Quantitative evaluation method for recessive diseases of semi-rigid base asphalt pavement

Abstract

The invention discloses a semi-rigid base asphalt pavement recessive disease quantitative evaluation method, which comprises the following steps: S1, detecting a pavement in a detection area by using a ground penetrating radar, and carrying out image processing on acquired data to obtain high-quality detection data; s2, analyzing the detection data, performing classification marking on the images of the abnormal areas, recording the positions and sizes of the abnormal areas, and performing core drilling sampling on part of the abnormal areas to determine the disease types of the abnormal areas; s3, collecting deflection values of a normal road surface and an abnormal area by using a falling weight deflectometer, and determining an influence coefficient of the disease on the structural strength of the road surface; and S4, performing quantitative evaluation on the semi-rigid base asphalt pavement according to the influence coefficient and the disease area of the recessive disease. According to the method, the influence of the recessive diseases on the strength of the semi-rigid asphalt pavement can be quantified, the internal condition of the semi-rigid asphalt pavement can be evaluated, and reference can be provided for detection diagnosis and maintenance decision of the recessive diseases in the asphalt pavement.

Description

Technical Field

[0001] The invention relates to the technical field of road engineering, and in particular to a quantitative evaluation method for hidden diseases of a semi-rigid base asphalt pavement. Background Art

[0002] As the years of operation increase, some hidden defects that cannot be directly observed will occur inside the semi-rigid asphalt pavement. As an advanced road non-destructive testing equipment, ground penetrating radar is widely used in the road field. It has the characteristics of fast detection speed, non-destructive and fast, and can detect hidden defects inside the pavement, including hidden cracks, poor interlayer adhesion, looseness and other defects. However, the existing pavement internal condition evaluation method is mainly based on the type, location and area of ​​defects detected by ground penetrating radar. The evaluation method has a certain degree of subjectivity and lacks consideration of the impact of defects on the overall structural strength of the pavement. It cannot accurately and quantitatively evaluate the internal condition of the semi-rigid asphalt pavement. Summary of the invention

[0003] The purpose of the present invention is to provide a method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement, which can quantify the influence of internal defects of semi-rigid base asphalt pavement, obtain the type and area information of hidden defects by analyzing ground penetrating radar data, and use the deflection value index to analyze the influence coefficient of hidden defects on the overall structural strength of the pavement. The radar detection data and pavement deflection data are combined to obtain the internal damage condition index of the pavement structure to conduct an objective quantitative evaluation of the hidden defects.

[0004] The present invention provides a method for quantitatively evaluating hidden defects of a semi-rigid base asphalt pavement, comprising:

[0005] Use ground penetrating radar (GPR) of the same frequency to detect the road surface in the detection area, obtain the road surface structure layer data in the target section, and perform image processing on the collected data to obtain a high-quality road surface grayscale image;

[0006] Optionally, the signal processing process includes: performing zero bias removal, zero point adjustment, gain adjustment, bandpass filtering and background elimination on the pavement structure layer data through ground penetrating radar processing software.

[0007] Mark and locate the abnormal parts in the grayscale image, record the location and area of ​​the disease, and classify and summarize the diseases with similar characteristics;

[0008] Select some typical abnormal images, obtain the pavement core samples at their locations, determine the types of defects and defect characteristic maps. The defect types are divided into three types: hidden cracks, poor interlayer adhesion, and looseness. Supplement the defect type information in the grayscale image.

[0009] The process of using the falling weight deflectometer (FWD) device to obtain the representative deflection value of the disease includes: if the disease type is a crack disease, the deflection value is tested at the top of the disease, and the deflection value at the center point is taken as the representative value of the crack disease; if the disease type is poor interlayer bonding and looseness, the deflection value is tested at the middle position of the disease as the representative value of the disease.

[0010] Preferably, as many normal road surfaces and diseased road surfaces as possible are selected within the detection range to measure the deflection values, and their average values ​​are taken as the deflection values ​​of the normal road surface and the diseased road surface, respectively, for subsequent operation steps.

[0011] Take the difference between the deflection values ​​of the normal road surface and the diseased road surface, and divide the difference by the deflection value of the normal road surface as the influence coefficient of the pavement structure strength of the hidden disease. The calculation formula is as follows:

[0012]

[0013] In the formula, w i represents the influence coefficient of the pavement structure strength of the ith disease, l0 is the deflection value of the normal pavement, l i is the deflection value of the ith disease.

[0014] The process of obtaining the internal condition index of the pavement structure includes: multiplying the disease area and the corresponding influence coefficient for different disease types to obtain the product of different disease types, taking the sum of the products and the ratio of the detected pavement area and multiplying it by 100 to obtain the internal damage rate of the asphalt pavement, and obtaining the internal damage condition index of the pavement structure based on the internal damage rate of the asphalt pavement, and the formula is as follows:

[0015]

[0016] In the formula, A i is the area of ​​the ith disease (m 2 ), A is the area of ​​road surface detection (m 2 ), IDR is the internal damage rate of the pavement structure, IPCI is the internal damage index of the pavement structure, a0 is 15.327, and a1 is 0.405.

[0017] Taking the accumulated area of ​​10 meters of disease as a measurement unit, the internal condition of the pavement is graded according to the calculation results of the internal damage index of the pavement structure. The IPCI score is not less than 90 points for excellent, less than 90 points but not less than 80 points for good, less than 80 points but not less than 70 points for medium, less than 70 points but not less than 60 points for inferior, and less than 60 points for poor. The grading standards are shown in Table 1.

[0018] Table 1 Pavement internal condition classification table

[0019]

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention proposes a method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement, establishes a hidden defect evaluation index based on the difference characteristics of deflection values, reflects the degree of influence of the defect on the overall pavement, and realizes quantitative evaluation of hidden defects; uses the IPCI index to evaluate the damage condition of the internal structure of the pavement, takes into account the influence of the defect on the strength of the pavement structure, improves the accuracy of the evaluation of the internal condition of the pavement, realizes quantitative evaluation of hidden defects, and can provide a theoretical reference for the detection, diagnosis and maintenance decision-making of hidden defects in asphalt pavements. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a method for quantitatively evaluating hidden defects of a semi-rigid base asphalt pavement in an embodiment of the present invention;

[0023] Figure 2 Schematic diagram of FWD deflection test of hidden cracks in an embodiment of the present invention;

[0024] Figure 3 Schematic diagram of FWD deflection test for poor and loose interlayer bonding in an embodiment of the present invention.

[0025] In the figure:

[0026] Upper layer A, middle layer B, lower layer C, base layer D, drop weight 1, sensor 2, bearing plate 3, hidden cracks 4, poor bonding between layers / loose area 5. DETAILED DESCRIPTION

[0027] The following is a further detailed description of a method for quantitatively evaluating hidden defects of a semi-rigid base asphalt pavement according to the present invention in conjunction with the accompanying drawings and specific embodiments:

[0028] Example:

[0029] like Figure 1 As shown in the figure, a quantitative evaluation method for hidden diseases of semi-rigid base asphalt pavement is provided, and the steps are as follows:

[0030] Step 1: Before the test begins, determine the measurement line, radar antenna frequency, time window, gain and other parameters. During the acquisition process, the radar antenna main frequency uses 800MHz. Then use the ground penetrating radar to detect the target area, and perform image processing on the collected data to obtain a grayscale image that is easy to observe the disease.

[0031] Step 2: sort and divide the collected data, classify and summarize the radar images of the abnormal areas, and record their locations and sizes.

[0032] Select some locations for core sampling to determine the hidden disease types corresponding to the abnormal areas; establish a hidden disease feature atlas library for semi-rigid base asphalt pavement, and supplement the disease type information in the grayscale image;

[0033] The types of defects are cracks, poor interlayer adhesion and looseness.

[0034] The radar spectrum characteristics of the crack disease are: the dielectric constant of the road surface where the crack disease is located is smaller than that of the normal road surface, the phase axis is discontinuous, the waveform at the crack position is convex, and there is a strip-shaped strong reflection area. If the crack is air, it enters the crack from the asphalt pavement, the phase is negative, and there are few obvious negative peaks.

[0035] The poor interlayer bonding defect is as follows: poor interlayer bonding will produce gaps between layers, which is manifested as strong amplitude in the layer interface on the radar map. Compared with poor interlayer bonding, voids have a larger impact range in the vertical direction and a stronger reflection.

[0036] The radar spectrum characteristics of loose disease are: the dielectric constant of the road surface where the loose disease is located is smaller than that of the normal road surface, the phase axis is discontinuous, the waveform is messy and irregular, the degree of waveform disorder increases with the increase of looseness, and the change of dielectric constant causes the amplitude to increase.

[0037] Step 3: According to the defect atlas, count the defects in the entire road section, select some sections and use the falling weight deflectometer (FWD) to test the road surface deflection in the target area; the detection parameters of FWD are: the diameter of the detection bearing plate is 30cm, the weight of the weight is (200±10)kg, and the impact load is (50±2.5)kN; the detection section includes normal sections and defect sections, and the FWD test position of the defect position is as follows: Figure 2 and Figure 3 shown.

[0038] Step 4: Calculate the influence coefficient w of the disease on the pavement structure strength i The pavement structure strength influence coefficient of the asphalt pavement disease is calculated by the following formula:

[0039]

[0040] In the formula, w i represents the influence coefficient of the pavement structure strength of the ith disease, l0 is the deflection value of the normal pavement, l i is the deflection value of the ith disease.

[0041] Step 5: Calculate the internal condition evaluation index of the asphalt pavement structure according to the area of ​​the detected road surface and the disease conditions; calculate the damaged area of ​​each disease according to the ground penetrating radar results, multiply the damaged area by the pavement structure strength influence coefficient, and calculate the internal damage rate of the asphalt pavement structure. The damage rate IDR is calculated by the following formula:

[0042]

[0043] In the formula, A i is the area of ​​the ith disease (m 2 ), A is the area of ​​road surface detection (m 2 ), IDR is the internal damage rate of the pavement structure.

[0044] The internal damage index of the pavement structure is obtained by the following formula:

[0045]

[0046] Where, IPCI is the internal damage index of the pavement structure, a0 is 15.327, and a1 is 0.405.

[0047] In order to quantitatively evaluate the integrity of the pavement structure, the PCI evaluation method of the pavement damage condition index is used as a reference; scores are given according to the calculation results of the IPCI internal damage condition index of the pavement structure, with scores of no less than 90 being excellent, less than 90 but not less than 80 being good, less than 80 but not less than 70 being fair, less than 70 but not less than 60 being inferior, and less than 60 being poor.

[0048] The present invention can quantify the influence of hidden defects on the strength of semi-rigid asphalt pavement, realize the evaluation of the internal condition of the semi-rigid asphalt pavement, and provide a reference for the detection, diagnosis and maintenance decision of hidden defects inside the asphalt pavement.

[0049] The examples of the present invention are described in detail above in conjunction with the embodiments, but the present invention is not limited to the above examples. Various changes can be made within the knowledge scope of ordinary technicians in the field without departing from the purpose of the present invention, and should also be regarded as the protection scope of the present invention.

Claims

1. A quantitative evaluation method for hidden defects of semi-rigid base asphalt pavement, characterized in that: The following steps are involved: S1. Use the ground penetrating radar of the same frequency to detect the road surface in the detection area, obtain the grayscale image of the semi-rigid base asphalt road surface, and perform image processing on the collected data to obtain high-quality detection data; S2. Analyze the test data, classify, mark and locate the abnormal positions in the grayscale image, record the location, area and size of the disease, and perform core sampling on some abnormal areas to determine the type of disease; S3. Use a drop weight deflectometer to collect the deflection values ​​of the load center point of the normal road surface and the abnormal area and compare them to determine the influence coefficient of the disease on the pavement structure strength; S4. Quantitatively evaluate the semi-rigid base asphalt pavement based on the influence coefficient and disease area of ​​hidden diseases, and propose a pavement hidden disease damage index.

2. The method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement according to claim 1 is characterized in that: Ground penetrating radar is used to perform nondestructive testing on the semi-rigid base asphalt pavement in the target area. The transmission frequency of the ground penetrating radar antenna is 800MHz, and the radar data processing software is used to perform gain adjustment, background denoising, and bandpass filtering on the collected images to obtain high-quality test data.

3. The method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement according to claim 1 is characterized in that: The grayscale images obtained by the detection are processed, abnormal areas are manually identified, abnormal areas with similar characteristics are classified and summarized, and the location and area of ​​the disease are recorded.

4. The method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement according to claim 1 is characterized in that: Several representative disease locations are selected for core sampling to determine the types and characteristic maps of hidden diseases, and the disease conditions are marked on the grayscale map through marking software; the disease types include hidden cracks, poor interlayer adhesion and looseness; the disease conditions include the type of disease, pile number, depth and size.

5. The method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement according to claim 1 is characterized in that: The deflection of the road surface reflects the overall strength of the road surface. The influence coefficient of the disease on the road surface strength is determined by calculating the loss deflection value. The process of obtaining the disease influence coefficient includes: using FWD equipment to obtain the deflection value of the load center point of the normal road surface and the diseased road surface, taking the difference between the deflection values ​​as the loss deflection, and taking the ratio of the deflection value to the deflection value of the load center point of the normal road surface as the influence coefficient of the disease. The formula is as follows: In the formula, w i represents the influence coefficient of the pavement structure strength of the ith disease, l0 is the deflection value of the normal pavement, l i is the deflection value of the ith disease.

6. The method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement according to claim 5 is characterized in that: The process of obtaining the deflection value of the defect includes: using ground penetrating radar to determine the type of hidden defects inside the semi-rigid asphalt pavement. If the defect type is hidden cracks, using a drop weight deflectometer to detect the deflection value of the load center point of the pile number where the crack apex is located, and use it as the representative value of this defect; if the defect type is poor interlayer adhesion and looseness, using a drop weight deflectometer to detect the deflection value of the load center point of the pile number where the center point of such defects is located, and use it as the representative value of such defects.

7. The method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement according to claim 1 is characterized in that: The area of ​​the disease is multiplied by the corresponding pavement structure strength influence coefficient to obtain the product of different disease types. The ratio of the detection area of ​​the asphalt pavement to the sum of the products is taken and multiplied by 100 to obtain the internal damage rate of the asphalt pavement. The diseases are classified according to the characteristic spectrum of the disease. The internal damage condition index of the pavement structure is obtained based on the internal damage rate of the asphalt pavement. The formula is as follows: In the formula, A i is the area of ​​the ith disease (m 2 ), A is the area of ​​road surface detection (m 2 ), IDR is the internal damage rate of the pavement structure, IPCI is the internal damage condition index of the pavement structure, a0 and a1 are model parameters, and their values ​​are determined later by parameter fitting.

8. The method for quantitatively evaluating hidden defects of semi-rigid base asphalt pavement according to claim 7 is characterized in that: The calculation result of the internal condition index of the pavement structure is used as the score, and the IPCI evaluation of the disease is obtained based on the score of the disease. The evaluation levels are divided into five levels: excellent, good, medium, poor, and poor. An IPCI score of not less than 90 is excellent, less than 90 but not less than 80 is good, less than 80 but not less than 70 is medium, less than 70 but not less than 60 is poor, and less than 60 is poor.

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