A pulse intelligent grouting control method and system

Abstract

The present invention provides a pulse intelligent grouting control method and system, which relates to the field of civil engineering technology. The present invention obtains real-time displacement data of measuring points within the construction site and uses it to establish an optimal grouting parameter prediction and analysis model. Then, by incorporating grouting parameters into a finite element model, the fitness of each set of parameters is calculated, and the combination with the highest fitness is selected as the optimal grouting parameter. Utilizing pulse intelligent control technology, a grouting pulse index is comprehensively generated, and a pulse threshold is established. When the grouting pulse index exceeds the threshold, the system will automatically stop grouting, thereby achieving precise control of the grouting process. The present invention calculates the optimal value of grouting fitness and optimizes grouting parameters through a grouting parameter analysis model, thereby achieving precise control and optimization of the grouting process.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering, and in particular to a pulse intelligent grouting control method and system. Background Art

[0002] Currently, with the continuous advancement of construction engineering technology, traditional grouting methods are increasingly unable to meet the safety and efficiency requirements of complex construction environments. In recent years, pulse grouting technology has attracted attention due to its ability to precisely control soil reinforcement and stability by adjusting the frequency, intensity, and duration of grouting. In combination with real-time monitoring technology, a pulse intelligent grouting control method has emerged. By acquiring real-time displacement data from measurement points, grouting parameters can be dynamically adjusted. This method not only improves the accuracy and response speed of the grouting process, but also effectively reduces the impact on the environment, becoming a key means of achieving intelligent and refined management in modern construction.

[0003] In the prior art, publication number CN111058450A discloses a grouting control method, device and system, which obtains the current formation cement slurry pressure data in real time; compares the current formation cement slurry pressure data with a preset cement slurry pressure value and obtains a comparison result; generates a control signal based on the comparison result, and the control signal is used to control the actuator to execute cement slurry injection, suck out excess cement slurry or stop cement slurry injection.

[0004] Insufficient existing technology:

[0005] While existing grouting control methods and devices achieve real-time monitoring and dynamic adjustment, they still have some shortcomings. First, current methods primarily rely on formation cement slurry pressure data for control. This single parameter may not fully reflect the complexities of the grouting process. For example, under different formation conditions, the fluidity, viscosity, and environmental factors of the cement slurry all affect the grouting effect. Existing technologies fail to effectively account for the combined influence of these multi-dimensional factors, potentially resulting in poor grouting results or adverse impacts on the surrounding environment.

[0006] Secondly, the control logic of existing technologies is relatively simple, generating control signals based solely on pressure data comparisons and lacking a more intelligent decision-making support system. This approach has limited response time and sensitivity to the grouting process, making it unable to adapt promptly to unexpected situations such as changes in stratum structure or external environmental disturbances. Furthermore, the lack of in-depth data analysis and pattern recognition prevents dynamic optimization and self-learning capabilities, limiting the system's intelligence and adaptability. Therefore, enhancing the intelligence and comprehensiveness of grouting control will be a key direction for future technological development.

[0007] Therefore, it is necessary to provide a pulse intelligent grouting control method and system to solve the above problems.

[0008] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0009] The purpose of the present invention is to provide a pulse intelligent grouting control method and system to solve the problems raised in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A pulse intelligent grouting control method, the specific steps include:

[0012] Step 1: Acquire multiple sets of historical state data, including historical grouting parameters of individuals in the initial population and displacements of corresponding measurement points after grouting. The historical grouting parameters include grouting hole spacing, grouting hole diameter, and grouting hole pressure data;

[0013] Step 2: Establish an optimal grouting parameter prediction model. Take the historical grouting parameters as input and the displacement of the corresponding measurement points as labels. Train the optimal grouting parameter prediction model. Calculate the fitness values ​​of individuals in the initial population based on the state data. Minimize the fitness value as the optimization goal. Optimize the initial population based on the genetic algorithm and the optimal grouting parameter prediction model. Find the individual with the smallest fitness value as the optimal grouting parameter combination.

[0014] Step 3: Based on the optimal grouting parameters, pulse technology is used for grouting. The pulse frequency, pulse width and pulse intensity under the optimal combination of construction grouting parameters are collected in real time to comprehensively generate the grouting pulse index.

[0015] Step 4: Set a pulse threshold. When the grouting pulse index exceeds the threshold, stop grouting to achieve precise control of the grouting process.

[0016] Furthermore, the optimal grouting parameter prediction model is established based on the following method:

[0017] Taking historical grouting parameters as input and the displacement of the corresponding measurement point as the label, the mean square error is selected as the loss function. The model structure adopts a deep neural network, which consists of an input layer, multiple hidden layers and an output layer. The input layer is responsible for receiving historical grouting parameter data, multiple hidden layers are responsible for capturing complex nonlinear relationships, and the output layer is responsible for outputting the corresponding measurement point displacement. The prediction model is then trained, and cross-validation and other methods are used to prevent overfitting. The performance of the model is evaluated by training error and validation error. The back propagation algorithm is then combined with the optimization method to iteratively update the model parameters until the preset round is reached to minimize the loss function and achieve effective prediction of the measurement point displacement.

[0018] Furthermore, the calculation formula of the fitness value is:

[0019]

[0020] Among them, X j Represents the final displacement of measurement point j, m is the total number of measurement points, j is the index of the measurement point, j∈[1,m], Indicates the maximum displacement threshold of the measurement point, X av represents the average displacement of the measurement point, ω1, ω2, and ω3 are weight proportional coefficients used to represent the degree of influence of each part on the fitness function, and ω2>ω1>ω3>0, q is the grouting cost coefficient, d, s, and δ represent the grouting hole diameter, spacing, and grouting hole pressure of the grouting parameters, respectively, S is the area of ​​the grouting area, and D is the grouting hole depth; the smaller the F value, the better the grouting parameters; This part represents the safety value, X av This part represents the penalty value. This part represents the economic value, and F is the fitness value of the individual;

[0021] The grouting parameter combination with the smallest sum of safety value, economic value and penalty value is selected as the optimal grouting parameter combination.

[0022] Furthermore, the grouting pulse index is generated comprehensively, based on the following method:

[0023] Based on pulse intelligent control technology, the grouting parameters are dynamically adjusted according to the real-time monitoring of the displacement data of the measuring points to obtain the pulse frequency f under the optimal construction grouting parameter combination. best , pulse width T best and pulse intensity I best , the grouting pulse index is comprehensively generated based on the formula:

[0024]

[0025] Among them, a, b, and c correspond to the weight ratio of their respective parameters and c>a>b>0, f best 、T best , I best is the pulse frequency, pulse width and pulse intensity under the optimal construction grouting parameter combination, f max 、T max , I max The maximum allowable values ​​for grouting frequency, width and intensity.

[0026] Furthermore, a pulse threshold is set to determine whether the grouting pulse index exceeds the pulse threshold. The logic formula is as follows:

[0027]

[0028] Among them, Q represents the logic value for judging whether the pulp pulse index exceeds the pulse threshold, J th It is the preset pulse threshold. When Q=0, it indicates that the pulse index has not exceeded the pulse threshold and the grouting construction continues; when Q=1, it indicates that the pulse index has exceeded the pulse threshold and the grouting construction is stopped.

[0029] The present invention further provides a pulse intelligent grouting control system, which is used to execute the above-mentioned pulse intelligent grouting control method, including:

[0030] A data acquisition module, wherein the data acquisition module is used to obtain multiple sets of historical status data, the historical status data including historical grouting parameters of individuals in the initial population and displacements of corresponding measurement points after grouting, the historical grouting parameters including grouting hole spacing, grouting hole diameter, and grouting hole pressure data;

[0031] A fitness value calculation and model construction module is used to establish an optimal grouting parameter prediction model. The module uses historical grouting parameters as input and the displacement of corresponding measurement points as labels to train the optimal grouting parameter prediction model. The module calculates the fitness values ​​of individuals in the initial population based on state data, and takes minimizing the fitness value as the optimization goal. The module optimizes the initial population based on a genetic algorithm and the optimal grouting parameter prediction model to find the individual with the smallest fitness value as the optimal grouting parameter combination.

[0032] A grouting pulse index generation module is used to perform grouting using pulse technology based on optimal grouting parameters, collect pulse frequency, pulse width, and pulse intensity under the optimal combination of construction grouting parameters in real time, and comprehensively generate a grouting pulse index;

[0033] The grouting process monitoring and threshold control module is used to set a pulse threshold. When the grouting pulse index exceeds the threshold, the grouting is stopped to achieve precise control of the grouting process.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention solves the shortcomings of the existing technology in grouting control methods and devices through multiple innovative steps and modular design. First, the invention uses the construction model to comprehensively consider the displacement data of the measurement points. This multi-parameter input method makes the grouting process no longer rely solely on a single pressure data. By establishing a model based on multi-dimensional parameters, the invention can more comprehensively reflect the complexity of the construction environment and formation conditions, thereby improving the accuracy and effectiveness of the grouting effect. This method can adapt to the fluidity, viscosity and environmental changes of different formations, significantly reducing the potential adverse effects on the surrounding environment;

[0036] Secondly, the present invention introduces a fitness function calculation module, making the generation and optimization process of grouting parameters more intelligent. This method can not only generate multiple sets of grouting parameters in real time, but also select the optimal combination through fitness evaluation, thereby enhancing the system's responsiveness to changes in construction conditions. In addition, combined with pulse intelligent control technology, the invention realizes dynamic adjustment in the way grouting parameters are applied, and can automatically optimize the grouting process based on real-time monitoring data. This intelligent decision support system improves the sensitivity of the system, ensuring that the system can quickly adapt and adjust operations when the stratum structure changes or external interference occurs, reflecting a higher level of intelligence and comprehensiveness, thereby overcoming the limitations of existing technologies;

[0037] The present invention uses a finite element numerical analysis model and a particle swarm optimization algorithm to generate and optimize grouting parameters, thereby achieving precise control and optimization of the grouting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the overall method of the present invention.

[0039] Figure 2 It is a schematic diagram of the system module flow of the present invention. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0041] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0042] Example:

[0043] See also Figure 1 , a pulse intelligent grouting control method, the specific steps include:

[0044] Step 1: Acquire multiple sets of historical state data, including historical grouting parameters of individuals in the initial population and displacements of corresponding measurement points after grouting. The historical grouting parameters include grouting hole spacing, grouting hole diameter, and grouting hole pressure data;

[0045] Step 2: Establish an optimal grouting parameter prediction model. Take the historical grouting parameters as input and the displacement of the corresponding measurement points as labels. Train the optimal grouting parameter prediction model. Calculate the fitness values ​​of individuals in the initial population based on the state data. Minimize the fitness value as the optimization goal. Optimize the initial population based on the genetic algorithm and the optimal grouting parameter prediction model. Find the individual with the smallest fitness value as the optimal grouting parameter combination.

[0046] Step 3: Based on the optimal grouting parameters, pulse technology is used for grouting. The pulse frequency, pulse width and pulse intensity under the optimal combination of construction grouting parameters are collected in real time to comprehensively generate the grouting pulse index.

[0047] Step 4: Set a pulse threshold. When the grouting pulse index exceeds the threshold, stop grouting to achieve precise control of the grouting process.

[0048] It should be noted that the use of deep neural network for training is a key step in the grouting control method, because this process can effectively capture and represent the complex nonlinear relationship between grouting parameters and construction effects. The historical grouting parameters are used as input and the displacement of the corresponding measurement points are used as labels to train the optimal grouting parameter prediction model. This model can not only quantify the difference between the model prediction and the actual situation through the mean square error loss function, but also iteratively update through the back propagation algorithm and optimization method to improve the prediction accuracy, aiming to improve the intelligence of the system so that the grouting parameters can be dynamically optimized according to changes in the actual construction environment, thereby ensuring the reliability and safety of the construction effect, and ultimately achieving more efficient construction management and resource utilization.

[0049] Measuring points should be evenly distributed at different locations around the foundation pit, especially the four corners and central area of ​​the foundation pit, to comprehensively monitor the displacement of the foundation pit and the building. Measuring points should also be arranged around the grouting holes, especially the distance between the grouting holes and the measuring points should ensure that the impact of grouting on the surrounding environment can be effectively captured.

[0050] Therefore, the optimal grouting parameter prediction model is established based on the following method:

[0051] Taking historical grouting parameters as input and the displacement of the corresponding measurement points as labels, the mean square error is selected as the loss function. The model structure adopts a deep neural network, which consists of an input layer, multiple hidden layers, and an output layer. The input layer is responsible for receiving historical grouting parameter data, the multiple hidden layers are responsible for capturing complex nonlinear relationships, and the output layer is responsible for outputting the corresponding measurement point displacement.

[0052] The fitness value of each individual in the initial population is calculated, and the individuals are sorted from small to large according to the fitness value. The individuals in the top 50% of the sort are selected as parents, and crossover and mutation are performed to generate new offspring. The offspring and parents form a new population, and the individuals in the population are input into the optimal grouting parameter prediction model to obtain the displacement of the measuring point. The fitness value of the individual is calculated based on the grouting parameters and the displacement of the measuring point. The selection, crossover and mutation operations are repeated until the predetermined number of iterations is reached. The individual with the smallest fitness value is selected as the optimal grouting parameter combination. The number of iterations is within 50-100.

[0053] It is important to note that by comprehensively considering safety, economic efficiency, and penalty values, we can comprehensively evaluate the impact of each set of grouting parameters on construction results. This process ensures that the selected parameters not only meet safety standards but also achieve optimal economic benefits. By setting a fitness function, we can effectively quantify and compare the advantages and disadvantages of different parameter combinations, providing a scientific basis for decision-making. In particular, the introduction of weight coefficients allows for flexible adjustment of the relative importance of various indicators in the fitness calculation, making the model more adaptable to changes in actual construction conditions.

[0054] This method incorporates a comprehensive evaluation mechanism combining safety, economic, and penalty values, breaking through the limitations of traditional single-objective optimization and achieving multi-objective optimization. This not only improves construction quality and safety, but also reduces costs while maximizing resource efficiency. Furthermore, the design of the fitness function enables dynamic adaptation to varying construction environments and requirements, further enhancing the method's flexibility and operability. This demonstrates the advantages of intelligent decision support systems and promotes the development of grouting construction technology.

[0055] Therefore, the calculation formula of the fitness value is:

[0056]

[0057] Among them, X j Represents the final displacement of measurement point j, m is the total number of measurement points, j is the index of the measurement point, j∈[1,m], Indicates the maximum displacement threshold of the measurement point, X av represents the average displacement of the measurement point, ω1, ω2, and ω3 are weight proportional coefficients used to represent the degree of influence of each part on the fitness function, and ω2>ω1>ω3>0, q is the grouting cost coefficient, d, s, and δ represent the grouting hole diameter, spacing, and grouting hole pressure of the grouting parameters, respectively, S is the area of ​​the grouting area, and D is the grouting hole depth; the smaller the F value, the better the grouting parameters; This part represents the safety value, X av This part represents the penalty value. This part represents the economic value, and F is the fitness value of the individual. The grouting parameter combination corresponding to the minimum sum of the safety value, economic value and penalty value is selected as the optimal grouting parameter combination. In the above fitness function calculation formula, is the total displacement of all m measuring points during construction. The smaller this value is, the better, which means that the foundation has a strong bearing deformation capacity during construction and is less likely to cause structural damage, which is conducive to subsequent grouting construction; is the average displacement of all m measuring points during construction. The smaller the value, the better, indicating that the displacement of each measuring point is close to the construction displacement standard. In the formula, S represents the size of the area that needs to be grouting, which is usually related to the total amount and cost of grouting. The larger the area, the higher the cost and the greater the economic value. Therefore, a reasonable grouting area should be selected to ensure that no material waste occurs. The depth of the grouting hole D directly affects the distribution and effect of the grouting material. The greater the depth, the more grouting material is required and the higher the construction cost. Therefore, under the condition that the economic value does not increase significantly, the grouting depth should be detected and determined in advance to avoid cost waste. In the formula, the natural logarithm and square root are taken to smooth and standardize the effect of the grouting hole diameter on the economic value, ensuring that it is quantified in an appropriate way in the fitness function, emphasizing the nonlinear effect of the hole diameter on the grouting effect. The larger the d value, the greater the economic value and the higher the cost; e qδ The effect of grouting hole pressure δ on cost is exponential, which means that as grouting hole pressure δ increases, cost and economic value will increase significantly. The grouting cost coefficient q is also considered, which partially reflects the economic factor and focuses on the high cost of construction under high pressure. The grouting cost coefficient q can be obtained by consulting the grouting engineering manual. The grouting hole spacing s affects the uniformity of grouting and the efficiency of material use. Since smaller spacing usually provides better grouting effect, but leads to higher cost and material waste, the smaller s is, the greater the economic value and the higher the cost. Therefore, the hole spacing should be reasonably planned to ensure the best economic benefits.

[0058] The weight ratio is set to ω2 > ω1 > ω3 > 0 because economic efficiency is a key consideration. The cost of grouting directly impacts the overall project budget and economic benefits. Therefore, the economic value weight ω2 is set higher than the safety value weight ω1 to emphasize the importance of cost control during the optimization process. The priority of economic efficiency reflects the common practice of pursuing cost-effectiveness in project management. During grouting construction, ensuring structural safety and avoiding displacement exceeding the maximum allowable value are crucial. Therefore, the safety value weight ω1 is set higher than the penalty value weight ω3 to ensure that safety is not sacrificed while pursuing economic efficiency. Even when economic efficiency is strong, safety must still be prioritized to prevent potential structural risks. The penalty value is primarily used to penalize situations that exceed the safety threshold, ensuring that displacements during construction are not excessive. Because the penalty value is a penalty mechanism for unsafe behavior, it has a relatively low relative importance in decision-making. Therefore, the penalty value weight ω3 is set to the lowest, reflecting the priority of avoiding the penalty zone in actual construction.

[0059] The grouting parameter combination with the smallest sum of safety value, economic value and penalty value is selected as the optimal grouting parameter combination.

[0060] It should be noted that by combining real-time monitoring data with intelligent control technology, the accuracy and effectiveness of the grouting process can be ensured. By dynamically adjusting the pulse frequency f best , pulse width T best and pulse intensity I best, timely optimizing grouting parameters based on the actual displacement of the measurement points, thereby improving construction safety and effectiveness. The weighting in this formula reflects the importance of pulse intensity over frequency and width in the pulse index, ensuring that grouting effectiveness is prioritized during construction. This synthetically generated pulse index not only helps control construction quality and safety risks, but also maximizes the overall performance of the grouting project. The maximum allowable values ​​for grouting frequency, width, and intensity can be found in the grouting project manual.

[0061] Therefore, it is necessary to comprehensively generate the grouting pulse index based on the pulse frequency, pulse width and pulse intensity under the optimal combination of construction grouting parameters. The method is as follows:

[0062] Based on pulse intelligent control technology, the grouting parameters are dynamically adjusted according to the real-time monitoring of the displacement data of the measuring points to obtain the pulse frequency f under the optimal construction grouting parameter combination. best , pulse width T best and pulse intensity I best , the grouting pulse index is comprehensively generated based on the formula:

[0063]

[0064] Among them, a, b, and c correspond to the weight ratio of their respective parameters and c>a>b>0, f best 、T best , I best is the pulse frequency, pulse width and pulse intensity under the optimal construction grouting parameter combination, f max 、T max , I max are the maximum allowable values ​​of grouting frequency, width, and strength; the reason why c>a>b>0 is that during the grouting process, the pulse intensity directly affects the injection capacity of the liquid and the effective reinforcement of the soil. Pulses with higher intensity can more effectively overcome the pore pressure of the soil, enhance the permeability and diffusion of cement slurry, and thus better fill the soil or cracks. Therefore, it is reasonable to set the weight of intensity to the highest c to ensure that the effectiveness of the grouting effect is given priority during construction; the pulse frequency affects the time interval of grouting application and the continuity of construction. The appropriate frequency can improve the uniformity of grouting and control the stress distribution of the soil. Although the importance of frequency is slightly lower than that of strength, it is still crucial to the construction effect, so the weight a is set in the middle position; the pulse width mainly affects the duration of each pulse. Compared with the intensity and frequency, its impact on the overall grouting effect is relatively small, so the weight b is set to the lowest. This does not mean that the width is not important, but because in the actual grouting process, changes in intensity and frequency often have a more significant impact on the results;

[0065] When f bestThe higher the value, the more times the grouting is applied per unit time, which helps to improve the uniformity and effectiveness of the grouting. In the form of, the greater the frequency, the closer the score is to 1, the larger the corresponding value is, thus increasing J p The value of T best The larger it is, the longer each grouting lasts, which may be beneficial to the full penetration and distribution of cement slurry, similar to the frequency. The increase of J p The increase of best When it increases, it shows that the strength directly affects the injection capacity of the grouting material. Higher strength can better enhance the bearing capacity and stability of the soil. Similarly, the increase in strength is achieved through Improve J p The value of pulse index J p The larger the value, the better the grouting parameter combination and the better the construction effect. Therefore, in practical application, the goal is to maximize the J p To ensure the best reinforcement effect and safety performance during the grouting process; the formula uses a proportional form The goal is to standardize different parameters so that they can be compared within the same framework. This approach allows parameters of different units and magnitudes to be rationally integrated, facilitating comprehensive evaluation. The use of a square root formula reduces the impact of the ratios between different parameters on the index, ensuring that the contribution of changes in each parameter to the overall index does not appear linearly amplified. This design better reflects the complex relationships between parameters in actual construction, for example, the impact of certain parameters on construction performance may gradually decrease with changes within a certain range.

[0066] It should be noted that the importance of setting the pulse threshold is that it provides a key safety control mechanism for the grouting construction process. p And with the preset pulse threshold J th By comparing the pulse index, it's possible to promptly determine whether the construction process is within a safe range. This logical judgment ensures that grouting operations are immediately halted if the pulse index exceeds the threshold, effectively preventing soil damage, excessive structural displacement, and even potential safety accidents caused by excessive grouting. This early warning mechanism not only ensures construction safety, but also reduces economic losses and improves construction efficiency. Therefore, setting pulse thresholds is an essential step in the entire grouting process.

[0067] Therefore, it is necessary to set a pulse threshold to determine whether the grouting pulse index exceeds the pulse threshold. The logic formula is as follows:

[0068]

[0069] Among them, Q represents the logic value for judging whether the pulp pulse index exceeds the pulse threshold, J th It is the preset pulse threshold. When Q=0, it indicates that the pulse index has not exceeded the pulse threshold and the grouting construction continues; when Q=1, it indicates that the pulse index has exceeded the pulse threshold and the grouting construction is stopped.

[0070] See also Figure 2 The present invention further provides a pulse intelligent grouting control system, which is used to execute the above-mentioned pulse intelligent grouting control method, including:

[0071] A data acquisition module, wherein the data acquisition module is used to obtain multiple sets of historical status data, the historical status data including historical grouting parameters of individuals in the initial population and displacements of corresponding measurement points after grouting, the historical grouting parameters including grouting hole spacing, grouting hole diameter, and grouting hole pressure data;

[0072] A fitness value calculation and model construction module is used to establish an optimal grouting parameter prediction model. The module uses historical grouting parameters as input and the displacement of corresponding measurement points as labels to train the optimal grouting parameter prediction model. The module calculates the fitness values ​​of individuals in the initial population based on state data, and takes minimizing the fitness value as the optimization goal. The module optimizes the initial population based on a genetic algorithm and the optimal grouting parameter prediction model to find the individual with the smallest fitness value as the optimal grouting parameter combination.

[0073] A grouting pulse index generation module is used to perform grouting using pulse technology based on optimal grouting parameters, collect pulse frequency, pulse width, and pulse intensity under the optimal combination of construction grouting parameters in real time, and comprehensively generate a grouting pulse index;

[0074] The grouting process monitoring and threshold control module is used to set a pulse threshold. When the grouting pulse index exceeds the threshold, the grouting is stopped to achieve precise control of the grouting process.

[0075] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0076] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0077] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0078] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A pulse intelligent grouting control method, characterized in that: The specific steps include: Step 1: Acquire multiple sets of historical state data, including historical grouting parameters of individuals in the initial population and displacements of corresponding measurement points after grouting. The historical grouting parameters include grouting hole spacing, grouting hole diameter, and grouting hole pressure data; Step 2: Establish an optimal grouting parameter prediction model. Take the historical grouting parameters as input and the displacement of the corresponding measurement points as labels. Train the optimal grouting parameter prediction model. Calculate the fitness values ​​of individuals in the initial population based on the state data. Minimize the fitness value as the optimization goal. Optimize the initial population based on the genetic algorithm and the optimal grouting parameter prediction model. Find the individual with the smallest fitness value as the optimal grouting parameter combination. Step 3: Based on the optimal grouting parameters, pulse technology is used for grouting. The pulse frequency, pulse width and pulse intensity under the optimal combination of construction grouting parameters are collected in real time to comprehensively generate the grouting pulse index. Step 4: Set a pulse threshold. When the grouting pulse index exceeds the threshold, stop grouting to achieve precise control of the grouting process. The calculation formula of fitness value is: Among them, X j represents the final displacement of measurement point j, m is the total number of measurement points, j is the index of the measurement point, j∈[1,m], Indicates the maximum displacement threshold of the measurement point, X av represents the average displacement of the measurement point, ω1, ω2, and ω3 are weight proportional coefficients used to represent the degree of influence of each part on the fitness function, and ω2>ω1>ω3>0, q is the grouting cost coefficient, d, s, and δ represent the grouting hole diameter, spacing, and grouting hole pressure of the grouting parameters, respectively, S is the area of ​​the grouting area, and D is the grouting hole depth; the smaller the F value, the better the grouting parameters; This part represents the safety value, X av This part represents the penalty value. This part represents the economic value, and F is the fitness value of the individual; The grouting parameter combination with the smallest sum of safety value, economic value and penalty value is selected as the optimal grouting parameter combination.

2. A pulse intelligent grouting control method according to claim 1, characterized in that: The optimal grouting parameter prediction model is established based on the following method: Taking historical grouting parameters as input and the displacement of the corresponding measurement point as the label, the mean square error is selected as the loss function. The model structure adopts a deep neural network, which consists of an input layer, multiple hidden layers and an output layer. The input layer is responsible for receiving historical grouting parameter data, multiple hidden layers are responsible for capturing complex nonlinear relationships, and the output layer is responsible for outputting the corresponding measurement point displacement. The prediction model is then trained, and cross-validation and other methods are used to prevent overfitting. The performance of the model is evaluated by training error and validation error. The back propagation algorithm is then combined with the optimization method to iteratively update the model parameters until the preset round is reached to minimize the loss function and achieve effective prediction of the measurement point displacement.

3. A pulse intelligent grouting control method according to claim 1, characterized in that: The grouting pulse index is generated comprehensively based on the following method: Based on pulse intelligent control technology, the grouting parameters are dynamically adjusted according to the real-time monitoring of the displacement data of the measuring points to obtain the pulse frequency f under the optimal construction grouting parameter combination. best , pulse width T best and pulse intensity I best , the grouting pulse index is comprehensively generated based on the formula: Among them, a, b, and c correspond to the weight ratio of their respective parameters and c>a>b>0, f best 、T best , I best is the pulse frequency, pulse width and pulse intensity under the optimal construction grouting parameter combination, f max 、T max , I max The maximum allowable values ​​for grouting frequency, width and intensity.

4. A pulse intelligent grouting control method according to claim 3, characterized in that: A pulse threshold is established to determine whether the grouting pulse index exceeds the pulse threshold. The logic formula is as follows: Among them, Q represents the logic value for judging whether the pulp pulse index exceeds the pulse threshold, J th It is the preset pulse threshold. When Q=0, it indicates that the pulse index has not exceeded the pulse threshold and the grouting construction continues; when Q=1, it indicates that the pulse index has exceeded the pulse threshold and the grouting construction is stopped.

5. A pulse intelligent grouting control system, characterized in that: The control system is used to execute the pulse intelligent grouting control method according to any one of claims 1 to 4, comprising: A data acquisition module, wherein the data acquisition module is used to obtain multiple sets of historical status data, the historical status data including historical grouting parameters of individuals in the initial population and displacements of corresponding measurement points after grouting, the historical grouting parameters including grouting hole spacing, grouting hole diameter, and grouting hole pressure data; A fitness value calculation and model construction module is used to establish an optimal grouting parameter prediction model. The module uses historical grouting parameters as input and the displacement of corresponding measurement points as labels to train the optimal grouting parameter prediction model. The module calculates the fitness values ​​of individuals in the initial population based on state data, and takes minimizing the fitness value as the optimization goal. The module optimizes the initial population based on a genetic algorithm and the optimal grouting parameter prediction model to find the individual with the smallest fitness value as the optimal grouting parameter combination. A grouting pulse index generation module is used to perform grouting using pulse technology based on optimal grouting parameters, collect pulse frequency, pulse width, and pulse intensity under the optimal combination of construction grouting parameters in real time, and comprehensively generate a grouting pulse index; The grouting process monitoring and threshold control module is used to set a pulse threshold. When the grouting pulse index exceeds the threshold, the grouting is stopped to achieve precise control of the grouting process.

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