Reconstruction and extension road differential settlement control method based on data driving

Abstract

The invention discloses a data-driven reconstruction and extension road differential settlement control method, particularly relates to the technical field of road engineering, and is used for solving the problems of environmental response mismatch and overcompensation caused by parameter solidification in an existing settlement control method. By synchronously collecting an old roadbed settlement value, a newly-filled roadbed settlement value and environment temperature and humidity data, differential settlement space gradient distribution is calculated, and a settlement gradient mutation area is positioned; dynamically generating a reinforcement resource allocation weight value in combination with the temperature and humidity correction coefficient and the settlement gradient attenuation rate; the grouting equipment is driven by the industrial control actuator to execute reinforcement operation; after settlement reaches the standard, the stress redistribution rate of the surrounding roadbed is detected, and when the value is lower than a threshold value, a newly-added grouting instruction related to the spatial position is generated and executed in a closed-loop callback mode. According to the method, double-index standard control over the settlement gradient value and the roadbed stress redistribution rate is achieved, a load transfer balance mechanism of a dynamic response environment is established, and the hidden settlement risk caused by the stress shielding effect is eliminated.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and more specifically, to a data-driven method for controlling differential settlement in reconstructed and expanded highways. Background Technology

[0002] In highway reconstruction and expansion projects, the splicing of old and new roadbeds is a critical technical aspect. Because the existing roadbed has stabilized after long-term operation, while the newly filled roadbed experiences significant post-construction settlement, differential settlement is highly likely to occur between the two. This differential settlement can lead to pavement cracking, vehicle bounce, and other defects, directly affecting road lifespan and driving safety. Current technologies for controlling differential settlement typically rely on pre-designed reinforcement schemes (such as pile foundation treatment and geogrid installation) and fixed construction schedules. The implementation of these schemes is often scheduled and resource-allocated through industrial control systems (such as controlling the operating parameters and timing of compaction equipment, grouting equipment, and other actuators) to achieve load balance and settlement control.

[0003] However, existing control methods have significant shortcomings when dealing with complex real-world construction environments: settlement control systems lack the ability to adaptively adjust to dynamic environmental disturbances. Specifically, once preset reinforcement parameters (such as the weighting of reinforcement resource allocation) and construction cycle parameters are set, they are typically fixed during construction. When actual on-site environmental conditions (such as rainfall and temperature changes) or foundation response characteristics (such as changes in local settlement rates) deviate from design expectations, the system cannot adaptively perform closed-loop feedback and parameter correction based on real-time monitoring data. This fixed control mode results in the output of the actuators (reinforcement intensity, construction pace) failing to accurately match the dynamic needs on-site, often leading to settlement control effects deviating from expectations—manifesting as control commands lagging behind settlement development, or excessive reinforcement (overcompensation) to compensate for potential risks. Summary of the Invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a data-driven differential settlement control method for reconstructed and expanded highways to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A data-driven method for controlling differential settlement in reconstructed and expanded highways includes the following steps:

[0007] S1. The sensor array synchronously acquires the settlement values ​​of the old roadbed, the settlement values ​​of the newly filled roadbed, and the measured data of ambient temperature and humidity.

[0008] S2. Calculate the spatial gradient distribution of differential settlement based on the settlement values ​​of the old and newly filled roadbeds, and locate the abrupt settlement gradient areas according to the coordinates of the contact surface between the old and new roadbeds.

[0009] S3. Calculate the temperature and humidity correction coefficient using measured environmental temperature and humidity data, and dynamically generate reinforcement resource allocation weight values ​​by combining the settlement gradient attenuation rate in the settlement gradient abrupt change area.

[0010] S4. Transmit the reinforcement resource allocation weight value to the industrial control actuator to drive the grouting equipment controller to perform the grouting operation;

[0011] S5. Measure the settlement gradient value in the settlement gradient abrupt change area. When the settlement gradient value enters the preset threshold range, detect the roadbed stress redistribution rate within a set distance around the corresponding area.

[0012] S6. When the roadbed stress redistribution rate is lower than the set threshold, generate a new grouting weight instruction and execute S4; otherwise, repeat S1 to S5 until the settlement gradient value and the roadbed stress redistribution rate reach the standard at the same time.

[0013] Furthermore, the sensor array simultaneously acquires the settlement values ​​of the old roadbed, the settlement values ​​of the newly filled roadbed, and measured data of ambient temperature and humidity, including:

[0014] Settlement monitoring sensors were used to obtain the settlement values ​​of the old roadbed, and settlement monitoring sensors were used to obtain the settlement values ​​of the newly filled roadbed. Temperature and humidity composite sensors were used to obtain measured data of atmospheric temperature and humidity.

[0015] Add a soil moisture sensor to measure soil moisture content data at a set depth below the surface, and incorporate the soil moisture content data into the measured ambient temperature and humidity data.

[0016] Furthermore, the temperature and humidity composite sensor and the settlement monitoring sensor are triggered to acquire data through a unified clock source.

[0017] Furthermore, settlement monitoring sensors are installed symmetrically on both sides of the splice joint of the old roadbed and the newly filled roadbed, and all sensor data are marked with the same timestamp.

[0018] Furthermore, the spatial gradient distribution of differential settlement is calculated based on the settlement values ​​of the old and newly filled roadbeds. The areas of abrupt settlement gradient changes are located according to the coordinates of the contact surface between the old and new roadbeds, including:

[0019] The contact surface coordinate lattice is divided for the settlement values ​​of the old roadbed and the newly filled roadbed;

[0020] The differential settlement is obtained by calculating the difference in elevation changes at each coordinate point.

[0021] A spatial gradient calculation grid is constructed by extending a set distance to both sides from the centerline of the contact surface.

[0022] Based on the spatial gradient, the difference in elevation change of grid nodes is calculated, and neighborhood difference operation is performed to generate the spatial gradient distribution of differential settlement.

[0023] In the coordinate lattice of the contact surface, the point set whose spatial gradient value is greater than a set threshold is selected and marked as the settlement gradient abrupt change region.

[0024] Furthermore, temperature and humidity correction coefficients are calculated using measured environmental temperature and humidity data. Combined with the settlement gradient attenuation rate in areas of abrupt settlement gradient changes, dynamic weight values ​​for reinforcement resource allocation are generated, including:

[0025] Extract atmospheric temperature, atmospheric humidity, and soil moisture content values ​​from measured environmental temperature and humidity data;

[0026] The comprehensive temperature and humidity influence factor is calculated based on the proportional relationship between soil moisture content and atmospheric temperature and humidity.

[0027] Obtain the settlement gradient decay rate over a continuous time span in the region of abrupt settlement gradient change.

[0028] The settlement gradient attenuation rate was proportionally weighted using a comprehensive temperature and humidity influence factor as a correction term.

[0029] Based on the proportional weighting results, the reinforcement resource allocation weight values ​​are dynamically allocated within a preset weight range.

[0030] Furthermore, the settlement gradient attenuation rate is calculated by the difference in settlement gradient values ​​between adjacent time periods.

[0031] Furthermore, the reinforcement resource allocation weight value is transmitted to the industrial control actuator to drive the grouting equipment controller to perform the grouting operation, including:

[0032] Establish a conversion formula between reinforcement resource allocation weight values ​​and grouting equipment controller parameters;

[0033] Based on the conversion formula, the reinforcement resource allocation weight value is parsed into pressure and flow control commands of the grouting equipment controller;

[0034] The industrial control actuator sends pressure control commands and flow control commands to the grouting equipment controller.

[0035] The grouting equipment controller performs pressure regulation and flow regulation operations;

[0036] Real-time data collection of actual grouting pressure and actual grouting flow rate is used to compare with target control commands and correct command offset.

[0037] Furthermore, the settlement gradient value in the area of ​​abrupt settlement gradient change is measured. When the settlement gradient value enters a preset threshold range, the roadbed stress redistribution rate within a set distance around the corresponding area is detected, including:

[0038] Determine the set of boundary coordinate points for the region of abrupt change in settlement gradient;

[0039] Settlement monitoring points are uniformly distributed within the coverage area of ​​the boundary coordinate point set to measure the settlement gradient value;

[0040] A surrounding detection area is formed by radiating outwards from the geometric center of the abrupt change in settlement gradient region.

[0041] Stress monitoring points were set up in a rectangular grid within the surrounding detection area, and the vertical stress values ​​of the roadbed at each point were collected synchronously through an earth pressure sensor array.

[0042] Calculate the percentage deviation between the initial design stress distribution and the actual vertical stress value within the surrounding detection area to obtain the roadbed stress redistribution rate.

[0043] Furthermore, when the subgrade stress redistribution rate is lower than a set threshold, a new grouting weight instruction is generated and S4 is executed; otherwise, S1 to S5 are repeated until the settlement gradient value and the subgrade stress redistribution rate simultaneously meet the target, including:

[0044] The measured subgrade stress redistribution rate is numerically compared with a set threshold.

[0045] If the roadbed stress redistribution rate is lower than the set threshold, the specific value of the new grouting weight instruction is determined based on the spatial location of the settlement gradient abrupt change area.

[0046] Input the new grouting weight command into the grouting equipment controller associated with the industrial control actuator;

[0047] The grouting equipment controller executes the grouting operation corresponding to the newly added weight value;

[0048] If the roadbed stress redistribution rate is not lower than the set threshold, the settlement gradient value of the settlement gradient abrupt change area and the roadbed stress redistribution rate within the set distance around it will be re-collected.

[0049] The numerical comparison is performed repeatedly until both the settlement gradient value and the roadbed stress redistribution rate reach the corresponding preset threshold range.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] A dynamic coupling control mechanism between environmental response and foundation behavior is established. By integrating real-time temperature and humidity data correction coefficients with settlement gradient attenuation rates for weighted decision-making, the industry pain point of mismatch between solidification parameters and environmental disturbances is addressed. Grouting reinforcement focal points are precisely located in areas of abrupt settlement gradient changes, avoiding the resource waste of traditional full-area reinforcement. After settlement reaches the target, the roadbed stress redistribution rate is verified, achieving dual-objective synergistic optimization of differential settlement control and load transfer balance, fundamentally eliminating the risk of later-stage misalignment caused by stress shielding effects. This control process forms a complete monitoring-decision-execution-verification closed loop, ensuring real-time matching between reinforcement intensity and foundation dynamic response, significantly reducing overcompensation or undercompensation caused by lag control.

[0052] This design leverages an industrial control system to achieve quantifiable adaptive adjustment: reinforcement weights are converted into pressure / flow commands from the grouting equipment controller, establishing a direct mapping between engineering parameters and industrial actuators; dual compliance conditions—settlement gradient threshold and roadbed stress redistribution rate threshold—overcome the limitations of a single settlement index; and newly added grouting weight commands are dynamically generated based on spatial location, achieving targeted compensation rather than uniform increments. This design ensures that the reconstruction and expansion project maintains load transfer balance under complex climatic conditions, avoiding the reinforcement inaccuracies caused by neglecting environmental temperature and humidity changes in traditional methods, and significantly improving the structural stability of the road during its service life. Attached Figure Description

[0053] Figure 1 This is a flowchart of the data-driven differential settlement control method for highway reconstruction and expansion according to the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example: Figure 1 This invention presents a data-driven differential settlement control method for reconstructed and expanded highways, comprising the following steps:

[0056] S1. The sensor array synchronously acquires the settlement values ​​of the old roadbed, the settlement values ​​of the newly filled roadbed, and the measured data of ambient temperature and humidity.

[0057] S2. Calculate the spatial gradient distribution of differential settlement based on the settlement values ​​of the old and newly filled roadbeds, and locate the abrupt settlement gradient areas according to the coordinates of the contact surface between the old and new roadbeds.

[0058] S3. Calculate the temperature and humidity correction coefficient using measured environmental temperature and humidity data, and dynamically generate reinforcement resource allocation weight values ​​by combining the settlement gradient attenuation rate in the settlement gradient abrupt change area.

[0059] S4. Transmit the reinforcement resource allocation weight value to the industrial control actuator to drive the grouting equipment controller to perform the grouting operation;

[0060] S5. Measure the settlement gradient value in the settlement gradient abrupt change area. When the settlement gradient value enters the preset threshold range, detect the roadbed stress redistribution rate within a set distance around the corresponding area.

[0061] S6. When the roadbed stress redistribution rate is lower than the set threshold, generate a new grouting weight instruction and execute S4; otherwise, repeat S1 to S5 until the settlement gradient value and the roadbed stress redistribution rate reach the standard at the same time.

[0062] S1. The settlement values ​​of the old roadbed, the settlement values ​​of the newly filled roadbed, and the measured data of ambient temperature and humidity are simultaneously acquired through the sensor array. The specific implementation is as follows:

[0063] When conducting data collection at the road reconstruction and expansion project site, construction workers first install multiple sets of vibrating wire settlement monitoring sensors at a specific distance from the centerline of the joint between the old and new roadbed surfaces. For example, three vibrating wire settlement monitoring sensors are evenly installed 1 meter from the centerline of the joint, each sensor being securely fixed to the roadbed surface with bolts. The same installation method is applied to the newly filled roadbed surface, i.e., the same number and model of vibrating wire settlement monitoring sensors are installed symmetrically. All sensor installation positions are precisely located and calibrated using a total station to ensure that the horizontal position error of the installation point is less than 5 millimeters. A temperature and humidity composite sensor is deployed within the same construction area, placed on a height-adjustable support device, for example, at the top of a standard support 2 meters above the ground. Construction workers use specialized drilling equipment to drill holes to a specific depth near the sensor installation points, for example, burying soil moisture sensors at a depth of 0.5 meters underground.

[0064] Various sensors are connected through a unified data acquisition system, which integrates a Global Positioning System (GPS) timing module. This module periodically receives standard time signals transmitted by satellites, such as a time synchronization pulse every 60 seconds. When the time synchronization pulse arrives, it triggers all connected sensors to synchronously perform measurement operations, with measurement time deviation controlled within 10 milliseconds. All acquired data is labeled with the same precise time information, and the time format includes a complete sequence of year, month, day, hour, minute, second, and millisecond.

[0065] Each data record in the collected data includes a device identification number field, a time stamp field (format example: 2023-08-15 14:05:30.500), and a measurement value field (settlement value in millimeters; temperature in degrees Celsius; humidity in percentage; moisture content in percentage). A 32-bit cyclic redundancy check code is added during data transmission to ensure data integrity.

[0066] The burial depth of the soil moisture sensor is adjusted according to different geological conditions. Geological exploration is conducted before construction to determine soil characteristics; for example, the burial depth ranges from 0.3 to 0.5 meters in sandy soil areas and from 0.8 to 1.0 meters in cohesive soil areas. After installation, all sensors undergo initial accuracy calibration: the settlement monitoring sensor's linearity is verified by loading standard weights in stages (e.g., sequentially loading 50kg, 100kg, and 150kg weights); the temperature and humidity composite sensor is compared in parallel with a standard meteorological instrument for 24 hours; and the soil moisture sensor is verified through on-site sampling (soil samples are obtained by drilling within a 1-meter radius of the sensor location).

[0067] A complete monitoring network system has been established at the construction site. For example, each monitoring section has six settlement monitoring points (three for the old roadbed and three for the newly filled roadbed), one atmospheric environment monitoring point, and one set of soil moisture monitoring points. The system automatically detects the connection status of the equipment, and triggers a remote alarm when no data signal is received from a certain sensor for five consecutive minutes. The data management system has established a regular maintenance procedure, such as performing a full system inspection twice a day.

[0068] For soil moisture content data, on-site verification was conducted every 14 days: sampling points were excavated adjacent to the soil moisture sensor, soil samples were obtained by depth layering, and the samples were dried in an oven at 105℃ for 24 hours before the actual moisture content was calculated. The entire sensor system underwent a 48-hour continuous operation test before the project started, and complete records including factory serial numbers and calibration dates were created for all devices.

[0069] S2. Calculate the spatial gradient distribution of differential settlement based on the settlement values ​​of the old and newly filled roadbeds, and locate the areas of abrupt settlement gradient changes according to the coordinates of the contact surface between the old and new roadbeds. The specific implementation is as follows:

[0070] The system receives a set of measured environmental temperature and humidity data from the storage module, and extracts settlement value data sets for both the old and newly constructed roadbeds. Based on the centerline coordinates of the contact surface between the old and new roadbeds determined by engineering surveys, calculation sections are set at fixed intervals along the longitudinal direction of the road. For example, a calculation section is set every 5 meters, and coordinate points are evenly distributed along the width of the contact surface on each section. Within a contact surface width of, for example, 10 meters, a coordinate point is set every 1 meter. Each coordinate point is associated with a specific location number and mapped to the corresponding settlement monitoring sensor.

[0071] Differential settlement calculation is performed for each coordinate point. The settlement value of the old roadbed and the settlement value of the newly filled roadbed at the same coordinate point are subtracted. For example, if the old roadbed settlement value recorded at a point is 2.3 mm and the newly filled roadbed settlement value is 5.1 mm, the calculated differential settlement value for that point is 2.8 mm. After calculation for all coordinate points, a differential settlement distribution map of the contact surface area is generated. During the calculation process, outlier data points are automatically filtered out; for example, data records with differential settlement values ​​less than 0 or greater than 50 mm are marked as invalid data.

[0072] Using the centerline of the contact surface as a reference, a rectangular computational grid is constructed extending to both sides. The extension distance is set according to the dimensions of the roadbed structure, for example, extending 20 meters towards the old roadbed and 20 meters towards the newly filled roadbed. The grid cell size is set to a standard square, for example, 1 meter on each side. The coordinates of the grid nodes are determined by actual measurements using a total station, and each node is labeled with a unique identifier. For grid nodes where no monitoring points are directly set, a bilinear interpolation algorithm is used to supplement the differential settlement values: the values ​​of the four nearest monitoring points are selected and a weighted average is calculated, with the weights determined according to the inverse distance principle.

[0073] For each grid node, a neighborhood difference calculation is performed. The lateral gradient component is obtained by subtracting the differential settlement value of the target grid node from its eastern neighbor, and the longitudinal gradient component is obtained by subtracting the differential settlement value of the target grid node from its northern neighbor. The lateral and longitudinal gradient components are then squared, and the sum of the two squares is taken as the square root to obtain the spatial gradient magnitude. For example, if the differential settlement of a node is 3.5 mm, its eastern neighbor is 3.8 mm, and its northern neighbor is 3.2 mm, then the lateral gradient component is 0.3 mm, the longitudinal gradient component is -0.3 mm, and the final calculated spatial gradient magnitude is 0.42 mm.

[0074] The spatial gradient magnitude of all grid nodes is compared with a preset threshold. This threshold is set according to engineering design requirements, for example, using a gradient value of 3 mm per meter as a benchmark. Scanning detection is performed within the coordinate grid range of the contact surface. When the spatial gradient value of a coordinate point exceeds the threshold, that point is added to the anomaly point set. When consecutive anomalies form a spatial cluster, the minimum bounding rectangle of this cluster is calculated. The entire bounding rectangle region is marked as a settlement gradient abrupt change region, and its boundary corner point coordinate set is registered in the system, for example, the three-dimensional coordinate values ​​of the four corner points.

[0075] Multiple quality assurance mechanisms are implemented during the calculation process. During the data input phase, the consistency of the number of records in the old roadbed settlement data set and the newly filled roadbed settlement data set is verified. The rationality of the spacing parameters is checked during coordinate point matrix division; for example, the minimum spacing is not less than 0.5 meters. When encountering missing adjacent nodes during neighborhood difference calculations, the calculation is automatically expanded to the next nearest neighbor. A historical trend comparison function is added to the threshold judgment phase; for example, when a spatial gradient value mutation exceeding 20% ​​is detected, data from the previous 24 hours for that point is retrieved for fluctuation characteristic analysis.

[0076] Information on marked settlement gradient abrupt change areas is stored in a specific data structure. Each record includes a region ID field, a boundary coordinate string field, a maximum gradient value field, an average gradient value field, and an associated timestamp field. The system implements a periodic review mechanism, such as automatically comparing the spatial relationship between the latest marked areas and historical marked areas every hour. When the overlap rate between two areas exceeds 70%, a verification process is triggered, requiring technicians to conduct on-site inspections of the actual settlement conditions. The grid construction parameters have an open configuration interface, allowing for adjustments to the grid size as needed, such as using a 0.5m × 0.5m denser grid specification in roadbed slope areas.

[0077] The final output includes multiple visualization formats: a dot matrix coordinate distribution map showing the location distribution of all calculated points; a gradient contour map reflecting the spatial gradient distribution trend; a heat map visually displaying the clustering of anomalies; and annotation diagrams clearly indicating the boundary locations of areas where settlement gradient abrupt changes occur. All output charts are associated with precise time stamp information to ensure accurate correspondence with the time points of data collection. The entire processing process is meticulously recorded in the system log, detailing key parameters and processing status for each step, such as the time taken for dot matrix partitioning, the number of grid calculation nodes, and the proportion of anomalies.

[0078] S3. Calculate the temperature and humidity correction coefficient using measured environmental temperature and humidity data, and dynamically generate reinforcement resource allocation weight values ​​based on the settlement gradient attenuation rate in areas of abrupt settlement gradient changes. The specific implementation is as follows:

[0079] The system receives a set of measured environmental temperature and humidity data and extracts three specific parameters from each record: atmospheric temperature, atmospheric humidity, and soil moisture content. For example, a record might contain an atmospheric temperature of 25.3℃, atmospheric humidity of 62.5%, and soil moisture content of 18.7%. The extraction operation performs integrity verification: it checks for any missing values ​​for the three parameters. If any parameter is found to be missing, the record is automatically skipped, a default flag is generated, and the record is stored in the system log. The acquired parameter values ​​retain their original precision without rounding; atmospheric temperature values ​​are maintained in degrees Celsius, and atmospheric humidity and soil moisture content values ​​are maintained in percentage format.

[0080] The calculation process for the comprehensive temperature and humidity influencing factor is as follows: First, select the corresponding soil moisture conversion model based on the soil type. For example, in silty clay areas, an S-shaped conversion curve is used to convert the soil moisture content value into an equivalent humidity ratio. Example conversion calculation: A soil moisture content of 18.7% corresponds to an equivalent humidity ratio of 74% under silty clay conditions. Next, perform a weighted comprehensive calculation on the atmospheric humidity value and the equivalent humidity ratio: assign a weight of 0.6 to the atmospheric humidity value and a weight of 0.4 to the equivalent humidity ratio. Multiply the two to obtain the comprehensive humidity value. Simultaneously, convert the atmospheric temperature value into a temperature correction coefficient using a specific formula. For example, when the temperature value is in the range of 20℃ to 30℃, a linear conversion formula is used: Temperature correction coefficient = (measured temperature - 20) / 10 × 0.3 + 0.7. Finally, multiply the comprehensive humidity value by the temperature correction coefficient to obtain the comprehensive temperature and humidity influencing factor value. All calculation steps retain two decimal places of precision.

[0081] The formula for calculating the comprehensive temperature and humidity influencing factors is:

[0082] γ env =α×(0.6×H) air +0.4×H soil )×β temp

[0083] Where, γ env α represents the dimensionless parameter of the comprehensive temperature and humidity influencing factors; α represents the soil characteristic adjustment coefficient (1.0 for sandy soil and 0.9 for clayey soil); H air Indicates the measured percentage value of atmospheric humidity; H soil Hsoil represents the equivalent humidity value of soil moisture content (calculated as: Hsoil = 0.82 × θsoil); θsoil represents the measured percentage value of soil moisture content; β temp The temperature correction factor is represented by the formula: βtemp = 0.03 × Tair - 0.25; Tair represents the measured atmospheric temperature in degrees Celsius.

[0084] For the settlement gradient abrupt change areas marked in step S2, the settlement gradient value records for that area in a continuous time series are obtained. The time span selection rule is dynamically adjusted according to the project stage: a 24-hour span is used during the regular construction period, and shortened to an 8-hour span during the rainfall-affected period. The settlement gradient attenuation rate is calculated using the difference method: take the settlement gradient values ​​of the two most recent time points, subtract the two values, and divide by the time interval in hours. For example, if the current settlement gradient value of a certain area is 3.2 mm / m, and the value recorded 24 hours ago was 3.8 mm / m, then the calculated attenuation rate is 0.025 mm / m per hour. Time difference normalization compensation calculation is automatically performed for data with non-equidistant time intervals.

[0085] The correction calculation phase employs a proportional weighting operation: the comprehensive temperature and humidity influence factor is directly multiplied into the original value of the settlement gradient attenuation rate as a product coefficient. Limits are set for the influence factor's effect: when the calculated influence factor result is less than 0.6, it is uniformly corrected to 0.6; when it is greater than 1.2, it is uniformly corrected to 1.2. For example, if the original attenuation rate is 0.025 mm / m and the comprehensive temperature and humidity influence factor is 0.9, then the corrected attenuation rate will be 0.0225 mm / m. Each correction operation records the difference in correction amount and marks the correction timestamp, forming a complete record of the correction operation trajectory.

[0086] The generation of reinforcement resource allocation weights employs a segmented mapping method: a preset baseline attenuation rate range of 0.02 mm / m to 0.04 mm / m per hour, corresponding to a base weight value of 0.5. Weight variation rules are defined: for every 0.01 unit decrease in the corrected attenuation rate below the baseline lower limit, the weight value increases by 0.08; for every 0.01 unit increase above the baseline upper limit, the weight value decreases by 0.05. For example, when the corrected attenuation rate is 0.015 mm / m, the calculated weight value is 0.5 + 5 × 0.08 = 0.9. The final result is limited to the range of 0.3 to 0.8; results outside this range are automatically truncated as boundary values. Each calculation result is associated with the corresponding regional location code and timestamp information.

[0087] Multiple quality assurance mechanisms were implemented. During the data reception phase, numerical range checks were performed: the valid range for atmospheric temperature was -10℃ to 50℃, for atmospheric humidity it was 15% to 95%, and for soil moisture content it was 5% to 40%. Abnormal value handling rules: values ​​outside the valid range were automatically replaced with the average of the three previous normal records. A threshold protection was added to the attenuation rate calculation: when the change in settlement gradient values ​​between two separate calculations exceeded 50%, a verification process was automatically initiated to confirm the reasonableness of the change by querying earlier historical data. Before outputting weight values, regional consistency comparisons were performed: when the difference in calculation results between adjacent marked regions exceeded 15%, the system paused output and issued a manual verification request.

[0088] The calculation system provides full-process debugging and tracking support: the data processing interface displays the intermediate values ​​of the comprehensive temperature and humidity influencing factors in real time, such as the conversion results of soil moisture content equivalent humidity ratio. The attenuation rate calculation process is displayed in the form of a time axis chart, clearly marking the locations of abnormal fluctuation points. The final weight value generation result output table contains complete parameter source information, including key parameter values ​​such as the original attenuation rate, comprehensive temperature and humidity influencing factors, and corrected attenuation rate.

[0089] The data storage employs a multi-dimensional relational structure: the basic storage layer contains the original parameters (atmospheric temperature, atmospheric humidity, and soil moisture content) and their acquisition time; the intermediate processing layer stores derived parameters (comprehensive temperature and humidity influencing factors, original settlement gradient decay rate, and corrected decay rate); and the final output layer records the final weight values. All data is linked together using regional location codes and timestamps, for example, a data change sequence for region code R005. The system automatically generates a daily summary of computational quality analysis, including statistics on input parameter distribution, anomaly handling records, and weight value interval percentage analysis.

[0090] S4. Transmit the reinforcement resource allocation weight value to the industrial control actuator to drive the grouting equipment controller to perform the grouting operation. The specific implementation is as follows:

[0091] The system receives records of reinforcement resource allocation weight values ​​generated in step S3. Each record contains a region location code, a weight value, and a corresponding timestamp. A conversion rule is established between the weight values ​​and grouting operation parameters: a baseline grouting pressure value and a baseline grouting flow rate value are set as conversion benchmarks. For example, the baseline grouting pressure value is set to 8 MPa, and the baseline grouting flow rate value is set to 40 liters per minute. The conversion method uses a linear proportional relationship: the reinforcement resource allocation weight value is multiplied by the baseline grouting pressure value and the baseline grouting flow rate value, respectively, to obtain the target grouting pressure control value and the target grouting flow rate control value. For example, when the weight value is 0.6, the calculated target grouting pressure control value is 4.8 MPa, and the calculated target grouting flow rate control value is 24 liters per minute.

[0092] The converted target grouting pressure control value and target grouting flow control value are encapsulated into a device control command data packet. The data packet is organized using a specific data structure, including a command type identifier field, a target grouting pressure value field (unit: MPa, precision: 0.1), a target grouting flow rate value field (unit: liters per minute, precision: 0.1), and an associated area location code field. Checksum information is appended to the end of the data packet, such as a 32-bit checksum generated using a specific algorithm. Checksum calculation and verification are performed before data transmission to ensure data integrity.

[0093] Industrial control actuators receive equipment control command data packets via an industrial fieldbus. Communication employs a serial transmission protocol, such as the standard Modbus-RTU communication protocol, for data transmission. The actuator reads the area location code field from the data packet and directs the command to the grouting equipment controller for the corresponding construction area. Transmission operations are performed at fixed time intervals, for example, an update command is sent every 30 seconds. After each transmission, the actuator waits for an acknowledgment signal from the controller; if no acknowledgment is received within 5 seconds, the data packet is automatically retransmitted.

[0094] After receiving the target grouting pressure control command and the target grouting flow control command, the grouting equipment controller executes equipment parameter adjustment operations. The pressure adjustment process involves the controller driving the electro-hydraulic proportional valve actuator to gradually adjust the system pressure to the target pressure value. During adjustment, the rate of pressure change is controlled, for example, within a speed range of 0.4 MPa per minute. Flow adjustment is performed simultaneously: the controller adjusts the variable pump motor speed to precisely control the grout flow rate near the target flow rate. The adjustment rate is limited by speed constraints, for example, the flow rate change rate cannot exceed 10 liters per minute. During adjustment, the controller reads the pressure sensor and flow meter measurements installed on the hydraulic pipeline in real time.

[0095] During grouting operations, equipment operating parameters are collected in real time. Pressure sensors collect actual grouting pressure feedback data twice per second, and electromagnetic flowmeters collect actual grouting flow feedback data at the same frequency. The controller calculates the percentage deviation between the actual value and the target value at each sampling moment. For example, when the actual grouting pressure is 4.9 MPa and the target value is 4.8 MPa, the pressure deviation percentage is 2.08%. When a pressure or flow deviation exceeding 5% is detected within three consecutive sampling cycles (1.5 seconds), an automatic correction program is triggered to generate a compensation value. The compensation value is calculated by multiplying the deviation percentage by an adjustment coefficient (fixed at 0.7), and then adding the calculated result as an increment to the original target value to form a correction command.

[0096] Safety protection measures are implemented during adjustment operations. The target grouting pressure control value is limited to a safe operating range of 0.5 MPa to 10 MPa, and all values ​​exceeding this range are automatically adjusted to boundary values. An upper limit constraint is set for the flow rate adjustment speed, for example, the flow rate change per minute should not exceed 30% of the rated flow rate. The controller performs a performance evaluation every minute: calculating the average deviation rate between the most recent 60 actual flow rates and the target value. When the average deviation rate exceeds 3%, the adjustment coefficient parameters are automatically adjusted and a change log is recorded. Each complete adjustment operation generates a process record report, including all process parameters such as start time, end time, initial actual value, and final actual value.

[0097] A reliability assurance mechanism is implemented for data transmission. Shielded twisted-pair cables are used for on-site cabling, and all connectors use industrial-grade waterproof and dustproof fittings. Signal repeaters are installed on transmission lines exceeding 300 meters to ensure that signal attenuation does not affect data transmission quality. Communication loop testing is performed daily during equipment startup: test command sequences are sent to test equipment response performance, such as sending pressure step test commands (0-5 MPa step change) and flow rate linear change commands (0-50 liters per minute uniform change), and the response time data when the commands are sent to 90% of the target parameters is recorded.

[0098] A dedicated procedure is in place for handling abnormal operating conditions. If the pressure sensor outputs no valid data for 10 consecutive seconds, the grouting operation automatically stops and switches to a safe pressure mode. If the equipment response delay exceeds 30 seconds, the control system automatically switches to a preset parameter mode to continue operation. Detailed reports are generated for all abnormal events, including the trigger time, duration, equipment status snapshot parameters, and final handling measures. Maintenance personnel review the abnormal event list daily and perform preventative maintenance on recurring anomalies.

[0099] The control process data storage adopts a hierarchical storage structure: the raw instruction storage layer stores initial control instruction data, including target grouting pressure control values ​​and target grouting flow control values; the operating status storage layer records actual grouting pressure feedback data and actual grouting flow feedback data; and the calibration record storage layer stores all calibration operation parameters. All datasets are cross-linked through timestamp and regional location coding fields. The system automatically generates an operation analysis report weekly, including performance indicators such as average response time statistics, instruction execution success rate, and out-of-tolerance event distribution, providing a basis for equipment maintenance decisions. After each grouting operation is completed, an operation summary report is generated, detailing key operating indicators such as the actual start time, end time, cumulative grouting volume, and average control accuracy of the operation.

[0100] S5. Measure the settlement gradient value in the area of ​​abrupt settlement gradient change. When the settlement gradient value enters a preset threshold range, detect the roadbed stress redistribution rate within a set distance around the corresponding area. Specifically, this is implemented as follows:

[0101] The system receives settlement gradient abrupt change region object data from step S2 and extracts the boundary coordinate point set definition information for this region. The boundary coordinate point set consists of multiple spatial coordinate points connected sequentially to form a closed polygon contour, with each coordinate point containing three-dimensional coordinate values. Specifically, the coordinates of all monitoring points located at the edge of the region are arranged clockwise to form a point sequence; for example, a region may have a boundary formed by 8 control points (point coordinates are preserved to three decimal places). In the road engineering plane coordinate system, the first and last points of the point set have the same coordinate values ​​to ensure contour closure, with the closure error controlled within 2 cm.

[0102] Settlement gradient monitoring points were deployed within the planar area covered by the boundary coordinate point set. The deployment principle was uniform coverage while ensuring monitoring of key locations. The specific process involved generating monitoring points within the boundary area using a grid partitioning method, for example, using square grid nodes spaced 1 meter apart as measurement locations. Each measurement point was located on-site using a total station, and a fixed settlement monitoring instrument was installed to measure the settlement gradient value at that point in real time. The measurement time interval was set to collect data every 60 minutes, and the timestamp information of each measurement was recorded simultaneously. A spatial correlation index was established between the location coordinates of all monitoring points and the boundary coordinate point set of their respective areas.

[0103] Calculate the geometric center coordinates of the settlement gradient abrupt change region. The calculation method involves: projecting the boundary coordinate point set onto a horizontal plane, and calculating the arithmetic mean of the X and Y coordinates of all points to obtain the planar coordinates of the geometric center. The elevation Z-value of the geometric center is taken as the average of the elevations of the highest and lowest points within the region. For example, for a certain region, the calculated average X-value is K25+315.250, the average Y-value is 28.5 meters, and the average elevation is 48.320 meters. This point serves as the reference point for subsequent operations.

[0104] Using a defined geometric center point as a reference point, the perimeter detection area is outlined by extending outwards. The extension distance is set according to the roadbed structure characteristics: a standard distance of 5 meters is used in conventional road sections, and the distance is increased to 8 meters in high embankment sections. The extended area is constructed as a ring-shaped zone: the inner boundary is a 1-meter buffer zone extending outwards from the boundary of the settlement gradient abrupt change area, and the outer boundary is a circular boundary with the geometric center as the center and the extension distance as the radius. Within the ring-shaped area, monitoring sub-zones are divided, for example, an inner ring zone (1-3 meters range) and an outer ring zone (3-5 meters range), and each sub-zone is statistically analyzed independently.

[0105] A roadbed stress monitoring network is deployed within the surrounding monitoring area. The network structure adopts a regular rectangular grid layout, for example, setting standard grid cells of 2 meters by 2 meters. Drilling is carried out at each grid node, with the drilling depth reaching the main bearing layer of the roadbed (e.g., 1.5 meters deep), and resistance strain gauge earth pressure sensors are installed at the bottom of the holes. All earth pressure sensors are connected to a unified data acquisition device, which synchronously triggers the measurement of the roadbed vertical stress values ​​at each point at set time nodes (e.g., every 6 hours). The measurement unit is kilopascal (kPa), and the data is recorded to one decimal place.

[0106] Calculate the subgrade stress redistribution rate index for the surrounding monitoring area. The procedure is as follows: Obtain the initial design stress distribution data for the area (derived from the subgrade stress distribution map in the engineering design documents). Perform data comparison calculation for each monitoring point: Analyze the difference between the measured subgrade vertical stress value and the design value. The deviation percentage calculation formula is: the difference between the design stress value and the measured stress value, divided by the design stress value, and then multiplied by 100%. For example, if the design stress value at a certain point is 150.0 kPa and the measured stress value is 135.0 kPa, the deviation percentage is calculated to be 10.0%. The average deviation percentage for the entire area is the final subgrade stress redistribution rate value.

[0107] The roadbed stress redistribution rate is calculated as follows:

[0108]

[0109] Among them, R stress The value represents the percentage of roadbed stress redistribution rate; N represents the total number of valid monitoring points within the surrounding detection area; σ design_i σ represents the initial design stress value in kPa at the location of the i-th monitoring point; actual_i The value represents the actual vertical stress in kPa at the location of the i-th monitoring point; i represents the sequence number of the monitoring point (i = 1, 2, 3, ..., N).

[0110] A quality control mechanism was established during implementation. During the boundary point set processing stage, the polygon closure was verified by detecting the distance difference between the first and last points (threshold ≤ 2 cm). During the settlement gradient measurement stage, accuracy sampling was performed: after each measurement, 10% of the points were randomly selected for manual verification. When the difference between the measured value and the verified value was ≥ 1 mm, the equipment calibration process was triggered. During the earth pressure measurement stage, temperature compensation correction was implemented: the sensor's built-in temperature probe collected temperature values ​​in real time, and the measured values ​​were automatically corrected according to a preset temperature-stress correction table.

[0111] Special operating condition rules have been added to the surrounding detection area division: when the geometric center point is located in the bridge abutment transition section, an additional 2 meters of monitoring distance is added in the direction of the bridge abutment; when the area is in a super-high embankment section (embankment height ≥ 5 meters), the extended distance is adjusted to 1.5 times the roadbed height. The local redistribution rate value is calculated independently for the data of each ring zone. For example, the stress redistribution rate is output separately for the inner ring zone and the outer ring zone.

[0112] The stress redistribution rate calculation now includes a stability analysis function. When calculating the percentage deviation at each point, the fluctuation characteristics of recent measurements at that point are analyzed simultaneously: the standard deviation is calculated using the three most recent consecutive measurements. If the standard deviation exceeds 20% of the average deviation at that point, it is marked as an anomaly. When the proportion of anomaly points in the entire area reaches 15% of the total number of points, a stability anomaly marker is specially added next to the final roadbed stress redistribution rate value.

[0113] The data acquisition system incorporates a transmission assurance design. Each earth pressure sensor node is equipped with a wireless transmission unit, and time slot allocation employs a time-division multiple access protocol to ensure conflict-free transmission. The sensors initiate a warm-up preparation state 30 seconds before the scheduled acquisition time, triggered by a synchronization broadcast signal sent by the control center to ensure simultaneous measurement by all nodes. Data transmission encryption utilizes a dynamic key mechanism: a unique verification code is generated and appended to the data packet for each transmission.

[0114] Abnormal data is automatically compensated. When a sensor fails to return valid data three times consecutively, the system initiates a data compensation procedure: the weighted average of the data from the four nearest valid points is used as the replacement value (inverse distance weighting method). When more than 10% of the points in the same sub-area require compensation, a field equipment maintenance alarm is triggered, and the abnormal area is highlighted on the map interface.

[0115] The data storage adopts a hierarchical organizational structure: the raw foundation layer stores the boundary coordinate point set and geometric center coordinate data; the observation data layer stores the measured records of settlement gradient values ​​and roadbed vertical stress values; and the results analysis layer stores the roadbed stress redistribution rate and its derived indices. All datasets are linked through a unified time index encoding, supporting the tracing of historical change trends along the time dimension. The system periodically generates stress state assessment reports, including time-series change curves of redistribution rates, maps of abnormal distribution points, regional comparative analysis tables, and other visual analysis results.

[0116] S6. When the subgrade stress redistribution rate is lower than the set threshold, generate a new grouting weight instruction and execute S4; otherwise, repeat S1 to S5 until the settlement gradient value and the subgrade stress redistribution rate simultaneously meet the standard. The specific implementation is as follows:

[0117] The system receives the subgrade stress redistribution rate value measured in step S5 and compares it with a preset threshold. The threshold is set according to road type classification; for example, an 85% threshold is used for Class I highways, and an 80% threshold is used for Class II highways. The comparison operation is implemented as follows: two sets of data storage areas are prepared; one set stores the measured value of the current subgrade stress redistribution rate, and the other set stores the preset threshold benchmark value for the corresponding area. The difference is calculated through subtraction; a negative difference indicates the value is below the threshold, while a zero or positive difference indicates the value is not below the threshold. All comparison results are recorded with the associated area location code and a time stamp accurate to the second.

[0118] When the condition is determined to be below the threshold, the process of calculating the new grouting weight is executed. This involves calling the spatial location parameters of the settlement gradient abrupt change area corresponding to that region, including specific attributes such as geometric center coordinates, projected area, and boundary shape factor. The weight increment calculation rules are as follows: First, determine the vertical distance parameter from the geometric center point to the roadbed centerline, for example, a geometric center located 6.5 meters to the left of the centerline; then, set the increment coefficient segmented according to the distance parameter: for the range of 0 to 5 meters from the centerline, the weight increment is 0.08 per meter; for the range of 5 to 10 meters from the centerline, the weight increment is 0.04 per meter. The final new grouting weight value is limited to an additional range of 0 to 0.3. For example, the increment calculated for a distance of 6.5 meters is 0.04 × 1.5 + 0.08 × 5 = 0.46, and the final upper limit of 0.3 is taken.

[0119] The formula for calculating the new grouting weight is as follows:

[0120]

[0121] Where, Δω add Indicates the value of the newly added grouting weight instruction; D center This represents the horizontal distance in meters from the geometric center of the settlement gradient abrupt change region to the roadbed centerline; Note: The calculation results are limited to the interval [0, 0.3] (if Δω add <0 takes 0, if Δω add >0.3 (take 0.3).

[0122] The generated structured new grouting weight instruction data packet contains the following fields: instruction type code field (fixed identifier "ADD_WEIGHT"), target area location code field (inherited from the settlement gradient abrupt change area code), weight value field (retaining three decimal places of precision), and effective time field. The data packet is transmitted to the grouting equipment controller of the corresponding area through the physical communication interface of the industrial control actuator (e.g., a standard RS485 serial interface). Before transmission, a link test is performed: a test data packet is sent to test the round-trip time; if the delay exceeds 2 seconds, the system automatically switches to the backup communication channel for transmission.

[0123] After receiving and decoding the command, the grouting equipment controller executes the grouting parameter adjustment operation. The controller stores a pressure reference value (e.g., 8 MPa) and a flow reference value (e.g., 40 L / min). The target increment value is obtained by multiplying the newly added grouting weight value by the reference value: Pressure increment = Weight value × Pressure reference value, Flow increment = Weight value × Flow reference value. For example, when the newly added weight value is 0.2, the calculated pressure increment is 1.6 MPa and the flow increment is 8 L / min. The adjustment process implements graded rate control: pressure changes use a stepped adjustment strategy, with the adjustment range not exceeding 30% of the current value per hour; flow changes use a uniform adjustment strategy, with the change not exceeding 10 L / min per minute. During the adjustment process, the actual pressure gauge and flow meter readings are recorded every minute.

[0124] If the condition is determined to be above the threshold, a data re-acquisition process is triggered. A complete data update is performed on the designated settlement gradient abrupt change area and its associated areas (within the set distance range defined in S5): First, the old roadbed settlement value, the newly filled roadbed settlement value, and measured environmental temperature and humidity data are simultaneously collected via a sensor array (completely replicating step S1); second, the differential settlement spatial gradient distribution is calculated and the settlement gradient value is updated (replicating the core operation of S2); third, the roadbed stress redistribution rate in the surrounding area is remeasured (replicating operation S5). The entire update cycle is controlled within a preset time range, for example, completed within 4 hours for regular areas and within 2 hours for key monitoring areas.

[0125] Establish a cyclic execution control mechanism: After each grouting adjustment or data update, immediately obtain the latest settlement gradient value and subgrade stress redistribution rate value for the current area. Settlement gradient value verification method: Compare with a preset gradient threshold range (e.g., 1.0 to 3.0 mm / m) to check if it falls within the range. Subgrade stress redistribution rate verification method: Compare with a set threshold (e.g., 85%) to check if it meets the standard. The process terminates when both indicators meet the requirements; if either fails, record the cycle count counter (incrementing from 1) and return to the initial comparison and judgment step.

[0126] The loop process is equipped with multiple protection mechanisms: the loop counter is set to an upper limit of 10 times, and will stop immediately and activate the manual intervention procedure when the upper limit is reached. Loop stall monitoring rules: when the improvement rate of two consecutive operations is less than 5%, the newly added weight coefficient ratio is automatically increased by 20% before the third operation. Anomaly handling process: detailed information such as the operation type (grouting compensation or data update), start and end times, input parameters, and output results are recorded in the loop log.

[0127] Reliability enhancement measures for control command transmission: The main communication line uses shielded twisted-pair copper cable (wire diameter ≥ 0.5mm). 2 Signal repeater amplifiers are installed every 200 meters. The transmission protocol adopts the industry-standard Modbus-RTU format, and cyclic redundancy check codes are added to the data packets. The response information returned by the controller includes the reception timestamp, command execution status code, and a snapshot of the current device operating parameters.

[0128] Upon final compliance, a structured acceptance document is generated. The document includes: area location coding information, initial test indicator values, final compliance values, total number of iterations, and a sequence of execution operation types (e.g., "compensation-update-compensation"). The output format is compatible with the project management database system and supports displaying the changes in each indicator via a timeline chart. All process data is archived and retained for 90 days, including original monitoring data, intermediate calculation results, and execution operation records.

[0129] Quality assurance measures: Within 24 hours of each newly added weight instruction being executed, the affected area will be manually retested to compare the consistency between sensor data and actual core sampling results. Monthly statistical analysis will be conducted on the trigger records of the cyclic protection mechanism to optimize the threshold settings for various parameters. On-site operators will be equipped with handheld terminals to query the current cyclic execution status and operation history of each area in real time.

[0130] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0131] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0132] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0133] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0135] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0136] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data-driven method for controlling differential settlement in reconstructed and expanded highways, characterized in that, Includes the following steps: S1. The sensor array synchronously acquires the settlement values ​​of the old roadbed, the settlement values ​​of the newly filled roadbed, and the measured data of ambient temperature and humidity. S2. Calculate the spatial gradient distribution of differential settlement based on the settlement values ​​of the old and newly filled roadbeds, and locate the abrupt settlement gradient areas according to the coordinates of the contact surface between the old and new roadbeds. S3. Calculate the temperature and humidity correction coefficient using measured environmental temperature and humidity data, and dynamically generate reinforcement resource allocation weight values ​​by combining the settlement gradient attenuation rate in the settlement gradient abrupt change area. S4. Transmit the reinforcement resource allocation weight value to the industrial control actuator to drive the grouting equipment controller to perform the grouting operation; S5. Measure the settlement gradient value in the settlement gradient abrupt change area. When the settlement gradient value enters the preset threshold range, detect the roadbed stress redistribution rate within a set distance around the corresponding area. S6. When the roadbed stress redistribution rate is lower than the set threshold, generate a new grouting weight instruction and execute S4; otherwise, repeat S1 to S5 until the settlement gradient value and the roadbed stress redistribution rate reach the standard at the same time.

2. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 1, characterized in that, The sensor array synchronously acquires settlement values ​​of the old roadbed, settlement values ​​of the newly filled roadbed, and measured data of ambient temperature and humidity, including: Settlement monitoring sensors were used to obtain the settlement values ​​of the old roadbed, and settlement monitoring sensors were used to obtain the settlement values ​​of the newly filled roadbed. Temperature and humidity composite sensors were used to obtain measured data of atmospheric temperature and humidity. Add a soil moisture sensor to measure soil moisture content data at a set depth below the surface, and incorporate the soil moisture content data into the measured ambient temperature and humidity data.

3. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 2, characterized in that, The temperature and humidity composite sensor and the sedimentation monitoring sensor are triggered to acquire data through a unified clock source.

4. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 2, characterized in that, Settlement monitoring sensors were installed symmetrically on both sides of the joint between the old and newly filled roadbeds, and all sensor data were marked with the same timestamp.

5. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 2, characterized in that, The spatial gradient distribution of differential settlement is calculated based on the settlement values ​​of the old and newly constructed roadbeds. The areas of abrupt settlement gradient changes are located according to the coordinates of the contact surface between the old and new roadbeds, including: The contact surface coordinate lattice is divided for the settlement values ​​of the old roadbed and the newly filled roadbed; The differential settlement is obtained by calculating the difference in elevation changes at each coordinate point. A spatial gradient calculation grid is constructed by extending a set distance to both sides from the centerline of the contact surface. Based on the spatial gradient, the difference in elevation change of grid nodes is calculated, and neighborhood difference operation is performed to generate the spatial gradient distribution of differential settlement. In the coordinate lattice of the contact surface, select the point set whose spatial gradient value is greater than a set threshold and mark it as a settlement gradient abrupt change region.

6. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 5, characterized in that, Temperature and humidity correction coefficients are calculated using measured environmental temperature and humidity data. Combined with the settlement gradient attenuation rate in areas of abrupt settlement gradient changes, dynamic weight values ​​for reinforcement resource allocation are generated, including: Extract atmospheric temperature, atmospheric humidity, and soil moisture content values ​​from measured environmental temperature and humidity data; The comprehensive temperature and humidity influence factor is calculated based on the proportional relationship between soil moisture content and atmospheric temperature and humidity. Obtain the settlement gradient decay rate over a continuous time span in the region of abrupt settlement gradient change. The settlement gradient attenuation rate was proportionally weighted using a comprehensive temperature and humidity influence factor as a correction term. Based on the proportional weighting results, the reinforcement resource allocation weight values ​​are dynamically allocated within a preset weight range.

7. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 6, characterized in that, The settlement gradient decay rate is calculated by the difference in settlement gradient values ​​between adjacent time periods.

8. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 6, characterized in that, The reinforcement resource allocation weight values ​​are transmitted to the industrial control actuator, driving the grouting equipment controller to perform the grouting operation, including: Establish a conversion formula between reinforcement resource allocation weight values ​​and grouting equipment controller parameters; Based on the conversion formula, the reinforcement resource allocation weight value is parsed into pressure and flow control commands of the grouting equipment controller; The industrial control actuator sends pressure control commands and flow control commands to the grouting equipment controller. The grouting equipment controller performs pressure regulation and flow regulation operations; Real-time data collection of actual grouting pressure and actual grouting flow rate is used to compare with target control commands and correct command offset.

9. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 8, characterized in that, Measure the settlement gradient value in areas of abrupt settlement gradient change. When the settlement gradient value enters a preset threshold range, detect the roadbed stress redistribution rate within a set distance around the corresponding area, including: Determine the set of boundary coordinate points for the region of abrupt change in settlement gradient; Settlement monitoring points are uniformly distributed within the coverage area of ​​the boundary coordinate point set to measure the settlement gradient value; A surrounding detection area is formed by radiating outwards from the geometric center of the abrupt change in settlement gradient region. Stress monitoring points were set up in a rectangular grid within the surrounding detection area, and the vertical stress values ​​of the roadbed at each point were collected synchronously through an earth pressure sensor array. Calculate the percentage deviation between the initial design stress distribution and the actual vertical stress value within the surrounding detection area to obtain the roadbed stress redistribution rate.

10. The data-driven differential settlement control method for reconstructed and expanded highways according to claim 9, characterized in that, When the roadbed stress redistribution rate is lower than the set threshold, a new grouting weight instruction is generated and S4 is executed; Otherwise, repeat steps S1 to S5 until both the settlement gradient value and the roadbed stress redistribution rate meet the standards, including: The measured subgrade stress redistribution rate is numerically compared with a set threshold. If the roadbed stress redistribution rate is lower than the set threshold, the specific value of the new grouting weight instruction is determined based on the spatial location of the settlement gradient abrupt change area. Input the new grouting weight command into the grouting equipment controller associated with the industrial control actuator; The grouting equipment controller executes the grouting operation corresponding to the newly added weight value; If the roadbed stress redistribution rate is not lower than the set threshold, the settlement gradient value of the settlement gradient abrupt change area and the roadbed stress redistribution rate within the set distance around it will be re-collected. The numerical comparison is performed repeatedly until both the settlement gradient value and the roadbed stress redistribution rate reach the corresponding preset threshold range.

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