensation algorithm and the pressure deviation value to obtain the adjusted pump pressure. A real-time feedback mechanism is established based on the adjusted pump pressure and the real-time monitoring data, and grouting control is performed through the real-time feedback results.
7. A tunnel surrounding rock grouting control device, characterized in that, include: The acquisition module is used to acquire grouting parameters, including grouting pressure and grout viscosity; The module is used for multi-dimensional real-time monitoring of surrounding rock fractures and for building a dynamic three-dimensional geological model based on the data from the multi-dimensional real-time monitoring. The prediction module is used to predict the grout diffusion path based on the three-dimensional geological model and generate an initial grouting plan based on the predicted grout diffusion path. The optimization module is used to optimize the fractal dimension according to the genetic algorithm, and to dynamically correct the grouting parameters through the optimized fractal dimension to obtain the grout diffusion result; A module is established to establish multi-field coupling equations based on the slurry diffusion results, and to optimize the initial grouting scheme through the multi-field coupling equations to obtain the optimal grouting scheme; The establishment module includes: The interaction relationship of the coupled physical fields was determined based on the slurry diffusion results, and multi-field coupling equations were established. The multi-field coupling equations are solved by discretizing the finite element method to obtain the multi-field distribution results; The initial grouting scheme is input into the multi-field coupling equation and solved to obtain the initial grouting result; Based on the initial grouting results and the multi-field distribution results, the grouting parameters are adjusted and the grouting points are optimized to form an optimized grouting scheme; The optimized grouting scheme was input into the multi-field coupling equation for simulation and verification, and the optimal grouting scheme was obtained. The compensation module is used to perform dynamic pressure compensation and grouting control according to the optimal grouting scheme.
8. The tunnel surrounding rock grouting control device according to claim 7, characterized in that the prediction module comprises: The learning unit is used to learn the geological characteristics of the surrounding rock and the grouting environment based on the three-dimensional geological model through a deep reinforcement learning algorithm, and to optimize the grouting parameters by combining a quantum genetic algorithm to generate a grouting strategy. The first prediction unit is used to input the grouting strategy into a multi-physics coupled virtual simulation system to predict grout diffusion, and adjust the grouting parameters based on the prediction results to obtain an optimized grouting strategy. The simulation unit is used to simulate the grout diffusion path based on the optimized grouting strategy, and to generate an initial grouting scheme through the simulated grout diffusion path.
9. The tunnel surrounding rock grouting control device according to claim 7, characterized in that the building module comprises: The first building unit is used to monitor the surrounding rock fissures in real time using microseismic and ground-penetrating radar, and to build a three-dimensional fissure model using real-time monitoring data. The second building unit is used to monitor the porosity distribution of the surrounding rock and to construct a porosity digital twin based on the porosity distribution data. The monitoring unit is used to perform distributed monitoring of the stress and strain state of the surrounding rock based on fiber optic grating sensors to obtain the location of the slurry front. An optimization unit is used to optimize and adjust the porosity distribution characteristics in the porosity digital twin and the parameters of the slurry front position and the three-dimensional fracture model to obtain a three-dimensional geological model.
10. The tunnel surrounding rock grouting control device according to claim 7, characterized in that the optimization module comprises: The adjustment unit is used to adjust the parameters in the fractal dimension according to the genetic algorithm, and to learn the fractal characteristics of the surrounding rock fractures by combining the deep learning algorithm to obtain the fractal model of the surrounding rock fractures. The second prediction unit is used to predict the diffusion of grout based on the fractal model of the surrounding rock fracture, and to obtain the diffusion range of grout. The calculation unit is used to compare and calculate the slurry diffusion range and the preset slurry diffusion threshold according to the fitness function of the genetic algorithm, and obtain the error value; The correction unit is used to dynamically correct the grouting parameters based on the error value to obtain the grout diffusion result.