Dynamic resilient modulus intelligent self-consistent monitoring structure and method for fill

Abstract

The application discloses a kind of filling body dynamic resilience modulus intelligent self-consistent monitoring structure and method, monitoring structure, including energy collection module, dynamic parameter sensing module, energy management module, data processing module, energy collection module includes magnet array, located in the positive below of design wheel trace zone;Permanent magnet outside is provided with inductive coil, energy management module stores instantaneous high-power vibration energy, and dynamic parameter sensing module is woken up and powered after accumulation to preset threshold value;Data processing module carries out the data verification of dynamic parameter sensing module and energy management module transmission, and time stamp is aligned to effective data, and the dynamic resilience modulus of roadbed filling body top is calculated.This application generates electricity using the vibration energy generated by vehicle load acting on roadbed, while realizing intelligent monitoring of filling body dynamic resilience modulus, simplifying the measurement process, protecting the pavement structure, and providing a long-life, self-powered health monitoring solution for smart highways.

Description

Technical Field

[0001] This invention belongs to the field of roadbed engineering health monitoring and new energy technology, and relates to an intelligent self-consistent monitoring structure and method for dynamic resilient modulus of fill, which generates electricity by utilizing the vibration energy generated by vehicle loads acting on the roadbed. Background Technology

[0002] Long-term performance monitoring and energy self-sufficiency of road infrastructure have become core requirements in the development of modern transportation systems. According to China's "Highway Subgrade Design Code" (JTG D30-2015), dynamic resilient modulus, as a key parameter for evaluating the fatigue deformation resistance of subgrade fill, is directly related to the design life and service safety of pavement structures through accurate monitoring. Meanwhile, the vibration energy generated by vehicle loads acting on the subgrade has significant potential for utilization. Converting this energy into a self-powered power source for monitoring equipment can effectively reduce the dependence of traditional sensors on wired power supplies, promoting the evolution of fill infrastructure towards intelligence and low-carbon development. However, existing technologies still face many challenges in achieving this goal.

[0003] Current methods for measuring dynamic resilient modulus suffer from several technical drawbacks, including cumbersome operation, significant damage to road structure, strong dependence on external power supply, and insufficient lifespan of automated detection devices. Patent CN218823698U employs a drop hammer impact method on a bearing plate, collecting data through force and settlement sensors. This requires interrupting traffic for discrete point detection, failing to meet continuous monitoring needs and necessitating damage to the road structure for testing. Patent application CN109356009A's dynamic resilient modulus monitoring scheme uses earth pressure sensors to measure stress and LVDT displacement sensors to measure deformation, calculating the dynamic resilient modulus using a specific formula. While achieving real-time monitoring, it still relies on solar panels for power and has poor environmental adaptability. Its displacement measurement depends on a mechanical contact LVDT sensor, obtaining deformation data through a physical connection with the pressure sensor; long-term vibration can easily lead to loose connections or component wear. In addition, some existing technologies use piezoelectric accelerometers to measure acceleration and integrate the acceleration to obtain velocity and displacement. Although this can achieve indirect measurement of dynamic parameters, this method also has significant limitations: the core component of the piezoelectric accelerometer relies on mechanical vibration to generate charge signals. When subjected to high-frequency impacts from vehicle loads for a long time, it is prone to sensitivity decay or even failure due to fatigue of piezoelectric materials, loose lead wires, or aging of the packaging.

[0004] The aforementioned problems severely restrict the reliability, economy, and environmental adaptability of backfill health monitoring technology, and there is an urgent need for a collaborative solution that integrates simplified monitoring, low-destructiveness, energy self-sufficiency, and long lifespan. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an intelligent self-consistent monitoring structure for the dynamic resilient modulus of embankments. This structure utilizes the vibration energy generated by vehicle loads acting on the roadbed to generate electricity, while simultaneously achieving intelligent monitoring of the dynamic resilient modulus of the embankments. This simplifies the measurement process, protects the road structure, and provides a long-life, self-powered health monitoring solution for smart highways.

[0006] Another objective of this invention is to provide a monitoring method for an intelligent self-consistent monitoring structure for the dynamic resilient modulus of a filling material.

[0007] The technical solution adopted in this invention is an intelligent self-consistent monitoring structure for the dynamic rebound modulus of a roadbed fill, comprising an energy harvesting module. The energy harvesting module includes a magnet array, with each lane's magnet array comprising two rows of cylindrical permanent magnets. The permanent magnets are vertically axially magnetized and embedded in the top of the roadbed fill, directly below the designed wheel track. An induction coil is arranged around the outside of the permanent magnets, with the permanent magnets and induction coils coaxial. The induction coils are located outside the equivalent circular action range of the standard axle load, while the permanent magnets are located within the equivalent circular action range of the standard axle load. The invention also includes:

[0008] The dynamic parameter sensing module uses a piezoresistive sensor located directly above the permanent magnet to collect the dynamic stress time history of the permanent magnet in real time, extract the peak stress and valley stress, and transmit them to the data processing module.

[0009] The energy management module is used to capture the induced electromotive force generated by the induction coil in real time and transmit it synchronously to the data processing module; at the same time, it stores the instantaneous high-power vibration energy, and after accumulating to a preset threshold, it wakes up the dynamic parameter sensing module and supplies power; the data processing module is used to verify the data sent by the dynamic parameter sensing module and the energy management module, align the valid data with timestamps, and calculate the dynamic rebound modulus of the top of the roadbed fill.

[0010] Furthermore, the data processing module calculates the magnetic field movement rate through the induced electromotive force generated by the induction coil. After determining the monitoring period, it selects the magnetic field rate during the unloading phase from the peak stress time to the valley stress time for integration to obtain the recoverable elastic deformation of the permanent magnet. Thus, based on the definition of the dynamic elastic modulus of the roadbed, it calculates the dynamic elastic modulus of the top of the roadbed fill.

[0011] Furthermore, the diameter of a single permanent magnet is 150-250mm, the height is 80-120mm, and the center-to-center distance of a single permanent magnet is 60-100cm.

[0012] Furthermore, the induction coil uses 30-80 turns of twisted enameled copper wire with a wire diameter of 0.2-0.5mm and a diameter of 250-350mm.

[0013] Furthermore, the energy management module includes:

[0014] An electrical signal acquisition and transmission device is used to capture the induced electromotive force generated by the induction coil in real time and transmit it synchronously to the data processing module.

[0015] The rectifier circuit, a bridge circuit, is used to rectify the alternating current generated by the movement of the induction coil and the permanent magnet into direct current.

[0016] The voltage regulation module uses a DC / DC converter, which is connected to the rectifier circuit to provide a stable output voltage to the energy storage unit.

[0017] The energy storage unit, a supercapacitor, is used to store instantaneous high-power vibration energy. Once the energy accumulates to a preset threshold, it wakes up the dynamic parameter sensing module to sample and provides power to the dynamic parameter sensing module and the electrical signal acquisition and transmission device.

[0018] A monitoring method for a smart self-consistent monitoring structure for the dynamic resilient modulus of a filling material includes the following steps:

[0019] S1, Establish the benchmark dataset for the roadbed and generate the benchmark value for the dynamic resilient modulus;

[0020] S2, when a vehicle passes by, the permanent magnet, which moves synchronously with the roadbed, generates relative motion with the relatively stationary induction coil, producing an alternating current. After conversion, the current is stored in the form of pulses in the supercapacitor. Once the current accumulates to a preset threshold, it wakes up the dynamic parameter sensing module and supplies power to the dynamic parameter sensing module and the electrical signal acquisition and transmission device. The dynamic parameter sensing module collects the dynamic stress time history of the permanent magnet in real time, extracts the peak stress and valley stress, and transmits them to the data processing module. The electrical signal acquisition and transmission device in the energy management module captures the induced electromotive force generated by the induction coil in real time and transmits it synchronously to the data processing module.

[0021] S3, the data preprocessing layer of the data processing module verifies the validity of the received raw data and aligns the valid data with timestamps; the algorithm processing layer of the data processing module calculates the magnetic field movement rate through the induced electromotive force generated by the induction coil, determines the monitoring period, selects the magnetic field rate during the unloading phase from the peak stress time to the valley stress time for integration, obtains the recoverable rebound deformation of the permanent magnet, and thus calculates the dynamic rebound modulus of the top of the roadbed fill based on the definition of the roadbed dynamic rebound modulus, and transmits the data to the computer terminal; the decision output layer of the data processing module sets up multi-level early warning responses based on the comparison results of the monitored roadbed dynamic rebound modulus with the dynamic rebound modulus benchmark value in S1.

[0022] Furthermore, the roadbed dynamic parameter benchmark dataset in S1 includes: the dynamic stress time history of the permanent magnet collected by the dynamic parameter sensing module, the extracted peak stress and valley stress; the motion rate and integral displacement data of the induction coil and magnetic field, recording the valley stress when there is no load and the displacement change under load.

[0023] Furthermore, in step S3, the data preprocessing layer performs validity verification on the received raw data, including the following steps:

[0024] The infrared camera identifies vehicles on the positive pressure wheel track. The data preprocessing layer uses the raw data collected when the vehicle passes through the positive pressure wheel track as the initial valid data. After removing invalid data samples such as abnormal voltage waveforms, stress signals exceeding the range, and displacement gauge data jumps, the valid data is output.

[0025] Furthermore, in S3, the method for calculating the dynamic resilient modulus of the top of the roadbed fill is as follows:

[0026] S31, the magnetic field generated by the permanent magnet is considered a constant magnetic field, based on Maxwell's and Faraday's laws: Thus obtain ;in, This refers to the induced electromotive force generated by the induction coil. The velocity of the magnetic field generated by the permanent magnet relative to the induction coil. The magnetic field strength cut by the induction coil. The number of turns of the induction coil. This is the effective cutting length of the induction coil;

[0027] S32, Determine the monitoring cycle Then, the peak stress time is selected. Peak stress time Magnetic field rate during this unloading phase Integrating, we obtain the recoverable springback deformation of the permanent magnet (13). :

[0028]

[0029] in, Indicates time;

[0030] S33, The dynamic resilient modulus of the top of the subgrade fill is calculated based on the definition of the dynamic resilient modulus of the subgrade. :

[0031]

[0032] in, Pi; This refers to the dynamic stress amplitude. , Indicates peak stress. The valley stress is represented by the dynamic parameter sensing module; The equivalent circle diameter for standard axle load; The Poisson's ratio is used for the filling material.

[0033] Furthermore, in S3, the early warning response is divided into three levels:

[0034] Primary warning Mark abnormal locations;

[0035] Intermediate warning Warning information is released in the cloud, and a high-frequency sampling mode is activated;

[0036] Emergency Alert : Real-time push to the maintenance terminal;

[0037] in, This represents the dynamic resilient modulus of the roadbed as monitored in real time. This represents the reference value for dynamic resilience modulus.

[0038] The beneficial effects of this invention are:

[0039] (1) This invention combines electromagnetic induction power generation with roadbed dynamic parameter monitoring. Through a set of magnet-induction coil system, the energy conversion of vehicle vibration and the monitoring of dynamic rebound modulus of the fill are realized simultaneously, providing technical support for the intelligent operation and maintenance of infrastructure.

[0040] (2) This invention converts vehicle vibration energy into electrical energy, reducing the amount of traditional cables used, simplifying the wiring process when deploying the device, and reducing line maintenance costs; it gets rid of the dependence of traditional monitoring devices on fixed power supply points, and can be more flexibly adapted to different roadbed monitoring scenarios, while laying the core technical foundation for subsequent optimization of energy collection structure to achieve higher efficiency energy collection.

[0041] (3) The present invention uses non-destructive implantation of sensors and power generation devices, that is, embedding sensors and power generation devices into the fill body in advance during the roadbed construction stage, avoiding damage to the roadbed structure to monitor the dynamic rebound modulus of the roadbed, and can monitor the dynamic rebound modulus of the fill body roadbed in a long-term stable manner. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the overall structure of the monitoring structure in an embodiment of the present invention.

[0044] Figure 2 This is a plan view of the monitoring structure layout in an embodiment of the present invention.

[0045] Figure 3 This is a schematic diagram of the energy acquisition process of the monitoring structure in an embodiment of the present invention.

[0046] Figure 4 This is a flowchart of the intelligent monitoring method for dynamic resilient modulus of fill material according to an embodiment of the present invention.

[0047] In the diagram, 1. Car, 2. Road surface, 3. Concrete surface layer, 4. Data processing module, 5. Energy management module, 6. Shoulder, 7. Base course, 8. Subbase course, 9. Subgrade fill, 10. Dynamic earth pressure cell, 11. Induction coil, 12. Monitoring unit, 13. Permanent magnet, 14. Electrical signal acquisition and transmission device, 15. Diode, 16. Rectifier circuit, 17. DC / DC converter, 18. Supercapacitor, 19. Pole, 20. Infrared camera, 21. Photovoltaic panel. Detailed Implementation

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1,

[0050] A smart self-consistent monitoring structure for the dynamic resilient modulus of fill, such as Figures 1-2 As shown, it includes an energy harvesting module, an energy management module 5, a dynamic parameter sensing module, a data processing module 4, and an infrared camera recognition module. The roadbed, from top to bottom, includes a pavement 2, a concrete surface layer 3, a base layer 7, a subbase layer 8, and a roadbed fill 9.

[0051] The energy harvesting module includes a magnet array and an induction coil assembly. The magnet array uses permanent magnets 13 made of N52 grade neodymium iron boron material. Each permanent magnet 13 has a diameter of 200 mm and a height of 100 mm, and is axially magnetized (the magnetic field is radially distributed). The magnet array is buried on the top of the roadbed fill 9, directly below the corresponding designed wheel track zone (referring to the typical area where the wheels continuously act during normal vehicle (car 1) driving, determined according to the designed vehicle type and driving characteristics). The center-to-center distance of each permanent magnet 13 is 80 cm.

[0052] The induction coil assembly includes the same number of induction coils 11 as the permanent magnet 13. The induction coils 11 are wound around the outside of the permanent magnet 13, and their central axes are aligned. Specifically, the straight line of the geometric center of the permanent magnet 13 in the vertical direction (since the permanent magnet 13 is cylindrical, this axis is its own center of symmetry) completely coincides with the straight line of the geometric center of the ring structure formed by the induction coils 11. This ensures that the relative motion between the magnetic field and the coil is always along the axial direction, avoiding any impact on electromagnetic induction efficiency due to offset. The induction coils 11 use 50-turn twisted-pair enameled copper wire with a diameter of 0.3mm and a total diameter of 300mm.

[0053] Under vehicle load, the area directly below the wheel track (where the magnet array is embedded) and its adjacent area (where induction coil 11 is embedded) experience a difference in vertical compression deformation: the fill material directly below the wheel track undergoes greater compression deformation, and the embedded magnet array moves synchronously with the compression of the fill material, resulting in a downward vertical displacement. The "Specifications for Design of Highway Asphalt Pavement" (JTG D50-2017) clearly stipulates that a single-axle-dual-wheel axle load of 100kN should be used as the design axle load in pavement design, with a single-wheel ground contact equivalent circle diameter... The diameter is 212mm. In this embodiment of the invention, the permanent magnet 13 is located within the equivalent circle of the standard axle load, while the induction coil 11 has a diameter of 300mm and is located outside the equivalent circle of the standard axle load BZZ-100. Beyond the equivalent circle, the stress decreases exponentially according to the elastic layered system theory, and the deformation is minimized. It is minimally affected by vehicle load, and the deformation of the roadbed fill 9 is negligible. Therefore, the downward displacement of the induction coil 11 is negligible. Thus, the relative displacement between the induction coil 11 and the magnetic field is equivalent to the absolute displacement of the permanent magnet 13. The following descriptions of the motion displacement between the induction coil 11 and the magnetic field all refer to the absolute displacement of the permanent magnet 13.

[0054] During the displacement of the induction coil 11 relative to the magnetic field, the coil continuously cuts the magnetic field lines, generating Faraday's electromagnetic induction phenomenon and producing an induced electromotive force. The voltage sensor collects data in real time, while the dynamic earth pressure cell 10 simultaneously collects stress data. Induced electromotive force... This can be derived from the Maxwell-Faraday law:

[0055] (1)

[0056] In the formula: The induced electromotive force (the induced electromotive force-time graph is a half-sine wave). The number of turns of the induction coil. This represents the rate of change of the magnetic field over time (in this embodiment, a permanent magnet is used, so this value is 0). This is the motional electromotive force generated by the movement of the magnet and the induction coil. The area element enclosed by the induction coil is a small element. This is a micro-element of the induction coil path; This is the time of the relative motion between the coil and the permanent magnet; It is the magnetic flux passing through the closed area enclosed by the induction coil.

[0057] The magnetic field generated by permanent magnet 13 is considered to be a constant magnetic field, that is... Based on this setting, only the motional electromotive force is retained in formula (1), and the final simplified formula is as follows:

[0058] (2)

[0059] Further transform the variables in equation (2): divide both sides of the equation by... (wherein, the magnetic field strength cut by the coil) Number of turns of the induction coil Effective cutting length of induction coil These are all inherent hardware parameters of the device, belonging to known constants), the coil is completely located in the magnetic field, and the effective cutting length. The product of the circumference of a single turn of the coil and the number of turns is used to obtain the velocity of the induction coil 11 relative to the magnetic field. The analytical expression for is calculated as follows:

[0060] (3)

[0061] The obtained velocity of the induction coil 11 and the magnetic field By performing time-domain integration, the displacement of the induction coil 11 relative to the magnetic field can be obtained. :

[0062] (4)

[0063] A complete monitoring cycle is defined as the entire process from the initial application of a single wheel load to its complete departure from the area above the magnet array and coil. During this period Within the track zone, the recoverable rebound deformation and stress of the roadbed fill directly beneath the track zone exhibit a pattern of "increasing from zero to a maximum value, and then returning from the maximum value to zero." By synchronizing the sampling of the device and aligning the timestamps of all measurement data, the monitoring cycle can be determined. And the displacement integral intervals corresponding to the peak stress time and the trough stress time. Within a known monitoring period... Then, the peak stress time From the moment of maximum compression to the moment of minimum stress (At the moment when the compression reaches zero) During this unloading phase, the induction coil 11 and the magnetic field movement speed Integrating, the displacement of the induction coil relative to the magnetic field is obtained. This can be equivalent to the recoverable springback deformation of the filling material. .

[0064] (5)

[0065] The energy management module 5 includes an electrical signal acquisition and transmission device 14, a rectifier circuit 16, a voltage regulation module, and an energy storage unit.

[0066] The electrical signal acquisition and transmission device 14 consists of a high-precision voltage sensor, a low-power wireless transmission module, and a waterproof housing. Its core function is to capture the induced electromotive force generated by the induction coil 11 in real time and transmit the raw electrical signal synchronously to the data processing module 4, providing basic data for subsequent velocity, displacement inversion, and dynamic spring modulus calculation.

[0067] The rectifier circuit 16 is a bridge circuit. This circuit uses four diodes, each of which conducts only for one and a half cycles, to rectify the alternating current generated by the movement of the coil and the permanent magnet into direct current, effectively improving the efficiency of energy harvesting.

[0068] The voltage regulation module uses a DC / DC converter 17 (synchronous rectification type Buck-Boost), with adjustable output voltage and integrated overvoltage and overcurrent protection functions. It is connected to other components via a waterproof cable.

[0069] The energy storage unit uses a double-layer supercapacitor 18 as the energy storage device. The supercapacitor 18 captures millisecond-level pulse electrical energy caused by vehicle loads, providing a fluctuation-free DC power supply for the electrical signal acquisition and transmission device 14 and the dynamic earth pressure cell 10 described below.

[0070] The core components of the energy management module 5 are centrally located in a housing 30cm below the road shoulder green belt, avoiding the 1.5m range on the side of the road shoulder driving lane and away from areas affected by vehicle traffic and mechanical disturbance from the curb. All components are connected by waterproof cables, which extend through pre-embedded waterproof conduits to the induction coil 11 and high-precision voltage sensor within the roadbed fill 9. The energy management module 5 uses these core components to achieve electrical signal acquisition and transmission, as well as power rectification, voltage stabilization, and storage, providing a stable power supply for the monitoring equipment. The acquired electrical and stress signals are directly uploaded to the data processing module 4.

[0071] The dynamic parameter sensing module includes a dynamic earth pressure cell 10. The dynamic earth pressure cell 10 employs a piezoresistive sensor and is located directly above the permanent magnet 13. The center-to-center distance between the dynamic earth pressure cell 10 and the permanent magnet 13 is 6 cm, and their central axes are aligned to ensure spatial consistency between stress measurement and displacement inversion data. The dynamic earth pressure cell 10 acquires dynamic stress time histories in real time. Extracting peak stress Valley stress .

[0072] According to the definition of dynamic resilient modulus of subgrade in the "Specifications for Field Testing of Highway Subgrade and Pavement" (JTG 3450-2019), the stress data collected in real time by the dynamic earth pressure cell 10 and the Poisson's ratio of the fill volume are used. and the recoverable springback deformation obtained by formula (5) Substituting the numerical value into formula (6), the dynamic resilient modulus of the roadbed fill 9 is obtained through inversion calculation. :

[0073] (6)

[0074] In the formula: Pi The dynamic stress amplitude reflects the degree of stress fluctuation in the roadbed under dynamic loads. , The equivalent circle diameter, For the Poisson's ratio of the fill volume, It is capable of recovering and rebounding deformation; Indicates peak stress. This represents the valley stress.

[0075] Each monitoring unit 12 includes a set of N52 grade neodymium iron boron permanent magnets 13 arranged in a 1×2 matrix, two corresponding induction coils 11, a matching dynamic earth pressure box 10, and an energy management module 5, which is buried under the road shoulders 6 on both sides.

[0076] The data processing module 4 includes three layers (data preprocessing layer, algorithm processing layer, and decision output layer). It verifies the data sent by the dynamic parameter sensing module and the electrical signal acquisition and transmission device 14, timestamps the valid data, calculates the dynamic rebound modulus of the top of the roadbed filling, and finally performs a three-layer early warning response based on the set dynamic rebound modulus of the roadbed.

[0077] Figure 3 This is a schematic diagram of the power generation process, including an induction coil 11, an electrical signal acquisition and transmission device 14, a rectifier circuit 16, a diode 15, a DC / DC converter 17, and a supercapacitor 18. The electrical signal acquisition and transmission device 14 consists of a high-precision voltage sensor, a low-power wireless transmission module, and a waterproof housing. The high-precision voltage sensor acquires the induced electromotive force in real time. The original electrical signal is synchronously transmitted to the data processing module. The rectifier circuit 16 uses four diodes 15, each conducting for only one and a half cycles, converting the induced current output during power generation into direct current. The DC / DC converter 17 is connected to the rectifier circuit 16, optimizing energy conversion efficiency by controlling the switching frequency and duty cycle, and providing a stable output voltage. The supercapacitor 18 uses a double-layer supercapacitor module, capable of rapidly absorbing and releasing energy, handling the instantaneous high-power vibration energy generated by passing vehicles. The supercapacitor 18 supplies the captured instantaneous high-power vibration energy to the dynamic earth pressure box 10 and the electrical signal acquisition and transmission device 14, ensuring the continuous and stable operation of key functional modules.

[0078] Example 2,

[0079] A method for intelligent monitoring of the dynamic resilient modulus of fill, such as Figure 4 As shown, it includes the following steps:

[0080] S1: The monitoring device of Embodiment 1 of this invention is deployed during the construction of the new or reconstructed roadbed filling. After the road is opened to traffic, the system automatically enters the baseline data acquisition mode, and the cumulative effective sample size is not less than 1000 times, including the following core data chain: ① Acquiring basic parameters of the monitoring system, including real-time induced electromotive force, charging data of supercapacitor 18, and operating parameters of DC / DC converter 17. This part of the data is used to establish the energy system baseline: clarify the power generation and energy storage capacity of the energy system, and verify whether the self-powered system has reached the design indicators; at the same time, record the stable state of the energy conversion link to provide an initial reference for the later equipment fault diagnosis. ② Establishing a baseline dataset of roadbed dynamic parameters, including dynamic stress time history data collected by dynamic earth pressure cell 10, induction coil 11 and magnetic field movement rate and integral displacement data; recording the valley stress under no load and the displacement change under load, and verifying the synergy between the movement of permanent magnet 13 and induction coil 11 and soil deformation. This data is used to generate the dynamic resilient modulus baseline value: the arithmetic mean of the dynamic resilient modulus calculation results under 1000 effective axle loads over 30 days is taken as the dynamic resilient modulus baseline value. Simultaneously, the fluctuation range of the modulus value is statistically analyzed. This dynamic elastic modulus reference value... This will serve as the health threshold for subsequent service life monitoring. ③ Collect relevant data on the collaborative performance of the equipment, including timestamp deviations of induced electromotive force, stress, and displacement. This data is used to verify the overall reliability of the system, confirm the collaborative working capability of each module, and provide a basis for parameter correction during long-term monitoring.

[0081] Specific verification steps:

[0082] S11. Time Synchronization Verification: Using the PPS second pulse signal provided by GNSS as a unified time reference, the voltage data of the induction coil 11 acquired by the dynamic earth pressure cell 10 and the electrical signal acquisition and transmission device 14 are synchronized via the PTP protocol (IEEE 1588) to ensure the time stamp error of each sensor. Extract two key time points under the same vehicle load: the peak stress time of dynamic earth pressure cell 10. Peak moment of electromotive force of induction coil 11 Calculate the time difference And continuously collect 50 sets of effective load data and take the average value. When the time difference is equal to This indicates that the response times of the two are consistent, with no significant lag or lead. The above process is carried out automatically in the data preprocessing layer of data processing module 4 without the need for additional software installation. Time synchronization and timestamp marking are automatically completed by an embedded system integrating the PTP protocol (such as Linux + ptp4l tool). The verification process incorporates a data validity check step, and data that fails to meet the synchronization standard is directly discarded.

[0083] S12. Stress-displacement correlation verification: Peak stress under the same axial load. Peak displacement of permanent magnet 13 Perform linear fitting and calculate the correlation coefficient. . This proves that as stress increases, soil deformation increases synchronously, and the displacement of permanent magnet 13 is linearly correlated with soil deformation, with no abnormal offset. The above process is also completed within the data preprocessing layer of data processing module 4. After completing time alignment and deformation verification, the correlation coefficient is automatically calculated using the built-in linear fitting algorithm to generate a stress-displacement correlation report, which serves as the final core basis for synergy verification.

[0084] If the verification of the above two dimensions meets the judgment criteria, it can be confirmed that the movement of the permanent magnet 13 is completely driven by the dynamic deformation of the soil, the displacement signal captured by the induction coil 11 is highly coordinated with the soil deformation in terms of time, value and mechanical law, and the monitoring data can be directly used for dynamic rebound modulus calculation.

[0085] S2: When a vehicle passes through the monitoring section, the roadbed fill 9 undergoes vertical dynamic compression deformation. The magnet array, which moves synchronously with the roadbed fill 9, forms relative motion with the induction coil 11, whose vertical displacement is negligible (the relative motion is equivalent to absolute motion), thereby generating an alternating current. This alternating current is converted into direct current by a bridge rectifier circuit, and then the voltage is stabilized and the power is transmitted through a DC / DC converter 17. Finally, the power is stored in the supercapacitor 18 in the form of pulses. The stored power can directly power the electrical signal acquisition and transmission device 14, the wireless transmission device, the dynamic earth pressure cell 10, and other equipment. While generating power, each sensor simultaneously acquires data: the dynamic earth pressure cell 10 acquires the dynamic stress time history of the roadbed fill; the voltage sensor acquires the induced electromotive force. All data is uploaded to the computer cloud through the wireless transmission module.

[0086] S3: Data processing module 4 operates based on the baseline database established by S1, following a three-layer pipeline:

[0087] First, the data preprocessing layer performs real-time signal verification and data transmission of sensor data and electrical signals. It identifies vehicles on the positive pressure wheel track using an infrared camera, and uses the raw data collected when these vehicles pass as preliminary valid data. After removing invalid data samples with abnormal voltage waveforms, stress signals exceeding their range, and displacement gauge data jumps, the valid data is output. The valid data is then timestamped to ensure that speed, stress, and displacement signals match on the same time scale.

[0088] Infrared cameras 20 are mounted on poles 19 at 30m intervals along the roadside, with a height of 2.5m and a lens angle of 18°, to ensure the capture of the complete dynamic process of the wheel crushing device. They continuously capture infrared images 24 hours a day and transmit the images to the data processing module 4 in real time. Photovoltaic panels 21 are integrated with the infrared cameras 20 and poles 19, with an elevation angle of 30°. During the day, the photovoltaic panels 21 generate a high amount of electricity, which, in addition to powering the system, charges the supercapacitor 18 with any surplus. At night and on cloudy or rainy days, the supercapacitor 18 provides power.

[0089] Infrared camera 20 operates continuously around the clock. When there are no vehicles, infrared camera 20 and its sensors operate in low-frequency mode. When a vehicle enters the monitoring field of view, infrared camera 20 continues to capture images, and data processing module 4 runs the lightweight YOLOv8-nano algorithm in real time. When a wheel is detected in the image, wheel trajectory tracking is immediately initiated: First, the actual lateral coordinates of the wheel center are extracted from each frame of the image. (Perpendicular to the direction of travel in the lane) and longitudinal coordinate (Along the driving direction of the lane); secondly, calculate in real time the longitudinal distance between the wheel and the geometric center of the permanent magnet 13 (the permanent magnet 13, the induction coil 11, and the dynamic earth pressure box 10 have the same geometric center in the vertical direction, the left wheel track is group A, and the right wheel track is group B). , Let be the longitudinal coordinate of permanent magnet 13; when (As the wheel is about to enter the crushing area), the data processing module 4 sends an instruction: increase the sensor sampling frequency and the frame rate of the infrared camera 20.

[0090] When the vehicle runs over the designed wheel track (permanent magnet 13 and induction coil 11), the infrared camera 20 calculates the lateral offset between the wheel and the permanent magnet 13 in each frame. , It can determine whether there is positive pressure in real time. This indicates the lateral offset (perpendicular to the direction of travel) between the center of the left wheel and the geometric center of permanent magnet 13 in group A. This indicates the lateral offset between the center of the left wheel and the geometric center of permanent magnet 13 in group B. It is the lateral coordinate of the geometric center of permanent magnet 13 in group A (position coordinate perpendicular to the direction of lane travel); It is the lateral coordinate of the geometric center of permanent magnet 13 in group B; the geometric center of permanent magnet 13 is the same as that of induction coil 11 and dynamic earth pressure cell 10.

[0091] The offset is determined according to the following positive pressure threshold: (Left wheel positive pressure group A) or (Right wheel positive pressure group B); After the judgment is completed, real-time marking is performed, and the judgment result (positive pressure / bias pressure + offset) of each frame image is bound with the corresponding timestamp and transmitted to the data processing module 4. At this time, the sensor performs high-speed sampling and synchronously records the voltage signal. , With stress signal , Each sampling point carries the same timestamp, corresponding one-to-one with the judgment result of the infrared camera 20.

[0092] Data processing module 4 compares the timestamps of the infrared camera 20's judgment result with the sensor's sampling data in real time. When the infrared camera 20 determines "positive pressure" for three consecutive frames, the offset is... At that time, the sensor data for the corresponding time period is locked and saved. If the infrared camera 20 determines "bias voltage"... If the judgment is inconsistent for 3 consecutive frames, the sensor data will only be temporarily stored and automatically deleted after 10 seconds, and will not be included in subsequent calculations.

[0093] When the infrared camera fails to detect the wheel (or wheel longitudinal distance) for 10 consecutive frames, To determine if the vehicle has left: the sensor sampling frequency is reduced to a low-frequency mode; the data processing module 4 calculates the locked valid data through the algorithm processing layer.

[0094] Secondly, the algorithm processing layer performs displacement integral and dynamic modulus inversion based on the relevant formulas derived in this embodiment of the invention: the motion speed of induction coil 11 and magnetic field is calculated based on the simplified formula (3) of Maxwell-Faraday law. The displacement of the induction coil and the magnetic field is obtained by time-domain integration of the velocity of the induction coil 11 and the magnetic field using formula (4). After determining the monitoring period, the induction coil and magnetic field velocity during the unloading phase from the peak stress time to the trough stress time are integrated, and the recoverable elastic deformation can be obtained through formula (5). The peak stress measured by the dynamic earth pressure cell 10 Valley stress Combined with soil, it can recover and rebound deformation. Poisson's ratio of the fill body Substitute into formula (6) to calculate the dynamic resilient modulus. And transmit the data to the computer terminal.

[0095] Finally, the decision output layer sets up a three-layer early warning response based on the monitored dynamic resilient modulus of the roadbed:

[0096] Primary warning The system automatically marks abnormal points, uploads them to the cloud platform, and generates a preliminary diagnostic report (including historical data comparison and suggested test items).

[0097] Intermediate warning Warning information is released in the cloud, and a high-frequency sampling mode is activated;

[0098] Emergency Alert The system pushes data to maintenance personnel's terminals in real time, triggering personnel inspections and ground-penetrating radar scans, and prompting them to take corresponding maintenance and repair measures.

[0099] Calculation example:

[0100] Data from a highway subgrade monitoring system was used. This section of road has a newly constructed asphalt pavement, and the subgrade filler is silty gravel. A single-axle-dual-wheel set with an axle load of 100kN was used as the design axle load, and the test speed was 80km / h. Monitoring period: ; Magnetic induction intensity of coil cutting The number of coil turns is Effective cutting length of coil The induced electromotive force-time graph is in the form of a half-sine wave, with a peak voltage of... angular frequency Induced electromotive force Peak stress measured by earth pressure cell Valley stress Poisson's ratio of the monitoring site's fill volume Equivalent circle diameter .

[0101] Calculation process:

[0102] (1) Calculate the recoverable rebound deformation of the filling body ;

[0103] monitoring cycle Induced electromotive force Magnetic induction intensity of coil cutting Number of coil turns Effective cutting length of coil Substituting into formulas (3), (4), and (5), we obtain the recoverable springback deformation of the filling material. :

[0104]

[0105] Since the recoverable rebound deformation of the fill material is considered a scalar, the absolute value of the above calculation result is taken as the recoverable rebound deformation. It is 0.6mm.

[0106] (2) Calculate the dynamic elastic modulus of the filling body ;

[0107] Peak stress Valley stress Poisson's ratio of the monitoring site fill Equivalent circle diameter The filling material can recover its elastic deformation. Substituting into formula (6), we obtain the dynamic elastic modulus of the filling body. :

[0108]

[0109] The "Specifications for Design of Highway Subgrade" (JTG D30-2015) clearly states that when the subgrade fill material is silty gravel, its dynamic resilient modulus should be within the range of 100~125 MPa. The dynamic resilient modulus of the silty gravel subgrade calculated above is approximately 113.72 MPa. This result not only falls entirely within the reasonable range specified in the specification, but also closely matches the actual dynamic mechanical properties of silty gravel in subgrade engineering. This result fully demonstrates that the monitoring structure and method of this invention have clear practical application value and can provide strong support for subgrade quality control and stability monitoring.

[0110] (3) Calculate electrical energy;

[0111] Based on the effective value formula of sinusoidal alternating current (Formula (7) and Formula (8)), the active power formula of pure resistive circuit (Formula (9)) and the energy calculation formula (Formula (10)), the power generation and energy of the monitoring structure in a single monitoring cycle are calculated.

[0112] (7)

[0113] (8)

[0114] (9)

[0115] (10)

[0116] Indicates the effective value of the voltage. Indicates the effective value of the current. Indicates the active power of an AC circuit. This indicates the electrical energy generated within a single cycle.

[0117] monitoring cycle Peak voltage Measured external load resistance (Matching the resistance of the low-power signal acquisition module in the roadbed monitoring to construct a closed loop), substituting into formulas (7)-(10), we obtain the power generation and electrical energy in a single cycle:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] This represents the average daily total power generation, with an average of 30,000 effective actions per day (15,000 vehicles × 2 actions / vehicle).

[0124] The above calculations show that the electrical energy can meet the power supply requirements of the low-power signal acquisition module and wireless transmission module in roadbed monitoring, which is in line with the design goal of "energy self-sufficiency and reduced operation and maintenance costs" in the project, and has practical application value in the field of intelligent roadbed monitoring.

[0125] This invention converts the vibration energy generated by vehicle loads into electrical energy to power the monitoring system. Simultaneously, this energy is used to monitor the dynamic resilience modulus of the roadbed. Both are synchronized through a magnet-induction coil system. This achieves self-powering, simplifies the monitoring process, and reduces energy consumption and maintenance costs. In this embodiment, the permanent magnet 13 employs a "radial radiating magnetic field," with the induction coil 11 completely within the magnetic field, ensuring a linear correlation between the induced electromotive force and displacement. The dynamic earth pressure cell 10 is perfectly aligned with the central axis of the permanent magnet 13, ensuring spatial consistency between stress measurement and displacement inversion data. Both the induced electromotive force and dynamic stress are triggered by the same physical event (vehicle tire rolling monitoring section), naturally synchronizing them at the physical time starting point. The same acquisition unit synchronously samples the voltage and stress signals. In the data processing module 4, using "peak stress → valley stress" as a benchmark, the corresponding induced electromotive force interval is aligned and extracted for integration, thereby achieving time alignment. Initially, when there is no power, the sensors (dynamic earth pressure cell 10 and electrical signal acquisition and transmission device 14) are in a low-power sleep state and do not require an external power supply. Therefore, they cannot sample and calculate the resilient modulus at the beginning. Within 30 days of the road opening, the monitoring system will conduct a baseline data acquisition in advance. This step avoids the situation where sampling is not possible at the beginning when subsequent formal monitoring begins. The electrical energy generated by the first compaction (regardless of positive or negative pressure) is preferentially charged into the supercapacitor 18. After accumulating to a preset threshold (i.e., the sensor activation threshold), the sensor is awakened and sampled. The power generation process and the sampling process are carried out simultaneously, achieving energy self-sufficiency and on-demand power supply. This provides a fluctuation-free DC power supply for sensors such as the dynamic earth pressure cell 10, reduces dependence on external power supply, reduces operation and maintenance costs, and improves environmental performance.

[0126] When a vehicle load is applied, the induction coil 11 generates electrical energy and outputs an electromotive force signal, with both functions operating in parallel. The same hardware system achieves dual functionality; the magnet-coil acts as both an energy harvester and a "sensitive element" of the displacement sensor, inverting displacement through the induced electromotive force, eliminating the need for additional displacement sensors (such as LVDTs or laser displacement gauges). LVDTs (Linear Variable Differential Transformers) rely on mechanical contact between the core and the coil; long-term vibration can easily lead to core wear and coil loosening, causing displacement drift. Laser displacement gauges are susceptible to interference from road dust and moisture, with accuracy dropping sharply in adverse weather conditions. This invention, based on Faraday's law of electromagnetic induction, exhibits a strictly linear relationship between the induced electromotive force and the relative motion rate of the coil-magnet. Displacement inversion through integration is achieved without mechanical wear or drift (non-contact principle), making it less susceptible to environmental factors such as adverse weather, thus overcoming the problems of LVDTs and laser displacement gauges.

[0127] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A smart self-consistent monitoring structure for the dynamic resilient modulus of a fill material, comprising an energy harvesting module, characterized in that, The energy harvesting module includes a magnet array, with each lane's magnet array comprising two rows of cylindrical permanent magnets (13). The permanent magnets (13) are vertically axially magnetized and embedded in the top of the roadbed fill (9), directly below the designed wheel track. An induction coil (11) is arranged around the outside of the permanent magnets (13), with the permanent magnets (13) and the induction coils (11) coaxial. The induction coils (11) are located outside the equivalent circular range of the standard axle load, while the permanent magnets (13) are located within the equivalent circular range of the standard axle load. The module also includes: The dynamic parameter sensing module is used to collect the dynamic stress time history of the permanent magnet (13) in real time, extract the peak stress and valley stress, and transmit them to the data processing module (4). The energy management module (5) is used to capture the induced electromotive force generated by the induction coil (11) in real time and transmit it synchronously to the data processing module (4); at the same time, it stores the instantaneous high-power vibration energy, and after accumulating to a preset threshold, it wakes up the dynamic parameter sensing module and supplies power; the data processing module (4) is used to align the effective data with timestamps and calculate the dynamic rebound modulus of the top of the roadbed filling body.

2. The intelligent self-consistent monitoring structure for the dynamic resilient modulus of a fill body according to claim 1, characterized in that, The data processing module (4) calculates the magnetic field movement rate through the induced electromotive force generated by the induction coil (11), determines the monitoring period, selects the magnetic field rate during the unloading stage from the peak stress time to the valley stress time for integration, and obtains the recoverable elastic deformation of the permanent magnet (13). Thus, the dynamic elastic modulus of the top of the roadbed fill is calculated based on the definition of the roadbed dynamic elastic modulus.

3. The intelligent self-consistent monitoring structure for the dynamic resilient modulus of a fill body according to claim 1, characterized in that, The diameter of a single permanent magnet (13) is 150~250mm and the height is 80~120mm. The center-to-center distance of a single permanent magnet (13) is 60~100cm.

4. The intelligent self-consistent monitoring structure for the dynamic resilient modulus of a fill body according to claim 1, characterized in that, The induction coil (11) uses 30~80 turns of twisted enameled copper wire with a wire diameter of 0.2~0.5mm and a diameter of 250~350mm.

5. The intelligent self-consistent monitoring structure for the dynamic resilient modulus of a fill body according to claim 1, characterized in that, The energy management module (5) includes: The electrical signal acquisition and transmission device (14) is used to capture the induced electromotive force generated by the induction coil (11) in real time and transmit it synchronously to the data processing module (4). The rectifier circuit (16) is a bridge circuit used to rectify the alternating current generated by the movement of the induction coil (11) and the permanent magnet (13) into direct current; The voltage regulation module uses a DC / DC converter (17), which is connected to the rectifier circuit (16) to provide a stable output voltage to the energy storage unit. The energy storage unit is a supercapacitor (18) used to store instantaneous high-power vibration energy. After accumulating to a preset threshold, it wakes up the dynamic parameter sensing module to sample and provides power to the dynamic parameter sensing module and the electrical signal acquisition and transmission device (14).

6. The monitoring method for the intelligent self-consistent monitoring structure of dynamic resilient modulus of fill as described in claim 1, characterized in that, Includes the following steps: S1, Establish the benchmark dataset for the roadbed and generate the benchmark value for the dynamic resilient modulus; S2, when the vehicle passes by, the permanent magnet (13) moving synchronously with the roadbed and the relatively stationary induction coil (11) generate relative motion, generating alternating current. After conversion, the current is finally stored in the supercapacitor (18) in the form of pulses. After accumulating to a preset threshold, the dynamic parameter sensing module is awakened and powered on the dynamic parameter sensing module and the electrical signal acquisition and transmission device (14). The dynamic parameter sensing module collects the dynamic stress time history of the permanent magnet (13) in real time, extracts the peak stress and valley stress, and transmits them to the data processing module (4). The electrical signal acquisition and transmission device (14) in the energy management module (5) captures the induced electromotive force generated by the induction coil (11) in real time and transmits it synchronously to the data processing module (4). S3, the data preprocessing layer of the data processing module (4) verifies the validity of the received raw data and aligns the valid data with timestamps; the algorithm processing layer of the data processing module (4) calculates the magnetic field movement rate through the induced electromotive force generated by the induction coil (11), determines the monitoring period, selects the magnetic field rate of the unloading stage from the peak stress time to the valley stress time for integration, obtains the recoverable rebound deformation of the permanent magnet (13), and calculates the dynamic rebound modulus of the top of the subgrade fill body based on the definition of the subgrade dynamic rebound modulus, and transmits the data to the computer terminal; the decision output layer of the data processing module (4) sets up multi-level early warning response based on the comparison result of the monitored subgrade dynamic rebound modulus and the dynamic rebound modulus benchmark value in S1.

7. The monitoring method for the intelligent self-consistent monitoring structure of dynamic resilient modulus of a fill body according to claim 6, characterized in that, The S1 roadbed dynamic parameter benchmark dataset includes: the dynamic stress time history of the permanent magnet (13) collected by the dynamic parameter sensing module, the extracted peak stress and valley stress; the induction coil (11) and magnetic field movement rate and integral displacement data, recording the valley stress when there is no load and the displacement change under load.

8. The monitoring method for the intelligent self-consistent monitoring structure of dynamic resilient modulus of fill material according to claim 6, characterized in that, In step S3, the data preprocessing layer performs validity verification on the received raw data, including the following steps: The infrared camera identifies vehicles on the positive pressure wheel track. The data preprocessing layer uses the raw data collected when the vehicle passes through the positive pressure wheel track as the initial valid data. After removing invalid data samples such as abnormal voltage waveforms, stress signals exceeding the range, and displacement gauge data jumps, the valid data is output.

9. The monitoring method for the intelligent self-consistent monitoring structure of dynamic resilient modulus of a fill body according to claim 6, characterized in that, In S3, the method for calculating the dynamic resilient modulus of the top of the roadbed fill is as follows: S31, the magnetic field generated by the permanent magnet (13) is considered to be a constant magnetic field, based on the Maxwell-Faraday law: Thus obtain ;in, The induced electromotive force generated by the induction coil (11), The velocity of the magnetic field generated by the permanent magnet (13) relative to the induction coil (11) The magnetic field strength cut by the induction coil (11) The number of turns of the induction coil (11) The effective cutting length of the induction coil (11); S32, Determine the monitoring cycle Then, the peak stress time is selected. Peak stress time Magnetic field rate during this unloading phase Integrating, we obtain the recoverable springback deformation of the permanent magnet (13). : ； in, Indicates time; S33, The dynamic resilient modulus of the top of the subgrade fill is calculated based on the definition of the dynamic resilient modulus of the subgrade. : ； in, Pi; This refers to the dynamic stress amplitude. , Indicates peak stress. The valley stress is represented by the dynamic parameter sensing module; The equivalent circle diameter for standard axle load; The Poisson's ratio is used for the filling material.

10. The monitoring method for the intelligent self-consistent monitoring structure of dynamic resilient modulus of a filling body according to claim 6, characterized in that, In S3, the early warning response is divided into three levels: Primary warning Mark abnormal locations; Intermediate warning The system issues a warning message in the cloud and initiates a high-frequency sampling mode. Emergency Alert : Real-time push to the maintenance terminal; in, This represents the dynamic resilient modulus of the roadbed as monitored in real time. This represents the reference value for dynamic resilience modulus.

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