Preparation method and system of grouting material for underground pipe gallery construction

Abstract

The invention provides a preparation method and system of a grouting material for underground pipe gallery construction, and relates to the technical field of grouting material preparation. A material construction demand set is established through material construction demand analysis; carrying out matching simulation optimization by utilizing material construction requirements, and establishing a calibration preparation process; after the raw material proportion is configured based on the calibration preparation process, the particle size is optimized; after grouting parameters are set according to the calibration preparation technology, cyclic stirring is executed; monitoring and collecting the slurry data set to generate dynamic feedback, optimizing and calibrating the preparation process according to the dynamic feedback, and then preparing the grouting material. The technical problems that in the prior art, due to the fact that accurate analysis on underground pipe gallery construction requirements is lacked, the grouting material proportion is difficult to optimize in a targeted mode, and the grouting material performance is not matched with the actual construction requirements are solved, and the purposes that the comprehensive performance of the grouting material is optimized to adapt to different construction requirements, and the construction efficiency is improved are achieved. And therefore, the construction quality and durability of the underground pipe gallery are improved.

Description

Technical Field

[0001] This application relates to the technical field of grouting material preparation, and specifically to a method and system for preparing grouting materials for the construction of underground utility tunnels. Background Art

[0002] Through filling, reinforcement and sealing effects, grouting materials can improve the bearing capacity of the soil around the underground utility tunnel, prevent leakage and deformation, thus ensuring the long-term safe operation of the tunnel. At present, the commonly used grouting materials in the construction of underground utility tunnels mainly include cement-based, chemical grouts and composite grouts. Traditional preparation methods usually adopt fixed ratios and mix materials based on experience or laboratory data. However, due to the complex geological conditions and variable construction environments of underground utility tunnels, different geological conditions, hydrological conditions and the characteristics of the tunnel structure, etc., have different emphases on the performance of grouting materials. The existing preparation methods lack accurate analysis of the construction requirements of underground utility tunnels and do not fully consider the specific requirements under different construction environments and conditions. This lack of targeted preparation methods makes the grouting materials unable to fully match the construction requirements in terms of key performance indicators such as strength, durability and impermeability, thus affecting the overall construction quality and durability of underground utility tunnels. Summary of the Invention

[0003] This application provides a method and system for preparing grouting materials for the construction of underground utility tunnels, solving the technical problem that in the prior art, due to the lack of accurate analysis of the construction requirements of underground utility tunnels, it is difficult to optimize the grouting material ratio targetedly, and the performance of the grouting material does not match the actual construction requirements, achieving the technical effect of optimizing the comprehensive performance of the grouting material to adapt to different construction requirements, and further improving the construction quality and durability of underground utility tunnels.

[0004] In view of the above problems, on the one hand, this application provides a method for preparing grouting materials for the construction of underground utility tunnels, and the method includes: analyzing the material construction requirements for the construction of underground utility tunnels to establish a set of material construction requirements, where the material construction requirements include the requirement for strengthening the bottom layer, the requirement for anti-seepage and plugging, and the requirement for high strength and durability; using the material construction requirements to perform ratio simulation optimization of the material database, and establishing a standard preparation process according to the ratio simulation optimization results; after configuring the raw material ratio based on the standard preparation process, optimizing the particle size through a mechanical chemical composite grinding technology; setting grouting parameters according to the standard preparation process and then performing cyclic stirring; activating the feedback adjustment unit, performing slurry data monitoring, and establishing a slurry data set, where the slurry data set includes viscosity and fluidity; generating dynamic feedback using the slurry data set, optimizing the standard preparation process according to the dynamic feedback, and then performing the preparation of grouting materials.

[0005] On the other hand, the present application also provides a grouting material preparation system for underground utility tunnel construction. The system includes: a construction requirement analysis module for analyzing the material construction requirements of underground utility tunnel construction and establishing a set of material construction requirements, where the material construction requirements include the requirement for strengthening the bottom layer, the requirement for anti-seepage and leakage stoppage, and the requirement for high strength and durability; a ratio simulation and optimization module for using the material construction requirements to perform ratio simulation and optimization of the material database and establishing a standard preparation process according to the ratio simulation and optimization results; a particle size optimization module for optimizing the particle size through a mechanical-chemical composite grinding technology after configuring the raw material ratio based on the standard preparation process; a circulating stirring module for performing circulating stirring after setting the grouting parameters according to the standard preparation process; a slurry data monitoring module for activating a feedback adjustment unit, performing slurry data monitoring, and establishing a slurry data set, where the slurry data set includes viscosity and fluidity; a dynamic feedback optimization module for generating a dynamic feedback using the slurry data set, optimizing the standard preparation process according to the dynamic feedback, and then performing the preparation of the grouting material.

[0006] One or more technical solutions provided in the present application have at least the following beneficial effects:

[0007] By analyzing the material construction requirements of underground utility tunnel construction and establishing a set of material construction requirements, it is ensured that the performance indicators of the grouting material can be targeted to match different construction scenarios, providing data support for subsequent material optimization. Using the material database for ratio optimization, by simulating and analyzing the performance of different ratio schemes, the optimal formula is determined, avoiding the limitations of blind tests and traditional fixed ratios, and ensuring the rationality and reliability of the material. Adopting a mechanical-chemical composite grinding technology to make the material particle size uniform, improving the fluidity and stability of the slurry, reducing segregation and sedimentation problems, and at the same time enhancing the chemical activity of the material, improving the bonding force and durability. Setting reasonable grouting parameters according to the optimized preparation process to ensure the uniformity of the slurry preparation process, and further enhancing the stability of the material through circulating stirring, making the slurry have more excellent rheological properties. Activating the feedback adjustment unit, performing slurry data monitoring, real-time monitoring the key parameters of the slurry, and optimizing the material ratio through dynamic feedback adjustment, so that the slurry performance remains in the best state throughout the preparation process, reducing performance fluctuations and improving construction adaptability.

[0008] In summary, through systematic steps such as requirement analysis, ratio optimization, particle size control, and real-time monitoring and feedback, the present application realizes the full-process optimization of the grouting material from demand to preparation and then to quality control. This systematic preparation scheme not only improves the comprehensive performance of the grouting material, making it better adapt to the diverse requirements of underground utility tunnel construction, but also ensures the stability of the material quality through a dynamic feedback mechanism, thus significantly improving the construction quality and durability of underground utility tunnels, and providing a more reliable and efficient grouting material preparation solution for underground utility tunnel construction.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of a method for preparing grouting materials for underground pipe gallery construction provided in an embodiment of the present application.

[0011] Figure 2 This is a schematic structural diagram of a grouting material preparation system for underground pipeline corridor construction provided in an embodiment of the present application.

[0012] Explanation of the accompanying symbols: construction demand analysis module 10, ratio simulation optimization module 20, particle size optimization module 30, circulation stirring module 40, slurry data monitoring module 50, dynamic feedback optimization module 60. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a method and system for preparing grouting materials for underground pipeline corridor construction, thereby solving the technical problems in the prior art of difficulty in targeted optimization of grouting material ratios and mismatch between grouting material performance and actual construction requirements due to the lack of accurate analysis of underground pipeline corridor construction requirements. This achieves the technical effect of optimizing the comprehensive performance of grouting materials to adapt to different construction requirements, thereby improving the construction quality and durability of underground pipeline corridors.

[0014] Example 1, as Figure 1 As shown, the embodiment of the present application provides a method for preparing grouting material for underground pipe gallery construction, the method comprising:

[0015] Step S1: Analyze the material construction requirements for underground pipe gallery construction and establish a material construction requirement set, wherein the material construction requirements include requirements for reinforcing the bottom layer, requirements for anti-seepage and plugging leakage, and requirements for high strength and durability.

[0016] Specifically, material construction demand analysis refers to analyzing the performance, functionality, and other requirements of the materials needed during the construction process to determine appropriate material properties. Material construction requirements include base reinforcement requirements, anti-seepage and leak-proofing requirements, and high-strength durability requirements. Base reinforcement requirements refer to reinforcing the bottom of underground tunnels to ensure their load-bearing capacity and stability; anti-seepage and leak-proofing requirements refer to preventing groundwater infiltration and liquid leakage within the tunnels; and high-strength durability requirements refer to the need for materials to possess high strength and durability for long-term use. The material construction demand set is a collection of various material construction requirements that guides material selection and preparation.

[0017] First, collect the geological exploration data of the construction site, including parameters such as soil layer structure, water content, and porosity. Then, determine the material construction requirements based on these parameters, including aspects such as strengthening the bottom layer, preventing seepage and plugging leaks, and high strength and durability. Organize these requirements into a set of material construction requirements, which will be used as the basis for subsequent material selection and preparation. For example, in a certain underground pipe gallery construction project, through on-site investigation, it is found that the geological conditions are poor and there is a certain settlement risk. Therefore, the requirement for strengthening the bottom layer is listed as one of the important requirements. At the same time, due to the high groundwater level, the requirement for preventing seepage and plugging leaks is also very important. In addition, considering the long-term use of the pipe gallery, the requirement for high strength and durability is also included in the set of material construction requirements. Finite element analysis (FEA) can be used to simulate the stress conditions of the underground pipe gallery, or seepage tests can be conducted to test the waterproof performance of the materials, so as to determine the specific requirement indicators of the materials.

[0018] Through a comprehensive analysis of the material construction requirements, the specific requirements of the construction for the materials are clarified, providing an accurate direction for subsequent material proportioning and preparation, ensuring the matching of material properties with actual construction requirements, and improving the overall quality and reliability of the construction.

[0019] Step S2: Use the material construction requirements to perform proportion simulation optimization of the material database, and establish a calibrated preparation process based on the proportion simulation optimization results.

[0020] Specifically, the material database is a data set storing different types of grouting materials and their physical and chemical properties, such as cement-based grouts, polyurethane grouts, two-component grouts, etc., as well as the performance data such as compressive strength, bonding force, and fluidity corresponding to these materials.

[0021] Input the requirement data determined in step S1 into the material database, and then use a multi-objective optimization algorithm (such as the genetic algorithm GA) to simulate different material proportions. For example, assuming that a certain area requires grouts with high fluidity and high strength, the water-cement ratio, admixture ratio (such as silica fume, fly ash), etc. can be adjusted to simulate their strength and rheological properties, and finally the optimal proportion is selected. After determining the proportion, use standard experiments (such as measuring the consistency with a Vicat apparatus, flowability test, etc.) for verification, and establish a calibrated preparation process based on this, including standard processes such as stirring time and temperature control.

[0022] By performing proportion simulation optimization in the material database, the material proportioning scheme that best meets the construction requirements can be found, providing a scientific basis for the establishment of the calibrated preparation process, not only improving the accuracy of material preparation, but also ensuring the optimization of material properties, and enhancing the construction quality and durability.

[0023] Step S3: After configuring the raw material ratio based on the calibrated preparation process, optimize the particle size through mechanochemical composite grinding technology.

[0024] Specifically, the mechanochemical composite grinding technology uses a method that combines mechanical force and chemical additives to break, homogenize solid particles during the grinding process and activate their chemical activity. The particle size refers to the size of the solid particles in the slurry. It is configured according to the raw material ratio specified in the standard preparation process. Then, the mechanochemical composite grinding technology is used to process the raw materials to optimize the particle size distribution of the particles. Through the synergy of mechanical force and chemical action, this technology enables the particles to reach a more ideal particle size range, improving the fluidity and filling property of the slurry. For example, in the preparation of a certain grouting material, the ratio of raw materials such as cement, sand, and additives is configured according to the standard preparation process. Then, the mixed materials are ground using a mechanochemical composite grinding device (such as a ball mill or a jet mill). By adjusting the grinding time and parameters, the particle size distribution becomes more uniform, improving the fluidity and filling property of the slurry, which is beneficial to enhancing the grouting effect and the final performance of the material, and strengthening the quality and reliability of the construction.

[0025] Step S4: After setting the grouting parameters according to the standard preparation process, perform cyclic stirring.

[0026] Specifically, the grouting parameters are the parameters that need to be controlled during the grouting process, such as key parameters like pressure, flow rate, and time, which determine the properties of the slurry. The grouting parameters are set according to the standard preparation process. Then, the prepared slurry is cyclically stirred to ensure that the raw materials are fully and evenly mixed, making the properties of the slurry consistent. Cyclic stirring can be achieved through a stirring device, such as using a mixer to stir at a certain speed and for a certain time. For example, in a certain underground pipe gallery grouting project, the grouting pressure is set to 0.5 MPa and the flow rate is set to 10 L / min according to the standard preparation process. Then, the slurry is cyclically stirred using a mixer for 30 minutes to ensure that the slurry is fully and evenly mixed, preparing for the subsequent grouting construction.

[0027] By setting the grouting parameters and performing cyclic stirring, the slurry is fully and evenly mixed, ensuring the consistency of the slurry properties, providing a good foundation for the subsequent grouting construction, and improving the efficiency and quality of the construction.

[0028] Step S5: Activate the feedback adjustment unit, perform slurry data monitoring, and establish a slurry data set, where the slurry data set includes viscosity and fluidity.

[0029] Specifically, the feedback regulation unit is a device or system for monitoring and providing feedback on slurry data. Activate the feedback regulation unit to start monitoring the slurry data. Through monitoring devices such as viscometers and fluidity testers, data such as the viscosity and fluidity of the slurry are obtained to establish a slurry dataset. These data will be used for subsequent dynamic feedback and process optimization. For example, during the preparation of a certain grouting material, a viscometer is used to monitor the viscosity of the slurry, and a fluidity tester is used to monitor the fluidity. The monitored data are recorded in the slurry dataset, such as a viscosity of 1000 mPa·s and a fluidity of 20 cm. These data provide a basis for subsequent process adjustments.

[0030] By activating the feedback regulation unit and performing slurry data monitoring, the performance data of the slurry can be obtained in real time, providing a basis for subsequent dynamic feedback and process optimization, ensuring the stability and reliability of the slurry performance, and improving the construction quality.

[0031] Step S6: Generate a dynamic feedback using the slurry dataset, and after optimizing the standard preparation process according to the dynamic feedback, perform the preparation of the grouting material.

[0032] Specifically, a dynamic feedback is generated using the slurry dataset established in step S5. By analyzing slurry data such as viscosity and fluidity, it is judged whether the slurry performance meets the requirements. If not, the standard preparation process is optimized according to the feedback information, such as adjusting the raw material ratio, grinding time, etc. Then, the preparation of the grouting material is performed according to the optimized process. For example, in a certain project, it is found through analyzing the slurry dataset that the fluidity of the slurry is lower than expected. Therefore, the standard preparation process is adjusted according to the dynamic feedback, the time of mechanical-chemical composite grinding is increased, and the particle size is optimized. The grouting material is re-prepared according to the optimized process to make the fluidity of the slurry meet the expected requirements.

[0033] By generating a dynamic feedback using the slurry dataset and optimizing the standard preparation process according to the feedback, problems in the preparation process can be adjusted in a timely manner, ensuring the stable and reliable quality of the grouting material, improving the efficiency and quality of construction, and reducing construction costs and environmental impacts.

[0034] Furthermore, step S4 includes:

[0035] Step S41: Activate the environmental perception layer, and use the environmental perception layer to read the monitoring environmental dataset of the environmental perception sensors to obtain temperature data, humidity data, and pressure data.

[0036] Step S42: Optimize the grouting parameter requirements of the material construction requirements set according to the temperature data, humidity data, and pressure data to generate an optimized result of the grouting parameter requirements.

[0037] Step S43: Set the grouting parameters by using the optimized result of the grouting parameter requirements.

[0038] Specifically, the preparation of the grouting material not only depends on the mixing ratio and stirring method, but also needs to be optimized in combination with the environmental conditions at the construction site. The environmental perception system collects data such as temperature, humidity, and pressure, and optimizes the grouting parameters based on these data to ensure the performance stability and adaptability of the grout under different construction environments.

[0039] The environmental perception layer refers to the sensor network used to collect construction environment data, which is usually composed of various environmental perception sensors and conducts real-time monitoring and data transmission through a data processing system. The environmental perception sensor is a device used to detect the environmental parameters at the construction site, such as temperature and humidity sensors, air pressure sensors, ultrasonic sensors, etc. The environmental data set refers to the set of environmental parameter data collected during the construction process, including numerical information such as temperature, humidity, and pressure, which is used for subsequent calculations and optimizations. Activating the environmental perception layer means starting the monitoring equipment. Then, the environmental perception sensors are used to read the monitored environmental data set to obtain the data such as temperature, humidity, and pressure of the current construction environment. For example, in a certain underground utility tunnel construction project, through the temperature sensor, humidity sensor, and pressure sensor in the environmental perception layer, the temperature of the current construction environment is obtained as 25°C, the humidity is 60%, and the pressure is 0.1 MPa. These data will be used as the basis for subsequent optimization of the grouting parameters.

[0040] According to the acquired data such as temperature, humidity, and pressure, optimize the grouting parameter requirements concentrated on the construction needs of the material. By analyzing the influence of these environmental data on the performance of the grouting material, adjust the grouting parameters such as grouting pressure and flow rate to generate the optimized grouting parameter requirement results. For example, in a high-temperature environment, it is necessary to reduce the dosage of additives to prevent the slurry from solidifying quickly, while in a high-humidity environment, it is necessary to increase the water-cement ratio to enhance fluidity. This optimization process can use machine learning algorithms (such as BP neural network, fuzzy control), combined with construction experience and database data, to analyze the environmental data and optimize the grouting parameters. For example: when the temperature rises (above 35 °C), reduce the water-cement ratio (such as from 0.5 to 0.45), reduce the dosage of additives, and prevent the slurry from coagulating prematurely; when the humidity is relatively high, appropriately increase the proportion of admixtures (such as silica fume) to improve the bonding performance of the slurry and avoid material failure caused by excessive humidity; when the pressure fluctuates greatly, adjust the grouting pressure to ensure uniform penetration of the slurry. Exemplarily, according to the acquired data of temperature 25 °C, humidity 60%, and pressure 0.1 MPa, it is analyzed that in the current environment, in order to ensure the fluidity and filling of the slurry, the grouting pressure needs to be adjusted to 0.5 MPa and the flow rate to 10 L / min. These optimized parameters will be used for subsequent grouting construction. By optimizing the grouting parameters according to the environmental data, the grouting parameters can be more adapted to the current construction environment, improve the grouting effect and construction quality, and reduce construction problems caused by environmental factors.

[0041] According to the optimized results of the generated grouting parameter requirements, set the parameters of the grouting equipment. For example, set the grouting pressure to 0.5 MPa and the flow rate to 10 L / min. These parameter settings will guide the operation of the grouting equipment to ensure that the grouting process is carried out according to the optimized parameters.

[0042] Further, the generation of the dynamic feedback in step S6 described above includes:

[0043] Step S61: Match the slurry mixing data based on the set raw material ratio to establish a matching data set.

[0044] Step S62: Identify the matching data set according to the matching degree and trust degree of the matching data set.

[0045] Step S63: Use the identified matching data set to construct a slurry performance prediction model.

[0046] Step S64: After receiving the slurry data set based on the slurry performance prediction model, perform slurry performance prediction, and generate the dynamic feedback according to the slurry performance prediction result.

[0047] Specifically, the slurry is stirred according to the set raw material ratio. During the stirring process, an online monitoring system (such as a rheometer, rotational viscometer) is used to record the viscosity and fluidity of the slurry during stirring. Then, the actually measured data (viscosity and fluidity) during the stirring process is compared and matched with the set raw material ratio to establish a matching data set.

[0048] The matching degree is used to measure the deviation degree between the actual slurry performance and the theoretical set target. An error analysis method (such as mean square error MSE) is used to calculate the matching degree, and the confidence level is calculated in combination with the confidence interval. For example: Sample A (high matching, high confidence): The theoretical fluidity is 25 cm, the actual fluidity is 24.8 cm, the error is 0.8%, and the confidence level is 95%. Sample B (low matching, low confidence): The theoretical fluidity is 25 cm, the actual is 22 cm, the error is 12%, and the confidence level is 75%. By evaluating the accuracy and reliability of the matching data, the data is classified and labeled.

[0049] The labeled matching data set is used to construct a slurry performance prediction model. This slurry performance prediction model is a mathematical model trained using machine learning algorithms based on the matching data set, which can predict the physical properties (such as fluidity, viscosity) of the slurry under different mixing ratios and environmental conditions. By selecting appropriate modeling methods, such as linear regression, neural network, etc., the features in the data set (such as raw material ratio, stirring parameters, etc.) are associated with the slurry performance (such as viscosity, fluidity, etc.). For example, in a certain grouting material preparation project, a neural network is used to construct a slurry performance prediction model. The raw material ratio and stirring parameters in the labeled matching data set are used as input features, and the viscosity and fluidity of the slurry are used as output targets to train the neural network model. By adjusting the network structure and parameters, the prediction performance of the model is optimized. By using the labeled matching data set to construct a slurry performance prediction model, the performance of the slurry can be accurately predicted, providing a scientific basis for subsequent dynamic feedback and process optimization, and improving the intelligent and automated level of the preparation process.

[0050] The slurry data set is input into the slurry performance prediction model to perform slurry performance prediction. According to the prediction results, dynamic feedback information is generated to guide the optimization of the standard preparation process. For example, in a certain project, the slurry data set (such as viscosity, fluidity, etc.) obtained from real-time monitoring is input into the trained neural network model to predict the performance of the slurry. If the prediction result shows that the fluidity of the slurry is lower than expected, dynamic feedback is generated, suggesting increasing the time of mechanical-chemical composite grinding to optimize the particle size. According to this feedback information, the standard preparation process is adjusted and the grouting material is prepared again. By using the slurry performance prediction model to generate dynamic feedback, problems in the preparation process can be adjusted in a timely manner to ensure the stable and reliable quality of the grouting material.

[0051] The above steps utilize the slurry dataset for real-time analysis and construct a prediction model to optimize the preparation of grouting materials. Through matching, identification, prediction, and feedback adjustment, the performance of the slurry is ensured to be stable and meet the construction requirements.

[0052] Further, generating the dynamic feedback according to the slurry performance prediction result in step S64 further includes:

[0053] Step S641: Establish an adjustable parameter space, and the adjustable parameters of the adjustable parameter space include water-cement ratio, admixture ratio, stirring rate, and curing time.

[0054] Step S642: Establish an optimization target according to the calibrated preparation process, and establish a performance deviation according to the slurry performance prediction result and the optimization target.

[0055] Step S643: Use the performance deviation as an optimization constraint, perform parameter combination optimization within the adjustable parameter space, and generate the dynamic feedback according to the parameter combination optimization result.

[0056] Specifically, first establish an adjustable parameter space to define the adjustable parameter ranges and combinations. These adjustable parameters include water-cement ratio, admixture ratio, stirring rate, and curing time. By setting the adjustable ranges of these parameters, it provides a basis for subsequent parameter combination optimization. For example, in the preparation of a certain grouting material, the adjustable range of the water-cement ratio is set to 0.4 to 0.6, the adjustable range of the admixture ratio is set to 2% to 5%, the adjustable range of the stirring rate is set to 50 to 100 revolutions per minute, and the adjustable range of the curing time is set to 1 to 3 hours. The setting of these parameter ranges is based on the requirements of material performance and construction experience.

[0057] Establish an optimization target according to the calibrated preparation process, such as high strength, high fluidity, etc. Then, compare the slurry performance prediction result with the optimization target to calculate the performance deviation, that is, the difference between the predicted slurry performance and the optimization target. For example, in a certain project, the optimization target is that the fluidity of the slurry reaches 20 cm. The fluidity predicted by the slurry performance prediction model is 18 cm, so the performance deviation is 2 cm. This deviation will be used as the basis for subsequent parameter optimization.

[0058] Using performance deviation as the optimization constraint, parameter combination optimization is carried out within the adjustable parameter space. Through optimization algorithms such as genetic algorithms and simulated annealing, parameter combinations that can minimize performance deviation are searched for. Dynamic feedback is generated based on the optimization results to guide the adjustment of the calibration preparation process. For example, in a certain project, with the fluidity deviation as the optimization constraint, optimization is carried out within the adjustable parameter space of water-cement ratio, admixture ratio, stirring rate, and curing time. The optimal parameter combination is found through the genetic algorithm: water-cement ratio 0.5, admixture ratio 3%, stirring rate 80 revolutions per minute, and curing time 2 hours. Dynamic feedback is generated based on this result to recommend adjusting the relevant parameters in the calibration preparation process.

[0059] By establishing an adjustable parameter space, setting optimization goals, calculating performance deviation, and performing parameter optimization, the performance of the slurry can be intelligently adjusted during the construction process, improving the stability and adaptability of the slurry.

[0060] Further, activating the feedback adjustment unit in step S5 and performing slurry data monitoring further includes:

[0061] Activating an acoustic sensor to perform bubble monitoring during the slurry stirring process and generating bubble feedback; adding the bubble feedback as additional feedback to the slurry data set.

[0062] Specifically, in addition to monitoring conventional data such as the viscosity and fluidity of the slurry, an acoustic sensor (such as an ultrasonic sensor) is activated to monitor the bubbles during the slurry stirring process. The presence of bubbles will affect the fluidity and filling properties of the slurry. By monitoring the bubbles and including their data in the analysis, the preparation process can be optimized more precisely, improving the uniformity and stability of the slurry, thereby enhancing the grouting effect and construction quality. The acoustic sensor judges the size, quantity, and distribution of the bubbles by detecting the propagation characteristics of sound waves in the slurry and generates bubble feedback data. Then, these bubble feedback data are added as additional feedback to the slurry data set to form a more comprehensive data set.

[0063] Further, the method further includes:

[0064] Activating a visual detection sensor to perform slurry monitoring during the stirring of the slurry, generating a layering recognition result and a uniformity recognition result; adding the layering recognition result and the uniformity recognition result as additional feedback to the slurry data set.

[0065] Specifically, activate a visual detection sensor (such as a high-resolution camera or an infrared camera) to monitor the slurry during the mixing process. The visual detection sensor analyzes the appearance characteristics and physical state of the slurry through image recognition and processing technologies, generating a stratification recognition result and a uniformity recognition result. Among them, the stratification recognition result is the stratification situation of the monitored slurry; the uniformity recognition result is the uniformity of the monitored slurry, which is used to evaluate the quality of the slurry. Exemplarily, using computer vision technologies such as edge detection, color analysis, and image segmentation algorithms, detect whether the slurry shows a stratification phenomenon. If the image shows that the upper layer of the slurry has a lighter color and the lower layer has a darker color, it indicates that there may be a stratification phenomenon. By analyzing the distribution of the slurry in the image, judge whether there is uniform flow or distribution. For example, if some areas are darker or lighter in color, it indicates that the mixing may be uneven. Then, these stratification recognition results and uniformity recognition results are also added as additional feedback to the slurry dataset to further improve the slurry dataset. By activating the visual detection sensor to monitor the stratification and uniformity of the slurry and feeding back the results to the slurry dataset, it is possible to more comprehensively understand the performance and state of the slurry. This helps to promptly detect possible stratification and non-uniformity of the slurry during the mixing process, thereby more accurately optimizing the preparation process, improving the uniformity and stability of the slurry, and enhancing the grouting effect and construction quality.

[0066] Furthermore, after establishing the slurry dataset, it also includes:

[0067] Perform deviation verification on the slurry dataset to generate a deviation verification result; use the deviation verification result for early warning matching to generate an early warning signal and report the early warning signal.

[0068] Specifically, after establishing the slurry dataset, perform deviation verification on the dataset. Compare the actually monitored slurry performance data (such as viscosity, fluidity, bubble content, stratification situation, and uniformity, etc.) with the preset standard values or expected values, and calculate the deviation value as the deviation verification result. Next, use the deviation verification result for early warning matching. Set the allowable error range. For example: allowable deviation of fluidity: ±2 cm; allowable deviation of viscosity: ±10 mPa·s; allowable deviation of bubble content: ±1%. If a certain piece of data exceeds the set range, generate an anomaly flag, and generate an early warning signal and report it according to the preset early warning rules. The early warning information can be reported in different forms, such as: visual alarm: display a yellow warning on the monitoring system screen; sound alarm: such as a buzzer to remind the operator to pay attention to the slurry performance.

[0069] Through deviation verification and early warning matching, it is possible to monitor the slurry performance in real time, promptly discover potential problems, and prompt the operator to take measures through the early warning signal, which helps to improve the quality control level of the grouting material, reduce the uncertainty and risk during construction, and ensure the reliability of the final quality.

[0070] In summary, the grouting material preparation method for the construction of underground pipe corridors provided by the embodiments of the present application has the following beneficial effects:

[0071] By analyzing the material construction requirements for the construction of underground pipe corridors, a set of material construction requirements is established to ensure that the performance indicators of the grouting material can be targeted to match different construction scenarios, providing data support for subsequent material optimization. The mixing ratio is optimized using the material database. By simulating and analyzing the performance of different mixing ratio schemes, the optimal formula is determined, avoiding the limitations of blind tests and traditional fixed mixing ratios, and ensuring the rationality and reliability of the material. The mechanical-chemical composite grinding technology is adopted to make the particle size of the material uniform, improve the fluidity and stability of the slurry, reduce segregation and sedimentation problems, and at the same time enhance the chemical activity of the material, improving the bonding strength and durability. Reasonable grouting parameters are set according to the optimized preparation process to ensure the uniformity of the slurry preparation process, and the stability of the material is further enhanced through cyclic stirring, making the slurry have better rheological properties. The feedback adjustment unit is activated to perform data monitoring of the slurry, real-time monitoring of the key parameters of the slurry, and optimizing the material mixing ratio through dynamic feedback adjustment, so that the slurry performance remains in the best state throughout the preparation process, reducing performance fluctuations and improving construction adaptability. Acoustic sensors and visual inspection sensors are introduced to monitor bubbles, stratification, and uniformity, further enriching the slurry data set and improving the controllability of the slurry quality; deviation verification and early warning matching are performed on the slurry data set to timely detect potential problems and send out early warning signals, ensuring the smooth progress of the construction process and the reliability of the final quality.

[0072] Overall, through the above series of steps, the embodiments of the present application achieve the full-process optimization of the grouting material from demand to preparation and then to quality control. This systematic preparation scheme not only improves the comprehensive performance of the grouting material, making it better adapt to the diverse needs of underground pipe corridor construction, but also ensures the stability of the material quality through a dynamic feedback mechanism, thus significantly improving the construction quality and durability of underground pipe corridors, providing a more reliable and efficient grouting material preparation solution for underground pipe corridor construction.

[0073] Embodiment 2, as Figure 2 shown, based on the same inventive concept as the foregoing Embodiment 1, the embodiments of the present application provide a grouting material preparation system for the construction of underground pipe corridors, and the system includes:

[0074] A construction requirement analysis module 10 for analyzing the material construction requirements for the construction of underground pipe corridors and establishing a set of material construction requirements, where the material construction requirements include the requirements for reinforcing the bottom layer, anti-seepage and leakage prevention, and high strength and durability.

[0075] The proportion simulation optimization module 20 is used to perform proportion simulation optimization on the material database by using the construction requirements of the materials, and establish a standard preparation process according to the proportion simulation optimization results.

[0076] The particle size optimization module 30 is used to optimize the particle size through the mechanochemical composite grinding technology after configuring the raw material ratio based on the standard preparation process.

[0077] The cyclic stirring module 40 is used to perform cyclic stirring after setting the grouting parameters according to the standard preparation process.

[0078] The slurry data monitoring module 50 is used to activate the feedback adjustment unit, perform slurry data monitoring, and establish a slurry data set, where the slurry data set includes viscosity and fluidity.

[0079] The dynamic feedback optimization module 60 is used to generate a dynamic feedback by using the slurry data set, optimize the standard preparation process according to the dynamic feedback, and then perform the preparation of the grouting material.

[0080] Furthermore, the cyclic stirring module 40 in the embodiment of the present application is further used to perform the following steps:

[0081] Activate the environment perception layer, use the environment perception layer to read the monitoring environment data set of the environment perception sensor, and obtain temperature data, humidity data, and pressure data; optimize the grouting parameter requirements of the material construction requirement set according to the temperature data, humidity data, and pressure data, and generate an optimized result of the grouting parameter requirements; set the grouting parameters by using the optimized result of the grouting parameter requirements.

[0082] Furthermore, the dynamic feedback optimization module 60 in the embodiment of the present application is further used to perform the following steps:

[0083] Perform slurry stirring data matching based on the set raw material ratio, establish a matching data set; identify the matching data set according to the matching degree and trust degree of the matching data set; construct a slurry performance prediction model by using the identified matching data set; perform slurry performance prediction after receiving the slurry data set based on the slurry performance prediction model, and generate the dynamic feedback according to the slurry performance prediction result.

[0084] Furthermore, the dynamic feedback optimization module 60 in the embodiment of the present application is further used to perform the following steps:

[0085] Establish an adjustable parameter space, where the adjustable parameters of the adjustable parameter space include water-cement ratio, admixture ratio, stirring rate, and curing time; establish an optimization target according to the standard preparation process, and establish a performance deviation according to the slurry performance prediction result and the optimization target; use the performance deviation as an optimization constraint, perform parameter combination optimization in the adjustable parameter space, and generate the dynamic feedback according to the parameter combination optimization result.

[0086] Further, the system according to the embodiment of the present application further includes a bubble monitoring module, and the bubble monitoring module is used to perform the following steps:

[0087] Activate the acoustic sensor, monitor the bubbles during the slurry stirring process, generate a bubble feedback; add the bubble feedback as an additional feedback to the slurry data set.

[0088] Further, the system according to the embodiment of the present application further includes a visual detection module, and the visual detection module is used to perform the following steps:

[0089] Activate the visual detection sensor, monitor the slurry during the stirring process, generate a stratification recognition result and a uniformity recognition result; add the stratification recognition result and the uniformity recognition result as additional feedback to the slurry data set.

[0090] Further, the system according to the embodiment of the present application further includes a deviation verification module, and the deviation verification module is used to perform the following steps:

[0091] Perform deviation verification on the slurry data set, generate a deviation verification result; use the deviation verification result to perform early warning matching, generate an early warning signal, and report the early warning signal.

[0092] Through the foregoing detailed description of the preparation method of the grouting material for the construction of the underground pipe gallery in this specification, those skilled in the art can clearly know the underground pipe gallery construction grouting material preparation system in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For the relevant parts, refer to the description in the method part.

[0093] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Preparation method of grouting material for construction of underground pipe gallery, characterized in that, The method includes: Analyze the material construction requirements for the construction of the underground utility tunnel, establish a set of material construction requirements, and the material construction requirements include the requirements for strengthening the bottom layer, anti-seepage and leakage stoppage, and high-strength durability; Use the material construction requirements to perform ratio simulation optimization of the material database, and establish a standard preparation process according to the ratio simulation optimization results; After configuring the raw material ratio based on the standard preparation process, optimize the particle size through the mechanochemical composite grinding technology; After setting the grouting parameters according to the standard preparation process, perform cyclic stirring; Activate the feedback adjustment unit, perform slurry data monitoring, and establish a slurry data set, and the slurry data set includes viscosity and fluidity; Generate dynamic feedback using the slurry data set, optimize the standard preparation process according to the dynamic feedback, and then perform the preparation of the grouting material.

2. The preparation method of the grouting material for the construction of the underground pipe gallery according to claim 1, wherein, The step of performing cyclic stirring after setting the grouting parameters according to the standard preparation process includes: Activate the environmental perception layer, use the environmental perception layer to read the monitoring environmental data set of the environmental perception sensor, and obtain temperature data, humidity data, and pressure data; Optimize the grouting parameter requirements of the material construction requirement set according to the temperature data, humidity data, and pressure data, and generate an optimized result of the grouting parameter requirements; Set the grouting parameters using the optimized result of the grouting parameter requirements.

3. The preparation method of the grouting material for the construction of the underground pipe gallery according to claim 1, characterized in that The step of generating dynamic feedback using the slurry data set includes: Perform slurry stirring data matching based on the set raw material ratio, and establish a matching data set; Identify the matching data set according to the matching degree and trust degree of the matching data set; Construct a slurry performance prediction model using the identified matching data set; After receiving the slurry data set based on the slurry performance prediction model, perform slurry performance prediction, and generate the dynamic feedback according to the slurry performance prediction result.

4. The preparation method of the grouting material for the construction of the underground pipe gallery according to claim 3, wherein, The step of generating the dynamic feedback according to the slurry performance prediction result further includes: Establish an adjustable parameter space, and the adjustable parameters of the adjustable parameter space include water-cement ratio, admixture ratio, stirring rate, and curing time; Establish an optimization target according to the standard preparation process, and establish a performance deviation according to the slurry performance prediction result and the optimization target; Use the performance deviation as an optimization constraint, perform parameter combination optimization within the adjustable parameter space, and generate the dynamic feedback according to the parameter combination optimization result.

5. The preparation method of the grouting material for the construction of the underground pipe gallery according to claim 1, characterized in that, The step of activating the feedback adjustment unit and performing slurry data monitoring further includes: Activate the acoustic sensor, perform bubble monitoring during the slurry stirring process, and generate a bubble feedback; Add the bubble feedback as an additional feedback to the slurry data set.

6. The preparation method of the grouting material for the construction of the underground pipe gallery according to claim 5, characterized in that, The method further includes: Activate the visual detection sensor, perform slurry monitoring during the stirring of the slurry, and generate a stratification identification result and a uniformity identification result; Add the stratification identification result and the uniformity identification result as additional feedback to the slurry data set.

7. The preparation method of the grouting material for the construction of the underground pipe gallery according to claim 1, characterized in that, After establishing the slurry data set, it further includes: Perform deviation verification on the slurry data set, and generate a deviation verification result; Perform warning matching using the deviation verification result, generate a warning signal, and report the warning signal.

8. A grouting material preparation system for the construction of underground pipe corridors, characterized in that, The system is used to execute the grouting material preparation method for the construction of underground pipe corridors described in any one of claims 1-7, including: A construction requirement analysis module, which is used to analyze the material construction requirements for the construction of underground pipe corridors, establish a set of material construction requirements, and the material construction requirements include the requirement for strengthening the bottom layer, the requirement for anti-seepage and leakage stoppage, and the requirement for high strength and durability; A ratio simulation and optimization module, which is used to perform ratio simulation and optimization of the material database using the material construction requirements, and establish a standard preparation process according to the ratio simulation and optimization results; A particle size optimization module, which is used to optimize the particle size through mechanical-chemical composite grinding technology after configuring the raw material ratio based on the standard preparation process; A cyclic stirring module, which is used to perform cyclic stirring after setting the grouting parameters according to the standard preparation process; A slurry data monitoring module, which is used to activate the feedback adjustment unit, perform slurry data monitoring, and establish a slurry data set, and the slurry data set includes viscosity and fluidity; A dynamic feedback optimization module, which is used to generate dynamic feedback using the slurry data set, optimize the standard preparation process according to the dynamic feedback, and then execute the preparation of grouting materials.

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