Rock mass grouting numerical simulation method, device, equipment, medium and program product

Abstract

The present application relates to the technical field of numerical simulation, and discloses a rock mass grouting numerical simulation method, device, equipment, medium and program product, the method comprises the following steps: inputting grouting pressure data, slurry density data, grouting rate, slurry dynamic viscosity and slurry type data of a to-be-predicted grouting engineering at multiple moments in the construction process into a target grouting numerical model constructed in advance for numerical simulation, obtaining the corresponding instantaneous injection rate of the to-be-predicted grouting engineering at different moments in the construction process, the target grouting numerical model is provided with a slurry diffusion model corresponding to different types of slurry, the water-cement ratio of different types of slurry is different, the target grouting numerical model is used for determining the target slurry diffusion model at the corresponding moment according to the slurry type data at each moment, and the instantaneous injection rate at the corresponding moment is calculated according to the grouting pressure data, the slurry density data, the grouting rate, the slurry dynamic viscosity and the target slurry diffusion model at each moment.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology, specifically to numerical simulation methods, apparatus, equipment, media, and program products for rock mass grouting. Background Technology

[0002] In the field of geotechnical grouting construction, for the same construction section of the same grouting hole, the engineering process usually involves actively and dynamically adjusting the grout mix parameters, including water-cement ratio, grout density, and additive dosage, based on the real-time grouting effect, dynamic changes in grouting pressure, and on-site geological conditions of the rock mass. The adjustment of the grout mix directly changes its rheological properties, such as dynamic viscosity and yield strength, in order to adapt to the construction needs of different stages of grouting and achieve the goal of optimizing the overall grouting reinforcement and seepage prevention effect.

[0003] In relevant grouting numerical simulation techniques, a pre-constructed grout diffusion model is typically embedded into the numerical simulation model. This diffusion model is used to calculate the unit width flow rate of the fracture, and then the instantaneous injection rate during the grouting process is solved based on the unit width flow rate. However, this simulation scheme relies solely on a fixed form of grout diffusion model to calculate the flow rate and injection rate, failing to consider the actual engineering reality of dynamic changes in grout mix ratios and real-time alterations in rheological properties at different times within the same grouting section during construction. It cannot reflect the impact of differences in grout mix ratios at different construction stages on the grout diffusion law and grouting effect, causing the numerical simulation process to deviate from the actual grouting conditions. Ultimately, this results in low accuracy of the simulation calculations, making it difficult to accurately reflect the actual grouting evolution law on site. Summary of the Invention

[0004] This invention provides a numerical simulation method, apparatus, equipment, medium, and program product for rock mass grouting. It addresses the problem that related technologies fail to consider the dynamic changes in grout mix ratio and real-time changes in rheological properties at different times during the actual construction process. This results in the inability to reflect the impact of differences in grout mix ratio at different construction stages on the grout diffusion law and grouting effect, causing the numerical simulation process to deviate from the actual grouting conditions. Ultimately, this leads to low accuracy in the simulation calculation results and makes it difficult to accurately reflect the actual grouting evolution law on site.

[0005] In a first aspect, the present invention provides a numerical simulation method for rock mass grouting. The method includes: acquiring grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and grout type data at multiple moments during the construction process of the grouting project to be predicted; inputting the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and grout type data at multiple moments into a pre-constructed target grouting numerical model for numerical simulation to obtain the instantaneous injection rate corresponding to different moments during the construction process of the grouting project to be predicted; the target grouting numerical model has preset grout diffusion models corresponding to different types of grout, with different water-cement ratios for different types of grout; the grout diffusion model is used to characterize the correlation between grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and unit width flow rate; the target grouting numerical model is used to determine the target grout diffusion model for the corresponding moment based on the grout type data at each moment; calculating the unit width flow rate for the corresponding moment based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and target grout diffusion model at each moment; and determining the instantaneous injection rate for the corresponding moment based on the unit width flow rate at each moment.

[0006] The rock mass grouting numerical simulation method provided by this invention obtains time-series data on grouting pressure, grout density, injection rate, grout dynamic viscosity, and grout type at multiple moments during the entire construction process of the grouting project to be predicted. Based on the target grouting numerical model with built-in dedicated grout diffusion models corresponding to different types of grout, the method can adaptively match and adapt the target grout diffusion model according to the actual grout type at each moment. This fully adapts to the differentiated characteristics of grout rheological properties under different grout types and water-cement ratios, and can accurately establish the intrinsic correlation between grouting pressure, grout density, injection rate, grout dynamic viscosity, and fracture unit width flow rate under various working conditions. It fully takes into account the real-time changes in the rheological properties of grout caused by dynamic adjustments of grout mix ratio and water-cement ratio at different times in the same grouting section during actual grouting construction. It effectively makes up for the shortcomings of existing technologies that cannot reflect the impact of differences in grout mix ratio at different construction stages on grout diffusion law and grouting effect. It significantly reduces the deviation between the numerical simulation process and the actual grouting conditions on site, and significantly improves the simulation calculation accuracy of key indicators such as instantaneous injection rate. It can more realistically and accurately restore the evolution law of the entire rock mass grouting process, and also provides reliable and accurate numerical simulation support for subsequent grouting construction process optimization, rock mass seepage prevention and reinforcement effect prediction and engineering design decisions.

[0007] In one optional embodiment, the slurry diffusion model includes a first diffusion model for a first type of slurry and a second diffusion model for a second type of slurry. The first diffusion model is used to characterize the relationship between the unit width flow rate of the first type of slurry and the dynamic viscosity, density, grouting pressure, and injection rate of the slurry. The second diffusion model is used to characterize the relationship between the unit width flow rate of the second type of slurry and the dynamic viscosity, density, grouting pressure, injection rate, and yield strength of the slurry. The yield strength of the slurry is determined based on the density of the slurry.

[0008] The method provided in this optional implementation, by dividing the slurry into two types and configuring corresponding slurry diffusion models for each, can be specifically adapted to the seepage and diffusion patterns of slurries with different rheological properties. The first diffusion model is adapted to slurry conditions without yield strength characteristics, while the second diffusion model can also take into account the simulation needs of slurries with yield strength characteristics. At the same time, the slurry yield strength is determined by correlation with the slurry density, eliminating the need for separate detection and acquisition of yield strength parameters, simplifying the data acquisition process, and accurately establishing the intrinsic correlation between various influencing parameters and unit width flow rate. This overcomes the drawback of a single diffusion model being incompatible with the simulation of different types of slurries, effectively broadening the applicability of numerical simulation, and further improving the accuracy and engineering practicality of grouting unit width flow rate and instantaneous injection rate simulation calculations under different slurry conditions.

[0009] In one optional implementation, the target grouting numerical model further includes a first coupling model and a second coupling model. The first coupling model is used to correct the dynamic viscosity of the grout based on the grouting pressure data and the maximum design grouting pressure to obtain the coupled dynamic viscosity of the grout. The second coupling model is used to correct the yield strength of the grout based on the grout density data and the maximum design grout density to obtain the coupled yield strength of the grout. The unit width flow rate at each time point is calculated based on the grouting pressure data, grout density data, grouting rate, dynamic viscosity of the grout, and the target grout diffusion model. This includes: if at least one first time point exists among multiple time points, the target grouting numerical model inputs the grouting pressure data, dynamic viscosity of the grout, and the maximum design grouting pressure at each first time point into the first coupling model for calculation to obtain the coupled dynamic viscosity of the grout at the corresponding first time point. The grout type corresponding to the first time point is... The first grout type; based on the coupled grout dynamic viscosity, grouting pressure data, grout density data, grouting rate, and the first diffusion model at each first moment, the unit width flow rate at the corresponding first moment is calculated; if there is at least one second moment among multiple moments, the target grouting numerical model inputs the grouting pressure data and grout dynamic viscosity at each second moment into the second coupled model for calculation to obtain the coupled grout dynamic viscosity at the corresponding second moment, and the grout type corresponding to the second moment is the second grout type; the target grouting numerical model inputs the grout yield strength, grout density data, and grout design maximum density at each second moment into the second coupled model for calculation to obtain the coupled yield strength at the corresponding second moment; based on the coupled yield strength, coupled grout dynamic viscosity, grouting pressure data, grout density data, grouting rate, and the second diffusion model at each second moment, the unit width flow rate at the corresponding second moment is calculated.

[0010] The method provided in this optional implementation adds a first coupling model and a second coupling model to the target grouting numerical model. Based on the grouting pressure and the design maximum pressure, and the grout density and the design maximum density, the dynamic viscosity and yield strength of the grout are coupled and corrected respectively, so as to obtain coupled rheological parameters that are more in line with the actual working conditions on site. At the same time, the method performs differentiated coupling model calls and parameter calculations for the first grout type and the second grout type, matches the rheological characteristics and diffusion mechanisms of the two types of grout, and substitutes the corrected coupled dynamic viscosity and coupled yield strength of the grout into the diffusion model of the corresponding grout to solve for the unit width flow rate. This approach fully considers the coupled influence of dynamic changes in grouting pressure and grout density on the rheological properties of the grout, abandoning the traditional method of directly using the original rheological parameters for simulation calculation. It adapts to the complex construction scenarios of pressure fluctuations and dynamic adjustments of grout mix ratio during actual grouting, making up for the shortcomings of existing simulations that do not consider the coupling correction of rheological parameters. It further improves the simulation accuracy of unit width flow rate and instantaneous injection rate under different types of grout working conditions, and also effectively enhances the adaptability of the grouting numerical model to various field working conditions and the versatility of engineering applications.

[0011] In one optional implementation, the target grouting numerical model is constructed through the following steps: acquiring a pre-constructed three-dimensional random fracture grouting numerical model, grouting pressure data, grout density data, grouting rate, and yield strength measured at multiple moments during construction; constructing a first diffusion model for a first type of fluid, a second diffusion model for a second type of fluid, a first coupling model, a second coupling model, and a feedback adjustment model for grouting pressure. The feedback adjustment model is used to adjust the grouting pressure data based on the simulated instantaneous injection rate when the deviation between the measured grouting rate and the simulated instantaneous injection rate exceeds a preset threshold, thereby obtaining coupled grouting pressure data; embedding the first diffusion model, the second diffusion model, the first coupling model, the second coupling model, and the feedback adjustment model into the three-dimensional random fracture grouting numerical model to obtain an initial grouting numerical model; and iteratively optimizing the initial grouting numerical model based on the grouting pressure data, grout density data, grouting rate, yield strength, and grout dynamic viscosity measured at multiple moments during construction to obtain the target grouting numerical model.

[0012] The method provided in this optional implementation uses a three-dimensional random fracture grouting numerical model as the basic carrier. It integrates a diffusion model adapted to different grout types, a rheological parameter coupling model, and a grouting pressure feedback adjustment model to form an initial grouting numerical model. At the same time, with the help of the feedback adjustment model, when the deviation between the simulated instantaneous injection rate and the measured grouting rate exceeds a preset threshold, it adaptively adjusts the coupled grouting pressure data based on the simulation results to achieve dynamic correction of the working parameters within the model. Furthermore, it utilizes real engineering data such as grouting pressure, grout density, grouting rate, yield strength, and grout dynamic viscosity measured at multiple moments on the construction site. The data was used to iteratively optimize and calibrate the initial model, ensuring that the model's internal structure, correlations, and related parameters closely match the actual rock mass fracture distribution characteristics and grout rheological evolution laws. This effectively overcomes the shortcomings of traditional grouting numerical models, such as a single structure, lack of coupling correction and pressure feedback adjustment mechanisms, and lack of engineering measurement verification of model parameters. It significantly improves the fitting degree, stability, and adaptability of the target grouting numerical model, laying a solid foundation for the accurate simulation and prediction of instantaneous injection rate of rock mass grouting under different construction scenarios and grout types. It also further enhances the applicability and reliability of this numerical simulation method in actual geotechnical grouting engineering.

[0013] In one optional implementation, the three-dimensional random fracture grouting numerical model is constructed through the following steps: obtaining the grouting hole parameters, modeling range, and rock fracture orientation distribution parameters of the rock mass grouting project; sampling the rock fracture orientation distribution parameters using a preset sampling method to obtain a three-dimensional random fracture network; and importing the grouting hole parameters, modeling range, and three-dimensional random fracture network into a preset numerical simulation software for model construction to obtain the three-dimensional random fracture grouting numerical model.

[0014] The method provided in this optional implementation uses the actual grouting hole parameters, modeling range, and rock mass fracture orientation and distribution parameters as the modeling basis, ensuring that the modeling is consistent with the actual conditions of the project. By sampling the rock mass fracture orientation and distribution parameters through a preset sampling method, a three-dimensional random fracture network is generated. This can realistically restore the objective geological characteristics of the random development and disordered orientation distribution of natural rock mass fractures, avoiding the defects of traditional homogeneous simplified modeling that ignores the random spatial distribution characteristics of rock mass fractures. Then, the relevant parameters and fracture network are imported into numerical simulation software to complete integrated modeling. The modeling process is standardized and controllable. The constructed three-dimensional random fracture grouting numerical model can accurately depict the real structure of the rock mass and the spatial distribution of fractures. It provides a basic carrier that is consistent with the real geological conditions for the subsequent embedding and integration of various grout diffusion models, coupling models, and feedback adjustment models, as well as the numerical simulation calculation of the entire grouting process. This ensures the geological authenticity and computational reliability of subsequent grouting simulation analysis from the source.

[0015] In an optional implementation, the method further includes: calculating the cumulative injection volume of the grouting project to be predicted based on the instantaneous injection rate corresponding to different times during the construction process of the grouting project to be predicted.

[0016] The method provided by this optional implementation method, based on accurately obtaining the instantaneous injection rate at each moment of the grouting project to be predicted, further completes the calculation of the cumulative injection volume. It can fully restore the dynamic cumulative change law of grouting volume throughout the entire grouting construction process by relying on the time-series instantaneous injection rate data, breaking through the limitation of only obtaining the grouting rate at a single moment. It can not only accurately quantify the total amount of grouting materials consumed, which is convenient for material control and construction progress coordination during the construction process, but also intuitively evaluate the fullness of rock mass fissure filling and the effectiveness of seepage prevention and reinforcement construction through the cumulative injection volume. It enriches the output dimensions of grouting numerical simulation results and greatly enhances the practical application value of this simulation method in engineering construction control, quality evaluation and construction scheme optimization.

[0017] Secondly, the present invention provides a numerical simulation device for rock mass grouting. The device includes: an acquisition module for acquiring grouting pressure data, grout density data, injection rate, grout dynamic viscosity, and grout type data at multiple moments during the construction process of the grouting project to be predicted; and a simulation module for inputting the grouting pressure data, grout density data, injection rate, grout dynamic viscosity, and grout type data at multiple moments into a pre-constructed target grouting numerical model for numerical simulation, thereby obtaining the instantaneous injection rate corresponding to different moments during the construction process of the grouting project to be predicted, and the target grouting numerical model. The system includes pre-set grout diffusion models for different types of grout. Different types of grout have different water-cement ratios. The grout diffusion model is used to characterize the relationship between grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and unit width flow rate. The target grouting numerical model is used to determine the target grout diffusion model for each time step based on the grout type data at each time step. The unit width flow rate for each time step is calculated based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and target grout diffusion model at each time step. The instantaneous injection rate for each time step is determined based on the unit width flow rate at each time step.

[0018] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the rock mass grouting numerical simulation method of the first aspect or any corresponding embodiment described above.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the rock mass grouting numerical simulation method of the first aspect or any corresponding embodiment described above.

[0020] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the rock mass grouting numerical simulation method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2This is a schematic diagram of the first process of the numerical simulation method for rock mass grouting according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the numerical simulation method for rock mass grouting according to an embodiment of the present invention; Figure 4 This is a structural block diagram of a rock mass grouting numerical simulation device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0025] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the execution of the rock mass grouting numerical simulation method depends is described here. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0027] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0028] In relevant grouting numerical simulation techniques, a pre-constructed grout diffusion model is typically embedded into the numerical simulation model. This diffusion model is used to calculate the unit width flow rate of the fracture, and then the instantaneous injection rate during the grouting process is solved based on the unit width flow rate. However, this simulation scheme relies solely on a fixed form of grout diffusion model to calculate the flow rate and injection rate, failing to consider the actual engineering reality of dynamic changes in grout mix ratios and real-time alterations in rheological properties at different times within the same grouting section during construction. It cannot reflect the impact of differences in grout mix ratios at different construction stages on the grout diffusion law and grouting effect, causing the numerical simulation process to deviate from the actual grouting conditions. Ultimately, this results in low accuracy of the simulation calculations, making it difficult to accurately reflect the actual grouting evolution law on site.

[0029] In view of this, this application provides a numerical simulation method for rock mass grouting, which can be applied to a server to realize numerical simulation of rock mass / fractured rock mass grouting. The method provided in this application obtains time-series data of grouting pressure, grout density, injection rate, grout dynamic viscosity, and grout type at multiple moments throughout the entire construction process of the grouting project to be predicted. Relying on a target grouting numerical model with built-in dedicated grout diffusion models corresponding to different types of grout, the method can adaptively match and adapt the target grout diffusion model according to the actual grout type at each moment. This fully adapts to the differentiated characteristics of grout rheological properties under different grout types and different water-cement ratios, and can accurately establish the intrinsic correlation between grouting pressure, grout density, injection rate, grout dynamic viscosity, and fracture unit width flow rate under various working conditions. It fully takes into account the real-time changes in the rheological properties of grout caused by dynamic adjustments of grout mix ratio and water-cement ratio at different times in the same grouting section during actual grouting construction. It effectively makes up for the shortcomings of existing technologies that cannot reflect the impact of differences in grout mix ratio at different construction stages on grout diffusion law and grouting effect. It significantly reduces the deviation between the numerical simulation process and the actual grouting conditions on site, and significantly improves the simulation calculation accuracy of key indicators such as instantaneous injection rate. It can more realistically and accurately restore the evolution law of the entire rock mass grouting process, and also provides reliable and accurate numerical simulation support for subsequent grouting construction process optimization, rock mass seepage prevention and reinforcement effect prediction and engineering design decisions.

[0030] According to an embodiment of the present invention, a numerical simulation method for rock mass grouting is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] This embodiment provides a numerical simulation method for rock mass grouting, which can be used in the aforementioned server. Figure 2 This is a flowchart of a numerical simulation method for rock mass grouting according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain grouting pressure data, grout density data, grouting rate, grout dynamic viscosity and grout type data at multiple moments during the construction process of the grouting project to be predicted.

[0032] For example, the grouting project to be predicted can be a geotechnical grouting project that requires simulation and prediction of the grouting construction process, grouting parameters, and grouting effect. It can be a planned grouting project or a grouting project under construction. Multiple time points refer to the pre-defined grouting construction sequence time nodes according to the grouting construction organization plan, graded pressurization, and variable grouting process planning, used to discretely simulate different construction stages of the entire grouting process. Grouting pressure data are the pre-set design grouting pressures at each time point based on the engineering grouting construction design plan, serving as the pressure boundary input conditions for numerical simulation. Grout density data are the theoretically calculated design grout density at each time point obtained from the pre-set water-cement ratio and grout mix proportion scheme, directly determined by the planned mix proportion, used to characterize the physical properties of the grout under the pre-set working conditions. Grouting rate is the pre-set design grouting rate at each time point, combined with the geological conditions of the rock mass and the requirements of the construction process, serving as a benchmark reference parameter for matching the simulation working conditions. The dynamic viscosity of the slurry is the design dynamic viscosity determined by indoor rheological tests or empirical formulas based on the preset slurry mix ratio and slurry type. It characterizes the flow resistance characteristics of the slurry used in the plan. The slurry type data is a preset slurry type identifier corresponding to each construction moment according to the construction slurry variation process plan, which is used by the model to automatically match and call the corresponding suitable slurry diffusion model.

[0033] Step S202: Input the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and grout type data at multiple times into the pre-constructed target grouting numerical model for numerical simulation to obtain the instantaneous injection rate corresponding to different times during the construction process of the grouting project to be predicted. The target grouting numerical model has preset grout diffusion models corresponding to different types of grout. Different types of grout have different water-cement ratios. The grout diffusion model is used to characterize the correlation between grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and unit width flow rate. The target grouting numerical model is used to determine the target grout diffusion model at the corresponding time based on the grout type data at each time. The unit width flow rate at the corresponding time is calculated based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and target grout diffusion model at each time. The instantaneous injection rate at the corresponding time is determined based on the unit width flow rate at each time.

[0034] For example, different types of grout can be various grouting grouts classified according to water-cement ratio and rheological properties, with different types corresponding to different mix proportions and flow characteristics. The grout diffusion model is a pre-set calculation model for each type of grout, used to establish the mathematical correlation between grouting pressure, grout density, grouting rate, grout dynamic viscosity, and fracture unit width flow rate. The target grout diffusion model is a dedicated grout diffusion model automatically matched and adapted to the current working condition based on the pre-set grout type at each time. Unit width flow rate is the grout flow rate per unit width of a single fracture in the rock mass, and is a basic intermediate calculation parameter for grouting numerical simulation. The instantaneous injection rate is the grout flow rate injected into the rock mass per unit time at each time, which is obtained by integrating and converting the fracture unit width flow rate, and is a key result parameter for grouting simulation.

[0035] In this embodiment, the grouting pressure, grout density, grouting rate, grout dynamic viscosity, and grout type parameters preset for each construction moment of the grouting project to be predicted are uniformly input into the already constructed target grouting numerical model. Based on the grout type corresponding to each moment, the model automatically matches a dedicated grout diffusion model adapted to its water-cement ratio and rheological properties. Substituting the parameters for the corresponding moment, the unit width flow rate of the fracture at each moment is calculated through the matched diffusion model. Finally, the unit width flow rate is further converted and solved to simulate the instantaneous injection rate corresponding to different moments during the construction process of the project to be predicted. A FISH language calculation subroutine is written in the software and embedded in the grouting simulation calculation process to calculate the instantaneous injection rate in real time. The instantaneous injection rate is calculated using the superposition method of flow rates at the intersection of the fracture and the grouting hole, and the formula is:

[0036] in, This represents the i-th time node. This indicates the number of fractures intersecting with the grouting holes. This represents the number of sides of the regular octagonal cross-section formed by the intersection of the k-th fracture and the grouting hole. express The unit width flow vector (unit: m2 / s) of the j-th fracture at time k. Let be the unit normal vector (dimensionless) of the j-th edge of the k-th crack. The length of the j-th side of the k-th crack (in meters).

[0037] The rock mass grouting numerical simulation method provided in this embodiment obtains time-series data on grouting pressure, grout density, injection rate, grout dynamic viscosity, and grout type at multiple moments during the entire construction process of the grouting project to be predicted. Based on the target grouting numerical model with built-in dedicated grout diffusion models corresponding to different types of grout, the method can adaptively match and adapt the target grout diffusion model according to the actual grout type at each moment. This fully adapts to the differentiated characteristics of grout rheological properties under different grout types and different water-cement ratios, and can accurately establish the intrinsic correlation between grouting pressure, grout density, injection rate, grout dynamic viscosity, and fracture unit width flow rate under various working conditions. It fully takes into account the real-time changes in the rheological properties of grout caused by dynamic adjustments of grout mix ratio and water-cement ratio at different times in the same grouting section during actual grouting construction. It effectively makes up for the shortcomings of existing technologies that cannot reflect the impact of differences in grout mix ratio at different construction stages on grout diffusion law and grouting effect. It significantly reduces the deviation between the numerical simulation process and the actual grouting conditions on site, and significantly improves the simulation calculation accuracy of key indicators such as instantaneous injection rate. It can more realistically and accurately restore the evolution law of the entire rock mass grouting process, and also provides reliable and accurate numerical simulation support for subsequent grouting construction process optimization, rock mass seepage prevention and reinforcement effect prediction and engineering design decisions.

[0038] This embodiment provides a numerical simulation method for rock mass grouting, which can be used in the aforementioned server. Figure 3 This is a flowchart of a numerical simulation method for rock mass grouting according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and grout type data at multiple moments during the construction process of the grouting project to be predicted. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0039] Step S302: Input the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and grout type data at multiple times into the pre-constructed target grouting numerical model for numerical simulation to obtain the instantaneous injection rate corresponding to different times during the construction process of the grouting project to be predicted. The target grouting numerical model has preset grout diffusion models corresponding to different types of grout. Different types of grout have different water-cement ratios. The grout diffusion model is used to characterize the correlation between grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and unit width flow rate. The target grouting numerical model is used to determine the target grout diffusion model at the corresponding time based on the grout type data at each time. The unit width flow rate at the corresponding time is calculated based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and target grout diffusion model at each time. The instantaneous injection rate at the corresponding time is determined based on the unit width flow rate at each time.

[0040] In some optional embodiments, the slurry diffusion model includes a first diffusion model for a first type of slurry and a second diffusion model for a second type of slurry. The first diffusion model is used to characterize the relationship between the unit width flow rate of the first type of slurry and the dynamic viscosity, density, grouting pressure, and injection rate of the slurry. The second diffusion model is used to characterize the relationship between the unit width flow rate of the second type of slurry and the dynamic viscosity, density, grouting pressure, injection rate, and yield strength of the slurry. The yield strength of the slurry is determined based on the density of the slurry.

[0041] For example, in this embodiment of the application, two rheological types are distinguished based on the quantitative characteristics of the water-cement ratio w / c of ​​the slurry: Newtonian fluid and Bingham fluid. The first type of slurry can be a Newtonian fluid, and the second type of slurry can be a Bingham fluid. For Newtonian fluids, when the water-cement ratio w / c > 0.8, the constitutive equation is: ,in Shear stress (unit: Pa). The dynamic viscosity of the slurry at time t (unit: Pa) s), Shear rate (unit: s) -1 ), The slurry flow rate is expressed in m / s. The distance perpendicular to the flow direction (unit: m). For Ham fluid: when the water-cement ratio w / c ≤ 0.8, the constitutive equation is: ,in Let be the grout yield strength (in Pa) at time t, calculated using the correlation model between grout density and yield strength. Let be the dynamic viscosity of the slurry at time t. Let be the shear rate. The correlation model between slurry density and yield strength is shown in the following equation:

[0042] in, Indicates the i-th time. (Unit: Pa) m 3 / kg), (Unit: Pa), parameters , The results were obtained by fitting the data from indoor experiments (with a minimum of 30 samples and a goodness-of-fit R-value). 2 ≥0.95), Let represent the slurry density at time i.

[0043] Ignoring the permeability of the grout in the rock pores, and assuming that the grout diffuses only in the fracture network, is incompressible, and flows in a laminar state, the corresponding diffusion formula is adopted according to the fluid type. The dynamic fracture roughness coefficient F(t) (calculated by coupling the grouting rate and grouting pressure) is introduced to modify the formula. The diffusion formula serves as the seepage control equation for software calculation: The first diffusion model can be the Newtonian fluid diffusion model (modified roughness fracture cubic law), as shown in the following equation:

[0044] in, This represents the unit width flow rate at time t (unit: m2 / s). The crack aperture (unit: m) is the value of the crack. Let g be the density of the slurry at time t, and g = 9.8 m / s². Let be the dynamic viscosity of the slurry at time t. , These are empirical coefficients, calibrated from measured engineering data. In this embodiment of the application, the grouting pressure at time t is represented as follows: , Hydraulic gradient (dimensionless). .

[0045] The second diffusion model is the Bingham fluid diffusion model (based on the roughness correction formula of the Buckingham equation):

[0046] in, Let be the yield strength of the Bingham fluid at time t.

[0047] In some optional implementations, the target grouting numerical model is further pre-set with a first coupling model and a second coupling model. The first coupling model is used to correct the dynamic viscosity of the grout based on the grouting pressure data and the maximum design grouting pressure to obtain the coupled dynamic viscosity of the grout. The second coupling model is used to correct the yield strength of the grout based on the grout density data and the maximum design density of the grout to obtain the coupled yield strength of the grout.

[0048] For example, in an embodiment of this application, the first coupling model can be as follows:

[0049] in, This indicates the maximum design pressure for grouting (unit: MPa). This represents the dynamic viscosity of the coupled slurry at time t. Represents the coupling coefficient. This can include, but is not limited to, 0.3; the meanings of the other variables will not be elaborated here.

[0050] The second coupling model can be represented by the following equation:

[0051] in, This represents the yield strength of the coupled slurry at time t. Represents the coupling coefficient. It can include, but is not limited to, 0.25. This represents the maximum design density of the slurry at time t (unit: kg / m3). This represents the slurry density at time t; the meanings of the other variables will not be elaborated further.

[0052] Based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and target grout diffusion model at each time point, the unit width flow rate at the corresponding time point is calculated, including: Step a1: If there is at least one first moment among multiple moments, the target grouting numerical model inputs the grouting pressure data, grout dynamic viscosity and grouting design maximum pressure of each first moment into the first coupled model for calculation to obtain the coupled grout dynamic viscosity of the corresponding first moment. The grout type corresponding to the first moment is the first grout type.

[0053] For example, in the embodiments of this application, for each first moment belonging to the first slurry type, the model inputs the grouting pressure, slurry dynamic viscosity and grouting design maximum pressure at that moment into the first coupled model, and obtains the coupled slurry dynamic viscosity at that moment after coupling correction calculation.

[0054] Step a2: Based on the coupled slurry dynamic viscosity, grouting pressure data, slurry density data, grouting rate, and the first diffusion model at each first moment, the unit width flow rate at the corresponding first moment is calculated.

[0055] For example, in this embodiment of the application, the dynamic viscosity of the slurry after coupling correction at the first moment, combined with the grouting pressure, slurry density and grouting rate at that moment, is substituted into the first diffusion model adapted to the first slurry type to calculate the fracture unit width flow rate at the corresponding moment.

[0056] Step a3: If there is at least one second time among multiple times, the target grouting numerical model inputs the grouting pressure data and slurry dynamic viscosity of each second time into the second coupled model for calculation to obtain the coupled slurry dynamic viscosity of the corresponding second time. The slurry type corresponding to the second time is the second slurry type.

[0057] For example, in the embodiments of this application, for each second moment belonging to the second slurry type, the target grouting numerical model inputs the grouting pressure data and slurry dynamic viscosity at that moment into the second coupling model, and obtains the corresponding coupled slurry dynamic viscosity after calculation and correction.

[0058] Step a4: The target grouting numerical model inputs the grout yield strength, grout density data and the maximum designed grout density at each second moment into the second coupled model for calculation, and obtains the coupled yield strength at the corresponding second moment.

[0059] For example, in the embodiments of this application, for each second time corresponding to the second slurry type, the model inputs the slurry yield strength, slurry density data and the slurry design maximum density at that time into the second coupling model, and obtains the corrected coupled slurry yield strength after correction calculation.

[0060] Step a5: Based on the coupled yield strength, coupled grout dynamic viscosity, grouting pressure data, grout density data, grouting rate, and the second diffusion model at each second moment, the unit width flow rate at the corresponding second moment is calculated.

[0061] For example, the coupled yield strength and dynamic viscosity of the coupled grout at the second moment are combined with the grouting pressure, grout density and grouting rate at that moment, and substituted into the second diffusion model adapted to the second grout type to calculate and solve for the fracture unit width flow rate at the corresponding moment.

[0062] In some alternative implementations, the target grouting numerical model is constructed through the following steps: Step b1: Obtain the pre-constructed three-dimensional random fracture grouting numerical model, grouting pressure data, grout density data, grouting rate, and yield strength measured at multiple moments during construction.

[0063] Specifically, the above-mentioned three-dimensional stochastic fracture grouting numerical model is constructed through the following steps: Step c1: Obtain the grouting hole parameters, modeling range, and rock mass fracture orientation distribution parameters for the rock mass grouting project.

[0064] For example, in this embodiment of the application, based on the engineering construction design scheme, the core parameters such as the grouting hole diameter D (unit: m), the grouting section division length H (unit: m), the hole spacing S (unit: m), and the construction sequence (I / II sequence) are clearly defined. Along the curtain axis, the center of the nearest constructed grouting hole to be simulated is selected as the side boundary, and the single-sided extension distance is the center distance L1 (unit: m) between the constructed hole and the hole to be constructed; perpendicular to the curtain axis, it extends 5S upstream and downstream as the front and rear boundaries, where 5S represents the distance to the vertical axis boundary; in the elevation direction, the top and bottom of a single grouting section are used as the vertical boundaries, and the vertical boundary spacing is the design length H of the grouting section. The model space is defined using a three-dimensional coordinate system: with the opening of the grouting hole to be simulated as the origin O(0,0,0), the curtain axis as the X-axis, the perpendicular curtain axis as the Y-axis, and the grouting hole depth direction as the Z-axis, the model range is X∈[ L1,L2]、Y∈[ 5S,5S]、Z∈[Z 顶 Z 底 [], where L2 is the center distance from the simulated hole to the constructed hole on the other side, and top and bottom are the elevation values ​​(unit: m) of the top and bottom of the grouting section, respectively. Based on the borehole and joint surface survey data from the field geological exploration, the key parameters of all dominant fracture groups in the grouting area are statistically analyzed: dip angle θ (unit: °), dip angle α (unit: °), fracture aperture u (unit: mm), spacing d (unit: m), and trace length L (unit: m). The mean, standard deviation, and distribution type (normal distribution / log-normal distribution) of each parameter are determined, and a fracture group parameter statistical table is formed. The table should clearly specify the above 6 parameters and distribution characteristics of each fracture group.

[0065] Step c2 involves sampling the rock mass fracture orientation distribution parameters using a preset sampling method to obtain a three-dimensional random fracture network.

[0066] For example, in this embodiment of the application, the Monte Carlo random sampling method is used to sample the dip, dip angle, aperture, spacing, and trace length parameters of each fracture group, and the number of samplings is as follows. The model's fracture count must match the actual fracture density in the rock mass (fracture density). , (The model volume is in m3). Based on the sampling results, a three-dimensional random fracture network is generated in the three-dimensional coordinate system that is consistent with the actual rock mass fracture distribution, attitude, and aperture. The fracture surface is characterized by the plane equation Ax+By+Cz+D=0, where A, B, C, and D are calculated from the sampled values ​​of fracture dip, dip angle, and trace length.

[0067] The specific calculation steps are as follows: The plane equation of the fracture surface is calculated using the fracture dip direction θ and dip angle α obtained from Monte Carlo sampling, and the coordinates (x0, y0, z0) of any point within the fracture surface (the trace length is used to determine the extent of the fracture surface and is not included in the calculation of the plane equation coefficients). First, the normal vector n=(A,B,C) of the fracture surface is determined. The fracture dip direction θ (the horizontal angle of the fracture direction rotated counterclockwise from the positive X-axis to the fracture strike, 0°≤θ≤360°), dip angle α (the angle between the fracture surface and the horizontal plane, 0°<α≤90°), and normal vector are calculated using the following formula:

[0068] in, This represents the component of the normal vector of the fracture surface along the X-axis. This represents the component of the normal vector of the fracture surface along the Y-axis. This represents the component of the fracture surface normal vector along the Z-axis (vertical direction).

[0069] Second, determine the reference point (x0, y0, z0) within the fracture surface. The key point is the fracture center coordinates obtained by Monte Carlo sampling (randomly generated from the model range to ensure the fracture surface is located within the model space), which are known constant values.

[0070] Third, calculate the constant term D of the plane equation. Substitute the normal vector (A,B,C) and the reference point (x0,y0,z0) into the plane equation to find D: D = (A×x0+B×y0+C×z0).

[0071] Fourth, obtain the complete plane equation of the fracture surface. Substitute the calculated A, B, C, and D into the equation to obtain the unique plane equation of the fracture surface: Ax + By + Cz + D = 0. The purpose of the trace length is to determine the extension length and width of the fracture surface in the plane based on the trace length, and to cut out the actual fracture surface size (to avoid the infinite extension of the fracture surface).

[0072] Step c3: Import the grouting hole parameters, modeling range, and three-dimensional random fracture network into the preset numerical simulation software to construct the model and obtain the three-dimensional random fracture grouting numerical model.

[0073] For example, in this embodiment of the application, based on a preset numerical simulation software, the three-dimensional random fracture network, grouting hole parameters (diameter D, coordinate position), and model range parameters are imported into the software. The fracture surface is meshed (the mesh size is no greater than 1 / 10 of the minimum trace length of the fracture). The grouting hole adopts a regular octagon to simulate a circular cross section. The diameter of the circumcircle of the regular octagon is equal to the design diameter D of the grouting hole. Finally, the three-dimensional random fracture grouting numerical model is constructed.

[0074] Step b2: Construct a first diffusion model for the first type of fluid, a second diffusion model for the second type of fluid, a first coupling model, a second coupling model, and a feedback adjustment model for grouting pressure. The feedback adjustment model is used to adjust the grouting pressure data based on the simulated instantaneous injection rate when the deviation between the measured grouting rate and the simulated instantaneous injection rate is greater than a preset threshold, so as to obtain coupled grouting pressure data.

[0075] For example, in this embodiment of the application, a diffusion model adapted to two types of fluids, a coupling model of two types of rheological parameters, and a grouting pressure feedback adjustment model are constructed respectively. The feedback adjustment model can adaptively adjust the grouting pressure based on the simulated instantaneous injection rate when the deviation between the measured grouting rate and the simulated instantaneous injection rate exceeds a set threshold, thus obtaining corrected coupled grouting pressure data. The feedback adjustment model can be expressed as follows:

[0076]

[0077] in, This represents the coupled grouting pressure data at time t. This represents the pressure correction increment at time t; , representing the pressure regulation coefficient; Convert the grouting rate to m 3 The value of / s; Represents the simulated instantaneous injection rate at time t; This represents the measured grouting rate at time t.

[0078] Step b3: Embed the first diffusion model, the second diffusion model, the first coupling model, the second coupling model, and the feedback adjustment model into the three-dimensional random fracture grouting numerical model to obtain the initial grouting numerical model.

[0079] For example, in this embodiment of the application, two types of diffusion models, two types of coupling models and pressure feedback adjustment models that have been built in advance are uniformly integrated and embedded into the three-dimensional random fracture grouting numerical model to form a complete initial grouting numerical model.

[0080] Step b4: Based on the measured grouting pressure data, grout density data, grouting rate, yield strength, and grout dynamic viscosity at multiple moments during construction, the initial grouting numerical model is iteratively optimized to obtain the target grouting numerical model.

[0081] For example, in this embodiment of the application, dynamically applying coupling parameters and performing simulation specifically includes: (1) Initialize simulation parameters: Import the initial dynamic parameters into the software and set the parameters at the initial simulation time t=0 as follows. , , , Initial dynamic parameters refer to the grouting pressure data, grout density data, grouting rate, yield strength, and grout dynamic viscosity measured at multiple moments during the construction process.

[0082] (2) Step-by-step coupling calculation: The simulation is carried out in 30-second time steps. In each time step, the pressure after coupling correction is calculated based on the measured parameters at the current time. Viscosity Yield strength Then, the crack roughness coefficient F(t) is updated, and finally, the unit width flow rate of the diffusion model is substituted to calculate the injection rate based on the mechanical loss. F(t) is a parameter that is dynamically updated with the grouting time step, and the update period is consistent with the simulation time step (30s). The specific steps are as follows (executed within each time step): Obtain the basic parameters for the current time step. Extract. Measured grouting pressure at any time (MPa), measured grouting rate (L / min); Substitute the basic parameters into the formula ,in, , is a constant.

[0083] (3) The calculated Import into numerical simulation software and replace the previous time step. The crack roughness coefficient is updated, and the updated data is directly used as the input parameters for the slurry diffusion model. Specifically, based on the water-cement ratio of the slurry at the current time step, the fluid type is determined and substituted into the corresponding diffusion model formula to calculate the unit width flow rate. Then, the total injection rate Q(t) is calculated using the instantaneous injection rate formula. The specific correspondence is as follows: ① If the slurry is a Newtonian fluid (water-cement ratio w / c > 0.8): Substitute the modified cubic law of roughness cracks (Newtonian fluid diffusion model) to calculate the unit width flow rate. ; ② If the slurry is a Bingham fluid (water-cement ratio w / c ≤ 0.8): Substitute the formula based on the Buckingham equation for rough crack correction (Bingham fluid diffusion model) to calculate the unit width flow rate. ; ③ The above formula calculates the unit width flow rate. Then, the total instantaneous injection rate for the current time step is calculated by uniformly substituting the values ​​into the instantaneous injection rate calculation formula. .

[0084] (4) Iterative convergence determination: Iterate 3 times in each time step until the relative change of the coupling parameters is reached. To ensure the stability of the coupling process, Indicates the pressure after coupling correction. Viscosity Yield strength and F .

[0085] (5) Output simulation results: After the simulation is completed, output the instantaneous injection rate during the entire construction period. Cumulative injection amount Data, forming a simulation results dataset .

[0086] (6) Quantitative verification of core indicators. The grouting injection rate was selected as the core verification indicator. Unstable monitoring data in the first 5 minutes of grouting were removed to obtain the field measurement dataset. ; Calculate the relative error and root mean square error (RMSE) between simulated and measured values, and the relative error at a single time point:

[0087] in, This represents the relative error at a single time point; the meanings of the other variables will not be elaborated further.

[0088] The average relative error is shown in the following formula:

[0089] in, This represents the average relative error; the meanings of the other variables will not be elaborated further.

[0090] The root mean square error is shown in the following formula:

[0091] in, This represents the root mean square error. This represents the number of valid time points used for verification; the meanings of the other variables will not be elaborated further.

[0092] like If the simulation accuracy meets the engineering requirements, then the model is iteratively optimized.

[0093] (7) Comparison and verification using traditional methods. Based on the same three-dimensional random fracture grouting numerical model, a comparison simulation was conducted using the traditional fixed-parameter simulation method. The parameters for the comparison simulation were set as follows: the grouting pressure was taken as the design fixed value. (Unit: MPa), the grout density and dynamic viscosity are taken according to the grouting design grade and remain unchanged within the same grade; the other boundary conditions and calculation methods are consistent with this method; the injection rate results of the calculation are compared with those of the simulation. Calculate separately and average relative error ,contrast and To verify the superior accuracy of this method, the average relative error of this method should be reduced by at least 60% compared with the traditional method.

[0094] (8) Iterative optimization of model parameters. For cases where the simulation accuracy does not meet requirements, the gradient descent method is used to fine-tune and calibrate the key parameters of the model. The optimized parameters include: crack aperture u, coupling coefficient, etc. , , , Parameters for calculating the yield strength of grout , The optimization steps are as follows: Determine the adjustment range of the optimization parameters: crack aperture Coupling coefficient , , , Yield strength parameter , ,in For on-site measurement of crack aperture; With average relative error Minimize the objective function, and the objective function is: The adjustment step size for each parameter is calculated using the gradient descent method: ( For parameters to be optimized, (for learning rate) Update the parameters by adjusting the step size, re-execute the numerical simulation steps, and calculate the updated mean relative error. ; Repeat the above steps until... The optimized parameter values ​​are recorded and used as the optimal parameters for the rock mass grouting simulation of the project, forming a parameter library.

[0095] (9) Optimize the simulation result dataset Integrating with measured datasets and traditional method result datasets, the system outputs simulated grouting process curves (injection rate-time curve, cumulative injection volume-time curve) and provides grouting construction control suggestions, such as the grouting pressure adjustment threshold corresponding to sudden changes in injection rate (when...). When it is recommended that the pressure adjustment range not exceed 0.2MPa, and the optimal time node for switching the grout ratio (when the cumulative injection volume reaches 80% of the design value and the injection rate is stable within ±10% of the design value), etc., provide quantitative guidance for the actual grouting construction of the project.

[0096] Step S303: Calculate the cumulative injection volume of the grouting project to be predicted based on the instantaneous injection rate corresponding to different times during the construction process of the grouting project to be predicted.

[0097] For example, in this embodiment of the application, the cumulative injection amount is calculated using the trapezoidal numerical integration method based on the integration principle, as shown in the following formula:

[0098] in, Indicates the current grouting time. , They are respectively , Instantaneous injection rate at any given moment For a fixed time step, (The initial injection rate at the moment of grouting is 0).

[0099] Furthermore, in this embodiment, the grouting pressure and grout density measured by the grouting recorder are used as the core data sources. The dynamic viscosity of the grout is dynamically calculated using piecewise linear interpolation, constructing a four-dimensional linkage parameter system of "pressure-density-viscosity-yield strength." Combined with a grouting rate feedback adjustment mechanism, this overcomes the limitation of incomplete dynamic coupling of parameters in traditional simulations, fully restoring the dynamic evolution process of multiple parameters in actual construction. Measured data are collected in a fixed 30-second time step, and linear interpolation ensures a smooth transition of parameters between adjacent monitoring times. An outlier judgment standard is introduced to ensure data reliability. A quantitative mapping relationship between grout density, viscosity, and yield strength is established, eliminating the need for dedicated sensors or indoor test data, and enabling real-time dynamic conversion and standardized processing of measured data to simulated parameters. A dynamic fracture roughness coefficient (calculated by coupling grouting pressure and grouting rate) is introduced to correct the diffusion model of Newtonian fluid and Bingham fluid, accurately characterizing the grout seepage law in natural rough fractures; a three-dimensional random fracture network is generated by Monte Carlo method, and the distribution characteristics of key parameters such as fracture dip, dip angle and aperture are comprehensively statistically analyzed to improve the model's accuracy in reproducing the real geological conditions of the rock mass.

[0100] Differentiated boundary conditions are set (classified according to permeability / impermeability and groundwater level), and vertical segmented independent simulation design is adopted to avoid cross-flow interference; the instantaneous injection rate formula based on the superposition of flow rates at the intersection of fracture and grouting hole and the cumulative injection volume formula based on the integral principle are clarified to provide clear quantitative basis for construction control; through linear pressure transition and fixed step size update strategy, step-like abrupt changes in simulation results are avoided to ensure the continuity and reliability of simulation.

[0101] A dual system of "quantitative verification with measured data + comparative verification with traditional methods" was established, and the simulation accuracy was evaluated using two indicators: relative error and root mean square error. For cases where the accuracy did not meet the requirements, the key parameters such as crack parameters and coupling coefficients were fine-tuned and calibrated using the gradient descent method, forming a closed-loop mechanism of "simulation-verification-optimization" to continuously improve the simulation accuracy and promote the transformation of grouting construction from "experience-driven" to "scientific quantification".

[0102] The rock grouting numerical simulation method provided in this application constructs a five-dimensional dynamic linkage system of "pressure-density-viscosity-yield strength-grouting rate", and combines a feedback adjustment model of pressure and grouting rate with a coupling model of grout parameters to achieve deep synergy of dynamic parameters of pressure and grout, thus completely solving the problem of incomplete parameter coupling in traditional simulation. The simulation results are significantly improved in their fit with the actual construction process, and the maximum relative error can be controlled within 8%, which is more than 60% lower than the traditional method (maximum relative error 21.21%).

[0103] Measured data are collected using a fixed time step of 30 seconds. Linear interpolation is used to achieve a smooth transition of parameters between adjacent monitoring times. An outlier judgment standard is introduced to ensure data reliability. A quantitative mapping relationship and dynamic calculation model between slurry density, viscosity and yield strength are established. Without relying on dedicated sensors or additional indoor test data, the measured data can be dynamically converted and standardized in real time to simulated parameters, and the precision of data application is significantly improved.

[0104] A fracture roughness coefficient based on dynamic calculation of grouting pressure and grouting rate is introduced to correct the diffusion models of Newtonian fluid and Bingham fluid, accurately characterizing the grout seepage law in natural rough fractures. A three-dimensional random fracture network is generated by Monte Carlo method, and the distribution characteristics of key parameters such as fracture dip, dip angle and aperture are comprehensively statistically analyzed. The model has a higher degree of restoration of the real geological conditions of the rock mass and effectively avoids the systematic deviations brought about by ideal models.

[0105] Differentiated boundary conditions and vertical segmented independent simulation design are adopted to effectively avoid cross-flow interference. The instantaneous injection rate formula based on the superposition of flow rates at the intersection of the fracture and grouting hole and the cumulative injection volume formula based on the integral principle are clearly defined, providing a clear quantitative basis for construction control. Through linear pressure transition, fixed step size update and iterative convergence judgment strategy, the stability and continuity of the variable pressure and variable grout coupling process are ensured, and the situation of step-like abrupt changes in simulation results that do not match reality is avoided.

[0106] A dual system of "quantitative verification with measured data + comparative verification with traditional methods" was established, and the simulation accuracy was evaluated using both relative error and root mean square error. For cases where the accuracy did not meet the requirements, the gradient descent method was used to fine-tune and calibrate key parameters such as crack parameters and coupling coefficients, forming a closed-loop mechanism of "simulation-verification-optimization" to continuously improve the simulation accuracy. This promoted the transformation of grouting construction from "experience-driven" to "scientific quantification," effectively avoiding problems such as excessive or insufficient grouting, and ensuring the anti-seepage and reinforcement effect of the project.

[0107] All coupling coefficients and empirical coefficients are given clear values ​​and units. The coupling model formulas are clear and calculable. The operation standards and parameter meanings of each step are clear. Those skilled in the art can implement them directly without controversy. The output grouting process curves and quantitative construction suggestions such as pressure adjustment thresholds and grout ratio switching nodes can directly provide accurate guidance for on-site construction, reduce construction risks and grout waste, and improve the quality and efficiency of engineering construction.

[0108] This embodiment also provides a rock mass grouting numerical simulation device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0109] This embodiment provides a numerical simulation device for rock mass grouting, such as... Figure 4 As shown, it includes: The acquisition module 401 is used to acquire grouting pressure data, grout density data, grouting rate, grout dynamic viscosity and grout type data at multiple moments during the construction process of the grouting project to be predicted; The simulation module 402 is used to input grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and grout type data at multiple times into a pre-constructed target grouting numerical model for numerical simulation. This allows for the acquisition of the instantaneous injection rate at different times during the construction of the grouting project to be predicted. The target grouting numerical model includes pre-set grout diffusion models for different types of grout. The water-cement ratio varies for different types of grout. The grout diffusion model is used to characterize the relationship between grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and unit width flow rate. The target grouting numerical model is used to determine the target grout diffusion model at each time based on the grout type data at each time. The unit width flow rate at each time is calculated based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and the target grout diffusion model at each time. Finally, the instantaneous injection rate at each time is determined based on the unit width flow rate at each time.

[0110] In some optional embodiments, the slurry diffusion model includes a first diffusion model for a first type of slurry and a second diffusion model for a second type of slurry. The first diffusion model is used to characterize the relationship between the unit width flow rate of the first type of slurry and the dynamic viscosity, density, grouting pressure, and injection rate of the slurry. The second diffusion model is used to characterize the relationship between the unit width flow rate of the second type of slurry and the dynamic viscosity, density, grouting pressure, injection rate, and yield strength of the slurry. The yield strength of the slurry is determined based on the density of the slurry.

[0111] In some optional implementations, the target grouting numerical model is further pre-set with a first coupling model and a second coupling model. The first coupling model is used to correct the dynamic viscosity of the grout based on the grouting pressure data and the maximum design grouting pressure to obtain the coupled dynamic viscosity of the grout. The second coupling model is used to correct the yield strength of the grout based on the grout density data and the maximum design grout density to obtain the coupled yield strength of the grout. Based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and target grout diffusion model at each time point, the unit width flow rate at the corresponding time point is calculated, including: If there is at least one first moment among multiple moments, the target grouting numerical model will input the grouting pressure data, grout dynamic viscosity and grouting design maximum pressure of each first moment into the first coupled model for calculation to obtain the coupled grout dynamic viscosity of the corresponding first moment. The grout type corresponding to the first moment is the first grout type. The unit width flow rate at each first moment is calculated based on the coupled slurry dynamic viscosity, grouting pressure data, slurry density data, grouting rate, and the first diffusion model. If there is at least one second time among multiple times, the target grouting numerical model will input the grouting pressure data and slurry dynamic viscosity of each second time into the second coupled model for calculation to obtain the coupled slurry dynamic viscosity of the corresponding second time. The slurry type corresponding to the second time is the second slurry type. The target grouting numerical model inputs the grout yield strength, grout density data and the maximum designed grout density at each second time point into the second coupled model for calculation, and obtains the coupled yield strength at the corresponding second time point; The unit width flow rate at each second moment is calculated based on the coupled yield strength, coupled grout dynamic viscosity, grouting pressure data, grout density data, grouting rate, and the second diffusion model.

[0112] In some alternative implementations, the target grouting numerical model is constructed through the following steps: Obtain a pre-constructed three-dimensional random fracture grouting numerical model, as well as grouting pressure data, grout density data, grouting rate, and yield strength measured at multiple moments during construction. A first diffusion model for the first type of fluid, a second diffusion model for the second type of fluid, a first coupling model, a second coupling model, and a feedback adjustment model for grouting pressure are constructed. The feedback adjustment model is used to adjust the grouting pressure data based on the simulated instantaneous injection rate when the deviation between the measured grouting rate and the simulated instantaneous injection rate is greater than a preset threshold, so as to obtain coupled grouting pressure data. The first diffusion model, the second diffusion model, the first coupling model, the second coupling model, and the feedback adjustment model are embedded into the three-dimensional stochastic fracture grouting numerical model to obtain the initial grouting numerical model. Based on the measured grouting pressure data, grout density data, grouting rate, yield strength, and grout dynamic viscosity at multiple moments during construction, the initial grouting numerical model was iteratively optimized to obtain the target grouting numerical model.

[0113] In some alternative implementations, the three-dimensional stochastic fracture grouting numerical model is constructed through the following steps: Obtain the grouting hole parameters, modeling range, and rock mass fracture orientation distribution parameters for rock grouting projects; The rock mass fracture orientation distribution parameters were sampled using a pre-defined sampling method to obtain a three-dimensional random fracture network. The grouting hole parameters, modeling range, and three-dimensional random fracture network are imported into a preset numerical simulation software to construct the model, thus obtaining a three-dimensional random fracture grouting numerical model.

[0114] In some alternative embodiments, the above-described apparatus further includes: The calculation module is used to calculate the cumulative injection volume of the grouting project to be predicted based on the instantaneous injection rate corresponding to different moments during the construction process.

[0115] The rock grouting numerical simulation device provided in this embodiment of the invention can execute the rock grouting numerical simulation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0116] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0117] The following is a detailed reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from memory 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0118] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0119] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the rock mass grouting numerical simulation method of the embodiments of the present invention.

[0120] Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0121] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the rock grouting numerical simulation method shown in the above embodiments is implemented.

[0122] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0123] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A numerical simulation method for rock mass grouting, characterized in that, The method includes: Obtain grouting pressure data, grout density data, grouting rate, grout dynamic viscosity and grout type data at multiple moments during the construction process of the grouting project to be predicted; The grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and grout type data at multiple time points are input into a pre-constructed target grouting numerical model for numerical simulation. This yields the instantaneous injection rate at different times during the construction process of the grouting project to be predicted. The target grouting numerical model includes pre-set grout diffusion models for different types of grout, each with a different water-cement ratio. The grout diffusion model is used to characterize the correlation between grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and unit width flow rate. The target grouting numerical model is used to determine the target grout diffusion model for each time point based on the grout type data. The unit width flow rate for each time point is calculated based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and the target grout diffusion model. The instantaneous injection rate for each time point is then determined based on the unit width flow rate.

2. The method according to claim 1, characterized in that, The slurry diffusion model includes a first diffusion model for a first type of slurry and a second diffusion model for a second type of slurry. The first diffusion model is used to characterize the relationship between the unit width flow rate of the first type of slurry and the dynamic viscosity, density, grouting pressure, and injection rate of the slurry. The second diffusion model is used to characterize the relationship between the unit width flow rate of the second type of slurry and the dynamic viscosity, density, grouting pressure, injection rate, and yield strength of the slurry. The yield strength of the slurry is determined based on the density of the slurry.

3. The method according to claim 2, characterized in that, The target grouting numerical model also includes a first coupling model and a second coupling model. The first coupling model is used to correct the dynamic viscosity of the grout based on the grouting pressure data and the maximum design grouting pressure to obtain the coupled dynamic viscosity of the grout. The second coupling model is used to correct the yield strength of the grout based on the grout density data and the maximum design grout density to obtain the coupled yield strength of the grout. The step of calculating the unit width flow rate at each time point based on grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and the target grout diffusion model includes: If there is at least one first moment among multiple moments, the target grouting numerical model will input the grouting pressure data, grout dynamic viscosity and grouting design maximum pressure of each first moment into the first coupled model for calculation to obtain the coupled grout dynamic viscosity of the corresponding first moment. The grout type corresponding to the first moment is the first grout type. The unit width flow rate at each first moment is calculated based on the coupled slurry dynamic viscosity, grouting pressure data, slurry density data, grouting rate, and the first diffusion model. If there is at least one second time among multiple times, the target grouting numerical model will input the grouting pressure data and slurry dynamic viscosity of each second time into the second coupled model for calculation to obtain the coupled slurry dynamic viscosity of the corresponding second time. The slurry type corresponding to the second time is the second slurry type. The target grouting numerical model inputs the grout yield strength, grout density data and the maximum designed grout density at each second time point into the second coupled model for calculation, and obtains the coupled yield strength at the corresponding second time point. Based on the coupled yield strength, coupled grout dynamic viscosity, grouting pressure data, grout density data, grouting rate, and the second diffusion model at each second time point, the unit width flow rate at the corresponding second time point is calculated.

4. The method according to claim 3, characterized in that, The target grouting numerical model is constructed through the following steps: Obtain a pre-constructed three-dimensional random fracture grouting numerical model, as well as grouting pressure data, grout density data, grouting rate, and yield strength measured at multiple moments during construction. A first diffusion model for a first type of fluid, a second diffusion model for a second type of fluid, a first coupling model, a second coupling model, and a feedback adjustment model for grouting pressure are constructed. The feedback adjustment model is used to adjust the grouting pressure data based on the simulated instantaneous injection rate when the deviation between the measured grouting rate and the simulated instantaneous injection rate is greater than a preset threshold, so as to obtain coupled grouting pressure data. The first diffusion model, the second diffusion model, the first coupling model, the second coupling model, and the feedback adjustment model are embedded into the three-dimensional random fracture grouting numerical model to obtain the initial grouting numerical model; The initial grouting numerical model was iteratively optimized based on grouting pressure data, grout density data, grouting rate, yield strength, and grout dynamic viscosity measured at multiple moments during construction to obtain the target grouting numerical model.

5. The method according to claim 4, characterized in that, The three-dimensional random fracture grouting numerical model was constructed through the following steps: Obtain the grouting hole parameters, modeling range, and rock mass fracture orientation distribution parameters for rock grouting projects; The rock mass fracture orientation distribution parameters are sampled using a preset sampling method to obtain a three-dimensional random fracture network; The grouting hole parameters, modeling range, and three-dimensional random fracture network are imported into a preset numerical simulation software to construct a model, thereby obtaining a three-dimensional random fracture grouting numerical model.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: The cumulative injection volume of the grouting project to be predicted is calculated based on the instantaneous injection rate at different times during the construction process.

7. A numerical simulation device for rock mass grouting, characterized in that, The device includes: The acquisition module is used to acquire grouting pressure data, grout density data, grouting rate, grout dynamic viscosity and grout type data at multiple moments during the construction process of the grouting project to be predicted; The simulation module is used to input grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and grout type data at multiple time points into a pre-constructed target grouting numerical model for numerical simulation. This simulation yields the instantaneous injection rate at different times during the construction process of the grouting project to be predicted. The target grouting numerical model includes pre-set grout diffusion models for different types of grout, each with a different water-cement ratio. These grout diffusion models characterize the relationship between grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and unit width flow rate. The target grouting numerical model determines the target grout diffusion model for each time point based on the grout type data. It also calculates the unit width flow rate at each time point based on the grouting pressure data, grout density data, grouting rate, grout dynamic viscosity, and the target grout diffusion model, and determines the instantaneous injection rate based on the unit width flow rate at each time point.

8. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the rock mass grouting numerical simulation method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the rock mass grouting numerical simulation method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the rock mass grouting numerical simulation method according to any one of claims 1 to 6.

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