A real-time dynamic prediction system and method for the three-dimensional shape of high-pressure jet grouting piles

Abstract

The present invention relates to the technical field of three-dimensional form real-time prediction, and particularly to a real-time dynamic prediction system and method for the three-dimensional form of high-pressure jet grouting pile formation. The method includes the following steps: identifying the formation characteristics of the construction site to obtain formation distribution data; determining key monitoring points according to the formation distribution data and establishing a multi-source sensor monitoring network; performing anti-interference optimization processing on the multi-source sensor monitoring network and collecting real-time high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; performing spatio-temporal evolution processing on the real-time high-pressure jet grouting data based on the jetting pressure, lifting speed, and rotation speed to generate parameter change characteristic data. By obtaining and analyzing the construction formation distribution and high-pressure jet grouting parameters in real time, establishing a parameter-formation response relationship model, and specifically predicting the slurry diffusion and pile body forming dynamics for different formation characteristics, the present invention greatly improves the precision construction ability under complex formation conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional form real-time prediction, and particularly to a real-time dynamic prediction system and method for the three-dimensional form of high-pressure jet grouting piles. Background Art

[0002] High-pressure jet grouting pile is a new type of foundation engineering construction technology, which is widely used in civil engineering, foundation treatment and underground engineering. High-pressure jet grouting pile technology is a foundation treatment method based on high-pressure jetting slurry to cut, stir and solidify soil, mainly used for soft soil foundation reinforcement and groundwater control projects. Its core lies in injecting slurry into the soil at high pressure by rotating and lifting the nozzle, so as to form continuous columnar piles or walls, improving soil strength and stability.

[0003] Traditional real-time dynamic prediction methods for the three-dimensional form of high-pressure jet grouting piles often have the following problems: High-pressure jet grouting construction needs to adapt to various stratum types, and different strata (such as sand layers, clay layers, etc.) have different responses to the diffusion and penetration of high-pressure jetting slurry. Previous pile-forming methods usually process different strata based on empirical data, but lack a mechanism for dynamic prediction and real-time adjustment, making it difficult to accurately control the pile body form. Previous high-pressure jet grouting construction mainly estimates slurry diffusion and pile body formation through experience or limited monitoring means. However, due to the lack of effective prediction and control models, it is difficult to accurately control the pile body shape, and the pile-forming quality and reinforcement effect are uncertain, increasing the risk of project construction. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a real-time dynamic prediction system and method for the three-dimensional form of high-pressure jet grouting piles to solve at least one of the above technical problems.

[0005] To achieve the above object, a real-time dynamic prediction method for the three-dimensional form of high-pressure jet grouting piles includes the following steps:

[0006] Step S1: Identify the stratum characteristics of the construction site to obtain stratum distribution data; determine key monitoring points according to the stratum distribution data, and establish a multi-source sensor monitoring network;

[0007] Step S2: Perform anti-interference optimization processing on the multi-source sensor monitoring network, and collect real-time high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; perform spatio-temporal evolution processing on the real-time high-pressure jet grouting data based on jet pressure, lifting speed and rotation speed to generate parameter change characteristic data;

[0008] Step S3: Establish a parameter-stratum response relationship model based on the parameter change characteristic data and the stratum distribution data; analyze the slurry diffusion characteristics according to the parameter-stratum response relationship model, and evaluate the diffusion radius related to depth to generate slurry diffusion dynamic data;

[0009] Step S4: Conduct soil improvement correlation processing based on the slurry diffusion dynamic data and the real-time high-pressure jet grouting data to generate soil improvement dynamic characteristic data; identify the pile-forming influence area based on the soil improvement dynamic characteristic data to generate pile influence range data; perform stratum stress mode coupling based on the jet pressure, soil stress, and improvement effect according to the pile influence range data to generate soil action coupling data;

[0010] Step S5: Construct a deep learning prediction model based on the soil action coupling data, and use the deep learning prediction model to perform dynamic prediction of the three-dimensional shape of the pile to generate shape prediction data; perform real-time optimization of construction parameters based on the shape prediction data to obtain high-pressure jet grouting construction control feedback data.

[0011] Through the identification of formation characteristics, the present invention can obtain detailed formation distribution information of the construction area before construction, including soil layer type, thickness, mechanical properties, etc. By using this information as basic data and determining key monitoring points, the monitoring accuracy and coverage of the sensor network can be effectively improved, ensuring the integrity and accuracy of the data. At the same time, the establishment of a multi-source sensor network can monitor the changes in formation conditions in real time, providing high-precision data support for subsequent predictions. The anti-interference optimization process effectively reduces the interference of the complex construction site environment on the monitoring data through electromagnetic shielding and vibration isolation technologies, improving the reliability of the monitoring data. The parameters of high-pressure jet grouting construction (such as jetting pressure, lifting speed, rotation speed) are collected in real time, and these data are processed for spatio-temporal evolution to capture the dynamic changes during the construction process, generating parameter change characteristic data, which provides accurate input characteristics for the prediction model. Through the parameter-stratum response model constructed by the parameter change characteristic data and the formation distribution data, the response relationship between the construction parameters and the formation can be accurately simulated. This model not only provides a basis for the analysis of slurry diffusion characteristics, but also helps to identify the diffusion radius at different depths and formations, realizing the accurate prediction of the slurry diffusion range. This helps to prevent resource waste caused by excessive diffusion and ensure the depth and uniformity of soil improvement. By combining the slurry diffusion dynamic data with the real-time high-pressure jet grouting data, the effect of different parameter combinations on soil improvement can be analyzed. The dynamic characteristic data of soil improvement generated in this process provides an accurate basis for identifying the pile formation influence area. Based on the pile body influence range data, the formation stress mode coupling can clarify the stress changes and their transmission during the construction process, thereby optimizing the forming quality of the pile body. The deep learning model trained with the soil action coupling data can predict the three-dimensional shape changes of the pile body in real time, including cross-section, longitudinal changes and spatial distribution, etc. The shape prediction data generated by this model not only provides intuitive shape feedback for construction personnel, but also provides a reliable basis for real-time optimization of construction parameters. Through continuous feedback and optimization, it is ensured that the high-pressure jet grouting parameters during the construction process are always in the best state, improving the construction quality and the consistency of pile formation. This method realizes the comprehensive monitoring and dynamic prediction of the high-pressure jet grouting pile formation process, forming a closed-loop control system from formation characteristic identification to real-time construction optimization. Through real-time data collection, accurate diffusion analysis, soil improvement evaluation and deep learning prediction, not only the construction efficiency is improved, but also the pile formation quality and stability are significantly enhanced.

[0012] The present invention also provides a real-time dynamic prediction system for the three-dimensional shape of high-pressure jet grouting pile formation, which is used to execute the above-mentioned real-time dynamic prediction method for the three-dimensional shape of high-pressure jet grouting pile formation. The real-time dynamic prediction system for the three-dimensional shape of high-pressure jet grouting pile formation includes:

[0013] The site monitoring module is used to identify the formation characteristics of the construction site, obtain formation distribution data; determine key monitoring points according to the formation distribution data, and establish a multi-source sensor monitoring network;

[0014] The parameter acquisition module is used to perform anti-interference optimization processing on the multi-source sensor monitoring network, and collect real-time high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; perform spatio-temporal evolution processing on the real-time high-pressure jet grouting data based on jet pressure, lifting speed, and rotation speed to generate parameter change characteristic data;

[0015] The diffusion prediction module is used to establish a parameter-formation response relationship model according to the parameter change characteristic data and the formation distribution data; analyze the slurry diffusion characteristics according to the parameter-formation response relationship model, and evaluate the diffusion radius related to depth to generate slurry diffusion dynamic data;

[0016] The influence assessment module is used to perform soil improvement correlation processing according to the slurry diffusion dynamic data and the real-time high-pressure jet grouting data to generate soil improvement dynamic characteristic data; identify the pile formation influence area based on the soil improvement dynamic characteristic data to generate pile body influence range data; perform formation stress mode coupling based on jet pressure, soil stress, and improvement effect according to the pile body influence range data to generate soil action coupling data;

[0017] The shape control module is used to construct a deep learning prediction model according to the soil action coupling data, and use the deep learning prediction model to perform three-dimensional shape dynamic prediction of the pile body to generate shape prediction data; perform real-time optimization of construction parameters based on the shape prediction data to obtain high-pressure jet grouting construction control feedback data.

[0018] Through the detailed identification of formation characteristics, the present invention can obtain accurate formation distribution information, providing basic data for subsequent construction; determine key monitoring points to concentrate on monitoring the most important areas and improve monitoring efficiency; establish a multi-source sensor network to ensure the comprehensiveness of monitoring data, improve data reliability and accuracy, and provide support for construction decision-making. Through optimization processing, the influence of external interference on sensor data can be effectively reduced, and the reliability of data acquisition can be improved. Real-time collection of high-pressure jet grouting parameters enables every key data in the construction process to be captured in a timely manner, providing a basis for dynamically adjusting the construction plan. Spatiotemporal evolution processing based on jet pressure, lifting speed, and rotation speed can comprehensively understand the variation law of construction parameters, laying a foundation for subsequent analysis. By establishing a parameter-formation response relationship model, the influence of different construction parameters on formation response can be quantified, enhancing the scientific nature of construction; analyzing the diffusion characteristics of the slurry and the diffusion radius related to depth can better understand the behavior of the slurry in the soil body, optimize the slurry formula and jetting strategy; generating dynamic slurry diffusion data can provide basic information for subsequent impact assessment. By analyzing the dynamic characteristic data of soil improvement, the influence of the slurry on the soil body can be evaluated in a timely manner, providing a basis for construction adjustment. Identifying the data of the influence range of the pile body can accurately evaluate the influence of pile construction on the surrounding soil body and ensure construction safety. Through formation stress mode coupling, jet pressure, soil stress, and improvement effect can be comprehensively considered to ensure the scientific and reasonable nature of the construction plan. Through the deep learning prediction model, dynamic prediction of the three-dimensional shape of the pile body can be realized, improving the prediction accuracy; real-time optimization of construction parameters based on the shape prediction data can ensure flexibility and adaptability during the construction process. Generating high-pressure jet grouting construction control feedback data can timely adjust the construction strategy, improve construction efficiency, and reduce risks. Through the collaborative work of the above modules, the entire system not only improves the automation and intelligent level of high-pressure jet grouting construction, but also provides comprehensive guarantee for real-time monitoring, data analysis, and decision support during the construction process. This integrated solution can effectively improve the safety, efficiency, and quality of construction and meet the high requirements of modern engineering construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0020] Figure 1 It is a schematic flow chart of the steps of the method for real-time dynamic prediction of the three-dimensional shape of a high-pressure jet grouting pile formed by the present invention;

[0021] Figure 2 is Figure 1 a detailed schematic flow chart of step S1 in

[0022] Figure 3 is Figure 1Schematic diagram of the detailed step - by - step process of step S2 in Detailed implementation mode

[0023] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, so repeated descriptions thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that although terms such as "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0026] To achieve the above - mentioned purpose, please refer to Figures 1 to 3 , the present invention provides a real - time dynamic prediction method for the three - dimensional shape of high - pressure jet grouting piles. The method includes the following steps:

[0027] Step S1: Identify the formation characteristics of the construction site to obtain formation distribution data; determine key monitoring points according to the formation distribution data, and establish a multi - source sensor monitoring network;

[0028] Step S2: Perform anti - interference optimization processing on the multi - source sensor monitoring network, and perform real - time acquisition of high - pressure jet grouting parameters to obtain real - time high - pressure jet grouting data; perform spatio - temporal evolution processing on the real - time high - pressure jet grouting data based on jet pressure, lifting speed, and rotation speed to generate parameter change characteristic data;

[0029] Step S3: Establish a parameter - formation response relationship model according to the parameter change characteristic data and the formation distribution data; analyze the slurry diffusion characteristics according to the parameter - formation response relationship model, and evaluate the diffusion radius related to depth to generate slurry diffusion dynamic data;

[0030] Step S4: Perform soil improvement correlation processing based on the slurry diffusion dynamic data and real-time high-pressure jet grouting data to generate soil improvement dynamic characteristic data; identify the pile-forming influence area based on the soil improvement dynamic characteristic data to generate pile influence range data; perform formation stress mode coupling based on the jet pressure, soil stress, and improvement effect according to the pile influence range data to generate soil action coupling data;

[0031] Step S5: Construct a deep learning prediction model based on the soil action coupling data, and use the deep learning prediction model to perform three-dimensional shape dynamic prediction of the pile to generate shape prediction data; perform real-time optimization of construction parameters based on the shape prediction data to obtain high-pressure jet grouting construction control feedback data.

[0032] In the embodiment of the present invention, with reference to Figure 1 As shown, it is a schematic flow chart of the steps of a method for real-time dynamic prediction of the three-dimensional shape of high-pressure jet grouting piles in the present invention. In this example, the method for real-time dynamic prediction of the three-dimensional shape of high-pressure jet grouting piles includes the following steps:

[0033] Step S1: Identify the formation characteristics of the construction site to obtain formation distribution data; determine key monitoring points according to the formation distribution data, and establish a multi-source sensor monitoring network;

[0034] In the embodiment of the present invention, in the construction site, technologies such as borehole sampling or ground penetrating radar are used to identify the formation characteristics to obtain specific formation distribution data, covering information such as soil layer thickness, soil density, and permeability coefficient. According to these data, geological information analysis software is used to determine the positions suitable for installing monitoring equipment, and key monitoring points are set. Then, a multi-source monitoring network including sensors, such as piezoelectric sensors and accelerometers, is arranged based on these points to continuously collect key parameters and realize real-time monitoring of the formation. In the actual scenario of urban subway foundation pit construction, the key points can be set at 5 meters, 10 meters, and 15 meters away from the center of the jet grouting pile to track the formation response at different radii.

[0035] Step S2: Perform anti-interference optimization processing on the multi-source sensor monitoring network, and perform real-time acquisition of high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; perform spatio-temporal evolution processing on the real-time high-pressure jet grouting data based on the jet pressure, lifting speed, and rotation speed to generate parameter change characteristic data;

[0036] In the embodiment of the present invention, anti-interference optimization is implemented for the multi-source sensor monitoring network, and filtering technology is used to reduce the influence of electromagnetic interference from construction equipment on signals. Subsequently, through the embedded acquisition system, high-pressure jet grouting parameters such as jet pressure, lifting speed, rotation speed, etc. are collected in real time to generate real-time jet grouting data. During the data acquisition process, combined with the jet grouting equipment used in the actual site, the jet pressure is set at 15 MPa, the lifting speed is controlled at 0.2 m / min, and the rotation speed is adjusted according to the formation conditions for spatio-temporal evolution analysis to obtain parameter change characteristic data, ensuring that the monitoring data reflects the dynamic characteristics of the formation response.

[0037] Step S3: Establish a parameter-formation response relationship model based on the parameter change characteristic data and the formation distribution data; analyze the slurry diffusion characteristics according to the parameter-formation response relationship model, and evaluate the diffusion radius related to depth to generate slurry diffusion dynamic data;

[0038] In the embodiment of the present invention, the parameter change characteristic data and the formation distribution data in Step S2 are used to construct a parameter-formation response relationship model to analyze the diffusion effects of parameters such as jet pressure and lifting speed in different formations. Based on this model, using fluid diffusion simulation software, the diffusion characteristics of the slurry are analyzed, and the diffusion radius of the slurry at different depths is evaluated. For example, in the sandy formation, the slurry diffusion radius is 0.6 meters, while in the clay layer, it diffuses to 0.3 meters, generating corresponding slurry diffusion dynamic data to ensure that the diffusion effect meets the design requirements.

[0039] Step S4: Perform soil improvement correlation processing based on the slurry diffusion dynamic data and the real-time high-pressure jet grouting data to generate soil improvement dynamic characteristic data; identify the influence area of pile formation based on the soil improvement dynamic characteristic data to generate pile body influence range data; perform formation stress mode coupling based on the jet pressure, soil stress, and improvement effect according to the pile body influence range data to generate soil action coupling data;

[0040] In the embodiment of the present invention, the slurry diffusion dynamic data and the real-time high-pressure jet grouting data are used to establish a soil improvement correlation model, and parameters such as the diffusion radius, jet grouting speed, and jet pressure are associated with the soil improvement effect to generate dynamic characteristic data. Further, based on this data, the influence area of pile formation is identified to analyze the influence range of the pile body on the surrounding soil. Then, based on the pile body influence range data, the jet pressure, soil stress, and improvement effect are coupled and analyzed to generate soil action coupling data. In the soft soil layer, through the coupling analysis, it can be known that under the condition of a jet pressure of 12 MPa, the soil improvement effect is significant, and area data with an influence radius of 0.5 meters is generated.

[0041] Step S5: Construct a deep learning prediction model based on the soil-acting coupling data, and use the deep learning prediction model to perform dynamic prediction of the three-dimensional shape of the pile body to generate shape prediction data; perform real-time optimization of construction parameters based on the shape prediction data to obtain high-pressure jet grouting construction control feedback data.

[0042] In an embodiment of the present invention, according to the soil-acting coupling data, a convolutional neural network is applied to construct a deep learning model, and the model is trained to predict the three-dimensional shape of the pile body. The input includes features such as jet grouting parameters and formation stress. Taking the jet grouting pile data at different time nodes as training samples, the model weights are optimized to generate shape prediction data. During on-site construction, the construction parameters are optimized in real time based on the predicted shape. For example, in a clay formation, the jetting pressure is appropriately reduced to 10 MPa to ensure uniform formation of the pile body. The generated control feedback data is transmitted back to the construction system in real time for dynamically adjusting the jet grouting construction parameters.

[0043] Through the identification of formation characteristics, the detailed formation distribution information of the construction area can be obtained before construction, including soil layer types, thicknesses, mechanical properties, etc. By using this information as basic data and determining key monitoring points, the monitoring accuracy and coverage of the sensor network can be effectively improved, ensuring the integrity and accuracy of the data. At the same time, the establishment of a multi-source sensor network can monitor the changes in formation conditions in real time, providing high-precision data support for subsequent predictions. Through anti-interference optimization processing using electromagnetic shielding and vibration isolation technologies, the interference of the complex construction site environment on the monitoring data is effectively reduced, improving the reliability of the monitoring data. The parameters of high-pressure jet grouting construction (such as jetting pressure, lifting speed, rotation speed) are collected in real time, and these data are processed for spatio-temporal evolution to capture the dynamic changes during the construction process, generating parameter change characteristic data, which provides accurate input characteristics for the prediction model. Through the parameter-formation response model constructed by the parameter change characteristic data and the formation distribution data, the response relationship between the construction parameters and the formation can be accurately simulated. This model not only provides a basis for the analysis of slurry diffusion characteristics, but also helps to identify the diffusion radius at different depths and formations, realizing the accurate prediction of the slurry diffusion range. This helps to prevent resource waste caused by excessive diffusion and ensure the depth and uniformity of soil improvement. By combining the slurry diffusion dynamic data with the real-time high-pressure jet grouting data, the effects of different parameter combinations on soil improvement can be analyzed. The dynamic characteristic data of soil improvement generated in this process provides an accurate basis for identifying the pile-forming influence area. Based on the pile body influence range data, the formation stress mode coupling can clarify the stress changes and their transmission during the construction process, thereby optimizing the forming quality of the pile body. The deep learning model trained with the soil action coupling data can predict the three-dimensional shape changes of the pile body in real time, including cross-section, longitudinal changes, and spatial distribution, etc. The shape prediction data generated by this model not only provides intuitive shape feedback for construction personnel, but also provides a reliable basis for real-time optimization of construction parameters. Through continuous feedback and optimization, it is ensured that the high-pressure jet grouting parameters during the construction process are always in the best state, improving the construction quality and the consistency of pile formation. This method realizes the comprehensive monitoring and dynamic prediction of the high-pressure jet grouting pile formation process, forming a closed-loop control system from formation characteristic identification to real-time construction optimization. Through real-time data collection, accurate diffusion analysis, soil improvement evaluation, and deep learning prediction, not only the construction efficiency is improved, but also the pile formation quality and stability are significantly enhanced.

[0044] Preferably, step S1 includes the following steps:

[0045] Step S11: Drill and sample at multiple points on the construction site to obtain formation physical sample data;

[0046] Step S12: Identify the types, distribution depths, and thicknesses of soil layers in the formation physical sample data to obtain formation structure data;

[0047] Step S13: Analyze the mechanical properties of the formation physical sample data based on density, cohesion, internal friction angle, and water content according to the formation structure data, so as to obtain the soil layer mechanical property data;

[0048] Step S14: Conduct hydrogeological characteristic analysis based on the soil layer mechanical property data to obtain hydrogeological characteristic data, where the hydrogeological characteristic analysis includes water level observation around the construction site, permeability test of the formation physical sample data, and groundwater distribution analysis based on water content and permeability;

[0049] Step S15: Combine the hydrogeological characteristic data, soil layer mechanical property data, and formation structure data into formation distribution data;

[0050] Step S16: Determine the key monitoring points according to the formation distribution data and establish a multi-source sensor monitoring network.

[0051] As an embodiment of the present invention, refer to Figure 2 shown in Figure 1 is the detailed step flow diagram of Step S1 in

[0052] Step S11: Conduct multi-point drilling sampling on the construction site to obtain formation physical sample data;

[0053] In the embodiment of the present invention, distributed drilling sampling is carried out at multiple positions on the construction site using drilling equipment to ensure that the samples can represent the characteristics of different depths and formation distributions. Usually, samples are taken at a depth of every 10 - 15 meters at key positions. During drilling, a suitable sampler, such as a rotary sampler or a double-tube sampler, is selected to keep the soil in its original state to the greatest extent. After the samples are taken out, they are sealed and preserved for subsequent laboratory analysis. Each sample is marked with specific depth information and borehole position information during sampling for position correspondence in subsequent analysis.

[0054] Step S12: Identify the type, distribution depth, and thickness of the soil layer for the formation physical sample data to obtain formation structure data;

[0055] In the embodiment of the present invention, the collected formation physical sample data is analyzed in detail in the laboratory. The particle size distribution of the soil layer is tested using a laser particle size analyzer or sieving method, and the mineral components in the soil are identified using magnetic or optical recognition techniques. For the identification of depth and thickness, the depth changes and thickness distributions of different soil layers are confirmed layer by layer by comparing the sample data of different boreholes and combining formation similarity speculation, so as to form complete formation structure data, providing a basis for subsequent geological analysis.

[0056] Step S13: Analyze the mechanical properties of the formation physical sample data based on density, cohesion, internal friction angle, and water content according to the formation structure data, so as to obtain the soil layer mechanical property data;

[0057] In the embodiment of the present invention, the mechanical properties of the physical sample are analyzed based on the formation structure data. The density is measured using a soil density meter, and the cohesion and internal friction angle are measured using a uniaxial compression test and a direct shear test. Triaxial shear tests are carried out under different water content conditions to accurately obtain the mechanical property data of the soil layer under different hydrogeological conditions. Through multi-point testing and statistical processing, comprehensive mechanical property data are obtained, providing a reference for further formation stability analysis.

[0058] Step S14: Analyze the hydrogeological characteristics according to the soil layer mechanical property data, so as to obtain the hydrogeological characteristic data, where the hydrogeological characteristic analysis includes water level observation around the construction site, permeability test of the formation physical sample data, and groundwater distribution analysis based on water content and permeability;

[0059] In the embodiment of the present invention, the hydrogeological characteristics are analyzed in detail based on the soil layer mechanical property data. Water level monitoring is carried out on site, and the water level changes are regularly recorded by installing water level observation wells. At the same time, the permeability of the formation physical sample is tested in the laboratory, and the permeability coefficient is measured using a permeameter. The groundwater distribution characteristics are analyzed by combining the soil water content and permeability data, and the finite element method is used to simulate the flow of groundwater in different soil layers to obtain accurate hydrogeological characteristic data, providing guidance for construction design.

[0060] Step S15: Combine the hydrogeological characteristic data, the soil layer mechanical property data, and the formation structure data into formation distribution data;

[0061] In the embodiment of the present invention, the hydrogeological characteristic data, the soil layer mechanical property data, and the formation structure data are integrated. The geographic information system (GIS) platform is used to perform spatial processing on various data to generate a three-dimensional formation distribution model. The structures, mechanical properties, and hydrogeological characteristics of different formations are displayed in layers in the GIS platform to ensure that the comprehensive situation of each attribute of the formation can be intuitively viewed during the analysis. The generated formation distribution data has a layered visualization function, facilitating real-time reference during the construction process.

[0062] Step S16: Determine the key monitoring points according to the formation distribution data and establish a multi-source sensor monitoring network.

[0063] In the embodiment of the present invention, the formation area that has a greater impact on the construction stability is selected as the key monitoring point according to the formation distribution data, such as the junction of highly permeable soil layer and cohesive soil layer. Multi-source sensors, including earth pressure gauges, piezometers, water level monitors, etc., are installed at the key points to form a full-coverage sensor monitoring network. To ensure the timeliness and reliability of the data, the sensor network transmits the monitoring data to the central control system in real time through wireless communication technology for subsequent construction monitoring and analysis.

[0064] Through multi-point drilling and sampling, the present invention can directly obtain the formation physical samples at different depths of the construction site, ensuring accurate actual measurement data of the geological conditions. The drilling and sampling form multiple data points within the area, laying a foundation for subsequent analysis and reducing the uncertainty brought by the formation inhomogeneity. The identification of the type, depth, and thickness of the soil layer can clarify the distribution of each soil layer. The formation structure data obtained in this process helps in the formulation and optimization of the construction plan, especially in the key soil layer areas that need attention during pile formation, thereby reducing the construction risks brought by misjudgment and unexpected formation mutations. The mechanical properties of the soil layer directly affect the pressure and slurry diffusion behavior during pile formation. Through the analysis of mechanical parameters such as density, cohesion, and internal friction angle, the compressive capacity and stability of each soil layer can be deeply understood, providing guidance for the optimization of parameters such as pressure, speed, and slurry volume required during pile formation. These data can also help predict the resistance and deformation characteristics that may be encountered during the construction process, reducing construction risks. The hydrogeological characteristics have a direct impact on the slurry diffusion and soil improvement effect. Water level observation, permeability test, and groundwater distribution analysis help to understand the fluidity and diffusibility of water in the soil layer. By clarifying the hydrogeological conditions, the construction team can reasonably adjust the slurry pressure and diffusion rate, prevent excessive slurry diffusion or leakage, improve construction accuracy, and avoid unnecessary construction costs. Integrating the hydrogeological characteristics, mechanical properties, and formation structure data into a comprehensive formation distribution data can provide an overall geological profile, providing a more intuitive reference for construction decisions. This integrated data facilitates the construction team to comprehensively grasp the geological characteristics of the site, improving the efficiency and accuracy of data use. Based on the formation distribution data, key monitoring points are reasonably arranged and a multi-source sensor network is established, enabling real-time monitoring of formation and construction parameters during pile formation construction. Through the sensor monitoring network, the construction team can continuously track key indicators such as soil stress, slurry diffusion, and formation displacement, ensuring that all operations during the construction process meet the predetermined safety and quality standards, promptly discovering and handling abnormal situations, and guaranteeing construction quality and safety. These steps, from formation sampling to data integration and then to the establishment of the monitoring network, provide detailed geological data and real-time monitoring means for the construction process. The integrated data and monitoring network provide a scientific basis for the adjustment and optimization of subsequent high-pressure jet grouting construction parameters, helping the construction team to achieve efficient and accurate control during the construction process, ultimately improving the quality and safety of pile formation and reducing the potential risks and costs of engineering construction.

[0065] Preferably, step S16 includes the following steps:

[0066] Step S161: Conduct a mutation analysis on the soil layer interface characteristics according to the formation distribution data to obtain soil layer mutation point data, where the mutation analysis is specifically to identify the positions where the soil layer type and mechanical properties change significantly;

[0067] In the embodiment of the present invention for mutation analysis, first, the formation distribution data is input into geological modeling software. By analyzing the soil layer profile diagram and combining the collected physical sample data of the soil layer, the interfaces where the soil layer types and mechanical properties in the formation change significantly are identified. Specifically, using data such as density, cohesion, and internal friction angle, the sudden change positions of the soil layer in these properties are observed. For example, if the density suddenly changes from 1800 kg / m 3 to 2100 kg / m 3 in a certain area, then this point can be considered as a soil layer mutation point. All mutation points are marked in three-dimensional space to form complete soil layer mutation point data for identifying possible impacts on engineering stability.

[0068] Step S162: Conduct a sensitivity analysis of the soil layer stress state based on the formation distribution data to identify the areas where the soil body stress changes significantly, and obtain stress-sensitive area data. The stress sensitivity analysis includes shear strength evaluation based on cohesion and internal friction angle, compressibility evaluation based on density and water content, and consolidation characteristic evaluation based on permeability;

[0069] In the embodiment of the present invention, a sensitivity analysis of the soil layer stress state is conducted on the formation distribution data to identify the areas where the soil body stress changes significantly. During the analysis process, first, the shear strength is evaluated to calculate the influence of cohesion and internal friction angle on the shear resistance of different soil layers. Then, based on the density and water content of the soil body, its compressibility characteristics are evaluated, and the compression modulus is obtained through a one-dimensional compression test to further analyze the stability of the soil body. Finally, based on the permeability test, the consolidation characteristics of different soil layers are evaluated using the consolidation theory. The areas with significant changes in shear strength, compressibility, or consolidation are marked as stress-sensitive areas, and their spatial coordinates and depth information are recorded to form stress-sensitive area data, providing basic data for the subsequent layout of monitoring points.

[0070] Step S163: Determine the spatial distribution of key monitoring points based on the preset site construction conditions, engineering requirement data, soil layer mutation point data, and stress-sensitive area data to obtain monitoring point layout data;

[0071] In the embodiment of the present invention, according to the construction conditions of the site (such as excavation depth, support type, etc.) and engineering requirements, combined with the soil layer mutation point data and stress-sensitive area data, the key monitoring points are arranged. By analyzing the distribution characteristics of the mutation points and stress-sensitive areas, monitoring points are preferentially arranged in these areas to monitor the formation state of the key areas in real time. For example, stress and displacement sensors are arranged at the mutation points found in the area with a foundation excavation depth of 10 meters, or water level and pressure sensors are arranged in the high compressibility stress-sensitive area to monitor the stress changes of the soil body. Finally, the monitoring point layout data is generated to provide guidance for the next configuration of sensor types.

[0072] Step S164: Determine the types of sensors according to the monitoring point layout data, and establish a multi-source sensor monitoring network including pressure sensors, displacement sensors, strain sensors, and water level sensors.

[0073] In the embodiment of the present invention, according to the monitoring point layout data, appropriate sensor types are selected to establish a multi-source sensor monitoring network. For the points with displacement monitoring requirements, displacement sensors are selected; pressure sensors are installed at the points where the stress change of the soil layer needs to be monitored; water level sensors are configured in the areas where the groundwater level changes significantly; strain sensors are configured in the soil layer areas sensitive to stress. The sensors are connected to the central control system through wired or wireless networks to ensure the transmission and recording of real-time data, forming a complete multi-source sensor monitoring network, so as to provide real-time feedback and early warning for subsequent engineering construction.

[0074] Through mutation analysis, the present invention identifies positions where the soil layer type and mechanical properties change significantly, and can locate areas where geological features change suddenly (i.e., soil layer mutation points), such as the interface between hard soil layers and loose soil layers or significant change areas of the groundwater level. These mutation points have an important impact on the construction process. Mutation analysis helps to consider in advance the requirements of different soil layers for pile-forming parameters in the construction design, avoid excessive disturbance of sensitive areas during the construction process, thereby reducing the probability of unexpected situations and ensuring the smooth progress of the construction. Stress sensitivity analysis can identify areas with large changes in soil stress (stress-sensitive areas) and evaluate in detail the shear strength, compressibility, and consolidation characteristics of the soil layer. During the pile-forming process, the soil layer in the stress-sensitive area is prone to deformation or instability due to the influence of force. By identifying these areas, the jetting pressure and construction speed can be more targeted controlled. Understanding the stress characteristics of these areas in advance can avoid formation instability caused by excessive diffusion of slurry or uneven pressure distribution in sensitive areas, and further improve the pile-forming quality and construction safety. According to the site construction conditions, engineering requirements, and data of soil layer mutation points and stress-sensitive areas, key monitoring points are arranged to ensure that the monitoring network reasonably covers the key positions of the construction area. In this way, the monitoring points can accurately cover important geological structures and stress-sensitive areas, avoiding monitoring blind spots. This layout can monitor in real time the stress, deformation, and water level changes of important soil layers during the construction process, enabling any anomalies during the construction process to be detected and adjusted in a timely manner, thereby effectively improving the construction accuracy and safety. Installing multi-source sensors including pressure sensors, displacement sensors, strain sensors, and water level sensors at the determined monitoring points can comprehensively capture various key parameters of the soil layer. This multi-source sensor network can not only monitor the pressure and deformation of the soil layer in real time, but also detect the changes in the groundwater level, helping the construction team to obtain the soil state information immediately. The establishment of the monitoring network enables the construction team to master the mechanical response of the soil layer and the slurry diffusion situation in the first time, which is beneficial to dynamically adjust the construction parameters according to the monitoring data, and further enhance the intelligence and safety of the construction process. Through soil layer mutation and stress sensitivity analysis, the construction team can deeply understand the formation characteristics and stress distribution characteristics of the site, and ensure that the monitoring network covers important positions. The reasonably arranged monitoring points and multi-source sensor network can provide real-time feedback of key parameters during the construction process, helping the construction team to carry out precise control and dynamic optimization, significantly improving the quality and safety of the pile-forming construction. At the same time, these measures can effectively reduce the construction risks brought by formation inhomogeneity, provide a more scientific monitoring basis for the entire project, ensure the smooth progress of the construction, and reduce costs.

[0075] Preferably, step S2 includes the following steps:

[0076] Step S21: Perform anti-interference optimization processing on the multi-source sensor monitoring network based on electromagnetic shielding and vibration isolation, and collect real-time parameters during the high-pressure jet grouting construction process to obtain real-time high-pressure jet grouting data;

[0077] Step S22: Segment the injection pressure in the real-time high-pressure jet grouting data based on time series to establish a pressure-time relationship curve;

[0078] Step S23: Calculate the frequency characteristics and amplitude distribution of the pressure fluctuation according to the pressure-time relationship curve, and perform correlation analysis between the pressure fluctuation and the depth to obtain pressure-depth evolution characteristic data;

[0079] Step S24: Establish a relationship curve between the lifting speed and the depth based on the real-time high-pressure jet grouting data, and calculate the speed change rate in different depth segments to obtain speed spatio-temporal evolution characteristic data;

[0080] Step S25: Record the real-time changes of the rotation speed in the real-time high-pressure jet grouting data, and perform correlation analysis between the rotation speed and the depth to obtain rotation spatio-temporal evolution characteristic data;

[0081] Step S26: Analyze the comprehensive influence of the parameter combination on the pile body according to the pressure-depth evolution characteristic data, the speed spatio-temporal evolution characteristic data, and the rotation spatio-temporal evolution characteristic data, and identify the critical values and optimal intervals of the key parameter combinations to generate spatio-temporal evolution characteristic data of the parameter combination;

[0082] Step S27: Establish a dynamic evolution model of the three-dimensional parameter space according to the spatio-temporal evolution characteristic data, and extract the parameter change characteristics to obtain parameter change characteristic data.

[0083] As an embodiment of the present invention, refer to Figure 3 shown, for Figure 1 the detailed step flow diagram of step S2 in

[0084] Step S21: Perform anti-interference optimization processing on the multi-source sensor monitoring network based on electromagnetic shielding and vibration isolation, and collect real-time parameters during the high-pressure jet grouting construction process to obtain real-time high-pressure jet grouting data;

[0085] When the embodiment of the present invention performs anti-interference optimization processing on the multi-source sensor monitoring network, first, an electromagnetic shielding device is installed for the sensors and lines to reduce the electromagnetic radiation interference generated by surrounding devices. In addition, rubber pads or shock absorbers are added to the brackets where the sensors are installed, so as to effectively isolate the vibration interference from construction equipment. Next, key parameters are collected in real time during the high-pressure jet grouting construction process, including jetting pressure, lifting speed, and rotation speed, to ensure the continuity and accuracy of data collection, so as to provide high-quality real-time high-pressure jet grouting data for subsequent analysis.

[0086] Step S22: Segment the jetting pressure in the real-time high-pressure jet grouting data based on time series, so as to establish a pressure-time relationship curve;

[0087] The embodiment of the present invention performs segmentation processing on the jetting pressure in the collected real-time high-pressure jet grouting data according to time series. Specifically, using the time series analysis method, the pressure data is segmented at a fixed interval of every 10 seconds, so as to draw a pressure-time relationship curve. Through these segmented data, the variation law of the jetting pressure in different time periods can be accurately displayed, laying a data foundation for subsequent pressure fluctuation analysis.

[0088] Step S23: Calculate the frequency characteristics and amplitude distribution of the pressure fluctuation according to the pressure-time relationship curve, and perform a correlation analysis between the pressure fluctuation and the depth, so as to obtain pressure-depth evolution characteristic data;

[0089] The embodiment of the present invention calculates the frequency characteristics and amplitude distribution of the pressure fluctuation based on the pressure-time relationship curve. First, the frequency characteristics of the pressure fluctuation are extracted through Fourier transform to obtain the main fluctuation frequency and the secondary frequency, and then the amplitude distribution at each frequency is calculated. In addition, combining the data at different depths, a correlation analysis between the pressure fluctuation and the depth is performed, and the significance of the pressure fluctuation at different depths is calculated through the regression analysis method, so as to obtain pressure-depth evolution characteristic data, providing basic data for judging the influence of the pressure on the pile formation at different depths.

[0090] Step S24: Establish a relationship curve between the lifting speed and the depth based on the real-time high-pressure jet grouting data, and calculate the rate of change of the speed in different depth segments, so as to obtain speed spatio-temporal evolution characteristic data;

[0091] In the embodiment of the present invention, when analyzing the lifting speed in real-time high-pressure jet grouting data, the lifting speed is first segmented by depth, and a relationship curve between depth and lifting speed is established. Specifically, the segmentation interval is set to every 0.5 meters, the rate of change of speed in each segment is calculated, and the spatio-temporal evolution characteristic data of speed is formed. By observing the curve, the trend of speed change in different depth segments can be found. For example, the situation where the speed is relatively small at a deeper position may be affected by the soil layer density, providing the evolution characteristics of depth and speed, and providing a basis for optimizing the control parameters of the lifting speed.

[0092] Step S25: Record the real-time change of the rotation speed in the real-time high-pressure jet grouting data, and conduct an analysis of the correlation characteristics between the rotation speed and the depth, so as to obtain the spatio-temporal evolution characteristic data of rotation;

[0093] The embodiment of the present invention records the rotation speed in the real-time high-pressure jet grouting data, especially the change of the rotation speed at different depths. Whenever the depth changes by 0.5 meters, the rotation speed is recorded and its correlation characteristics with the depth are analyzed. For example, at a depth of 10 meters, the rotation speed is stable at 20 revolutions per minute, and at a deeper depth, the rotation speed increases to 25 revolutions per minute. Through this analysis of depth correlation characteristics, the spatio-temporal evolution characteristic data of rotation is obtained, providing control suggestions for the rotation speed and helping to optimize the construction effect at different depths.

[0094] Step S26: Analyze the comprehensive influence of parameter combinations on the formation of the pile body according to the pressure-depth evolution characteristic data, the spatio-temporal evolution characteristic data of speed, and the spatio-temporal evolution characteristic data of rotation, and identify the critical values and optimal intervals of the key parameter combinations, so as to generate the spatio-temporal evolution characteristic data of the parameter combinations;

[0095] The embodiment of the present invention analyzes the comprehensive influence of different parameter combinations on the formation of the pile body according to the pressure-depth evolution characteristic data, the spatio-temporal evolution characteristic data of speed, and the spatio-temporal evolution characteristic data of rotation. The critical values of the key parameter combinations are identified through statistical analysis methods. For example, the combination of jetting pressure of 15 MPa, lifting speed of 2 m / min, and rotation speed of 20 revolutions per minute has the best effect on the formation of the pile body. Further, the optimal intervals of these parameters are determined, and the spatio-temporal evolution characteristic data of the parameter combinations is generated, providing clear parameter control suggestions for subsequent construction.

[0096] Step S27: Establish a dynamic evolution model of the three-dimensional parameter space according to the spatio-temporal evolution characteristic data, and extract the parameter change characteristics, so as to obtain the parameter change characteristic data.

[0097] In an embodiment of the present invention, based on spatio-temporal evolution characteristic data, a dynamic evolution model of the parameter space is established using 3D modeling software to show the changes in pressure, velocity, and rotational velocity at different depths and times. Next, key parameter change characteristics are identified through feature extraction techniques, such as the peak or mutation point of parameter changes at a specific depth. Through these data, parameter change characteristic data is obtained, providing an accurate basis for real-time monitoring and parameter adjustment during the construction process to ensure that the pile formation meets the expected quality.

[0098] Through anti-interference processing of electromagnetic shielding and vibration isolation, the present invention can effectively reduce the interference of the external environment on sensor data, improve the accuracy and stability of data acquisition. Accurately collect real-time parameters such as jet pressure, lifting speed, and rotation speed during the high-pressure jet grouting construction process to ensure the reliability of monitoring data and provide high-quality basic data for subsequent analysis. By segmenting the jet pressure in time series and establishing a pressure-time relationship curve, the change trend of pressure can be visually observed. This process can identify the stage characteristics of pressure during the construction process, help the construction team master the pressure changes at different time points, and provide basic data support for the further optimization of construction parameters. Calculate the frequency characteristics and amplitude distribution of pressure fluctuations, and analyze them in combination with depth to obtain the correlation characteristics between pressure fluctuations and depth. This data can help the construction team better understand the pressure response characteristics at different depths. Especially when abnormal pressure fluctuations occur, adjustments can be made in a timely manner to ensure stability under depth changes and effectively prevent the risks of soil layer mutation or instability. By establishing a relationship curve between the lifting speed and depth and calculating the speed change rate in different depth segments, it can help the construction team identify the differences in speed with depth, which is helpful for optimizing construction parameters in different depth segments. The speed change rate analysis can also avoid construction quality problems caused by uneven lifting speed, and contribute to maintaining the uniformity and stability of the pile body. By recording the real-time changes in the rotation speed and analyzing them in combination with depth, the performance characteristics of the rotation speed at different depths can be deeply understood. The analysis of the correlation characteristics between the rotation speed and depth can reveal the influence of depth changes on the rotation effect, which is helpful for adjusting the rotation speed in key depth segments to achieve the best jet grouting effect and ensure the integrity and uniformity of the pile body structure. By comprehensively analyzing the evolution characteristics of pressure, lifting speed, and rotation speed in space and time, the critical values and optimal intervals of key parameter combinations during the pile formation process can be identified. This process can help the construction team determine the most effective parameter range for pile formation, so as to make real-time parameter adjustments during construction to ensure the quality and structural stability of the pile body. By establishing a dynamic evolution model of the three-dimensional parameter space, a comprehensive analysis of the spatio-temporal characteristics of parameter changes can be carried out. The extraction of the dynamic characteristics of the model helps the construction team understand the change trend and influence mechanism of parameters during the entire construction process, so as to achieve the optimal control of parameters. The model can provide a scientific basis for real-time construction optimization, realize intelligent construction control, and improve construction efficiency and pile formation quality. The systematic analysis method collects and analyzes multi-source sensor data, establishes the depth correlation and spatio-temporal evolution model of various parameters, enabling the construction process of high-pressure jet grouting pile formation to be dynamically adjusted according to real-time data. This data-based parameter control method not only improves the accuracy and stability of construction, but also greatly reduces the risks caused by uncertain factors during the construction process.At the same time, through comprehensive analysis and optimization of parameters, the construction team can grasp the key influencing factors in the pile-building process, optimize the pile-building quality, and ultimately improve the safety and reliability of the entire project.

[0099] Preferably, step S21 includes the following steps:

[0100] The multi-source sensor monitoring network is optimized for anti-interference based on electromagnetic shielding and vibration isolation, and real-time parameters during the high-pressure rotary spraying construction process are collected to obtain real-time high-pressure rotary spraying data; the electromagnetic shielding includes metal shell packaging of the sensor, shielding layer grounding of the signal cable, and planning of the sensor grounding point; the vibration isolation includes setting shock-absorbing brackets, using flexible connectors, and anti-vibration optimization of the sensor installation position.

[0101] When implementing the anti-interference optimization processing of the multi-source sensor monitoring network, the embodiment of the present invention first installs a metal shell for each sensor to form an electromagnetic shielding layer, and selects an aluminum alloy shell with a thickness of 1mm to resist external electromagnetic interference; at the same time, the signal cable is equipped with a shielding layer, and one end of it is grounded to form a good shielding effect. The specific grounding point is selected at the shortest distance from the sensor to reduce signal attenuation. In order to further improve the anti-interference effect, the grounding point of the sensor is also planned, and its grounding point is separated from other power grounding systems to avoid interference caused by ground return. In addition, in terms of vibration isolation, a dedicated shock-absorbing bracket is installed for each sensor bracket, and a shock-absorbing cushion layer made of rubber material is used to absorb high-frequency vibrations. At the same time, a flexible connector is used to connect the sensor to the fixed bracket to effectively reduce the vibration transmission generated during construction. Finally, based on the vibration characteristics of the construction site, the installation position of the sensor is optimized, and it is kept as far away from high-frequency vibration sources or areas with greater vibration interference as possible, such as away from the main engine of the rotary spraying machine and set in a stable area to ensure the stability and accuracy of data acquisition. After the implementation of the above-mentioned anti-interference optimization measures, real-time parameters of the high-pressure rotary grouting construction process were collected, including injection pressure, lifting speed, rotation speed, etc. These data were recorded in real time for subsequent analysis, thereby obtaining high-precision, low-interference real-time high-pressure rotary grouting data, which provided reliable data support for dynamic monitoring during subsequent construction.

[0102] The present invention encapsulates the sensor with a metal shell, which can effectively block external electromagnetic interference (EMI). In the high-pressure jet grouting construction environment, there are a large number of mechanical equipment and electrical equipment, generating strong electromagnetic fields, which may have an adverse impact on the data acquisition of the sensor. The metal shell can form the Faraday cage effect, isolating the electronic components inside the sensor from the external electromagnetic field and significantly improving the signal stability of the sensor. Grounding the shielding layer of the signal cable can effectively suppress the external electromagnetic interference conducted along the cable. The grounded shielding layer can provide a reliable grounding path for the cable, transferring the interference signal to the ground and preventing the interference signal from entering the data stream of the sensor, ensuring the accuracy of the data output by the sensor. Reasonably planning the grounding points of the sensor and ensuring that the grounding path of each sensor is short and stable can minimize the electromagnetic interference caused by the grounding current. At the same time, the reasonable arrangement of the grounding points can also avoid the formation of potential differences between different grounding points, avoiding the interference signals caused thereby and ensuring the stable operation of the sensor network. The shock-absorbing bracket can absorb and buffer the mechanical vibrations from the high-pressure jet grouting equipment and the construction site. During the construction process, the operation of the equipment will generate large vibrations, which will directly act on the sensor, causing data fluctuations and reducing the measurement accuracy. By installing the shock-absorbing bracket, this kind of vibration can be effectively isolated and attenuated, ensuring that the sensor collects data in a stable environment and improving the reliability of the monitoring data. The flexible connector has certain elasticity and flexibility, which can provide buffering between the sensor and the mounting bracket, further absorbing external vibrations and preventing the vibrations from being directly transmitted to the sensor. In this way, the sensor can reduce the vibration impact under the protection of the flexible connector and thus obtain more accurate real-time data. By analyzing the distribution of the vibration sources at the construction site, select an area far from the vibration sources and with less vibration to install the sensor. Optimizing the installation position can significantly reduce the vibration impact on the sensor, thereby improving the accuracy of the data it collects. Through the above electromagnetic shielding and vibration isolation measures, the multi-source sensor monitoring network can significantly improve the anti-interference ability of data acquisition in the complex environment of high-pressure jet grouting construction. Electromagnetic shielding and vibration isolation not only ensure the stability of the sensor network, but also reduce the influence of the external environment on the monitoring data, making the real-time data of high-pressure jet grouting collected more accurate and reliable. These optimization processes provide solid data support for subsequent data analysis, parameter regulation, and construction optimization, while reducing the construction errors caused by interference, thereby improving the construction efficiency and the reliability of the project quality.

[0103] Preferably, step S27 includes the following steps:

[0104] Step S271: Construct a three-dimensional parameter space with injection pressure, lifting speed, and rotation speed as the coordinate axes based on the spatio-temporal evolution characteristic data, so as to obtain the parameter space coordinate data;

[0105] In an embodiment of the present invention, based on the data of the injection pressure, lifting speed, and rotation speed collected in real time, these three parameters are respectively used as the X, Y, and Z axes of a three-dimensional space to construct a parameter space coordinate system. Then, each data point is mapped into this coordinate system in sequence according to the construction time to generate parameter space coordinate data, which is used to show the position relationship of each parameter in the three-dimensional space at different construction moments, laying a foundation for analyzing the parameter change trend. This operation needs to consider the specific depth of each data sampling point, and realizes the association and visualization of multi-layer data through depth marking.

[0106] Step S272: Establish a parameter change trajectory based on the parameter space coordinate data, and construct a parameter motion trajectory curve through the spatial mapping of time-series sampling points, so as to obtain parameter trajectory data;

[0107] In an embodiment of the present invention, using the aforementioned parameter space coordinate data, the parameter change trajectories of the injection pressure, lifting speed, and rotation speed are sequentially drawn according to the construction time sequence, and a parameter motion trajectory curve is generated by connecting adjacent time-series points. This curve can reflect the change trend of each parameter at different depths and times, so as to obtain parameter trajectory data. This trajectory helps to identify how the parameters change with time and depth during the construction process, providing a basis for analyzing the stability and mutation of parameter changes during the construction process.

[0108] Step S273: Perform depth-based hierarchical processing on the parameter trajectory data to identify the parameter change characteristics including the change trend, change rate, and mutual correlation in different depth segments, so as to obtain hierarchical feature data;

[0109] In an embodiment of the present invention, depth-based hierarchical processing is performed based on the parameter trajectory data. According to the trajectory data of different depth segments, the change trend, rate, and mutual correlation of the parameters are decomposed layer by layer to identify the parameter change characteristics of each depth layer and generate hierarchical feature data. For example, the parameter change rate and trend in the depth segment of 10-20 meters can reveal the injection effect in this depth segment, providing a basis for adjusting the process parameters at different depths.

[0110] Step S274: Construct a dynamic model of the three-dimensional parameter space according to the hierarchical feature data, and establish a continuous expression of parameter change through spatial interpolation and numerical fitting, so as to obtain dynamic evolution model data;

[0111] In an embodiment of the present invention, a dynamic model of the three-dimensional parameter space is constructed in combination with the hierarchical feature data. By performing spatial interpolation and numerical fitting on the data of each depth segment in this model, a continuous expression of parameter change is generated to obtain dynamic evolution model data. This operation smooths the discrete sampling data into a continuous surface curve, thus more intuitively showing the parameter change situation in different depth levels and facilitating a more comprehensive observation of the parameter evolution process.

[0112] Step S275: Extract features of gradient, curvature, and velocity based on parameter changes from the dynamic evolution model data to obtain model feature data;

[0113] In the embodiment of the present invention, detailed feature extraction is performed on the dynamic evolution model data. Specifically, the rate of change of parameters is extracted using gradient calculation, the shape change of the parameter trajectory is reflected by curvature calculation, and velocity feature analysis is used to identify the amplitude and direction of parameter fluctuations, generating model feature data. For example, the gradient change of the pressure parameter can reveal the stability of the injection pressure and provide suggestions for process adjustment.

[0114] Step S276: Perform parameter correlation analysis based on the coupling relationship and mutual feedback mechanism between parameters based on the model feature data to obtain parameter correlation data;

[0115] In the embodiment of the present invention, the coupling relationship analysis between parameters is carried out based on the model feature data. Through the mutual feedback mechanism between parameters, the correlation between injection pressure, lifting speed, and rotation speed is further identified, generating parameter correlation data. This step analyzes the correlation and the degree of mutual influence of the three parameters through numerical simulation and regression analysis methods, revealing the coupling behavior between parameters during the construction process.

[0116] Step S277: Extract indicators characterizing the dynamic change law of parameters according to the model feature data and the parameter correlation data to generate parameter change feature data.

[0117] In the embodiment of the present invention, key indicators capable of characterizing the dynamic change law of parameters, such as average change rate, maximum change amplitude, and parameter correlation coefficient, are extracted by combining the model feature data and the parameter correlation data, generating parameter change feature data. These indicators are used for further optimization of construction parameters. For example, when the injection pressure is low and the change is stable, the lifting speed can be appropriately increased to improve efficiency, thus completing the feedback of parameter optimization and construction control.

[0118] In the present invention, a three-dimensional parameter space is constructed with injection pressure, lifting speed, and rotation speed as coordinate axes, making the dynamic changes of each parameter clearly visible within the same space. This allows for a comprehensive observation of the interrelationships among the three key parameters, facilitating a more accurate analysis of their combined impact on pile formation and laying the foundation for subsequent trajectory analysis. The parameter change trajectory can visually display the evolution path of the parameters at different construction stages. By constructing the parameter motion trajectory curve, it is possible to observe the dynamic change trends of the parameters in the time series, which helps to identify key time points and the rate of parameter changes, effectively revealing the mutual influence and coupling relationship among the parameters and enhancing the understanding of the dynamic behavior of the parameters. By performing in-depth hierarchical processing on the parameter trajectory data, it is possible to more clearly identify the parameter change characteristics in different depth segments, including change trends, rates, and mutual correlations. The hierarchical feature data not only helps to deeply understand the variation law of the parameters with depth but also provides a reference basis for parameter adjustment and optimization under different formation conditions. After establishing the dynamic model, the change trends of the parameters can be presented in a continuous expression form, facilitating a more intuitive observation of their evolution process. Through spatial interpolation and numerical fitting, the dynamic model data has high-precision temporal and spatial continuity, which helps to optimize the construction parameters in real time and improve the accuracy of prediction and control. By extracting the gradient, curvature, and velocity characteristics of the parameters, it is possible to more deeply understand the rate, direction, and amplitude of parameter changes. This feature extraction provides a more representative quantitative index for analyzing the dynamic change patterns of the parameters, helping to determine the sensitive areas and key intervals of parameter changes during construction, thereby guiding the fine adjustment of construction parameters. Based on the coupling relationship and mutual feedback mechanism among the parameters, analyzing the parameter correlation can reveal the internal connections between different parameters and identify potential interactions. The parameter correlation data helps to judge the degree of influence of parameter changes on other parameters, enhancing the grasp of the comprehensive effects of multiple parameters during construction and thus achieving the overall optimization of construction effects. By extracting the characterization indexes of the dynamic change laws, it provides a scientific basis for parameter control in high-pressure jet grouting construction. This step not only helps to master the change laws of the parameters but can also be further used for monitoring the key indexes of construction control, realizing real-time feedback and adjustment, and ensuring the accuracy and stability of the construction process. This series of steps not only establishes a three-dimensional parameter space, a dynamic model, and hierarchical feature data, providing multi-dimensional and refined characterization means for parameter analysis in high-pressure jet grouting construction, but also reveals the dynamic characteristics during the construction process by extracting the coupling relationship and change laws among the parameters. The finally generated parameter change characteristic data can guide the real-time optimization of construction parameters, providing key data support for improving the pile forming quality and construction efficiency.

[0119] Preferably, step S3 includes the following steps:

[0120] Step S31: Establish a parameter-stratum characteristic correspondence table including the mapping relationship between parameter changes and soil body responses in different strata based on the parameter change characteristic data and the stratum distribution data, so as to obtain parameter response data;

[0121] In the embodiment of the present invention, according to the parameter change characteristic data and the stratum distribution data, a parameter-stratum characteristic correspondence table is constructed. The specific operation is first to associate the stratum information in different depth segments with the corresponding parameter change data, such as jetting pressure, lifting speed, and rotation speed, etc., and these data come from the real-time monitoring results of the previous steps. Then, combine these parameter change data with the corresponding stratum types, thicknesses, and mechanical properties (such as cohesion and internal friction angle) to form a mapping relationship table, and finally generate parameter response data. This table can reflect the influence of construction parameters on soil body reactions under different stratum conditions and provide a basis for subsequent analysis.

[0122] Step S32: Perform data standardization and feature dimensionality reduction processing on the parameter response data, and establish a mathematical model of parameter-stratum response, so as to obtain response model data;

[0123] In the embodiment of the present invention, the parameter response data is standardized so that parameters with different magnitudes and units can be compared within the same range. Common methods include Z-score standardization or Min-Max scaling. Subsequently, apply feature dimensionality reduction techniques such as principal component analysis (PCA) or t-SNE to convert high-dimensional data into low-dimensional data, extract the features that have important effects on the response, and then establish a mathematical model of parameter-stratum response to obtain response model data. This model will make the response trends of different parameters when the stratum characteristics change clearer and lay a foundation for the effectiveness of the model.

[0124] Step S33: Perform machine learning training based on the response model data, construct a non-linear mapping relationship between parameters and stratum responses, so as to obtain a parameter-stratum response relationship model;

[0125] In the embodiment of the present invention, based on the response model data, machine learning algorithms such as support vector machine (SVM), random forest, or neural network are used for training to construct a non-linear mapping relationship between parameters and stratum responses. During the training process, the training data needs to be divided into a training set and a validation set, and the model parameters are adjusted through an iterative optimization algorithm until the model can accurately predict the responses of the stratum to different construction parameters. This will make the obtained parameter-stratum response relationship model have good generalization ability and can be applied to unseen data scenarios.

[0126] Step S34: Perform slurry diffusion characteristic analysis including pressure propagation, soil body penetration, and slurry solidification according to the parameter-stratum response relationship model, so as to obtain diffusion characteristic data;

[0127] In the embodiments of the present invention, based on the parameter-stratum response relationship model, the analysis of slurry diffusion characteristics is carried out, specifically including the comprehensive analysis of the pressure propagation, soil penetration, and slurry solidification processes. First, the model is used to predict the slurry pressure change under different stratum conditions and its diffusion process in the soil; secondly, analyze how the penetration characteristics of the soil affect the solidification effect of the slurry at different depths; finally, through the combination of numerical simulation or experimental data, the diffusion characteristic data of the slurry under different conditions are obtained to ensure the comprehensiveness and accuracy of the analysis.

[0128] Step S35: Calculate the depth-related diffusion range based on the diffusion characteristic data, so as to obtain the diffusion radius data, where the diffusion range calculation includes the analysis of the slurry diffusion pressure attenuation law and the influence of soil shear strength;

[0129] The embodiments of the present invention use the diffusion characteristic data to calculate the depth-related diffusion range. The specific operations include studying the pressure attenuation law during the slurry diffusion process, and using non-linear regression or finite element analysis methods to simulate the diffusion behavior of the slurry in the soil. At the same time, the influence of soil shear strength on the diffusion range also needs to be considered, and a calculation model of the diffusion radius is established to identify the effective influence range of the slurry in different depth segments, and finally the diffusion radius data are obtained to optimize the construction parameters.

[0130] Step S36: Identify the key features and mutation points during the slurry diffusion process for the diffusion dynamic data, so as to obtain the slurry diffusion dynamic data.

[0131] The embodiments of the present invention identify the key features and mutation points for the diffusion dynamic data. This process first requires setting thresholds for monitoring parameters such as pressure and permeability, and identifying the mutation points in the data through time series analysis methods (such as moving average or autoregressive moving average model). These mutation points often indicate major changes or abnormal phenomena during the slurry diffusion process. The identified key features and mutation points will provide important information for the slurry diffusion dynamic data, helping to understand the performance and potential risks of the slurry during the construction process, and thus optimizing the subsequent construction decisions.

[0132] The present invention combines parameter change characteristic data with formation distribution data to create a parameter-formation characteristic correspondence table, which helps to identify the influence of parameter changes in different formations on the soil response. This mapping relationship provides comprehensive data support for subsequent analysis, can accurately capture the response characteristics of parameters under different formation conditions, and improves the adaptability to formation conditions. Through standardization processing, the dimensional differences in parameter response data are eliminated to ensure data consistency; through feature dimensionality reduction, the data structure is simplified and the model complexity is reduced, thereby improving the accuracy and efficiency of subsequent modeling. This step provides a concise and reliable data basis for constructing a parameter-formation response mathematical model, which helps to improve the stability and computational efficiency of the response model. By machine learning training, a non-linear mapping model is constructed to capture the complex non-linear relationship between parameters and formation response, and accurate prediction of parameter changes and soil response is realized. The model can adjust parameters such as jetting pressure and speed according to different formation conditions, effectively improve the adaptability and accuracy of slurry diffusion control, and ensure the uniformity and stability of slurry diffusion during pile formation. Based on the parameter-formation response relationship model, the diffusion characteristics of pressure propagation, soil penetration and slurry solidification are analyzed to comprehensively understand the diffusion law of slurry in different formations. This step ensures the uniform diffusion and effective solidification of slurry in the soil, prevents insufficient or excessive diffusion, avoids unnecessary influence on the surrounding soil, and thus improves the forming quality and structural stability of the pile body. By analyzing the attenuation law of slurry diffusion pressure and the shear strength of the soil, the diffusion range and diffusion radius data are accurately calculated to facilitate the control of the effective range of slurry diffusion. This calculation provides a reference for pile formation design, ensures that the slurry can effectively fill the soil voids during pile construction, improves the bonding strength between the pile body and the surrounding soil layer, and thus enhances the bearing capacity and durability of the pile body. Through dynamic data analysis, the key characteristics and mutation points in the slurry diffusion process are identified, abnormal situations in the diffusion process can be detected in time, and necessary adjustments can be made. This identification process effectively prevents the unstable phenomenon of slurry during diffusion, improves the uniformity and control accuracy of slurry diffusion, and ensures the overall quality of pile formation. These steps together construct a systematic method from the parameter-formation response relationship model to the analysis of slurry diffusion characteristics, which not only improves the prediction accuracy of the model, but also ensures the efficient diffusion of slurry during pile construction. This process provides scientific guidance for pile construction, guarantees the pile quality and construction controllability through real-time monitoring and parameter adjustment, optimizes the construction efficiency and pile formation effect, and provides a solid technical foundation for subsequent construction management and pile quality improvement.

[0133] Preferably, step S4 includes the following steps:

[0134] Step S41: Analyze the influence law of different parameter combinations on the soil improvement effect according to the slurry diffusion dynamic data and real-time high-pressure jet grouting data, so as to obtain the improvement influence data;

[0135] In the embodiments of the present invention, according to the slurry diffusion dynamic data and real-time high-pressure jet grouting data, the influence law of different parameter combinations on the soil improvement effect is analyzed. The specific operation includes first collecting key parameters related to slurry diffusion and high-pressure jet grouting, such as jetting pressure, slurry curing time, and physical properties of the soil, etc., and then establishing a relationship model between the parameter combination and the soil improvement effect by using multivariate regression analysis or machine learning methods. Through the experimental design of different parameter combinations (such as orthogonal test or response surface method), the influence of each parameter on the soil improvement effect is systematically evaluated, so as to obtain the improvement influence data to help guide the subsequent construction optimization.

[0136] Step S42: Conduct a performance evaluation of the improved body based on the strength development law, curing time characteristics, and deformation characteristics of the improvement influence data, so as to obtain performance evaluation data;

[0137] In the embodiments of the present invention, a performance evaluation of the improved body is conducted on the improvement influence data based on the strength development law, curing time characteristics, and deformation characteristics. The specific operation includes using standard test methods (such as unconfined compressive strength test, permeability test, etc.) to evaluate the performance of the improved soil, and combining the improvement influence data, and using statistical analysis (such as variance analysis) to study the strength development trend after soil improvement, the influence of curing time on the performance, and the deformation characteristics of the improved body, so as to obtain performance evaluation data. These data will provide information on the effectiveness and applicability of soil improvement for engineers.

[0138] Step S43: Conduct an evolutionary characteristic analysis of the improvement effect over time on the performance evaluation data, so as to obtain the dynamic characteristic data of soil improvement;

[0139] In the embodiments of the present invention, an evolutionary characteristic analysis of the improvement effect over time is conducted on the performance evaluation data. This process requires collecting the performance evaluation data at different time points, applying time series analysis methods (such as autoregressive integrated moving average model, ARIMA) to analyze the change trend of performance over time, and identifying the key time nodes that affect the soil improvement effect. By analyzing these dynamic characteristic data, the change of the improvement effect at different stages after construction can be revealed, and finally the dynamic characteristic data of soil improvement can be obtained.

[0140] Step S44: Conduct a numerical simulation of the pile-forming influence area based on the dynamic characteristic data of soil improvement, and establish a multi-field coupling analysis model including stress field, displacement field, and seepage field, so as to obtain the influence area data;

[0141] In an embodiment of the present invention, numerical simulation of the pile-forming influence area is carried out according to the dynamic characteristic data of soil improvement. The specific method includes establishing a three-dimensional numerical model using finite element analysis (FEA) software to simulate the influence of the load applied by the pile on the stress field, displacement field and seepage field of the surrounding soil. The physical properties of the soil (such as elastic modulus, Poisson's ratio, etc.) and boundary conditions need to be set in the model, and the simulation is run to obtain the influence area data, analyze the influence of pile construction on the surrounding environment, and provide a basis for subsequent analysis.

[0142] Step S45: Perform boundary recognition and spatial partitioning on the influence area data to obtain the pile influence range data;

[0143] In an embodiment of the present invention, boundary recognition and spatial partitioning are performed on the influence area data. The specific operations include using the numerical simulation results and applying image processing techniques (such as edge detection algorithms) to identify the boundaries of the stress and displacement fields. At the same time, the influence area is divided into different spatial partitions (such as according to stress magnitude, displacement change, etc.) for easy analysis and visualization. This process will obtain the pile influence range data and clarify the specific influence area of the pile on the surrounding soil.

[0144] Step S46: Perform coupling of the formation stress modes based on the jet pressure, soil stress, and improvement effect according to the pile influence range data, identify the evolution law of the stress field and the critical state characteristics, and generate the coupled soil action data.

[0145] In an embodiment of the present invention, coupling of the formation stress modes based on the jet pressure, soil stress, and improvement effect is performed according to the pile influence range data. The specific operations include establishing a multi-physical field coupling model that comprehensively considers various influencing factors, simulating the evolution law of the stress field during soil improvement by introducing the changes in jet pressure and soil stress. Combining experimental data and numerical simulation results, the key critical state characteristics are identified, and finally the coupled soil action data is generated to provide a scientific basis for further soil improvement and construction optimization.

[0146] By analyzing different parameter combinations of slurry diffusion dynamic data and real-time high-pressure jet grouting data, the present invention can identify which parameter combinations have the most significant effect on soil improvement. This analysis provides a scientific basis for optimizing construction parameters, helps to dynamically adjust parameters during construction, thereby improving the efficiency and effect of soil improvement. Evaluating the improvement influence data based on the strength development law, curing time characteristics, and deformation characteristics can comprehensively understand the performance of the improved body. This evaluation can help engineers predict the mechanical properties of the improved soil during construction, provide data support for subsequent construction plan adjustment, and thus ensure the effectiveness and durability of soil improvement. Analyzing the evolution characteristics of performance evaluation data over time can identify the variation law of soil improvement effect over time, providing dynamic monitoring of the improvement effect. This dynamic characteristic data is crucial for the maintenance and evaluation in the later stage of construction, helping to ensure the stability and safety of the soil during long-term use. By establishing a multi-field coupling analysis model of stress field, displacement field, and seepage field, it is possible to accurately simulate the affected area of the pile body after soil improvement. This simulation provides important theoretical support for construction, ensuring that the interaction of various environmental factors during pile construction is fully considered, and thus optimizing the design and construction strategy of the pile body. Identifying the boundary and spatial partitioning of the affected area data can clarify the influence range of the pile body. This step helps to accurately delimit the construction area, ensure that construction activities will not have a negative impact on the surrounding environment, and at the same time provide basic data for subsequent monitoring and evaluation. According to the pile body influence range data, coupling analysis of the formation stress mode can identify the evolution law of the stress field and the characteristics of the critical state. This analysis helps to reveal the interaction relationship between the pile body and the surrounding soil, ensuring the safety and stability of the pile body during construction and long-term use. By understanding the influence of different construction parameters on the stress distribution, it is possible to optimize the design of the pile body, ensuring that the pile body can adapt to the actual construction conditions and environmental changes. These steps together construct a systematic analysis framework to help engineers comprehensively understand the influence of slurry diffusion on soil improvement and, under dynamically changing construction conditions, carry out scientific and effective optimization and evaluation of construction parameters. Through these analyses, the effectiveness and stability of soil improvement can be ensured, the bearing capacity and service life of the pile body can be improved, thereby effectively reducing construction risks and ensuring the successful implementation of the project.

[0147] Preferably, step S5 includes the following steps:

[0148] Step S51: Extract the characteristics of the coupled data acting on the soil and construct a training sample set for the deep learning model to obtain training sample data, where the training sample data includes input features and morphological labels;

[0149] In the embodiments of the present invention, feature extraction is performed on the coupled data of soil body action, and a training sample set for a deep learning model is constructed. The specific operations include first cleaning and preprocessing the coupled data of soil body action, extracting key features such as stress distribution, displacement change, slurry diffusion radius, etc., and then organizing these features into an input feature set. At the same time, the corresponding soil body morphology is marked to generate morphology labels. For example, image segmentation technology is used to mark different soil body morphologies (such as diffusion morphology, solidification morphology, etc.). In this way, training sample data including input features and morphology labels is constructed, providing a basis for the training of subsequent deep learning models.

[0150] Step S52: Construct a deep learning network structure based on a hybrid architecture of a convolutional neural network and a recurrent neural network according to the training sample data, and perform model training to obtain a morphology prediction model.

[0151] In the embodiments of the present invention, a deep learning network structure based on a hybrid architecture of a convolutional neural network (CNN) and a recurrent neural network (RNN) is constructed according to the training sample data. The specific method is to design a multi-layer convolutional neural network to extract spatial features, and combine a long short-term memory network (LSTM) to process temporal information to form an effective hybrid architecture. The backpropagation algorithm and optimization algorithms (such as Adam or SGD) are used for model training, iteratively adjusting the network parameters to enable the model to effectively capture the relationship between the input features and the morphology labels, and finally obtaining a morphology prediction model with prediction ability.

[0152] Step S53: Optimize the prediction accuracy of the morphology prediction model based on the cross-validation method to obtain prediction model data.

[0153] In the embodiments of the present invention, the prediction accuracy of the morphology prediction model is optimized based on the cross-validation method. The specific operation is to divide the training sample data into multiple subsets, and use the K-fold cross-validation method for model training and validation. Through multiple trainings and evaluations, calculate the prediction accuracy of the model on different subsets (such as accuracy rate, F1 value, etc.), and then adjust the hyperparameters of the model (such as learning rate, batch size, etc.) to improve the generalization ability of the model. Finally, optimized prediction model data is obtained to ensure the stability and accuracy of the model on new data.

[0154] Step S54: Use the prediction model data to perform dynamic prediction of the three-dimensional morphology of the pile body to obtain morphology prediction data, where the morphology prediction data includes cross-sectional shape, vertical change, and spatial distribution.

[0155] In the embodiments of the present invention, the dynamic prediction of the three-dimensional shape of the pile body is carried out by using the prediction model data. The specific operation includes inputting the real-time high-pressure jet grouting data collected into the optimized shape prediction model to generate the three-dimensional shape prediction results of the pile body during the construction process. This process can use software visualization tools to present the prediction results as a three-dimensional model, showing the cross-sectional shape, vertical changes, and spatial distribution of the pile body, helping construction personnel to monitor and adjust the construction strategy in real time to ensure the construction quality.

[0156] Step S55: Analyze the key stages and morphological mutation characteristics during the pile body formation process for the morphological prediction data, so as to obtain the morphological evolution data, where the key stages include the initial jetting stage, the diffusion and development stage, the initial curing stage, the strength development stage, the stable forming stage, and the adjacent pile body influence stage;

[0157] In the embodiments of the present invention, the key stages and morphological mutation characteristics during the pile body formation process are analyzed for the morphological prediction data. This process first identifies the key stages in the morphological prediction data, including the initial jetting stage, the diffusion and development stage, the initial curing stage, the strength development stage, the stable forming stage, and the adjacent pile body influence stage. By analyzing the morphological changes of the pile body in different stages and using data mining techniques (such as clustering analysis and feature extraction) to identify the morphological mutation points, the morphological evolution data is obtained. These data will provide an important basis for understanding the dynamic changes during the pile body formation process.

[0158] Step S56: Optimize the construction parameters in real time for each stage according to the morphological evolution data, so as to obtain the high-pressure jet grouting construction control feedback data.

[0159] In the embodiments of the present invention, the construction parameters in each stage are optimized in real time according to the morphological evolution data. The specific operation includes establishing a relationship model between the construction parameters and the pile body morphological evolution. By analyzing the influence of construction parameters in different stages (such as jetting pressure, slurry curing time, lifting speed, etc.) on the pile body morphology, the key parameters during the construction process are monitored in real time, and optimization algorithms (such as genetic algorithms or particle swarm optimization) are used to adjust the construction parameters to achieve real-time feedback and optimization, and finally the high-pressure jet grouting construction control feedback data is obtained, providing a scientific basis and effective guidance for the construction process.

[0160] By extracting the characteristics of the coupled data of the soil body, the important factors affecting the pile body morphology can be identified. The training sample set constructed in this step includes input features (such as soil parameters, construction conditions, etc.) and morphology labels (pile body morphology features), providing rich data support for the subsequent deep learning model and ensuring the effectiveness and accuracy of model training. Using a hybrid architecture of convolutional neural network (CNN) and recurrent neural network (RNN) can effectively capture the complex relationships of spatial features and time series features. This deep learning network structure can improve the model's prediction ability for the changes in pile body morphology, enhance the model's adaptability, and thus improve the prediction accuracy. By optimizing the morphology prediction model through the cross-validation method, overfitting can be effectively avoided, ensuring the generalization ability of the model on unseen data. This optimization process helps to improve the reliability of the model and provide more accurate morphology prediction for actual construction. Using the trained prediction model for the dynamic prediction of the three-dimensional morphology of the pile body can obtain comprehensive information on the cross-sectional shape, vertical changes, and spatial distribution. These prediction data provide a scientific basis for the monitoring and adjustment during the construction process, helping to grasp the formation situation of the pile body in real time and ensure the construction quality. Identifying the key stages and morphology mutation characteristics during the pile body formation process can deeply understand the growth process and influencing factors of the pile body. This analysis helps engineers to grasp the key links during the construction process, take timely measures to deal with potential problems, and thus improve the safety and effectiveness of the construction. According to the morphology evolution data, the construction parameters in each stage can be optimized in real time, dynamically adjusting the construction strategy of high-pressure jet grouting. This real-time feedback mechanism ensures flexible response during the construction process, maximally improving the construction efficiency, reducing the construction risk, and ensuring the final quality and performance of the pile body. Through this series of steps, the accurate prediction and dynamic optimization of the three-dimensional morphology of the pile body can be achieved, effectively improving the intelligent level of high-pressure jet grouting construction and the overall efficiency of the project. Using deep learning technology to analyze the complex dynamics during the soil improvement process can better cope with the uncertainties in construction and ensure the safety and reliability of the pile body structure. This method not only improves the construction accuracy but also provides strong data support for subsequent monitoring and evaluation, providing a scientific decision-making basis for modern engineering construction management.

[0161] The present invention also provides a real-time dynamic prediction system for the three-dimensional morphology of high-pressure jet grouting piles, which is used to execute the above-mentioned real-time dynamic prediction method for the three-dimensional morphology of high-pressure jet grouting piles. The real-time dynamic prediction system for the three-dimensional morphology of high-pressure jet grouting piles includes:

[0162] A site monitoring module, which is used to identify the formation characteristics of the construction site, obtain formation distribution data; determine key monitoring points according to the formation distribution data, and establish a multi-source sensor monitoring network;

[0163] The parameter acquisition module is used to perform anti-interference optimization processing on the multi-source sensor monitoring network, and perform real-time acquisition of high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; perform spatio-temporal evolution processing on the real-time high-pressure jet grouting data based on jet pressure, lifting speed, and rotation speed to generate parameter change characteristic data;

[0164] The diffusion prediction module is used to establish a parameter-stratum response relationship model based on the parameter change characteristic data and the stratum distribution data; perform slurry diffusion characteristic analysis according to the parameter-stratum response relationship model, and perform diffusion radius evaluation related to depth to generate slurry diffusion dynamic data;

[0165] The influence evaluation module is used to perform soil improvement correlation processing based on the slurry diffusion dynamic data and the real-time high-pressure jet grouting data to generate soil improvement dynamic characteristic data; identify the pile formation influence area based on the soil improvement dynamic characteristic data to generate pile body influence range data; perform stratum stress mode coupling based on the jet pressure, soil stress, and improvement effect according to the pile body influence range data to generate soil action coupling data;

[0166] The shape control module is used to construct a deep learning prediction model based on the soil action coupling data, and use the deep learning prediction model to perform three-dimensional shape dynamic prediction of the pile body to generate shape prediction data; perform real-time optimization of construction parameters based on the shape prediction data to obtain high-pressure jet grouting construction control feedback data.

[0167] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0168] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A real-time dynamic prediction method for the three-dimensional shape of high-pressure jet grouting piles, characterized in that: The following steps are involved: Step S1: Identify the stratum characteristics of the construction site and obtain stratum distribution data; determine key monitoring points based on the stratum distribution data and establish a multi-source sensor monitoring network; Step S2: performing anti-interference optimization processing on the multi-source sensor monitoring network, and performing real-time collection of high-pressure rotary spraying parameters to obtain real-time high-pressure rotary spraying data; The real-time high-pressure jet spraying data is processed based on the spatiotemporal evolution of the jet pressure, lifting speed and rotation speed to generate parameter change characteristic data; Step S3: establishing a parameter-formation response relationship model based on the parameter change characteristic data and the formation distribution data; performing slurry diffusion characteristic analysis based on the parameter-formation response relationship model, and performing depth-related diffusion radius evaluation to generate slurry diffusion dynamic data; Step S4: performing soil improvement correlation processing according to the slurry diffusion dynamic data and the real-time high-pressure rotary jetting data to generate soil improvement dynamic characteristic data; identifying the pile formation influence area based on the soil improvement dynamic characteristic data to generate pile influence range data; coupling the formation stress mode based on the injection pressure, soil stress and improvement effect according to the pile influence range data to generate soil action coupling data; Step S5: construct a deep learning prediction model based on the soil action coupling data, and use the deep learning prediction model to dynamically predict the three-dimensional morphology of the pile body to generate morphology prediction data; optimize the construction parameters in real time based on the morphology prediction data to obtain high-pressure rotary jet construction control feedback data.

2. The real-time dynamic prediction method of the three-dimensional morphology of high-pressure jet grouting piles according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Perform multi-point drilling sampling on the construction site to obtain formation physical sample data; Step S12: Identify the type, distribution depth and thickness of the soil layer of the stratum physical sample data, so as to obtain stratum structure data; Step S13: performing mechanical property analysis based on density, cohesion, internal friction angle and water content on the physical sample data of the stratum according to the stratum structure data, thereby obtaining mechanical property data of the soil layer; Step S14: Performing hydrogeological characteristic analysis based on the soil mechanical property data to obtain hydrogeological characteristic data, wherein the hydrogeological characteristic analysis includes water level observation around the construction site, permeability testing of stratum physical sample data, and groundwater distribution analysis based on water content and permeability; Step S15: merging the hydrogeological characteristic data, the soil layer mechanical property data and the stratum structure data into stratum distribution data; Step S16: Determine key monitoring points based on the stratum distribution data and establish a multi-source sensor monitoring network.

3. The real-time dynamic prediction method of the three-dimensional morphology of high-pressure jet grouting piles according to claim 2 is characterized in that: Step S16 includes the following steps: Step S161: performing mutation analysis on soil layer interface characteristics according to stratum distribution data to obtain soil layer mutation point data, wherein the mutation analysis specifically identifies locations where soil layer types and mechanical properties change significantly; Step S162: performing sensitivity analysis on the soil layer stress state according to the stratum distribution data, identifying areas where soil stress changes greatly, and obtaining stress sensitive area data, wherein the stress sensitivity analysis includes shear strength evaluation based on cohesion and internal friction angle, compressibility evaluation based on density and water content, and consolidation characteristic evaluation based on permeability; Step S163: Determine the spatial distribution of key monitoring points according to preset site construction conditions and engineering requirements data, soil layer mutation point data, and stress sensitive area data to obtain monitoring point layout data; Step S164: Determine the sensor type according to the monitoring point layout data, and establish a multi-source sensor monitoring network including a pressure sensor, a displacement sensor, a strain sensor, and a water level sensor.

4. The real-time dynamic prediction method of the three-dimensional morphology of high-pressure jet grouting piles according to claim 3 is characterized in that: Step S2 includes the following steps: Step S21: performing anti-interference optimization processing based on electromagnetic shielding and vibration isolation on the multi-source sensor monitoring network, and collecting real-time parameters during the high-pressure rotary grouting construction process, thereby obtaining real-time high-pressure rotary grouting data; Step S22: segmenting the injection pressure in the real-time high-pressure rotary spraying data based on the time series, thereby establishing a pressure-time relationship curve; Step S23: Calculate the frequency characteristics and amplitude distribution of pressure fluctuations according to the pressure-time relationship curve, and perform correlation analysis between pressure fluctuations and depth, so as to obtain pressure-depth evolution characteristic data; Step S24: establishing a relationship curve between lifting speed and depth based on real-time high-pressure jetting data, and calculating the speed change rate at different depths, thereby obtaining speed spatiotemporal evolution characteristic data; Step S25: recording the real-time changes of the rotation speed in the real-time high-pressure rotary spraying data, and performing correlation characteristic analysis between the rotation speed and the depth, thereby obtaining rotation spatiotemporal evolution characteristic data; Step S26: performing a comprehensive analysis of the influence of parameter combination on pile formation based on the pressure-depth evolution characteristic data, the velocity spatiotemporal evolution characteristic data, and the rotation spatiotemporal evolution characteristic data, and identifying the critical value and optimal interval of the key parameter combination, thereby generating spatiotemporal evolution characteristic data of the parameter combination; Step S27: establishing a dynamic evolution model of the three-dimensional parameter space according to the spatiotemporal evolution characteristic data, and extracting parameter change characteristics, thereby obtaining parameter change characteristic data.

5. The real-time dynamic prediction method of the three-dimensional shape of high-pressure jet grouting piles according to claim 4 is characterized in that: Step S21 includes the following steps: The multi-source sensor monitoring network is optimized for anti-interference based on electromagnetic shielding and vibration isolation, and real-time parameters during the high-pressure rotary spraying construction process are collected to obtain real-time high-pressure rotary spraying data; the electromagnetic shielding includes metal shell packaging of the sensor, shielding layer grounding of the signal cable, and planning of the sensor grounding point; the vibration isolation includes setting shock-absorbing brackets, using flexible connectors, and anti-vibration optimization of the sensor installation position.

6. The real-time dynamic prediction method of the three-dimensional shape of high-pressure jet grouting piles according to claim 5 is characterized in that: Step S27 includes the following steps: Step S271: constructing a three-dimensional parameter space with injection pressure, lifting speed and rotation speed as coordinate axes according to the spatiotemporal evolution characteristic data, thereby obtaining parameter space coordinate data; Step S272: establishing a parameter change trajectory based on the parameter space coordinate data, and constructing a parameter motion trajectory curve through spatial mapping of the time series sampling points, thereby obtaining parameter trajectory data; Step S273: performing depth-based layered processing on the parameter trajectory data to identify parameter change characteristics of different depth segments, including change trends, change rates, and mutual correlations, thereby obtaining layered feature data; Step S274: constructing a dynamic model of the three-dimensional parameter space according to the hierarchical feature data, establishing a continuous expression of parameter changes through spatial interpolation and numerical fitting, thereby obtaining dynamic evolution model data; Step S275: extracting the gradient features, curvature features and speed features of the dynamic evolution model data based on parameter changes, thereby obtaining model feature data; Step S276: performing parameter correlation analysis based on the coupling relationship and mutual feedback mechanism between parameters based on the model characteristic data, thereby obtaining parameter correlation data; Step S277: extracting indicators characterizing the dynamic change rules of parameters based on the model characteristic data and the parameter association data, thereby generating parameter change characteristic data.

7. The real-time dynamic prediction method of the three-dimensional shape of high-pressure jet grouting piles according to claim 6 is characterized in that: Step S3 includes the following steps: Step S31: establishing a parameter-stratum characteristic correspondence table including a mapping relationship between parameter changes and soil responses in different strata according to the parameter change characteristic data and the stratum distribution data, thereby obtaining parameter response data; Step S32: performing data standardization and feature dimension reduction processing on the parameter response data, and establishing a mathematical model of parameter-formation response, thereby obtaining response model data; Step S33: Perform machine learning training based on the response model data to construct a nonlinear mapping relationship between the parameter and the formation response, thereby obtaining a parameter-formation response relationship model; Step S34: performing slurry diffusion characteristic analysis including pressure propagation, soil penetration and slurry solidification according to the parameter-stratum response relationship model, thereby obtaining diffusion characteristic data; Step S35: Calculating the diffusion range related to the depth based on the diffusion characteristic data to obtain diffusion radius data, wherein the diffusion range calculation includes the influence analysis of the slurry diffusion pressure attenuation law and the soil shear strength; Step S36: identifying key features and mutation points in the slurry diffusion process on the diffusion dynamic data, thereby obtaining the slurry diffusion dynamic data.

8. The real-time dynamic prediction method of the three-dimensional shape of high-pressure jet grouting piles according to claim 7 is characterized in that: Step S4 includes the following steps: Step S41: analyzing the influence of different parameter combinations on soil improvement effects based on slurry diffusion dynamic data and real-time high-pressure rotary jetting data, thereby obtaining improvement impact data; Step S42: evaluating the performance of the improved body based on the strength development law, curing time characteristics and deformation characteristics of the improved impact data, thereby obtaining performance evaluation data; Step S43: analyzing the evolution characteristics of the improvement effect over time on the performance evaluation data, thereby obtaining soil improvement dynamic characteristic data; Step S44: numerically simulate the pile influence area according to the soil improvement dynamic characteristic data, and establish a multi-field coupling analysis model including stress field, displacement field and seepage field, so as to obtain the influence area data; Step S45: performing boundary recognition and spatial partitioning on the impact area data, thereby obtaining pile body impact range data; Step S46: coupling the stratum stress mode based on the injection pressure, soil stress and improvement effect according to the pile influence range data, identifying the stress field evolution law and critical state characteristics, and generating soil action coupling data.

9. The real-time dynamic prediction method of the three-dimensional shape of high-pressure jet grouting piles according to claim 8 is characterized in that: Step S5 includes the following steps: Step S51: extracting features from soil action coupling data and constructing a training sample set for a deep learning model to obtain training sample data, wherein the training sample data includes input features and morphological labels; Step S52: constructing a deep learning network structure based on a hybrid framework of a convolutional neural network and a recurrent neural network according to the training sample data, and performing model training to obtain a morphology prediction model; Step S53: optimizing the model prediction accuracy of the morphology prediction model based on a cross-validation method, thereby obtaining prediction model data; Step S54: dynamically predicting the three-dimensional shape of the pile body using the prediction model data, thereby obtaining shape prediction data, wherein the shape prediction data includes cross-sectional shape, vertical change, and spatial distribution; Step S55: The key stages and morphological mutation characteristics of the pile body formation process are analyzed for the morphological prediction data, thereby obtaining morphological evolution data, wherein the key stages include the initial injection stage, the diffusion development stage, the initial solidification stage, the strength development stage, the stable forming stage, and the adjacent pile body influence stage; Step S56: Optimize the construction parameters of each stage in real time according to the morphological evolution data, so as to obtain high-pressure jet grouting construction control feedback data.

10. A real-time dynamic prediction system for the three-dimensional shape of high-pressure jet grouting piles, characterized in that: The method for real-time dynamic prediction of the three-dimensional shape of high-pressure jet grouting piles according to claim 1, wherein the real-time dynamic prediction system for the three-dimensional shape of high-pressure jet grouting piles comprises: The site monitoring module is used to identify the stratum characteristics of the construction site and obtain stratum distribution data; determine the key monitoring points based on the stratum distribution data and establish a multi-source sensor monitoring network; The parameter acquisition module is used to perform anti-interference optimization processing on the multi-source sensor monitoring network and to collect high-pressure rotary spray parameters in real time to obtain real-time high-pressure rotary spray data; the real-time high-pressure rotary spray data is processed based on the spatiotemporal evolution of the injection pressure, lifting speed and rotation speed to generate parameter change characteristic data; The diffusion prediction module is used to establish a parameter-formation response relationship model based on parameter change characteristic data and formation distribution data; analyze the slurry diffusion characteristics based on the parameter-formation response relationship model, and evaluate the depth-related diffusion radius to generate slurry diffusion dynamic data; The impact assessment module is used to perform soil improvement correlation processing based on the slurry diffusion dynamic data and real-time high-pressure rotary jetting data to generate soil improvement dynamic characteristic data; to identify the pile impact area based on the soil improvement dynamic characteristic data to generate pile impact range data; to couple the formation stress mode based on the injection pressure, soil stress and improvement effect based on the pile impact range data to generate soil action coupling data; The morphology control module is used to build a deep learning prediction model based on the soil action coupling data, and use the deep learning prediction model to dynamically predict the three-dimensional morphology of the pile to generate morphology prediction data; based on the morphology prediction data, the construction parameters are optimized in real time to obtain high-pressure rotary jet construction control feedback data.

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