Method and system for high-liquid-limit soft soil roadbed construction

Abstract

The invention provides a method and system for high-liquid-limit soft soil roadbed construction, and relates to the technical field of data processing, and the method comprises the steps: obtaining geological monitoring data and drainage monitoring data of a construction site; generating a three-dimensional model of the construction area through the geological monitoring data and the drainage monitoring data, and generating a preliminary construction scheme according to the three-dimensional model; obtaining construction dynamic monitoring data; dynamically adjusting the ratio of the thickness of the rubble layer to the particle size of the gravel layer according to the real-time monitoring data so as to obtain an adjustment suggestion for dynamically adjusting the thickness of the rubble layer and the particle size of the gravel layer; according to the construction feedback data of the rubble layer and the gravel layer and the external environment data, a compaction process construction adjustment scheme is generated; according to the adjustment suggestions of the thickness of the rubble layer and the particle size of the gravel layer and the compaction process construction adjustment scheme, the construction process data are recorded in real time, and the construction log is generated, the construction quality and efficiency of roadbed construction can be further improved, so that the long-term stability of the roadbed is improved, and the later maintenance cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for high liquid limit soft soil roadbed construction. Background Art

[0002] In the roadbed construction in high liquid limit soft soil areas, the currently commonly used methods mainly include the use of bagged mortar, plastic drainage boards combined with vacuum preloading dewatering technology, or the reinforcement of soil through granular piles, CFG piles (cement fly ash piles), etc.

[0003] Although the above methods have certain advantages in improving the bearing capacity and stability of soft soil roadbed, they also have a series of disadvantages, which are mainly manifested in the following aspects: The vacuum preloading dewatering and soft soil consolidation process usually takes a long time, especially in areas with high groundwater levels. The consolidation process may last for several months, resulting in a long construction period.

[0004] Whether it is bagged mortar, plastic drainage boards, or the reinforcement of granular piles and CFG piles, a large amount of material and equipment investment is required, resulting in a significant increase in construction costs, especially when the soft soil layer is thick or the project scope is wide, the cost is more significant.

[0005] Although existing technologies have improved the bearing capacity of soft soil foundations through reinforcement and drainage measures, the stability of the roadbed may still be affected by changes in groundwater levels after construction is completed. Especially in areas with large seasonal or climatic changes, groundwater level fluctuations may cause roadbed settlement, water accumulation and other problems. In severe cases, water damage to the pavement structure may occur, posing a threat to the long-term stability of the roadbed. Although soft soil can provide short-term stability through initial reinforcement and drainage improvement, the roadbed may experience gradual or uneven settlement due to long-term groundwater level changes and soil settlement, requiring continuous monitoring and maintenance in the later stage, which increases the difficulty and cost of maintenance. Summary of the invention

[0006] The purpose of this application is to provide a method and system for high liquid limit soft soil roadbed construction, so as to solve the shortcomings of the prior art in high liquid limit soft soil roadbed construction, such as long construction period, high cost, stability problems caused by groundwater level changes, and difficulty in later maintenance. Through the proposal of this solution, combined with real-time monitoring, geological and drainage model construction, dynamic optimization and long-term maintenance suggestions, it can not only significantly reduce the construction cost and period, but also ensure the construction quality, reduce the impact of groundwater level fluctuations on roadbed stability, thereby improving the long-term stability of the roadbed and reducing later maintenance costs. The specific solution is as follows.

[0007] A method for high liquid limit soft soil roadbed construction, characterized by comprising: Obtain geological monitoring data and drainage monitoring data of the construction site; Generate a 3D model of the construction area using geological monitoring data and drainage monitoring data, and generate a preliminary construction plan based on this; Obtain construction dynamic monitoring data; Dynamically adjust the thickness of the stone layer and the particle size ratio of the gravel layer according to the real-time monitoring data to obtain adjustment suggestions for the dynamic adjustment of the thickness of the stone layer and the particle size of the gravel layer; Generate compaction process construction adjustment plan based on construction feedback data of the rubble layer and gravel layer as well as external environment data; According to the adjustment suggestions for the thickness of the stone layer and the particle size of the gravel layer and the compaction process construction adjustment plan, the construction plan is adjusted, and the construction process data is recorded in real time to generate a construction log.

[0008] Furthermore, the geological monitoring data includes the thickness and distribution of the soft soil layer at the construction site and the physical parameters of the soil, and the drainage monitoring data includes the groundwater level distribution, pore water pressure and flow data of the drainage ditch.

[0009] Furthermore, the method for generating a three-dimensional model of the construction area using geological monitoring data and drainage monitoring data is as follows: Standardize geological monitoring data and drainage monitoring data into a unified spatial grid format; Generate complete 3D data distribution of the construction area based on limited monitoring point data in geological monitoring data; The processed geological monitoring data and drainage monitoring data are input into the 3D modeling tool, and the 3D model is constructed layer by layer according to the thickness of the soft soil layer, soil layer type, groundwater level, etc.

[0010] Further, the method of generating a preliminary construction plan is as follows; Through the 3D model and extracted key parameters, specific construction operation steps and parameters are generated, including the excavation depth, riprap thickness, and gravel grading ratio of each section, to obtain a preliminary construction plan.

[0011] Furthermore, the construction dynamic monitoring data includes the compaction degree of the stone layer, the particle size distribution of the gravel layer, the sedimentation rate, and the status data of the construction equipment.

[0012] Furthermore, the thickness of the stone layer and the particle size ratio of the gravel layer are dynamically adjusted according to the real-time monitoring data to obtain the following method for dynamically adjusting the thickness of the stone layer and the particle size of the gravel layer: Obtain the construction data of the rubble layer and gravel layer in the real-time monitoring data as well as the on-site monitoring data; Conduct statistical analysis on the compaction data of the slab layer to identify low-compactness areas; Combined with geological stratification data, determine whether it is necessary to adjust the thickness of the rubble layer or increase the filling volume; Generate a suggested adjustment plan for the thickness of the sheet rock layer; Through the particle size distribution of the gravel layer, the uniformity coefficient and curvature coefficient of its multi-level proportion are calculated; Compare the actual particle size distribution with the design standard curve to evaluate whether the gravel layer meets the drainage performance requirements; If it deviates from the design standard, the particle size ratio is optimized using a linear programming algorithm to generate particle size optimization parameters.

[0013] Furthermore, according to the construction feedback data of the rubble layer and the gravel layer and the external environment data, the method for generating the compaction process construction adjustment plan is as follows: Obtaining construction feedback data of the rubble layer and the gravel layer, the construction feedback data of the rubble layer and the gravel layer including compaction degree, particle size distribution and fill layer thickness, and external environmental data including dynamic changes in groundwater level and soft soil settlement trend; Classify and count the compaction data to generate a spatial distribution map of the compaction effect; Apply machine learning models (such as support vector machines (SVM)) to predict the impact of different compaction parameters (such as vibration force and compaction speed) on compaction results; Based on the prediction results, a compaction parameter optimization plan is generated (such as increasing the vibration force to 2.5 tons and reducing the compaction speed to 0.5 m / s) and the adjustment parameters of the compaction equipment are generated (including vibration force, compaction speed and layer thickness control values).

[0014] Extract quality indicators such as compaction pass rate, settlement rate, drainage efficiency, etc. from construction logs; Assess whether the construction area meets the design requirements; Output construction quality report and mark the areas that do not meet the standards; Settlement monitoring: Track the long-term changes in roadbed settlement through settlement plate sensors, and monitor the flow rate and groundwater level of drainage ditches; Based on long-term data, evaluate the long-term stability of the roadbed and generate maintenance recommendations (such as drainage system improvement plans and settlement area repair plans); On the other hand, the present application provides a system for high liquid limit soft soil roadbed construction, comprising: Data acquisition module: used to collect geological, drainage and construction dynamic data, and pass them to the data processing and model building module after preprocessing; Data processing and model building module: used to build a three-dimensional model using the collected data and generate a preliminary construction plan; Dynamic adjustment module: used to adjust the thickness of the stone layer, the ratio of sand and gravel particle size and the compaction process according to real-time monitoring data to generate an optimization plan; Execution and recording module: used to convert the optimization plan into equipment control instructions and record the construction process data; The quality assessment and maintenance module is used to assess construction quality and long-term roadbed stability through construction logs and long-term monitoring data, and generate quality reports and maintenance recommendations.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages: 1. This application avoids the long preloading process of traditional reinforcement methods and reduces the construction period through real-time dynamic monitoring and optimization adjustment. For example, by monitoring the soil compaction and dynamically adjusting the thickness of the stone layer, the construction process can be accelerated and the construction period can be shortened while maintaining quality.

[0016] 2. This application reduces the reliance on expensive soil reinforcement materials (such as granular piles and CFG piles) by backfilling economical water-stable materials (such as gravel, crushed stone, blocks, etc.) and dynamically optimizing the construction process, thereby significantly reducing construction costs.

[0017] 3. This solution combines intelligent control systems with long-term monitoring to ensure precise control of roadbed settlement, compaction and groundwater level changes during construction, thereby improving the long-term stability of the roadbed.

[0018] 4. Through the optimization of the drainage system and the use of three-dimensional models, this application can evaluate the impact of groundwater level fluctuations on the roadbed in real time, and avoid roadbed settlement and water damage caused by water level changes through dynamic adjustments.

[0019] 5. This application uses real-time monitoring and data feedback mechanisms to promptly identify potential construction problems and avoid future subsidence and water damage through dynamic adjustments. Combined with long-term monitoring data, early warning can be provided to reduce the frequency and cost of subsequent roadbed maintenance.

[0020] 6. In this phase, by combining real-time monitoring data during the construction process with optimization algorithms, various construction parameters are precisely controlled to ensure that the quality of each floor’s construction meets the design requirements and avoid uneven quality problems caused by human errors.

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0023] Figure 1 A schematic diagram of a process for the construction of a high liquid limit soft soil roadbed in this application; Figure 2 A schematic diagram of the structure of a system for high liquid limit soft soil roadbed construction in this application; Figure 3 This is a schematic diagram of a structure for a high liquid limit soft soil roadbed in the present application.

[0024] Explanation of the accompanying drawings: 11. Data acquisition module, 12. Data processing and model building module, 13. Dynamic adjustment module, 14. Execution and recording module, 15. Quality assessment and maintenance module, 1. Drainage ditch, 2. Slag layer, 3. Drainage layer, 4. Roadbed backfill layer, 5. Pavement structure layer. DETAILED DESCRIPTION

[0025] The present application provides a method and system for high liquid limit soft soil roadbed construction. By combining real-time construction monitoring data (such as settlement, compaction, groundwater level, etc.) for dynamic optimization, the construction process can be controlled more accurately, avoiding an excessively long pre-compression consolidation process, reducing the time required for construction, and using backfill gravel, crushed stone and block stone with good water stability to replace traditional soil reinforcement methods during construction, reducing the need for expensive reinforcement materials (such as granular piles and CFG piles). By optimizing material selection, the material cost during the construction process is reduced; by using three-dimensional models and drainage system optimization, the possible groundwater level change areas are identified in advance, the construction plan is adjusted, and the unstable factors after the construction is completed are reduced to ensure the stability of the roadbed in long-term use; and through continuous settlement monitoring and drainage system evaluation, combined with construction logs and long-term monitoring data, long-term maintenance recommendations are generated. Through these monitoring data and predictive analysis, potential hidden dangers can be warned in time, such as abnormal increase in groundwater level or excessive settlement rate, and then optimized and adjusted to reduce the maintenance cost in the later stage.

[0026] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0027] Example 1, please refer to the attached Figure 1 The present application provides a method and system for high liquid limit soft soil roadbed construction, wherein a method for high liquid limit soft soil roadbed construction includes: S100 obtains geological monitoring data and drainage monitoring data of the construction site, wherein the geological monitoring data includes the thickness and distribution of the soft soil layer at the construction site and physical parameters of the soil, and the drainage monitoring data includes groundwater level distribution, pore water pressure, and flow data of the drainage ditch; Specifically: The data acquisition method is as follows: By slowly pressing the cone head of the static penetration device into the soil, the cone tip resistance, side wall friction resistance and pore water pressure are measured to obtain the shear strength, compressibility and layer thickness of the soft soil layer; Soil samples at different depths are extracted by drilling equipment and sent to the laboratory for testing of liquid limit, plasticity index, water content and compression modulus to obtain the soil structure and its physical parameters. The density of the soil layer is evaluated by hammering the standard penetration rod into the soil and recording the number of hammering to obtain the relative density and bearing capacity of the soil layer; Thus, a three-dimensional geological stratification map, mechanical properties of each layer (such as shear strength, compression modulus) and key physical parameters (liquid limit, plasticity index) are obtained for subsequent analysis.

[0028] Water level sensors (such as piezoresistive or capacitive water level gauges) can be installed in drainage ditches or monitoring wells to record the groundwater level in real time to obtain a groundwater level change curve; By burying the pore water pressure sensor in the soft soil layer, the water pressure change in the soil is recorded to obtain the data of the change of pore water pressure over time; Install a flow sensor (such as an ultrasonic flow meter) at the outlet of the drainage ditch to monitor the drainage rate. In order to obtain the time series data of drainage rate and drainage volume; This allows it to integrate these data to generate groundwater level dynamic curves, pore water pressure change graphs, and drainage system efficiency reports for subsequent analysis.

[0029] S200 generates a 3D model of the construction area based on geological monitoring data and drainage monitoring data, and generates a preliminary construction plan based on this; Specifically: This step is the core part of the entire construction plan design and optimization. It mainly uses geological and drainage monitoring data to build a three-dimensional geological model of the construction area and generate a preliminary construction plan based on the model. The specific steps and supplementary content of step S200 are described in detail below; S210 standardizes geological monitoring data and drainage monitoring data into a unified spatial grid format; Specifically: First, all the collected geological monitoring data (such as soft soil layer thickness, soil physical parameters) and drainage monitoring data (such as groundwater level, pore water pressure, drainage ditch flow) are converted to ensure that they all conform to unified data standards (such as CSV, GeoJSON, NetCDF, etc.); Convert geological and drainage data into a grid format at a certain spatial resolution. For example, use a grid of grids at a certain interval to convert data such as groundwater level and pore water pressure into values ​​at each grid point. This is done to facilitate subsequent interpolation and modeling in three-dimensional space; For data from different sources (such as soil physical parameters, groundwater level data, etc.), standardization is performed to unify units and dimensions. For example, the liquid limit and plasticity index of the soil can be unified into standard dimensions, and the groundwater level data can be standardized to the height relative to the benchmark point; S211 generates complete three-dimensional data distribution of the construction area based on the limited monitoring point data in the geological monitoring data using interpolation method; Specifically: Since geological monitoring and drainage monitoring data are usually point data, and the spatial distribution between points may be uneven, it is necessary to generate the complete data distribution of the construction area through interpolation methods; Considering the autocorrelation of spatial data, Kriging interpolation can be used to process the data. Kriging interpolation can better handle the spatial continuity problem of geological and drainage data. For simpler geological data, inverse distance weighted interpolation can be used to simply and efficiently expand sparse point data to the entire area; Adjust the interpolation accuracy and grid size according to the density of geological monitoring points and the changing nature of geological layers. For example, if the geological changes in a certain area are drastic, a smaller grid size can be selected to improve accuracy; Through interpolation processing, the numerical values ​​of each grid in three-dimensional space (such as soil layer thickness, groundwater level, etc. at each grid point) are generated, so that the geological structure of the entire construction area can be understood and a complete data set can be formed that can be viewed and analyzed in three-dimensional space.

[0030] S212 inputs the processed geological monitoring data and drainage monitoring data into the 3D modeling tool, and constructs the 3D model layer by layer according to the thickness of the soft soil layer, soil layer type, groundwater level, etc.

[0031] Specifically, it can use appropriate 3D modeling software tools, such as ArcGIS, AutoCAD Civil 3D, Surfer, etc., to import standardized and interpolated geological data (such as soil layer thickness, soil properties) and drainage data (such as groundwater level, pore water pressure) into the modeling tool to build a 3D geological model of the construction area; According to the distribution of soft soil layers, the model is constructed layer by layer in the 3D modeling tool. For example, areas with thinner soil layers can show finer layers in the 3D model, while areas with thicker soil layers can appropriately reduce the fineness of the layers.

[0032] The attribute data of each layer (such as soil type and physical parameters) should be assigned according to the geological monitoring data, and the groundwater level data and pore water pressure should be displayed at the corresponding level.

[0033] In actual operation, the resolution and details of the model can be optimized according to the geological characteristics of different regions. For example, in areas with large geological changes or key construction areas, the resolution can be increased to generate a more detailed model.

[0034] S213 generates specific construction operation steps and parameters through the 3D model and extracted key parameters, including the excavation depth, riprap thickness, and gravel grading ratio of each section, and obtains a preliminary construction plan; Specifically, it can extract key geological parameters such as soft soil thickness, groundwater level, soil compressibility, etc. in the construction area based on the three-dimensional geological model. These parameters will directly affect the formulation of the construction process.

[0035] Extract drainage system related data, such as drainage ditch location, flow rate, pore water pressure, etc., to evaluate the drainage effect of soil.

[0036] The excavation depth of each section of the roadbed is determined according to the construction objectives. For example, in areas with thicker soft soil layers, deeper excavation may be required.

[0037] According to the bearing capacity and compressibility of each layer of soil in the 3D model, the thickness of the stone sheet is determined reasonably. For example, in areas with weak bearing capacity, the stone sheet layer needs to be thickened to increase the bearing capacity.

[0038] Determine the sand and gravel grading ratio to ensure it meets drainage performance requirements and optimize the material input ratio.

[0039] Using the 3D geological model and extracted key parameters (soft soil thickness, groundwater level, pore water pressure, etc.), specific construction plans are generated. These plans include: Drainage ditch construction: According to the groundwater level distribution, calculate the optimal position and depth of drainage ditch 6 to ensure that the position and depth of the drainage ditch are 50 cm below the drainage layer, and generate the slope gradient of drainage ditch 6 (for example, the slope gradient is 1:1.5) to ensure that all high groundwater level areas are covered; Roadbed excavation construction: According to the thickness of the soft soil layer, the excavation depth (i.e., design elevation) of different construction sections is designed, and a processing plan is generated accordingly (for example, whether to excavate the roadbed earthwork to the design elevation from one end or both ends according to the full width of the section or in sections); Construction of the rubble layer: according to the compressibility of the soft soil, determine the thickness and gradation requirements of the rubble layer 1, and optimize the density of the dumping to ensure the density and uniformity of the dumping (generally speaking, the thickness or diameter of the rubble should not be less than 30cm), and fill the upper 10cm after the riprap is completed; Construction of slag layer: The thickness of slag layer 2 and the particle size of slag backfill are generated according to the required thickness of the slag layer and gravel layer, that is, the particle size of slag backfill should not exceed the maximum thickness or diameter of the slag.

[0040] Drainage layer construction: according to the soil permeability coefficient, determine the thickness of drainage layer 3 and design a layered filling plan, and select high-permeability gravel materials to ensure that the permeability meets the drainage requirements, for example (gravel should be medium-coarse sand with high permeability, the content of sand larger than 0.5mm should not be less than 50% of the total mass, the mud content should not be greater than 3%, and the permeability coefficient should not be less than 5×10-3cm / s); Construction of roadbed backfill layer: The thickness of the roadbed backfill layer 4 is determined according to the required height of the pavement structure layer and the height of other construction layers. The roadbed is backfilled and compacted in layers, that is, the maximum compacted thickness of each layer should not be greater than 30cm, and the compacted thickness of the last layer on the top surface should not be less than 10cm. The compaction degree is greater than or equal to 96%.

[0041] Construction of pavement structure layer: The specific construction method can be determined according to the construction requirements. The construction of pavement structure layer 5 is carried out after the roadbed construction is completed.

[0042] According to the construction plan, the specific construction operation steps and parameters are output, that is, a standardized construction data table is generated, which contains the construction parameters of each section (including the excavation depth, riprap thickness, gravel grading ratio, etc. of each section), and the three-dimensional geological model and construction plan drawings of the construction area are output accordingly.

[0043] S300 obtains construction dynamic monitoring data, wherein the construction dynamic monitoring data includes compaction degree of the stone layer, particle size distribution of the gravel layer, sedimentation rate, and construction equipment status data; Specifically, the acquisition method is as follows: Real-time monitoring of compaction of the rubble layer: By burying settlement plate sensors in the construction area, the settlement amount and settlement rate of the roadbed can be recorded in real time. The settlement plate sensor can detect the change of settlement according to the degree of soil deformation and send data in real time. By burying compaction sensors (such as nuclear density meters, static compaction sensors, etc.) in the construction area of ​​the rubble layer, the compaction of each layer can be monitored in real time. The sensor can estimate the density of the soil through the propagation speed of electromagnetic waves or mechanical waves; The sensor transmits the collected compaction data to the central data processing system in real time via a wireless network (such as LoRa, 5G or Wi-Fi); Compare the real-time collected compaction data with the design requirements and mark the low compaction areas; The compaction data were analyzed using statistical methods to generate a compaction distribution map showing the compaction conditions in different areas and marking low-density areas as a basis for subsequent adjustments to construction parameters.

[0044] Real-time monitoring of particle size distribution in the gravel layer: Real-time monitoring of particle size distribution in the gravel layer is achieved by using dynamic particle size analyzers (such as laser particle size analyzers, image recognition technology, etc.). These devices can measure the content of different particle sizes in the gravel in real time and transmit the data to the central control system in real time; Compare the collected particle size distribution data with the design standard curve to evaluate whether the particle size distribution meets the drainage requirements; Calculate the uniformity coefficient (Cu) and curvature coefficient (Cc) of particle size distribution. These indicators can reflect the drainage and construction uniformity of the gravel layer; If the particle size distribution deviates from the design standard, the system will generate optimization suggestions, such as adjusting the particle size ratio.

[0045] The system will generate a grain size distribution report for the gravel layer, showing the difference between the current mix and the design standard.

[0046] In actual operation, based on the deviation of particle size distribution, the system can use linear programming algorithm to optimize the particle size ratio of the gravel layer to ensure that the drainage performance meets the design requirements; and according to the optimization results, it can automatically adjust the material feeding ratio or the screen aperture of the screening equipment to ensure the uniformity of gravel particle size distribution during construction.

[0047] Real-time monitoring of settlement rate: By burying settlement plate sensors in the construction area, the settlement amount and settlement rate of the roadbed can be recorded in real time. The settlement plate sensor can detect the change of settlement according to the degree of soil deformation and send data in real time; The settlement data is sent to the monitoring center via wireless transmission (such as LoRa or Wi-Fi). Every settlement change is recorded to generate time series data; The real-time sedimentation data is statistically analyzed and the sedimentation rate is calculated.

[0048] Use finite element analysis to simulate current settlement trends, predict future settlement trends, and help identify potential settlement risk areas to generate time series data of settlement rates and settlement trend graphs to help identify abnormal settlement areas. Based on the predicted settlement trends, generate adjustment plans for future construction phases, such as adjusting the construction schedule or optimizing the construction process; Real-time monitoring of construction equipment status data: It monitors the working status of construction equipment in real time through sensors installed on the construction equipment (such as GPS sensors, vibration force sensors, compaction sensors, etc.).

[0049] The collected data include the equipment’s operating position, operating speed, vibration force, compaction speed, etc. Compare the real-time data of construction equipment with the set construction standards to ensure that the operation of the equipment meets the construction requirements; Use statistical methods to analyze the operating efficiency of equipment and generate equipment performance reports; Generate equipment status reports and adjust equipment parameters (such as adjusting vibration force or working speed) according to the equipment's working status.

[0050] Provide equipment maintenance and adjustment suggestions to ensure the efficiency and stability of equipment operation; S400 dynamically adjusts the thickness of the stone layer and the particle size ratio of the gravel layer according to real-time monitoring data to obtain adjustment suggestions for dynamically adjusting the thickness of the stone layer and the particle size of the gravel layer; Step S400 of the present application also includes: S401 obtains the construction data of the slate layer and the gravel layer and the on-site monitoring data in the real-time monitoring data; The specific steps are as follows: The compaction sensor at the construction site monitors the compaction of the slab layer in real time. The sensor transmits the compaction data to the central control system in time, so as to analyze the compaction of the construction area in real time. Monitor the particle size distribution of the gravel layer through a dynamic particle size analyzer. This device records the proportion of gravel with different particle sizes in real time and sends the data to the central processing system to ensure that the particle size distribution of the gravel layer meets the design requirements; Obtain on-site monitoring data, including settlement rate, groundwater level changes, temperature and humidity, etc. These data help to understand changes in the construction environment and their impact on the soil. By deploying settlement plates and environmental sensors on site, data is regularly collected and transmitted to the system.

[0051] S402 performs statistical analysis on the compactness data of the slab stone layer, identifies low-density areas, and generates a thermal map of the compactness distribution of the slab stone layer using a spatial interpolation algorithm (such as Kriging interpolation) to intuitively display the compactness changes; The specific steps are as follows; Analyze the compaction data collected in real time using statistical methods to generate compaction distribution maps for each area. Common methods include calculation of statistical quantities such as mean, standard deviation, maximum and minimum values ​​to help identify low-density areas that may exist during construction; Use spatial interpolation algorithms (such as Kriging interpolation) to expand the collected discrete compaction data to the entire construction area and generate a spatial distribution map of compaction. This can clearly show which areas have compaction levels lower than the design requirements; Based on the compaction distribution map, low-density areas are marked as key areas for subsequent construction adjustments.

[0052] S403 determines whether it is necessary to adjust the thickness of the slab stone layer or increase the filling volume in combination with the geological stratification data; The specific steps are as follows: Combined with the geological survey data before construction, analyze the bearing capacity, compressibility and other parameters of the soft soil layer. Determine the minimum thickness requirement of the rubble layer based on the thickness of the soil layer, compression modulus, etc. If the bearing capacity of the soil layer in some areas is weak, it may be necessary to thicken the rubble layer to improve the stability of the roadbed; If the low-density area is closely related to the distribution of soft soil layers (e.g., the soft soil layer is thick and highly compressible), it may be necessary to increase the thickness of the stone layer or increase the amount of fill in specific areas to ensure the bearing capacity and stability of the roadbed; According to the analysis results, a specific adjustment plan for the rubble layer is generated. The adjustment plan may include increasing the filling volume, thickening the rubble layer, etc., and determining the adjustment area and specific adjustment values.

[0053] S404 generates a suggested adjustment plan for the thickness of the slab stone layer; The specific steps are as follows: Based on the analysis results in S403, an adjustment plan for the thickness of the rubble layer is generated. For example, for low-density areas, it is recommended to increase the thickness of the rubble layer by 2 cm to 5 cm; for areas with deep soft soil layers or strong compressibility, it is recommended to increase the thickness of the rubble layer; The adjustment plan generated will be converted into construction instructions, clarify the thickness requirements of the stone layer in each construction section, and guide the construction equipment to carry out construction.

[0054] S410 calculates the uniformity coefficient (Cu) and curvature coefficient (Cc) of the multi-grade mix through the particle size distribution of the gravel layer; The specific steps are as follows: Use a dynamic particle size analyzer or other monitoring tools to obtain the particle size distribution data of the gravel layer. Evaluate the multi-grade ratio of the gravel layer by measuring the proportion of gravel with different particle sizes; Calculate the uniformity coefficient (Cu) and curvature coefficient (Cc) of the gravel layer. The uniformity coefficient reflects whether the particle size distribution of the gravel layer is uniform, and the curvature coefficient describes the changing trend of the particle size distribution; Cu and Cc values ​​are used to evaluate whether the gravel layer meets the drainage design requirements. Generally speaking, a good gravel layer should have moderate Cu and Cc values ​​to ensure good drainage.

[0055] S411 compares the actual particle size distribution with the design standard curve to evaluate whether the gravel layer meets the drainage performance requirements; The specific steps are as follows: Compare the actual collected gravel particle size distribution with the design standard curve to check whether the actual particle size distribution is consistent with the design value; Based on the comparison results, the degree to which the particle size distribution of the gravel layer deviates from the design standard is evaluated. If the deviation is large, it may affect the drainage performance and roadbed stability.

[0056] If S412 deviates from the design standard, the particle size ratio is optimized using a linear programming algorithm to generate particle size optimization parameters; The specific steps are as follows: Use a linear programming algorithm to calculate the optimal particle size ratio based on the deviation between the design standard curve and the actual particle size distribution. For example, increase the proportion of sand and gravel in a certain particle size range to supplement larger particles, or reduce the proportion of fine particles; Generate optimized gravel particle size ratio parameters and output construction adjustment suggestions to guide the adjustment of gravel material ratio during construction; In actual operation, after the particle size ratio is adjusted, the particle size distribution of the gravel layer can continue to be monitored and compared with the design standard to ensure the effectiveness of the optimization plan.

[0057] S500 generates a compaction process construction adjustment plan based on the construction feedback data of the rubble layer and gravel layer as well as the external environment data; Step S500 of the present application also includes: S501 obtains construction feedback data of the rubble layer and the gravel layer, wherein the construction feedback data of the rubble layer and the gravel layer include compaction degree, particle size distribution and filling layer thickness, and external environment data, wherein the external environment data includes dynamic change of groundwater level and soft soil settlement trend; The specific steps are as follows: The compaction sensor monitors the compaction of the rubble and gravel layers in real time. The compaction of each layer is calculated based on the real-time data and compared with the design standards; Monitor the particle size distribution of the gravel layer through a particle size analyzer to ensure that it meets the design requirements. Record the proportion of different particle sizes to assess whether drainage performance requirements are met; And record the filling thickness of each construction layer to ensure that the thickness of each layer meets the construction specifications; Use water level sensors to monitor groundwater level changes in real time, especially to assess the impact of possible groundwater fluctuations on the roadbed during construction; Monitor the settlement amount and settlement rate of the construction area through the settlement plate to understand the progress of settlement and predict future settlement trends; The feedback data of the rubble layer and gravel layer are synchronized with the external environmental data to ensure accurate comparison and analysis of all data at the same time point.

[0058] S502 classifies and counts the compaction degree data to generate a spatial distribution map of the compaction effect; The specific steps are as follows: According to the compaction data collected in real time, the data is classified and counted by construction area. According to the compaction values ​​of different areas, different compaction quality grades (such as qualified, unqualified, to be adjusted, etc.) are determined; Use spatial interpolation methods (such as Kriging interpolation or inverse distance weighted method) to expand the discrete compaction data to the entire construction area and generate a spatial distribution map of the compaction effect. This can clearly show which areas do not meet the compaction standards and which areas have better compaction quality; Through statistical analysis and spatial distribution maps, areas where the compaction level is below the standard are clearly marked for reference when subsequently adjusting construction parameters.

[0059] S503 applies machine learning models (such as support vector machines SVM) to predict the impact of different compaction parameters (such as vibration force and compaction speed) on compaction results; The specific steps are as follows: It can collect a large amount of historical data, including compaction effects under different compaction parameters, environmental conditions (such as groundwater level, soil moisture, etc.) and corresponding construction feedback data; And use machine learning models (such as support vector machine SVM, regression analysis, etc.) to train models and analyze the impact of compaction parameters (such as vibration force, compaction speed) on compaction degree and construction quality; During the construction process, by inputting compaction parameters and external environment data in real time, the trained machine learning model is used for prediction, the impact of different parameters on the compaction effect is evaluated, and the optimal vibration force, compaction speed and construction method are found through the prediction results to maximize the compaction effect.

[0060] In actual operation, as the construction progresses, new construction data can be continuously collected and used to update the model, ensuring the real-time effectiveness of the machine learning model.

[0061] S504 generates a compaction parameter optimization plan based on the prediction results (such as increasing the vibration force to 2.5 tons and reducing the compaction speed to 0.5 m / s) and generates adjustment parameters for the compaction equipment (including vibration force, compaction speed and layer thickness control values).

[0062] Specifically: It can determine the optimal compaction parameters (such as vibration force, compaction speed and layer thickness) based on the prediction results of the machine learning model. For example, if the model recommends increasing the vibration force to 2.5 tons and reducing the compaction speed to 0.5 m / s, this parameter will become part of the optimization plan; The optimized compaction parameters are converted into construction instructions and transmitted to the construction equipment. The equipment will make real-time adjustments based on these adjustment parameters (such as vibration force, compaction speed, etc.) to ensure that the compaction effect of each layer meets the design requirements; Combine settlement rate data with construction schedule to analyze settlement conditions at the current construction stage; Use finite element analysis to simulate future subsidence trends and identify areas where abnormal subsidence may occur; Generate construction adjustment suggestions for abnormal areas (such as increasing preloading time or reducing filling rate); Generate regional construction adjustment plans (such as suspending local construction and adjusting filling rate); Analyze drainage system efficiency data (such as drainage volume, pore water pressure changes) and evaluate the operating status of the current drainage system.

[0063] A dynamic drainage model was applied to predict the impact of groundwater level changes on the construction area.

[0064] Optimize drainage technology based on prediction results (such as increasing the number of drainage wells or adjusting the slope of drainage ditches); Generate subsequent drainage process optimization and adjustment plan; S600 adjusts the construction plan based on the adjustment suggestions for the thickness of the stone layer and the particle size of the gravel layer and the compaction process construction adjustment plan, and records the construction process data in real time to generate a construction log.

[0065] S601 Optimize the thickness of the stone layer, the particle size distribution of the gravel layer and the compaction process parameters during the construction process according to the adjustment suggestions and construction adjustment plan; Specifically: The following is an example of the adjustment of the slab layer construction: according to the adjustment suggestions for the slab layer thickness, adjust the hopper height of the paver and the speed of slab stone delivery; in low-density areas, increase the filling volume of slab stones, or adjust the slab stone grading to improve the overall density; to ensure that the thickness of the slab layer meets the requirements of the adjustment plan; An example of the drainage layer construction adjustment is as follows: according to the gravel particle size adjustment suggestions, the screen aperture or feed ratio of the screening equipment is adjusted to optimize the particle size distribution of the gravel. During the construction process, the particle size distribution of the gravel can be continuously monitored and dynamically adjusted based on real-time data to ensure the uniformity and drainage performance of the gravel layer.

[0066] An example of compaction process construction adjustment is as follows: According to the compaction parameter optimization plan, the vibration force, compaction speed and compaction layer thickness of the compaction equipment are adjusted.

[0067] The specific operations include: Increase or decrease vibration force to ensure that low-density areas are fully compacted; Adjust the travel speed of compaction equipment to meet the compaction needs of different areas; In low-density areas, increase the compaction times or adopt a layered compaction method to further improve the compaction quality.

[0068] 602 records the changes of all key parameters during the construction process through real-time data, providing reliable data support for subsequent analysis and evaluation; The specific operations are as follows: Use sensors installed on construction equipment to record key data such as the thickness of the rubble layer, the distribution of gravel particle size, compaction degree, and the status of compaction equipment in real time. And collect environmental data (such as groundwater level changes, temperature and humidity, etc.) in real time through the sensor network deployed on site. The collected data is transmitted to the central data storage system in real time via wireless networks (such as LoRa, 5G).

[0069] The collected data is classified and stored, and information such as construction parameters, environmental data and equipment status are recorded separately; The system automatically generates a construction log based on real-time data, including: the thickness and density of the rubble layer in each construction area; the particle size distribution of the gravel layer and the proportion that meets the standards; the operating parameters of the compaction equipment (such as vibration force, travel speed, etc.); and the environmental data of the construction area (such as groundwater level, drainage system operation, etc.).

[0070] 603 Extract key quality indicators based on construction log data to evaluate whether the construction quality meets the design requirements; The specific operations are as follows; Extract key quality indicators: Compactness qualification rate: collect the compaction data of each layer and calculate the proportion of areas that meet the design standards; Settlement rate: Analyze the data recorded by the settlement plate, calculate the average settlement rate of each construction stage, and compare it with the design standard; Drainage Efficiency: Combined with drain flow data, evaluate the operating efficiency of the drainage system.

[0071] Based on the extracted quality indicators and design standards, evaluate whether the construction area meets the design requirements; Specifically, it can evaluate the overall quality of the construction area based on the compaction qualification rate, settlement rate and drainage efficiency, and analyze each construction area one by one to determine whether it meets the design requirements; If the quality indicators of some areas do not meet the standards (such as compaction below the standard or settlement rate too fast), the system should automatically mark these risk areas for reference in subsequent repairs and adjustments, so that it can analyze the changing trend of construction quality by comparing the quality indicators of multiple construction stages. If it is found that the quality indicators of a certain stage continue to decline, the reasons can be analyzed in time and the construction strategy can be adjusted; Generate construction quality reports, mark substandard areas and provide repair suggestions to guide subsequent construction adjustments; The S700 uses settlement plate sensors to track long-term changes in roadbed settlement and monitor drainage ditch flow and groundwater levels; Evaluate the long-term stability of the roadbed based on long-term data; The specific steps are as follows: Settlement plate sensors are buried in key areas of the roadbed. These sensors can record the vertical displacement of the ground or roadbed in real time; Settlement data should be collected at regular intervals (e.g. weekly or monthly) to track the settlement rate and total settlement. Settlement panels should be located in different areas to ensure that the overall settlement trend of the roadbed is captured.

[0072] The settlement data will be sent to the central data processing system in real time via wireless transmission (such as LoRa, Wi-Fi, etc.) for data storage and analysis.

[0073] Install flow sensors (such as ultrasonic flow meters) at the outlet of the gutter or at key locations to monitor the drainage volume in real time. These sensors can provide flow velocity and flow data of water in the gutter.

[0074] By continuously monitoring gutter flow, you can assess how efficiently your drainage system is working and determine if there is any drainage obstruction or blockage.

[0075] Groundwater level sensors are installed on and around the roadbed to monitor groundwater level changes in real time. Water level changes may directly affect soil moisture, compaction and settlement rate, so it is particularly important to monitor groundwater level changes.

[0076] Groundwater level sensors are usually installed in the soil layer near drainage ditches, wells or roadbeds to record the height and changing trends of groundwater levels in real time.

[0077] By using the collected long-term settlement data, the settlement trend is analyzed. Based on the change in settlement rate, it is determined whether there is a risk of uneven settlement or excessive settlement of the roadbed.

[0078] Regression analysis was performed on the settlement data to predict future settlement trends and identify areas that may lead to roadbed instability.

[0079] Combined with groundwater level monitoring data, the impact of groundwater level fluctuations on soil is analyzed. Groundwater level changes may cause soft soil to swell or settle, especially when the water level is too high or too low, which may lead to uneven settlement.

[0080] Based on the relationship between groundwater level and settlement, determine whether additional drainage measures need to be taken or the construction process needs to be adjusted.

[0081] Use gutter flow data to evaluate the efficiency of the drainage system. This will reflect whether the drainage system is running smoothly, or whether there are any blockages or leaks.

[0082] Combine settlement data and groundwater level data to comprehensively evaluate the overall performance of the drainage system and determine whether there are drainage problems that affect the stability of the roadbed; It can use machine learning or statistical methods (such as time series analysis, linear regression, etc.) to predict long-term trends in settlement, groundwater levels, and drainage effectiveness, allowing it to assess roadbed stability by analyzing long-term monitoring data.

[0083] Embodiment 2 A system for high liquid limit soft soil roadbed construction, comprising: Data acquisition module 11: used to collect geological, drainage and construction dynamic data, and pass them to the data processing and model building module after pre-processing; The data acquisition module 11 includes: Geological monitoring unit: used to collect geological monitoring data of the construction area; Drainage monitoring unit: used to collect drainage data in the construction area; Construction dynamic monitoring unit: used to monitor the construction status in real time.

[0084] Data processing and model building module 12: used to build a three-dimensional model using the collected data and generate a preliminary construction plan; The data processing and model building module 12 includes: Data preprocessing unit: Used to pre-process the data collected by the data acquisition module; The preprocessing method is as follows: format unification, outlier detection and denoising of geological data and drainage data are performed, and the processed data are converted into a gridded spatial data format to generate standardized and cleaned geological and drainage data.

[0085] 3D model building unit: builds a 3D geological model of the construction area based on geological and drainage data; The construction method is as follows: use interpolation methods (such as Kriging interpolation) to generate the three-dimensional distribution of soft soil layer thickness and groundwater level, and use modeling tools (such as GIS software) based on this to construct a layered three-dimensional geological model to support the preliminary construction plan design.

[0086] Construction plan generation unit: extract key parameters based on the 3D geological model and generate a preliminary construction plan; The specific steps are as follows: extract key parameters such as soft soil thickness, groundwater level, pore water pressure in the model, and design construction steps and parameters (such as excavation depth, riprap thickness, and gravel grading ratio) accordingly.

[0087] Dynamic adjustment module 13: used to adjust the thickness of the stone layer, the ratio of the sand and gravel particle size and the compaction process according to the real-time monitoring data to generate an optimization plan; The dynamic adjustment module 13 includes: Dynamic adjustment unit: The dynamic adjustment unit is used to analyze the density data of the stone layer, identify the low-density area and adjust the stone thickness, and analyze the gravel particle size distribution, compare it with the design standard curve, and use the linear programming algorithm to optimize the particle size ratio to generate stone layer thickness adjustment suggestions and gravel layer particle size ratio adjustment plans.

[0088] Compaction process optimization unit: The compaction process optimization unit predicts the impact of compaction parameters on construction quality based on construction feedback data (such as compaction degree, particle size distribution) and external environmental data (such as groundwater level changes, soft soil settlement trends), and uses machine learning models such as support vector machines (SVM) to generate compaction parameter optimization solutions to generate adjustment parameters for compaction equipment (such as vibration force, compaction speed, layer thickness control value).

[0089] Execution and recording module 14: used to convert the optimization plan into equipment control instructions and record construction process data; The execution and recording module 14 includes: Execution control unit: used to convert the dynamic adjustment plan into construction equipment control instructions, that is, to control the operating parameters of the paver, compaction equipment, etc. according to the optimization plan; Construction log recording unit: used to record construction process data in real time, such as recording construction parameters (such as stone thickness, compaction degree, particle size distribution), and then generate a construction log based on the recorded construction process data, the construction log includes the construction quality and adjustment records of each layer.

[0090] The quality assessment and maintenance module 15 is used to assess the construction quality and long-term stability of the roadbed through construction logs and long-term monitoring data, and to generate quality reports and maintenance recommendations.

[0091] The quality assessment and maintenance module 15 includes: Quality assessment unit: used to assess construction quality through construction log data. Specifically, it can extract quality indicators such as compaction qualification rate, settlement rate, drainage efficiency, etc. from the log, mark substandard areas based on quality assessment results, and output construction quality reports; Long-term monitoring unit: It is used to monitor the long-term stability of the roadbed after the construction is completed. Specifically, it can monitor the long-term settlement and drainage efficiency through sensors such as settlement plate sensors and drainage sensors preset during the construction process, and analyze the long-term monitoring data based on this to evaluate the stability of the roadbed.

[0092] Any of the above methods or steps can be stored as computer instructions or programs in various types of computer memories, and the computer instructions or programs can be recognized by various types of computer processors to implement any of the above methods or steps; Secondly, the above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0093] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for high liquid limit soft soil roadbed construction, characterized in that: include: Obtain geological monitoring data and drainage monitoring data of the construction site; Generate a 3D model of the construction area using geological monitoring data and drainage monitoring data, and generate a preliminary construction plan based on this; Obtain construction dynamic monitoring data; Dynamically adjust the thickness of the stone layer and the particle size ratio of the gravel layer according to the real-time monitoring data to obtain adjustment suggestions for the dynamic adjustment of the thickness of the stone layer and the particle size of the gravel layer; Generate compaction process construction adjustment plan based on construction feedback data of the rubble layer and gravel layer as well as external environment data; The construction plan is adjusted according to the adjustment suggestions for the thickness of the stone layer and the particle size of the gravel layer and the compaction process construction adjustment plan.

2. A method for high liquid limit soft soil roadbed construction according to claim 1, characterized in that: The geological monitoring data include the thickness and distribution of the soft soil layer at the construction site and the physical parameters of the soil, and the drainage monitoring data include the groundwater level distribution, pore water pressure and flow data of the drainage ditch.

3. A method for high liquid limit soft soil roadbed construction according to claim 2, characterized in that: The method of generating a 3D model of the construction area using geological monitoring data and drainage monitoring data is as follows: Standardize geological monitoring data and drainage monitoring data into a unified spatial grid format; Generate complete 3D data distribution of the construction area based on limited monitoring point data in geological monitoring data; The processed geological monitoring data and drainage monitoring data are input into the 3D modeling tool, and the 3D model is constructed layer by layer according to the thickness of the soft soil layer, soil layer type, groundwater level, etc.

4. A method for high liquid limit soft soil roadbed construction according to claim 3, characterized in that: The method for generating a preliminary construction plan is as follows; Through the 3D model and extracted key parameters, specific construction operation steps and parameters are generated, including the excavation depth, riprap thickness, and gravel grading ratio of each section, to obtain a preliminary construction plan.

5. The method for high liquid limit soft soil roadbed construction according to claim 1, characterized in that: The construction dynamic monitoring data includes the compaction degree of the rubble layer, the particle size distribution of the gravel layer, the sedimentation rate, and the status data of the construction equipment.

6. A method for high liquid limit soft soil roadbed construction according to claim 1, characterized in that: The method for dynamically adjusting the thickness of the stone layer and the particle size ratio of the gravel layer according to the real-time monitoring data to obtain the adjustment suggestions for dynamically adjusting the thickness of the stone layer and the particle size of the gravel layer is as follows: Obtain the construction data of the rubble layer and gravel layer in the real-time monitoring data as well as the on-site monitoring data; Conduct statistical analysis on the compaction data of the slab layer to identify low-compactness areas; Combined with geological stratification data, determine whether it is necessary to adjust the thickness of the rubble layer or increase the filling volume; Generate a suggested adjustment plan for the thickness of the sheet rock layer; Through the particle size distribution of the gravel layer, the uniformity coefficient and curvature coefficient of its multi-level proportion are calculated; Compare the actual particle size distribution with the design standard curve to evaluate whether the gravel layer meets the drainage performance requirements; If it deviates from the design standard, the particle size ratio is optimized using a linear programming algorithm to generate particle size optimization parameters.

7. The method for high liquid limit soft soil roadbed construction according to claim 1, characterized in that: According to the construction feedback data of the rubble layer and the gravel layer and the external environment data, the method for generating the compaction process construction adjustment plan is as follows: Obtaining construction feedback data of the rubble layer and the gravel layer, the construction feedback data of the rubble layer and the gravel layer including compaction degree, particle size distribution and fill layer thickness, and external environmental data including dynamic changes in groundwater level and soft soil settlement trend; Classify and count the compaction data to generate a spatial distribution map of the compaction effect; Apply machine learning models to predict the impact of different compaction parameters on compaction performance; A compaction parameter optimization plan is generated based on the prediction results, and adjustment parameters of the compaction equipment are generated.

8. A system for high liquid limit soft soil roadbed construction, characterized in that: include: Data acquisition module: used to collect geological, drainage and construction dynamic data, and pass them to the data processing and model building module after preprocessing; Data processing and model building module: used to build a three-dimensional model using the collected data and generate a preliminary construction plan; Dynamic adjustment module: used to adjust the thickness of the stone layer, the ratio of sand and gravel particle size and the compaction process according to real-time monitoring data to generate an optimization plan; Execution and recording module: used to convert the optimization plan into equipment control instructions and record construction process data.

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