A high-precision, continuous, and rapid detection method and system for roadbed humidity without damaging the road surface

Abstract

The present invention discloses a high-precision, continuous, and rapid detection method and system for roadbed moisture without damaging the road surface. The method comprises the following steps: acquiring GPR and CCR atlas data from the road to be inspected; constructing a pavement material dielectric constant-water content relationship model, a pavement material resistivity-water content relationship model, and a roadbed filler resistivity-water content relationship model; obtaining the pavement layer thickness and dielectric constant based on the GPR atlas data, and obtaining the pavement moisture content based on the obtained pavement layer thickness and dielectric constant and the pavement material dielectric constant-water content relationship model; and obtaining the spatial distribution of the roadbed moisture content based on the CCR atlas data, the pavement thickness and moisture content, and the roadbed filler resistivity-water content relationship model. This application provides a method for indirectly obtaining the spatial distribution of the roadbed moisture content by collecting imaging data of the road to be inspected and constructing a moisture content relationship model. This method has the advantages of not damaging the road surface, rapid detection, continuous detection, and high detection accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of roadbed humidity detection, and in particular to a high-precision continuous and rapid detection method and system for roadbed humidity without damaging the road surface. Background Art

[0002] The road subgrade is the foundation of the pavement structure. The subgrade moisture content (moisture content) is an important indicator of subgrade strength and stability, and is also a key indicator for evaluating the operational status of the subgrade. It directly impacts the operational safety and service life of the rebuilt road. Understanding the subgrade moisture content is crucial for road maintenance and renovation and expansion. In existing engineering practices, subgrade moisture content is primarily tested through destructive methods such as grooving and drilling. Traditional destructive testing is inefficient and causes significant damage to the pavement structure. Furthermore, subgrade soil moisture sampling methods such as drying, electrical resistance, neutron testing, gamma-ray (transmission), and time domain reflectometry (TDR) lack single-point representativeness and low test efficiency.

[0003] As research into geophysical exploration matures, geophysical methods such as electromagnetic and electrical methods are increasingly being used in road inspection. These methods offer significant advantages in efficiency and spatial resolution, providing essential data on the operational status of roadbeds at all times. While ground-penetrating radar (GPR) is a relatively mature technology for qualitatively identifying roadbed defects, quantitative detection primarily focuses on surface layer research. Research and application of subgrade moisture content is limited, and accuracy is insufficient to meet application requirements. Similarly, due to the limited accuracy of electrical methods, they are primarily used for qualitative identification, while quantitative identification of moisture content remains uncertain.

[0004] Unlike railway subgrades and road subgrades during construction, operational road subgrades are often covered by hardened pavements such as asphalt or cement concrete. This makes the existing road subgrade a hidden structure, and the overlying pavement structure complicates testing the moisture content of the existing subgrade. On the one hand, the pavement structure affects the propagation of radar waves through the subgrade soil, which in turn affects test accuracy and depth. Existing railway subgrade moisture testing methods are not suitable for testing subgrade moisture content on existing roads with asphalt or cement pavements. On the other hand, the traditional electrical method's electrode installation is difficult to implement on hardened pavement structures, and the test efficiency is insufficient for non-destructive testing of subgrade moisture content over long distances. Testing the moisture content of existing subgrades covered by asphalt and cement pavements urgently requires the development of a testing equipment system and technical methods that can achieve non-destructive and continuous detection of road subgrade moisture content. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned background technology and provide a high-precision, continuous and rapid detection method and system for roadbed moisture without damaging the road surface.

[0006] In a first aspect, the present application provides a method for high-precision, continuous, and rapid detection of roadbed moisture without damaging the road surface, comprising the following steps:

[0007] Step S1, obtaining GPR map data and CCR map data of the road to be inspected;

[0008] Step S2, constructing a pavement material dielectric constant-moisture content relationship model, a pavement material resistivity-moisture content relationship model, and a roadbed filler resistivity-moisture content relationship model;

[0009] Step S3: Obtain the pavement layer thickness and dielectric constant based on the acquired GPR map data, and obtain the pavement moisture content based on the acquired pavement layer thickness and dielectric constant and the pavement material dielectric constant-moisture content relationship model;

[0010] Step S4: Obtain the spatial distribution of roadbed moisture content based on the CCR map data, road surface thickness and road surface moisture content, and the roadbed filler resistivity-moisture content relationship model.

[0011] According to the first aspect, in a first possible implementation manner of the first aspect, step S2 specifically includes the following steps:

[0012] Step S21: Collect or acquire representative pavement material and roadbed filler samples based on the data, perform dielectric constant and resistivity tests, and obtain test results;

[0013] Step S22: fitting the test results to obtain a pavement material dielectric constant-moisture content relationship model, a pavement material resistivity-moisture content relationship model, and a roadbed filler resistivity-moisture content relationship model.

[0014] According to the first possible implementation manner of the first aspect, in the second possible implementation manner of the first aspect, step S3 specifically includes the following steps:

[0015] Step S31, pre-processing the acquired GPR atlas data;

[0016] Step S32: Identify the pre-processed GPR map data to obtain the pavement structure interface of the road to be inspected;

[0017] Step S33: selecting the propagation time of the radar wave reflected from the bottom of the road surface at different data collection positions on the obtained road surface structure interface;

[0018] Step S34: Obtain the pavement layer thickness and dielectric constant by inversion calculation based on the acquired multiple propagation times;

[0019] Step S35: Obtain the pavement moisture content based on the obtained dielectric constant and the pavement material dielectric constant-moisture content relationship model.

[0020] According to the first aspect, in a third possible implementation manner of the first aspect, step S4 specifically includes the following steps:

[0021] Step S41: Obtaining the resistivity spatial distribution within the roadbed range based on the acquired CCR map data, pavement layer thickness, and pavement moisture content;

[0022] Step S42: Obtain the spatial distribution of the moisture content of the roadbed according to the obtained resistivity and the roadbed filler resistivity-moisture content relationship model.

[0023] According to the third possible implementation manner of the first aspect, in the fourth possible implementation manner of the first aspect, step S41 specifically includes the following steps:

[0024] Step S410, pre-process the acquired CCR map data, taking the acquired pavement layer thickness and pavement moisture content as constraints, and taking the least squares of the pre-processed CCR map data and the forward calculation results of the model parameters, and the sum of the regularization terms that stabilize the inversion results as the objective function, and using the Gauss-Newton iterative method to solve, and obtain the resistivity spatial distribution within the roadbed range.

[0025] In a second aspect, the present application provides a high-precision, continuous, and rapid detection system for roadbed moisture without damaging the road surface, comprising:

[0026] Imaging data acquisition module, used to obtain GPR map data and CCR map data of the road to be inspected;

[0027] Moisture content relationship model construction module, used to construct the pavement material dielectric constant-moisture content relationship model, the pavement material resistivity-moisture content relationship model, and the roadbed filler resistivity-moisture content relationship model;

[0028] a pavement moisture content acquisition module, in communication with the imaging data acquisition module and the moisture content relationship model construction module, for acquiring the pavement layer thickness and dielectric constant based on the acquired GPR atlas data, and acquiring the pavement moisture content based on the acquired pavement layer thickness and dielectric constant and the pavement material dielectric constant-moisture content relationship model;

[0029] The subgrade moisture content acquisition module is in communication with the imaging data acquisition module, the pavement moisture content acquisition module and the moisture content relationship model construction module, and is used to obtain the spatial distribution of subgrade moisture content based on CCR map data, pavement thickness and pavement moisture content, and the subgrade filler resistivity-moisture content relationship model.

[0030] According to the second aspect, in a first possible implementation of the second aspect, the imaging data acquisition module includes a multi-channel ground penetrating radar device, a capacitive coupling resistivity meter and a traction vehicle, and the traction vehicle is used to carry the multi-channel ground penetrating radar device and the capacitive coupling resistivity meter to the road to be inspected for data collection.

[0031] According to the second aspect, in a second possible implementation manner of the second aspect, the moisture content relationship model construction module includes:

[0032] The test result acquisition submodule is used to collect data or collect representative pavement material and roadbed filler samples to conduct dielectric constant and resistivity tests and obtain test results;

[0033] The moisture content relationship model acquisition submodule is in communication with the test result acquisition submodule and is used to fit the test results to obtain the pavement material dielectric constant-moisture content relationship model, the pavement material resistivity-moisture content relationship model, and the roadbed filler resistivity-moisture content relationship model.

[0034] According to the second aspect, in a third possible implementation of the second aspect, the road surface moisture content acquisition module includes:

[0035] A GPR atlas data preprocessing submodule, communicatively connected to the imaging data acquisition module, for preprocessing the acquired GPR atlas data;

[0036] A pavement structure interface acquisition submodule is in communication with the GPR map data preprocessing submodule and is used to identify the preprocessed GPR map data and acquire the pavement structure interface of the road to be inspected;

[0037] a propagation time acquisition submodule, which is in communication with the pavement structure interface acquisition submodule and is used to select the propagation time of the radar wave reflected from the bottom of the road surface at different data collection positions on the pavement structure interface;

[0038] a pavement parameter acquisition submodule, which is in communication with the propagation time acquisition submodule and is used to inversely calculate and obtain the pavement layer thickness and dielectric constant based on the acquired multiple propagation times;

[0039] The pavement moisture content acquisition submodule is in communication with the pavement parameter acquisition submodule and the imaging data acquisition module, and is used to acquire the pavement moisture content based on the acquired dielectric constant and the pavement material dielectric constant-moisture content relationship model.

[0040] According to the second aspect, in a fourth possible implementation of the second aspect, the subgrade moisture content acquisition module includes:

[0041] an apparent resistivity acquisition submodule, which is in communication with the imaging data acquisition module and the pavement parameter acquisition submodule and is used to acquire the resistivity spatial distribution within the roadbed range based on the CCR atlas data, the pavement layer thickness and the pavement moisture content;

[0042] The roadbed moisture content acquisition submodule is in communication with the apparent resistivity acquisition submodule and the moisture content relationship model construction module, and is used to obtain the spatial distribution of roadbed moisture content based on the acquired resistivity and the roadbed filler resistivity-moisture content relationship model.

[0043] Compared with the prior art, the advantages of the present invention are as follows:

[0044] The pavement moisture detection method provided in this application collects imaging data of the road to be inspected, constructs a moisture content relationship model, and indirectly obtains the roadbed moisture content of the road. It has the advantages of not damaging the road surface, rapid detection, and the ability to achieve high-resolution continuous detection in space and high detection accuracy, providing reliable data support for maintaining roadbed quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flow chart of a method for high-precision, continuous, and rapid detection of roadbed moisture without damaging the road surface provided by an embodiment of the present invention;

[0046] Figure 2 A functional module block diagram of a high-precision, continuous, and rapid detection system for roadbed moisture without damaging the road surface provided by an embodiment of the present invention;

[0047] Figure 3 Another method flow chart of the method for high-precision, continuous and rapid detection of roadbed moisture without damaging the road surface provided by an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of the arrangement of imaging data acquisition modules of a road surface moisture detection system provided in an embodiment of the present invention;

[0049] Figure 5 A radar map obtained by a ground-penetrating radar provided in an embodiment of the present invention;

[0050] Figure 6 A resistivity spectrum obtained by the galvanic coupling resistivity system provided in an embodiment of the present invention;

[0051] Figure 7 The thickness of the pavement layer quantitatively identified by using ground penetrating radar provided in an embodiment of the present invention;

[0052] Figure 8 The resistivity distribution obtained by the method of the present invention is provided in an embodiment of the present invention;

[0053] Figure 9The embodiment of the present invention provides a roadbed moisture content distribution obtained by using the method of the present invention.

[0054] In the figure, 1. traction vehicle, 2. ground penetrating radar device (GPR), 3. capacitive coupling resistivity meter (CCR), 4. roadbed and pavement structure; 5. main vehicle, 6. trailer 1, 7. trailer 2, 8. traction rope; 9. radar host, 10. geodetic antenna 1, 11. geodetic antenna 2, 12. portable computer, 13. power supply, 14. radar wave, 15. receiving electrode, 16. transmitting electrode, 17. cable, 18. insulating rope, 19. geodetic instrument host, 20. display tablet, 21. current, 22. pavement, 23. roadbed, 100. imaging data acquisition module, 200. moisture content relationship model acquisition module, 300. pavement moisture content acquisition module, 400. roadbed moisture content acquisition module. DETAILED DESCRIPTION

[0055] Reference will now be made in detail to specific embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Although the present invention will be described in conjunction with specific embodiments, it will be understood that the present invention is not intended to be limited to those embodiments. On the contrary, it is intended to cover variations, modifications, and equivalents within the spirit and scope of the present invention as defined by the appended claims. It should be noted that the method steps described herein can be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of the two.

[0056] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Note: The following example is only a specific example and is not intended to limit the embodiments of the present invention to the following specific steps, values, conditions, data, sequence, etc. Those skilled in the art can apply the concepts of the present invention to construct more embodiments not described in this specification by reading this specification.

[0058] The road subgrade is the foundation of the pavement structure, and subgrade moisture is an important indicator affecting the strength and stability of the subgrade. Therefore, understanding the subgrade moisture is crucial for road maintenance and expansion. In existing engineering practices, subgrade moisture is mainly measured through grooving and drilling sampling, but this has the disadvantages of low detection efficiency and significant damage to the pavement structure. Existing non-destructive testing methods, such as electromagnetic and electrical methods, mainly focus on pavement layer research, and there are still many uncertainties in the quantitative identification of moisture content. Existing railway subgrade moisture content testing methods are not suitable for subgrade moisture content testing on existing roads with asphalt or cement pavement conditions. Traditional electrical methods are difficult to implement and have low testing efficiency.

[0059] In view of this, the present application provides a high-precision continuous and rapid detection method for roadbed moisture without damaging the road surface, which effectively solves the technical problems of low detection efficiency, low detection accuracy and damage to the road surface structure in the existing high-precision continuous and rapid detection method for roadbed moisture without damaging the road surface.

[0060] Please refer to Figure 1 and Figure 3 The present application provides a high-precision, continuous and rapid detection method for roadbed moisture without damaging the road surface, comprising the following steps:

[0061] Step S1: At a road site to be inspected, obtain GPR map data and CCR map data of the road site to be inspected;

[0062] Step S2, constructing a pavement material dielectric constant-moisture content relationship model, a pavement material resistivity-moisture content relationship model, and a roadbed filler resistivity-moisture content relationship model;

[0063] Step S3: Obtain the pavement layer thickness and dielectric constant based on the acquired GPR map data, and obtain the pavement moisture content based on the acquired pavement layer thickness and dielectric constant and the pavement material dielectric constant-moisture content relationship model;

[0064] Step S4: Obtain the spatial distribution of roadbed moisture content based on the CCR map data, road surface thickness and road surface moisture content, and the roadbed filler resistivity-moisture content relationship model.

[0065] The pavement moisture detection method provided in this application collects imaging data of the road to be inspected, constructs a moisture content relationship model, and indirectly obtains the roadbed moisture content of the road. It has the advantages of not damaging the road surface, rapid detection, and the ability to achieve high-resolution continuous detection in space and high detection accuracy, providing reliable data support for maintaining roadbed quality.

[0066] The high-precision, continuous, and rapid detection method and system for roadbed moisture without damaging the road surface described in the present invention have the following beneficial effects compared to the prior art:

[0067] (1) In terms of technical reliability, compared with the existing slot sampling test technology, the slot sampling test can only detect the moisture content of a single point on the road subgrade and pavement structure each time. The present application can realize linear long-distance continuous testing on the road subgrade and pavement structure, and obtain a high-density continuous spatial distribution of the subgrade moisture content at different subgrade depths in the measurement line direction. Compared with the single ground penetrating radar method or the resistivity method, the ground penetrating radar has better recognition accuracy for the pavement structure, but has limited recognition information for the subgrade structure below the pavement. The resistivity method can recognize the entire subgrade and pavement, but has limited recognition accuracy. The present invention combines the advantages of ground penetrating radar and resistivity method, uses ground penetrating radar to identify the structure and moisture content of the pavement, and uses this to constrain the recognition of the subgrade moisture content by the resistivity method, which can significantly improve the recognition accuracy of the subgrade moisture content. The absolute error of the subgrade moisture content detected by the present invention method is less than 3%. The specific verification process is shown in the examples.

[0068] (2) In terms of detection efficiency, compared with the existing slot detection, the existing road subgrade and pavement structure slot test of subgrade moisture content can usually only test 2-3 points a day. The non-destructive testing technology of the present invention can achieve continuous testing of 5-10km a day, and the testing efficiency has been improved by an order of magnitude. Compared with the existing traditional electrical methods such as high-density resistivity method, the traditional electrical method usually requires the electrodes to be inserted into the ground or laid and buried on the road surface. The electrode layout takes a lot of time and usually only 2-3 sections of 100-300m can be tested a day. The GPR and CCR devices of the present invention can both achieve ground contact on asphalt or cement hardened pavement. During the test, the test is carried out manually or by vehicle towing and dragging, without the need for special laying and arrangement of electrodes, and the test efficiency is greatly improved.

[0069] (3) In terms of economy and environmental protection, compared with the existing widely used slotting test, the slotting test requires the crushing and excavation of the road surface 4 and the roadbed 5 superstructure at the test point, which usually requires the use of large equipment such as excavators. The trench after the excavation test needs to be backfilled and repaired, but it is usually difficult to restore the original roadbed structure state, which is easy to cause local secondary road surface diseases. Therefore, the cost of testing a single point is high, and it is time-consuming, labor-intensive, and environmentally unfriendly. The present invention uses GPR and CCR to achieve non-destructive testing, which does not damage the roadbed and pavement structure, has zero emissions, is clean and efficient, and reduces testing costs by at least 60%.

[0070] As mentioned above, the humidity may also be referred to as moisture content.

[0071] In one embodiment, before step S1, the following steps are further included:

[0072] Step S0: Based on the structure of the road to be inspected, the type of roadbed filler material, and environmental conditions, a suitable multi-channel ground penetrating radar (GPR), capacitive coupled resistivity (CCR), and traction vehicle are selected to construct a high-precision, continuous, and rapid roadbed moisture detection system that does not damage the road surface. Parameters such as the GPR test frequency and the CCR electrode spacing are also configured.

[0073] In one embodiment, step S1 specifically includes the following steps:

[0074] At the site of the road to be inspected, the multi-channel ground penetrating radar test equipment and the capacitive coupling resistivity meter are connected, and the GPR test frequency and CCR electrode distance and other parameter configurations are debugged. The towing vehicle equipped with GPR and CCR is tested at a certain driving speed on the subgrade and pavement structure of the road to be inspected to collect GPR and CCR map data of the road section to be inspected.

[0075] In one embodiment, the certain driving speed is achieved as a preset speed.

[0076] In one embodiment, step S2 specifically includes the following steps:

[0077] Step S21: Collect or acquire representative pavement material and roadbed filler samples based on the data, perform dielectric constant and resistivity tests, and obtain test results;

[0078] Step S22: fitting the test results to obtain a pavement material dielectric constant-moisture content relationship model, a pavement material resistivity-moisture content relationship model, and a roadbed filler resistivity-moisture content relationship model.

[0079] In a more specific embodiment, step S2 is specifically implemented by collecting 1-2 groups of samples for each pavement material and roadbed filler. The pavement material needs to be tested for dielectric constant and resistivity under 3-5 different moisture contents, and the roadbed filler needs to be tested for resistivity under 3-5 different moisture contents. The pavement material dielectric constant-moisture content relationship model, the pavement material resistivity-moisture content relationship model, and the roadbed filler resistivity-moisture content relationship model are fitted to obtain.

[0080] In one embodiment, the relationship model between the dielectric constant and the moisture content in step S2 is obtained by using the empirical formula (S2-0-1) through indoor experiments or similar engineering research to obtain specific model parameters C 00 、C 01 、C 02 、C 03 .

[0081] w v =C 00 +C 01 ε b +C 02 εb 2 +C 03 ε b 3 Formula (S2-0-1)

[0082] Among them, ε b is the dielectric constant; w v is the volumetric water content; C 00 、C 01 、C 02 、C 03 is an empirical constant.

[0083] As a preferred technical solution of the present invention, the resistivity-water content relationship model in step S2 can adopt the Archie formula for asphalt concrete, cement-stabilized crushed stone, and coarse-grained fillers such as gravel and sandy soil:

[0084] ρ=ρ w n s -x S w -y Formula (S2-0-2)

[0085] Where ρ is the resistivity of the filler, ρ w is the pore water resistivity; n s is the soil porosity; S w is the pore water saturation of the subgrade soil, S w =w v / n s x is an index related to soil cohesion, and y is the saturation index. For coarse-grained soil roadbed fillers such as crushed stone, gravel, and sandy soil, the x value range is 1.0 to 5.0, and the y value range is usually 1.5 to 2.5.

[0086] For fine soil fillers such as clay soil and lime-improved soil, the empirical formula is used:

[0087]

[0088] Where c c and p are fitting parameters related to the soil particle size, and θ is the volume percentage of clay in the filler. c Related, c c =x1θ c y1 , p=x2θ c y2 ; When θ c ≥5%, x1=0.6,y1=0.55,x2=0.92,y1=0.2;When θc<5%,c c =1.45, p=1.25.

[0089] In one embodiment, step S3 specifically includes the following steps:

[0090] Step S31, pre-processing the acquired GPR atlas data;

[0091] Step S32: Identify the pre-processed GPR map data to obtain the pavement structure interface of the road to be inspected;

[0092] Step S33: selecting the propagation time of the radar wave reflected from the bottom of the road surface at different data collection positions on the obtained road surface structure interface;

[0093] Step S34: Obtain the pavement layer thickness and dielectric constant by inversion calculation based on the acquired multiple propagation times;

[0094] Step S35: Obtain the pavement moisture content based on the obtained dielectric constant and the pavement material dielectric constant-moisture content relationship model.

[0095] In one embodiment, step S34 is specifically implemented as follows:

[0096] As a preferred technical solution of the present invention, the GPR map data preprocessing in step S3 first uses a de-averaging algorithm (DEWOW) to interfere with low-frequency noise, and then selects the propagation time ti corresponding to the reflection signal from the bottom of the road surface 22 at different collection positions.

[0097] The calculation of the road surface thickness and dielectric constant in step S3 is based on the principle that the reflected wave propagation time ti is a function of three parameters: the reflection depth (i.e., the road surface thickness) hi, the dielectric constant εbi, and the reflected wave reflection surface inclination angle αi, according to the equation (S3-0-1). By combining the collected data from multiple antennas of a multi-channel ground-penetrating radar, each data collection point can obtain the propagation time corresponding to no fewer than three different antenna spacings. Using equation (S3-0-1), the three sets of data can be combined to solve for the three unknowns: the road surface thickness hi, the dielectric constant εbi, and the reflected wave reflection surface inclination angle αi.

[0098]

[0099] When calculating the pavement thickness and dielectric constant, the n observation times t collected by multi-channel ground penetrating radar at different test locations are used. obs (Observation time) data, use formula (S3-0-1) to simulate and calculate the n corresponding radar wave propagation times t mod (Simulation time), combine all tobs and tmod to construct the objective function (S3-0-2), and obtain the parameter combination (hi, ε) that minimizes the difference between the observed and simulated parameters through optimization algorithms such as Gaussian iteration. bi, αi), that is, the road surface thickness hi and dielectric constant ε at different test locations are obtained bi .

[0100]

[0101] Among them, t obs is the propagation time of the electromagnetic wave signal collected by the on-site radar from the transmitting end to the receiving end after being reflected by the bottom of the road surface, t mod is the electromagnetic wave propagation time obtained by parameter simulation, d i h is the distance between the transmitting and receiving antennas, i is the road surface thickness, α i is the angle between the radar wave reflecting surface and the horizontal plane, ε bi is the dielectric constant of the filler, c0 is a constant, i is the number of the data point collected by the radar, and n is the total number of data collection points.

[0102] In one embodiment, step S4 specifically includes the following steps:

[0103] Step S41: Obtaining the resistivity spatial distribution within the roadbed range based on the acquired CCR map data, pavement layer thickness, and pavement moisture content;

[0104] Step S42: Obtain the spatial distribution of the moisture content of the roadbed according to the obtained resistivity and the roadbed filler resistivity-moisture content relationship model.

[0105] In one embodiment, the step S41 specifically includes the following steps:

[0106] Step S410, pre-process the acquired CCR map data, taking the acquired pavement layer thickness and pavement moisture content as constraints, and taking the least squares of the pre-processed CCR map data and the forward calculation results of the model parameters, and the sum of the regularization terms that stabilize the inversion results as the objective function, and using the Gauss-Newton iterative method to solve, and obtain the resistivity spatial distribution within the roadbed range.

[0107] In one embodiment, step S410 is specifically implemented as follows:

[0108] In one embodiment, the CCR map data preprocessing in step S4 involves first preprocessing the measured apparent resistivity data to remove outliers. The apparent resistivity data is then input into the inversion algorithm, using the pavement thickness and moisture content acquired by the ground-penetrating radar (GPR) as prior constraints for the inversion. Using the pavement moisture content as a constraint, the pavement material resistivity-moisture content relationship model obtained in step S2 is first used to convert the moisture content calculated by the GPR into pavement resistivity.

[0109] As a preferred technical solution of the present invention, the resistivity calculation method in step S4 uses the least squares of the CCR observation data and the forward calculation results of the model parameters as the fitting term:

[0110] Φ ρ =(ρ-F(m)) T W ρ T W ρ (ρ-F(m)) Formula (S4-0-1)

[0111] Where ρ is the measured apparent resistivity data (calculated by Equation S4-0-2), m is the resistivity model, a spatial resistivity distribution matrix composed of simulated resistivity values ​​at different grid points, F(m) is the forward modeling of the resistivity model m, and Wρ is the data weight matrix. If the observed data errors are assumed to be uncorrelated and the forward modeling numerical errors are ignored, the data weight matrix is ​​a diagonal matrix equal to the standard deviation of the measured data.

[0112] ρ=KV / I Formula (S4-0-2)

[0113]

[0114] Where I is the supply current, V is the measured potential, ρ is the apparent resistivity, K is the device coefficient (calculated by E-0-3), b = 2r / l, l is the length of the transmitting and receiving dipoles, and r is the dipole center distance.

[0115] In order to make the inversion results stable and unique, a regularization term is added:

[0116] Φ m =||C(mm ref )|| Formula (S4-0-4)

[0117] Among them, m ref is the initial resistivity model. If the initial resistivity model does not contain known information, the matrix C is usually a smooth matrix to obtain a model with smooth resistivity changes. In order to use the radar results as prior information to constrain the inversion and achieve the purpose of independently controlling the inversion model boundary and model unit characteristics, the matrix C is set to:

[0118] C=diag(w i s )C1diag(w j n ) Formula (S4-0-5)

[0119] Among them, C1 is the difference matrix, diag(w j n ) is the diagonal weight matrix that controls the n grid cells in the model, diag(wi s ) is the diagonal weight matrix that controls the s individual structures in the model, w i s 、w j n is the diagonal weight value, i = 1…s, j = 1…n, such as at the interface between subgrade soil and pavement or bedrock, set

[0120] The final inversion objective function is:

[0121] Φ=Φ ρ +λΦ m Formula (S4-0-6)

[0122] Among them, Φ ρ is the observed data fitting term, Φ m is the regularization term, and λ is the weight coefficient for controlling the observation data fitting term and the regularization term.

[0123] The inversion process is to find the parameter value that minimizes the objective function. The Gauss-Newton iteration method is used to solve it. The model parameter change matrix for each iteration is obtained by the following equation:

[0124]

[0125] Where J is the Jacobian matrix J at the kth iteration i,j , m k is the model parameter value at the kth iteration; Δm is the parameter change at the kth iteration. λ is the weight coefficient, which is continuously varied during the iteration process. A larger λ is used at the beginning of the iteration, and a smaller λ is used as the inversion nears convergence, which is more conducive to convergence. Through iterative solution, the resistivity model m is ultimately obtained, i.e., the subgrade resistivity distribution is solved.

[0126] Based on the same invention concept, please refer to Figure 2 The present application provides a high-precision, continuous, and rapid detection system for roadbed moisture without damaging the road surface, comprising:

[0127] The imaging data acquisition module 100 is used to acquire GPR map data and CCR map data of the road site to be inspected;

[0128] The moisture content relationship model building module 200 is used to build a pavement material dielectric constant-moisture content relationship model, a pavement material resistivity-moisture content relationship model, and a roadbed filler resistivity-moisture content relationship model;

[0129] a pavement moisture content acquisition module 300, in communication with the imaging data acquisition module 100 and the moisture content relationship model construction module 200, for acquiring the pavement layer thickness and dielectric constant based on the acquired GPR atlas data, and acquiring the pavement moisture content based on the acquired pavement layer thickness and dielectric constant and the pavement material dielectric constant-moisture content relationship model;

[0130] The subgrade moisture content acquisition module 400 is in communication with the imaging data acquisition module 100, the pavement moisture content acquisition module 200 and the moisture content relationship model construction module 300, and is used to obtain the spatial distribution of subgrade moisture content based on CCR map data, pavement thickness and pavement moisture content, and the subgrade filler resistivity-moisture content relationship model.

[0131] In one embodiment, the imaging data acquisition module includes a multi-channel ground penetrating radar device, a capacitive coupling resistivity meter and a towing vehicle, please refer to Figure 4 The traction vehicle is used to carry the multi-channel ground penetrating radar device and the capacitive coupling resistivity meter to the road to be inspected for data collection.

[0132] The ground-penetrating radar device 2 (GPR) includes a radar host 7, two or more ground antennas (including ground antenna 1 8 and ground antenna 2 9), a portable computer 10, and a power supply 11. The ground antennas are connected to the GPR host via signal cables, the power supply is connected to the GPR host via a power cable, and the portable computer is connected to the GPR host via a signal cable. The ground antennas are mounted on the lower portion of the trailer body, and the GPR host, power supply, and portable computer are mounted on the upper portion of the trailer. During testing, a towing vehicle carrying the channel ground-penetrating radar device travels on the road section to be tested. The portable computer controls the GPR host to send and receive commands. The transmitting antenna transmits radar waves toward the roadbed and pavement structure according to the GPR host's transmission commands. The receiving antenna collects radar waves reflected from the roadbed and pavement structure according to the reception commands. The received reflected radar wave signals are displayed and stored in the portable computer.

[0133] The capacitive coupling resistivity meter (CCR) includes one transmitting electrode, two or more receiving electrodes, a cable, a host, and a display tablet computer. Cables are connected at both ends of the transmitting electrode as a transmitting dipole, and cables are connected at both ends of the receiving electrode as a receiving dipole. The transmitting dipole and the receiving dipole are connected by an insulating rope of adjustable length. The receiving dipole is connected to the host via an optical cable, and the transmitting dipole is not connected to the host. The host is connected to the display tablet computer via Bluetooth. The display tablet computer has a built-in GPS module that records the position coordinates during data collection in real time. During the test, the power switches of all receiving antennas and transmitting antennas are turned on, and the trailer of the traction vehicle drags the capacitive coupling resistivity meter on the road section to be tested. The transmitting dipole supplies current 21 to the road subgrade and pavement structure, and the receiving dipole collects the output current passing through the road subgrade and pavement structure. The host computer is controlled by the tablet computer to collect the current information data of the receiving dipole, and the current information data is displayed and stored in the tablet computer.

[0134] The tractor vehicle consists of a main vehicle and two trailers. The main vehicle is connected to trailer one by a 2-3m long traction rope, and trailer one is connected to trailer two by a 10-20m long traction rope. The ground antenna of the multi-channel ground penetrating radar device is placed at the bottom of the trailer. The radar host, portable computer, and power supply are connected by power lines or signal lines and placed on the top of trailer one. The host and tablet computer of the capacitive coupling resistivity meter are placed on trailer two, and the transmitting electrode and the receiving electrode are connected in series in sequence by cables and insulating ropes and connected to the rear of trailer two. During the test, a traction vehicle equipped with GPR and CCR runs at a certain driving speed on the road subgrade and pavement structure. The GPR host controls the transmitting antenna to transmit radar waves to the road subgrade and pavement structure. The receiving antenna collects radar wave signals reflected from the road subgrade and pavement structure. The radar wave spectrum is saved to a portable computer. The CCR transmitting dipole supplies current to the road subgrade and pavement structure, and the receiving dipole collects the output current passing through the road subgrade and pavement structure. The CCR host collects the current information data of the receiving dipole and saves it to a tablet computer. The collected GPR and CCR spectrum data are imported into the data analysis algorithm for processing to obtain the subgrade moisture content distribution.

[0135] The main vehicle can be a standard commercial off-road vehicle or small truck. The tractor has a plastic body and is equipped with four high-strength rubber wheels. It is 1.5-2.0 meters long and 0.6-1.0 meters wide. The tractor has a load capacity of over 100 kg and can safely and stably carry GPR and CCR components. The traction rope has a tensile strength of over 5 kN.

[0136] The GPR host uses a conventional commercial multi-channel ground penetrating radar, which can control the collection of data from multiple antenna channels. The data collection time window range is continuously adjustable from 20ns to 200s, the number of sampling points is 512 to 32767 samples / scan, the power consumption of the whole machine is less than 6W, and the minimum sampling interval is less than 2ps.

[0137] The ground-coupled antenna is a ground-coupled antenna with a fixed distance between the transmitting end and the receiving end, and there are more than two ground-coupled antennas. The antenna frequency is generally 200-1200 MHz, among which 600 MHz is recommended for roads with a road surface thickness of about 80 cm, such as expressways and first-class highways, and 800 MHz is recommended for roads with a road surface thickness of about 50 cm, such as second-class and below highways.

[0138] The portable computer is a ruggedized, triple-proof portable computer with drop resistance of at least 1 meter. It has a display that is at least 10 inches and highly readable in sunlight. It has at least 8GB of memory and at least 256GB of hard disk storage. It runs Windows 7 or higher and is installed with radar data acquisition and processing software compatible with the host computer, enabling interactive control of the host computer and data storage.

[0139] The power supply is a rechargeable mobile power supply with an output power of 220V and a power capacity of more than 10A.

[0140] The transmitting electrode has an operating frequency of about 16.5KHz, an output power of up to 2W, an output voltage of less than 1000Vrms, and an output current of 0.125-16mA.

[0141] The input voltage of the receiving electrode is 0-2Vrms.

[0142] The host is a capacitive coupling resistivity meter host, with a maximum acquisition time of 2 scans per second and can work continuously for more than 24 hours.

[0143] The cable is a pole-dip coaxial cable with an outer diameter of 1.5-3.0 cm. From the outside in, it includes a coaxial insulation layer, a coaxial conductive layer, and a conductive core. Each cable section is 2.5 m or 5.0 m long. A 2.5 m cable is typically used for a test depth of 2 m (within the roadbed 5), while a 5 m cable is used for a test depth of 4 m (above the roadbed 5 and embankment 6).

[0144] The tablet computer is a tablet computer with Bluetooth communication function, a memory of more than 8G, a hard disk storage of more than 256G, and is installed with resistivity imaging data acquisition and processing software adapted to the host, which can realize interactive control of the host and data storage.

[0145] The insulating rope has a tensile bearing capacity of more than 2 kN and a length of 2.5-10.0 m.

[0146] The roadbed and pavement structure 1 includes, from bottom to top, an embankment, a roadbed and a pavement. The roadbed is made of crushed stone soil, gravel soil or clay soil, and the pavement is made of graded crushed stone, cement stabilized crushed stone, cement concrete, asphalt concrete and the like in layers.

[0147] The driving speed of the traction vehicle is generally 3.0-10.0 km / h.

[0148] In one embodiment, the moisture content relationship model building module includes:

[0149] The test result acquisition submodule is used to collect data or collect representative pavement material and roadbed filler samples to conduct dielectric constant and resistivity tests and obtain test results;

[0150] The moisture content relationship model acquisition submodule is in communication with the test result acquisition submodule and is used to fit the test results to obtain the pavement material dielectric constant-moisture content relationship model, the pavement material resistivity-moisture content relationship model, and the roadbed filler resistivity-moisture content relationship model.

[0151] In one embodiment, the road surface moisture content acquisition module includes:

[0152] A GPR atlas data preprocessing submodule, communicatively connected to the imaging data acquisition module, for preprocessing the acquired GPR atlas data;

[0153] A pavement structure interface acquisition submodule is in communication with the GPR map data preprocessing submodule and is used to identify the preprocessed GPR map data and acquire the pavement structure interface of the road to be inspected;

[0154] a propagation time acquisition submodule, which is in communication with the pavement structure interface acquisition submodule and is used to select the propagation time of the radar wave reflected from the bottom of the road surface at different data collection positions on the pavement structure interface;

[0155] a pavement parameter acquisition submodule, which is in communication with the propagation time acquisition submodule and is used to inversely calculate and obtain the pavement layer thickness and dielectric constant based on the acquired multiple propagation times;

[0156] The pavement moisture content acquisition submodule is in communication with the pavement parameter acquisition submodule and the imaging data acquisition module, and is used to acquire the pavement moisture content based on the acquired dielectric constant and the pavement material dielectric constant-moisture content relationship model.

[0157] In one embodiment, the subgrade moisture content acquisition module includes:

[0158] An apparent resistivity acquisition submodule, which is in communication with the imaging data acquisition module and the pavement parameter acquisition submodule, and is used to acquire the apparent resistivity within the roadbed range based on the CCR atlas data, the pavement layer thickness, and the pavement moisture content;

[0159] The roadbed moisture content acquisition submodule is in communication with the apparent resistivity acquisition submodule and the moisture content relationship model construction module, and is used to obtain the spatial distribution of roadbed moisture content based on the acquired apparent resistivity and the roadbed filler resistivity-moisture content relationship model.

[0160] In a specific test case, a six-lane highway expansion project in South China had a design speed of 80 km / h. The pavement structure was 15 cm asphalt concrete + 68 cm cement-stabilized gravel layer, and the roadbed filler was fine-grained soil gravel. The test section was the slow lane. The test included six sections, each 1500 m long, totaling 9000 m. The test process was as follows:

[0161] 1. Select a multi-channel ground penetrating radar device 2 (GPR), a capacitive coupling resistivity meter 3 (CCR), and a traction vehicle to form an imaging data acquisition module of a high-precision continuous and rapid detection system for roadbed moisture without damaging the road surface, and conduct tests on the roadbed 23 and road surface 22. Figure 4 The GPR system is a multi-channel ground-penetrating radar (HI-MOD) that can control the data collection of multiple antenna channels. The data acquisition time window range is continuously adjustable from 20ns to 200s, and the number of sampling points is 512 to 32767 samples per scan. Two ground-pair antennas are configured with an antenna frequency of 900MHz. The ground-pair antennas are connected to the GPR host via signal cables, and the power supply is connected to the GPR host via a power cable. The portable computer is connected to the GPR host via a signal cable. The transmitting antenna and receiving antenna are mounted on the front body bracket of the trolley 12, the GPR host and power supply are mounted on the middle body bracket of the trolley, and the portable computer is placed on the support platform at the rear of the trolley. The CCR system is an OhmMapper TRN capacitively coupled resistivity meter, equipped with one transmitting electrode 16 and four receiving electrodes 15. The operating frequency of the transmitting electrode is about 16.5KHz, the output current is 0.125-16mA, and the input voltage of the receiving electrode is 0-2Vrms. Cables are connected to both ends of the transmitting electrode as a transmitting dipole, and cables 17 are connected to both ends of the receiving electrode as a receiving dipole. The transmitting dipole and the receiving dipole are connected by an insulating rope 18 with adjustable length. The receiving dipole is connected to the host via an optical cable, and the transmitting dipole is not connected to the host 19. The host 19 is connected to the display tablet 20 via Bluetooth.

[0162] 2. Configure the test system parameters according to the site conditions. The spacing between the ground dipole antennas is set to 1.0m, and the distance between the transmitting and receiving dipole moments of the capacitive coupling resistivity meter is set to 2.5m. After the parameters are set, the test operator 13 starts the towing vehicle on the road to be tested at a speed of 3.0-5.0km / h. The ground penetrating radar device and the capacitive coupling resistivity system are towed by trailers 1 and 2 to detect the road. The GPR and CCR are tested independently and synchronously to complete the data collection. Figure 5、 Figure 6 The representative collection results are for the test section 1, 200m;

[0163] 3. Collect two sets of samples each of pavement materials, such as asphalt concrete, cement-stabilized crushed stone, and subgrade filler (including fine-grained soil and gravel). The pavement material should be tested for dielectric constant and resistivity at three to five different moisture contents, while the subgrade filler should be tested for resistivity at five different moisture contents. Fitting models were used to determine the dielectric constant-moisture content relationship and the resistivity-moisture content relationship. The dielectric constant-moisture content relationship model for asphalt concrete and cement-stabilized crushed stone uses the empirical formula (S2-0-1): C00 = -6.216, C01 = 2.383, C02 = -0.0598, and C03 = 0.0006. The resistivity-moisture content relationship model of asphalt concrete, cement-stabilized macadam, and fine-grained soil gravel adopts the Archie formula (S2-0-2), where the comprehensive parameter values ​​of asphalt concrete and cement-stabilized macadam pavement materials are: ρw is 60Ωm, ns is 0.12, x is 1.5, and y is 1.8; the parameter values ​​of fine-grained soil gravel roadbed materials are: ρw is 60Ωm, ns is 0.36, x is 3.4, and y is 2.3.

[0164] 4. Pre-process the collected GPR map data and identify the road surface structure interface through the processed imaging map, such as Figure 7 As shown, the pavement thickness is 0.70-0.85m. The propagation time of the radar wave reflected from the bottom of the pavement at different collection locations is selected. According to the n observation time tobs (observation time) data collected by the multi-channel ground penetrating radar at different test locations, the n corresponding radar wave propagation times tmod (simulation time) are simulated and calculated using formula (S3-0-1). All tobs and tmod are combined to construct the objective function formula (S3-0-2). Through optimization algorithms such as Gaussian iteration, the parameter combination (h) that minimizes the difference between the observed and simulated parameters is obtained. i , ε bi , α i ), that is, to obtain the road surface thickness h at different test locations i and dielectric constant ε bi , and then the dielectric constant of the pavement layer is converted into the pavement moisture content through the determined dielectric constant-moisture content relationship model.

[0165] 5. Preprocess the collected CCR atlas data, use the pavement thickness and moisture content obtained in the previous step as the constraint conditions for resistivity calculation (S4-0-4, S4-0-5), take the least squares formula of the CCR observation data and the forward calculation results of the model parameters (S4-0-1), and the sum of the regularization term that stabilizes the inversion results as the objective function (S4-0-6), and use the Gauss-Newton iterative method to solve the formula (S4-0-7), so as to obtain the resistivity distribution within the roadbed range. The joint inversion results are as follows: Figure 8 shown.

[0166] 6. Using the determined roadbed filler resistivity-moisture content relationship model, convert the roadbed soil apparent resistivity obtained in the previous step into moisture content. The final obtained spatial distribution of moisture content of the pavement layer and the upper part of the roadbed (roadbed) is as follows: Figure 9 shown.

[0167] Through the above steps, a rapid and continuous test of road subgrade moisture can be achieved without damaging the road surface. To verify the accuracy of the method of the present invention in detecting the moisture content of the subgrade, a trench was opened in each test section, and the trench depth was 30 cm below the top surface of the roadbed. Samples were taken and dried to measure the moisture content of the subgrade. The results were compared with the moisture content obtained by the non-destructive testing method of the present invention. The results are shown in Table 1. The absolute error of the detected moisture content is -2.8% to 2.1%, which is higher than the accuracy of the existing single resistivity method or radar detection method. It can be seen that the high-precision continuous and rapid detection method of subgrade moisture without damaging the road surface provided by the present application has high accuracy and reliability in detecting subgrade moisture content.

[0168] Table 1 Verification and accuracy analysis of roadbed moisture content

[0169]

[0170] Based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the method steps of the above method are implemented.

[0171] The present invention implements all or part of the process in the above method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0172] Based on the same inventive concept, an embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program running on the processor, and when the processor executes the computer program, all or part of the method steps in the above method are implemented.

[0173] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of a computer device and connects various parts of the entire computer device using various interfaces and lines.

[0174] The memory can be used to store computer programs and / or modules. The processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, video data, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0175] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, servers, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0176] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), servers, and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0177] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0179] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A high-precision, continuous and rapid detection method for roadbed moisture without damaging the road surface, characterized in that: The following steps are involved: Step S1, obtaining GPR map data and capacitive coupling resistivity (CCR) map data of the road to be inspected; Step S2, constructing a pavement material dielectric constant-moisture content relationship model, a pavement material resistivity-moisture content relationship model, and a roadbed filler resistivity-moisture content relationship model; Step S3: Obtain the pavement layer thickness and dielectric constant based on the acquired GPR map data, and obtain the pavement moisture content based on the acquired pavement layer thickness and dielectric constant and the pavement material dielectric constant-moisture content relationship model; Step S4, obtaining the spatial distribution of subgrade moisture content based on the CCR map data, the pavement thickness and moisture content, and the subgrade filler resistivity-moisture content relationship model; The step S3 specifically includes the following steps: Step S31, pre-processing the acquired GPR atlas data; Step S32: Identify the pre-processed GPR map data to obtain the pavement structure interface of the road to be inspected; Step S33: selecting the propagation time of the radar wave reflected from the bottom of the road surface at different data collection positions on the obtained road surface structure interface; Step S34: Obtain the pavement layer thickness and dielectric constant by inversion calculation based on the acquired multiple propagation times; Step S35: Obtain the pavement moisture content based on the obtained dielectric constant and the pavement material dielectric constant-moisture content relationship model; The step S4 specifically includes the following steps: Step S41: Obtaining the resistivity spatial distribution within the roadbed range based on the acquired CCR map data, pavement layer thickness, and pavement moisture content; Step S42: Obtain the spatial distribution of the water content of the roadbed according to the obtained resistivity and the roadbed filler resistivity-water content relationship model; The step S41 specifically includes the following steps: Step S410, pre-process the acquired CCR map data, taking the acquired pavement layer thickness and pavement moisture content as constraints, and taking the least squares of the pre-processed CCR map data and the forward calculation results of the model parameters, and the sum of the regularization terms that stabilize the inversion results as the objective function, and using the Gauss-Newton iterative method to solve, and obtain the resistivity spatial distribution within the roadbed range.

2. The high-precision, continuous and rapid detection method for roadbed moisture without damaging the road surface as claimed in claim 1, characterized in that: The step S2 specifically includes the following steps: Step S21: Collect or acquire representative pavement material and roadbed filler samples based on the data, perform dielectric constant and resistivity tests, and obtain test results; Step S22: fitting the test results to obtain a pavement material dielectric constant-moisture content relationship model, a pavement material resistivity-moisture content relationship model, and a roadbed filler resistivity-moisture content relationship model.

3. A high-precision, continuous, and rapid detection system for roadbed moisture without damaging the road surface, based on the high-precision, continuous, and rapid detection method for roadbed moisture without damaging the road surface as claimed in claim 1 or 2, characterized in that: include: Imaging data acquisition module, used to obtain GPR map data and CCR map data of the road to be inspected; Moisture content relationship model construction module, used to construct the pavement material dielectric constant-moisture content relationship model, the pavement material resistivity-moisture content relationship model, and the roadbed filler resistivity-moisture content relationship model; a pavement moisture content acquisition module, in communication with the imaging data acquisition module and the moisture content relationship model construction module, for acquiring the pavement layer thickness and dielectric constant based on the acquired GPR atlas data, and acquiring the pavement moisture content based on the acquired pavement layer thickness and dielectric constant and the pavement material dielectric constant-moisture content relationship model; The subgrade moisture content acquisition module is in communication with the imaging data acquisition module, the pavement moisture content acquisition module and the moisture content relationship model construction module, and is used to obtain the spatial distribution of subgrade moisture content based on CCR map data, pavement thickness and pavement moisture content, and the subgrade filler resistivity-moisture content relationship model.

4. The high-precision continuous rapid detection system for roadbed moisture without damaging the road surface as claimed in claim 3, characterized in that: The imaging data acquisition module includes a multi-channel ground penetrating radar device, a capacitive coupling resistivity meter and a traction vehicle. The traction vehicle is used to carry the multi-channel ground penetrating radar device and the capacitive coupling resistivity meter to the road to be inspected for data collection.

5. The high-precision continuous rapid detection system for roadbed moisture without damaging the road surface as claimed in claim 3, characterized in that: The moisture content relationship model building module includes: The test result acquisition submodule is used to collect data or collect representative pavement material and roadbed filler samples to conduct dielectric constant and resistivity tests and obtain test results; The moisture content relationship model acquisition submodule is in communication with the test result acquisition submodule and is used to fit the test results to obtain the pavement material dielectric constant-moisture content relationship model, the pavement material resistivity-moisture content relationship model, and the roadbed filler resistivity-moisture content relationship model.

6. The high-precision continuous rapid detection system for roadbed moisture without damaging the road surface as claimed in claim 3, characterized in that: The road surface moisture content acquisition module includes: A GPR atlas data preprocessing submodule, communicatively connected to the imaging data acquisition module, for preprocessing the acquired GPR atlas data; A pavement structure interface acquisition submodule is in communication with the GPR map data preprocessing submodule and is used to identify the preprocessed GPR map data and acquire the pavement structure interface of the road to be inspected; a propagation time acquisition submodule, which is in communication with the pavement structure interface acquisition submodule and is used to select the propagation time of the radar wave reflected from the bottom of the road surface at different data collection positions on the pavement structure interface; a pavement parameter acquisition submodule, which is in communication with the propagation time acquisition submodule and is used to inversely calculate and obtain the pavement layer thickness and dielectric constant based on the acquired multiple propagation times; The pavement moisture content acquisition submodule is in communication with the pavement parameter acquisition submodule and the imaging data acquisition module, and is used to acquire the pavement moisture content based on the acquired dielectric constant and the pavement material dielectric constant-moisture content relationship model.

7. The high-precision continuous rapid detection system for roadbed moisture without damaging the road surface as claimed in claim 6, characterized in that: The roadbed moisture content acquisition module includes: A resistivity acquisition submodule, which is in communication with the imaging data acquisition module and the pavement parameter acquisition submodule, and is used to acquire the resistivity within the roadbed range based on the CCR map data, the pavement layer thickness, and the pavement moisture content; The roadbed moisture content acquisition submodule is in communication with the resistivity acquisition submodule and the moisture content relationship model construction module, and is used to obtain the spatial distribution of roadbed moisture content based on the acquired resistivity and the roadbed filler resistivity-moisture content relationship model.

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