A grouting path optimization method and system based on multi-field coupling constraints

Abstract

The application provides a grouting path optimization method and system based on multi-field coupling constraints, and belongs to the technical field of rock-soil engineering grouting, and comprises the following steps: collecting geological data, including rock layer distribution and physical and mechanical parameters, and underground water data; establishing a three-dimensional multi-physical field coupling model based on the collected geological data, including establishing a three-dimensional rock-soil body model, performing stress field analysis on the three-dimensional rock-soil body model, constructing a seepage field, simulating a temperature field, quantifying the mutual influence among the stress field, the seepage field and the temperature field, and obtaining the coupling relationship among the physical fields; obtaining rheological property data of slurry, calibrating the established three-dimensional multi-physical field coupling model based on the obtained rheological property data of slurry, and obtaining a calibrated three-dimensional multi-physical field coupling model; and optimizing a grouting path, slurry pressure and slurry proportioning parameters for the calibrated three-dimensional multi-physical field coupling model, and generating an optimal grouting path planning scheme.

Description

Technical Field

[0001] This invention belongs to the field of grouting technology in geotechnical engineering, and particularly relates to a grouting path optimization method and system based on multi-field coupling constraints. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Grouting reinforcement technology plays a crucial role in numerous geotechnical engineering construction and geological disaster control projects. However, actual geological conditions are complex and variable, including intricate stress distributions, varying seepage characteristics, and different temperature environments.

[0004] Traditional grouting path planning methods mainly rely on static geological models, often considering a single factor in isolation and ignoring the comprehensive influence of the interaction between various physical fields on grout diffusion behavior. This makes it difficult to adapt to the dynamic changes of complex geological conditions during construction in real time, often resulting in inaccurate grouting path planning, low grout diffusion efficiency, and inconsistent grouting effects. Problems such as uneven grout diffusion, unstable reinforcement strength, and stratum disturbance frequently occur, seriously restricting project quality, efficiency, and safety, and failing to meet the higher requirements of modern engineering for grouting reinforcement effect and construction efficiency.

[0005] In addition, for tunnel engineering monitoring, existing technologies include the following in the specific monitoring process: S1, real-time geological exploration; S2, shield attitude and mechanical monitoring; S3, hydrological and seepage monitoring; S4, surrounding rock deformation and stress monitoring; and S5, comprehensive early warning and feedback.

[0006] Although the above scheme involves the detection of seepage, stress, etc., the above process is only used for tunnel engineering monitoring, with the aim of ensuring tunnel safety. However, in the field of grouting reinforcement technology, how to consider multiple factors to achieve grouting path optimization is one of the important technical problems that need to be solved.

[0007] Therefore, the existing technologies in the field of grouting reinforcement technology have the following problems:

[0008] First, existing technologies rely on traditional models based on a single factor to determine grouting paths, which cannot accurately simulate actual geology. Based on simulation results that do not match reality, subsequent calculations and path planning cannot meet actual needs, resulting in inaccurate grouting path planning.

[0009] Second: In the process of implementing the grouting path, the existing technology uses a fixed grouting path and does not dynamically adjust the grouting path based on real-time data, which cannot ensure that the grouting process can flexibly respond to changes in geological conditions. Summary of the Invention

[0010] To overcome the shortcomings of the existing technology, this invention provides a grouting path optimization method based on multi-field coupling constraints. By introducing multi-physics field coupling modeling technology such as stress field, seepage field and temperature field, the method comprehensively considers the influence of various physical factors on the grouting path. It combines real-time monitoring data with optimization algorithms and dynamically adjusts the grouting path and parameters to ensure that the grouting process can flexibly respond to changes in geological conditions.

[0011] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0012] Firstly, a grouting path optimization method based on multi-field coupling constraints is disclosed, including:

[0013] Collect geological data, including: rock strata distribution and physical and mechanical parameters, and groundwater data;

[0014] A three-dimensional multiphysics coupling model is established based on the collected geological data, including: establishing a three-dimensional rock and soil model, performing stress field analysis on the three-dimensional rock and soil model, constructing a seepage field, simulating a temperature field, quantifying the mutual influence between the stress field, seepage field and temperature field, and obtaining the coupling relationship between each physical field;

[0015] The rheological properties of the slurry are acquired, and the established three-dimensional multiphysics coupling model is calibrated based on the acquired rheological properties of the slurry to obtain the calibrated three-dimensional multiphysics coupling model.

[0016] For the calibrated three-dimensional multiphysics coupling model, the grouting path, grout pressure, and grout mix ratio parameters are optimized to generate the optimal grouting path planning scheme.

[0017] As a further technical solution, it also includes: dynamically adjusting and optimizing the grouting path planning by acquiring real-time dynamic change data of the geological body, including stress, seepage, and temperature.

[0018] As a further technical solution, each piece of data in the collected geological data is accompanied by its corresponding spatial coordinates, depth information and timestamp.

[0019] As a further technical solution, when establishing a three-dimensional rock and soil model, the geometry in the model should match the actual tunnel location and the layout of the surrounding rock strata.

[0020] The soil and rock layers are divided into multiple sub-regions, each with different physical and mechanical parameters. The input parameters include compressive strength, shear strength, elastic modulus, Poisson's ratio, and porosity.

[0021] As a further technical solution, the interaction between the stress field, seepage field, and temperature field is quantified, specifically including:

[0022] When the stress field is coupled with the seepage field, a specific expression for the influence of the stress field on the permeability coefficient is obtained;

[0023] When the seepage field is coupled with the temperature field, a specific expression for the coupling between the seepage field and the temperature field is obtained;

[0024] When the stress field and the temperature field are coupled, the specific expression for the coupling between the stress field and the temperature field can be obtained.

[0025] As a further technical solution, the established three-dimensional multiphysics coupling model is calibrated based on the acquired rheological property data of the slurry, including:

[0026] Establishing a finite element model: Constructing a three-dimensional model of the geological body and the grouting area;

[0027] Solving multi-field coupling equations: Discretizing stress field, seepage field and heat conduction equations, setting boundary conditions according to actual engineering conditions, and then numerically solving them using the finite element method to obtain the slurry flow, pressure field, temperature field and diffusion range under different temperature, stress and seepage conditions.

[0028] Results verification and calibration: After completing the finite element solution, the actual data obtained through experiments, including slurry viscosity, permeability, and curing time, are compared with the finite element simulation results to determine the deviation between the simulation results and the experimental data, and to optimize the model parameters.

[0029] As a further technical solution, the grouting path, grout pressure, and grout mix ratio parameters are optimized to generate an optimal grouting path planning scheme, specifically including:

[0030] Setting optimization goals: The goal of global optimization is to maximize the diffusion radius of the grout and the uniformity of the solidified strength, while minimizing construction costs;

[0031] The process of global optimization using the particle swarm optimization algorithm includes:

[0032] Initialize the particle swarm: First, set the particle swarm size to... Perform particle position initialization. The position of each particle represents a grouting path planning scheme, and each dimension of the particle represents a design variable.

[0033] Fitness evaluation: The fitness function evaluates the solution quality of each particle;

[0034] Particle position and velocity update: The PSO algorithm updates the position and velocity of each particle based on its performance in the search space.

[0035] Update individual and global optimal solutions: For each particle, check if the fitness of its current solution is better than the historical optimal solution. If so, update the historical optimal solution of that particle.

[0036] By checking the fitness of all particles, the particle with the best fitness is found, and the global optimal solution is updated.

[0037] Iteration and Output: The PSO algorithm will output the optimal grouting path planning scheme, that is, the particle position of the global historical optimal solution, including: grouting path position, grouting pressure, grout mix ratio scheme, and optimal construction cost.

[0038] As a further technical solution, it also includes: pressure gradient stability control, specifically including:

[0039] The flow of grout in the rock and soil under different grouting pressures was simulated using a multi-field coupling model.

[0040] Calculate the distribution of the pressure gradient to identify regions where the pressure gradient may be too large or too small.

[0041] After simulating the grout diffusion behavior and pressure gradient changes under different pressures, a grouting pressure control strategy was formulated to ensure the stability of the pressure gradient during the grouting process.

[0042] According to the preset pressure control strategy, the stress, seepage and temperature data of the soil and rock mass are monitored and collected in real time. When the monitoring data indicates that the pressure gradient in a certain area fluctuates drastically, dynamic adjustments are made.

[0043] If the diffusion radius of a certain area is found to be insufficient, the grouting pressure is increased;

[0044] During the grouting process, the pressure gradient changes are continuously monitored and the results of real-time adjustments are fed back.

[0045] As a further technical solution, it also includes: verifying the effectiveness of the grouting path and parameters through grout diffusion and reinforcement effect evaluation steps, combined with model simulation and field verification.

[0046] Slurry diffusion simulation: Based on the established multi-field coupling model, the diffusion process of slurry under different pressures is simulated, the diffusion radius and penetration depth of slurry are calculated, and the spatial distribution map of slurry under different schemes is obtained through numerical simulation.

[0047] Simulation of the strength distribution of the reinforced body: Based on the simulation of grout diffusion, the strength distribution of the reinforced body after grouting is further simulated. The simulation results are used to predict the strength distribution of the grout after solidification in the rock and soil mass, and the compressive strength model is used to characterize it.

[0048] Diffusion and reinforcement effect evaluation: Combining the diffusion radius and intensity distribution obtained from simulation, the effects of different grouting schemes are evaluated, and a comprehensive evaluation index is defined. Used to measure the overall performance of slurry diffusion and reinforcement effect;

[0049] In the selected construction area, grouting is carried out according to the optimized path and grouting parameters. During the construction process, the grouting pressure, grout ratio and grouting rate are strictly controlled to ensure consistency with the optimized model. After the construction is completed, several typical locations in the construction area are selected to collect solidified samples. The samples should represent the grout diffusion effect and reinforcement strength in different areas.

[0050] The collected samples were subjected to diffusion range testing, compressive strength testing, and permeability testing to obtain test data. The field verification results were compared with the simulation results to assess the deviation between the simulation and actual construction. For areas with significant deviations, the causes were analyzed and the model parameters were optimized accordingly.

[0051] If the permeability of the field sample is high, but the simulation results underestimate the diffusion depth of the slurry, then the model needs to be optimized by increasing the permeability coefficient.

[0052] If the slurry diffusion range of the field sample is large, and the diffusion range of the slurry is underestimated in the simulation results, it is necessary to adjust the simulation results by increasing the rheological parameters to better match the actual situation.

[0053] If the simulation results underestimate the diffusion radius or the increase in strength, the grouting pressure needs to be adjusted.

[0054] After completing the above adjustments, it is necessary to re-simulate through model iteration and verification steps, substitute the updated parameters into the model for a new simulation, compare with the field data, and continue to correct and optimize until the error reaches an acceptable range.

[0055] Secondly, a grouting path optimization system based on multi-field coupling constraints is disclosed, including:

[0056] The geological data acquisition module is configured to acquire geological data, including: rock strata distribution and physical and mechanical parameters, and groundwater data;

[0057] The three-dimensional multiphysics coupling model building module is configured to: build a three-dimensional multiphysics coupling model based on the collected geological data, including: building a three-dimensional rock and soil model, performing stress field analysis on the three-dimensional rock and soil model, constructing a seepage field, simulating a temperature field, quantifying the mutual influence between the stress field, seepage field and temperature field, and obtaining the coupling relationship between each physical field;

[0058] The three-dimensional multiphysics coupling model calibration module is configured to: acquire the rheological property data of the slurry, and calibrate the established three-dimensional multiphysics coupling model based on the acquired rheological property data of the slurry, and obtain the calibrated three-dimensional multiphysics coupling model.

[0059] The grouting path planning module is configured to optimize the grouting path, grout pressure, and grout mix ratio parameters for the calibrated three-dimensional multiphysics coupling model, and generate the optimal grouting path planning scheme.

[0060] The above one or more technical solutions have the following beneficial effects:

[0061] (1) This invention comprehensively considers the influence of various physical factors on the grouting path by introducing multi-physics field coupling modeling technology, such as stress field, seepage field, and temperature field. This method can not only accurately simulate the constraints of stress changes, fracture seepage characteristics, and temperature changes on grout diffusion behavior in rock strata, but also provide a scientific basis for path optimization under complex geological conditions. Compared with traditional single-physics field models, the multi-field coupling finite element model can more realistically reflect the physical phenomena in actual engineering, thereby improving the accuracy and operability of the grouting path and ensuring that the grout diffusion efficiency and the uniformity of the reinforced body achieve the best results.

[0062] (2) This invention innovatively combines real-time monitoring data with optimization algorithms, and ensures that the grouting process can flexibly respond to changes in geological conditions by dynamically adjusting the grouting path and parameters. The dynamic data of the geological body (such as stress, seepage, temperature, etc.) acquired by the system in real time are input into the optimization model to continuously update the grouting path planning. This dynamic optimization mechanism can cope with complex geological changes such as stress concentration in fault zones and changes in fracture networks, ensuring that the path planning remains accurate and flexible throughout the construction process, thereby significantly improving construction safety and grouting effect.

[0063] (3) This invention combines particle swarm optimization algorithm to globally optimize the grouting path, comprehensively considering multiple objectives such as grout diffusion radius, uniformity of reinforced body strength, and construction cost, thereby generating the optimal grouting path planning scheme. Traditional path planning methods are difficult to satisfy multiple objectives simultaneously, but by introducing optimization algorithm, the best combination of grouting parameters can be found globally, effectively improving the economy and stability of construction results. At the same time, this optimization framework can be applied to different geological environments and has wide applicability.

[0064] (4) This invention addresses the potential pressure instability during grouting by proposing a dynamic pressure regulation strategy based on a multi-field coupling model. By analyzing the impact of grouting pressure on grout diffusion in real time, a reasonable pressure control scheme is formulated, avoiding formation damage caused by excessively high pressure or insufficient grout diffusion caused by excessively low pressure. The implementation of this strategy can maintain the stability of the pressure gradient throughout the grouting process, ensuring that the grout can diffuse uniformly and fully reinforce the formation, significantly improving the grouting effect and the safety of the project.

[0065] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0066] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0067] Figure 1 This is an overall flowchart of the method in an embodiment of the present invention. Detailed Implementation

[0068] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0069] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0070] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0071] Example 1

[0072] This embodiment discloses a grouting path optimization method based on multi-field coupling constraints. Combining multi-sensor fusion of real-time monitoring data with intelligent optimization algorithms, it dynamically adjusts the grouting path to improve construction accuracy and efficiency. This achieves dynamic optimization and intelligent adjustment of the grouting path under complex geological conditions, accurately planning the grouting path and optimizing the pressure gradient to achieve ideal grout diffusion and reinforcement effects, ensuring the safe, stable, and economically sound progress of the project. The method includes:

[0073] Step 1: Construct a multi-field coupling model.

[0074] 1-1) Geological data acquisition and processing.

[0075] In the early stages of tunnel or underground engineering construction, comprehensive geological exploration and data acquisition are fundamental to building accurate models. Key steps in geological data acquisition include detailed investigation of rock strata, monitoring of groundwater levels and flow velocities, and experimental testing of mechanical properties, specifically:

[0076] (1) Obtaining Rock Strata Distribution and Physical and Mechanical Parameters. Geological exploration boreholes were drilled in the tunnel construction area to obtain rock strata data at different depths. At each borehole point, rock strata distribution parameters were obtained through drilling, including the depth, thickness, strike, and dip angle of the strata. These parameters describe the geometric shape and spatial arrangement of the rock strata in three-dimensional space. Simultaneously, after each drilling to a specified depth, core samples were obtained using a corer. Each core sample was categorized and stored according to depth and rock strata properties to avoid cross-contamination. Relevant physical and mechanical parameters (compressive strength, shear strength, elastic modulus, Poisson's ratio, etc.) were obtained through laboratory testing for each core sample. The laboratory data results and borehole exploration data were recorded in detail in a data table.

[0077] (2) Groundwater data collection. During drilling, groundwater level monitoring wells are installed at designated depths according to exploration needs, ensuring that the lower end of the well pipe is in contact with the groundwater layer. A water level gauge is installed at the wellhead to record groundwater level changes in real time. Simultaneously, a closed section is set up around the monitoring well for a water pressure test: water pressure is gradually applied to the closed section area using a pumping system, and water pressure gauges and flow meters are used to monitor the pressure changes and flow velocity in real time and continuously record the data. Then, based on the water level changes and flow velocity data recorded in the water pressure test, the permeability coefficient of the rock strata or soil is calculated in combination with the flow rate and pressure. The specific formula is as follows:

[0078]

[0079] in, The permeability coefficient represents the water permeability of a rock or soil layer. The larger the coefficient, the stronger the permeability and the easier it is for water to flow through. The water flow rate is measured in real time by a flow meter. This is the length of the water flow path, i.e., the vertical distance from the water injection point to the monitoring point; This refers to the cross-sectional area of ​​the fluid flow region, i.e., the cross-sectional area of ​​the test region. The change in head is the amount of change in water level during the experiment.

[0080] (3) Geological Data Integration and Database Construction. A database system is used to uniformly store data such as rock strata information, groundwater data, and geotechnical physical and mechanical parameters, forming a multi-dimensional dataset. Each data item is accompanied by its corresponding spatial coordinates (X, Y, Z coordinates), depth information (depth from the surface to the measuring point), and timestamp (the specific time of data collection). This facilitates dynamic tracking and updating during subsequent modeling and analysis. Next, data cleaning algorithms are used to remove outliers and duplicates from the database. Then, geological data with different dimensions are standardized to convert them into the same dimension, facilitating subsequent comprehensive analysis and modeling.

[0081] 1-2) Multiphysics Collaborative Modeling

[0082] A three-dimensional multiphysics coupled model is established using finite element analysis software. This stage of modeling includes establishing the soil and rock mass model, stress field analysis, seepage field construction, temperature field simulation, and coupling of physical fields. Specifically:

[0083] 1-2-1) Establishment of the Soil and Rock Mass Model: Based on the geological data obtained from the exploration, including rock strata information, groundwater data, and physical and mechanical parameters of the soil and rock, a three-dimensional soil and rock mass model is first established in finite element analysis software. This model includes the division of different rock strata, which is based on the rock strata information to create different sub-regions, the contact relationships between rock strata, and the various physical and mechanical parameters of the rock strata. The geometry in the model should match the actual tunnel location and the layout of the surrounding rock strata. The soil and rock layers are divided into multiple sub-regions, each with different physical and mechanical parameters. The input parameters include: compressive strength, shear strength, elastic modulus, Poisson's ratio, and porosity.

[0084] 1-2-2) Stress Field Analysis: The stress field affects the deformation of soil and rock masses, which in turn affects the opening and closing of fractures. Changes in fractures directly affect the flow path and permeability of the grout. During grout injection, the stress field in the soil and rock mass causes the fractures to reopen and close, altering the pore structure of the soil and rock mass, thereby affecting the diffusion range and flow velocity of the grout. The stress field equation is: .in, For stress tensor, it describes the stress distribution inside the rock or soil mass; External force sources (water pressure, rock mass weight, etc.); The divergence of stress represents the distribution of stress.

[0085] In step 1-2-2), input the physical and mechanical parameters of the soil and rock (compressive strength, etc.) into the stress field equation to calculate the stress distribution of the soil and rock mass during the grouting process.

[0086] The above analysis is a stress field analysis of the established three-dimensional rock and soil model. The analysis results are as follows: ① Stress distribution characteristics: determine the stress concentration locations in various regions of the rock and soil (such as fault zones and near fracture networks); ② Fracture opening and closing state: stress changes (such as grouting pressure or rock mass self-weight) cause dynamic changes in the degree of fracture opening and closing, which directly affect the permeability.

[0087] The results of stress field analysis directly affect subsequent grouting path optimization and construction control: In subsequent global optimization, the PSO algorithm is used to prioritize the grouting path in areas with low stress concentration to avoid formation damage caused by high-pressure injection of grout. The objective function includes a stress constraint term.

[0088] 1-2-3) Construction of the seepage field: The seepage field describes the flow behavior of grout in soil or rock. Based on Darcy's law, the movement of grout in the soil and rock mass is simulated to establish the seepage field model. The seepage field equation is: .in, The permeability coefficient represents the permeability of the rock and soil mass. The pressure of seepage; This represents the pressure gradient.

[0089] A seepage field model is used to simulate the flow behavior of grout in soil and rock masses, predicting its diffusion path and range. Based on Darcy's law, combined with the permeability coefficient and pressure gradient, the grout diffusion radius is calculated. The pressure distribution output from the seepage field model is input into the PSO algorithm, which, combined with stress and temperature field data, generates a globally optimal grouting scheme. The seepage field equation is only the core mathematical expression of the seepage field model; the seepage field model is a comprehensive computational framework, including equations, parameters, and coupling relationships with other fields.

[0090] 1-2-4) Temperature Field Simulation: The temperature field directly affects the physical properties of the slurry, especially its viscosity and solidification process, thus influencing the slurry's diffusion rate within the formation. This field is described by the heat conduction equation, which is: .in, For temperature; is the thermal diffusivity, which represents the ability of heat to diffuse in the slurry; Let be the Laplace operator for temperature, representing the spatial distribution of the temperature field; The heat source term represents the heat release caused by external heat sources (such as the thermal effects during grouting). In finite element analysis software, the thermal effects during grouting are defined by the exothermic characteristics of the cement hydration reaction, and this term is incorporated into the temperature field equations.

[0091] In steps 1-2-3), the slurry seepage path is calculated based on groundwater data (permeability coefficient, etc.) and Darcy's law.

[0092] The temperature field simulation here is performed on the established 3D soil and rock model. After the simulation is completed, the following key parameters will be output: T(x,y,z,t) temperature distribution, describing the temperature change over time at various points within the soil and rock mass; ▽T temperature gradient, reflecting the rate and direction of temperature change. This provides data for subsequent global optimization of the grouting path (PSO algorithm).

[0093] Determining the coupling relationships between various physical fields is a crucial step in constructing a multi-field coupled model. By quantifying the interactions between the stress field, seepage field, and temperature field, their interactions are accurately described, ensuring the model accurately reflects on-site engineering conditions. This quantification is achieved through multi-field coupling, specifically: the coupling between the stress field and the seepage field: stress changes affect the permeability of the soil and rock mass, specifically through the dynamic relationship between the permeability coefficient and stress; the coupling between the seepage field and the temperature field: temperature changes affect seepage behavior through the temperature dependence of slurry viscosity; and the coupling between the stress field and the temperature field: temperature changes cause thermal expansion or contraction of the soil and rock mass, leading to additional stress. Specifically:

[0094] Coupling of stress and seepage fields: The coupling of stress and seepage fields reflects the influence of stress changes in soil and rock masses on the permeability of grout. Cracks or porosity in soil and rock masses change under stress, thus altering the permeability coefficient. The effect of the stress field on the permeability coefficient is specifically expressed as follows: .in, The initial permeability coefficient, This is the coupling coefficient between permeability and stress variation. This represents the stress variation in the soil and rock mass. As the stress field changes, the permeability coefficient... It will adjust dynamically, thus affecting the diffusion of the slurry.

[0095] The calculation outputs of the aforementioned stress-seepage field coupling include: dynamic changes in the permeability coefficient at different time and spatial nodes, and the permeability coefficient response function. These serve as input parameters in seepage simulation and path optimization calculations, used to dynamically adjust the grout diffusion range and grouting path. Through stress-seepage coupling modeling, the influence of rock mass deformation on the grout diffusion capacity can be accurately reflected.

[0096] Coupling of the seepage field and the temperature field: The coupling between the seepage field and the temperature field is mainly reflected in the influence of temperature on the fluidity and diffusivity of the slurry. Temperature changes alter the viscosity of the slurry, thus affecting its fluidity. The permeability, seepage velocity, and diffusion radius in the seepage field are directly related to the viscosity of the slurry, thereby affecting its diffusivity. The coupling between the seepage field and the temperature field is specifically expressed as follows: .in, The rate of change of seepage pressure represents the pressure change over time; Indicates penetration rate; The viscosity of the slurry; The pressure gradient represents the pressure change along the seepage direction.

[0097] The output results of the above-mentioned coupling of the seepage field and temperature field include: the nonlinear relationship function μ(T) between slurry viscosity and temperature, etc.

[0098] Stress field coupling with temperature field: Temperature changes cause thermal expansion or contraction of soil and rock masses, thereby altering the stress distribution. Stress changes caused by temperature can be represented by thermal expansion stress. The coupling between the stress field and temperature field can be specifically expressed as follows: .in, For stress changes in rock and soil, The coefficient of thermal expansion represents a material's ability to expand or contract under changes in temperature. This refers to temperature changes.

[0099] The above coupling output results: Updated total stress field σ , which is the superposition of the initial stress field and thermal stress.

[0100] In step one, the system integrates information from drilling, ground-penetrating radar, geophysical exploration, etc., to construct a three-dimensional geological model that includes rock strata structure and physical property zoning, providing a spatial framework for subsequent analysis.

[0101] Step 2: Finite element simulation and experimental calibration.

[0102] After the multi-field coupling model was constructed, the rheological properties of the slurry were obtained through precise laboratory experiments. The model was then precisely calibrated using finite element simulation to ensure that the simulation results were highly consistent with the actual slurry behavior in engineering projects. The multi-field coupling model is a comprehensive mathematical model; the interaction between the stress field, seepage field, and temperature field is described here using the three coupling equations mentioned above. Specifically:

[0103] 2-1) Detailed Laboratory Testing. Before performing finite element simulation calibration, it is necessary to obtain rheological property data of the slurry through laboratory tests to provide a reference for parameter optimization in the finite element simulation. In order to comprehensively obtain experimental data on the rheological properties, permeability, solidification properties, and temperature field changes of the slurry, an integrated experimental system was designed.

[0104] Among them, permeability data serves as a key input parameter for seepage field modeling, used to set the K value (permeability coefficient). Solidification characteristic data serves as input for simulating the strength distribution of the grouted body, thereby predicting the strength distribution of the grouted body after injection. Temperature field variation data is used for temperature field simulation and calibration of coupled modeling parameters (heat source term Q and heat conduction parameter α). While rheological characteristic data is indeed core to modeling grout flow behavior, it cannot solely determine the diffusion process, as diffusion is also influenced by other factors. Ultimately, these four types of experimental data are used together for model calibration, optimization, and training.

[0105] This system will simulate the behavior of slurry under different working conditions, providing detailed experimental data for the calibration of the finite element simulation model. The specific experimental system includes the following functional modules:

[0106] 2-1-1) Grout Rheological Testing Module: This module is used to test the rheological properties of grout under different cement and admixture ratios. The viscosity and rheological index of the grout at different temperatures are obtained using a rotational viscometer or rheometer. Combining the rotational viscometer measurement results, the rheological curve of the grout can be plotted, and important parameters such as the yield stress, viscosity, and rheological index of the grout can be calculated.

[0107] Calculation Process: A rotational viscometer records shear rate and shear stress data in real time and transmits them to a data acquisition and processing system. The system uses the Herschel-Bulkley rheological model to perform nonlinear fitting on the raw data, and calculates the yield stress τ0, viscosity coefficient K, and rheological exponent n of the slurry using the least squares method. Simultaneously, it outputs the fitting curve and goodness-of-fit index. These parameters will serve as inputs to the rheological field model, providing fundamental support for multiphysics coupling modeling.

[0108] 2-1-2) Grout Permeability Testing Module: This module is used to test the permeability characteristics of grout under different pressure gradients and stress environments, and to obtain the permeability coefficient of the grout. The relationship between the permeability coefficient and stress and temperature was analyzed.

[0109] Specifically, the slurry permeability testing module includes a triaxial permeability testing system, a constant pressure slurry supply system, a temperature control system, a flow detection system, and a data acquisition system. It is used to determine the permeability of slurry under different pressure gradients and stress environments, thereby obtaining the permeability coefficient K and analyzing its trend with stress and temperature.

[0110] The triaxial permeability testing system includes a triaxial permeameter / permeometer for applying axial, confining, and back pressures to simulate different geostress states; a constant pressure grout supply system for adjusting the grout injection pressure to create different pressure gradients; a flow detection system for real-time monitoring of the rate at which grout flows into the sample medium; a temperature control system for controlled heating to obtain flow data under different temperature conditions; and a data acquisition system for uniformly collecting and storing the above monitoring data for subsequent seepage field modeling.

[0111] The test results will be used for parameter setting and calibration of the "stress-seepage field" and "temperature-seepage field" in the multi-field coupling model to ensure that the grout flow behavior in the grouting simulation is consistent with the actual behavior.

[0112] 2-1-3) Grout Curing Characteristics Testing Module: This module obtains parameters such as grout setting time and curing strength through laboratory curing tests. Specifically, a Vicat apparatus is used to test the grout setting time at different temperatures to obtain the effect of temperature on the grout curing process. Grout specimens are cured at different temperatures, and the curing strength of the grout at different temperatures is measured through compressive strength tests.

[0113] Specifically, the slurry curing characteristic testing module includes a setting time testing system, a temperature-controlled curing system, a compressive strength testing system, a sample preparation system, and a data recording module. This module is used to obtain the initial setting time, final setting time, and compressive strength at different ages of the slurry under different temperature conditions, and is used for experimental analysis of the influence of temperature on the curing process.

[0114] The setting time testing system uses a standard Vicat apparatus combined with a constant temperature water bath to test the setting time of slurry under different temperature conditions; the curing system uses equipment such as a constant temperature and humidity chamber and a curing tank to cure test blocks under set environmental conditions; the strength testing system uses an electric compression testing machine to conduct compressive tests on cured test blocks at different ages; the data recording module can automatically collect temperature, setting time and strength results and upload them to the modeling platform for use in finite element model parameter calibration and temperature coupling analysis.

[0115] 2-1-4) Temperature Field Change Experiment Module: A temperature field experiment is designed to simulate the effect of temperature changes on grout diffusion during actual grouting. Temperature sensors are used for monitoring to obtain temperature distribution and temperature gradient data.

[0116] Specifically, the temperature field change experiment module includes a temperature control simulation chamber, a temperature regulation system, a multi-point temperature acquisition system, a grout injection system, and a data processing system. This module obtains the temperature distribution and its changing trends during the grouting process by artificially simulating a temperature gradient environment, which is used for temperature field modeling and multiphysics coupling analysis.

[0117] The temperature-controlled simulation chamber is used to construct a test platform simulating the environment surrounding a tunnel; the temperature control system is used to control the temperature distribution in different areas of the chamber and can apply a set temperature difference; the multi-point temperature sensing system includes a thermistor / thermocouple array, which is deployed inside the test medium to collect temperature change data in real time; the grout injection system controls the grouting pressure and rate to complete the grout injection in the simulation chamber; the data acquisition system synchronously uploads all temperature point data to the computing platform for finite element modeling of the temperature field and thermo-mechanical coupling analysis.

[0118] The test results will be used to calibrate the thermal diffusivity in the heat conduction equation model. The temperature source term Q is used, and combined with the results of rheological and curing experiments, to achieve a full-process modeling of the effect of temperature on grouting performance.

[0119] The relationship between the templates is as follows: Sample configuration → (rheological test → permeability test → curing test) → temperature field test → data collection and uploading → finite element parameter calibration and simulation feedback.

[0120] 2-2) Finite Element Simulation and Calibration. The multiphysics coupling model described above was calibrated based on experimental data to ensure that the input parameters of the finite element model were consistent with the slurry properties measured in the laboratory. Specifically:

[0121] 2-2-1) Finite Element Model Establishment: Construct a three-dimensional model of the geological body and the grouting area. Based on collected actual geological exploration data (such as rock strata, porosity, permeability, etc.), establish the geometry of the medium and set the material properties (stress, permeability, temperature, etc.) of different layers. Adaptive meshing technology is used to mesh the model.

[0122] It should be noted that in this step, based on the aforementioned three-dimensional geological model, a finite element model with numerical computation capabilities is further established. This finite element model, while inheriting the stratigraphic structure and material property distribution, performs finite element mesh generation and applies corresponding boundary conditions, initial physical fields, and coupled solution equations to achieve accurate simulation of the multi-field physical behavior of the grouting diffusion process.

[0123] 2-2-2). Solving multi-field coupling equations: Discretize the stress field, seepage field and heat conduction equations, set boundary conditions according to actual engineering conditions, and then perform numerical solutions using the finite element method to obtain the slurry flow, pressure field, temperature field and diffusion range under different temperature, stress and seepage conditions.

[0124] 2-2-3) Result Verification and Calibration: After completing the finite element solution, the actual data obtained from experiments (slurry viscosity, permeability, curing time, etc.) are compared with the finite element simulation results to determine the deviation between the simulation results and the experimental data, thereby optimizing the model parameters. In this application, the root mean square error is used for error assessment. .in For experimental data, For simulation data, This represents the number of data points. Based on the error assessment results, the model parameters (slurry viscosity) are adjusted. Permeability coefficient coefficient of thermal expansion Adjustments are made until the simulation results highly match the experimental data. This application uses the least squares method to minimize the sum of squared errors between the experimental and simulated data. Specifically:

[0125]

[0126] in, For experimental data, For simulation data, For data points. By applying the objective function... By differentiating and minimizing, the model parameters can be updated. (slurry viscosity) Permeability coefficient coefficient of thermal expansion ).

[0127] Step 3: Global optimization and path planning.

[0128] Based on the calculation results of the multiphysics coupling model, a genetic algorithm or particle swarm optimization algorithm is used to optimize multi-dimensional parameters such as grouting path, grout pressure, and grout mix ratio, thereby generating the optimal grouting path planning scheme. The specific steps are as follows:

[0129] 3-1) Optimization objective setting. The objective of global optimization is to simultaneously consider the grout diffusion radius, the uniformity of the reinforced body strength, and the minimization of construction costs.

[0130] To ensure effective grout distribution, the grouting path design needs to maximize the grout diffusion radius, thereby achieving a wider reinforcement area, reducing grout waste, and improving the reinforcement effect. This application maximizes the diffusion radius by controlling the direction of the grouting path and the injection pressure, based on the seepage field calculation results from finite element simulation. The post-grouting reinforcement effect must ensure the uniformity of strength throughout the reinforced area; that is, the distribution and permeability of the grout should be as uniform as possible, avoiding localized over-density or under-density, and ensuring the structural stability of the reinforced area. This application achieves this goal by optimizing the grouting path and grout mix ratio based on solidification strength data obtained from experiments and simulations. Construction cost is a crucial indicator for evaluating the rationality of the grouting scheme. Optimizing the grouting path and parameters not only ensures effective grout diffusion and reinforcement effect but also considers the optimal allocation of grout volume, construction time, and equipment resources during construction, thereby achieving cost control. This application obtains an efficient and low-cost grouting path planning scheme through simulation and optimization.

[0131] 3-2) Application of Optimization Algorithm. To achieve the above optimization objectives, this application employs the Particle Swarm Optimization (PSO) algorithm for global optimization. Specifically:

[0132] 3-2-1) First, the optimization objective is to maximize the diffusion radius of the grout and the uniformity of the solidified body strength using the particle swarm optimization algorithm, while minimizing the construction cost. The specific objective function is established as follows:

[0133]

[0134] in, This represents the diffusion radius of the slurry; the goal is to maximize this value so that the slurry can cover a larger area. This indicates the uniformity of the strength of the reinforced body. The goal is to make the strength distribution within the reinforced area as uniform as possible, avoiding localized areas that are too dense or too sparse. Reflecting the economic efficiency of construction, the goal is to minimize the amount of grouting and construction time, thereby controlling costs; , where is the weighting coefficient, representing the relative importance of different optimization objectives.

[0135] 3-2-2) Next, the Particle Swarm Optimization (PSO) algorithm is used to achieve global objective optimization, specifically as follows:

[0136] 1. Initialize the particle swarm: First, set the particle swarm size to... The particle positions are initialized, with each particle's position representing a grouting path planning scheme, and each dimension of the particle representing a design variable. In this example, the particle dimensions are set to [number missing]. The particle position is then represented as: .in Indicates the first The particle in the first The position in each dimension represents the corresponding grouting design variables (grouting path location, grouting pressure, grout mix ratio, etc.). (Number of variables). Initial position: .in, and These are the minimum and maximum values ​​of the grouting path design variables, respectively. Next, particle velocity initialization is performed: the velocity of each particle represents the change in position of the particle in the search space, and the particle velocity is expressed as... .in For the first The particle in the first Velocity in each dimension. Initial velocity: .in, This represents the maximum particle velocity.

[0137] 2. Fitness Evaluation: The fitness function evaluates the solution quality of each particle. In grouting path optimization, the fitness function includes multiple objectives: maximizing the grout diffusion radius. .in The slurry diffusion radius; the strength of the homogenized solidified body. .in It is the first Each node's strength, It is the average strength of the solidified body. It refers to the number of nodes; minimizing construction costs. .in, For the cost of slurry materials, For construction labor costs, For construction time cost. The final fitness function. Based on the objective function To calculate the solution mass of each particle: .when The larger the value, the higher the quality of the solution.

[0138] 3. Particle Position and Velocity Update: The PSO algorithm updates the position and velocity of each particle based on its performance in the search space. The particle velocity update formula is as follows:

[0139]

[0140] in, Indicates the first The velocity of each particle after iterative update; Inertial weights are used to control the search range of particles; Indicates the first The velocity of each particle before the update; The learning factor controls the speed at which particles approach the local and global optima. It is a random number. This increases the randomness of the algorithm; For particles In the The historical best position in the next iteration; It represents the globally optimal position in the particle swarm.

[0141] The particle position update formula is:

[0142]

[0143] in, Indicates the first The position of each particle after iterative update Indicates the first The particle's position before the update. The particle's current position is determined by the updated velocity and the current velocity.

[0144] 4. Update individual and global optimal solutions: For each particle, check if the fitness of its current solution is better than the historical optimal solution. If so, update the historical optimal solution for that particle.

[0145]

[0146] in, Represents particles The historical optimal solution for the location; Indicates the particle position after iteration; Indicates particle position The fitness of; This represents the fitness of the position when the historical optimal solution is reached.

[0147] By checking the fitness of all particles, the particle with the best fitness is found, and the global optimal solution is updated:

[0148]

[0149] in, Represents the global historical optimal solution for all particle positions; Indicates the particle position after iteration; Indicates particle position The fitness of; This represents the fitness of the position when the global historical optimal solution is reached.

[0150] 5. Iteration and Output: This application sets the maximum number of iterations to be [number missing]. If the number of iterations If the condition is not met, the algorithm stops. Ultimately, the PSO algorithm will output the optimal grouting path planning scheme, that is, the particle position of the global historical best solution, including: grouting path position (geometry of the grouting path, grouting node positions on the path), grouting pressure, grout mix ratio, and optimal construction cost.

[0151] Step 4: Real-time monitoring and local fine-tuning.

[0152] By acquiring dynamic change data of the geological body (including stress, seepage, temperature, etc.) through a real-time monitoring system, the grouting path planning is dynamically adjusted and optimized, thereby ensuring the accuracy and flexibility of the grouting scheme. Specifically:

[0153] 4-1) Data Acquisition and Processing of the Real-Time Monitoring System. During construction, stress sensors monitor the stress distribution within the soil and rock mass in real time, especially stress concentration near fault zones or fracture networks; seepage monitoring instruments acquire information on the flow of fluids (grout) within the soil and rock mass, particularly in areas with fractures or water channels, to monitor grout permeability in real time; temperature sensors monitor temperature changes during the grout's solidification process after injection, helping to analyze the grout's setting and hardening rates and the presence of abnormal temperature rises or falls. The data acquired by these sensors is transmitted in real time to the central control system for data preprocessing and preliminary analysis, forming a dynamic monitoring data stream.

[0154] 4-2) Data Input and Function Update. Based on the real-time monitoring data mentioned above, the real-time monitoring results are fed back to the particle swarm optimization model. The objective function is then updated:

[0155]

[0156] in, , and These represent the changes in stress, seepage, and temperature, respectively, with weighting coefficients. , , This indicates the degree of influence of these factors on the objective function.

[0157] 4-3) Path and Parameter Fine-tuning. After obtaining real-time monitoring data, the particle positions and velocities in the Particle Swarm Optimization (PSO) algorithm need to be adjusted based on the new monitoring data to ensure that the updated path is more adapted to the current geological conditions. Dynamic updates based on the influence of monitoring data are added to the original PSO formula:

[0158] 1. Particle velocity update:

[0159]

[0160] Among them, new items This indicates adjustments to the velocity based on real-time monitoring data, reflecting fine-tuning caused by factors such as stress and seepage.

[0161]

[0162] in, The number of factors affecting the monitoring data, The weighting coefficients for the monitoring data. For the first Path changes caused by monitoring data.

[0163] 2. Particle position update:

[0164]

[0165] The position update formula remains unchanged, but as the velocity is updated, the particle's position will change based on the influence of real-time monitoring data.

[0166] 3. Fitness assessment update:

[0167]

[0168] objective function The fitness of particles is dynamically adjusted based on changes in real-time monitoring data. It is updated in real time.

[0169] 4-4) Iterative Calculation and Feedback Optimization. The iterative calculation is the same as above. The PSO algorithm optimizes the global solution by updating particle velocity and position, combining real-time monitoring data and dynamic adjustments to the objective function, and adjusts the path and parameters. After each path fine-tuning, the optimization system provides feedback on the new grouting path and parameters, and verifies them using real-time monitoring data. Based on the feedback results, the path planning is adjusted again to ensure that the final path scheme and construction parameters can adapt to the constantly changing geological environment.

[0170] Step 5: Pressure gradient stability control.

[0171] Pressure gradient stability control is a crucial component of grouting path optimization and construction safety. By combining multi-field coupling models and real-time monitoring data, grouting pressure strategies are formulated and adjusted to ensure the stability of the pressure gradient during grouting, thus preventing formation damage or insufficient diffusion caused by inappropriate pressure. Specifically:

[0172] 1) Pressure Gradient Analysis and Simulation. First, a multi-field coupled model was used to simulate the flow of grout within the soil and rock mass under different grouting pressures (the changes in the grout's diffusion radius, diffusion rate, and permeability under different pressures). The model considered the rheological properties of the grout, the injection pressure, and the permeability of the porous medium. The grout flow equation was set as follows:

[0173]

[0174] in, It is the permeability coefficient; It is the dynamic viscosity of the slurry, which changes with pressure; This represents the grouting pressure gradient.

[0175] Calculate the distribution of the pressure gradient to identify regions where pressure gradients may be too large or too small:

[0176]

[0177] in, For pressure gradient, For the pressure change injected, This is the length of the injection path. Within the diffusion region, the pressure gradient should remain stable to avoid drastic fluctuations.

[0178] 2) Pressure Control Strategy Formulation. After simulating the grout diffusion behavior and pressure gradient changes under different pressures, a grouting pressure control strategy was formulated to ensure the stability of the pressure gradient during grouting. First, the control objectives were clarified: to avoid formation damage caused by excessively high pressure; and to avoid insufficient diffusion caused by excessively low pressure. Next, based on the physical properties of the soil and rock mass (such as compressive strength, permeability, etc.), a suitable pressure range was defined. Assumptions... and These are the minimum and maximum allowable grouting pressures, respectively. For real-time injection pressure, it should satisfy the following: The system adjusts the grouting pressure in real time based on real-time monitoring data (stress, seepage, temperature, etc.). Specifically, if the pressure gradient in a certain area changes too rapidly (e.g., increases sharply), the system will automatically reduce the grouting pressure to avoid formation damage caused by excessive pressure. If insufficient grout diffusion (i.e., a small diffusion radius) is detected in a certain area, the system will increase the grouting pressure in a timely manner to ensure effective grout penetration.

[0179] 3) Real-time monitoring and dynamic adjustment. The system monitors and collects data on stress, seepage, and temperature changes in the soil and rock mass in real time according to a preset pressure control strategy. When the monitoring data indicates that the pressure gradient in a certain area fluctuates drastically (potentially leading to damage), dynamic adjustments are made according to the following rules:

[0180]

[0181] in, This is the adjusted grouting pressure; This is the current grouting pressure; This is the current pressure gradient; It is a regulatory factor; It is the set pressure gradient threshold.

[0182] When the diffusion radius of a certain area is found to be insufficient (low permeability), the grouting pressure can be increased according to the following formula:

[0183]

[0184] in, It is a regulating factor, with a value of ; The target diffusion radius; This represents the current diffusion radius.

[0185] 4) Feedback and Optimization. During grouting, the system continuously monitors changes in the pressure gradient and provides real-time adjustment results. Based on changes in the pressure gradient and the diffusion behavior of the grout, the monitoring system relays the need for pressure changes to the control system. Based on the real-time feedback data, the optimization system fine-tunes the grouting path and pressure to ensure the optimization of the overall objectives.

[0186] Step 6: Evaluation of slurry diffusion and reinforcement effect.

[0187] Through the grout diffusion and reinforcement effect evaluation process, combined with model simulation and field verification, the effectiveness of the grouting path and parameters is validated to ensure that the reinforcement effect achieves the expected goals. Specifically:

[0188] 1) Simulation evaluation. The simulation model is based on previous multi-field coupling models (rheology, mechanics, permeability, etc.) and the flow and diffusion behavior of grout. Combined with parameters such as grouting pressure, grouting path, and grout mix ratio, it predicts the spatial distribution of grout diffusion and calculates the strength distribution of the reinforced body.

[0189] 1. Slurry Diffusion Simulation: Based on the established multi-field coupling model, the diffusion process of slurry under different pressures is simulated, and the diffusion radius and penetration depth of the slurry are calculated. Through numerical simulation, the spatial distribution maps of the slurry under different scenarios are obtained. The specific formula is:

[0190]

[0191] in, The diffusion radius of the slurry; This refers to the grouting pressure; The viscosity of the slurry; Permeability coefficient; The diffusion coefficient represents the speed and direction of slurry diffusion.

[0192] 2. Simulation of Grout Strength Distribution: Based on the grout diffusion simulation, the strength distribution of the grouted body after injection is further simulated. The simulation results are used to predict the strength distribution of the grout after solidification in the soil and rock mass, and a compressive strength model is used for characterization. The specific formula is:

[0193]

[0194] in, This refers to the compressive strength of the solidified soil and rock mass. This refers to the grouting pressure; The viscosity of the slurry; The physicochemical properties of the slurry.

[0195] 3. Evaluation of Diffusion and Reinforcement Effects: Based on the diffusion radius and intensity distribution obtained from simulations, the effectiveness of different grouting schemes is evaluated. A comprehensive evaluation index is defined. This is used to measure the overall performance of grout diffusion and reinforcement effect. The specific formula is:

[0196]

[0197] in, Weighted by diffusion radius. As a weight for reinforcement strength, ; Inject slurry flow rate; Permeability coefficient; This refers to the grouting pressure; This refers to the curing time; These are model parameters, obtained through calibration using experimental data.

[0198] 2) Field Verification. Grouting was carried out in the selected construction area according to the optimized path and grouting parameters. During construction, parameters such as grouting pressure, grout mix ratio, and grouting rate were strictly controlled to ensure consistency with the optimized model. After construction, samples of the reinforced body were collected from several typical locations within the construction area. The samples should represent the grout diffusion effect and reinforcement strength in different areas.

[0199] The collected samples were subjected to diffusion range testing, compressive strength testing, and permeability testing to obtain test data. The field verification results were compared with the simulation results to assess the deviation between the simulation and actual construction. For areas with significant deviations, the causes were analyzed and the model parameters were optimized accordingly.

[0200] The formula for evaluating error is:

[0201]

[0202] in, These are the actual measured data indicators. These are data indicators obtained from simulation and prediction.

[0203] If the field sample has high permeability, but the simulation results underestimate the diffusion depth of the slurry, then it is necessary to increase the permeability coefficient. To optimize the model:

[0204]

[0205] in, This is the adjusted permeability coefficient; The original permeability coefficient; The adjustment factor is determined by comparing the errors in field verification. If the error is large, it is increased. .

[0206] If the slurry diffusion range of the field sample is large, and the diffusion range of the slurry is underestimated in the simulation results, then it is necessary to adjust the simulation results by increasing the rheological parameters to better match the actual situation.

[0207]

[0208] in, These are the adjusted rheological parameters; These are the original rheological parameters; The adjustment factor was determined through error analysis.

[0209] Grouting pressure has a significant impact on the diffusion range and penetration effect of the grout. If the simulation results underestimate the diffusion radius or strength improvement, the grouting pressure needs to be adjusted.

[0210]

[0211] in, The adjusted grouting pressure; The original grouting pressure; To adjust the factors, selection was made by comparing field results.

[0212] After making the above adjustments, a resimulation is required through model iteration and verification steps. The updated parameters (permeability coefficient) should be applied. Rheological parameters and grouting pressure Substitute the data into the model for a new simulation, compare it with the field data, and continue to correct and optimize until the error reaches an acceptable range. .

[0213] Example 2

[0214] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0215] Example 3

[0216] The purpose of this embodiment is to provide a computer-readable storage medium.

[0217] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0218] Example 4

[0219] The purpose of this embodiment is to provide a grouting path optimization system based on multi-field coupling constraints, including:

[0220] The geological data acquisition module is configured to acquire geological data, including: rock strata distribution and physical and mechanical parameters, and groundwater data;

[0221] The three-dimensional multiphysics coupling model building module is configured to: build a three-dimensional multiphysics coupling model based on the collected geological data, including: building a three-dimensional rock and soil model, performing stress field analysis on the three-dimensional rock and soil model, constructing a seepage field, simulating a temperature field, quantifying the mutual influence between the stress field, seepage field and temperature field, and obtaining the coupling relationship between each physical field;

[0222] The three-dimensional multiphysics coupling model calibration module is configured to: acquire the rheological property data of the slurry, and calibrate the established three-dimensional multiphysics coupling model based on the acquired rheological property data of the slurry, and obtain the calibrated three-dimensional multiphysics coupling model.

[0223] The grouting path planning module is configured to optimize the grouting path, grout pressure, and grout mix ratio parameters for the calibrated three-dimensional multiphysics coupling model, and generate the optimal grouting path planning scheme.

[0224] Example 5

[0225] The purpose of this embodiment is to provide a computer program product containing instructions that, when run on a computer, causes the computer to perform the methods and functions involved in any of the embodiments described above.

[0226] The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0227] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0228] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A grouting path optimization method based on multi-field coupling constraints, characterized in that, The method comprises the following steps: Collecting geological data, including: stratum distribution and physical and mechanical parameters, groundwater data; Based on the collected geological data, a three-dimensional multi-physical field coupling model is established, including: establishing a three-dimensional rock-soil model, conducting stress field analysis on the three-dimensional rock-soil model, constructing a seepage field, simulating a temperature field, quantifying the mutual influence between the stress field, the seepage field and the temperature field, and obtaining the coupling relationship between the physical fields; The mutual influence between the stress field, the seepage field and the temperature field is quantified, specifically including: When the stress field and the seepage field are coupled, the specific expression of the influence of the stress field on the permeability coefficient is obtained, wherein, is the initial permeability coefficient, is the coupling coefficient of permeability and stress change, is the stress change of the rock mass, with the change of the stress field, the permeability coefficient will be dynamically adjusted, thereby affecting the diffusion of the slurry; When the seepage field and the temperature field are coupled, the specific expression of the coupling between the seepage field and the temperature field is obtained, wherein, is the rate of change of seepage pressure, representing the change in pressure with time; represents the permeability; is the slurry viscosity; is the pressure gradient, representing the change in pressure in the direction of seepage; When the stress field and the temperature field are coupled, the specific expression of the coupling between the stress field and the temperature field is obtained, wherein is a stress change of the geotechnical body, is a coefficient of thermal expansion, representing the ability of a material to change in dimensions in response to a change in temperature, is a temperature change; Obtaining the rheological property data of the slurry, and calibrating the established three-dimensional multi-physical field coupling model based on the obtained rheological property data of the slurry to obtain a calibrated three-dimensional multi-physical field coupling model; For the calibrated three-dimensional multi-physical field coupling model, the grouting path, the slurry pressure and the slurry proportioning parameters are optimized to generate an optimal grouting path planning scheme.

2. The method of claim 1, wherein the method is characterized by, Calibration of the established three-dimensional multi-physical field coupling model based on the obtained rheological property data of the slurry, including: Establishing a finite element model: constructing a three-dimensional model of the geological body and the grouting area; Solving multi-field coupling equations: discretizing the stress field, seepage field and heat conduction equations, setting boundary conditions according to the actual engineering conditions, then numerically solving by the finite element method, and then obtaining the slurry flow conditions, pressure field, temperature field and diffusion range under different temperature, stress and permeability conditions; Result verification and calibration: after completing the finite element solution, the actual data obtained through experiments, including slurry viscosity, permeability, solidification time, are compared with the finite element simulation results to determine the deviation between the simulation results and the experimental data, and the model parameters are optimized.

3. The method of claim 1, wherein the method is characterized by, The grouting path, the slurry pressure and the slurry proportioning parameters are optimized to generate an optimal grouting path planning scheme, specifically including: Setting optimization goals: the global optimization goal is to maximize the diffusion radius of the slurry and the uniformity of the reinforcement body strength, while minimizing the construction cost; The process of global optimization using the particle swarm optimization algorithm includes: Initialize the particle swarm: first set the particle swarm size as , initialize the particle position, the position of each particle represents a grouting path planning scheme, and each dimension of the particle represents a design variable; Fitness evaluation: the fitness function evaluates the solution quality of each particle; Particle position and velocity update: the PSO algorithm updates the position and velocity of each particle in the search space according to its performance; Updating individual optimal solution and global optimal solution: for each particle, check whether the fitness of its current solution is better than the historical optimal solution, if so, update the historical optimal solution of the particle; By checking the fitness of all particles, find the particle with the best fitness, and update the global optimal solution; Iteration and output: the PSO algorithm outputs the optimal grouting path planning scheme, i.e. the particle position of the global historical optimal solution, including: grouting path position, grouting pressure, slurry proportioning scheme, optimal construction cost.

4. The method of claim 1, further comprising: Including: Pressure gradient stability control, specifically including: Through the multi-field coupling model, the flow of the slurry in the rock-soil body under different grouting pressures is simulated; Calculate the distribution of pressure gradients to identify areas where excessive or insufficient pressure gradients may occur; After simulating the slurry diffusion behavior and pressure gradient changes under different pressures, develop a control strategy for grouting pressure to ensure the stability of the pressure gradient during the grouting process; According to the pre-set pressure control strategy, real-time monitoring and collection of geotechnical stress, seepage, and temperature data are performed. When the monitoring data indicate that the pressure gradient in a certain area fluctuates sharply, dynamic adjustment is carried out; When the diffusion radius of a certain area is insufficient, the grouting pressure is increased; During the grouting process, the pressure gradient changes are continuously monitored and the real-time adjustment results are fed back.

5. The method of claim 1, further comprising: It includes: Through the slurry diffusion and reinforcement effect evaluation steps, combined with model simulation and field verification, the effectiveness of the grouting path and parameters is verified: Slurry diffusion simulation: Based on the established multi-field coupling model, the diffusion process of slurry under different pressures is simulated, the diffusion radius and penetration depth of slurry are calculated, and the spatial distribution of slurry under different schemes is obtained through numerical simulation; Reinforced body strength distribution simulation: Based on the slurry diffusion simulation, the strength distribution of the reinforced body after grouting is further simulated. The simulated results are used to predict the strength distribution of the solidified slurry in the geotechnical body, and the compressive strength model is used to represent it; Diffusion and reinforcement effect evaluation: combined with the simulated diffusion radius and intensity distribution, the effect of different grouting schemes is evaluated, and a comprehensive evaluation index is defined Overall performance for measuring the diffusion and reinforcement effect of the slurry In the selected construction area, grouting construction is carried out according to the optimized path and grouting parameters. During the construction process, the grouting pressure, slurry ratio, and grouting rate are strictly controlled to ensure consistency with the optimized model. After the construction is completed, samples of the reinforced body are collected from several typical locations in the construction area, which should represent the slurry diffusion effect and reinforcement strength in different areas; The collected samples are subjected to diffusion range detection, compressive strength detection, and permeability detection. The detection data are compared with the simulation results to evaluate the deviation between the simulation and actual construction. For areas with large deviations, the reasons are analyzed and the model parameters are optimized accordingly; If the permeability of the field samples is high and the simulation results underestimate the diffusion depth of the slurry, the model needs to be optimized by increasing the permeability coefficient; If the slurry diffusion range of the field samples is large and the simulation results underestimate the diffusion range of the slurry, the simulation results need to be adjusted by improving the rheological parameters to better match the actual situation; If the simulation results underestimate the diffusion radius or strength improvement, the grouting pressure needs to be adjusted; After the above adjustments, the model iteration and verification steps need to be repeated for re-simulation. The updated parameters are input into the model for new simulation, and the results are compared with the field data for continuous correction and optimization until the error reaches an acceptable range.

6. A grouting path optimization system based on multi-field coupling constraints, characterized in that, It includes: The geological data acquisition module is configured to acquire geological data, including rock layer distribution and physical and mechanical parameters, and underground water data; The three-dimensional multi-physical field coupling model establishment module is configured to establish a three-dimensional multi-physical field coupling model based on the acquired geological data, including establishing a three-dimensional geotechnical model, performing stress field analysis on the three-dimensional geotechnical model, constructing a seepage field, simulating a temperature field, quantifying the mutual influence between the stress field, seepage field, and temperature field, and obtaining the coupling relationship between the physical fields; Quantify the mutual influence among the stress field, the seepage field and the temperature field, specifically including: When the stress field and the seepage field are coupled, the specific expression of the influence of the stress field on the permeability coefficient is obtained, wherein, is the initial permeability coefficient, is the coupling coefficient of permeability and stress change, is the stress change of the rock mass, with the change of the stress field, the permeability coefficient will be dynamically adjusted, thereby affecting the diffusion of the slurry; When the seepage field and the temperature field are coupled, the specific expression of the coupling between the seepage field and the temperature field is obtained, wherein, is the rate of change of seepage pressure, representing the change in pressure with time; represents the permeability; is the slurry viscosity; is the pressure gradient, representing the change in pressure in the direction of seepage; When the stress field and the temperature field are coupled, the specific expression of the coupling between the stress field and the temperature field is obtained, wherein is the change in stress of the geotechnical body, is the coefficient of thermal expansion, which represents the ability of a material to expand or contract in response to a change in temperature, is the change in temperature; The three-dimensional multi-physical field coupling model calibration module is configured to: obtain rheological property data of the slurry, and calibrate the established three-dimensional multi-physical field coupling model based on the obtained rheological property data of the slurry, and obtain a calibrated three-dimensional multi-physical field coupling model; The grouting path planning module is configured to: for the calibrated three-dimensional multi-physical field coupling model, optimize the grouting path, the slurry pressure and the slurry ratio parameters, and generate an optimal grouting path planning scheme.

7. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1 to 5.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps of the method of any one of claims 1-5.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to perform the steps of the method of any one of claims 1-5.

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