Intelligent grouting method for shield tunnel construction in water-rich sandy cobble stratum and related equipment

Abstract

An intelligent grouting method and related equipment for shield tunnel construction in water-rich sandy cobble strata, comprising: obtaining stratum characteristic data of the water-rich sandy cobble strata to be grouted, constructing a shield tunnel three-dimensional finite element model and a seepage potential erosion two-dimensional discrete element model of the water-rich sandy cobble strata; using the shield tunnel three-dimensional finite element model to simulate and determine the initial grouting parameters of the water-rich sandy cobble strata; using the seepage potential erosion two-dimensional discrete element model to determine the stress amplitude change value and the porosity change gradient of the water-rich sandy cobble strata under the action of groundwater seepage potential erosion, and then dividing the water-rich sandy cobble strata into multiple risk areas with different risk levels; for different risk areas, adjusting the corresponding initial grouting parameters according to different supplementary grouting adjustment strategies; and performing grouting construction processing on the corresponding risk areas according to the adjusted grouting parameters. The present application can meet the requirements of uniformity and timeliness of grouting in sandy cobble strata for shield construction.

Description

Technical Field

[0001] This application relates to the field of tunnel engineering technology, specifically to an intelligent grouting method and related equipment for shield tunnel construction in water-rich sandy and gravelly strata. Background Technology

[0002] Water-rich sandy and gravelly strata are typical complex geological conditions in shield tunnel construction, characterized by heterogeneous rock properties and loose structure. Due to their loose granular structure and high porosity, they are prone to problems such as tunnel water inrush, leakage, and ground settlement during construction, directly affecting the safety and stability of tunnel construction. Especially in long-distance underpass construction in densely populated urban areas, the complexity of sandy and gravelly strata significantly increases the construction difficulty, placing higher demands on the precision, efficiency, and adaptability of grouting technology.

[0003] However, the grouting methods in related technologies usually rely on human experience to control the grout ratio and grouting pressure, lacking the ability to make precise adjustments in real time, making it difficult to meet the requirements of uniformity and timeliness of grouting in shield tunneling in gravel strata. Summary of the Invention

[0004] This application provides an intelligent grouting method and related equipment for shield tunnel construction in water-rich sandy and gravelly strata, aiming to meet the requirements of uniformity and timeliness of grouting in shield tunnel construction in sandy and gravelly strata.

[0005] Firstly, this application provides an intelligent grouting method for shield tunnel construction in water-rich sandy gravel strata. The intelligent grouting method for shield tunnel construction in water-rich sandy gravel strata includes: acquiring geological characteristic data of the water-rich sandy gravel strata to be grouted; constructing a grout diffusion numerical simulation model of the water-rich sandy gravel strata based on the geological characteristic data, the grout diffusion numerical simulation model including a three-dimensional finite element model of the shield tunnel and a two-dimensional discrete element model of seepage and erosion; performing simulation using the three-dimensional finite element model of the shield tunnel to determine the initial grouting parameters of the water-rich sandy gravel strata; determining the stress reduction amplitude and porosity gradient of the water-rich sandy gravel strata under the action of groundwater seepage and erosion using the two-dimensional discrete element model of seepage and erosion; dividing the water-rich sandy gravel strata into multiple risk zones with different risk levels based on the stress reduction amplitude and porosity gradient; adjusting the corresponding initial grouting parameters according to different supplementary grouting adjustment strategies for different risk zones to obtain adjusted grouting parameters; and performing grouting construction treatment on the corresponding risk zones according to the adjusted grouting parameters.

[0006] In the above embodiments, a comprehensive simulation of the grouting process in water-rich sandy gravel strata was achieved by constructing a dual-scale simulation system comprising a macroscopic three-dimensional finite element model and a microscopic two-dimensional discrete element model. The three-dimensional finite element model is used to predict grout diffusion and stratum settlement from a global perspective, providing a macroscopic basis for setting initial grouting parameters. More importantly, a two-dimensional discrete element model capable of simulating particle behavior was introduced, specifically for quantifying the erosion caused by groundwater seepage. This involves calculating stress reduction and porosity gradients, transforming the previously unpredictable risk of delayed settlement into a concrete and measurable indicator. Based on this risk indicator, zoning was performed, and differentiated supplementary grouting adjustment strategies were formulated, making the grouting construction process a targeted and refined risk management process. This technical logic of first determining macroscopic parameters, then quantifying microscopic risks, and finally implementing precise measures for each zone significantly improves the scientific rigor, uniformity, and timeliness of grouting, thereby ensuring the construction safety and long-term stability of shield tunnels.

[0007] In conjunction with some embodiments of the first aspect, in some embodiments, obtaining stratigraphic characteristic data of the water-rich sandy gravel stratum to be grouted includes: conducting advanced geological surveys of the water-rich sandy gravel stratum to obtain geological survey data; using monitoring points deployed in the water-rich sandy gravel stratum to obtain stratigraphic monitoring data, including pressure monitoring points and porosity monitoring points; and determining stratigraphic characteristic data based on the geological survey data and stratigraphic monitoring data.

[0008] In the above embodiments, the data acquisition process is divided into two levels: macroscopic surveying and fixed-point monitoring. Advanced geological surveying provides the basic geological physical and mechanical parameters for constructing the simulation model, ensuring the initial accuracy of the model. Meanwhile, by deploying pressure and porosity monitoring points, a dynamic monitoring network capable of real-time sensing of the internal state of the formation is established. This approach, combining macroscopic static data with microscopic dynamic data, yields more comprehensive and reliable formation characteristic data, effectively overcoming the limitations of a single data source.

[0009] In conjunction with some embodiments of the first aspect, in some embodiments, a three-dimensional finite element model of a shield tunnel is used for simulation to determine the initial grouting parameters of the water-rich sandy gravel strata. This includes: simulation of grouting construction based on the three-dimensional finite element model of the shield tunnel to predict the grout diffusion range and stratum settlement trend after grouting construction in the water-rich sandy gravel strata; and determining the initial grouting parameters of the water-rich sandy gravel strata based on the grout diffusion range and stratum settlement trend.

[0010] In the above embodiments, the entire grouting process is simulated using a three-dimensional finite element model, which can intuitively predict the three-dimensional diffusion pattern of the grout underground and the possible ground settlement response under specific grouting parameters. This parameter setting method based on macroscopic simulation replaces the extensive estimation relying on human experience, avoiding under-grouting, over-grouting, or ground disturbance caused by inappropriate parameters. This ensures that the initial grouting scheme has high feasibility and safety, thereby improving the success rate and economic benefits of construction.

[0011] In conjunction with some embodiments of the first aspect, in some embodiments, for different risk areas, the corresponding initial grouting parameters are adjusted according to different supplementary grouting adjustment strategies to obtain adjusted grouting parameters, including: determining the loss coefficient associated with the risk level of different risk areas; using a two-dimensional discrete element model of seepage erosion to determine at least one of the volume, average porosity, and cross-sectional area of ​​the erosion zone in water-rich sandy gravel strata under the action of groundwater seepage erosion; and determining the adjusted grouting parameters based on at least one of the volume, average porosity, and cross-sectional area of ​​the erosion zone, as well as the loss coefficient.

[0012] In the above embodiments, a loss coefficient is associated with the risk level, directly linking the degree of risk to the potential loss of the grout. Then, using microscopic physical quantities such as the volume of the eroded area and the average porosity output by the discrete element model, the amount of base grout required to fill the eroded area is accurately calculated. This quantitative calculation method based on a microscopic physical model ensures that the adjusted grouting parameters not only meet the filling requirements but also reflect the severity of the risk, achieving refined and scientific control of the adjusted grouting parameters to ensure the economy and effectiveness of grouting.

[0013] In conjunction with some embodiments of the first aspect, in some embodiments, the adjusted grouting parameters are determined based on at least one of the volume of the burrowing zone, the average porosity, the cross-sectional area of ​​the burrowing zone, and the loss coefficient, including: determining a pore channel compensation coefficient that is positively correlated with the porosity change gradient; and determining the adjusted grouting parameters based on at least one of the volume of the burrowing zone, the average porosity, the cross-sectional area of ​​the burrowing zone, the loss coefficient, and the pore channel compensation coefficient.

[0014] In the above embodiments, a pore channel compensation coefficient positively correlated with the porosity gradient was introduced when calculating the adjusted grouting parameters. This design enables the supplementary grouting adjustment strategy to achieve "dual compensation": it both fills the pores increased by undercutting and blocks the channels that exacerbate undercutting. This dual-control model greatly enhances the targeting of grouting, more effectively curbs the further development of delayed settlement, and fundamentally improves the long-term stability of the formation.

[0015] In conjunction with some embodiments of the first aspect, in some embodiments, after dividing the water-rich sandy gravel strata into multiple risk zones with different risk levels based on the stress reduction change value and porosity change gradient, the method further includes: determining an optimization formula for the grouting path of the grouting construction treatment, wherein the optimization formula includes the total path length between grouting points, the distance between adjacent grouting points, and the correlation between the risk weights of grouting points, and the risk weights of grouting points are determined based on the risk level of the corresponding risk zone; and determining the target grouting path of the grouting construction treatment based on the optimization formula.

[0016] In the above embodiments, an optimization formula for the grouting path is introduced. By converting the risk level into the risk weight of the grouting point in the path planning, the process avoids passing through high-risk areas while finding the shortest path, thereby improving the efficiency and safety of the grouting construction.

[0017] In conjunction with some embodiments of the first aspect, in some embodiments, after grouting construction treatment is carried out on the corresponding risk area according to the adjusted grouting parameters, the method further includes: real-time monitoring of at least one of the current grouting pressure, current grout flow rate, current grout diffusion range, and current formation stability parameters during the grouting construction treatment process; if at least one of the current grouting pressure, current grout flow rate, current grout diffusion range, and current formation stability parameters is not within the corresponding preset allowable range, a gradual stopping strategy is adopted to stop the grouting construction treatment.

[0018] In the above embodiments, by performing high-frequency real-time monitoring of at least one of the current grouting pressure, current grout flow rate, current grout diffusion range, and current formation stability parameters, and by setting preset allowable ranges, abnormal situations during construction can be detected in a timely manner. Once a parameter exceeds the limit, a gradual shutdown strategy will be automatically triggered, rather than a sudden shutdown. This "soft shutdown" method can avoid secondary disturbance to the formation caused by sudden pressure changes, ensuring the smoothness and safety of the construction process.

[0019] In a second aspect, embodiments of this application provide an intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata, which is used to perform the method described in any possible implementation of the first aspect.

[0020] Thirdly, embodiments of this application provide an intelligent grouting device for shield tunnel construction in water-rich sandy gravel strata. The intelligent grouting device for shield tunnel construction in water-rich sandy gravel strata includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the intelligent grouting device for shield tunnel construction in water-rich sandy gravel strata to perform the method described in the first aspect and any possible implementation thereof.

[0021] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an intelligent grouting system for shield tunnel construction in water-rich sandy gravel strata, causes the intelligent grouting system for shield tunnel construction in water-rich sandy gravel strata to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fifthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an intelligent grouting system for shield tunnel construction in water-rich sandy gravel strata, cause the intelligent grouting system for shield tunnel construction in water-rich sandy gravel strata to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata provided in the second aspect, the intelligent grouting device for shield tunnel construction in water-rich sandy and gravelly strata provided in the third aspect, the computer program product provided in the fourth aspect, and the computer storage medium provided in the fifth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By adopting the technical solution of acquiring stratum characteristic data, constructing a dual-scale simulation model including a three-dimensional finite element model and a two-dimensional discrete element model, using the three-dimensional finite element model to determine the initial grouting parameters, using the two-dimensional discrete element model to quantify the risk of erosion and perform risk zoning, and finally formulating differentiated supplementary grouting adjustment strategies for different risk areas and carrying out construction treatment, this application can comprehensively grasp and predict the complex behavior of water-rich sandy gravel strata from both macroscopic and microscopic levels, and perform risk zoning and differentiated measures based on stress reduction amplitude and porosity change gradient indicators, so that grouting resources can be accurately deployed to the most needed areas, which can significantly improve the scientific nature, uniformity and timeliness of grouting, thereby ensuring the construction safety and long-term stability of shield tunnels. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating an intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata, as described in this application.

[0026] Figure 2 This is another schematic diagram of the intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata in this application embodiment;

[0027] Figure 3 This is an overall logical schematic diagram of the intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata in the embodiments of this application;

[0028] Figure 4 This is a schematic diagram of at least part of the physical structure of the intelligent grouting device for shield tunnel construction in water-rich sandy and gravelly strata, as described in this application embodiment. Detailed Implementation

[0029] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0031] In related technologies, grouting methods typically rely on manual experience to control grout ratio and grouting pressure, lacking the ability to precisely adjust to the real-time response of the strata. This makes it difficult to meet the requirements of uniformity and timeliness in shield tunneling construction in sandy and gravelly strata. The following problems commonly exist in grouting processes in complex strata: First, it is impossible to accurately monitor strata changes and grout diffusion range, leading to difficulties in ensuring grouting quality; second, the adjustment of grouting pressure and grout ratio is lagging, making it difficult to cope with rapid changes in geological conditions in sandy and gravelly strata; and third, grouting construction efficiency is low, easily leading to extended construction periods.

[0032] Since grouting methods are closely related to construction efficiency and ground stability, intelligent grouting methods are crucial for solving these problems in gravelly strata. For example, by combining advanced sensing technology, real-time monitoring technology, and automated control technology, dynamic perception of the ground condition and intelligent adjustment of grouting parameters can be achieved, thereby effectively improving grouting quality and reducing construction risks. Currently, research on intelligent grouting for shield tunneling in gravelly strata is still in its early stages, and efficient grouting methods tailored to the characteristics of this type of strata are lacking. Therefore, developing intelligent grouting methods suitable for shield tunnels in gravelly strata has significant engineering implications.

[0033] Furthermore, research on shield tunneling grouting technology generally overlooks the delayed settlement effect in water-rich sandy and gravelly strata. Groundwater seepage triggers the continuous migration and loss of fine particles, especially in water-rich sandy and gravelly strata, which can easily lead to a sharp increase in stratum porosity and consequently degradation of the macroscopic mechanical parameters of the soil and rock mass. This seepage-induced erosion significantly weakens the stability of the strata, and the grouting methods used in related technologies lack a quantitative understanding of this evolution, making it impossible to effectively prevent delayed settlement disasters.

[0034] This application aims to propose an intelligent grouting method suitable for shield tunnel construction in sandy and gravelly strata. This method achieves real-time monitoring of the stratum state based on a monitoring point network, dynamically optimizes and adjusts grouting parameters through intelligent algorithms, and possesses advantages such as high precision, controllability, and strong adaptability. It utilizes particle flow discrete element method (PFM) simulation to calculate the seepage process, generating porosity change gradients and stress reduction values ​​to quantify the hysteresis settlement effect. This effectively improves the grouting effect during shield tunnel construction, reduces stratum deformation and construction risks, and provides reliable technical support for shield construction under complex geological conditions.

[0035] The embodiments of this application have been specifically optimized for the following characteristics of water-rich sandy and gravel strata:

[0036] 1. High permeability: This makes the grout easy to spread, but it also makes it prone to over-grouting or leakage. Simulation modeling is needed to optimize the grouting pressure and diffusion range.

[0037] 2. Heterogeneity: The particle distribution in different areas of the water-rich sandy gravel strata is uneven, and the permeability coefficient k varies greatly in space. The diffusion process needs to be dynamically adjusted in conjunction with on-site monitoring.

[0038] 3. Low cementation: The formation lacks cementing materials and has a low bearing capacity, requiring rapid grout consolidation to enhance formation stability.

[0039] The grouting diffusion process in shield tunnels located in water-rich sandy and gravelly strata can be divided into the following three stages:

[0040] 1. Infiltration stage: The slurry diffuses through the formation pores under the drive of the pressure gradient.

[0041] 2. Filling stage: The grout fills the pore space and gradually solidifies.

[0042] 3. Steady-state stage: After the slurry solidifies, it forms a continuous body, and the formation stability is improved.

[0043] The evolution of subsidence and instability caused by groundwater seepage:

[0044] 1. Seepage and erosion stage: Fine particles migrate and the pore structure deteriorates.

[0045] 2. Formation weakening stage: Macroscopic mechanical parameters degrade, and bearing capacity decreases.

[0046] 3. Deformation and instability stage: Effective stress is redistributed, triggering a catastrophe.

[0047] In the specific implementation scenario of this application, a series of technical terms are involved, which, combined with the special environment of shield tunneling in water-rich sandy gravel strata, have specific engineering connotations and processing logic. Water-rich sandy gravel strata refer to loose strata rich in groundwater, composed of a mixture of sand and gravel of different particle sizes; their high permeability and heterogeneity are the main challenges of construction. Shield tunneling is a modern construction method that utilizes a shield machine for tunnel excavation and lining. Intelligent grouting combines sensing, computing, and control technologies with grouting processes in related technologies to achieve automation and optimization.

[0048] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating an intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata, as described in this application.

[0049] S101. Obtain the stratigraphic characteristics data of the water-rich sandy gravel strata to be grouted.

[0050] Among them, water-rich sandy gravel strata refer to geological structures with high groundwater content and composed of loose particles such as sand and pebbles. Areas awaiting grouting refer to areas where the tunnel boring machine is about to begin excavation or is currently excavating, requiring pre-reinforcement or simultaneous reinforcement through grouting. Stratigraphic characteristic data is a set of quantitative parameters describing the physical and mechanical properties of the area, including soil density, particle size distribution, porosity, permeability coefficient, internal friction angle, and cohesion.

[0051] S102. Based on the geological characteristic data, a numerical simulation model of grouting diffusion in water-rich sandy gravel strata is constructed. The numerical simulation model of grouting diffusion includes a three-dimensional finite element model of a shield tunnel and a two-dimensional discrete element model of seepage and erosion.

[0052] The grouting diffusion numerical simulation model refers to a mathematical and physical model established in a computer using the geological characteristic data obtained in step S101, which can simulate the flow, diffusion, and solidification process of grout in the strata. This grouting diffusion numerical simulation model is the core of the prediction and analysis in this application. It specifically refers to a composite model containing two different scale models: a three-dimensional finite element method (FEM) model of the shield tunnel for macroscopic analysis, and a two-dimensional discrete element method (DEM) model of seepage and erosion for microscopic mechanism analysis.

[0053] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata utilizes geotechnical engineering simulation software to generate geometric and physical models that match the actual engineering area based on stratum characteristic data. First, a three-dimensional finite element model encompassing the tunnel, strata, and surrounding environment is established and assigned macroscopic mechanical properties. Then, a representative high-risk two-dimensional profile is selected from the three-dimensional finite element model to establish a discrete element model that reflects the true size and distribution of particles, and is assigned microscopic contact parameters and fluid-structure interaction properties.

[0054] S103. Simulation was performed using a three-dimensional finite element model of the shield tunnel to determine the initial grouting parameters for the water-rich sandy gravel strata.

[0055] The simulation refers to inputting different combinations of grouting parameters into the three-dimensional finite element model and calculating the resulting changes in the stress field, seepage field, and displacement field. The initial grouting parameters refer to a set of basic grouting construction parameters applicable to the entire area before considering differentiated adjustments for potential erosion risks. These parameters mainly include the initial grouting pressure, initial grouting flow rate, and initial grout mix ratio.

[0056] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata uses this three-dimensional finite element model as the computational object, applies a gravity field and initial boundary conditions, and simulates the grouting process by applying pressure or flow loads at nodes representing grouting holes. By performing transient or steady-state solutions on multiple combinations of initial grouting parameters, the final grout diffusion range and ground settlement value under each parameter set are obtained. The intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata compares these predicted results with engineering control standards (such as grout sleeve thickness and surface settlement limits) to select the optimal set of initial grouting parameters.

[0057] S104. Using a two-dimensional discrete element model of seepage erosion, determine the stress reduction and porosity gradient of water-rich sandy gravel strata under the action of groundwater seepage erosion.

[0058] Among them, groundwater seepage erosion refers to the dragging force exerted on fine soil particles by groundwater flowing through pores. When this force is large enough, the particles will detach from the soil skeleton and be carried away by the water flow, leading to the deterioration of the stratum structure. This is the fundamental reason for the delayed settlement of water-rich sandy gravel strata. The stress reduction amplitude and porosity gradient are two core indicators for quantifying this process. The former reflects the degree of loss of the soil skeleton's bearing capacity, while the latter reflects the rate of increase in the internal pores of the soil.

[0059] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata applies seepage boundary conditions consistent with the on-site hydraulic head difference to the two-dimensional discrete element model for fluid-structure interaction calculations. During the simulation, when the drag force of the water flow on a fine particle exceeds its contact force, the particle is considered to have migrated. After the simulation, the stress reduction field and porosity gradient field of the entire region are calculated through post-processing of the model data.

[0060] In one specific embodiment of this application, the core of the two-dimensional discrete element model for seepage-induced erosion is a contact mechanics model describing the interaction between particles and a seepage drag force model simulating the effect of groundwater on particles. For example, the contact mechanics model can employ a spring-damper model, calculating the particle trajectory by determining the normal and tangential contact forces between particles. The seepage drag force model calculates the drag force based on the relative velocity between the fluid and the particles; when the drag force exceeds the constraint force on the particles, particle migration occurs. By constructing a two-dimensional discrete element model capable of precisely simulating the interaction between particles and the fluid-structure interaction effect, the microscopic evolution process of delayed settlement caused by seepage-induced erosion can be reproduced from basic physical principles. This allows for the quantitative and mechanistic analysis of previously unpredictable delayed settlement risks, providing a theoretical basis and data support for subsequent risk zoning and refined grouting.

[0061] S105. Based on the stress reduction amplitude and porosity gradient, the water-rich sandy gravel strata are divided into multiple risk zones with different risk levels.

[0062] Risk level refers to the classification of the likelihood and severity of delayed subsidence caused by erosion. Multiple risk zones refer to sub-regions on the digital map of the area to be grouted, which require different treatment measures based on risk level, such as high-risk, medium-risk, and low-risk zones.

[0063] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata establishes a risk assessment matrix or rule base, comparing the stress reduction change value and porosity change gradient calculated in step S104 with preset thresholds. For example, when the stress reduction is greater than 30 kPa and the porosity change gradient is greater than 3% / m, the area is identified as a high-risk zone. By traversing all computational grids, a visualized "hysteresis settlement risk map" is finally generated.

[0064] S106. For different risk areas, adjust the corresponding initial grouting parameters according to different supplementary grouting adjustment strategies to obtain the adjusted grouting parameters.

[0065] The supplementary grouting adjustment strategy refers to a grouting plan specifically designed for medium- and high-risk areas, which involves additional reinforcement based on the initial grouting parameters. The adjusted grouting parameters are the final, differentiated construction instructions issued to the construction equipment for each different risk area.

[0066] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata uses the "lagging settlement risk map" generated in step S105 to call a grouting volume adjustment calculation model for medium-risk and high-risk areas. This model accurately calculates the amount of grouting that needs to be added based on the risk level of the area and parameters such as the volume of the erosion zone calculated by the discrete element model, and superimposes it on the initial grouting volume to generate a set of adjusted grouting parameters that are precisely matched with the risk level.

[0067] S107. According to the adjusted grouting parameters, carry out grouting construction treatment on the corresponding risk areas.

[0068] Grouting construction refers to the physical process of pumping grout into pre-designed grouting holes in tunnel segments using on-site grouting equipment and injecting it into the strata.

[0069] Specifically, the control module of the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata sends the final adjusted grouting parameter instruction set to the intelligent grouting pump station on site via an industrial control network. The actuators of the intelligent grouting pump station automatically execute the grouting operation precisely according to the parameters set for each grouting hole in the instructions, ensuring the accuracy and consistency of the construction.

[0070] To solve the problem that the preset parameters are not applicable due to the ever-changing on-site construction conditions, the embodiments of this application introduce a real-time monitoring and feedback closed-loop mechanism throughout the construction process. During the grouting construction, the intelligent grouting system for shield tunneling in water-rich sandy cobble strata continuously monitors the actual on-site response. If a large deviation from the expectation is found, the current execution is paused, and rapid re-analysis and re-decision are carried out starting from step S104 or S105 to generate new adjusted grouting parameters and then continue the execution. This dynamic "execution - monitoring - re-decision - re-execution" cycle ensures that the final implementation of the plan can adapt to on-site changes in real time.

[0071] In a specific embodiment of this application, the grouting construction process is completed by an automated system with multiple intelligent devices collaborating and closed-loop feedback control. This automated system includes: a precision-controlled grouting pump capable of independently controlling the pressure and flow rate of each grouting point; a three-dimensional laser scanner for real-time monitoring of the slurry filling effect; customized shield tunnel segments with optimized layout of grouting holes; and a high-frequency pressure and flow monitoring network arranged at the pipelines and grouting holes. These devices are interconnected through an industrial network and are centrally scheduled by a central computer. The central computer issues instructions based on the planned target grouting path and adjusted grouting parameters, and adopts a multi-point collaborative dynamic correction strategy based on the real-time feedback data of the sensor network. It not only corrects the grouting parameters of abnormal points but also actively adjusts the parameters of its adjacent grouting points according to the collaborative influence model, ensuring the uniformity and stability of the entire grouting area.

[0072] The following provides a further and more specific process description of the method provided in this embodiment. Please refer to Figure 2 and Figure 3 , Figure 2 which is another process schematic diagram of the intelligent grouting method for shield tunneling in water-rich sandy cobble strata in the embodiments of this application.

[0073] S201. Conduct advanced geological exploration on the water-rich sandy cobble strata to obtain geological exploration data.

[0074] Among them, advanced geological exploration refers to detecting the geological conditions of the unexcavated strata ahead through physical or drilling means before the excavation face of the shield machine. Geological exploration data are the original data describing the lithology, structure, and physical and mechanical properties of the strata, such as borehole columnar diagrams, geotechnical sample test reports, and geophysical exploration profiles.

[0075] Specifically, during the preparation stage or tunneling process of shield construction, the intelligent grouting system for shield tunneling in water-rich sandy cobble strata first plans and executes the advanced geological exploration task to obtain the geological exploration data necessary for constructing a simulation model. For example, an advanced drilling rig配套 with the shield machine can be controlled to drill and take samples at a certain distance in front of the shield machine, and the sample information and indoor test results are entered into the formation characteristic database.

[0076] In one embodiment of this application, to obtain comprehensive geological survey data, the following calculations are also included:

[0077] Calculation of initial formation pressure distribution: Since the initial formation pressure in water-rich sandy gravel strata is generated by the combined action of the strata's own weight and pore water pressure, according to soil mechanics principles, its initial formation pressure distribution can be calculated using the following formula:

[0078] P1=P_atm+ρgh

[0079] Where P1 is the initial formation pressure, P_atm is the atmospheric pressure (a surface constant), ρ is the soil density (a soil physical property), g is the gravitational acceleration, and h is the depth of the measuring point, representing the vertical distance from the measuring point to the ground surface. The calculated initial formation pressure is part of the geological survey data and can be used to describe the initial formation pressure distribution at different depths and locations, ensuring that the pressure design matches the actual formation.

[0080] Porosity Calculation: Porosity represents the proportion of pore volume to the total volume of soil in a formation, and is an important indicator of the permeability of water-rich sandy and gravelly formations. According to soil mechanics principles, porosity can be calculated using the following formula:

[0081] n=V_v / V_t

[0082] Where n is the formation porosity, V_v is the pore volume, i.e., the portion of the soil not occupied by solid particles, and V_t is the total soil volume, including particle volume and pore volume. Since formations with high porosity require more grout to fill, the porosity calculation results can be used as part of the geological survey data for subsequent simulation calculations to guide the distribution of grouting points.

[0083] S202. Use monitoring points set up on water-rich sandy gravel strata to obtain stratum monitoring data. The monitoring points include pressure monitoring points and porosity monitoring points.

[0084] Monitoring points refer to various sensors pre-embedded in the strata or installed on the tunnel structure. Stratum monitoring data refers to the dynamic data stream reflecting changes in the strata's state, collected in real-time or periodically by these sensors. Pressure monitoring points (such as pore water pressure gauges and earth pressure cells) are used to measure the stress state within the strata, while porosity monitoring points (such as time-domain reflectometry (TDR)) are used to indirectly measure changes in the strata's density.

[0085] Specifically, before tunnel construction or during segment assembly, the intelligent grouting system for shield tunneling in water-rich sandy and gravelly strata deploys a sensor network at key locations around the tunnel according to the design plan. During construction, the system uses a data acquisition unit to poll all monitoring points at a set frequency, reads their measurement values, and stores them in a real-time database along with timestamps and location information. This data is used not only to determine the initial stratum state but also to verify simulation results and provide feedback control during construction.

[0086] In some embodiments, pressure monitoring points and porosity monitoring points are deployed within the grouting area. The spacing between the monitoring points is set according to the characteristics of the water-rich sandy gravel strata, typically ranging from 2 to 5 meters.

[0087] S203. Based on geological survey data and stratigraphic monitoring data, determine stratigraphic characteristic data.

[0088] Geological survey data and stratigraphic monitoring data serve as common inputs, and through data fusion processing, the final stratigraphic characteristic data used for modeling are determined.

[0089] In some embodiments, the specific process of data fusion processing is described, including:

[0090] Data preprocessing and spatiotemporal alignment: The intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata first standardizes all input data. The coordinates of all geological survey points (boreholes) and monitoring points (sensors) are unified into the construction coordinate system. For dynamically changing strata monitoring data, a time average is performed over a fixed time window (e.g., 24 hours before construction begins) to obtain a monitoring value representing the "initial steady state," thus eliminating short-term fluctuations.

[0091] Constructing a basic trend field: Using high-precision geological survey data (such as particle size distribution, seepage parameters, internal friction angle, and cohesion obtained from borehole sampling), a smooth "basic trend field" covering the entire shield tunnel grouting area is constructed through Kriging interpolation or trend surface analysis. This "basic trend field" represents the macroscopic spatial distribution law of parameters in highly permeable, heterogeneous strata. For example, based on borehole data interpolation, a three-dimensional density field is obtained in which the soil density gradually increases with depth.

[0092] The system generates a residual field and performs local corrections: It compares the predicted values ​​of the baseline trend field at each monitoring point with the average measured values ​​at those points within the given time window, calculating the "residual" (i.e., residual = measured value - predicted value) for each monitoring point. This residual represents local geological anomalies (groundwater channels, local pebble voids) or model interpolation errors. Then, using these residual values, a spatial interpolation algorithm (such as inverse distance weighting) is applied again to generate a "residual field."

[0093] Final Data Field Generation: The intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata spatially superimposes the "basic trend field" and the "residual field" point-by-point (i.e., final value = trend field value + residual field value). In this way, a stratigraphic characteristic data field reflecting complex hydrogeological features is ultimately generated. This preserves the macroscopic trends revealed by geological survey data while precisely correcting for local areas using measured values ​​from monitoring data, achieving complementary advantages from the two data sources. For example, if the pressure monitoring value in a certain area is significantly higher than the prediction of the basic trend field, the soil density and stress values ​​in that area will be correspondingly increased after superimposing the residual field, more realistically reflecting the existence of locally dense, large-particle-size sandy and gravelly layers or localized quicksand.

[0094] S204. Based on geological characteristic data, a numerical simulation model of grouting diffusion in water-rich sandy gravel strata is constructed. The numerical simulation model of grouting diffusion includes a three-dimensional finite element model of a shield tunnel and a two-dimensional discrete element model of seepage and erosion.

[0095] Establishment of a three-dimensional finite element model of a shield tunnel: Select the shield tunnel excavation area and use three-dimensional modeling software to establish a simulation model of water-rich sandy gravel strata that matches the on-site construction conditions. This model accurately reflects the stratum morphology and distribution, shield tunnel dimensions, and various construction facilities.

[0096] Establishment of a two-dimensional discrete element model for seepage-induced erosion: Based on geological survey data and the analysis results of a three-dimensional finite element model of a shield tunnel, key sections were selected to construct a particle flow discrete element model, in which the particle parameters matched the results of field tests. By simulating seepage-induced erosion on typical sections and studying its evolution process, data such as the porosity gradient, stress reduction variation, cross-sectional area of ​​the eroded zone, and average porosity can be obtained, and the volume of the eroded zone can be calculated.

[0097] The basic calculation principle of two-dimensional discrete element method for seepage and undercutting: The basic principle of the discrete element method is to establish a spring-damper model between particles within the contact range to calculate the contact force between the particles. This spring-damper model is based on Newton's second law and considers the combined effect of elastic force and damping force. Its basic equations are as follows:

[0098] F_c=k×Δx+c×v1+m×a

[0099] Where F_c is the contact force between particles, k is the spring stiffness, Δx is the relative displacement between particles, c is the damping coefficient, v1 is the relative velocity, m is the particle mass, and a is the particle acceleration.

[0100] Discrete Element Method (DEM) Formula for the Influence of Seepage: In water-rich sandy and gravelly formations, seepage exerts a drag force on particles. When a sufficiently large seepage space exists, it can cause the migration of fine particles, leading to changes in the formation structure. The formula for the drag force of seepage on particles is as follows:

[0101] F_d=0.5×C_d×ρ_f×A_p×|u_f-u_p|×(u_f-u_p)

[0102] Where F_d is the drag force, C_d is the drag coefficient, ρ_f is the fluid density, A_p is the particle frontal area, u_f is the seepage velocity, and u_p is the particle velocity.

[0103] S205. Simulation of grouting construction based on three-dimensional finite element model of shield tunnel to predict the grout diffusion range and stratum settlement trend after grouting construction in water-rich sandy gravel strata.

[0104] The grout diffusion range refers to the area enclosed by the boundary that the grout can reach underground. The ground settlement trend refers to the change in vertical displacement of the surface or tunnel structure over time under the influence of grouting.

[0105] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata calculates the stress redistribution and deformation of the stratum skeleton by simulating the increase in pore pressure and additional load caused by grout injection within the three-dimensional finite element model of the shield tunnel. The intelligent grouting system outputs a series of visualized cloud maps and curves, such as grout pressure contour maps and surface settlement trough curves, thus intuitively demonstrating the range of reinforcement that the grout can provide and the resulting stratum settlement under specific grouting parameters.

[0106] In one embodiment of this application, the core equations describing the slurry diffusion process and ground response in the three-dimensional finite element model of a shield tunnel are as follows:

[0107] Fluid control equations: Since water-rich sand and gravel are porous media, according to seepage theory, the slurry diffusion behavior is determined by permeability, pressure gradient, and time. The control equations for slurry diffusion behavior in the three-dimensional finite element model of a shield tunnel are as follows:

[0108] v2 = -(k / μ) × D P

[0109] Where v2 is the slurry flow velocity, k is the permeability coefficient, representing the formation's ability to permeate fluids, μ is the slurry viscosity, affecting the fluid diffusion rate, and D... P The pressure gradient represents the rate of change of formation pressure with space.

[0110] Diffusion radius formula: To accurately describe the grout diffusion range and ensure uniform grout coverage of the target area, the diffusion radius formula in the three-dimensional finite element model of a shield tunnel is defined as follows, based on Darcy's law of permeability and diffusion theory:

[0111] R = sqrt(2 × k × P³ × t / μ)

[0112] Where R is the diffusion radius, representing the effective range of grout filling, sqrt represents the square root operation, k is the permeability coefficient, P3 is the grouting pressure, t is the grouting time, and μ is the grout viscosity.

[0113] Non-Darcy Flow Correction Formula: When the grouting pressure is high or the porosity of the water-rich sandy gravel formation is large, the grout flow may deviate from the Darcy flow model, exhibiting nonlinear characteristics. In this case, it is necessary to correct the grout flow behavior. The formula for correcting the grout flow velocity is defined as follows:

[0114] v'2=k×D P / μ+β×(D P ) 2

[0115] Where v'2 is the corrected slurry flow velocity, D P β is the pressure gradient, and β is the non-Darcy flow correction factor.

[0116] Ground settlement prediction: To avoid ground instability caused by settlement, the ground settlement formula in the three-dimensional finite element model of a shield tunnel is defined as follows:

[0117] S=ΔP4 / E

[0118] Where S is the settlement, ΔP4 is the additional pressure change caused by grouting, and E is the elastic modulus of the formation.

[0119] S206. Based on the grout diffusion range and the subsidence trend of the strata, determine the initial grouting parameters for the water-rich sandy gravel strata.

[0120] The grout diffusion range and the formation settlement trend are the direct basis for determining the initial grouting parameters. The initial grouting parameters include the initial grouting pressure and the initial grouting flow rate.

[0121] Initial grouting pressure setting: To ensure that the grouting pressure meets the filling requirements of water-rich sandy gravel strata without compromising the stability of the strata structure, a safety factor is introduced based on simulation optimization. The formula for setting the initial grouting pressure is as follows:

[0122] P0 = P_sim × K_s

[0123] Where P0 is the initial grouting pressure, P_sim is the grouting pressure predicted by simulation, and K_s is the safety factor, which is usually taken as 1.1 to 1.3.

[0124] Grouting flow rate setting: To ensure that the grout injection rate meets the diffusion requirements of water-rich sandy gravel formations and to avoid formation disturbance caused by excessively rapid grouting, the grouting flow rate setting formula is as follows, based on the fluid continuity equation:

[0125] Q=A_hole×v2

[0126] Where Q is the slurry flow rate, A_hole is the cross-sectional area of ​​the grouting hole, and v2 is the slurry flow velocity.

[0127] S207. Using a two-dimensional discrete element model of seepage erosion, determine the stress reduction and porosity gradient of water-rich sandy gravel strata under the action of groundwater seepage erosion.

[0128] Among them, the seepage and erosion process of key sections of water-rich sandy gravel strata is simulated at a microscale using a two-dimensional discrete element model of seepage and erosion. For details, please refer to step S104, which will not be elaborated here.

[0129] S208. Based on the stress reduction change value and the porosity change gradient, the water-rich sandy gravel strata are divided into multiple risk zones with different risk levels.

[0130] Referring to step S105, based on the seepage process evolution analysis, points with significant changes in stress reduction and porosity gradient are preferentially designated as special control points. Based on the particle loss priority and porosity evolution characteristics of the seepage-induced erosion influence zone in the discrete element simulation, the risk level classification is further defined using the soil state changes obtained from the simulation as follows:

[0131] Low risk: Stress reduction change Δσ ≤ 15, porosity change gradient D n ≤1.5;

[0132] Medium risk: Stress reduction change value 15 < Δσ ≤ 30, porosity change gradient 1.5 < D n ≤3.0;

[0133] High risk: Stress reduction change Δσ > 30, porosity change gradient D n >3.0;

[0134] Where Δσ is in kilopascals (kPa), D n The unit is a percentage per meter (% / m).

[0135] Low-risk latent erosion mechanisms are described as localized fine particle migration and skeletal stability; medium-risk latent erosion mechanisms are described as the loss of medium-sized particles and enhanced pore connectivity; high-risk latent erosion mechanisms are described as skeletal particle instability and latent erosion channel formation.

[0136] In some embodiments, the division of multiple risk zones with different risk levels can adopt a dynamic risk index zoning scheme based on "spatiotemporal weight of tunneling disturbance" and "hydraulic-structural coupling deterioration", specifically including the following steps:

[0137] (1) Construct and update the spatiotemporal weight field of tunneling disturbance in real time.

[0138] First, a spatiotemporal weight field for tunneling disturbances is constructed to reflect the impact of dynamic disturbances during tunneling.

[0139] The meaning of the spatiotemporal weight field of tunneling disturbance is a four-dimensional weight matrix defined within the entire three-dimensional finite element model of the shield tunnel, with its value dynamically changing over time. Its scope covers all strata areas that may be affected by the squeezing, shearing, and vibration of the shield tunneling. Its significance lies in quantifying the urgency and importance of the risks posed by any point in the strata at the present and future moments due to its relative position to the tunnel boring machine. This allows risk assessment to focus on key areas during construction, especially sections with loose structures that are highly susceptible to groundwater flow induced by disturbance.

[0140] In the embodiments of this application, the specific method for constructing and updating the spatiotemporal weight field of the tunneling perturbation is as follows:

[0141] Establish a dynamic coordinate system: with the center point of the shield tunnel as the origin (0, 0, 0) and the tunneling direction of the shield machine as the positive X-axis, establish a local dynamic coordinate system that moves with the center point of the shield tunnel.

[0142] Define a three-dimensional spatial weighting function: Based on this dynamic coordinate system, a three-dimensional spatial weighting function is established to calculate the weight value of any point (x, y, z) in the three-dimensional finite element model of the shield tunnel. This function is preferably a piecewise function or a Gaussian decay function to simulate the distribution of disturbance effects. A typical example of a piecewise function is as follows:

[0143] In the X-axis direction (tunneling direction), the area in front of the tunnel face from 0.5 to 1.5 times the tunnel diameter is defined as the core influence zone, with a weight of 1.0; the area behind the shield tail corresponding to the width of 0 to 3 ring segments is defined as the secondary influence zone, with a weight of 0.8.

[0144] On the YZ plane (tunnel radial direction), the area within 0.5 times the tunnel diameter outside the tunnel outline is defined as the strong influence zone, with its weight value multiplied by an attenuation coefficient of 0.9; the area within 0.5 to 2.0 times the tunnel diameter is defined as the general influence zone, with its weight value multiplied by an attenuation coefficient of 0.5.

[0145] The weight values ​​for other regions are set to lower values, such as 0.1.

[0146] Real-time updated weight field: The intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata acquires the position and attitude data (e.g., mileage, elevation, azimuth) of the tunnel boring machine in real time via an interface. Based on this data, the weight values ​​of all elements in the three-dimensional finite element model of the entire shield tunnel are recalculated at a preset time step for synchronous grouting (e.g., every ring of tunnel segments excavated or every 10 minutes), thus forming a four-dimensional weight field that dynamically evolves with the construction progress. This closely links static geological risks with dynamic construction behavior, making risk assessment forward-looking and timely. It can automatically focus on key areas such as the tunnel face or shield tail gap, which are extremely sensitive to stratum disturbance, improving the pertinence and predictability of risk identification.

[0147] (2) Calculate the hydraulic-structure coupling degradation index.

[0148] The hydraulic-structural coupling degradation index is a dimensionless parameter used to quantify the ratio of the internal shear stress level to the shear strength of a soil element under the combined action of the hydraulic gradient and effective stress generated by the current high groundwater head pressure. Its range is typically from 0 to above 1; the closer the value is to or exceeds 1.0, the closer the soil element is to or has reached a state of seepage instability or shear failure. Its significance lies in the fact that the hydraulic-structural coupling degradation index, based on the fundamental failure criteria of soil mechanics, directly assesses the intrinsic stability margin of a soil element, avoiding the subjectivity of empirically weighted combinations of multiple indirect indicators.

[0149] In the embodiments of this application, the specific method for calculating the hydraulic-structural coupling degradation index is as follows:

[0150] Data extraction: From the three-dimensional finite element model of the shield tunnel containing the calculation results of the steady seepage field, the calculation results of each stratum element are extracted, including the effective normal stress (σ'_x, σ'_y, σ'_z) and shear stress (τ_xy, τ_yz, τ_zx) in three directions, as well as the water head or pore water pressure, and the hydraulic gradient i is calculated from it.

[0151] Shear strength calculation: The shear strength limit τ_f of the element is calculated according to the Mohr-Coulomb strength criterion. Specifically, the effective normal stress σ'_n on any shear surface within the element is first calculated using the stress tensor. This σ'_n is the effective stress considering the influence of pore water pressure. Then, the influence of seepage force j is considered, which further changes the effective normal stress on the shear surface. The corrected effective normal stress is σ'_n_correcte = σ'_n - j × sin(θ) (where θ is the angle between the seepage direction and the normal direction of the shear surface). Finally, the shear strength is τ_f = c' + σ'_n_corrected × tan(φ'), where c' and φ' are the effective cohesion and effective internal friction angle of the formation.

[0152] Deterioration index calculation: The maximum shear stress τ_max currently experienced by the element is calculated using the stress tensor. Finally, the hydraulic-structural coupling deterioration index I_HC is defined as: I_HC = τ_max / τ_f.

[0153] The hydraulic-structural coupling degradation index has a clear physical meaning and is directly related to the ultimate equilibrium state of the soil. It can accurately quantify the degree of soil structural weakening caused by groundwater seepage and excavation unloading. Its assessment results are more reliable and sensitive than indirect indicators based on deformation or stress changes.

[0154] (3) Construct and calculate the dynamic risk index.

[0155] Then, the spatiotemporal weight field of tunneling disturbance is multiplied by the hydraulic-structure coupling deterioration index to construct and calculate the dynamic risk index.

[0156] The dynamic risk index is a comprehensive risk measure that reflects the inherent structural weakening and permeability instability tendency of a geological unit, as well as its importance in the construction process, and varies with time and space. It is a dimensionless value greater than or equal to zero. Its significance lies in its ability to highlight geological units that not only have poor inherent stability but are also in or about to enter areas of strong construction disturbance; these units are the direct sources of engineering risks.

[0157] In the embodiments of this application, the specific method for constructing and calculating the Dynamic Risk Index (DRI) is as follows: for each element in the three-dimensional finite element model of the shield tunnel, its hydraulic-structural coupling deterioration index I_HC(x, y, z, t) at time t is multiplied by its spatiotemporal weight value W_TD(x, y, z, t) of the tunneling disturbance in the same time and space, that is:

[0158] DRI(x,y,z,t)=I_HC(x,y,z,t)×W_TD(x,y,z,t).

[0159] The calculation results can generate a series of three-dimensional dynamic risk cloud maps that evolve over time.

[0160] (4) Adaptive risk zoning based on dynamic risk index.

[0161] Finally, based on the calculation results of the dynamic risk index, adaptive risk zoning is carried out for water-rich sandy gravel strata with strong permeability and structural instability.

[0162] The meaning of the evolution of the dynamic risk cloud map is a series of three-dimensional risk index distribution maps that are continuous or discrete in time, which intuitively show the process of the position migration, range expansion or contraction of high-risk areas as the shield tunneling progresses. The meaning of the dynamic threshold is one or more critical values for determining the risk level, which can be adjusted according to the engineering risk control requirements, formation condition changes or expert experience, rather than being fixed.

[0163] In the embodiments of the present application, the specific method of adaptive risk zoning is as follows:

[0164] Set two dynamic thresholds, for example, the high-risk threshold \(T_H = 0.7\) and the medium-risk threshold \(T_M = 0.4\). Mark all the formation units that satisfy \(DRI\geq T_H\) and the set of their adjacent buffer units as the "high-risk area"; mark the set of units that satisfy \(T_M\leq DRI < T_H\) as the "medium-risk area"; and the rest are the "low-risk area". It can be seen that by using this dynamic risk index, it is possible to achieve a forward-looking and accompanying dynamic assessment and zoning of the shield construction lag settlement risk, improving the accuracy and timeliness of risk identification.

[0165] S209. Determine the optimization formula for the grouting path of the grouting construction treatment. The optimization formula includes the total path length between grouting points, the distance between adjacent grouting points, and the correlation relationship between the risk weights of grouting points, and the risk weight of the grouting point is determined based on the risk level of the corresponding risk area.

[0166] Among them, the grouting path refers to the number, position and grouting execution sequence of multiple grouting holes. The optimization formula refers to the mathematical expression for evaluating the quality of a grouting path. The risk weight of the grouting point is a dimensionless value, which is used to represent the grouting risk and safety level of the area where the grouting point is located, and the grouting points in the high-risk area are assigned high weights.

[0167] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sand and gravel strata first maps the divided risk levels (such as high, medium, low) to specific weight values (such as 3, 2, 1), and then constructs a graph with all grouting holes as nodes, and the connection cost between nodes is defined by the optimization formula.

[0168] In an embodiment of the present application, the A* algorithm is used to plan the optimal grouting path to avoid treating high-risk areas, while reducing unnecessary grouting points and improving the grouting efficiency. The optimization formula for its grouting path is as follows:

[0169] \(Lg = argmin\sum d\) i，i+1 \(\times W\) i

[0170] Among them, \(Lg\) is the target grouting path, \(i\) is the \(i\)-th grouting point, \(d\) i，i+1W represents the distance between adjacent grouting points. i Let be the grouting point risk weight for the i-th grouting point.

[0171] In some embodiments, to address the randomness of grout diffusion in water-rich sandy gravel strata and the complexity of construction constraints, a Pareto optimal grouting path set generation scheme based on a multi-objective genetic algorithm can be adopted. The multi-objective genetic algorithm is a global optimization search algorithm that simulates biological evolution. Its core lies in its ability to simultaneously handle multiple conflicting or unrelated optimization objectives (such as the contradiction between maximizing grouting reinforcement effect and minimizing construction cost), rather than weighting and aggregating them into a single objective. The Pareto optimal grouting path set refers to a set consisting of multiple different grouting paths (i.e., a combination sequence of grouting hole opening order, grouting timing, and grouting duration). No other grouting path in this set is superior to any other grouting path in all optimization objectives. This means that each grouting path in the set is a unique "optimal solution" without an absolutely better alternative, making different trade-offs between different optimization objectives. The scheme specifically includes the following steps:

[0172] (1) Define multiple optimization objective functions.

[0173] Define at least three conflicting optimization objective functions:

[0174] Objective Function 1: Minimize Total Weighted Risk f1: min f1 = Σ(d_i / w_i). This objective function prioritizes high-risk points that may lead to structural instability. The core of this objective function is to quantify the "opportunity cost" of handling the disaster risk in water-rich sandy gravel strata. "d_i" refers to the distance between adjacent grouting points in the i-th segment of the grouting path, representing the movement cost. "w_i" refers to the grouting point risk weight at the endpoint of the i-th segment. The physical meaning of d_i / w_i is "the distance cost incurred to reach a grouting point with a unit risk." By minimizing their sum, the algorithm prioritizes planning a grouting path that first traverses and handles the high-risk areas most prone to geological disasters, even if this grouting path may not be the shortest in total length.

[0175] Objective Function 2: Minimize total construction time f2: min f2 = Σ(t_grout_i + t_move_i). Where t_grout_i is the estimated grouting time at point i, and t_move_i is the time to move to that point. This objective function aims for maximum efficiency. This objective function is directly related to the project schedule. "t_grout_i" is the estimated grouting time at the i-th grouting point, which can be obtained by dividing the estimated grouting volume by the estimated average grouting flow rate. "t_move_i" is the time required for the grouting equipment to move from the previous point to the i-th point, which can be calculated based on d_i and the average moving speed of the equipment. This objective function makes the algorithm tend to select grouting paths that are short in time for the grouting points themselves and allow for quick movement between points, avoiding tunnel boring machine downtime due to delayed grouting operations.

[0176] Objective Function 3: Minimize the total travel distance f3: min f3 = Σ(d_i). This objective function aims to minimize equipment movement costs and energy consumption. It focuses on resource consumption. Minimizing the total travel distance directly corresponds to minimizing equipment energy consumption, mechanical wear, and operator workload.

[0177] It can be seen that these three objectives are often in conflict. For example, a grouting path that prioritizes high-risk points (f1 is optimal) may need to shuttle back and forth on the construction site, resulting in a longer total distance and time (f2 and f3 are not optimal).

[0178] (2) Solve the problem using a multi-objective genetic algorithm.

[0179] A multi-objective genetic algorithm is used to solve the problem.

[0180] Population initialization: Randomly generate a set (e.g., 100) of grouting paths as the initial population.

[0181] Iterative Evolution: The algorithm enters an iterative loop, performing the following operations in each generation: a. Fitness Assessment: For each grouting path in the population, the three objective functions mentioned above are used to reflect the construction constraints of water-rich sandy gravel strata, and the corresponding three objective values ​​are calculated. b. Non-dominated Sort and Crowding Calculation: The algorithm sorts all grouting paths according to the "Pareto dominance" relationship, assigning them to different "non-dominated fronts" (Pareto Fronts). For grouting paths within the same front, their "crowding degree" is calculated to maintain solution diversity. c. Selection, Crossover, and Mutation: Using methods such as "binary tournament," grouting paths with high non-dominated levels and high crowding degrees are preferentially selected as parents. Through crossover (e.g., sequential crossover) and mutation (e.g., reverse mutation), new offspring populations that are better adapted to the current geological and construction conditions are generated.

[0182] Here, "population" refers to the set of all candidate solutions (i.e., grouting paths) held by the algorithm at any given time. A "grouting path" is a permutation sequence containing all points to be grouted, with each point appearing only once. "Fitness evaluation" involves calculating the three objective function values ​​f1, f2, and f3 for each grouting path. "Pareto dominance" is a core concept in multi-objective optimization: if grouting path A is not inferior to grouting path B on all objectives, and is strictly superior to grouting path B on at least one objective, then "grouting path A dominates grouting path B." "Non-dominated sorting" is the process of stratifying all grouting paths in the population. Grouting paths not dominated by any other grouting path form the first layer (i.e., the first non-dominated front), and from the remaining grouting paths, those not dominated form the second layer, and so on.

[0183] (3) Output the Pareto optimal grouting path set.

[0184] After a set number of iterations, the algorithm converges. The intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata outputs all grouting paths in the final population that are at the first non-dominant front. This set of grouting paths is called the Pareto optimal grouting path set. The criteria for algorithm convergence can be reaching the preset maximum number of iterations, or the Pareto optimal front no longer showing significant improvement after several consecutive generations. Relevant technical personnel can select the most suitable grouting path as the final target grouting path based on the current construction priorities (e.g., when the construction period is tight or the strata are relatively stable, select the grouting path with the smallest f2 value; when safety is paramount, select the grouting path with the smallest f1 value; or select a relatively balanced "inflection point" solution that balances all aspects, minimizing shield equipment wear and worker labor intensity while ensuring safety).

[0185] As can be seen, the embodiments of this application transform the grouting path planning problem of shield tunneling into a multi-objective collaborative optimization, and introduce Pareto optimality theory to generate a set of grouting path schemes that achieve optimal trade-offs in multiple dimensions such as risk, time and cost, so that the decision can adapt to the complex and ever-changing engineering needs of water-rich sandy gravel strata.

[0186] S210. Based on the optimization formula, determine the target grouting path for grouting construction.

[0187] The target grouting path is the final execution order calculated by the optimization algorithm in step S209.

[0188] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata runs a built-in path planning solver (such as the A* algorithm), using the optimization formula determined in step S209 as the objective function, searching through all possible grouting sequences, and finally outputting a grouting path sequence with the minimum total length of grouting paths between grouting points. This grouting path sequence is the target grouting path.

[0189] S211. For different risk areas, determine the loss coefficient associated with the risk level of the risk area.

[0190] The loss factor is a correction factor used to estimate the volume loss of grout during the grouting process due to bleeding, dilution by groundwater, or loss to non-target areas. This loss factor is related to the risk level, meaning that areas with higher risk typically have larger formation porosity and more complex hydraulic connections, and the loss of grout may be greater. The value range of the loss factor is, for example, [0.2, 0.4].

[0191] S212. Using a two-dimensional discrete element model of seepage-induced erosion, determine at least one of the following: volume of the erosion zone, average porosity, and cross-sectional area of ​​the erosion zone in water-rich sandy gravel strata under the action of groundwater seepage-induced erosion.

[0192] Among them, the volume of the erosion zone, the average porosity, and the cross-sectional area of ​​the erosion zone are obtained based on the two-dimensional discrete element model of seepage erosion in step S204, and are several key geometric and physical parameters used to quantitatively describe the degree of erosion damage.

[0193] Specifically, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata performs post-processing on the simulation results of the two-dimensional discrete element model of seepage and erosion. For example, a porosity threshold can be set, and regions in the two-dimensional discrete element model with porosity exceeding this threshold can be identified as "emergence zones". Then, by integrating the unit volumes of these regions, the total volume of the emergence zone is calculated; by calculating the ratio of the pore volume in this region to the total volume, the average porosity is obtained; and by projecting this region in a specific direction, the cross-sectional area of ​​the emergence zone is calculated.

[0194] S213. Determine the pore channel compensation coefficient that is positively correlated with the porosity change gradient.

[0195] The pore channel compensation coefficient is an adjustment coefficient used to calculate the amount of grout required to seal undercut channels. It is positively correlated with the porosity gradient, meaning that the more severe the undercut (the faster the pore growth), the faster the channel will grow, requiring a larger compensation coefficient to calculate the amount of sealing grout. The value range of the pore channel compensation coefficient is, for example, [0.05, 0.1].

[0196] S214. Based on at least one of the following: volume of the eroded zone, average porosity, and cross-sectional area of ​​the eroded zone, as well as the loss coefficient and pore channel compensation coefficient, determine the adjusted grouting parameters.

[0197] In this embodiment, more specific parameters (such as the volume of the eroded zone, the loss coefficient, and the pore channel compensation coefficient) are used to implement the supplementary grouting adjustment strategy, making the adjustment process of the grouting parameters more explicit and quantifiable.

[0198] Specifically, a dual-control model based on seepage-induced erosion and pore filling and erosion channel compensation is used to determine the adjusted grouting parameters. Taking the adjusted grouting flow rate as an example, the calculation formula for the adjusted grouting flow rate is as follows:

[0199] Q_adj=V_p×n_avg×(1+K_loss)+k_comp×A_p×D n

[0200] Where Q_adj is the adjusted grouting flow rate, V_p is the volume of the undercut zone, n_avg is the average porosity of the undercut zone, K_loss is the loss coefficient, k_comp is the pore channel compensation coefficient, A_p is the cross-sectional area of ​​the undercut zone, and D... n This represents the porosity gradient of the eroded zone.

[0201] S215. According to the adjusted grouting parameters, carry out grouting construction treatment on the corresponding risk areas.

[0202] In the embodiments of this application, the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata can perform grouting construction treatment on each risk area according to the target grouting path and the corresponding adjusted grouting parameters.

[0203] In some embodiments, intelligent devices can be used to dynamically optimize and finely control the grouting path, adjust the grouting pressure, flow rate, and diffusion path in real time, and achieve collaborative optimization among intelligent devices, as detailed below:

[0204] The intelligent equipment consists of: grouting pressure monitoring points to collect real-time data on grout diffusion pressure distribution; a 3D laser scanner to map the topography and diffusion status of the grouting area in real time; customized shield tunnel segments with pre-set grouting holes to accurately cover the grouting area; a precision-controlled grouting pump for independent control of grout pressure and flow rate at multiple points; and real-time coordination of each device by a computer with a built-in path planning algorithm and feedback control system.

[0205] Arrangement method: Grouting pressure monitoring points are arranged along the shield tunneling direction and linked with the grouting equipment.

[0206] Intelligent grouting implementation process: The pressure at the grouting points is dynamically adjusted based on real-time feedback from monitoring points; considering the heterogeneity of water-rich sandy gravel strata, the flow rate at each grouting point is independently adjusted by precision-controlled grouting pumps; a three-dimensional laser scanner is used to monitor the grout diffusion range in real time, provide real-time feedback, and update the grouting path plan.

[0207] Intelligent equipment collaborative optimization: Multi-point grouting equipment works synchronously, adopts a zoned grouting scheme, and each grouting area is independently controlled to ensure that the grouting diffusion range does not overlap or miss any areas; intelligent equipment is connected through a wireless network to achieve global optimization.

[0208] S216. Real-time monitoring of at least one of the following parameters during the grouting construction process: current grouting pressure, current grout flow rate, current grout diffusion range, and current formation stability.

[0209] The current grouting pressure and current grout flow rate are real-time values ​​directly measured by sensors installed at the grouting pump outlet or grouting orifice. The current grout diffusion range can be monitored through various indirect methods, such as temperature sensing (heat of hydration), electrical resistivity tomography (ERT), or three-dimensional laser scanning. Current formation stability parameters refer to indicators reflecting the safety of the formation during grouting, such as the settlement rate at key points and the rate of change of pore water pressure.

[0210] Specifically, during the grouting process, the data acquisition module of the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata continuously reads data from all relevant sensors at a high frequency (e.g., 10 to 30 times per second). These data streams constitute a real-time "health check report" of the construction process, which is the basis for the system's closed-loop feedback control and safety judgment, ensuring high-frequency real-time data updates.

[0211] In some embodiments, the grouting construction process employs a closed-loop control strategy: current grouting pressure, current grout flow rate, current grout diffusion range, and current formation stability parameters are fed back to the control center in real time to achieve dynamic parameter adjustment, ensuring that the grouting parameters meet planning requirements. Simultaneously, multi-grouting-hole collaborative correction is performed to avoid single-point correction affecting other grouting areas. For example, the correction formula for the current grouting pressure is as follows:

[0212] P5 = P6 + K_p × ΔP7

[0213] Wherein, P5 is the corrected grouting pressure, P6 is the current grouting pressure, K_p is the grouting pressure correction coefficient, which is usually taken as 1.1 to 1.3, and ΔP7 is the grouting pressure deviation value (i.e. the difference between the target grouting pressure and the current grouting pressure).

[0214] S217. If at least one of the following parameters—current grouting pressure, current grout flow rate, current grout diffusion range, and current formation stability parameter—is not within the corresponding preset allowable range, a gradual stop strategy shall be adopted to stop the grouting construction process.

[0215] The preset allowable range refers to the upper and lower safety thresholds set for each monitoring parameter. The gradual shutdown strategy is a smooth and slow reduction in grouting pressure and flow rate to avoid impacting the formation.

[0216] Specifically, the control module of the intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata continuously compares at least one of the following parameters—current grouting pressure, current grout flow rate, current grout diffusion range, and current stratum stability parameters—with the corresponding preset allowable ranges. If any parameter is found to be "out of bounds"—for example, if the current grouting pressure exceeds the stratum fracturing pressure, or if the surface settlement rate in the stratum stability parameters exceeds the warning value—a safety shutdown procedure is immediately triggered. This procedure sends a series of instructions to the grouting pump to gradually reduce the pressure and flow rate until it stops completely, while simultaneously issuing an audible and visual alarm to the operator. Furthermore, after grouting operations cease, the grouting holes can be immediately sealed to prevent grout backflow or stratum water seepage.

[0217] As can be seen, compared with related technologies, the main effects and advantages of the embodiments of this application are as follows:

[0218] 1. Through real-time monitoring and intelligent control system, the grouting pressure, flow rate and diffusion range can be dynamically adjusted to adapt to the high permeability and complexity of water-rich sandy gravel strata, ensuring uniform diffusion and filling of grout, and effectively improving grouting accuracy and construction quality.

[0219] 2. It can predict and judge the settlement of water-rich sand and gravel strata and changes in the stability of surrounding rock in a timely manner during grouting, and dynamically adjust grouting parameters during construction to avoid excessive disturbance of strata or reduction of bearing capacity, thereby reducing potential hazards in shield tunnel construction.

[0220] 3. Combining high-precision grouting equipment with an intelligent monitoring system, it provides real-time feedback on grout diffusion and stratum changes, keeping pace with the tunnel boring machine's construction progress. This facilitates rapid adjustments to the grouting strategy, improving efficiency and saving time. Furthermore, the grouting operation is simple, and the grouting path planning is intelligent. It can be flexibly adjusted according to complex environments such as water-rich sandy gravel strata, making it highly adaptable.

[0221] 4. By combining discrete element simulation analysis of particle flow, a particle contact mechanics model under the influence of seepage and erosion is constructed to accurately simulate the structural evolution process caused by groundwater seepage in water-rich sandy gravel strata, thereby effectively predicting the lag settlement of the strata. Based on the simulation results, the range and boundary conditions of the grouting control zone can be clearly defined, providing a quantitative basis for setting grouting parameters during construction. This can help avoid the risk of strata instability caused by lag settlement in advance, ensuring construction safety and strata stability.

[0222] 5. Through the precise control of intelligent equipment, slurry waste is reduced, construction costs are lowered, and the impact on the surrounding strata and environment is reduced, resulting in significant economic benefits and environmental value.

[0223] 6. It is applicable to tunnel construction in water-rich sandy gravel strata and other complex strata, and features high precision, high efficiency and high safety. It provides a new intelligent and efficient method for shield tunnel construction, and has significant engineering practice value and prospects for promotion.

[0224] In one embodiment, this application also provides an intelligent grouting system for shield tunnel construction in water-rich sandy gravel strata. The intelligent grouting system for shield tunnel construction in water-rich sandy gravel strata is used to perform the intelligent grouting method for shield tunnel construction in water-rich sandy gravel strata as described in any possible implementation.

[0225] The intelligent grouting device for shield tunnel construction in water-rich sandy and gravelly strata, as described in this application embodiment, is presented below from a hardware processing perspective. Please refer to [link to relevant documentation]. Figure 4 This is a schematic diagram of at least a portion of the physical structure of the intelligent grouting device for shield tunnel construction in water-rich sandy and gravelly strata, as described in this application embodiment.

[0226] It should be noted that, Figure 4 The structure of the intelligent grouting device for shield tunnel construction in water-rich sandy and gravel strata shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application. The intelligent grouting device for shield tunnel construction in water-rich sandy and gravel strata can be integrated with an intelligent grouting system for shield tunnel construction in water-rich sandy and gravel strata, or integrated into an intelligent grouting system for shield tunnel construction in water-rich sandy and gravel strata.

[0227] like Figure 4As shown, the intelligent grouting device for shield tunnel construction in water-rich sandy and gravelly strata includes a CPU 401, which can perform various appropriate actions and processes according to a program stored in ROM 402 or a program loaded from storage section 408 into RAM 403, such as executing the methods described in the above embodiments. RAM 403 also stores various programs and data required for device operation. The CPU 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O interface 405 is also connected to bus 404.

[0228] The following components are connected to I / O interface 405: input section 406 including audio input devices, push-button switches, etc.; output section 407 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 408 including a hard disk, etc.; and communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0229] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by CPU 401, it performs the various functions defined in this application.

[0230] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0231] Specifically, the intelligent grouting device for shield tunnel construction in water-rich sandy gravel strata in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the intelligent grouting method for shield tunnel construction in water-rich sandy gravel strata provided in the above embodiment.

[0232] In another aspect, this application also provides a computer-readable storage medium, which may be included in the intelligent grouting device for shield tunnel construction in water-rich sandy gravel strata described in the above embodiments; or it may exist independently and not assembled into the intelligent grouting device for shield tunnel construction in water-rich sandy gravel strata. The storage medium carries one or more computer programs, which, when executed by a processor of the intelligent grouting device for shield tunnel construction in water-rich sandy gravel strata, cause the intelligent grouting device to implement the intelligent grouting method for shield tunnel construction in water-rich sandy gravel strata provided in the above embodiments.

[0233] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A smart grouting method for shield tunnel construction in water-rich sandy and gravelly strata, characterized in that, The intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata includes: Obtain stratigraphic characteristic data of the water-rich sandy gravel strata to be grouted; Based on the geological characteristic data, a numerical simulation model of grouting diffusion in water-rich sandy gravel strata is constructed. The numerical simulation model of grouting diffusion includes a three-dimensional finite element model of a shield tunnel and a two-dimensional discrete element model of seepage and erosion. The three-dimensional finite element model of the shield tunnel was used for simulation to determine the initial grouting parameters of the water-rich sandy gravel strata. Using the aforementioned two-dimensional discrete element model of seepage and erosion, the stress reduction and porosity gradient of water-rich sandy gravel strata under the action of groundwater seepage and erosion were determined. Based on the stress reduction change value and the porosity change gradient, the water-rich sandy gravel strata are divided into multiple risk zones with different risk levels. For different risk areas, the corresponding initial grouting parameters are adjusted according to different supplementary grouting adjustment strategies to obtain the adjusted grouting parameters; According to the adjusted grouting parameters, grouting construction treatment is carried out on the corresponding risk areas; The construction steps of the three-dimensional finite element model of the shield tunnel include: based on the selected shield tunnel excavation area, using three-dimensional modeling software to establish a simulation model of water-rich sandy gravel strata to characterize the morphology and distribution of the strata, the size of the shield tunnel and various construction facilities, and using it as the three-dimensional finite element model of the shield tunnel. The construction steps of the two-dimensional discrete element model for seepage and erosion include: determining the target profile based on geological survey data of water-rich sandy gravel strata and analysis results of the three-dimensional finite element model of the shield tunnel, in order to construct a corresponding particle flow discrete element model, which serves as the two-dimensional discrete element model for seepage and erosion; the two-dimensional discrete element model for seepage and erosion is used to simulate seepage and erosion on the cross-section, thereby calculating the porosity change gradient, stress reduction change value, cross-sectional area of ​​the erosion zone, average porosity, and volume of the erosion zone; The calculation rules of the two-dimensional discrete element model for seepage and undercutting include: establishing a spring-damper model between particles within the contact range, and using it to calculate the contact force between particles; the spring-damper model is based on Newton's second law, and the equation is as follows: F_c=k×Δx+c×v1+m×a Where F_c is the contact force between particles, k is the spring stiffness, Δx is the relative displacement between particles, c is the damping coefficient, v1 is the relative velocity, m is the particle mass, and a is the particle acceleration. The calculation rules also include a discrete element flow influence formula, which is as follows: F_d=0.5×C_d×ρ_f×A_p×|u_f-u_p|×(u_f-u_p) Where F_d is the drag force, C_d is the drag coefficient, ρ_f is the fluid density, A_p is the particle frontal area, u_f is the seepage velocity, and u_p is the particle velocity.

2. The intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata as described in claim 1, characterized in that, The acquisition of stratigraphic characteristic data of the water-rich sandy gravel strata to be grouted includes: Advanced geological surveys were conducted on water-rich sandy and gravelly strata to obtain the aforementioned geological survey data; By using monitoring points set up in water-rich sandy gravel strata, stratum monitoring data is obtained, including pressure monitoring points and porosity monitoring points; Based on the geological survey data and the stratigraphic monitoring data, the stratigraphic characteristic data are determined.

3. The intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata as described in claim 1, characterized in that, The simulation using the three-dimensional finite element model of the shield tunnel to determine the initial grouting parameters for the water-rich sandy and gravelly strata includes: The simulation of grouting construction is carried out based on the three-dimensional finite element model of the shield tunnel to predict the grout diffusion range and stratum settlement trend after grouting construction in water-rich sandy gravel strata. Based on the grout diffusion range and the formation subsidence trend, the initial grouting parameters for the water-rich sandy gravel formation were determined.

4. The intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata as described in claim 1, characterized in that, The method involves adjusting the initial grouting parameters according to different supplementary grouting adjustment strategies for different risk areas to obtain adjusted grouting parameters, including: For different risk areas, determine the loss coefficient associated with the risk level of the risk area; Using the aforementioned two-dimensional discrete element model of seepage erosion, at least one of the following can be determined for water-rich sandy gravel strata under the action of groundwater seepage erosion: volume of the erosion zone, average porosity, and cross-sectional area of ​​the erosion zone. The adjusted grouting parameters are determined based on at least one of the following: the volume of the eroded zone, the average porosity, the cross-sectional area of ​​the eroded zone, and the loss coefficient.

5. The intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata as described in claim 4, characterized in that, The determination of the adjusted grouting parameters based on at least one of the erosion zone volume, average porosity, and erosion zone cross-sectional area, and the loss coefficient, includes: Determine the pore channel compensation coefficient that is positively correlated with the porosity change gradient; The adjusted grouting parameters are determined based on at least one of the volume of the eroded zone, the average porosity, and the cross-sectional area of ​​the eroded zone, as well as the loss coefficient and the pore channel compensation coefficient.

6. The intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata as described in claim 1, characterized in that, After dividing the water-rich sandy gravel strata into multiple risk zones with different risk levels based on the stress reduction change value and the porosity change gradient, the method further includes: An optimization formula for determining the grouting path in grouting construction is provided. The optimization formula includes the total path length between grouting points, the distance between adjacent grouting points, and the correlation between the risk weights of grouting points. The risk weights of grouting points are determined based on the risk level of the corresponding risk area. Based on the optimization formula, the target grouting path for grouting construction is determined.

7. The intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata as described in claim 1, characterized in that, After performing grouting treatment on the corresponding risk areas according to the adjusted grouting parameters, the process further includes: Real-time monitoring of at least one of the following parameters during the grouting construction process: current grouting pressure, current grout flow rate, current grout diffusion range, and current formation stability parameter; If at least one of the current grouting pressure, current grout flow rate, current grout diffusion range, and current formation stability parameters is not within the corresponding preset allowable range, a gradual stop strategy will be adopted to stop the grouting construction process.

8. An intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata, characterized in that, The intelligent grouting system for shield tunnel construction in water-rich sandy and gravelly strata is used to perform the intelligent grouting method for shield tunnel construction in water-rich sandy and gravelly strata as described in any one of claims 1 to 7.

9. An intelligent grouting device for shield tunnel construction in water-rich sandy and gravelly strata, characterized in that, The intelligent grouting device for shield tunnel construction in water-rich sandy and gravelly strata includes: One or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the intelligent grouting device for shield tunnel construction in water-rich sandy gravel strata to perform the intelligent grouting method for shield tunnel construction in water-rich sandy gravel strata as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes instructions that, when executed on an intelligent grouting system for shield tunnel construction in water-rich sandy gravel strata, cause the intelligent grouting system for shield tunnel construction in water-rich sandy gravel strata to perform the intelligent grouting method for shield tunnel construction in water-rich sandy gravel strata as described in any one of claims 1 to 7.

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