Method for detecting void blockage silt content of drainage asphalt pavement

Abstract

The invention discloses a method for detecting the silt content of void blockage of a drainage asphalt pavement. According to the method, characteristic parameters of blockages in pores of the drainage asphalt pavement are obtained through a portable detection device, and the soil content in the blockages is deduced by utilizing a pre-established reference model, and the method comprises the following steps: preparing a plurality of standard samples for simulating the blockages and measuring dielectric and spectral characteristic parameters of the standard samples; establishing a correlation model between the soil content and the characteristic parameters; during field detection, a dual-frequency dielectric sensor and a spectrum sensor are used for synchronously measuring dielectric parameters and characteristic spectrum reflectivity of blockages in gaps of the road surface, and the dielectric frequency difference and the spectrum absorption index of the blockages are obtained; the environmental influence is compensated by combining humidity and temperature sensors, and actually measured parameters are substituted into the correlation model to calculate the soil content of the blockages. The method realizes lossless rapid quantitative evaluation of the soil content in the pore blockage state of the drainage asphalt pavement, is simple and convenient to operate, and can provide a scientific basis for pavement maintenance decision-making.

Description

Technical Field

[0001] This invention relates to the field of pavement void detection technology, and in particular to a method for detecting the mud content in voids of drainage asphalt pavement. Background Technology

[0002] Permeable asphalt pavement, due to its high porosity structure, effectively drains water, prevents water accumulation, and absorbs sound to reduce noise, making it an important component of sponge city road construction. However, in actual service, rainwater runoff carries fine particles such as silt and dust into the pavement pores, which gradually deposit and form blockages over time. Blockages reduce the pavement porosity, significantly diminishing drainage and noise reduction performance, thus affecting the road's service quality and lifespan. In particular, the soil (clay and silt components) has small particle size and strong adhesion; once it enters the interconnected pores of the asphalt pavement, it is often more difficult to be washed away or removed, leading to persistent blockage problems.

[0003] Currently, the common method for evaluating the degree of blockage in permeable asphalt pavements is the permeability coefficient test. By injecting water into the pavement voids on-site and measuring the infiltration rate, the degree of blockage can be inferred. However, the permeability test can only provide an overall indicator of the degree of blockage and cannot distinguish the composition of the blockage, let alone directly quantify the proportion of soil in the blockage. In addition, there are methods that use pavement core sample experiments for analysis, such as removing the blockage and measuring the soil content by sieving or washing and weighing. However, this method is destructive, cumbersome, and inefficient, and is not suitable for large-scale rapid detection. With the increasing requirements for road maintenance and management, there is an urgent need for a method that can non-destructively, quickly, and quantitatively assess the composition of void blockage in permeable asphalt pavements on-site to guide targeted maintenance measures. For example, if the key parameter of soil content in the blockage can be accurately obtained, maintenance personnel can determine whether conventional vacuuming and sweeping is sufficient or whether more powerful methods such as high-pressure washing are needed, thereby improving the targeting and effectiveness of maintenance. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a method for detecting the mud content in voids of permeable asphalt pavement, in order to solve the problem that existing methods for detecting blockages in permeable asphalt pavement cannot accurately distinguish the components of the blockage and quantitatively assess the mud content under non-destructive and rapid conditions, thus making it difficult to provide targeted and scientific decision-making basis for road maintenance.

[0005] To achieve the above objectives, the present invention provides a method for detecting the mud content in the voids of drainage asphalt pavement, comprising the following steps:

[0006] S1: First, prepare several standard samples of drainage asphalt pavement pore blockage with known soil content. Mix the soil with inert granular materials at different mass percentages to form simulated blockages and adjust the moisture content to the set level.

[0007] S2: Measure the characteristic parameters of each of the standard samples using a portable blockage detection device equipped with a dual-frequency dielectric sensor and a spectral sensor. The characteristic parameters include at least the dielectric constant obtained at the first frequency and the reflectance of the target spectral band; record the actual soil content and corresponding characteristic parameter data of each standard sample.

[0008] S3: Based on the data obtained in step S2, establish a soil content prediction model, use the actual soil content as the dependent variable and the feature parameters as the independent variables to perform correlation fitting, and obtain a regression model for soil content inference.

[0009] S4: Select the target detection area on the drainage asphalt pavement to be tested, clean the surface soil, and place the blockage detection device on the pavement in the area to obtain measured characteristic parameters such as dielectric constant and spectral reflectance of the pavement pore blockage at that location.

[0010] S5: Simultaneously collect environmental parameters of the blockage in the detection area, including the moisture content and temperature of the blockage. Based on the environmental parameters, compensate and correct the feature parameters obtained in step S4 to make them correspond to the standard dry state.

[0011] S6: Input the compensated and corrected measured characteristic parameters into the regression model established in step S3 to calculate the soil content of the blockage in the detection area;

[0012] S7: Output the soil content of the blockage in the detection area, and compare the soil content with the preset threshold for the soil content of the blockage in the voids to assess the severity of the blockage in the road surface voids.

[0013] Preferably, the portable blockage detection device in step S2 includes a dual-frequency dielectric sensor, a spectral sensor, a humidity and temperature sensor, and a data acquisition and processing system connected to the sensors; the data acquisition and processing system is used to control each sensor to acquire signals and record characteristic parameters, and to calculate soil content based on the regression model and output the results.

[0014] Preferably, the dual-frequency dielectric sensor includes two electrodes and an excitation source disposed on the same plane for measuring the dielectric constant of the blockage at the first frequency and the second frequency; the spectral sensor is used to detect the spectral reflectance of the blockage in a predetermined absorption band and a reference band to calculate the spectral absorption characteristic index.

[0015] Preferably, the dual-frequency dielectric sensor is a planar capacitive sensor, consisting of copper coplanar electrodes on a flexible substrate, with electrode dimensions of 50mm × 10mm and electrode spacing of 5mm. During detection, the sensor probe is tightly attached to the location to be measured on the road surface, and the dielectric value is read every 30 seconds. The average value of four consecutive readings is taken as the dielectric characteristic parameter of that location.

[0016] Preferably, the spectral absorption characteristic index is calculated according to the formula I = (R(λ0) - R(λ)) / R(λ0), where I is the spectral absorption characteristic index, R(λ) is the reflectance of the blockage at the target absorption wavelength λ, and R(λ0) is the reflectance of the blockage at the reference wavelength λ0 where there is no significant absorption.

[0017] Preferably, the soil content prediction model is a multiple regression model, which uses the spectral absorption characteristic index and the difference in dielectric constant as independent variables for predicting soil content. The model form is P = a0 + a1·I + a2·Δε, where Δε is the difference in dielectric constant, Δε = ε(f1) - ε(f2), ε(f1) and ε(f2) are the dielectric constants of the blockage at the first frequency f1 and the second frequency f2, a0, a1, and a2 are coefficients obtained by fitting through standard samples, and P is the soil content.

[0018] Preferably, the standard sample in step S1 is prepared using a drainage asphalt mixture specimen with an actual void structure. First, the voids inside the asphalt mixture specimen are filled with a mixture of soil and sand with different mass fractions to simulate the blockage state with different soil contents. Then, each specimen is cured to a constant moisture content before being used for parameter determination.

[0019] Preferably, before performing the on-site measurement in step S4, the blockage detection device is calibrated, including: measuring the dielectric background value on a clean pavement with unblocked gaps as a zero-point reference for ε(f1) and ε(f2), and calibrating the reflectivity of the spectral sensor using a whiteboard standard to eliminate the influence of instrument drift on the measurement results.

[0020] The beneficial effects of this invention are:

[0021] This method for detecting mud content in voids of drainage asphalt pavement employs a combination of dual-frequency dielectric parameters and characteristic spectral reflectance. By utilizing the differences in dielectric dispersion and near-infrared absorption bands of mud, water, and air, a correlation model between mud mass fraction and characteristic parameters is established. This enables direct quantitative inversion of "mud content (mass fraction)," compensating for the inability to distinguish the composition of the blockage medium solely based on changes in permeability or capacitance. The resulting judgment is closer to the essential properties of the blockage.

[0022] The detection device is portable and can complete point measurements without core sampling or damage to the road surface. It also introduces synchronous acquisition of moisture content and temperature, and performs standard state conversion on dielectric and spectral parameters, effectively reducing the influence of moisture content and temperature fluctuations on the readings, ensuring the comparability and transferability of results between different seasons and different road sections, and is suitable for large-scale inspections and multi-time period retests.

[0023] The system instantly generates soil content percentages on-site and correlates them with preset thresholds, triggering maintenance recommendations such as "routine vacuuming, high-pressure flushing, and intensive cleaning" based on light / medium / heavy levels. The model supports road segment-level calibration and rapid verification, improving detection efficiency and reducing over / under-maintenance costs due to misjudgments, thus forming a closed-loop management capability encompassing detection, assessment, and treatment. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0026] Figure 2 This is a schematic diagram showing the characteristic parameters and soil content of the standard sample of this invention;

[0027] Figure 3 This is a schematic diagram showing the actual soil content and the predicted soil content of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0029] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0030] During the research process, the applicant recognized that soil (mainly composed of silicate clay particles) differs significantly from water and air in dielectric properties and optical spectra. For example, the dielectric constant of water is much higher than that of soil and air, and it varies significantly with frequency, while the dielectric constant of dry soil is moderate and its dispersion characteristics are weak. At the same time, different substances have different spectral reflectance characteristics at specific wavelengths. Soil, due to its iron oxide and clay mineral content, exhibits characteristic absorption peaks in the near-infrared band. Based on these differences, if the relevant signals of each component in the blockage can be obtained simultaneously through appropriate sensors, it is possible to infer the proportion of soil without directly weighing the soil. This invention is based on this idea and uses a multi-parameter sensor fusion detection method to assess the soil content of the pore blockage in drainage asphalt pavement.

[0031] A method for detecting mud content in voids of drainage asphalt pavement includes the following steps;

[0032] In S1, when preparing several sets of standard samples simulating blockages in permeable asphalt pavements, specifically, firstly, dried and sieved soil (particles <0.075mm) and inert fine particles (such as quartz sand or mineral powder, particle size 0.075mm~2mm) are mixed in different mass ratios to form mixtures with different soil mass fractions to simulate various compositions of blockages; then, an appropriate amount of water is added to the mixture and stirred evenly to ensure that the moisture content of each standard sample is consistent; if necessary, the above mixture can be filled into the pores of permeable asphalt mixture specimens to more realistically simulate the pavement blockage state, with each specimen's upper surface evenly spread and flush with the pavement; the soil mass fraction of the standard samples covers a range from 0% (pure inert particles, no mud) to 100% (pure soil), with several representative gradients (e.g., 10%, 30%, 50%, 70%, etc.) selected in between; each standard sample is allowed to stand for a period of time before measurement to ensure uniform and stable moisture distribution;

[0033] In step S2, a portable blockage detection device is used to measure each standard sample prepared in step S1 to obtain its electrical and optical characteristic parameters. Specifically, the sensor probe of the detection device is placed on the upper surface of the standard sample (or on the surface of the sample if it is a sample filled into a specimen), ensuring close contact between the sensor working surface and the sample. The device is activated, and the dielectric constant of the sample is measured at a first frequency f1 (e.g., a lower frequency in the range of 20MHz-100MHz) and a second frequency f2 (e.g., a higher frequency in the range of 200MHz-1GHz), yielding ε(f1) and ε(f2). By selecting two significantly different frequencies for measurement, the "effect of moisture" can be effectively separated from the measurement signal, thus more accurately reflecting the "soil" content. Simultaneously, the sample... The reflectance values ​​in the predetermined spectral bands include the reflectance R(λ) at the target absorption wavelength λ and the reflectance R(λ0) at the reference wavelength λ0. To improve measurement accuracy, each parameter of each sample can be measured repeatedly and the average value is taken as the final reading. Preferably, the dielectric measurement adopts the planar capacitance sensor method: the planar capacitance sensor probe is placed flat on the sample surface, and the dielectric reading is read every 30 seconds. The spectral measurement can be performed using a portable near-infrared spectrometer, which illuminates the sample surface with a uniform light source and collects the reflectance spectrum, and takes the reflectance in the relevant band. After the data collection of all standard samples is completed, the actual soil content P_actual (known ratio) of each standard sample and the corresponding characteristic parameter set {ε(f1), ε(f2), R(λ), R(λ0)} are established in a corresponding record table.

[0034] In step S3, the standard data obtained in step S2 is analyzed to establish a mapping model between soil content P and characteristic parameters. For example, the dielectric constant difference and spectral absorption index are selected as the main independent variables, i.e., the dielectric frequency difference Δε = ε(f1) - ε(f2) and the spectral absorption characteristic index I = (R(λ0) - R(λ)) / R(λ0) are calculated for each sample. Multiple linear regression or machine learning algorithms are used to fit the model with P_actual as the dependent variable and Δε and I as independent variables to obtain the soil content prediction model P = f(Δε, I). For example, a linear model P_pred = a0 + a1·I + a2·Δε can be used, where the coefficients a0, a1, and a2 are determined by least squares fitting. If it is necessary to improve the applicability of the model, higher-order terms or cross terms can be added, or nonlinear methods such as artificial neural networks can be used to train the model. The obtained model should be statistically tested to ensure good fit and prediction accuracy. R can be selected. 2 The model performance was evaluated using indicators such as mean square error, and the soil content regression model was finally stored in the data processing system for future use.

[0035] In S4, when measuring the characteristic parameters of blockages in the test area of ​​an actual drainage asphalt pavement, representative test points should be selected before testing, avoiding obviously damaged or abnormal areas. It is preferable to use the same portable blockage detection device as the one used for standard sample measurements to ensure data consistency. The sensor probe should be placed on the pavement test point, covering an area of ​​several square centimeters to tens of square centimeters (approximately equivalent to the effective detection area of ​​the sensor). Ensure that the blockages in the pavement voids below the probe are the objects to be evaluated. If the detection device is mounted on a trolley, it can be pushed to the target location and the sensor head lowered to contact the pavement. Touch the sensor to activate the measuring device and obtain the dielectric constants ε(f1) and ε(f2) and the spectral reflectances R(λ) and R(λ0) at that point. Similarly, the measurement can be repeated several times and the average value can be taken. Since the actual road surface material and structure may have a certain influence on the absolute measurement value, it is necessary to perform on-site calibration of the device before testing if necessary: ​​for example, measure the background dielectric value of the void in a nearby clean and unblocked area as a zero reference for ε(f1) and ε(f2); and use a standard white board to calibrate the reflectance reading of the spectral sensor on-site. Through the above methods, more accurate and reliable on-site blockage characteristic parameters can be obtained.

[0036] In step S5, considering that the blockage material may contain some moisture and the temperature may differ from that of the standard sample, humidity and temperature compensation is needed for the measured parameters to ensure the accuracy of subsequent soil content inference. First, the moisture content W or humidity level of the blockage material at the detection point in step S4 is measured using a humidity sensor or other means. For example, the moisture content of the blockage material can be calculated using the dielectric frequency difference Δε_field: the high dielectric properties of water make Δε positively correlated with the moisture content, which can be estimated based on the experimental calibration curve W(%) = k*Δε_field (where k is a pre-calibrated proportionality coefficient). Simultaneously, the ambient temperature T is recorded. Then, standard state conversions are performed on the dielectric and spectral parameters respectively: regarding the dielectric constant, the standard state conversions can be performed... The measured ε_field value is converted to a dry state (e.g., the influence of moisture is subtracted); in terms of spectrum, R_field(λ) can be corrected according to the moisture content. For example, when the moisture content is high, the characteristic absorption of soil will be enhanced. The excessively high absorption index I_field can be weakened by existing empirical models so that it corresponds to the equivalent value I_corr under dry conditions; temperature also has a certain influence on dielectric constant. If necessary, known formulas can be used for temperature correction to convert ε_field to the value at the reference temperature (e.g., 20℃); after the above processing, the compensated and corrected parameters Δε_corr and I_corr are obtained; if the measured moisture content of the blockage material on site is extremely low (close to dry) and the temperature is close to room temperature, this step can be omitted or simplified;

[0037] In step S6, the corrected feature parameters obtained in step S5 are first input into the soil content regression model established in step S3 to calculate the soil content P_pred (predicted value) of the blockage at the detection point. The data processing system calculates the input parameters according to the stored model and outputs the results. For example, when using a linear model, the corresponding I_corr and Δε_corr are substituted into the formula P_pred=a0+a1·I_corr+a2·Δε_corr to calculate the percentage of soil content. The output P_pred is the mass fraction (%) of soil in the blockage. If necessary, the confidence level or error range of the prediction can be given at the same time, for example, the ±σ estimate based on the model fitting error. For situations requiring high accuracy, a small number of points can be selected on-site, and the soil content of a small number of blockage samples can be measured (e.g., by drying and sieving) to correct the model deviation. Then, it can be used for large-scale detection in the surrounding area to ensure the reliability of the results.

[0038] In step S7, the calculated soil content P_pred is compared with a preset threshold or evaluation standard to determine the severity of the pavement clogging. If P_pred is much lower than a certain limit, such as below 30%, it indicates that the clogging material contains little soil and is mostly sand and gravel powder. In this case, routine sweeping or suction can easily restore the pavement function. If P_pred is close to or exceeds the threshold, such as exceeding 50%, it indicates that the soil content is high and the clogging is mainly composed of fine clay particles, which may have seriously affected the pavement's permeability. More intensive maintenance measures (such as high-pressure washing or more frequent cleaning) are required. The test results can be displayed in real time on the device's built-in display screen to show the soil content percentage and evaluation conclusion. They can also be stored in the device's memory or uploaded to the maintenance management system for further analysis and decision-making. This step allows maintenance personnel to intuitively obtain the soil clogging content information at each test point, thereby quantitatively assessing the clogging status of the drainage asphalt pavement and providing a basis for subsequent maintenance.

[0039] Furthermore, for newly constructed drainage asphalt pavement projects, specific models can be established in the early stages of road construction using steps S1 to S6 above. First, standard samples are prepared and parameters are fitted using asphalt mixture specimens from the new road in an indoor environment to obtain a soil content prediction model. Then, after the road has been in use for a period of time (e.g., six months or one year), representative points are selected on-site for simultaneous sensor detection and blockage sampling analysis to obtain the actual soil content. The model prediction result P_pred is then verified and fine-tuned to better reflect the actual conditions of the road, ultimately used for long-term monitoring of the road's void blockage status. For older drainage asphalt roads that have been in service for many years, if prior model data is lacking, sensor detection can be performed at multiple measuring points according to steps S4 to S7 above, combined with necessary blockage sampling experiments, to obtain a set of sensor readings and measured values ​​of soil content. These data are then fitted to establish a soil content prediction model, which is then applied to large-scale testing of the road. These two modes are respectively suitable for prior modeling and verification of newly constructed roads and on-site modeling needs of existing roads, both falling within the scope of this invention.

[0040] In a preferred embodiment:

[0041] Using drainage asphalt mixture specimens as the matrix, standard samples of blockage material were prepared with soil mass fractions (P_actual) of 0%, 20%, 40%, 60%, and 80%. The portion with a particle size ≤0.075mm was the soil component, and 0.075-2mm quartz sand was the inert particle; all samples had the same moisture content. The detection device included a dual-frequency dielectric sensor (example frequency: f1≈50MHz, f2≈1GHz), a near-infrared spectral unit (example band: λ≈1450nm; reference λ0≈1300nm), a temperature and humidity module, and a data processing terminal. The dielectric value was read every 30 seconds, and the average of four consecutive readings was taken; the spectrum was measured close to a self-emitting light source and calibrated using a white board.

[0042] Then, the dielectric frequency difference Δε = ε(f1) - ε(f2) and the spectral absorption index I = (R(λ0) - R(λ)) / R(λ0) were extracted, and a linear regression model P = f(Δε, I) was established with P_actual as the dependent variable. Least square fitting and leave-one-out cross-validation were used to determine the coefficients and solidify them in the terminal.

[0043] The results are shown in Table 1 and... Figure 2 and Figure 3 The following is a set of example data and prediction results:

[0044] Table 1

[0045] sample P_actual(%) <![CDATA[ε(f1)]]> <![CDATA[ε(f2)]]> Δε <![CDATA[R(λ0)]]> R(λ) I P_Pred(%) Residual (%) S0 0 3.8 3.75 0.05 0.4 0.36 0.1 2 2 S1 20 3.9 3.83 0.07 0.38 0.31 0.18 21 1 S2 40 4.01 3.92 0.09 0.36 0.27 0.25 39 -1 S3 60 4.13 4.02 0.11 0.34 0.22 0.35 61 1 S4 80 4.25 4.12 0.13 0.32 0.19 0.41 79 -1

[0046] From Table 1 and Figure 2It can be seen that as the actual soil content increases, the dielectric frequency difference and spectral absorption index also increase, and I, Δε, and P_actual show a monotonically positive correlation; according to Figure 3 The model's predicted values ​​largely coincide with the ideal predicted values, demonstrating high accuracy and stability. Furthermore, in Table 1, the linear model's R² is approximately 0.98 and RMSE is approximately 2-3 percentage points for this data set, indicating its effectiveness in quantitative prediction of soil content. The coefficients are stored on the terminal based on actual calibration results and will not be elaborated upon here.

[0047] In the on-site testing and grading stage, a section of drainage asphalt pavement that has been in service for two years was first selected, and five representative measuring points were set up. After cleaning the surface dust, dielectric / spectral / temperature and humidity parameters were obtained according to the same process as in the experiment. The dry equivalent Δε_corr and I_corr were obtained by estimating humidity and converting temperature. These were then substituted into the terminal's built-in model to output the predicted soil content P_pred. Refer to Table 2 for an example as follows:

[0048] Table 2

[0049] measuring point Δε_corr I_corr P_Pred(%) Evaluation Conclusion P1 0.1 0.29 59 If the "severe" threshold (≥50%) is reached, high-pressure flushing is recommended. P2 0.08 0.25 49 If the test is close to the threshold, it is recommended to retest and schedule enhanced cleaning. P3 0.12 0.33 66 Severe congestion requires immediate attention. P4 0.09 0.27 54 Severe boundary, included in the list of key road sections P5 0.06 0.2 40 For moderate cases, conventional suction and scanning can be performed first.

[0050] The reference value is not a fixed constant, but a graded range, which is determined based on the previous laboratory modeling and road performance testing. The threshold is determined based on the regression relationship between the laboratory permeability coefficient and the mud content of the blockage: when the mud mass fraction exceeds 50%, the permeability coefficient generally drops to below 30% of the original value, which is considered the "severe blockage threshold".

[0051] It is worth noting that before testing, the dielectric zero point should be obtained in a clean, unblocked area, and the spectrum should be calibrated using a white board. To verify the extrapolation of the model, a small amount of blockage material should be taken from 1-2 randomly selected points, dried, weighed, and rechecked (without damaging the road surface structure). The error should be controlled within ±5 percentage points. After treatment, the same procedure can be followed to retest and verify the cleaning effect and the re-blocking trend.

[0052] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in the details for the sake of brevity.

[0053] This invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting the amount of silt in a void blockage of a drained asphalt pavement, characterized by, The method comprises the following steps: S1: first prepare a plurality of standard samples of pore blockage of drainage asphalt pavement with known soil content, mix soil with inert particulate material to form simulated blockage at different mass percentages, and adjust the water content to a set level; S2: measure the characteristic parameters of each standard sample using a portable blockage detection device equipped with a dual-frequency dielectric sensor and a spectral sensor, the characteristic parameters at least including the dielectric constant obtained at a first frequency and a second frequency and the reflectivity of the target spectral band; record the actual soil content of each standard sample and the corresponding characteristic parameter data; S3: based on the data obtained in step S2, establish a soil content prediction model, correlate and fit the actual soil content as the dependent variable and the characteristic parameters as the independent variable to obtain a regression model for inferring soil content; S4: select a target detection area on the drainage asphalt pavement to be tested, clean the surface soil, and place the blockage detection device on the pavement in the area to obtain the measured characteristic parameters such as the dielectric constant and spectral reflectivity of the pore blockage of the pavement in the area; S5: simultaneously collect the environmental parameters of the blockage in the detection area, including the water content and temperature of the blockage, and compensate and correct the characteristic parameters obtained in step S4 based on the environmental parameters so that they correspond to the standard dry state; S6: input the compensated and corrected measured characteristic parameters into the regression model established in step S3 to calculate the soil content of the blockage in the detection area; S7: output the soil content of the blockage in the detection area, and compare the soil content with the pre-set void blockage soil content threshold to evaluate the severity of the void blockage of the pavement.

2. The method for detecting void blockage of a drainage asphalt pavement according to claim 1, characterized by, The portable blockage detection device in step S2 comprises a dual-frequency dielectric sensor, a spectral sensor, a humidity and temperature sensor, and a data acquisition and processing system connected with the sensors; the data acquisition and processing system is used to control the acquisition of signals by each sensor and record the characteristic parameters, and calculate the soil content based on the regression model and output the results.

3. The method for detecting void blockage of a drainage asphalt pavement according to claim 2, characterized by, The dual-frequency dielectric sensor comprises two electrodes and an excitation source arranged on the same plane, which is used to measure the dielectric constant of the blockage at a first frequency and a second frequency; the spectral sensor is used to detect the spectral reflectivity of the blockage in the predetermined absorption band and the reference band to calculate the spectral absorption characteristic index.

4. The method for detecting void blockage of a drained asphalt pavement according to claim 3, characterized by, The dual-frequency dielectric sensor is a planar capacitive sensor composed of copper coplanar electrodes on a flexible substrate, the electrode size is 50mm×10mm, and the electrode spacing is 5mm; during detection, the sensor probe is tightly attached to the position to be detected on the pavement, the dielectric value is read every 30 seconds, and the average value of 4 continuous readings is taken as the dielectric characteristic parameter of the position.

5. The method for detecting void blockage of a drainage asphalt pavement according to claim 3, wherein The spectral absorption feature index is calculated according to the formula I = (R(λ o )-R(λ)) / R(λ0), where I is the spectral absorption feature index, R(λ) is the reflectance of the clog at a target absorption wavelength λ, and R(λ0) is the reflectance of the clog at a reference wavelength λ0that has no significant absorption.

6. The method for detecting void blockage of a drained asphalt pavement according to claim 5, wherein The soil content prediction model is a multiple regression model, taking the spectral absorption characteristic index and the dielectric constant difference as independent variables for the prediction of the soil content, and the model form is P=a0+a1·I+a2·Δε, wherein Δε is the dielectric constant difference, Δε=ε(f1)-ε(f2), ε(f1) and ε(f2) are the dielectric constants at the first frequency f1 and the second frequency f2, a0, a1 and a2 are the coefficients obtained by fitting the standard sample, and P is the soil content.

7. The method for detecting void blockage of a drainage asphalt pavement according to claim 1, wherein The standard sample in step S1 is prepared by using a drainage asphalt mixture test piece with an actual void structure, the internal voids of the asphalt mixture test piece are first filled with a soil-sand mixture with different mass fractions to simulate the clogging state of different soil contents, and then each test piece is cured to a constant water content for parameter determination.

8. The method for detecting void blockage of a drained asphalt pavement according to claim 1, wherein Before performing the field measurement of step S4, the clogging detection device is calibrated, including: measuring the dielectric background value on the clean voids of the unblocked pavement as the zero point reference of ε(f1) and ε(f2), and using a whiteboard standard to calibrate the reflectivity of the spectral sensor to eliminate the influence of instrument drift on the measurement results.

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