Imaging Method and Device for Latent Defects in Asphalt Layers Based on Dynamic Coplanar Array Capacitors

Abstract

This invention relates to an imaging method and apparatus for latent defects in asphalt layers based on dynamic coplanar array capacitance, applicable to the field of urban traffic road engineering maintenance technology. The method includes the following steps: determining the penetration depth of a movable array electrode coplanar capacitance sensor; selecting a corresponding movable array electrode coplanar capacitance sensor based on the determined optimal structural parameters; performing dynamic detection at a set penetration depth based on dynamic detection and electrode excitation scheme to determine the depth of latent defects in the asphalt layer; constructing a dynamic sensitive field distribution matrix of the measured area using the finite element method; constructing a normalized coplanar capacitance distribution matrix for different scanning steps at the depth of the latent defects; and reconstructing the relative permittivity distribution map of the latent defects in the asphalt layer under dynamic detection using a capacitance tomography reconstruction algorithm to identify the planar location and shape characteristics of the latent defects. Compared with the prior art, this invention has the advantages of rapid detection and accurate identification.

Description

Technical Field

[0001] This invention belongs to the field of urban traffic road engineering maintenance technology, and in particular relates to a method and device for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance. Background Technology

[0002] Hidden damage to asphalt pavements is often difficult to detect. Due to prolonged exposure to external environments, including repeated loads, moisture, and temperature, these internal damages can rapidly develop into more severe macroscopic damage, posing a potential threat to traffic safety. Therefore, regular inspection and preventative maintenance are crucial for maintaining the long-term performance of asphalt pavements. Non-destructive testing methods are frequently used in pavement inspection to identify the internal condition of asphalt pavements due to their advantages over destructive testing, such as being non-destructive, portable, and fast.

[0003] Ground-penetrating radar (GPR) can determine the location and geometry of hidden damage in asphalt pavements by identifying waveform images, but the reliability of the identification largely depends on the subjective experience of the inspection engineer. Ultrasonic technology can indirectly identify hidden cracks based on modulus attenuation and Rayleigh wave correlation indicators. However, its disadvantages include slow detection speed and the need for ultrasonic equipment to have full contact with the asphalt surface, making it difficult to apply in field testing. Visible light images converted from infrared thermal imagers can identify reflective cracking and bonding layer delamination in asphalt pavements. However, the accuracy of hidden damage identification is affected by many external environmental factors (such as humidity, solar radiation, and temperature differences between different asphalt layers). Compared with the above non-destructive testing techniques, coplanar capacitance imaging technology is more advanced in identifying hidden damage in the tested object, allowing for accurate and objective visual identification with less external environmental interference.

[0004] Planar capacitance imaging technology mainly includes static imaging technology using arrayed coplanar electrode capacitance sensors and dynamic imaging technology based on single-pair coplanar capacitance sensors. A single-pair coplanar capacitance sensor has a pair of parallel electrodes used to scan along the surface of the object under test to identify complete and detailed hidden damage features. However, the process of acquiring coplanar capacitance data and performing inverse imaging calculations using dynamic imaging technology based on single-pair coplanar capacitance sensors is too complex, making it difficult to image and identify hidden damage within large detection areas. Arrayed coplanar electrode capacitance sensors, on the other hand, have high detection efficiency within large test areas, as illustrated by the method for detecting latent defects in asphalt layers using arrayed electrode coplanar capacitance imaging disclosed in Chinese patent application CN117890444A. Existing methods based on arrayed coplanar electrode capacitance sensors typically employ static imaging, which results in varying degrees of artifacts during image reconstruction, making it difficult to accurately identify internal damage. Furthermore, static detection using arrayed coplanar electrode capacitance sensors has blind spots, making it difficult to identify hidden damage at specific locations.

[0005] Therefore, it is necessary to study a new imaging technique for latent defects in asphalt layers based on arrayed coplanar array capacitance. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and apparatus for rapid detection and accurate identification of latent defects in asphalt layers based on dynamic coplanar array capacitance.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] An imaging method for latent defects in asphalt layers based on dynamic coplanar array capacitance includes the following steps:

[0009] The penetration depth of the mobile array electrode coplanar capacitance sensor is determined according to the asphalt layer detection depth requirements. Based on the predetermined optimal structural parameters, the corresponding mobile array electrode coplanar capacitance sensor is selected. The optimal structural parameters are optimized and determined with the penetration depth, sensor signal strength and sensitive field uniformity coefficient as the collaborative optimization objectives.

[0010] Based on the predetermined dynamic detection and electrode excitation scheme, the mobile array electrode coplanar capacitance sensor is used to perform dynamic detection of the measured area at a set penetration depth. The coplanar capacitance values ​​of adjacent diagonal electrode pairs in the mobile array electrode coplanar capacitance sensor are collected, and a normalized coplanar capacitance distribution matrix for different penetration depths is constructed. Based on the normalized coplanar capacitance distribution matrix for different penetration depths, the depth of the latent defects in the asphalt layer is determined.

[0011] The dynamic sensitive field distribution matrix of the measured area is constructed using the finite element method. Based on the dynamic detection and electrode excitation scheme, the measured area is dynamically detected using the mobile array electrode coplanar capacitance sensor at the depth of the latent disease. Based on the determined scanning step size, the coplanar capacitance values ​​of adjacent diagonal electrode pairs in the mobile array electrode coplanar capacitance sensor at different scanning steps are collected to construct the normalized coplanar capacitance distribution matrix for different scanning steps.

[0012] Based on the normalized coplanar capacitance distribution matrix and dynamic sensitive field distribution matrix of the different scanning steps, the relative permittivity distribution map of the latent defects in the asphalt layer under dynamic detection is reconstructed using the capacitance tomography reconstruction algorithm.

[0013] Based on the relative permittivity distribution diagram of the latent defects in the asphalt layer, the planar location and shape characteristics of the latent defects are identified.

[0014] Furthermore, the optimal structural parameters are obtained through optimization using a third-generation non-dominated sorting genetic algorithm.

[0015] Furthermore, the optimal structural parameters include electrode length, width, and spacing.

[0016] Furthermore, the dynamic detection and electrode excitation scheme is specifically as follows:

[0017] The mobile array electrode coplanar capacitance sensor adopts a lateral scanning method. It starts from one side of the measured area and moves at a set scanning step size until it reaches the other side of the measured area. Each time it moves, it sequentially excites the adjacent diagonal electrode pairs in the mobile array electrode coplanar capacitance sensor and collects the corresponding coplanar capacitance values.

[0018] Furthermore, the normalized coplanar capacitance distribution matrix is ​​obtained based on the coplanar capacitance values ​​of each electrode pair in the empty field, the object field, and the full field.

[0019] Furthermore, the penetration depth corresponding to the normalized coplanar capacitance distribution matrix with a value greater than zero is determined as the depth at which the latent disease is located.

[0020] Furthermore, the construction of the dynamic sensitive field distribution matrix includes:

[0021] The three-dimensional sensitive field space is determined, and the electric field intensity distribution of each electrode pair in the movable array electrode coplanar capacitance sensor is solved by the finite element method to calculate the sensitivity distribution of the electrode pair.

[0022] Based on the dynamic detection and electrode excitation scheme, the sensitivity distributions of each electrode pair are superimposed to construct the static sensitivity field distribution matrix of the sensor, thereby determining the optimal static sensitive layer and its corresponding static sensitivity field distribution matrix.

[0023] The static sensitive field distribution matrices corresponding to the optimal static sensitive layer under different scanning steps are superimposed to construct the dynamic sensitive field of the measured area and the corresponding dynamic sensitive field distribution matrix.

[0024] Furthermore, the lower space of the movable array electrode coplanar capacitive sensor in the measured area is defined as the three-dimensional sensitive field space.

[0025] Furthermore, the capacitance tomography reconstruction algorithm adopts the Tikhonov framework.

[0026] The present invention also provides an imaging device for latent defects in asphalt layers based on dynamic coplanar array capacitance, comprising:

[0027] The dynamic sensitivity matrix calculation unit is used to calculate the dynamic sensitivity field distribution matrix of the measured area;

[0028] A dynamic coplanar capacitance distribution matrix acquisition unit is used to acquire coplanar capacitance measurements at different penetration depths and scanning steps in the tested area of ​​an asphalt layer containing latent defects, according to a predetermined dynamic detection and electrode excitation scheme. The dynamic coplanar capacitance distribution matrix acquisition unit includes a movable array electrode coplanar capacitance sensor with optimal structural parameters. The optimal structural parameters are determined by optimizing penetration depth, sensor signal strength, and sensitive field uniformity coefficient as collaborative optimization objectives.

[0029] The computer post-processing unit is used to determine the depth of latent defects in the asphalt layer based on the coplanar capacitance measurement values ​​at different penetration depths, construct normalized coplanar capacitance distribution matrices for different scanning steps based on the coplanar capacitance measurement values ​​for different scanning steps, and use the capacitance tomography reconstruction algorithm to reconstruct the relative permittivity distribution map of latent defects in the asphalt layer under dynamic detection to identify the planar location and shape characteristics of latent defects based on the normalized coplanar capacitance distribution matrix and the dynamic sensitive field distribution matrix for different scanning steps.

[0030] Compared with existing technologies, this invention can be used for rapid detection and precise visual identification of latent defects in asphalt layers, providing a reliable basis for long-term pavement performance evaluation and preventive maintenance, and has the following beneficial effects:

[0031] 1. This invention provides an imaging technology for latent defects in asphalt layers based on dynamic coplanar array capacitance. The penetration depth of the mobile array electrode coplanar capacitance sensor is determined according to the requirements of the asphalt layer detection depth. The sensor signal strength and the uniformity coefficient of the sensitive field are taken as optimization targets to obtain the optimal structural parameters of the mobile array electrode coplanar capacitance sensor. The accurate dynamic coplanar capacitance value can be obtained. By using dynamic sensitive field distribution calculation and dynamic coplanar capacitance measurement, the location, shape and relative depth of latent defects in asphalt layers can be quickly and accurately identified, and the types of latent defects such as cavities and water damage can be effectively distinguished.

[0032] 2. This invention, through dynamic detection, can construct normalized coplanar capacitance distribution matrices with different penetration depths and different scanning steps, which can conveniently determine the depth of latent defects in asphalt layers. It can also establish a relative permittivity distribution map of the tested area based on the normalized coplanar capacitance distribution matrices of different scanning steps, quickly identify the location and shape characteristics of latent defects in asphalt layers, and effectively distinguish between defect types such as cavities and water damage. Attached Figure Description

[0033] Figure 1 This is a schematic flowchart of the method of the present invention;

[0034] Figure 2 This is a schematic diagram of the movable array electrode coplanar capacitance sensor of the present invention;

[0035] Figure 3This is a schematic diagram of dynamic detection based on the mobile array electrode coplanar capacitance sensor of the present invention;

[0036] Figure 4 This is a schematic diagram of the imaging device in an embodiment of the present invention;

[0037] Figure 5 This is a schematic diagram of the imaging system in an embodiment of the present invention;

[0038] In the figure: 1. Imaging method for latent defects in asphalt layer based on dynamic coplanar array capacitance; 2. Mobile array electrode coplanar capacitance sensor; 2.1 Edge shield; 2.2 Electrode plate; 2.3 Bottom shield; 2.4 Adhesion layer; 3. Measured area; 3.1 Latent defect; 4. Imaging device for latent defects in asphalt layer based on dynamic coplanar array capacitance; 4.1 Dynamic sensitivity matrix calculation unit; 4.2 Dynamic coplanar capacitance distribution acquisition unit; 4.3 Computer post-processing unit; 5. Imaging system for latent defects in asphalt layer based on dynamic coplanar array capacitance; 5.1 Defect dynamic detection module; 5.2 Dynamic sensitive field calculation module; 5.3 Dynamic coplanar capacitance acquisition module; 5.4 Latent defect identification module. Detailed Implementation

[0039] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0040] Example 1

[0041] This embodiment provides an imaging method 1 for latent defects in asphalt layers based on dynamic coplanar array capacitance, including processes such as sensor configuration determination, dynamic detection and electrode excitation scheme formulation, dynamic sensitive field distribution calculation, dynamic coplanar capacitance measurement, latent defect depth judgment, latent defect relative permittivity distribution reconstruction, and latent defect feature identification. Figure 1 As shown, the method specifically includes the following steps:

[0042] S1. Determine the penetration depth of the mobile array electrode coplanar capacitance sensor according to the asphalt layer detection depth requirements. Select the corresponding mobile array electrode coplanar capacitance sensor based on the pre-determined optimal structural parameters. The optimal structural parameters are determined by optimizing the penetration depth, sensor signal strength and sensitive field uniformity coefficient as the collaborative optimization objectives.

[0043] S2. Based on the predetermined dynamic detection and electrode excitation scheme, the mobile array electrode coplanar capacitance sensor is used to perform dynamic detection of the measured area at a set penetration depth, and the coplanar capacitance values ​​of adjacent diagonal electrode pairs in the mobile array electrode coplanar capacitance sensor are collected to construct a normalized coplanar capacitance distribution matrix for different penetration depths.

[0044] S3. Determine the depth of hidden defects in the asphalt layer based on the normalized coplanar capacitance distribution matrix of different penetration depths.

[0045] S4. Construct the dynamic sensitive field distribution matrix of the measured area using the finite element method;

[0046] S5. Based on the dynamic detection and electrode excitation scheme, at the depth of the latent disease, the mobile array electrode coplanar capacitance sensor is used to dynamically detect the area under test. Based on the determined scanning step length, the coplanar capacitance values ​​of adjacent diagonal electrode pairs in the mobile array electrode coplanar capacitance sensor at different scanning steps are collected to construct a normalized coplanar capacitance distribution matrix for different scanning steps.

[0047] S6. Based on the normalized coplanar capacitance distribution matrix and dynamic sensitive field distribution matrix of the different scanning steps, the relative permittivity distribution map of the asphalt layer hidden defects under dynamic detection is reconstructed using the capacitance tomography reconstruction algorithm.

[0048] S7. Based on the distribution map of the relative permittivity of the latent defects in the asphalt layer, identify the planar location and shape characteristics of the latent defects.

[0049] In step S1, when selecting the appropriate mobile array electrode coplanar capacitance sensor, the penetration depth, sensor signal strength, and sensitive field uniformity coefficient determined according to the asphalt layer detection depth requirements are used as the collaborative optimization objectives. The third-generation non-dominated sorting genetic algorithm is used for multi-objective optimization design to obtain the optimal structural parameters of the mobile array electrode coplanar capacitance sensor, including electrode length, width, and spacing.

[0050] In this embodiment, the third-generation non-dominated sorting genetic algorithm can be used to coordinately optimize several conflicting objectives such as penetration depth, sensor signal strength and sensitive field uniformity coefficient, in order to determine the optimal parameters that meet the sensor optimization design requirements, thereby improving the accuracy of sensor data acquisition and the final imaging effect of hidden diseases.

[0051] like Figure 2As shown, this embodiment of the mobile array electrode coplanar capacitance sensor 1 comprises an edge shield 2.1, an electrode plate 2.2, a bottom shield 2.3, and an adhesion layer 2.4. In this embodiment, an optimization target of a penetration depth of not less than 16 cm, a signal strength of not less than 1.2, and a sensitive field uniformity coefficient of not more than 1.76 is used as an example. The determined dimensions of the mobile array electrode coplanar capacitance sensor are 1086 × 1066 × 15 mm. The edge shield 2.1 and the bottom shield 2.3 are made of 3 mm thick copper plates. The edge shield 2.1 has a planar width of 40 mm and surrounds the outermost edge of the electrode. The bottom shield 2.3 has a planar dimension of 1086 × 1066 mm and surrounds the interior of the electrode, isolating the external electric field from interference with the internal electric field of the mobile array electrode coplanar capacitance sensor 2. The electrode plate 2.2 consists of 15 electrodes, each with dimensions of 321 × 289 × 5 mm, used to generate an electrostatic field in the measured area of ​​the asphalt layer, which is then used to calculate the dynamic sensitive field matrix and measure the dynamic coplanar capacitance value. The adhesion layer 2.4 is made of epoxy resin insulating board and is used to bond the electrode plate and the shielding layer, and to prevent leakage of internal electric field lines generated by the moving array electrode coplanar capacitive sensor.

[0052] In step S2, the predetermined dynamic detection and electrode excitation scheme of this embodiment is as follows: the movable array electrode coplanar capacitance sensor adopts a lateral scanning method, starting from one side of the measured area and moving at a set scanning step size until it reaches the other side of the measured area. Each time it moves, it sequentially excites adjacent diagonal electrode pairs in the movable array electrode coplanar capacitance sensor and collects the corresponding coplanar capacitance values. This embodiment uses a dynamic detection method, which effectively improves the detection accuracy compared to static detection.

[0053] like Figure 3 The diagram shows the dynamic detection process. At the beginning of the detection, the movable array electrode coplanar capacitance sensor 2 is placed on the left side of the area to be tested 3, and adjacent diagonal electrode pairs in the movable array electrode coplanar capacitance sensor are activated in sequence. The scanning step size is set to 2cm, and the movable array electrode coplanar capacitance sensor scans in the lateral direction, scanning through the latent disease 3.1 in sequence, until the right side of the movable array electrode coplanar capacitance sensor coincides with the right side of the area to be tested 3, and the scanning ends.

[0054] The excitation electrodes and corresponding adjacent diagonal electrode pairs of the movable array electrode coplanar capacitance sensor are shown in Table 1. In the 15-electrode movable array electrode coplanar capacitance sensor, there are a total of 16 independent measured capacitance values. The capacitance values ​​of adjacent diagonal electrode pairs are obtained using a sequential cyclic excitation scheme.

[0055] Table 1. Excitation electrodes and corresponding electrode pairs for a movable array electrode coplanar capacitance sensor.

[0056]

[0057]

[0058] Note: The thickened electrode pairs are repeated.

[0059] In step S2, the normalized coplanar capacitance distribution matrix is ​​obtained based on the coplanar capacitance values ​​of each electrode pair in the empty field, the object field, and the full field. The object field represents the actual testing scenario, i.e., the tested area is filled with asphalt material that may contain latent defects. The normalization method differs for different latent defects.

[0060] For void defects, when the measured area is filled with air, filled with asphalt material containing void defects, or filled with asphalt material, the output corresponds to the coplanar capacitance values ​​of void defects in the empty field, the material field, and the full-field asphalt material, respectively. The formula for the normalized capacitance value is as follows:

[0061]

[0062] In the formula: C N1 —Normalized capacitance value of asphalt layer voids; C f1 —Full-field capacitance value of asphalt layer voids; C m1 —The capacitance value of the physical field for asphalt layer cavities; C u1 —The open field capacitance value of voids in the asphalt layer.

[0063] For water-damaged defects, when the measured area is filled with asphalt material, filled with asphalt material containing water-damaged defects, or filled with water, the output is the coplanar capacitance value of the asphalt material water-damaged defects in the empty field, the object field, and the full field. The formula for its normalized capacitance value is as follows:

[0064]

[0065] In the formula: C N2 —Normalized capacitance value of water damage in asphalt layers; C f2 —Full-field capacitance value of water damage in asphalt layer; C m2 —The physical field capacitance value of water damage in asphalt layers; C u2 —The open field capacitance value of water damage in asphalt layers.

[0066] In step S3, the mobile array electrode coplanar capacitance sensors with different detection depths are arranged horizontally in groups from shallow to deep. Dynamic detection and electrode excitation schemes are used for detection. When there are some values ​​greater than zero in the calculated normalized capacitance values, the depth of the latent disease can be determined.

[0067] In step S4, the lower space of the movable array electrode coplanar capacitive sensor is divided into a three-dimensional sensitive field space. The finite element method is used to solve for the electric field intensity distribution of each electrode pair in the movable array electrode coplanar capacitive sensor, and the sensitivity distribution of the electrode pairs is calculated. Based on the dynamic detection and electrode excitation scheme, the sensitivity distributions of each electrode pair are superimposed to construct the static sensitive field distribution matrix of the sensor. The static sensitive field distribution of each layer of the sensor is plotted, and the optimal static sensitive layer and its corresponding static sensitive field distribution matrix are determined.

[0068] Based on a determined scanning step size, the static sensitive field distribution matrices of different scanning steps are superimposed to construct the dynamic sensitive field of the measured region and the corresponding dynamic sensitive field distribution matrix.

[0069] In this embodiment, the scanning step size of the movable array electrode coplanar capacitance sensor is 2 cm. The three-dimensional sensitive field space of the movable array electrode coplanar capacitance sensor is 1186×1166×250 mm, divided into 20 layers, with the 7th layer being the optimal sensitive layer.

[0070] In step S6, the capacitance tomography reconstruction algorithm adopts the Tikhonov framework, meaning that image reconstruction is solved using the Tikhonov framework. Tikhonov regularization is one of the most commonly used methods for determining approximations of linear equations. It replaces the minimization problem with a penalized least squares problem. The residuals in Tikhonov regularization are calculated using the semi-norm of the fractional power of the pseudo-inverse.

[0071] Image reconstruction using conventional least squares methods can lead to overfitting (multiple solutions) or underfitting (no solution). However, this embodiment uses the Tikhonov framework, which can effectively solve the problem of overfitting or underfitting.

[0072] By combining the above imaging methods with planar capacitance imaging and arrayed coplanar electrode capacitance sensors, the location, shape, and relative depth of latent defects in asphalt layers can be quickly and accurately identified. This effectively distinguishes latent defect types such as cavities and water damage, and can be used for rapid detection and precise visual identification of latent defects in asphalt layers, providing a reliable basis for long-term pavement performance evaluation and preventive maintenance.

[0073] Example 2

[0074] like Figure 4 As shown, this embodiment provides an imaging device 4 for latent defects in asphalt layers based on dynamic coplanar array capacitance, comprising:

[0075] Dynamic sensitivity matrix calculation unit 4.1 is used to calculate the dynamic sensitivity field distribution matrix of the measured area;

[0076] The dynamic coplanar capacitance distribution matrix acquisition unit 4.2 is used to acquire coplanar capacitance measurements at different penetration depths and scanning steps in the tested area of ​​the asphalt layer containing latent defects, according to a predetermined dynamic detection and electrode excitation scheme. The dynamic coplanar capacitance distribution matrix acquisition unit includes a movable array electrode coplanar capacitance sensor, which has optimal structural parameters. The optimal structural parameters are determined by optimizing the penetration depth, sensor signal strength, and sensitive field uniformity coefficient as collaborative optimization objectives.

[0077] The computer post-processing unit 4.3 is used to determine the depth of latent defects in the asphalt layer based on the coplanar capacitance measurement values ​​at different penetration depths, construct normalized coplanar capacitance distribution matrices for different scanning steps based on the coplanar capacitance measurement values ​​for different scanning steps, and use the capacitance tomography reconstruction algorithm to reconstruct the relative permittivity distribution map of latent defects in the asphalt layer under dynamic detection to identify the planar location and shape characteristics of latent defects.

[0078] The dynamic sensitivity matrix calculation unit 4.1 has built-in dynamic sensitivity matrix calculation software. Using finite element simulation, it superimposes the static sensitivity matrix distribution of different scanning steps in the moving array electrode coplanar capacitance sensor to construct the dynamic sensitivity field distribution matrix of the measured area.

[0079] In the dynamic coplanar capacitance distribution matrix acquisition unit 4.2, the movable array electrode coplanar capacitance sensor is connected to an LCR digital bridge for acquiring the coplanar capacitance values ​​of adjacent diagonal electrodes.

[0080] The computer post-processing unit 4.3 has built-in dynamic imaging detection software for latent defects in asphalt layers, which is used to determine the depth of latent defects in asphalt layers, reconstruct the relative permittivity distribution of asphalt layers containing latent defects, and identify the location and shape characteristics of latent defects.

[0081] The rest is the same as in Example 1.

[0082] Example 3

[0083] like Figure 5 As shown, this embodiment provides an imaging system 5 for latent defects in asphalt layers based on dynamic coplanar array capacitance, including a defect dynamic detection module 5.1, a dynamic sensitive field calculation module 5.2, a dynamic coplanar capacitance acquisition module 5.3, and a latent defect identification module 5.4.

[0084] The disease dynamic detection module 5.1 includes a movable array electrode coplanar capacitance sensor, which is used to perform lateral scanning on the asphalt layer containing latent diseases for dynamic disease detection.

[0085] The dynamic sensitive field calculation module 5.2 is used to calculate and construct the dynamic sensitive field distribution matrix of the measured area of ​​the asphalt layer containing hidden defects.

[0086] The dynamic coplanar capacitance acquisition module 5.3 includes an LCR digital bridge connected to the movable array electrode coplanar capacitance sensor, used to acquire the coplanar capacitance values ​​of adjacent diagonal electrode pairs obtained by the movable array electrode coplanar capacitance sensor in different scanning steps.

[0087] The latent disease identification module 5.4 is used to reconstruct and draw the relative permittivity distribution of latent diseases in the asphalt layer, and to identify the location and shape characteristics of latent diseases.

[0088] The rest is the same as in Example 1.

[0089] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance, characterized in that, Includes the following steps: The penetration depth of the mobile array electrode coplanar capacitance sensor is determined according to the asphalt layer detection depth requirements. Based on the predetermined optimal structural parameters, the corresponding mobile array electrode coplanar capacitance sensor is selected. The optimal structural parameters are optimized and determined with the penetration depth, sensor signal strength and sensitive field uniformity coefficient as the collaborative optimization objectives. Based on the predetermined dynamic detection and electrode excitation scheme, the mobile array electrode coplanar capacitance sensor is used to perform dynamic detection of the measured area at a set penetration depth. The coplanar capacitance values ​​of adjacent diagonal electrode pairs in the mobile array electrode coplanar capacitance sensor are collected, and a normalized coplanar capacitance distribution matrix for different penetration depths is constructed. Based on the normalized coplanar capacitance distribution matrix for different penetration depths, the depth of the latent defects in the asphalt layer is determined. The dynamic sensitive field distribution matrix of the measured area is constructed using the finite element method. Based on the dynamic detection and electrode excitation scheme, the measured area is dynamically detected using the mobile array electrode coplanar capacitance sensor at the depth of the latent disease. Based on the determined scanning step size, the coplanar capacitance values ​​of adjacent diagonal electrode pairs in the mobile array electrode coplanar capacitance sensor at different scanning steps are collected to construct the normalized coplanar capacitance distribution matrix for different scanning steps. Based on the normalized coplanar capacitance distribution matrix and dynamic sensitive field distribution matrix of the different scanning steps, the relative permittivity distribution map of the latent defects in the asphalt layer under dynamic detection is reconstructed using the capacitance tomography reconstruction algorithm. Based on the relative permittivity distribution diagram of the latent defects in the asphalt layer, the planar location and shape characteristics of the latent defects are identified.

2. The method for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance according to claim 1, characterized in that, The optimal structural parameters were obtained through optimization using a third-generation non-dominated sorting genetic algorithm.

3. The method for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance according to claim 1, characterized in that, The optimal structural parameters include electrode length, width, and spacing.

4. The method for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance according to claim 1, characterized in that, The dynamic detection and electrode excitation scheme is specifically as follows: The mobile array electrode coplanar capacitance sensor adopts a lateral scanning method. It starts from one side of the measured area and moves at a set scanning step size until it reaches the other side of the measured area. Each time it moves, it sequentially excites the adjacent diagonal electrode pairs in the mobile array electrode coplanar capacitance sensor and collects the corresponding coplanar capacitance values.

5. The imaging method for latent defects in asphalt layers based on dynamic coplanar array capacitance according to claim 1, characterized in that, The normalized coplanar capacitance distribution matrix is ​​obtained based on the coplanar capacitance values ​​of each electrode pair in the empty field, the object field, and the full field.

6. The method for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance according to claim 1, characterized in that, The depth corresponding to the normalized coplanar capacitance distribution matrix with a value greater than zero is determined as the depth of the latent disease.

7. The method for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance according to claim 1, characterized in that, The construction of the dynamic sensitive field distribution matrix includes: The three-dimensional sensitive field space is determined, and the electric field intensity distribution of each electrode pair in the movable array electrode coplanar capacitance sensor is solved by the finite element method to calculate the sensitivity distribution of the electrode pair. Based on the dynamic detection and electrode excitation scheme, the sensitivity distributions of each electrode pair are superimposed to construct the static sensitivity field distribution matrix of the sensor, thereby determining the optimal static sensitive layer and its corresponding static sensitivity field distribution matrix. The static sensitive field distribution matrices corresponding to the optimal static sensitive layer under different scanning steps are superimposed to construct the dynamic sensitive field of the measured area and the corresponding dynamic sensitive field distribution matrix.

8. The method for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance according to claim 7, characterized in that, The lower space of the movable array electrode coplanar capacitive sensor in the measured area is the three-dimensional sensitive field space.

9. The method for imaging latent defects in asphalt layers based on dynamic coplanar array capacitance according to claim 1, characterized in that, The capacitance tomography reconstruction algorithm adopts the Tikhonov framework.

Images