An efficient and rapid method for identifying highway pavement damage

Abstract

The present invention belongs to the technical field of pavement damage identification, and specifically discloses a method for efficiently and quickly identifying damage to highway pavements. The method comprises: locating the apparent damage position of the pavement of a selected highway, conducting in-depth detection of the image structure of the corresponding position, detecting the abnormality of the internal base structure at the damage position, and analyzing the degree of roadbed damage; identifying the main crack opening angle and expansion angle of the crack-damaged pavement, analyzing the axial damage gain of the main crack in the crack zone, identifying its axial gain type, and analyzing the superimposed damage gain in the crack zone. Similarly, damage gain identification is performed on the complex zone, which helps to discover potential pavement collapse and sudden cracking risks in advance; by detecting the roadbed curvature and load flow at different damage positions on the pavement, performing differentiated, targeted and in-depth analysis of crack-type and deformation-type damage positions, and exploring the damage gain between different damage points.

Description

Technical Field

[0001] The present invention belongs to the technical field of pavement damage identification, and relates to an efficient and rapid method for identifying pavement damage on expressways. Background Art

[0002] In the current field of traffic infrastructure construction and maintenance, expressways, as important traffic arteries, their pavement conditions directly affect the safety and efficiency of transportation. With the booming development of China's transportation industry, a series of large-scale expressway construction projects such as the Yilai Project (Yilai Expressway Construction Project) have emerged continuously.

[0003] As a traffic link connecting important regions, the Yilai Expressway has a large construction scale, a long route and a complex geographical environment. After completion, it will carry a large amount of traffic flow. In such large-scale projects, the expressway pavement faces various potential damage factors. On the one hand, the complex topographical and geological conditions (such as the mountainous areas passed by the Yilai Project have large terrain undulations and steep slopes) exert different degrees of stress on the pavement, which is likely to cause damage such as cracks, potholes and deformations on the pavement. On the other hand, the high-frequency vehicle driving will accelerate the wear and fatigue damage of the pavement. Therefore, there is an urgent need for an efficient and rapid pavement damage identification method that can meet the needs of the Yilai Project and similar large-scale expressway construction projects, timely and accurately detect pavement damage, ensure the safe operation of expressways, and reduce problems such as traffic interruption and increased maintenance costs caused by pavement damage.

[0004] In the prior art, there are also some solutions related to pavement damage identification. For example, the patent with the Chinese patent publication number CN114202511A discloses a method for detecting pavement marking damage based on computer vision. It collects pavement images, establishes an image sequence according to the pavement images; detects pavement markings in the pavement images and tracks the pavement markings; identifies the damage of pavement markings in the pavement images to obtain damage detection results; and further calculates the damage rate of pavement markings according to the damage detection results. This method improves the detection accuracy.

[0005] However, traditional pavement damage identification methods have many limitations when facing large and complex expressways such as the Yilai Project. For example, when analyzing road apparent defect data, it can often only observe the damage on the pavement surface, and lacks in-depth extended analysis of defect parameters in the defect area. It may not be able to comprehensively cover the internal structure of the pavement, resulting in insufficient in-depth structure detection of the damage location. At the same time, it cannot accurately identify the damage type and degree, which is far from enough for the Yilai Expressway that needs long-term stable operation. Summary of the Invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a method for efficiently and quickly identifying damage to highway pavement is proposed.

[0007] The objectives of the present invention can be achieved through the following technical solutions: The present invention provides a method for efficiently and quickly identifying damage to a highway pavement, the method comprising the following steps: Step 1, obtaining a road body cast contour model of a selected highway, performing an image scan of the selected highway pavement using an unmanned aerial vehicle or a vehicle-mounted scanning device, collecting various damage feature points of the pavement using image recognition technology, such as various feature points of cracks and various feature points of deformations, and numbering each damage feature point as 1, 2, ...point..., d.

[0008] Step 2: Use ray recognition technology to collect the internal image of each damage feature point, extract the internal base structure of each damage feature point, and construct a damage information image model of the selected highway.

[0009] Step 3: From the damage information image model of the selected highway, delineate the crack areas and complex areas, detect the surface damage degree and internal base structure damage degree of each damage feature point on the selected highway pavement, and analyze the roadbed damage degree D of each damage feature point on the selected highway pavement. point The fracture regions are numbered as 1, 2, …f…, d1, and the complex regions are numbered as 1, 2, …c…, d2.

[0010] Step 4: Obtain the geomorphic environment information of the selected highway and analyze a type of damage gain array of the selected highway pavement damage feature points.

[0011] Step 5: Obtain traffic load information of the selected highway and analyze the second-class damage gain array of the selected highway pavement damage feature points.

[0012] Step 6: Based on the first-class damage gain array and the second-class damage gain array of the selected highway pavement damage feature point, construct the damage iteration coefficient of the selected highway pavement damage feature point.

[0013] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention locates the apparent damage position of the road surface by performing image scanning on the road surface of the selected highway, and then conducts in-depth detection of the image structure at the corresponding position. By detecting the abnormal changes in the internal base structure at the damaged position and analyzing the degree of roadbed damage at each damage feature point of the selected highway road surface, the problems existing in the roadbed, such as loose base material, changes in compaction degree, and formation of underground voids, can be understood more accurately.

[0014] (2) By detecting the subgrade curvature and load flow conditions at different damaged positions on the road surface, the present invention can accurately analyze the root causes of crack generation, and accordingly conduct differential and targeted in-depth analysis on the damaged positions of crack types and deformation types, explore the damage gain between different damage points, and can clearly reveal the formation mechanisms of various damaged road surfaces (such as ruts, subsidence, and bumps), thereby contributing to the formulation of highly accurate repair plans.

[0015] (3) By identifying the main crack opening angle and expansion angle of the crack-type damaged road surface, analyzing the axial damage gain of the main crack in the crack area, the present invention can quantitatively evaluate the current state of the crack, and by identifying its axial gain type and analyzing the superimposed load damage gain in the crack area, it helps to determine the expansion speed and degree of the crack in the axial direction, so that the possible future expansion path of the crack can be predicted. Similarly, by identifying the damage gain in the complex area, the potential risks of road surface collapse and sudden cracking can be detected in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic flow chart of the implementation steps of the method of the present invention.

[0018] Figure 2 It is a schematic diagram of the damage information image model of the selected highway of the present invention.

[0019] Figure 3 It is a schematic diagram of the crack area of the present invention.

[0020] Figure 4 It is a schematic diagram of the road body curvature profile with reference to the crack area of the present invention.

[0021] Reference numerals: 1, internal base structure; 2, apparent crack profile; 3, branch crack; 4, spatial boundary of the horizontal plane; 5, spatial boundary of the vertical plane; 6, outer tangent; 7, expansion line. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0023] Please refer to Figure 1 As shown, the present invention provides a method for efficiently and rapidly identifying damages on highway pavements, and the method comprises the following steps: Step 1, obtain the roadbed construction contour model of a selected highway, perform image scanning on the pavement of the selected highway by using a scanning device carried by a drone or a vehicle, collect each damage feature point on the pavement through image recognition technology, such as each feature point of crack types and each feature point of deformation types, and number each damage feature point as 1, 2,... point..., d.

[0024] The crack types include longitudinal cracks and transverse cracks, and the deformation types include rutting, subsidence, and bumping.

[0025] The longitudinal cracks extend along the driving direction, with a relatively regular shape, usually presenting as straight lines or slightly curved; the transverse cracks are perpendicular to the driving direction and are mostly straight lines.

[0026] Step 2, collect the internal images of each damage feature point through ray recognition technology, extract the internal base structures of each damage feature point, and construct a damage information image model of the selected highway.

[0027] In a preferred embodiment, the specific method for constructing the damage information image model of the selected highway is as follows: mark each damage feature point on the pavement in the roadbed construction contour model of the selected highway, use computer vision technology to extract the internal base structures in the internal images of each damage feature point, and import them into the roadbed construction contour model of the selected highway to obtain the damage information image model of the selected highway.

[0028] Please refer to Figure 2 、 3 As shown, Step 3, demarcate each fissure area and each complex area from the damage information image model of the selected highway, detect the surface damage degrees and the internal base structure damage degrees of each damage feature point on the pavement of the selected highway, analyze the subgrade damage degree D point of each damage feature point on the pavement of the selected highway, and number each fissure area as 1, 2,... f..., d1, and number each complex area as 1, 2,... c..., d2.

[0029] In a preferred embodiment, the specific method for detecting the surface damage degree of each damage feature point on the pavement of the selected highway is as follows: extract the crack lengths and crack widths of each feature point of crack types and the shape depths and aggregate pushing degrees of each feature point of deformation types from the damage position coordinate distribution map of the selected highway, record them as the apparent damage feature detection values of each damage feature point, calculate the ratios of them to the corresponding preset reference values of the apparent damage features, and then sum them to obtain the surface damage degrees of each damage feature point on the pavement of the selected highway.

[0030] The shape depth of each deformation characteristic point includes rutting depth, subsidence depth, and bump height. The aggregate displacement degree of the deformation characteristic point refers to the ratio between the volume of the displaced accumulation of the laying base material due to the deformation of the road surface and the preset reference accumulation volume.

[0031] In a further preferred embodiment, the analysis of the degree of subgrade damage at each damage feature point on the highway pavement includes: screening each crack-type feature point from the damage information image model of the selected highway, and extracting the apparent crack contour of each crack-type feature point, obtaining the deepest point position of each crack-type feature point along the vertical and horizontal planes in the damage information image model, using them as the spatial boundaries of the horizontal and vertical planes, respectively, to delineate the spatial area, and accordingly delineate the crack damage area of each crack-type feature point, which is recorded as each crack area.

[0032] The internal base structure of each crack zone is extracted from the internal base structure of each damage feature point, the apparent crack contour of each crack zone is used as the main crack, and the intersection angle between each branch crack contour and the main crack is obtained from the internal base structure of each crack zone. And obtain the embedded crack volume V of each branch crack in each crack zone (f，b) , analyze the damage degree of the base structure in each crack area In the formula represents the preset cross angle compensation value, V0 represents the preset reference embedded crack volume, b represents the number of each branch crack corresponding to the crack area, b = 1, 2, ..., a.

[0033] Specifically, when the branch crack profile intersects the main crack at a small angle (acute angle), the stress concentration phenomenon will be more obvious near the intersection point, that is, the smaller the intersection angle, the more serious the damage to the base structure.

[0034] The embedded crack volume refers to the total volume of cracks formed by branch cracks extending toward the roadbed subgrade.

[0035] The deformation feature points are screened from the damage information image model of the selected highway, and the apparent deformation contours of the deformation feature points are extracted. The base layer corresponding to the internal base structure of each deformation feature point is identified, and the paving structure data of a certain base layer exceeds the preset standard value of the corresponding base layer corresponding to the paving structure data, and the paving structure data of each adjacent base layer do not exceed the preset standard value of the corresponding base layer corresponding to the paving structure data. The base layer is used as the deformation space boundary of the corresponding deformation feature point, and the deformation damage area of each deformation feature point is circled, recorded as each complex area, and the base structure damage degree D″ of each complex area is analyzed. c , including various paving structure data such as paving thickness and road crown slope.

[0036] The damage degree of the base structure of each crack area and each complex area is summarized to obtain the damage degree D″ of the base structure of each damage characteristic point on the highway pavement. point , and then analyze and obtain the roadbed damage degree D of each damage characteristic point on the highway pavement point =D″ point *D′ point , D′ point Indicates the surface damage degree of the point-th damage feature point on the selected highway pavement.

[0037] In a further preferred embodiment, the analysis of the damage degree of the base structure of each complex area includes: extracting the internal base structure of each complex area from the internal base structure of each damage feature point, obtaining the paving structure data of each base corresponding to the deformed damaged area of each complex area, subtracting the paving structure data of each base corresponding to the deformed damaged area of each complex area from the preset standard value of the paving structure data of the base corresponding to the corresponding road area, and then comparing the difference with the preset reference difference, and summing the difference to obtain the paving quality change coefficient P of each base corresponding to the deformed damaged area of each complex area. (c,r) , r represents the number of the base layer, r = 1, 2, ..., g.

[0038] Detect the interlayer impurity content Su of each base layer and its adjacent base layer in each complex area (c,r) and interlaminar crack opening Ca (c,r) , analyze the damage degree of the base structure in each complex area Where Su′ and Ca′ represent the preset interlayer reference impurity content and interlayer reference crack opening, respectively.

[0039] The interlayer debris includes debris left over from construction (uncleaned loose materials, discarded construction material fragments), debris brought into the environment (dust and soil, plant seeds and roots), and material aging or chemical reaction products (asphalt aging products, chemical crystals).

[0040] The interlayer crack refers to a crack extending along the horizontal plane between the layers of adjacent base layers.

[0041] The present invention scans the pavement of a selected highway to locate the apparent damage position of the pavement, and then conducts in-depth detection of the image structure at the corresponding position. By detecting the abnormal changes in the internal base structure at the damaged position and analyzing the degree of roadbed damage at each damage characteristic point on the selected highway pavement, the present invention can more accurately understand the problems existing in the roadbed, such as loose base material, changes in compaction, and the formation of underground voids.

[0042] Step 4: Obtain the geomorphic environment information of the selected highway and analyze a type of damage gain array of the selected highway pavement damage feature points.

[0043] The landform environment information includes the road slope and road curvature of the road section.

[0044] See also Figure 4 As shown, in a preferred embodiment, the damage gain array of each damage feature point of the statistically selected highway pavement includes: obtaining the road slope and road curvature of the road section to which each damage feature point belongs from the damage information image model of the selected highway.

[0045] Select any crack area as the reference crack area, obtain the road curvature of the reference crack area from the road curvature of the road section to which each damage characteristic point belongs, and obtain the road curvature profile of the reference crack area, locate the external tangent line, and compare it with the apparent crack profile of the reference crack area to obtain the main crack opening angle of the reference crack area

[0046] The external tangent positioning method is: extracting the maximum arc point from the road body curvature profile, and making a tangent to the road body curvature profile at this point.

[0047] The perpendicular line of the external tangent line of the road curvature contour of the reference crack area is used as the expansion line, and the intersection angle of the corresponding branch crack contour of the reference crack area with the expansion line in the horizontal direction is extracted. The embedded crack volume V of each branch crack in the reference crack zone is extracted from the embedded crack volume of each branch crack in each crack zone. b′ , identify the main crack expansion angle of the reference crack zone b′ represents the number of each branch crack corresponding to the transverse reference crack zone, b′=1, 2, ..., a′.

[0048] Analysis of the main crack axial damage gain in the reference crack zone in represents the preset reference opening angle of the main crack, γ represents the road slope of the reference crack area in the road slope of each damage characteristic point, Represents the preset reference road slope, and the corresponding main crack axial damage gain X of each crack zone is obtained in this way. f , π represents 180°.

[0049] Match the corresponding deformation type of each complex area with the preset corresponding morphological complexity basic damage gain of each deformation type to obtain the corresponding morphological complexity basic damage gain β of each complex area c , analyze the corresponding morphological complexity damage gain of each complex area where γ c represents the road slope of the cth complex area in the road slope of the road section to which each damage feature point belongs, λ cIt represents the road body curvature of the c-th complex area in the road body curvature of the section to which each damage characteristic point of the road surface belongs. It respectively represents the preset reference road body slope and reference road body curvature.

[0050] Exemplarily, the basic damage gain of the corresponding morphological complexity of each deformation type is set as follows: the basic damage gain of the morphological complexity of the rut type is set to β = 0.3, the basic damage gain of the morphological complexity of the subsidence type is set to β = 0.3, and the basic damage gain of the morphological complexity of the bump type is set to β = 0.4.

[0051] Statistically analyze the array of the first type of damage gain of the damage characteristic points on the selected highway road surface. Among them, X1 represents the axial damage gain of the main crack in the first crack area, Y1 respectively represents the damage gain of the corresponding morphological complexity of the first one, f + c = d, d is the total number of damage characteristic points, f is the total number of crack areas, and c is the total number of complex areas.

[0052] Step Five: Obtain the traffic load information of the selected highway, and analyze the array of the second type of damage gain of the damage characteristic points on the selected highway road surface.

[0053] The traffic load information is the daily traffic volume of each axle heavy vehicle on the section.

[0054] In a preferred implementation manner, the statistical analysis of the array of the second type of damage gain of each damage characteristic point on the selected highway road surface includes: monitoring the daily traffic volume of each axle heavy vehicle on the corresponding local section through the cameras set on the local sections of the highway, matching the daily traffic volume of each axle heavy vehicle on each local section with the section to which each damage characteristic point of the road surface belongs, and obtaining the daily traffic volume Q( point,h ) of each axle heavy vehicle on the section to which each damage characteristic point of the road surface belongs, and analyzing the load coefficient of the section to which each damage characteristic point of the road surface belongs. Among them, κh represents the corresponding tire set weight of each axle heavy vehicle preset, Q0 represents the preset reference daily traffic volume, and h represents the number of each axle heavy vehicle, h = 1, 2,..., m.

[0055] Each axle heavy vehicle such as a large truck, semi-trailer, etc. has different contact areas and contact pressures between its corresponding tires and the road surface.

[0056] Obtain each crack area among the damage characteristic points on the road surface, identify the main crack axial gain type of each crack area, and combine the road body slope and road body curvature of the section to which each damage characteristic point of the road surface belongs to analyze the superimposed load damage gain X′ f .

[0057] Obtain the corresponding deformation type of each complex area in each damage feature point of the road surface, and determine the corresponding basic damage gain of each complex area by complicating the basic form of the damage gain. Analyze the superimposed damage gain of each complex area Among them G c It represents the load coefficient of the section to which the cth complex area belongs among the load coefficients of the section to which each damage characteristic point of the road surface belongs.

[0058] Statistically select the second type of damage gain array of the highway pavement damage feature points Where X′1 represents the overlay damage gain of the first crack region, and Y′1 represents the overlay damage gain of the first complex region.

[0059] In a further preferred embodiment, the method for identifying the main crack axial gain type of each crack zone is: determining the main crack axial gain type indicator of the reference crack zone Among them, when T0=0, it means that the axial gain type of the main crack in the reference crack zone is the transverse roadbed settlement gain, and when T0=1, it means that the axial gain type of the main crack in the reference crack zone is the longitudinal side instability gain. Then, the axial gain type of the main crack in each crack zone is obtained in this way.

[0060] The axial types of the main cracks include transverse roadbed settlement gain and longitudinal side body instability gain, wherein the transverse roadbed settlement gain refers to the possibility of the roadbed being interrupted in a direction perpendicular to the driving direction, and the longitudinal side body instability gain refers to the possibility of the roadbed collapsing toward the edge of the road.

[0061] In a further preferred embodiment, the analysis of the overload damage gain of each crack zone is as follows: the main crack axial gain type of each crack zone is obtained. When the main crack axial gain type of a crack zone is the longitudinal side body instability gain, the boundary distance L0 between the center position of the corresponding main crack in the crack zone and the corresponding soil shoulder position of the roadbed is obtained, and the main crack length L'0 of the crack zone is extracted to analyze the longitudinal roadbed slope instability spatial weight of the crack zone. in They represent the preset reference boundary distance and main crack reference length respectively, and e is a natural constant.

[0062] When the main crack axial gain type of the crack zone is the transverse roadbed settlement gain, the corresponding main crack width w0 of the crack zone and the road body slope γ0 of the road section are obtained to analyze the spatial weight of the transverse roadbed settlement of the crack zone. Where V0′ represents the preset main crack reference area.

[0063] Obtain the superimposed load damage gain X′0 of this crack area, where X′0 = η0 * G0, and G0 represents the load coefficient of the section to which this crack area belongs among the load coefficients of the sections where each damage characteristic point of the road surface is located. Analyze the superimposed load damage gains of each crack area according to this method.

[0064] By identifying the main crack opening angle and expansion angle of the cracked pavement, and analyzing the axial damage gain of the crack area, the present invention can quantitatively evaluate the current state of the crack, and by identifying the type of its axial gain and analyzing the superimposed load damage gain of the crack area, it helps to determine the expansion speed and degree of the crack in the axial direction, so that the possible future expansion path of the crack can be predicted. Similarly, by identifying the damage gain of the complex area, potential road surface collapse and sudden crack risks can be detected in advance.

[0065] Step Six: Based on the first-class damage gain array and the second-class damage gain array of the selected highway pavement damage characteristic points, construct the damage iteration coefficient of the selected highway pavement damage characteristic points.

[0066] In a preferred embodiment, the construction of the damage iteration coefficient of the selected highway pavement damage characteristic points is specifically as follows: Take the dot product of the first-class damage gain array and the second-class damage gain array of each damage characteristic point of the selected highway pavement to obtain the damage gain scalar U of the selected highway pavement damage characteristic points, and then construct the damage iteration coefficient of the selected highway pavement damage characteristic points.

[0067] Exemplarily, the dot product calculation method is as follows:

[0068] By detecting the subgrade curvature and load flow conditions at different damage positions on the road surface, the present invention can accurately analyze the root cause of crack generation, and based on this, conduct differential and targeted in-depth analysis of crack-type and deformation-type damage positions, explore the damage gains between different damage points, and can clearly reveal the formation mechanism of each damaged road surface (such as rutting, subsidence, and bumping), thus helping to formulate a highly accurate repair plan.

[0069] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or adopt similar substitution methods, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. An efficient and rapid method for identifying highway pavement damage, characterized in that, The method includes the following steps: Step 1. Obtain the road body casting contour model of the selected expressway. Use the scanning equipment carried by drones or vehicles to scan the road surface of the selected expressway, and collect each damage feature point on the road surface through image recognition technology, including each feature point of crack type and each feature point of deformation type, and number each damage feature point as ; Step 2: Collect the internal images of the road body at each damage feature point through ray recognition technology, extract the internal base structure of each damage feature point, and construct a damage information image model of the selected highway. Step 3: Encircle each fissure area and each complex area from the damage information image model of the selected expressway, detect the surface damage degree and the internal subgrade structure damage degree of each damage feature point on the selected expressway pavement, and analyze the subgrade damage degree of each damage feature point on the selected expressway pavement , and number each fissure area as , and number each complex area as ; Step 4: Obtain the geomorphic environment information of the selected highway and analyze the first-class damage gain array of the pavement damage feature points of the selected highway. Step 5: Obtain the traffic load information of the selected highway and analyze the second-class damage gain array of the pavement damage feature points of the selected highway. Step 6: Based on the first-class damage gain array and the second-class damage gain array of the pavement damage feature points of the selected highway, construct the damage iteration coefficient of the pavement damage feature points of the selected highway. The damage iteration coefficient for constructing the damage characteristic points of the selected highway pavement is specifically as follows: perform a dot product on the first-class damage gain array and the second-class damage gain array of each damage characteristic point on the selected highway pavement to obtain the damage gain scalar of the damage characteristic points on the selected highway pavement , and then construct the damage iteration coefficient for the damage characteristic points of the selected highway pavement .

2. The efficient and rapid damage identification method for highway pavement according to claim 1, characterized in that The specific method for constructing the damage information image model of the selected highway is as follows: Mark each pavement damage feature point in the road body casting contour model of the selected highway, and use computer vision technology to extract the internal base structure in the internal image of the road body at each damage feature point, and import it into the road body casting contour model of the selected highway to obtain the damage information image model of the selected highway.

3. The high-efficiency and rapid damage identification method for highway pavement according to claim 1, characterized in that, The specific method for detecting the surface damage degree of each pavement damage feature point of the selected highway is as follows: Extract the crack length and crack width of each feature point of the crack type and the shape depth and aggregate pushing degree of each feature point of the deformation type from the damage position coordinate distribution map of the selected highway, record them as the detection values of the apparent damage features of each damage feature point, and take the ratio of them to the corresponding preset reference values of the apparent damage features, and then sum them to obtain the surface damage degree of each pavement damage feature point of the selected highway.

4. The efficient and rapid damage identification method for highway pavement according to claim 1, wherein The content of analyzing the subgrade damage degree of each pavement damage feature point of the selected highway includes: Screen each feature point of the crack type from the damage information image model of the selected highway, extract the apparent crack contour of each feature point of the crack type, obtain the deepest point position of each feature point of the crack type cracking along the vertical plane and the horizontal plane in the damage information image model, and use them as the spatial boundaries of the horizontal plane and the vertical plane respectively to conduct spatial region delineation, and accordingly delineate the fissure damage region of each feature point of the crack type, which is recorded as each fissure region. Extract the internal basic structure of each fracture zone. Take the apparent crack contour of each fracture zone as the main crack, and obtain the intersection angle between each branch crack contour and the main crack from the internal basic structure of each fracture zone , and obtain the embedded fracture volume of each branch crack in each fracture zone , and analyze the damage degree of the basic structure of each fracture zone , where represents the preset intersection angle compensation value, represents the preset reference embedded fracture volume, represents the number of each corresponding branch crack in the fracture zone, ; Screen deformation-related feature points from the damage information image model of the selected highway, extract the apparent deformation contours of deformation-related feature points, demarcate the deformation damage areas of deformation-related feature points, denoted as each complex area, and analyze the damage degree of the base structure of each complex area ; Summarize the damage degrees of the base structures in each crack area and each complex area to obtain the damage degrees of the base structures at each damage characteristic point on the expressway pavement , and then analyze to obtain the damage degrees of the subgrades at each damage characteristic point on the expressway pavement , Denote the surface damage degree of the th damage characteristic point on the selected expressway pavement.

5. The efficient and rapid damage identification method for highway pavement according to claim 4, wherein The content of analyzing the base structure damage degree of each complex region includes: Extract the internal base structure of each complex region to obtain the paving structure data of each base corresponding to the deformation damage region of each complex region, and calculate the paving quality change coefficient of each base corresponding to the deformation damage region of each complex region. Detect the interlayer debris content and interlayer crack opening degree between each base of each complex region and its adjacent base, and accordingly analyze the base structure damage degree of each complex region.

6. The efficient and rapid damage identification method for highway pavement according to claim 1, characterized in that, The content of counting the first-class damage gain array of each pavement damage feature point of the selected highway includes: Obtain the road body slope and road body curvature of the section where each pavement damage feature point of the selected highway is located from the damage information image model of the selected highway. Select any fissure area as the reference fissure area, obtain the road body curvature of the reference fissure area, and obtain the road body curvature contour of the reference fissure area. Locate the outer tangent line and compare it with the apparent crack contour of the reference fissure area to obtain the main crack opening angle of the reference fissure area , ; Taking the vertical line of the outer tangent corresponding to the road body curvature contour of the reference fissure area as the expansion line, extract the intersection angles of the corresponding branch crack contours in the reference fissure area with the expansion line in the horizontal plane direction , and extract the embedded fissure volume of the corresponding branch cracks in the reference fissure area , identify the main crack expansion angle of the reference fissure area , indicating the numbers of the corresponding branch cracks in the transverse reference fissure area, ; Analyze the axial damage gain of the main crack in the reference fracture zone , where represents the preset opening angle of the main crack reference, represents the slope of the road body in the reference fracture zone, represents the preset reference slope of the road body. The axial damage gain of the corresponding main crack in each fracture zone is obtained in this way , represents ; Obtain the corresponding morphological complication-based damage gains for each complex region, and analyze the corresponding morphological complication damage gains for each complex region ; A class of damage gain arrays for statistically analyzing the pavement damage characteristic points of selected expressways , where represents the axial damage gain of the main crack corresponding to the first crack area, respectively represent the corresponding morphological complexity damage gain of the first one, , is the total number of damage characteristic points, is the total number of crack areas, is the total number of complex areas.

7. An efficient and rapid damage identification method for highway pavement according to claim 6, characterized in that, The content of counting the second-class damage gain array of each pavement damage feature point of the selected highway includes: Monitor the daily traffic volume of heavy-duty vehicles on each axle of the corresponding local section through the cameras set on the highway, match the daily traffic volume of heavy-duty vehicles on each axle of each local section with the section to which each damage characteristic point of the road surface belongs, obtain the daily traffic volume of heavy-duty vehicles on each axle of the section to which each damage characteristic point of the road surface belongs, and analyze the load coefficient of the section to which each damage characteristic point of the road surface belongs ; Obtain each crack area among the damage characteristic points of the road surface, identify the main crack axial gain type of each crack area, and analyze the superimposed load damage gain of each crack area ; Obtain the corresponding deformation types of each complex area among the damage feature points on the road surface, determine the corresponding basic form load damage gain of each complex area, and analyze the superimposed load damage gain of each complex area ; Statistically select the two - type damage gain array of the pavement damage characteristic points on the expressway , where represents the superimposed load damage gain of the first crack area, respectively represent the superimposed load damage gain of the first complex area.

8. The method for efficiently and rapidly identifying damages on an expressway pavement according to claim 7, wherein The corresponding method for identifying the axial gain type of the main crack in each crack area is as follows: determining the indication of the axial gain type of the main crack in the reference crack area , where It indicates that the axial gain type of the main crack with reference to the fissure area is the lateral roadbed settlement gain, It indicates that the axial gain type of the main crack with reference to the fissure area is the longitudinal side body instability gain, and thus the axial gain type of the main crack in each fissure area is obtained according to this method.

9. The method for efficiently and rapidly identifying damages on a highway pavement according to claim 8, characterized in that, The process of analyzing the superimposed load damage gain of each fissure region is as follows: Obtain the axial gain type of the main crack in each fissure zone. When the axial gain type of the main crack in a certain fissure zone is longitudinal side body instability gain, obtain the boundary distance between the corresponding main crack center position in this fissure zone and the corresponding earthen shoulder position of the subgrade , and extract the corresponding main crack length of this fissure zone , and analyze the longitudinal subgrade slope instability spatial weight of this fissure zone , where respectively represent the preset reference boundary distance, the main crack reference length, and e is the natural constant; When the axial gain type of the main crack in the fissure area is the transverse roadbed settlement gain, obtain the corresponding main crack width of the fissure area , the roadbed slope of the road section , analyze the transverse roadbed settlement spatial weight of the fissure area , where represents the preset reference area of the main crack; Obtain the superimposed load damage gain of this crack area , represents the load coefficient of the section to which the crack area belongs among the load coefficients of the sections to which each damage characteristic point of the road surface belongs. The superimposed load damage gain of each crack area is obtained by analyzing in this way.

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