A method for correcting pavement structure mechanical response monitoring data

Abstract

The application discloses a kind of correction methods of road surface structure mechanics response monitoring data, comprising the following steps: S1, monitoring system layout;S2, environmental parameter record;S3, static loading test and data acquisition;S4, dynamic loading test and data acquisition;S5, data summary and model establishment;S6, actual monitoring data correction.The application realizes the calibration of optical fiber sensor monitoring data by field standardization static and dynamic loading test, establishes the accurate mapping relationship of load and mechanical response, provides reliable basis for the correction of actual service data, effectively improves the scientificity and accuracy of road surface structure performance evaluation.

Description

Technical Field

[0001] This invention belongs to the field of road engineering testing and monitoring technology, and in particular relates to a method for correcting monitoring data of pavement structure mechanical response. Background Technology

[0002] The service performance of pavement structures directly determines the quality of road traffic and its service life. Accurately understanding the mechanical response of pavement structures under traffic loads is a core prerequisite for scientifically assessing pavement damage and predicting the development of defects. Currently, road performance evaluation mainly relies on conventional road condition testing equipment and methods, such as using a 3m straightedge to test smoothness, using a Beckman beam to test deflection, and manual inspection to identify defects. These methods have significant drawbacks: First, the testing frequency is low, mostly consisting of periodic spot checks, making continuous monitoring impossible; second, they have a significant impact on traffic, requiring temporary lane closures during testing, disrupting normal traffic flow; third, the testing data is outdated, only reflecting the pavement's apparent condition at the moment of testing, making it difficult to trace the initial stage of defect occurrence; fourth, they cannot obtain the true internal mechanical parameters of the pavement structure, such as stress and strain, under service conditions, making it impossible to accurately locate the true source of damage to the pavement structure and materials, and also making it difficult to scientifically explain the occurrence and development mechanism of pavement defects.

[0003] To overcome the aforementioned shortcomings, fiber optic sensors, as a new generation of information technology, are gradually being applied to pavement structure monitoring. By embedding fiber optic sensing elements such as soil pressure sensors and strain sensors into the pavement structure layer, real-time data on the internal mechanical response of the pavement structure during its service life can be collected, thereby tracking the damage and evolution process of the pavement structure. Chinese patent document (application number: CN201921615503.4) discloses a pavement structure monitoring system based on multi-sensor fusion, comprising: a data acquisition device cluster, including at least two types of data acquisition devices for real-time acquisition of pavement condition data; and a data receiving and analysis device connected to the data acquisition device cluster for receiving and analyzing the pavement condition data to obtain the damage status. This system employs multiple data acquisition devices to monitor pavement conditions in real time, avoiding the subjective factors of human detection and more accurately reflecting the true condition of the pavement.

[0004] However, in actual service environments, the trajectories of vehicles are random, and the location and manner of vehicle load application are difficult to control precisely. This makes it impossible to effectively obtain key information such as the maximum stress and strain generated within the pavement structure under actual traffic loads, and to establish an accurate mapping model between traffic loads and the internal mechanical response of the structure. Under these circumstances, directly using raw monitoring data collected by fiber optic sensors for pavement performance evaluation is prone to data bias, leading to distorted evaluation results and failing to provide a reliable basis for pavement maintenance decisions.

[0005] Therefore, how to calibrate and correct the mechanical response information monitored by fiber optic sensors through standardized loading methods, establish an accurate mapping relationship between load and mechanical response, and realize the scientific correction of structural mechanical response data under traffic load in actual service environment has become an urgent technical problem to be solved in the field of road structure monitoring. Summary of the Invention

[0006] The purpose of this invention is to address the problems in existing pavement structure mechanical response monitoring technologies, such as the inaccuracy of monitoring data reflecting the true mechanical state due to the randomness of actual traffic loads, and the difficulty in establishing a load-mechanical response relationship model. This invention provides a method for correcting pavement structure mechanical response monitoring data. This method calibrates the monitoring data from fiber optic sensors through standardized on-site static and dynamic loading tests, establishing a precise mapping relationship between load and mechanical response. This provides a reliable basis for correcting actual service data and effectively improves the scientific rigor and accuracy of pavement structure performance evaluation.

[0007] To achieve the above objectives, the present invention employs the following technical effects: A method for correcting pavement structure mechanical response monitoring data includes the following steps: S1. Monitoring system deployment: Pre-set monitoring sections in the pavement structure layer to be monitored, and embed fiber optic stress sensors and strain sensors in layers along the pavement depth direction. Connect the sensors to the fiber optic demodulator and debug until the signal is stable and the data acquisition is accurate. S2. Environmental parameter recording: Before the loading test, environmental monitoring equipment is used to record the road surface temperature, air temperature and humidity; S3. Static Loading Test and Data Acquisition: Static loading was performed using standard test vehicles with different axle loads. During each loading, the center of the wheel gap was aligned with the sensor placement position. At the same time, static loading was performed using a 100kN standard test vehicle at loading positions at different distances from the center of the wheel gap directly above the sensor. The stress and strain data of the sensors at each depth layer were collected and recorded in real time through a fiber optic demodulator to form static loading standard data. S4. Dynamic loading test and data acquisition: A test vehicle with a standard axle load of 100kN travels at different speeds along the sensor layout trajectory. The stress and strain data of the sensors at each depth layer are continuously acquired through an optical fiber demodulator. At the same time, the relationship between the vehicle speed and time is recorded to form dynamic loading standard data. S5. Data Summary and Model Establishment: Summarize the static loading standard data collected in step S3 and the dynamic loading standard data collected in step S4 at different vehicle speeds, and remove invalid data; use the load magnitude, the lateral offset distance of the vehicle wheel track center relative to the sensor position, and the driving speed as independent variables, and the stress value and equivalent effect monitored by the sensor as dependent variables, and perform nonlinear fitting on the static loading standard data and the dynamic loading standard data respectively; select standard axle load, standard offset distance, and standard speed as reference working condition parameters, substitute the actual working condition parameters and the reference working condition parameters into the response fitting function respectively, obtain the theoretical response value of the actual working condition and the theoretical response value of the reference working condition, and establish a mapping relationship model of load-speed-mechanical response at this ambient temperature; S6. Correction of Actual Monitoring Data: During the service of the road surface, the original mechanical response data under actual traffic load is collected in real time by the deployed fiber optic sensors, and the real-time road surface temperature and traffic speed information are recorded. The real-time temperature is compared with the test environment temperature recorded in step S2, and the stress-strain temperature correction is performed according to the temperature-mechanical response relationship function. Then, the corrected temperature data and real-time traffic speed data are substituted into the mapping relationship model established in step S5 to correct the original mechanical response data, so as to obtain the real mechanical response data of the road surface structure under actual service conditions.

[0008] Preferably, in step S1, fiber optic stress sensors and strain sensors are buried in layers along the road surface depth direction to ensure complete coverage of the surface layer, base layer and subbase layer; the vertical spacing matches the thickness of each structural layer, and the horizontal spacing is 30-70cm.

[0009] Preferably, in step S3, standard test vehicles with axle loads of 50kN, 70kN, 100kN, 150kN and 200kN are used for static loading. During each loading, the wheel gap center is aligned with the sensor placement position to ensure that the tire pressure covers the sensor area. The loading rate is controlled at 0.5-1kN / s, and the load is kept stable for 3-5 minutes.

[0010] Preferably, in step S3, a 100kN standard test vehicle is used to perform static loading at loading positions 0cm, 30cm and 50cm away from the center of the wheel gap directly above the sensor. The stress and strain data of the sensors at each depth layer are collected and recorded in real time by a fiber optic demodulator. Each load state is collected repeatedly for 3-5 sets and the average value is taken to form static loading standard data.

[0011] Preferably, in step S3, the standard test vehicle needs to be calibrated for load before static loading to ensure that the loading accuracy error does not exceed ±1%; during the loading process, displacement sensors are used to assist in monitoring the vertical deformation of the road structure. If the deformation exceeds the preset threshold, loading is stopped immediately and the condition of the road structure is investigated.

[0012] Preferably, in step S4, a test vehicle with a standard axle load of 100kN travels at a constant speed along the sensor layout track at speeds of 10km / h, 30km / h, 50km / h and 70km / h.

[0013] Preferably, in step S4, during dynamic loading, the test road section is closed by traffic control equipment to ensure that the test vehicle travels at a constant speed and avoids interference from vehicle acceleration and deceleration on the loading effect; GPS positioning equipment is used to track the position of the test vehicle in real time to ensure that the deviation between the vehicle's trajectory and the sensor's deployment trajectory does not exceed 5cm.

[0014] Preferably, in step S5, during the data aggregation process, outlier data is removed using the 3σ principle; after establishing the mapping relationship model, the coefficient of determination R is used... 2 Verify model fit and ensure R 2 A value ≥0.95 ensures the reliability of the model.

[0015] Preferably, in step S6, the formula for temperature correction of stress-strain based on the temperature-mechanical response relationship function is as follows: In the formula: R 0 represents the mechanical response value (kPa / με) at the reference temperature. R The values ​​represent the mechanical response at different temperatures (kPa / με). T 0 is the reference temperature (°C); T Different temperatures (°C).

[0016] In summary, the present invention adopts the above technical solution, and the present invention has the following technical effects: (1) Solved the problem of monitoring data distortion caused by the randomness of vehicle load in actual service environment: This invention obtains mechanical response benchmark data under standard working conditions by accurately controlling the load size, vehicle speed and loading position through standardized static and dynamic loading tests on site, which provides a reliable basis for the correction of actual monitoring data and effectively improves the accuracy of monitoring data.

[0017] (2) A precise mapping relationship between load, speed and mechanical response was established: This invention comprehensively considers the influence of static load and dynamic load under different driving speeds on the road structure. Combined with the ambient temperature parameter, the established mapping relationship model can fully reflect the road mechanical response law under the action of multiple factors, and overcomes the limitation of the existing technology that only relies on a single load parameter to establish the model.

[0018] (3) Realized the synergy of real-time monitoring and dynamic correction: This invention combines standardized loading calibration with actual service monitoring. Through temperature correction and model correction, it can obtain real mechanical response data of pavement structure in real time, accurately locate the damage source of pavement structure, scientifically explain the occurrence and development process of disease, and provide accurate data support for pavement maintenance decision making.

[0019] (4) Convenient construction and minimal impact on traffic: The loading test of this invention can be completed under the condition of short-term closure of a local lane, and normal traffic can be restored after the test; the monitoring system uses fiber optic sensors, which have the characteristics of strong anti-interference ability and good durability, and can achieve long-term stable monitoring without frequent maintenance. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method for correcting monitoring data of pavement structure mechanical response.

[0021] Figure 2 This is a schematic diagram of the sensors deployed in the road section of Application Example 1. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many details listed in the specification are merely to provide the reader with a thorough understanding of one or more aspects of the present invention, and these aspects of the invention can be implemented even without these specific details.

[0023] Example 1 like Figure 1 As shown, a method for correcting pavement structure mechanical response monitoring data includes the following steps: S1. Monitoring System Deployment: Pre-set monitoring sections within the road surface structure layer to be monitored, and embed fiber optic stress sensors and strain sensors in layers along the road surface depth direction to ensure complete coverage of the surface layer, base layer, and subbase layer; the vertical spacing matches the thickness of each structural layer, and the horizontal spacing is 30-70cm; connect the fiber optic stress sensors and strain sensors to the fiber optic demodulator and debug until the signal is stable and the data acquisition is accurate. S2. Environmental parameter recording: Before the loading test, environmental monitoring equipment is used to record the road surface temperature, air temperature and humidity to ensure that the loading test is carried out under stable environmental conditions and to reduce the interference of environmental factors. S3. Static Loading Test and Data Acquisition: Before static loading, the standard test vehicle needs to be calibrated to ensure that the loading accuracy error does not exceed ±1%. During the loading process, displacement sensors are used to assist in monitoring the vertical deformation of the road structure. If the deformation exceeds the preset threshold, loading is stopped immediately and the road structure condition is investigated. Then, standard test vehicles with axle loads of 50kN, 70kN, 100kN, 150kN, and 200kN are used for static loading. During each loading, the wheel gap center is aligned with the sensor placement position to ensure that the tire pressure covers the sensor area. The loading rate is controlled at 0.5-1kN / s, and the load is kept stable for 3-5 minutes. At the same time, a 100kN standard test vehicle is used for static loading at loading positions 0cm, 30cm, and 50cm away from the center of the wheel gap directly above the sensor. The stress and strain data of the sensors at each depth layer are collected and recorded in real time through a fiber optic demodulator. Each load condition is collected repeatedly for 3-5 sets, and the average value is taken to form the static loading standard data. S4. Dynamic Loading Test and Data Acquisition: A test vehicle with a standard axle load of 100kN travels at a constant speed along the sensor deployment trajectory at speeds of 10km / h, 30km / h, 50km / h, and 70km / h. Stress and strain data from the sensors at each depth layer are continuously acquired using a fiber optic demodulator. The relationship between vehicle speed and time is also recorded. Each speed is repeated 3-5 times. Outlier data is removed, and the average value is used to form the standard dynamic loading data. During dynamic loading, the test section is closed using traffic control equipment to ensure the test vehicle travels at a constant speed, avoiding interference from acceleration and deceleration. GPS positioning equipment is used to track the test vehicle's position in real time, ensuring that the deviation between the vehicle's trajectory and the sensor deployment trajectory does not exceed 5cm. S5. Data Aggregation and Model Building: The static loading standard data collected in step S3 and the dynamic loading standard data at different vehicle speeds collected in step S4 are aggregated, and invalid data is removed. During the data aggregation process, the 3σ principle is used to remove outlier data. After establishing the mapping relationship model, the coefficient of determination R is used. 2 Verify model fit and ensure R 2 ≥0.95 to ensure the reliability of the model; using load magnitude and vehicle speed as independent variables, and stress and strain values ​​monitored by sensors as dependent variables, nonlinear fitting is performed on static loading standard data and dynamic loading standard data respectively; standard axle load, standard offset distance and standard speed are selected as reference working condition parameters, and the actual working condition parameters and reference working condition parameters are substituted into the response fitting function respectively to obtain the theoretical response value of the actual working condition and the theoretical response value of the reference working condition, and a mapping relationship model of load-speed-mechanical response under this ambient temperature is established; In this invention, the model establishment process in step S5 is based on function fitting of standard loading test data and equivalent conversion of benchmark working conditions. Specifically, under the same ambient temperature conditions, the static loading standard data obtained in step S3 and the dynamic loading standard data obtained in step S4 are first summarized, and invalid data are removed. Then, using vehicle axle load P, lateral offset distance d of vehicle wheel track center relative to sensor position, and vehicle speed v as independent variables, and stress value σ and equivalent strain εeq obtained by sensor as dependent variables, a nonlinear fitting relationship between each working condition parameter and the mechanical response of the road structure is established. Subsequently, the actual working condition parameters are substituted into each fitting function to obtain the theoretical mechanical response under the actual working conditions.

[0024] S6. Correction of Actual Monitoring Data: During the service of the road surface, the original mechanical response data under actual traffic load is collected in real time by the deployed fiber optic sensors, and the real-time road surface temperature and traffic speed information are recorded. The real-time temperature is compared with the test environment temperature recorded in step S2, and the stress-strain temperature correction is performed according to the temperature-mechanical response relationship function. Then, the corrected temperature data and real-time traffic speed data are substituted into the mapping relationship model established in step S5 to correct the original mechanical response data, so as to obtain the real mechanical response data of the road surface structure under actual service conditions.

[0025] The formula for temperature correction of stress and strain based on the temperature-mechanical response relationship function is as follows: In the formula: R 0 represents the mechanical response value (kPa / με) at the reference temperature. R The values ​​represent the mechanical response at different temperatures (kPa / με). T 0 is the reference temperature (°C); T Different temperatures (°C).

[0026] This formula is based on the master curve of the dynamic modulus of asphalt mixtures. By establishing the correspondence between temperature and dynamic modulus, the mechanical response obtained under different temperature conditions is uniformly corrected to the reference temperature condition, so as to improve the comparability between data at different loading stages.

[0027] Application Example 1 This embodiment takes the monitoring and calibration of the structural mechanical response of an asphalt pavement on a highway as an example. The specific steps are as follows: 1. Monitoring system deployment: such as Figure 2As shown, three monitoring sections were selected in the road section to be monitored. Fiber optic stress sensors and fiber optic strain sensors were installed along the road surface depth direction in each section, specifically in the upper layer (4cm), middle layer (10cm), lower layer (18cm), base layer (58cm), and subbase layer (78cm). All sensors were connected to a fiber optic demodulator (100MHz bandwidth, 1000Hz sampling rate) via fiber optic patch cords. After system debugging, an unloaded test was conducted to ensure that the sensor signals were free from interference and that data acquisition was normal.

[0028] 2. Environmental parameter recording: A portable environmental monitoring instrument was used to measure and record the road surface temperature of the test area as 32℃, the atmospheric temperature as 27℃, and the humidity as 65%.

[0029] 3. Static Loading Test and Data Acquisition: Standard test vehicles with load capacities of 50kN, 70kN, 100kN, 150kN, and 200kN were used. The rear axle loading wheels of the test vehicles were precisely aligned with the sensor placement positions using a guiding device. A hydraulic loading system was used to control the loading rate at 0.8kN / s, and the load was maintained at a stable level for 4 minutes after reaching the specified load. Simultaneously, a 100kN standard test vehicle was used to perform static loading at loading positions 0cm, 30cm, and 50cm away from the center of the wheel gap directly above the sensor. Stress and strain data of the sensors at each depth layer were collected using a fiber optic demodulator. Four sets of data were collected for each loading state. After removing the maximum and minimum values, the average value was taken to obtain the static loading standard data (partial data are shown in Table 1 below).

[0030] The static loading standard data is used to characterize the benchmark variation law of the pavement structure mechanical response under different axle load levels and different lateral action positions, and serves as the basis for determining the subsequent load correction factor and action position correction factor.

[0031] Table 1. Mechanical response data of the bottom layer (18cm) under different loads. Table 2. Mechanical response data of the bottom layer (18cm) at different offset distances. 4. Dynamic Loading Test and Data Acquisition: A portion of the test road section was closed, and the test vehicle's loaded wheel assembly was aligned with the sensor placement positions. The vehicle was driven at constant speeds of 10 km / h, 30 km / h, 50 km / h, and 70 km / h. During the test, sensor data was continuously acquired using a fiber optic demodulator, and the vehicle's position and speed were recorded using GPS positioning equipment. Each speed was repeated four times. The acquired data was filtered to remove abnormal data during vehicle start-up and braking, and the average value of the data during stable driving was taken as the dynamic loading standard data (partial data are shown in Table 3 below). This dynamic loading standard data is used to characterize the influence of vehicle speed changes on the stress, strain peak, and response amplitude of the road structure, and serves as the basis for determining subsequent speed correction factors.

[0032] Table 3 Standard data on the mechanical response of the surface layer (18cm) at different vehicle speeds (100kN dynamic loading). 5. Data Aggregation and Model Building: Aggregate static and dynamic loading standard data, and use the 3σ principle to remove outlier data; respectively, based on load... P (kN), offset distance d (cm) and driving speed v (km / h) is the independent variable, and stress is the inverse variable. σ (kPa), equivalent strain ε (με) is the dependent variable. The static loading standard data obtained in step 3 are fitted to obtain the load correction function and the position correction function; the dynamic loading standard data obtained in step 4 are fitted to obtain the velocity correction function. The mapping model is shown below: In the formula: P For load (kN); d Offset distance (cm); v The driving speed is (km / h).

[0033] 6. Correction of Actual Monitoring Data: In actual service monitoring, the axle load magnitude, application location, and travel speed of vehicle loads often exhibit significant randomness, leading to a lack of direct comparability of stress and strain responses collected under different vehicle operating conditions. To eliminate the influence of differences in vehicle axle load, wheel track offset, and travel speed on the monitoring data, it is necessary to select axle load, offset distance, and travel speed under standard operating conditions as benchmark data. The standard axle load is 100kN, the standard offset distance is 0, and the standard speed can be taken as the representative travel speed of the test road section. Based on this, the formulas in step 5 are comprehensively considered to establish a true mechanical response data model of the pavement structure.

[0034] During actual monitoring, the actual operating parameters are obtained based on the vehicle axle load identification results, vehicle speed, and the lateral offset distance between the vehicle wheel tracks and the sensor. , where are respectively the loads P (kN), offset distance d (cm) and driving speed v (km / h) is then substituted into the prediction model to obtain the theoretical response value under actual working conditions; simultaneously, the reference working condition parameters are substituted into the prediction model to obtain the theoretical response value under the reference working conditions. Subsequently, based on the proportional relationship between the theoretical response value under actual working conditions and the theoretical response value under the reference working conditions, the raw monitoring data collected by the fiber optic sensor is equivalently corrected to obtain pavement structural mechanical response data under a unified reference working condition.

[0035] The above methods effectively corrected the deviations in actual monitoring data, accurately reflected the mechanical state of the pavement structure under real traffic loads, and provided a reliable basis for pavement structure damage assessment and disease prediction.

[0036] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for correcting monitoring data of pavement structure mechanical response, characterized in that: Includes the following steps: S1. Monitoring system deployment: Pre-set monitoring sections in the pavement structure layer to be monitored, and embed fiber optic stress sensors and strain sensors in layers along the pavement depth direction. Connect the sensors to the fiber optic demodulator and debug until the signal is stable and the data acquisition is accurate. S2. Environmental parameter recording: Before the loading test, environmental monitoring equipment is used to record the road surface temperature, air temperature and humidity; S3. Static Loading Test and Data Acquisition: Static loading was performed using standard test vehicles with different axle loads. During each loading, the center of the wheel gap was aligned with the sensor placement position. At the same time, static loading was performed using a 100kN standard test vehicle at loading positions at different distances from the center of the wheel gap directly above the sensor. The stress and strain data of the sensors at each depth layer were collected and recorded in real time through a fiber optic demodulator to form static loading standard data. S4. Dynamic loading test and data acquisition: A test vehicle with a standard axle load of 100kN was used to travel at different speeds along the sensor layout track. The stress and strain data of sensors at each depth layer are continuously collected by the fiber optic demodulator, and the relationship between vehicle speed and time is recorded to form dynamic loading standard data. S5. Data Summary and Model Establishment: Summarize the static loading standard data collected in step S3 and the dynamic loading standard data collected in step S4 at different vehicle speeds, and remove invalid data; use the load magnitude, the lateral offset distance of the vehicle wheel track center relative to the sensor position, and the driving speed as independent variables, and the stress value and equivalent effect monitored by the sensor as dependent variables, and perform nonlinear fitting on the static loading standard data and the dynamic loading standard data respectively; select standard axle load, standard offset distance, and standard speed as reference working condition parameters, substitute the actual working condition parameters and the reference working condition parameters into the response fitting function respectively, obtain the theoretical response value of the actual working condition and the theoretical response value of the reference working condition, and establish a mapping relationship model of load-speed-mechanical response at this ambient temperature; S6. Correction of Actual Monitoring Data: During the service of the road surface, the original mechanical response data under actual traffic load is collected in real time by the deployed fiber optic sensors, and the real-time road surface temperature and traffic speed information are recorded. The real-time temperature is compared with the test environment temperature recorded in step S2, and the stress-strain temperature correction is performed according to the temperature-mechanical response relationship function. Then, the corrected temperature data and real-time traffic speed data are substituted into the mapping relationship model established in step S5 to correct the original mechanical response data, so as to obtain the real mechanical response data of the road surface structure under actual service conditions.

2. The method for correcting pavement structure mechanical response monitoring data according to claim 1, characterized in that: In step S1, fiber optic stress sensors and strain sensors are buried in layers along the road surface depth direction to ensure complete coverage of the surface layer, base layer and subbase layer; the vertical spacing matches the thickness of each structural layer, and the horizontal spacing is 30-70cm.

3. The method for correcting pavement structure mechanical response monitoring data according to claim 1, characterized in that: In step S3, standard test vehicles with axle loads of 50kN, 70kN, 100kN, 150kN and 200kN were used for static loading. During each loading, the wheel gap center was aligned with the sensor placement position to ensure that the tire pressure covered the sensor area. The loading rate was controlled at 0.5-1kN / s, and the load was kept stable for 3-5 minutes.

4. The method for correcting pavement structure mechanical response monitoring data according to claim 3, characterized in that: In step S3, a 100kN standard test vehicle is used to perform static loading at loading positions 0cm, 30cm and 50cm away from the center of the wheel gap directly above the sensor. The stress and strain data of the sensors at each depth layer are collected and recorded in real time through a fiber optic demodulator. Each load state is collected repeatedly for 3-5 sets and the average value is taken to form static loading standard data.

5. The method for correcting pavement structure mechanical response monitoring data according to claim 3 or 4, characterized in that: In step S3, the standard test vehicle needs to be calibrated before static loading to ensure that the loading accuracy error does not exceed ±1%. During the loading process, displacement sensors are used to assist in monitoring the vertical deformation of the road structure. If the deformation exceeds the preset threshold, loading is stopped immediately and the condition of the road structure is investigated.

6. The method for correcting pavement structure mechanical response monitoring data according to claim 1, characterized in that: In step S4, a test vehicle with a standard axle load of 100kN travels at a constant speed along the sensor layout track at speeds of 10km / h, 30km / h, 50km / h and 70km / h.

7. The method for correcting pavement structure mechanical response monitoring data according to claim 1 or 6, characterized in that: In step S4, during dynamic loading, the test road section is closed by traffic control equipment to ensure that the test vehicle travels at a constant speed and to avoid the vehicle's acceleration and deceleration from interfering with the loading effect; GPS positioning equipment is used to track the position of the test vehicle in real time to ensure that the deviation between the vehicle's trajectory and the sensor's trajectory does not exceed 5cm.

8. The method for correcting pavement structure mechanical response monitoring data according to claim 1, characterized in that: In step S5, during the data aggregation process, outlier data is removed using the 3σ principle; after establishing the mapping relationship model, the coefficient of determination R is used... 2 Verify model fit and ensure R 2 A value ≥0.95 ensures the reliability of the model.

9. The method for correcting pavement structure mechanical response monitoring data according to claim 1, characterized in that: In step S6, the formula for temperature correction of stress and strain based on the temperature-mechanical response relationship function is as follows: In the formula: R 0 represents the mechanical response value (kPa / με) at the reference temperature. R The values ​​represent the mechanical response at different temperatures (kPa / με). T 0 is the reference temperature (°C); T Different temperatures (°C).

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