Asphalt pavement maintenance decision-making method based on multi-layer disease data

Abstract

The invention discloses an asphalt pavement maintenance decision-making method based on multi-layer disease data, which comprises the following steps: S1, collecting information, and arranging a test line; s2, synchronously collecting appearance and internal structure detection data of the asphalt pavement; s3, classifying and identifying the diseases, and quantifying according to the road surface damage condition index; s4, identifying disease category, horizon, area and depth information; s5, quantifying the internal structure of the asphalt pavement through PSCI; s6, constructing a maintenance scheme decision tree, and determining a maintenance part and a scheme; and S7, collecting the unit price of each maintenance scheme, and calculating and determining the investment fund of the maintenance scheme. The pavement integrity is evaluated through PCI and PSCI, a corresponding maintenance scheme is formulated, and the cost of the maintenance scheme is calculated, so that the treatment precision of the surface layer and the base layer can be positioned to reach the centimeter level, the traditional full-section, full-depth and full-width extensive maintenance is avoided, a large amount of maintenance cost can be saved, and good economic and social benefits are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of asphalt pavement maintenance, and in particular to an asphalt pavement maintenance decision method based on multi-layer disease data. Background Art

[0002] The design service life of my country's expressways is about 10 to 15 years. With the extension of the road service life and the further influence of the natural environment and traffic load, a considerable number of asphalt pavements have developed rutting, cracks and other diseases, resulting in the degradation of pavement performance and structural diseases. Therefore, taking appropriate maintenance measures for asphalt pavements can not only improve driving comfort, but also extend the service life of the road, thereby improving the economy of the road maintenance process throughout its life cycle. The key issue is how to make scientific and reasonable asphalt pavement maintenance decisions.

[0003] At present, the formulation of asphalt pavement maintenance decision-making models still relies on traditional pavement technical condition evaluation methods, which include not only functional indicators such as PCI, RDI, SRI, RQI, PBI, etc. that reflect the apparent performance of the pavement, but also indicators that are difficult to obtain, such as the roadbed condition index SCI and the pavement structure strength index PSSI. Traditional SCI and PSSI detection and evaluation methods not only have problems such as low detection efficiency, strong subjectivity, easy missed detection, and poor accuracy, but also can only reflect the operating status of the pavement when the overall pavement has more serious structural problems. At this time, the best time for maintenance has been missed.

[0004] In summary, traditional detection methods have the problem of difficulty in collecting road data in a refined manner, especially the lack of important internal structural disease data when formulating pavement maintenance decision-making models. This will lead to the inability of the established maintenance decision-making models to accurately reflect the pavement structure behavior and formulate reasonable maintenance plans, making it difficult to guide design and construction units to carry out refined maintenance. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an asphalt pavement maintenance decision-making method based on multi-layer disease data.

[0006] The purpose of the present invention is achieved through the following technical solution: a method for asphalt pavement maintenance decision-making based on multi-layer disease data, comprising the following steps:

[0007] S1: Collect information on the surveyed road section and lay out the test line based on the information;

[0008] S2: Turn on the 3D ground penetrating radar and visual acquisition system to simultaneously collect the surface and internal structure detection data of the asphalt pavement;

[0009] S3: Classify and identify the damage according to its apparent characteristics and specific location, and quantify it according to the pavement damage condition index;

[0010] S4: According to the specific location of the structural damage and combined with the typical atlas of 3D ground penetrating radar damage, the damage type, layer, area and depth information are identified;

[0011] S5: Establish the PSCI, a comprehensive evaluation index for the technical condition of the internal structure of the pavement, to quantify the internal structure of the asphalt pavement;

[0012] S6: Construct a maintenance plan decision tree to determine the maintenance location and maintenance plan;

[0013] S7: Collect the unit price of each maintenance plan and calculate and determine the investment funds for the maintenance plan.

[0014] Preferably, in step S1, the information includes detection range, number of lanes, road surface ride quality index RQI, rutting depth index RDI and road surface skid resistance index SRI.

[0015] Preferably, step S3 further includes the following steps:

[0016] S31: Establish naming rules for apparent pavement damage;

[0017] S32: Calculate the road surface damage index PCI,

[0018]

[0019] Among them, DR is the road surface damage rate, a1 is 0.412, a0 is 15, A i is the cumulative area of ​​the i-th type of road damage, ω i is the weight of the i-th type of road damage, and A is the area of ​​the evaluated road section.

[0020] Preferably, in step S4, the surface information of the investigated road section is collected and defects are marked by a visual acquisition system, the internal structure of the road surface is imaged by a three-dimensional ground penetrating radar, and abnormal bodies are extracted based on the event axes, amplitudes, phases and frequencies of slices in different directions of the three-dimensional ground penetrating radar.

[0021] Preferably, step S5 further includes the following steps:

[0022] S51: Quantify the area of ​​internal structural damage.

[0023]

[0024] Among them, PSCI A SDR is a pavement structure condition index that quantifies the internal structural damage area. Ais the internal structure damage rate, i0 is the total number of damage types, which is 6, i is the disease type of the internal structure of the investigated section, A si is the cumulative area of ​​the i-th type of internal structural damage, A s is the area of ​​the evaluation section, ω si is the weight of the i-th type of internal structural damage, a2 and a3 are fitting parameters;

[0025] S52: Quantify the volume of internal structural damage.

[0026]

[0027] Among them, PSCI V It is a pavement structure condition index quantified by the volume of internal structural damage. If it is a negative value, it is recorded as 0. SDR V is the pavement structure damage rate, h i is the depth of the i-th structural defect, A vi is the area of ​​the ith structural defect, n is the total number of structural defects in the investigated road section, V is the volume of the investigated road surface, a4 and a5 are fitting parameters;

[0028] S53: Set up PSCI A With PSCI V The distance function is PSCI (PSCI A , PSCI V ), the weight distribution coefficients of the two are α and β respectively,

[0029]

[0030] PSCI A , PSCI V ) 2 =(α-β) 2 ;

[0031] Among them, PSCI Ai For PSCI A Sub-index values, PSCI Vi For PSCI V The sub-index values ​​are calculated based on the distribution coefficients α and β to obtain the PSCI, a comprehensive evaluation index of the technical condition of the internal structure of the pavement.

[0032] PSCI=α*PSCI A +β*PSCI V .

[0033] Preferably, in step S51, the types of damages of the internal structure of the road section investigated include loose bodies, voids, cracks, subsidence, delamination and water-rich.

[0034] Preferably, step S6 further includes the following steps:

[0035] S61: Collect the pavement ride quality index (RQI), rutting depth index (RDI) and pavement skid resistance index (SRI) of the investigated road sections from the management and maintenance units;

[0036] S62: Obtain the pavement damage condition index PCI obtained in step S3 and the pavement internal structure technical condition comprehensive evaluation index PSCI obtained in step S5, and then combine the pavement ride quality index RQI, the rutting depth index RDI and the pavement skid resistance index SRI to establish a decision tree strategy model consisting of 8 maintenance plans and 17 branches;

[0037] S63: Import the data of each surveyed road section into the decision tree strategy model to determine the maintenance location and maintenance plan.

[0038] Preferably, in step S7, the cost of the investigated road section is calculated based on the length, area and volume indicators of the treated road section and the maintenance plan formulated in combination with the decision tree strategy model.

[0039] The present invention has the following advantages:

[0040] 1. The present invention obtains the apparent disease data and structural disease data of the road section to be tested, and performs statistics on the distribution areas of the apparent diseases and internal structural diseases to obtain the quantitative indicators PCI and PSCI of the apparent and internal structures, thereby providing more scientific and accurate data support for the construction of the decision tree countermeasure model, thereby extending the service life of the highway and improving the highway service efficiency.

[0041] 2. Compared with the traditional pavement maintenance scheme, the present invention can locate the sections of the surface layer and the base layer that need to be treated with an accuracy of centimeters, avoiding the traditional extensive maintenance of the entire section, full depth and full width, saving a lot of maintenance costs, and having good economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of the asphalt pavement maintenance decision-making process;

[0043] Figure 2 A schematic diagram showing the layout of survey lines for the road section under investigation;

[0044] Figure 3 A schematic diagram of the test line layout for detailed inspection of abnormal locations;

[0045] Figure 4 Schematic diagram of the decision tree strategy model. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0047] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0049] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0050] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use, or the positions or positional relationships commonly understood by those skilled in the art, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0051] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] In this embodiment, if Figure 1 As shown, a method for making a decision on asphalt pavement maintenance based on multi-layer disease data includes the following steps:

[0053] S1: Collect information on the surveyed road section and lay out the test line based on the information;

[0054] S2: Turn on the 3D ground penetrating radar and visual acquisition system to simultaneously collect the surface and internal structure detection data of the asphalt pavement;

[0055] S3: Classify and identify the damage according to its apparent characteristics and specific location, and quantify it according to the pavement damage condition index;

[0056] S4: According to the specific location of the structural damage and combined with the typical atlas of 3D ground penetrating radar damage, the damage type, layer, area and depth information are identified;

[0057] S5: Establish the PSCI, a comprehensive evaluation index for the technical condition of the internal structure of the pavement, to quantify the internal structure of the asphalt pavement;

[0058] S6: Construct a maintenance plan decision tree to determine the maintenance location and maintenance plan;

[0059] S7: Collect the unit price of each maintenance plan, calculate and determine the investment funds for the maintenance plan. Obtain the apparent disease data and structural disease data of the road section to be tested, and statistically analyze the distribution area of ​​the apparent disease and internal structural disease, and obtain the quantitative indicators PCI and PSCI of the apparent and internal structure, so as to provide more scientific and accurate data support for the construction of the decision tree countermeasure model, thereby extending the service life of the highway and improving the highway service efficiency.

[0060] Further, such as Figure 2 As shown, in step S1, the information includes the detection range, the number of lanes, the road quality index RQI, the rutting depth index RDI and the road skid resistance index SRI. Specifically, the information details of the investigated road section are shown in Table 1.

[0061] Table 1

[0062]

[0063] According to the actual situation on site, the test line is laid out along the direction of the lane. The radiation width of the 3D ground penetrating radar is about 1.8 meters, and the width of each lane is about 3.5 meters. In order to ensure full coverage of the first detection, two test lines are laid out in each lane of the main road. After the abnormal position is found, such as Figure 3 As shown, the two-dimensional radar grid-like encrypted survey lines are used for re-survey. The spacing between the encrypted survey lines is no more than 0.5m. The survey lines in key detection areas or detection abnormal areas should be densely laid out or cross-laid.

[0064] Furthermore, step S3 further includes the following steps:

[0065] S31: Establish a naming rule for apparent pavement damage; specifically, the naming rule is as follows: numbered a.bX-c, where a represents the pavement type: 1 represents cement concrete pavement, 2 represents asphalt concrete pavement; b represents the damage category: 1 represents cracking, 2 represents block cracks, 3 represents longitudinal cracks, 4 represents transverse cracks, 5 represents subsidence, 6 represents rutting, 7 represents wave bumps, 8 represents potholes, 9 represents looseness, 10 represents oil spills, and 11 represents repairs; X represents the degree of damage: L represents mild, M represents general, and H represents severe; c represents the damage number, which is represented by a digital sequence starting from 1. Through statistical analysis of the apparent damage diseases in the investigated section, the statistical results of pavement damage in the investigated section are obtained, as shown in Table 2.

[0066] Table 2

[0067]

[0068]

[0069] S32: Calculate the road surface damage index PCI,

[0070]

[0071] Among them, DR is the road surface damage rate, a1 is 0.412, a0 is 15, A i is the cumulative area of ​​the i-th type of road damage, ω i is the weight of the i-th type of road damage, and A is the area of ​​the evaluated road section. Specifically, the road damage index PCI is divided into five levels: excellent, good, medium, poor, and bad. The standards are shown in Table 3.

[0072] Table 3

[0073] Rating excellent good middle Second-rate Difference PCI ≥90 80～90 70～80 60～70 <60

[0074] Among them, in the PCI grade classification standard for the road surface damage index of expressways, "excellent" should be PCI≥92, "good" should be 80≤PCI<92, and the others remain unchanged.

[0075] The pavement damage index PCI was calculated with 1 km as the evaluation unit, and the evaluation results of the pavement apparent technical condition of the investigated road section were obtained, as shown in Table 4.

[0076] Table 4

[0077]

[0078]

[0079] In this embodiment, in step S4, equipment is used to investigate road damage and structural defects, and the visual acquisition system is used to collect the apparent information of the investigated road section and mark the defects. The internal structure of the road surface is imaged by a three-dimensional ground penetrating radar, and abnormal bodies are extracted according to the phase axis, amplitude, phase and frequency of the three-dimensional ground penetrating radar slices in different directions. Specifically, the existing AI automatic recognition technology is used to mark the defects. For the internal structural defects of the road, the internal structure of the road surface is clearly imaged by a three-dimensional ground penetrating radar, and the abnormal bodies are extracted according to the phase axis and the attribute characteristics such as amplitude, phase and frequency of the three-dimensional ground penetrating radar slices in different directions, so as to interpret the three-dimensional ground penetrating radar data and analyze the defects, and then evaluate the structural defects investigated. The rules for expressing structural defects are as follows: The internal structural defects of the inspected road section are obtained through three-dimensional radar detection, and the defect number is XYZ, where X is the defect category, represented by the letters V (void), G (void), L (loose), C (crack), S (sinking), W (water-rich) and D (delamination); Y is the layer where the defect occurs, represented by M (surface layer), J (base layer), D (subbase layer) and T (soil base); Z is the defect number, represented by a digital sequence starting from 1, such as LJ-1, which represents a loose base layer defect with a sequence number of 1. Through statistical analysis of the internal structural defects in the investigated road section, the layer distribution of the internal structural defects of the road surface in the investigated road section is obtained, and the details are shown in Table 5:

[0080] Table 5

[0081]

[0082] Furthermore, step S5 further includes the following steps:

[0083] S51: Quantify the area of ​​internal structural damage.

[0084]

[0085] Among them, PSCI A SDR is a pavement structure condition index that quantifies the internal structural damage area. A is the internal structure damage rate, i0 is the total number of damage types, which is 6, i is the disease type of the internal structure of the investigated section, and preferably, the disease types of the internal structure of the investigated section include loose body, void, crack, subsidence, delamination and rich water, A si is the cumulative area of ​​the i-th type of internal structural damage, A s is the area of ​​the evaluation section, ω si is the weight of the i-th type of internal structural damage, and the pavement structure condition index PSCI based on the diseased area A The damage types and weights are shown in Table 6.

[0086] Table 6

[0087]

[0088] Among them, a2 and a3 are fitting parameters, a2 is 7.684, a3 is 0.546. According to the method of quantifying the internal structural damage area of ​​the pavement, the structural damage of the investigated road sections in Table 5 is quantified and scored, as shown in Table 7.

[0089] Table 7

[0090]

[0091]

[0092] S52: Quantify according to the volume of internal structural defects. In other words, the survey equipment can directly measure the layer, depth, area / length of each structural defect, thereby calculating the "excavation volume" when the structural defect is treated, thereby calibrating and quantifying the volume of the structural defect, which is convenient for subsequent evaluation.

[0093]

[0094] Among them, PSCI V It is a pavement structure condition index that quantifies the volume of internal structural damage. The value range is 0 to 100. If it is a negative value, it is recorded as 0. SDR V is the pavement structure damage rate, is the ratio of the sum of the internal disease volumes of each pavement to the pavement survey volume, h i is the depth of the i-th structural defect, A vi is the area of ​​the ith structural defect, n is the total number of structural defects in the investigated road section, V is the volume of the investigated road surface, which is calculated by multiplying the maximum depth of each structural defect by the area of ​​the investigated road section, a4 and a5 are fitting parameters, a4 is 8.902, and a5 is 0.512; specifically, the quantification method of the internal structural defect volume of the pavement is used to quantify and score the structural damage of the investigated road section, as shown in Table 8.

[0095] Table 8

[0096] Detection range <![CDATA[Pavement volume / m 3 > <![CDATA[∑h i *A vi / m 3 ]]> <![CDATA[SDR v / %]]> <![CDATA[PSCI V <!-- 8 -->]]> K942+265~K952+665 92456 5887.49 6.37 77.03

[0097] S53: To ensure PSCI A With PSCI V The difference between the indicators and their corresponding distribution coefficients remain the same. Let PSCI A With PSCI V The distance function is PSCI (PSCI A , PSCI V ), the weight distribution coefficients of the two are α and β respectively,

[0098]

[0099] PSCI A , PSCI V ) 2 =(α-β) 2 ;

[0100] Among them, PSCI Ai For PSCI A Sub-index values, PSCI Vi For PSCI V The sub-index values ​​of the road surface are calculated based on the distribution coefficients α and β, α is 0.585, β is 0.415, and the comprehensive evaluation index PSCI of the technical condition of the internal structure of the road surface is obtained.

[0101] PSCI=α*PSCI A +β*PSCI V .

[0102] Specifically, the PSCI evaluation standard for the comprehensive evaluation index of the internal structure of the pavement is divided into five levels: excellent, good, medium, poor, and poor. The details are shown in Table 9.

[0103] Table 9

[0104] Rating excellent good middle Second-rate Difference PSCI ≥90 80～90 70～80 60～70 ＜60

[0105] Thus, the internal technical condition scores and grades of the investigated road sections are obtained, as shown in Table 10.

[0106] Table 10

[0107] Detection range <![CDATA[PSCI A ]]> <![CDATA[PSCI V ]]> PSCI grade K942+265~K952+665 61.14 77.03 67.73 Difference

[0108] When the length of a road section is less than 1 km, 100 m is used as an evaluation unit; when the length of a road section is greater than 5 km, 1 km is used as an evaluation unit. Taking the investigated road section as an example, the division and calculation of the 1 km evaluation unit are performed, as shown in Table 11.

[0109] Table 11

[0110] Starting pile number DR / % PCI Rating HDR PSCI Rating K942+200-K943+000 0.63 87.60 good 10.19 62.34 Difference K943+000-K944+000 0.48 88.91 good 16.58 55.05 Very bad K944+000-K945+000 2.11 79.60 middle 3.56 73.06 middle K945+000-K946+000 0.02 96.99 excellent 4.86 70.46 middle K946+000-K947+000 0.03 96.35 excellent 4.53 71.07 middle K947+000-K948+000 1.68 81.43 good 6.68 67.33 Difference K948+000-K949+000 1.23 83.69 good 5.57 69.17 Difference K949+000-K950+000 0.69 87.12 good 5.15 69.92 Difference K950+000-K951+000 1.66 81.53 good 3.92 72.29 middle K951+000-K952+000 0.91 85.56 good 6.19 68.13 Difference K952+000-K952+700 0.14 93.30 excellent 7.43 66.18 Difference average 0.85 87.46 good 6.37 67.73 Difference

[0111] In this embodiment, if Figure 4 As shown, step S6 also includes the following steps:

[0112] S61: Collect the pavement driving quality index RQI, rutting depth index RDI and pavement skid resistance index SRI of the investigated road section from the maintenance unit; specifically, in the early preparation stage, it is necessary to collect the pavement driving quality index RQI, rutting depth index RDI and pavement skid resistance index SRI of the investigated road section from the maintenance unit, thereby obtaining all technical condition indicators for formulating the maintenance decision model, as shown in Table 12.

[0113] Table 12

[0114] Detection range RQI RDI SRI PCI PSCI K942+265~K952+665 85.39 83.70 82.49 87.46 67.73

[0115] S62: Obtain the pavement damage condition index PCI obtained in step S3 and the pavement internal structure technical condition comprehensive evaluation index PSCI obtained in step S5, and then combine the pavement ride quality index RQI, the rutting depth index RDI and the pavement skid resistance index SRI to establish a decision tree strategy model consisting of 8 maintenance plans and 17 branches, so as to achieve full coverage of road sections with various technical conditions;

[0116] S63: Import the data of each surveyed road section into the decision tree strategy model to determine the maintenance location and maintenance plan. Specifically, taking the surveyed road section as an example, its maintenance plan is shown in Table 13.

[0117] Table 13

[0118]

[0119] Furthermore, in step S7, the cost of the investigated road section is calculated based on the length, area and volume indicators of the treated road section, combined with the maintenance plan formulated by the decision tree countermeasure model. Specifically, the unit price of the maintenance treatment measures is collected: after the decision tree countermeasure model formulates the maintenance countermeasure, it is necessary to calculate the maintenance funds invested, according to the quality requirements of the raw materials in the relevant specifications, combined with the raw material prices, equipment usage fees, transportation costs and other factors at the location of the inspection section, and on the basis of visiting the ex-factory prices of various manufacturers, the approximate maintenance cost unit price of each maintenance plan is summarized, as shown in Table 14.

[0120] Table 14

[0121]

[0122] According to the length, area and volume indicators of the treated road section, combined with the maintenance plan formulated by the decision tree strategy model, the cost of the investigated road section is calculated, as shown in Table 15.

[0123] Table 15

[0124]

[0125] Therefore, compared with the traditional road maintenance scheme, the present invention can locate the sections of the surface layer and the base layer that need to be treated with an accuracy of centimeters, avoiding the traditional extensive maintenance and repair of the entire road section, full depth, and full width, saving a lot of maintenance costs. Taking the surveyed section as an example, compared with the traditional construction scheme of milling and resurfacing the entire surface layer and base layer, the present invention can accurately locate the sections of the road that need to be treated with centimeters, avoiding the traditional extensive maintenance and repair of the entire road section, full depth, and full width, saving a lot of maintenance costs. 2 The structural defects can be removed and re-paved, which can save 17500m3 of excavation 3 , accounting for 80.14% of the total excavation volume of the base; compared with the traditional pavement maintenance plan, with other costs unchanged, the total construction cost of the building and installation project is saved by 5.9835 million yuan.

[0126] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for asphalt pavement maintenance decision-making based on multi-layer disease data, characterized by: The following steps are involved: S1: Collect information on the surveyed road section and lay out the test line based on the information; S2: Turn on the 3D ground penetrating radar and visual acquisition system to simultaneously collect the surface and internal structure detection data of the asphalt pavement; S3: Classify and identify the damage according to its apparent characteristics and specific location, and quantify it according to the pavement damage condition index; S4: According to the specific location of the structural damage and combined with the typical atlas of 3D ground penetrating radar damage, the damage type, layer, area and depth information are identified; S5: Establish the PSCI, a comprehensive evaluation index for the technical condition of the internal structure of the pavement, to quantify the internal structure of the asphalt pavement; S6: Construct a maintenance plan decision tree to determine the maintenance location and maintenance plan; S7: Collect the unit price of each maintenance plan and calculate and determine the investment funds for the maintenance plan.

2. The asphalt pavement maintenance decision-making method based on multi-layer disease data according to claim 1 is characterized by: In the step S1, the information includes the detection range, the number of lanes, the road quality index RQI, the rutting depth index RDI and the road skid resistance index SRI.

3. The asphalt pavement maintenance decision-making method based on multi-layer disease data according to claim 1 is characterized by: The step S3 further includes the following steps: S31: Establish naming rules for apparent pavement damage; S32: Calculate the road surface damage index PCI, Among them, DR is the road surface damage rate, a1 is 0.412, a0 is 15, A i is the cumulative area of ​​the i-th type of road damage, ω i is the weight of the i-th type of road damage, and A is the area of ​​the evaluated road section.

4. The asphalt pavement maintenance decision-making method based on multi-layer disease data according to claim 1 is characterized by: In step S4, the visual acquisition system is used to collect the surface information of the investigated road section and mark the defects, the internal structure of the road surface is imaged by a three-dimensional ground penetrating radar, and the abnormal body is extracted according to the phase axis, amplitude, phase and frequency of the three-dimensional ground penetrating radar slices in different directions.

5. The asphalt pavement maintenance decision-making method based on multi-layer disease data according to claim 1 is characterized by: The step S5 further includes the following steps: S51: Quantify the area of ​​internal structural damage. Among them, PSCI A SDR is a pavement structure condition index that quantifies the internal structural damage area. A is the internal structure damage rate, i0 is the total number of damage types, which is 6, i is the disease type of the internal structure of the investigated section, A si is the cumulative area of ​​the i-th type of internal structural damage, A s is the area of ​​the evaluation section, ω si is the weight of the i-th type of internal structural damage, a2 and a3 are fitting parameters; S52: Quantify the volume of internal structural damage. Among them, PSCI V It is a pavement structure condition index quantified by the volume of internal structural damage. If it is a negative value, it is recorded as 0. SDR V is the pavement structure damage rate, h i is the depth of the i-th structural defect, A vi is the area of ​​the ith structural defect, n is the total number of structural defects in the investigated road section, V is the volume of the investigated road surface, a4 and a5 are fitting parameters; S53: Set up PSCI A With PSCI V The distance function is PSCI (PSCI A ,PSCIV ) , the weight distribution coefficients of the two are α and β respectively, PSCI(PSCI A ,PSCI V ) 2 =(α-β) 2 ; Among them, PSCI Ai For PSCI A Sub-index values, PSCI Vi For PSCI V The sub-index values ​​are calculated based on the distribution coefficients α and β to obtain the PSCI, a comprehensive evaluation index of the technical condition of the internal structure of the pavement. PSCI=α*PSCI A +β*PSCI V 。 6. The asphalt pavement maintenance decision-making method based on multi-layer disease data according to claim 5 is characterized by: In the step S51, the types of damages of the internal structure of the road section investigated include loose bodies, voids, cracks, subsidence, delamination and water-rich.

7. The asphalt pavement maintenance decision-making method based on multi-layer disease data according to claim 1 is characterized by: The step S6 further includes the following steps: S61: Collect the pavement ride quality index (RQI), rutting depth index (RDI) and pavement skid resistance index (SRI) of the investigated road sections from the management and maintenance units; S62: Obtain the pavement damage condition index PCI obtained in step S3 and the pavement internal structure technical condition comprehensive evaluation index PSCI obtained in step S5, and then combine the pavement ride quality index RQI, the rutting depth index RDI and the pavement skid resistance index SRI to establish a decision tree strategy model consisting of 8 maintenance plans and 17 branches; S63: Import the data of each surveyed road section into the decision tree strategy model to determine the maintenance location and maintenance plan.

8. The asphalt pavement maintenance decision-making method based on multi-layer disease data according to claim 1 is characterized by: In step S7, the cost of the investigated road section is calculated based on the length, area and volume indicators of the treated road section and the maintenance plan formulated in combination with the decision tree strategy model.

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