Method and device for monitoring ruts on asphalt pavement

Abstract

The invention discloses a method and a device for monitoring ruts on an asphalt pavement, and relates to the technical field of asphalt pavement detection. Receiving scanning data of a transverse full section of the target road, and calculating an old pavement area and a new pavement area in the scanning data to obtain a first rut depth and a second rut depth; when the first rut depth or the second rut depth is larger than or equal to the depth threshold value, it is determined that the old pavement area or the new pavement area is in a preliminary rut abnormal state; the sinking depth value of the longitudinal construction joint area is larger than or equal to the sinking depth threshold value, and if the first transverse distance is smaller than the second transverse distance and the old pavement area is in the initial rut abnormal state, it is confirmed that the old pavement local deterioration state caused by the construction joint exists; and if the first transverse distance is greater than the second transverse distance and the new pavement area is in the initial rut abnormal state, determining that a new pavement local deterioration state caused by the construction joint exists. By implementing the technical scheme provided by the invention, local deterioration caused by the construction joint can be accurately reflected.

Description

Technical Field

[0001] This application relates to the field of asphalt pavement testing technology, specifically to a method and device for monitoring rutting on asphalt pavements. Background Technology

[0002] In recent years, to meet the ever-increasing traffic demand, road expansion projects have accounted for a continuously rising proportion of highway construction. Due to their special structural form, expanded roads include both existing and newly constructed road surfaces, connected by longitudinal construction joints. This composite road surface has more complex stress characteristics and deformation patterns than ordinary roads.

[0003] In the field of road rutting detection, three-dimensional laser scanning technology has been widely applied. This technology utilizes a vehicle-mounted laser scanning system to continuously collect data on the road surface, acquiring cross-sectional elevation data, and extracting geometric parameters such as the depth and width of ruts and wheel tracks through digital signal processing methods. Combined with the virtual ruler method, the specific numerical values ​​of road rutting can be calculated, providing a quantitative basis for road condition assessment.

[0004] However, existing rutting detection technologies are mainly designed for ordinary roads with homogeneous structures, making them unsuitable for the inspection needs of expanded pavements. Due to differences in material properties and service life between new and old pavement sections in expanded pavements, and the tendency for stress concentration in construction joint areas, rutting development exhibits significant regional variations. Using a uniform standard for inspection and evaluation cannot accurately reflect the actual damage state of asphalt pavements in different areas caused by construction joints. Summary of the Invention

[0005] This application provides a method and device for monitoring rutting on asphalt pavement. This method effectively solves the problem that the unified evaluation standard cannot accurately reflect the problems caused by construction joints in the expanded pavement. It can accurately reflect whether the damage is caused by local deterioration due to construction joints or by the overall degradation of pavement performance.

[0006] Firstly, this application provides a method for monitoring rutting on asphalt pavement. The method includes: receiving transverse full-section scanning data of a target road; dividing the target road into an old pavement area, a new pavement area, and a longitudinal construction joint area based on the scanning data; calculating the first rutting depth and the second rutting depth for the old pavement area and the new pavement area respectively using a virtual string method; confirming that the old pavement area or the new pavement area is in a preliminary rutting anomaly state when the first rutting depth is greater than or equal to a first depth threshold, or the second rutting depth is greater than or equal to a second depth threshold; and when the old pavement area or the new pavement area is in a preliminary rutting anomaly state, determining that the new pavement area has been opened to traffic. The first duration determines the sinking depth threshold; if the sinking depth value of the longitudinal construction joint area is greater than or equal to the sinking depth threshold, the first lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the first rut depth, and the second lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the second rut depth are calculated; if the first lateral distance is less than the second lateral distance, and the old road surface area is in a preliminary rutting abnormal state, it is confirmed that the target road has a local deterioration state of the old road surface caused by the construction joint; if the first lateral distance is greater than the second lateral distance, and the new road surface area is in a preliminary rutting abnormal state, it is confirmed that the target road has a local deterioration state of the new road surface caused by the construction joint.

[0007] By adopting the above technical solution, transverse full-section scanning data of the target road is received, and the road is divided into old pavement area, new pavement area, and longitudinal construction joint area based on the scanning data. This enables accurate identification of the composite structural characteristics of the expanded pavement. The rutting depth of the old and new pavement areas is calculated separately using a virtual string line method, overcoming the problem that traditional single detection methods cannot reflect regional differences. By setting differentiated depth thresholds for anomaly judgment, the problem that a unified evaluation standard is not applicable to expanded pavements is effectively solved. After anomaly detection, a subsidence depth threshold judgment mechanism based on the opening time of the new pavement is used. By analyzing the spatial relationship between the subsidence depth of the construction joint area and the rutting location, accurate identification of the causes of rutting is achieved. In particular, by comparing the transverse distance between the construction joint centerline and the first and second rutting depths, a correlation criterion between the influence range of the construction joint and rutting development is established. This can accurately distinguish whether the damage is caused by local deterioration due to the construction joint or by overall pavement performance degradation, thereby improving the accuracy and reliability of rutting detection for expanded pavements.

[0008] Optionally, before the first rut depth is greater than or equal to a first depth threshold, the first depth threshold needs to be determined. This specifically includes: acquiring historical traffic data for the old road surface area; classifying vehicles in the historical traffic data into various vehicle types according to axle load; acquiring actual axle load data for each vehicle type; averaging multiple actual axle load data to obtain the average axle load value for each vehicle type; dividing the average axle load value by the standard axle load to obtain the vehicle type conversion coefficient for each vehicle type; acquiring the daily traffic volume for each vehicle type from the historical traffic data, and acquiring the second duration of open traffic in the old road surface area; multiplying the daily traffic volume, the second duration, and the vehicle type conversion coefficient to obtain the historical cumulative equivalent axle loads; conducting fatigue tests on the old road surface area to obtain fatigue life parameters, based on... The fatigue life parameter is used to calculate the fatigue damage degree of the material; historical maintenance records of the old pavement area are retrieved, and maintenance time and disease treatment effect scores are obtained from the historical maintenance records; the difference between the current time and the maintenance time is multiplied by the attenuation constant to obtain the time attenuation factor; the time attenuation factor is multiplied by the disease treatment effect score to obtain the single maintenance impact factor; the number of maintenance is obtained from the historical maintenance records, and the number of maintenance is multiplied by the single maintenance impact factor to obtain the sum of maintenance impact factors; the difference between the sum of maintenance impact factors and the preset sum is calculated, and the difference is used as the repair impact coefficient, with the preset sum being 1; the standard rut depth threshold, historical cumulative equivalent axle number, material fatigue damage degree, and repair impact coefficient are multiplied to obtain the first depth threshold.

[0009] By adopting the above technical solutions, historical traffic data was classified by vehicle type and axle load converted, establishing an equivalent axle load calculation method based on actual load characteristics, thus avoiding the load impact assessment bias caused by traditional simple accumulation methods. Material life parameters were obtained through fatigue testing and converted into fatigue damage degree, achieving a quantitative characterization of the degree of pavement structural performance degradation. Furthermore, a repair impact evaluation mechanism considering the timeliness of maintenance effects was implemented. By calculating the coupling effect of time decay factor and disease treatment effect, the continuous impact of each maintenance on pavement performance was accurately reflected. The threshold determination method, which organically combines the cumulative effect of traffic load, the evolution law of material properties, and the impact of maintenance repair, not only improves the rationality and reliability of rutting depth control indicators but also allows for dynamic adjustment based on the actual service condition of the pavement, providing a basis for rutting monitoring of expanded roads.

[0010] Optionally, before the second rut depth is greater than or equal to the second depth threshold, the second depth threshold needs to be determined. This specifically includes: obtaining the field compaction degree from multiple test points in the new pavement area; determining the theoretical maximum density of the new pavement area using a non-destructive method; and counting the number of test points in the new pavement area; dividing the quotient of the field compaction degree and the theoretical maximum density by the number of test points to obtain the average compaction degree index; drilling asphalt mixture core samples from the new pavement area; conducting dynamic creep tests on the asphalt mixture core samples to obtain dynamic stability values ​​under different stress levels; dividing the difference between the dynamic stability value and the dynamic stability benchmark value by the dynamic stability benchmark value to obtain the high-temperature stability coefficient; and continuously collecting data from the new pavement area. The system collects data on various vehicle types and axle loads within a preset time period. Axle load data is divided by the average axle load of each vehicle type to obtain a vehicle type conversion coefficient. This conversion coefficient is then multiplied by the daily traffic volume of each vehicle type to obtain the daily equivalent axle load. The system obtains the first daily equivalent axle load corresponding to a first time point and the second daily equivalent axle load corresponding to a second time point, with the first and second time points being adjacent. The system calculates the axle load growth rate based on the first and second daily equivalent axle loads. The initial load growth rate is determined based on the axle load growth rate and the preset time period. Finally, the system multiplies the standard rut depth threshold, average compaction index, high-temperature stability coefficient, and initial load growth rate to obtain the second depth threshold.

[0011] By adopting the above technical solutions, the on-site compaction degree was obtained and the average compaction degree index was calculated based on non-destructive testing methods, effectively reflecting the uniformity of the initial density of the pavement and providing a reliable basis for evaluating construction quality. Dynamic creep tests were conducted on asphalt mixture core samples to obtain dynamic stability values ​​under different stress levels. The high-temperature stability coefficient was calculated by comparing these values ​​with benchmark values, achieving an accurate assessment of the material's resistance to permanent deformation. A method for calculating the load growth rate based on the daily equivalent axle load comparison at adjacent time points was also established. By analyzing the dynamic changes in traffic load, the load action pattern in the initial stage of new pavement opening was accurately grasped. The threshold determination method, which organically combines construction compaction quality, material high-temperature performance, and early load development trends, not only overcomes the limitations of traditional single-index evaluation but also is particularly suitable for the characteristics of the evolution of the structural performance of newly constructed pavements over time, providing a monitoring basis for timely detection and prevention of early pavement defects.

[0012] Optionally, a virtual line-drawing method is used to calculate the first rut depth and the second rut depth for both the old and new road surfaces. Specifically, this includes: obtaining elevation point cloud data of the wheel path and wheel tracks from the scanned data; obtaining the first elevation value corresponding to the first elevation point and the second elevation value corresponding to the second elevation point from the elevation point cloud data, where the first and second elevation points are adjacent; calculating the difference between the first and second elevation values ​​to obtain the elevation difference; calculating the mean and standard deviation of multiple elevation differences; removing elevation points in the elevation point cloud data whose target elevation difference is greater than the standard deviation to obtain initial elevation point cloud data, where the target elevation difference is the difference between the elevation difference and the mean elevation difference; smoothing the initial elevation point cloud data using a moving average method to obtain processed elevation point cloud data; and projecting the processed elevation point cloud data... The image is projected onto the cross-sectional plane. In both the old and new road surface areas, the standard width of the wheel track is determined based on the vehicle's wheelbase. On the cross-sectional plane, using the lane centerline as a reference, the target width is extended to both sides to determine the theoretical range of the wheel track, where the target width is half the standard width. Within the theoretical range of the wheel track, the first and second points with the largest elevation change rate are searched, and these points are designated as the outer edge point and inner edge point, respectively. The line connecting the outer edge point and the inner edge point serves as the first and second virtual reference lines. The maximum first vertical distance between the first virtual reference line and the cross-section of the actual road surface wheel track is calculated, and this maximum first vertical distance is taken as the first rut depth. The maximum second vertical distance between the second virtual reference line and the cross-section of the actual road surface wheel track is calculated, and this maximum second vertical distance is taken as the second rut depth.

[0013] By employing the aforementioned technical solutions, a standard deviation-based outlier identification mechanism was established through difference analysis and statistical characteristic calculation of adjacent elevation points, effectively eliminating measurement noise caused by factors such as equipment vibration and road surface contamination. The initial elevation point cloud data was smoothed using a moving average method, preserving the true characteristics of road surface deformation while eliminating interference from local random fluctuations. Furthermore, a method for determining the theoretical range of the wheel track zone based on vehicle wheelbase was used, symmetrically extending the cross-sectional plane with the lane centerline as a reference to accurately define the effective area for rut measurement. In particular, the method of determining outer and inner edge points by searching for the point of maximum elevation change overcomes the limitations of traditional fixed-interval point selection, more accurately reflecting the actual contour of rut deformation. Finally, the calculation of the maximum vertical distance between the virtual baseline and the cross-section of the actual road surface wheel tracks ensures not only the accuracy of rut depth measurement but also adapts to the differences in deformation characteristics between new and old road surfaces.

[0014] Optionally, the target road is divided into old pavement area, new pavement area, and longitudinal construction joint area based on the scanned data. Specifically, this includes: retrieving preset road structure information for the target road, including construction joint location information, new pavement paving material information, old pavement paving material information, pavement cross-sectional elevation information, and construction joint process parameters; obtaining lateral elevation change characteristics from the scanned data, and determining the initial construction joint area based on these characteristics, wherein the initial construction joint area is the area where the lateral elevation change gradient is greater than a preset threshold; matching the initial construction joint area with the construction joint location information to determine the centerline position of the longitudinal construction joint; extracting surface texture features from the scanned data, and analyzing the surface texture features based on the new pavement paving material information and the old pavement paving material information to obtain the preliminary boundary position between the old and new pavements; using the centerline position as a reference, and combining the preliminary boundary position, dividing the scanned data laterally into old pavement area, new pavement area, and longitudinal construction joint area, wherein the width of the longitudinal construction joint area is determined based on the construction joint process parameters.

[0015] By employing the aforementioned technical solutions, complete road structure information, including construction joint location, material information, elevation information, and process parameters, was retrieved, establishing a priori knowledge base for pavement area division. The preliminary identification method for construction joint areas based on lateral elevation gradient changes effectively captured the local elevation abrupt changes caused by construction joints by setting reasonable preset thresholds. Matching the initial identification results with preset construction joint location information not only improved the accuracy of centerline location determination but also effectively avoided interference from elevation abrupt changes caused by other pavement defects. By analyzing the surface texture features in the scanned data and comparing them with the paving material information of new and old pavements, precise demarcation based on material properties was achieved, overcoming the problem of accurately distinguishing between new and old pavements relying solely on geometric features.

[0016] Optionally, if the subsidence depth of the longitudinal construction joint area is greater than or equal to the subsidence depth threshold, it is necessary to obtain the subsidence depth value of the longitudinal construction joint area. Specifically, this includes: on the cross-section of the longitudinal construction joint area, extending a preset width to both sides based on the centerline position to determine the subsidence calculation area; obtaining target elevation point cloud data from the subsidence calculation area, removing elevation points in the target elevation point cloud data whose elevation difference is greater than the preset elevation difference, and obtaining the removed elevation point cloud data; using the weighted moving average method to smooth the removed elevation point cloud data, and establishing a local coordinate system for the smoothed elevation point cloud data; selecting multiple points at each of the two boundaries of the subsidence calculation area, and fitting the multiple points using the least squares method to obtain the first boundary baseline and the second boundary baseline; calculating the average elevation of the first boundary baseline and the second boundary baseline to obtain the theoretical connection line; calculating the vertical distance from each elevation point to the theoretical connection line within the subsidence calculation area; and selecting the maximum value from the multiple vertical distances as the subsidence depth value.

[0017] By adopting the above technical solution, a subsidence calculation area of ​​preset width is established with the centerline of the construction joint as the reference, ensuring that the measurement range can completely cover the affected area of ​​the construction joint. In the data preprocessing stage, an outlier removal mechanism based on a preset elevation difference is used to effectively remove interference data caused by measurement errors or local road defects. Data smoothing is performed using a weighted moving average method, preserving the true characteristics of subsidence deformation while eliminating the influence of random fluctuations. Multiple feature points are selected at the boundaries of the calculation area, and the boundary baseline is obtained by fitting using the least squares method, avoiding the influence of local deformation on the measurement baseline. The theoretical connecting line is obtained by calculating the average elevation of the two boundary baselines, providing a reference benchmark for the quantitative assessment of subsidence depth. Finally, by calculating the vertical distance from each elevation point to the theoretical connecting line and selecting the maximum value as the subsidence depth value, not only is the accuracy of the measurement results ensured, but the most unfavorable deformation state of the construction joint area is also accurately reflected.

[0018] Optionally, after determining the subsidence depth threshold based on the first duration of traffic opening in the new pavement area, the method further includes: if the subsidence depth value of the longitudinal construction joint area is less than the subsidence depth threshold, then confirm that the longitudinal construction joint area is in a structurally normal state; when the old pavement area is in a preliminary rutting abnormal state and the new pavement area is in a preliminary rutting normal state, determine that the target road has an independent old pavement deterioration state; when the new pavement area is in a preliminary rutting abnormal state and the old pavement area is in a preliminary rutting normal state, determine that the target road has an independent new pavement deterioration state; when the new pavement area is in a preliminary rutting abnormal state and the old pavement area is in a preliminary rutting abnormal state, determine that the target road has an overall pavement deterioration state.

[0019] By adopting the above technical solutions, the subsidence depth of the construction joint area was compared with a threshold, establishing a basic criterion for determining the normal state of the structure, laying an important foundation for subsequent distress classification. Based on the distress classification method combining rutting conditions in new and old pavement areas, the method effectively distinguishes three typical distress patterns—isolated old pavement deterioration, isolated new pavement deterioration, and overall pavement deterioration—by analyzing whether the rutting development in the two areas reaches the critical conditions for an abnormal state. This multi-dimensional state assessment method not only overcomes the limitations of traditional single-indicator evaluation but also accurately reflects the spatial differences and diverse causes of distress development in expanded pavements.

[0020] A second aspect of this application provides a monitoring device for rutting on asphalt pavement. The device includes a receiving unit, a processing unit, and a confirmation unit. The receiving unit receives scanning data of the entire transverse section of a target road; based on the scanning data, it divides the target road into an old pavement area, a new pavement area, and a longitudinal construction joint area. The processing unit uses a virtual string method to calculate the first rutting depth and the second rutting depth for the old pavement area and the new pavement area, respectively. When the first rutting depth is greater than or equal to a first depth threshold, or the second rutting depth is greater than or equal to a second depth threshold, it confirms that the old pavement area or the new pavement area is in a preliminary rutting abnormality state. When the old pavement area or the new pavement area is in a preliminary rutting abnormality state, it further confirms that the old pavement area or the new pavement area is in a preliminary rutting abnormality state. The sinking depth threshold is determined based on the first duration of traffic opening in the new road surface area; if the sinking depth value of the longitudinal construction joint area is greater than or equal to the sinking depth threshold, the first lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the first rut depth, and the second lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the second rut depth are calculated; in the confirmation unit, if the first lateral distance is less than the second lateral distance and the old road surface area is in a preliminary rutting abnormal state, it is confirmed that the target road has a local deterioration state of the old road surface caused by the construction joint; if the first lateral distance is greater than the second lateral distance and the new road surface area is in a preliminary rutting abnormal state, it is confirmed that the target road has a local deterioration state of the new road surface caused by the construction joint. In a third aspect of this application, an electronic device is provided, comprising a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory, causing the electronic device to perform the method as described in any one of the above claims of this application.

[0021] In a fourth aspect, this application provides a computer-readable storage medium storing instructions that, when executed, perform any of the methods described above in this application.

[0022] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The system receives transverse full-section scanning data of the target road and divides the road into old pavement areas, new pavement areas, and longitudinal construction joint areas based on the scanning data. This enables accurate identification of the composite structural characteristics of the expanded pavement. A virtual string method is used to calculate the rutting depth in both the old and new pavement areas, overcoming the problem that traditional single detection methods cannot reflect regional differences. By setting differentiated depth thresholds for anomaly detection, the system effectively solves the problem that a unified evaluation standard is not applicable to expanded pavements. After anomaly detection, a subsidence depth threshold judgment mechanism based on the opening time of the new pavement is used. By analyzing the spatial relationship between the subsidence depth of the construction joint area and the rutting location, accurate identification of the causes of rutting is achieved. In particular, by comparing the transverse distance between the construction joint centerline and the locations of the first and second rutting depths, a correlation criterion between the influence range of the construction joint and rutting development is established. This accurately distinguishes between local deterioration caused by the construction joint and damage caused by overall pavement performance degradation, thereby improving the accuracy and reliability of rutting detection for expanded pavements.

[0023] 2. Historical traffic data was categorized by vehicle type and axle load, establishing an equivalent axle load calculation method based on actual load characteristics, avoiding the load impact assessment bias caused by traditional simple accumulation methods. Material life parameters were obtained through fatigue testing and converted into fatigue damage degree, achieving a quantitative characterization of the degree of pavement structural performance degradation. Furthermore, a repair impact evaluation mechanism considering the timeliness of maintenance effects was implemented. By calculating the coupling effect of time decay factor and disease treatment effect, the continuous impact of each maintenance on pavement performance was accurately reflected. A threshold determination method that organically combines the cumulative effect of traffic load, the evolution law of material performance, and the impact of maintenance repair not only improves the rationality and reliability of rutting depth control indicators but also allows for dynamic adjustment based on the actual service condition of the pavement, providing a basis for rutting monitoring of expanded roads. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a method for monitoring rutting on asphalt pavement provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a monitoring device for rutting on asphalt pavement provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0025] Explanation of reference numerals in the attached drawings: 201, receiving unit; 202, processing unit; 203, confirmation unit; 300, electronic device; 301, processor; 302, memory; 303, user interface; 304, network interface; 305, communication bus. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Therefore, how to solve the problem that a unified evaluation standard cannot accurately reflect the problems caused by construction joints in expanded pavements? This application provides a method for monitoring rutting in asphalt pavements, applied in a server. The server in this application can be a platform providing rutting detection services to asphalt highway companies. Figure 1 This is a flowchart illustrating a method for monitoring rutting on asphalt pavement provided in an embodiment of this application. (Refer to...) Figure 1 The method includes the following steps S101-S107.

[0030] S101: Receives transverse full-section scanning data of the target road; based on the scanning data, the target road is divided into old road surface area, new road surface area, and longitudinal construction joint area.

[0031] In step S101 above, the target road is first scanned transversely across its entire cross-section using a vehicle-mounted 3D laser scanning device to obtain high-precision scanning data containing road surface elevation information and surface texture features. In this embodiment, the target road refers to an expanded road. Since the expanded road surface structure has the characteristics of coexisting old and new road surface materials and construction joint connections, in order to achieve accurate identification and targeted evaluation of different areas, it is necessary to divide the acquired scanning data into regions.

[0032] Based on the scanned data, the target road is divided into old pavement area, new pavement area, and longitudinal construction joint area. Specifically, this includes: retrieving preset road structure information for the target road, including construction joint location information, new pavement paving material information, old pavement paving material information, pavement cross-sectional elevation information, and construction joint process parameters; acquiring lateral elevation change characteristics from the scanned data, and determining the initial construction joint area based on these characteristics, where the lateral elevation change gradient is greater than a preset threshold; matching the initial construction joint area with the construction joint location information to determine the centerline position of the longitudinal construction joint; extracting surface texture features from the scanned data, and analyzing these features based on the new and old pavement paving material information to obtain the preliminary boundary position between the old and new pavements; using the centerline position as a reference, and combining it with the preliminary boundary position, dividing the scanned data laterally into old pavement area, new pavement area, and longitudinal construction joint area, where the width of the longitudinal construction joint area is determined based on the construction joint process parameters.

[0033] Specifically, after acquiring the scanned data of the target road, when classifying and dividing the road surface based on the scanned data, it is necessary to retrieve the preset road structure information of the target road. This preset road structure information includes construction joint location information, such as the design centerline coordinates of the construction joint and the milled joint width; paving material information for the new and old pavements, such as the new pavement using SMA-13 ​​asphalt mixture and the old pavement using AC-13 asphalt mixture; pavement cross-sectional elevation information, including design cross slope, superelevation, and structural layer elevation; and construction joint process parameters, such as overlap width and compaction requirements. When acquiring lateral elevation change characteristics, the scanned data is sampled along the cross-sectional direction, and elevation data points are extracted at 10mm intervals. The elevation difference between adjacent points is calculated, and the central difference method is used to calculate the elevation change gradient. When the lateral elevation change gradient of a certain area exceeds a preset threshold (e.g., 2%), that area is marked as the initial construction joint area. This identification method based on elevation abrupt change characteristics can effectively capture the geometric discontinuities at construction joints. Next, the identified initial construction joint area is spatially matched with the preset construction joint location information. The centerline of the initial construction joint area can be fitted using the least squares method, and the deviation from the preset construction joint location is calculated. When the deviation is within the allowable range (e.g., ±50mm), the initial construction joint area is confirmed as a valid construction joint area, and its centerline position is accurately located. This dual verification mechanism significantly improves the accuracy of construction joint identification. In the surface texture feature extraction stage, laser intensity data and point cloud density features can be used for analysis. Since the old and new pavements use different paving materials, their surface textures differ significantly. By calculating the texture depth index and aggregate distribution characteristics of local areas, feature vectors are established, and pattern matching is performed in conjunction with preset material information to determine the preliminary boundary between the old and new pavements. This material property-based identification method overcomes the problem of accurately distinguishing between old and new pavements by relying solely on geometric features. Using the determined construction joint centerline position as a reference, the width specified by the construction joint process parameters (e.g., 300mm) is extended to both sides to delineate the longitudinal construction joint area. Based on this, combined with the preliminary boundary position between the old and new pavements, the remaining area is divided into old pavement area and new pavement area respectively.

[0034] The entire division process fully considered the structural characteristics and material differences of the expanded road surface, ensuring the engineering rationality of the division results and providing a reliable spatial reference for subsequent differential testing and evaluation.

[0035] S102: The virtual string method is used to calculate the first rut depth and the second rut depth for the old road surface area and the new road surface area respectively.

[0036] In S102 above, after dividing the target road, the virtual string method is used to measure the rut depth of the old road surface area and the new road surface area respectively, so as to accurately assess the deformation state of different areas.

[0037] In addition, a virtual line drawing method was used to calculate the first and second rut depths for both the old and new road surfaces. Specifically, this involved: acquiring elevation point cloud data of the wheel track and its tracks from the scanned data; obtaining the first elevation value corresponding to the first elevation point and the second elevation value corresponding to the second elevation point from the elevation point cloud data, where the first and second elevation points are adjacent; calculating the difference between the first and second elevation values ​​to obtain the elevation difference; calculating the mean and standard deviation of multiple elevation differences; removing elevation points in the elevation point cloud data whose target elevation difference is greater than the standard deviation to obtain the initial elevation point cloud data, where the target elevation difference is the difference between the elevation difference and the mean elevation difference; smoothing the initial elevation point cloud data using a moving average method to obtain the processed elevation point cloud data; and projecting the processed elevation point cloud data. Within the cross-sectional plane; in both the old and new road surface areas, determine the standard width of the wheel track based on the vehicle's wheelbase; on the cross-sectional plane, using the lane centerline as a reference, extend the target width distance to both sides to determine the theoretical range of the wheel track, where the target width is half the standard width; within the theoretical range of the wheel track, search for the first and second points with the largest elevation change rate, and determine the first and second points as the outer edge point and inner edge point, respectively; connect the outer edge point and the inner edge point as the first virtual baseline and the second virtual baseline; calculate the maximum first vertical distance between the first virtual baseline and the cross-section of the actual road wheel track, and take the maximum first vertical distance as the first rut depth; calculate the maximum second vertical distance between the second virtual baseline and the cross-section of the actual road wheel track, and take the maximum second vertical distance as the second rut depth.

[0038] Specifically, elevation point cloud data of the wheel track and wheel tracks is first extracted from the scanning data acquired by the vehicle-mounted 3D laser scanner. During the acquisition process, the scanner samples at 2mm lateral intervals to ensure that the data density meets the requirements for accurate measurement. In the acquired elevation point cloud data, adjacent first and second elevation points are paired, and their corresponding first and second elevation values ​​are extracted. The spacing between adjacent elevation points is maintained within the range of 2-5mm to ensure the continuity and representativeness of the data. To eliminate the influence of outliers, the difference between the first and second elevation values ​​is calculated to obtain an elevation difference sequence. Statistical analysis methods are used to calculate the mean elevation difference of the entire sequence, and the dispersion index of the data is obtained through the standard deviation calculation formula. Specifically, the sliding window method (window width of 50mm) is used to calculate the mean and standard deviation of local elevation differences to adapt to the spatial non-uniformity of road surface deformation. In the data filtering stage, outliers in the elevation point cloud data are removed. The specific method involves calculating the difference between each elevation difference and the mean elevation difference (i.e., the target elevation difference). When this difference exceeds the previously calculated standard deviation, the corresponding elevation point is marked as an outlier and removed. This statistical feature-based filtering method effectively removes interference data caused by factors such as equipment vibration and road surface contamination. The initial elevation point cloud data after outlier removal is smoothed using a moving average method. Specifically, an 11-point weighted moving average algorithm is used, with the center point having a weight of 1, and the weights of the points on either side decreasing exponentially with distance. This smoothing method preserves the true characteristics of road surface deformation while effectively eliminating the influence of random fluctuations. When projecting the smoothed elevation point cloud data onto the cross-sectional plane, an orthogonal projection method is used to ensure accurate positioning of the data on the cross-section. In both the old and new road surface areas, the standard width of the wheel track is determined based on the standard vehicle wheelbase (1800mm). Considering the influence of lateral vehicle drift, the standard width is set to 300mm. When dividing the path zone on the cross-sectional plane, the target width distance (half the standard width, 150mm) is extended to both sides of the lane centerline as the reference to determine the theoretical range of the wheel path zone. This symmetrical extension method can accurately cover the wheel's action area. Within the determined theoretical range, the locations with the largest elevation change rate are identified by calculating the first derivative of the elevation points, and the points corresponding to these locations are determined as the outer edge points and inner edge points, respectively. To improve the accuracy of identification, the elevation change rate is calculated using the 5-point difference method, and the peak detection algorithm is used to determine the maximum value point. The identified outer edge points and inner edge points are fitted with the least squares method to obtain the straight line equations, which are used as the first virtual baseline and the second virtual baseline, respectively. This baseline determination method based on actual deformation characteristics avoids the limitations of the traditional fixed-interval point selection method.

[0039] Finally, the vertical distances from each point on the cross-section of the wheel tracks to the virtual baseline were calculated, and the maximum values ​​were selected as the first and second rut depths, respectively. The calculation employed a point-to-line distance formula, and a grid search method was used to ensure no maximum value points were missed. This depth calculation method based on geometric relationships ensures both measurement accuracy and provides reliable quantitative evaluation indicators. The entire measurement process, through precise control of multiple stages, achieved high-precision measurement of rut depth, providing reliable basic data for road condition assessment.

[0040] S103: When the first rut depth is greater than or equal to the first depth threshold, or the second rut depth is greater than or equal to the second depth threshold, it is confirmed that the old road surface area or the new road surface area is in a preliminary rut abnormality state.

[0041] In S103 above, after obtaining the first rut depth corresponding to the old road surface area and the second rut depth corresponding to the new road surface area, the first depth threshold needs to be set based on factors such as historical traffic data, material properties and historical maintenance of the old road surface area.

[0042] Before the first rut depth is greater than or equal to the first depth threshold, the first depth threshold needs to be determined. This specifically includes: acquiring historical traffic data for the old road surface area; classifying vehicles in the historical traffic data into various vehicle types according to axle load; acquiring actual axle load data for each vehicle type; averaging multiple actual axle load data points to obtain the average axle load value for each vehicle type; dividing the average axle load value by the standard axle load to obtain the vehicle type conversion factor for each vehicle type; acquiring the daily traffic volume for each vehicle type from the historical traffic data, and acquiring the second duration of open traffic in the old road surface area; multiplying the daily traffic volume, the second duration, and the vehicle type conversion factor to obtain the historical cumulative equivalent axle loads; conducting fatigue tests on the old road surface area to obtain fatigue life parameters; and based on the fatigue... The lifespan parameter is used to calculate the material fatigue damage degree; historical maintenance records of the old pavement area are retrieved, and maintenance time and disease treatment effect scores are obtained from the historical maintenance records; the difference between the current time and the maintenance time is multiplied by the attenuation constant to obtain the time attenuation factor; the time attenuation factor is multiplied by the disease treatment effect score to obtain the single maintenance impact factor; the number of maintenance is obtained from the historical maintenance records, and the number of maintenance is multiplied by the single maintenance impact factor to obtain the sum of maintenance impact factors; the difference between the sum of maintenance impact factors and the preset sum is calculated, and the difference is used as the repair impact coefficient, with the preset sum being 1; the standard rut depth threshold, historical cumulative equivalent axle number, material fatigue damage degree, and repair impact coefficient are multiplied to obtain the first depth threshold.

[0043] Specifically, historical traffic data for the old road surface area is first extracted from the road management database. Based on monitoring data from the vehicle feature recognition system, vehicles are classified into various vehicle types according to axle load, such as small vehicles (≤2t), medium vehicles (2-5t), large vehicles (5-10t), and extra-large vehicles (≥10t). Axle load data of each type of vehicle is collected during actual operation through a weighing system, and statistical analysis is performed on the measured data for 30 consecutive days to calculate the average axle load value for each vehicle type. To achieve a standardized expression of traffic load, the average axle load value of each vehicle type is divided by the standard axle load (100kN) to obtain the corresponding vehicle type conversion factor. For example, if the average axle load value of a certain vehicle type is 80kN, then its vehicle type conversion factor is 0.8. Daily traffic volume data for each vehicle type is obtained from traffic flow detection equipment, and combined with the open traffic duration of the old road surface area (e.g., 8 years), the daily traffic volume, open duration, and vehicle type conversion factor are multiplied to obtain the historical cumulative equivalent axle load reflecting the cumulative traffic load effect. In terms of material performance evaluation, fatigue life parameters of asphalt mixtures in old pavement areas were obtained through indoor fatigue tests. Specifically, a four-point bending fatigue test method was used to test the fatigue life of the material under different stress levels. A stress-life relationship curve was established through regression analysis, and the fatigue damage degree of the material was calculated based on Miner's linear cumulative damage theory. Simultaneously, historical maintenance records of the old pavement area were retrieved, and the implementation time and treatment effectiveness score (using a percentage system) of each maintenance were extracted. To quantify the long-term impact of maintenance measures, the concept of a time decay factor was introduced. Specifically, the difference between the current time and the maintenance time (in years) was calculated and multiplied by an experimentally determined decay constant (e.g., 0.1 / year) to obtain the time decay factor reflecting the decay law of maintenance effectiveness over time. Multiplying the time decay factor by the treatment effectiveness score yielded the single maintenance impact factor. Considering the cumulative effect of multiple maintenance operations, the number of maintenance operations in the historical maintenance records was counted, and multiplied by the single maintenance impact factor to obtain the sum of maintenance impact factors. To standardize the impact of maintenance, the difference between the sum of maintenance impact factors and the preset sum (set to 1) is calculated, and this difference is used as the repair impact coefficient, which reflects the remaining bearing capacity of the pavement structure. Finally, the first depth threshold for old road sections is obtained by comprehensively calculating four factors: the standard rut depth threshold (e.g., 20mm), the historical cumulative equivalent axle loads, the material fatigue damage degree, and the repair impact coefficient.

[0044] This threshold determination method based on multi-factor coupling not only considers the cumulative effect of traffic load, but also includes the evolution of material properties and the impact of maintenance measures. It can more accurately reflect the actual service status of old pavement areas and provide a scientific and reasonable basis for judging abnormal rutting conditions.

[0045] After determining the first depth threshold corresponding to the old pavement area, it is necessary to obtain factors such as the construction quality of the new pavement area, the high-temperature performance of the materials, and the growth trend of traffic load to set a second depth threshold. Before the second rut depth is greater than or equal to the second depth threshold, the second depth threshold must be determined. Specifically, this includes: obtaining the field compaction degree from multiple test points in the new pavement area; determining the theoretical maximum density of the new pavement area using non-destructive methods; counting the number of test points in the new pavement area; dividing the quotient of the field compaction degree and the theoretical maximum density by the number of test points to obtain the average compaction degree index; drilling asphalt mixture core samples from the new pavement area; conducting dynamic creep tests on the asphalt mixture core samples to obtain dynamic stability values ​​under different stress levels; and comparing the dynamic stability values ​​with the dynamic stability benchmark value. Dividing the difference by the dynamic stability benchmark value yields the high-temperature stability coefficient; continuously collecting data on various vehicle types and axle loads in the new road area within a preset time period, dividing the axle load data by the average axle load data for each vehicle type yields the vehicle type conversion coefficient; multiplying the vehicle type conversion coefficient by the daily traffic volume of each vehicle type yields the daily average equivalent axle load; obtaining the first daily average equivalent axle load corresponding to the first time point and the second daily average equivalent axle load corresponding to the second time point, with the first and second time points being adjacent time points; calculating the axle load growth rate based on the first and second daily average equivalent axle loads; determining the initial load growth rate based on the axle load growth rate and the preset time; multiplying the standard rut depth threshold, average compaction index, high-temperature stability coefficient, and initial load growth rate yields the second depth threshold.

[0046] Specifically, the construction quality of the new pavement area was first assessed. A nuclear density meter was used to set up test points in a 10m × 10m grid in the new pavement area to obtain the on-site compaction data for each test point. Simultaneously, a non-destructive electromagnetic wave detection method was used to determine the theoretical maximum density of the new pavement area. All test points within the grid were statistically analyzed to calculate the total number of test points. The average compaction index, reflecting the overall compaction quality, was obtained by summing the ratios of the on-site compaction degree to the theoretical maximum density of all test points and dividing by the number of test points. To evaluate the high-temperature performance of the pavement material, asphalt mixture core samples were drilled from the new pavement area using a φ100mm core drill. Dynamic creep tests were conducted on the core samples at 60℃, testing the dynamic stability values ​​at three stress levels: 0.7MPa, 0.4MPa, and 0.2MPa. By comparing the measured dynamic stability values ​​with the specified dynamic stability benchmark value (e.g., 2000 cycles / mm) and dividing the difference by the benchmark value, the high-temperature stability coefficient, characterizing the material's resistance to deformation, was obtained. In traffic load characteristic analysis, vehicle data is continuously collected using dynamic weighing equipment over a preset period (e.g., 30 consecutive days). The collected axle load data is categorized and statistically analyzed to calculate the average axle load for each vehicle type, and this is used as a benchmark to calculate the vehicle type conversion factor. For example, if the measured axle load of a certain vehicle type is 60kN and its average axle load is 50kN, then the conversion factor for that type is 1.2. The calculated vehicle type conversion factor is multiplied by the daily traffic volume of each vehicle type to obtain the standardized daily equivalent axle load. To assess the growth trend of traffic load, daily equivalent axle load data is obtained for two adjacent time points (7-day intervals). The axle load growth rate is obtained by calculating the ratio of the daily equivalent axle load at the second time point to that at the first time point. Based on the axle load growth rate and combined with a preset time period (e.g., 180 days), extrapolation prediction is performed to determine the initial load growth rate reflecting the rapid growth characteristics of traffic load. Finally, the standard rut depth threshold (e.g., 15 mm) is used as a benchmark and multiplied by three correction factors: average compaction index, high temperature stability coefficient, and initial load growth rate, to obtain a second depth threshold adapted to the characteristics of the new pavement area.

[0047] This threshold determination method based on multiple corrections fully considers the construction quality, material properties, and early load characteristics of the new pavement, accurately reflecting the deformation resistance of the new pavement area. By introducing construction quality and material performance indicators, the influence of construction defects on threshold setting is effectively controlled; by considering the rapid growth characteristics of traffic load, the early warning performance of the threshold is improved. After determining the first and second depth thresholds for the old and new pavement areas respectively through the above method, for the old pavement area, considering that it has experienced a long period of traffic load, the first depth threshold can be set to 15mm; for the new pavement area, since it is in the initial stage of use and requires stricter control standards, the second depth threshold is set to 12mm. The first rutting depth can be compared with the first depth threshold. When the first rutting depth is greater than or equal to 15mm, it indicates that the deformation of the old pavement area has exceeded the allowable range, and the old pavement area is marked as a preliminary rutting anomaly. Similarly, the second rutting depth is compared with the second depth threshold. When the second rutting depth is greater than or equal to 12mm, the new pavement area is marked as a preliminary rutting anomaly. This differential threshold-based judgment method fully considers the differences in structural performance between new and old pavements. By setting reasonable control standards, it avoids false alarms caused by overly stringent judgments while promptly identifying potential damage risks. Furthermore, the use of the intermediate judgment result of "preliminary rutting anomaly" leaves room for subsequent in-depth analysis and precise diagnosis, improving the scientific rigor and reliability of pavement condition assessment.

[0048] S104: When an old or new road surface area is in an initial rutting abnormal state, the sinking depth threshold shall be determined based on the first duration of traffic opening in the new road surface area.

[0049] In S104 above, when the old pavement area or the new pavement area is in a preliminary rutting anomaly state, the open traffic time record of the new pavement area is obtained, and the time span from the opening of traffic to the current time is calculated, i.e., the first duration. Considering that the newly built pavement undergoes consolidation settlement and structural stabilization processes in the early service stage, a segmented control method is adopted to determine the subsidence depth threshold.

[0050] Specifically, when the first service period is less than 90 days, considering the natural increase in the compaction degree of the subgrade fill and the secondary compaction effect of the asphalt mixture, the subsidence depth threshold is set to 8 mm; when the first service period is between 90 and 180 days, the pavement structure enters a relatively stable stage, and the subsidence depth threshold is adjusted to 6 mm; when the first service period exceeds 180 days, the pavement structure is basically stable, and the subsidence depth threshold is set to 4 mm. This time-segmented threshold determination method not only considers the performance evolution of the new pavement structure during its early service, but also enables timely detection of potential defects such as uneven subgrade settlement. By establishing a correspondence between the first service period and the subsidence depth threshold, a more reasonable defect judgment standard is provided, effectively avoiding early misjudgment and omission, and thus accurately identifying pavement defects.

[0051] S105: If the depression depth of the longitudinal construction joint area is greater than or equal to the depression depth threshold, calculate the first lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the first rut depth, and the second lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the second rut depth.

[0052] In S105 above, before the sinking depth value of the longitudinal construction joint area is greater than or equal to the sinking depth threshold, it is necessary to obtain the sinking depth value of the longitudinal construction joint area. Specifically, this includes: on the cross section of the longitudinal construction joint area, extending a preset width to both sides based on the centerline position to determine the sinking calculation area; obtaining target elevation point cloud data from the sinking calculation area, removing elevation points in the target elevation point cloud data whose elevation difference is greater than the preset elevation difference, and obtaining the removed elevation point cloud data; using the weighted moving average method to smooth the removed elevation point cloud data, and establishing a local coordinate system for the smoothed elevation point cloud data; selecting multiple points at the boundaries on both sides of the sinking calculation area, and fitting the multiple points using the least squares method to obtain the first boundary baseline and the second boundary baseline; calculating the average elevation of the first boundary baseline and the second boundary baseline to obtain the theoretical connection line; calculating the vertical distance from each elevation point to the theoretical connection line within the sinking calculation area, and selecting the maximum value from multiple vertical distances as the sinking depth value.

[0053] Specifically, when obtaining the subsidence depth value of the longitudinal construction joint area, the scope of the subsidence calculation area is first determined. The centerline of the longitudinal construction joint is accurately located using image recognition technology, and based on this, a preset width of 300mm is extended to both sides, constructing a subsidence calculation area with a total width of 300mm. This area division method can completely cover the influence range of the construction joint while avoiding interference from adjacent defects. Within the determined subsidence calculation area, a vehicle-mounted 3D laser scanner is used to collect target elevation point cloud data, with a scanning accuracy set to 1mm horizontally and 2mm vertically. To ensure data quality, the raw point cloud data is preprocessed. By calculating the elevation difference between adjacent points, outliers exceeding the preset elevation difference (set to 5mm) are removed. This data filtering method based on elevation gradient can effectively remove interference data caused by factors such as equipment vibration and road debris. The elevation point cloud data after removing outliers is smoothed using a weighted moving average method. Specifically, a 15-point weighted window is used, with the center point having a weight of 1, and the weights of the points on both sides decreasing exponentially according to distance. This smoothing method preserves the true characteristics of subsidence deformation while effectively eliminating the influence of random noise. The smoothed data is transformed into a local coordinate system with the construction joint centerline as the origin, the road surface transverse direction as the X-axis, and the elevation direction as the Z-axis, facilitating subsequent calculations and analysis. In this local coordinate system, 21 evenly distributed control points are selected at each of the two boundaries of the subsidence calculation area (±300mm from the centerline). These control points are fitted with straight lines using the least squares method to obtain the first and second boundary baselines, reflecting the elevation characteristics of the area boundary. The fitting process employs an iterative weighted least squares algorithm to ensure that the fitted lines accurately reflect the overall trend of the boundary area. The elevation values ​​of the two boundary baselines at the same abscissa position are calculated, and their arithmetic mean is used to construct a theoretical connecting line. This theoretical connecting line represents the ideal elevation state of the road surface when no subsidence deformation has occurred. By calculating the vertical distance from each elevation point within the subsidence calculation area to the theoretical connecting line, a series of distance values ​​reflecting the degree of local subsidence are obtained. The maximum value among these vertical distances is then selected as the subsidence depth value. To improve measurement reliability, a sliding window method (window width 50mm) was used to confirm the maximum value, avoiding the influence of single-point outliers. This subsidence depth measurement method based on multiple verifications not only ensures data accuracy but also objectively reflects the deformation state of the longitudinal construction joint area.

[0054] Furthermore, after obtaining the subsidence depth value of the longitudinal construction joint area, the detected subsidence depth value is compared with a subsidence depth threshold. If the subsidence depth value is greater than or equal to the subsidence depth threshold, the lateral distance measurement program is initiated. Specifically, the centerline position of the longitudinal construction joint is first accurately located in the cross-sectional data. This position is determined by image recognition and edge detection algorithms of the construction joint surface features. Simultaneously, the aforementioned rut detection data is used to extract the lateral position coordinates of the first and second rut depths. The first lateral distance is obtained by calculating the lateral projection distance between the centerline position of the longitudinal construction joint and the first rut depth position; similarly, the second lateral distance is obtained by calculating the lateral projection distance between the centerline position of the longitudinal construction joint and the second rut depth position. This distance measurement method based on high-precision positioning can accurately reflect the spatial relationship between rut deformation and the longitudinal construction joint, providing important parameters for assessing the correlation of defects.

[0055] S106: If the first lateral distance is less than the second lateral distance, and the old road surface area is in a preliminary rutting abnormality state, then it is confirmed that the target road has a local deterioration state of the old road surface caused by the construction joint.

[0056] In S106 above, the numerical relationship between the first lateral distance and the second lateral distance is compared. For example, when the measured first lateral distance is 1.2 meters and the second lateral distance is 1.8 meters, the condition that the first lateral distance is less than the second lateral distance is met. This distance relationship indicates that the rutting deformation location is asymmetrically distributed relative to the longitudinal construction joint, and the rutting location on the old pavement side is closer to the construction joint. At the same time, the rutting condition of the old pavement area is checked. When the first rutting depth is detected to exceed the first depth threshold but has not yet reached the degree of severe deformation, it is confirmed that the old pavement area is in a preliminary rutting anomaly state, while the new pavement area is not in a preliminary rutting anomaly state. For example, when the first rutting depth is 22 mm, the first depth threshold is 20 mm, and the severe deformation threshold is 35 mm, the judgment condition for preliminary rutting anomaly is met. When both conditions are met simultaneously, comprehensive analysis suggests that the existence of the longitudinal construction joint leads to stress concentration, causing a local reduction in the bearing capacity of the old pavement structure, which in turn accelerates the development of rutting deformation, ultimately confirming that the target road has a local deterioration state of the old pavement caused by the construction joint. This comprehensive diagnostic method, based on spatial relationships and disease conditions, can accurately identify structural defects caused by construction joints, providing a clear basis for determining the causes of defects in subsequent maintenance plans. The implementation of this diagnostic method has significantly improved the accuracy of defect analysis in expanded pavements.

[0057] S107: If the first lateral distance is greater than the second lateral distance, and the new road surface area is in a preliminary rutting abnormality state, then it is confirmed that the target road has a local deterioration state of the new road surface caused by the construction joint.

[0058] In S107 above, when the first lateral distance is 2.1 meters and the second lateral distance is 1.5 meters, the condition that the first lateral distance is greater than the second lateral distance is met. This distance relationship indicates that the rutting deformation location is asymmetrically distributed relative to the longitudinal construction joint, and the rutting location on the new pavement side is closer to the construction joint. Simultaneously, the rutting condition of the new pavement area is checked. When the second rutting depth exceeds the second depth threshold but has not yet reached a severe deformation level, the new pavement area is confirmed to be in a preliminary rutting anomaly state, while the old pavement area is not. For example, when the second rutting depth is 17 mm, the second depth threshold is 15 mm, and the severe deformation threshold is 30 mm, the condition for determining preliminary rutting anomaly is met. When both conditions are met simultaneously, comprehensive analysis suggests that the presence of the longitudinal construction joint affects the collaborative working performance of the new and old pavement structures, causing the new pavement side to bear excessive load, thereby accelerating the development of rutting deformation, ultimately confirming that the target road has a localized deterioration state of the new pavement caused by the construction joint. This comprehensive diagnostic method, based on spatial relationships and disease conditions, can accurately identify structural defects in new pavements caused by construction joints.

[0059] If the first lateral distance equals the second lateral distance, and both the new and old road surfaces are in a preliminary rutting anomaly state, then it is confirmed that the target road has an overall road surface deterioration state caused by the construction joint. When the first lateral distance is measured to be 1.8 meters and the second lateral distance is also 1.8 meters, the condition that the two lateral distances are equal is met. This distance relationship indicates that the rutting deformation locations are symmetrically distributed relative to the longitudinal construction joint, indicating that both sides of the new and old road surfaces are affected by similar degrees of load. At the same time, the rutting state of the new and old road surfaces is checked. When the first rutting depth exceeds the first depth threshold (e.g., the first rutting depth is 22 mm and the first depth threshold is 20 mm), and the second rutting depth exceeds the second depth threshold (e.g., the second rutting depth is 17 mm and the second depth threshold is 15 mm), but neither reaches their respective severe deformation degree, it is confirmed that both the new and old road surfaces are in a preliminary rutting anomaly state. When these conditions are met simultaneously, a comprehensive analysis concludes that the presence of longitudinal construction joints disrupts the integrity of the pavement structure, leading to a decrease in the load-bearing capacity of both the old and new pavement structures. This, in turn, triggers symmetrical rutting deformation, ultimately confirming that the target road exhibits an overall pavement deterioration state caused by construction joints.

[0060] In one possible implementation, if the subsidence depth of the longitudinal construction joint area is less than the subsidence depth threshold, the longitudinal construction joint area is confirmed to be in a normal structural state; when the old pavement area is in a preliminary rutting abnormal state and the new pavement area is in a preliminary rutting normal state, the target road is determined to have an independent old pavement deterioration state; when the new pavement area is in a preliminary rutting abnormal state and the old pavement area is in a preliminary rutting normal state, the target road is determined to have an independent new pavement deterioration state; when the new pavement area is in a preliminary rutting abnormal state and the old pavement area is in a preliminary rutting abnormal state, the target road is determined to have an overall pavement deterioration state.

[0061] Specifically, the structural condition of the longitudinal construction joint area is first assessed. When the measured subsidence depth (e.g., 3 mm) is less than the subsidence depth threshold (e.g., 4 mm) determined based on the first duration, it indicates that no significant structural damage has occurred in the construction joint area, confirming that the longitudinal construction joint area is in a normal structural state. This assessment method based on subsidence depth can accurately reflect the structural integrity of the construction joint area. Under the premise that the construction joint structure is normal, the rutting condition of the old and new pavements is further assessed. When the first rutting depth (e.g., 22 mm) exceeds the first depth threshold (e.g., 20 mm), while the second rutting depth (e.g., 12 mm) does not exceed the second depth threshold (e.g., 15 mm), it indicates that the old pavement area has shown initial rutting anomalies, while the new pavement area remains in a normal state. This further confirms that the target road has an independent old pavement deterioration state. This state indicates that the load-bearing capacity of the old pavement may have decreased due to long-term service, requiring targeted maintenance. Conversely, when the second rut depth (e.g., 17 mm) exceeds the second depth threshold (e.g., 15 mm), while the first rut depth (e.g., 18 mm) does not exceed the first depth threshold (e.g., 20 mm), it indicates that preliminary rutting anomalies have appeared in the new pavement area, while the old pavement area remains normal. This confirms the existence of an independent new pavement deterioration state on the target road. This state may reflect problems with the construction quality or material performance of the new pavement, requiring timely quality investigation and handling. When the first rut depth (e.g., 22 mm) exceeds the first depth threshold (e.g., 20 mm), and the second rut depth (e.g., 17 mm) also exceeds the second depth threshold (e.g., 15 mm), it indicates that preliminary rutting anomalies have appeared in both the new and old pavement areas. This confirms the existence of an overall pavement deterioration state on the target road. This state indicates insufficient overall load-bearing capacity of the pavement structure, requiring full-section maintenance measures.

[0062] This application also provides a device for monitoring rutting on asphalt pavements. Figure 2 This is a schematic diagram of a monitoring device for rutting on asphalt pavement provided in an embodiment of this application. (Refer to...) Figure 2 The device includes a receiving unit 201, a processing unit 202, and a confirmation unit 203. The receiving unit 201 receives the transverse full-section scanning data of the target road; based on the scanning data, it divides the target road into old pavement area, new pavement area and longitudinal construction joint area; Processing unit 202 uses a virtual string method to calculate the first rut depth and the second rut depth for the old road surface area and the new road surface area respectively. When the first rut depth is greater than or equal to the first depth threshold, or the second rut depth is greater than or equal to the second depth threshold, it is confirmed that the old road surface area or the new road surface area is in a preliminary rut abnormality state. When the old road surface area or the new road surface area is in a preliminary rut abnormality state, the sinking depth threshold is determined according to the first time that the new road surface area has been open to traffic. If the sinking depth value of the longitudinal construction joint area is greater than or equal to the sinking depth threshold, the first lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the first rut depth, and the second lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the second rut depth are calculated. If the first lateral distance is less than the second lateral distance and the old road surface area is in a preliminary rutting abnormal state, the target road is confirmed to have a local deterioration state of the old road surface caused by the construction joint. If the first lateral distance is greater than the second lateral distance and the new road surface area is in a preliminary rutting abnormal state, the target road is confirmed to have a local deterioration state of the new road surface caused by the construction joint.

[0063] In one possible implementation, the receiving unit 201 is used to acquire historical traffic data of the old road surface area, classify vehicles in the historical traffic data into various vehicle types according to axle load; acquire actual axle load data of various vehicle types, calculate the average axle load value of various vehicle types by averaging multiple actual axle load data; the processing unit 202 is used to divide the average axle load value by the standard axle load to obtain the vehicle type conversion coefficient of various vehicle types; the receiving unit 201 is used to acquire the daily traffic volume of various vehicle types from the historical traffic data, and acquire the second duration of open traffic in the old road surface area, multiply the daily traffic volume, the second duration, and the vehicle type conversion coefficient to obtain the historical cumulative equivalent axle load; the processing unit 202 is used to conduct fatigue tests on the old road surface area to obtain fatigue life parameters. The process involves: calculating material fatigue damage based on fatigue life parameters; retrieving historical maintenance records for the old pavement area, obtaining maintenance time and damage treatment effectiveness scores from these records; multiplying the difference between the current time and maintenance time by an attenuation constant to obtain a time attenuation factor; multiplying the time attenuation factor by the damage treatment effectiveness score to obtain a single maintenance impact factor; obtaining the number of maintenance sessions from historical maintenance records, multiplying the number of maintenance sessions by the single maintenance impact factor to obtain the sum of maintenance impact factors; calculating the difference between the sum of maintenance impact factors and a preset total, using this difference as the repair impact coefficient, with the preset total being 1; and multiplying the standard rut depth threshold, historical cumulative equivalent axle number, material fatigue damage, and repair impact coefficient to obtain the first depth threshold.

[0064] In one possible implementation, the receiving unit 201 is used to acquire the on-site compaction degree of multiple test points in the new pavement area, determine the theoretical maximum density of the new pavement area using a non-destructive method, and count the number of test points in the new pavement area; the processing unit 202 is used to divide the quotient of the on-site compaction degree and the theoretical maximum density by the number of test points to obtain the average compaction degree index; drill asphalt mixture core samples from the new pavement area, conduct dynamic creep tests on the asphalt mixture core samples, and obtain dynamic stability values ​​under different stress levels; divide the difference between the dynamic stability value and the dynamic stability benchmark value by the dynamic stability benchmark value to obtain the high-temperature stability coefficient; continuously collect data on various vehicle types and... within a preset time period in the new pavement area. The axle load data is divided by the average axle load data of each vehicle type to obtain the vehicle type conversion coefficient. The vehicle type conversion coefficient is multiplied by the daily traffic volume of each vehicle type to obtain the daily average equivalent axle load. The receiving unit 201 is used to obtain the first daily average equivalent axle load corresponding to the first time point and the second daily average equivalent axle load corresponding to the second time point, where the first time point and the second time point are adjacent time points. The processing unit 202 is used to calculate the axle load growth rate based on the first daily average equivalent axle load and the second daily average equivalent axle load. The initial load growth rate is determined based on the axle load growth rate and the preset time. The standard rut depth threshold, the average compaction index, the high temperature stability coefficient, and the initial load growth rate are multiplied to obtain the second depth threshold.

[0065] In one possible implementation, the receiving unit 201 is used to acquire elevation point cloud data of the wheel path and wheel tracks from the scanned data, and to acquire a first elevation value corresponding to a first elevation point and a second elevation value corresponding to a second elevation point from the elevation point cloud data, wherein the first elevation point and the second elevation point are adjacent; the processing unit 202 is used to calculate the difference between the first elevation value and the second elevation value to obtain the elevation difference; to calculate multiple elevation differences to obtain the mean and standard deviation of the elevation differences; to remove elevation points in the elevation point cloud data whose target elevation difference is greater than the standard deviation, thereby obtaining initial elevation point cloud data, wherein the target elevation difference is the difference between the elevation difference and the mean elevation difference; to smooth the initial elevation point cloud data using a moving average method to obtain processed elevation point cloud data; and to project the processed elevation point cloud data onto the cross-sectional plane; in the old For both the existing road surface area and the new road surface area, the standard width of the wheel track is determined based on the vehicle's wheel track width. On the cross-sectional plane, using the lane centerline as a reference, the target width distance is extended to both sides to determine the theoretical range of the wheel track, where the target width is half of the standard width. Within the theoretical range of the wheel track, the first and second points with the largest elevation change rate are searched, and these points are designated as the outer edge point and the inner edge point, respectively. The line connecting the outer edge point and the inner edge point is used as the first virtual baseline and the second virtual baseline. The maximum first vertical distance between the first virtual baseline and the cross-section of the actual road surface wheel track is calculated, and this maximum first vertical distance is used as the first rut depth. The maximum second vertical distance between the second virtual baseline and the cross-section of the actual road surface wheel track is calculated, and this maximum second vertical distance is used as the second rut depth.

[0066] In one possible implementation, the receiving unit 201 is used to retrieve preset road structure information of the target road, including construction joint location information, paving material information of the new road surface, paving material information of the old road surface, road cross-sectional elevation information, and construction joint process parameters; acquire lateral elevation change features in the scanned data, and determine the initial construction joint area based on the lateral elevation change features, wherein the initial construction joint area is the area where the lateral elevation change gradient is greater than a preset threshold; the processing unit 202 is used to match the initial construction joint area with the construction joint location information to determine the centerline position of the longitudinal construction joint; extract surface texture features from the scanned data, analyze the surface texture features based on the paving material information of the new road surface and the paving material information of the old road surface, and obtain the preliminary boundary position of the new and old road surfaces; based on the centerline position and combined with the preliminary boundary position, divide the scanned data laterally into the old road surface area, the new road surface area, and the longitudinal construction joint area, wherein the width of the longitudinal construction joint area is determined according to the construction joint process parameters.

[0067] In one possible implementation, the processing unit 202 is used to determine the depression calculation area by extending a preset width to both sides of the cross section of the longitudinal construction joint area, with the center line position as the reference; the receiving unit 201 is used to acquire target elevation point cloud data from the depression calculation area, remove elevation points in the target elevation point cloud data whose elevation difference is greater than the preset elevation difference, and obtain the removed elevation point cloud data; the processing unit 202 is used to smooth the removed elevation point cloud data using a weighted moving average method, and establish a local coordinate system for the smoothed elevation point cloud data; select multiple points at the boundaries of both sides of the depression calculation area, and fit the multiple points using the least squares method to obtain the first boundary baseline and the second boundary baseline; calculate the average elevation of the first boundary baseline and the second boundary baseline to obtain the theoretical connection line; calculate the vertical distance from each elevation point to the theoretical connection line in the depression calculation area, and select the maximum value from multiple vertical distances as the depression depth value.

[0068] In one possible implementation, the processing unit 202 is used to confirm that the longitudinal construction joint area is in a normal structural state if the subsidence depth value of the longitudinal construction joint area is less than the subsidence depth threshold; the confirmation unit 203 is used to determine that the target road has an independent old pavement deterioration state when the old pavement area is in a preliminary rutting abnormal state and the new pavement area is in a preliminary rutting normal state; when the new pavement area is in a preliminary rutting abnormal state and the old pavement area is in a preliminary rutting normal state, the target road has an independent new pavement deterioration state; when the new pavement area is in a preliminary rutting abnormal state and the old pavement area is in a preliminary rutting abnormal state, the target road has an overall pavement deterioration state.

[0069] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0070] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This application provides a schematic diagram of the structure of an electronic device. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 302, and at least one communication bus 305.

[0071] The communication bus 305 is used to enable communication between these components.

[0072] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0073] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0074] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 302, and by calling data stored in memory 302. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and application requests; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0075] The memory 302 may include random access memory (RAM) or read-only memory. Optionally, the memory 302 may include a non-transitory computer-readable storage medium. The memory 302 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 302 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc. The data storage area may store data involved in the various method embodiments described above. Optionally, the memory 302 may also be at least one storage device located remotely from the aforementioned processor 301.

[0076] like Figure 3 As shown, the memory 302, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application for monitoring ruts on asphalt pavement.

[0077] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for users to input data and obtain user input data; while the processor 301 can be used to call the application program for monitoring asphalt pavement ruts stored in the memory 302. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0078] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0079] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0081] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0083] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0084] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truths. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A method for monitoring rutting on asphalt pavement, characterized in that, The method includes: Receive transverse full-section scanning data of the target road; based on the scanning data, divide the target road into old pavement area, new pavement area, and longitudinal construction joint area; The first rut depth and the second rut depth are obtained by calculating the old road surface area and the new road surface area respectively using the virtual line method; When the first rut depth is greater than or equal to the first depth threshold, or the second rut depth is greater than or equal to the second depth threshold, it is confirmed that the old road surface area or the new road surface area is in a preliminary rut abnormality state. When the old road surface area or the new road surface area is in the initial rutting abnormal state, the sinking depth threshold is determined based on the first duration during which the new road surface area has been open to traffic. If the depression depth of the longitudinal construction joint area is greater than or equal to the depression depth threshold, calculate the first lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the first rut depth, and the second lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the second rut depth. If the first lateral distance is less than the second lateral distance, and the old road surface area is in the initial rutting abnormal state, then it is confirmed that the target road has a local deterioration state of the old road surface caused by the construction joint. If the first lateral distance is greater than the second lateral distance, and the new road surface area is in the initial rutting abnormal state, then it is confirmed that the target road has a local deterioration state of the new road surface caused by the construction joint.

2. The method according to claim 1, characterized in that, Before the first rut depth is greater than or equal to the first depth threshold, the first depth threshold needs to be determined, specifically including: Obtain historical traffic data for the old road area, and classify the vehicles in the historical traffic data into various vehicle types according to axle load; Obtain the actual axle load data for each of the aforementioned vehicle models, and calculate the average axle load value for each of the aforementioned vehicle models by averaging the multiple actual axle load data. Divide the average axle load value by the standard axle load to obtain the vehicle conversion factor for each of the vehicle models. The daily traffic volume of each vehicle type is obtained from the historical traffic data, and the second duration of the open traffic in the old road area is obtained. The daily traffic volume, the second duration and the vehicle type conversion coefficient are multiplied together to obtain the historical cumulative equivalent axle times. Fatigue life parameters are obtained by conducting fatigue tests on the old road surface area, and the material fatigue damage degree is calculated based on the fatigue life parameters. Retrieve the historical maintenance records of the old road surface area, and obtain the maintenance time and disease treatment effect score from the historical maintenance records; Multiply the difference between the current time and the maintenance time by the decay constant to obtain the time decay factor; Multiply the time decay factor by the disease treatment effect score to obtain the single maintenance impact factor; The number of maintenance sessions is obtained from the historical maintenance records. The number of maintenance sessions is then multiplied by the single maintenance impact factor to obtain the sum of the maintenance impact factors. Calculate the difference between the sum of the maintenance influencing factors and the preset total, and use the difference as the repair influence coefficient, where the preset total is 1; The first depth threshold is obtained by multiplying the standard rut depth threshold, the historical cumulative equivalent axle count, the material fatigue damage degree, and the repair influence coefficient.

3. The method according to claim 2, characterized in that, Before the second rut depth is greater than or equal to the second depth threshold, the second depth threshold needs to be determined, specifically including: The on-site compaction degree of multiple test points in the new road surface area is obtained, the theoretical maximum density of the new road surface area is determined using a non-destructive method, and the number of test points in the new road surface area is counted. The average compaction index is obtained by dividing the quotient of the on-site compaction degree and the theoretical maximum density by the number of test points. Asphalt mixture core samples were drilled from the new road surface area, and dynamic creep tests were conducted on the asphalt mixture core samples to obtain dynamic stability values ​​under different stress levels. The difference between the dynamic stability value and the dynamic stability reference value is divided by the dynamic stability reference value to obtain the high-temperature stability coefficient. Continuously collect data on various vehicle types and axle loads in the new road area within a preset time period, and divide the axle load data by the average axle load data of each vehicle type to obtain the vehicle conversion coefficient for each vehicle type. Multiply the vehicle type conversion factor by the daily traffic volume of each vehicle type to obtain the daily equivalent axle loads; Obtain the first daily average equivalent axis corresponding to the first time point and the second daily average equivalent axis corresponding to the second time point, wherein the first time point and the second time point are adjacent time points; The growth rate of the number of axes is calculated by comparing the first daily average equivalent number of axes with the second daily average equivalent number of axes. The initial load growth rate is determined based on the axle growth rate and the preset time. The second depth threshold is obtained by multiplying the standard rut depth threshold, the average compaction index, the high temperature stability coefficient, and the initial load growth rate.

4. The method according to claim 1, characterized in that, The method of using virtual string lines to calculate the first rut depth and the second rut depth for the old road surface area and the new road surface area respectively includes: Elevation point cloud data of the wheel track area is obtained from the scan data. A first elevation value corresponding to a first elevation point and a second elevation value corresponding to a second elevation point are obtained from the elevation point cloud data. The first elevation point and the second elevation point are adjacent. The difference between the first elevation value and the second elevation value is calculated to obtain the elevation difference; Calculate the mean and standard deviation of the elevation differences for each of the aforementioned elevation differences; Elevation points in the elevation point cloud data whose target elevation difference is greater than the standard deviation are removed to obtain initial elevation point cloud data, wherein the target elevation difference is the difference between the elevation difference and the mean elevation difference. The initial elevation point cloud data is smoothed using a moving average method to obtain processed elevation point cloud data; the processed elevation point cloud data is then projected onto the cross-sectional plane. In both the old road surface area and the new road surface area, the standard width of the wheel track strip is determined according to the vehicle wheel track. On the cross-sectional plane, with the lane centerline as a reference, a target width distance is extended to both sides to determine the theoretical range of the wheel path band, wherein the target width is half of the standard width; Within the theoretical range of the wheel path zone, search for the first and second points with the largest elevation change rate, and determine the first point and the second point as the outer edge point and the inner edge point, respectively. The line connecting the outer edge point and the inner edge point is used as the first virtual baseline and the second virtual baseline. Calculate the maximum first vertical distance between the first virtual baseline and the cross section of the actual road wheel track, and use the maximum first vertical distance as the first rut depth; Calculate the maximum second vertical distance between the second virtual baseline and the cross section of the actual road surface wheel track, and use the maximum second vertical distance as the second rut depth.

5. The method according to claim 1, characterized in that, The process of dividing the target road into old road surface areas, new road surface areas, and longitudinal construction joint areas based on the scanned data specifically includes: Retrieve the preset road structure information of the target road, which includes construction joint location information, paving material information of the new road surface, paving material information of the old road surface, road cross-section elevation information, and construction joint process parameters. The lateral elevation change characteristics in the scanned data are obtained, and the initial construction joint area is determined based on the lateral elevation change characteristics, wherein the initial construction joint area is the area where the lateral elevation change gradient is greater than a preset threshold. The initial construction joint area is matched with the construction joint location information to determine the centerline position of the longitudinal construction joint; Surface texture features are extracted from the scanned data. The surface texture features are analyzed based on the paving material information of the new road surface and the paving material information of the old road surface to obtain the preliminary boundary position between the new and old road surfaces. Based on the centerline position and the preliminary boundary position, the scanned data is divided into the old road surface area, the new road surface area, and the longitudinal construction joint area in the horizontal direction. The width of the longitudinal construction joint area is determined according to the construction joint process parameters.

6. The method according to claim 5, characterized in that, Before the sinking depth value of the longitudinal construction joint area is greater than or equal to the sinking depth threshold, it is necessary to obtain the sinking depth value of the longitudinal construction joint area, specifically including: On the cross section of the longitudinal construction joint area, with the center line position as a reference, a preset width is extended to both sides to determine the depression calculation area; The target elevation point cloud data is obtained from the sunken calculation area, and the elevation points in the target elevation point cloud data with an elevation difference greater than a preset elevation difference are removed to obtain the removed elevation point cloud data. The weighted moving average method is used to smooth the removed elevation point cloud data, and a local coordinate system is established for the smoothed elevation point cloud data. Multiple points are selected at each of the two sides of the depression calculation region, and the first boundary baseline and the second boundary baseline are obtained by fitting the multiple points using the least squares method. Calculate the average elevation of the first boundary baseline and the second boundary baseline to obtain the theoretical connection line; Within the depression calculation area, the vertical distance from each elevation point to the theoretical connecting line is calculated, and the maximum value among the multiple vertical distances is selected as the depression depth value.

7. The method according to claim 1, characterized in that, After determining the subsidence depth threshold based on the first duration during which traffic has been opened in the new road surface area, the method further includes: If the depression depth of the longitudinal construction joint area is less than the depression depth threshold, then the longitudinal construction joint area is confirmed to be in a normal structural state. When the old road surface area is in the initial rutting abnormal state and the new road surface area is in the initial rutting normal state, it is determined that the target road has an independent old road surface deterioration state. When the new road surface area is in the initial rutting abnormal state and the old road surface area is in the initial rutting normal state, it is determined that the target road has an independent new road surface deterioration state. When the new road surface area is in the initial rutting anomaly state and the old road surface area is in the initial rutting anomaly state, it is determined that the target road has an overall road surface deterioration state.

8. A monitoring device for rutting on asphalt pavement, characterized in that, The device includes a receiving unit (201), a processing unit (202), and a confirmation unit (203). The receiving unit (201) receives scanning data of the transverse full section of the target road; based on the scanning data, the target road is divided into an old road surface area, a new road surface area, and a longitudinal construction joint area; The processing unit (202) uses a virtual line-drawing method to calculate the first rut depth and the second rut depth for the old road surface area and the new road surface area respectively. When the first rut depth is greater than or equal to the first depth threshold, or the second rut depth is greater than or equal to the second depth threshold, it is confirmed that the old road surface area or the new road surface area is in a preliminary rut abnormality state. When the old road surface area or the new road surface area is in the preliminary rut abnormality state, the sinking depth threshold is determined according to the first time that the new road surface area has been open to traffic. If the depression depth of the longitudinal construction joint area is greater than or equal to the depression depth threshold, calculate the first lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the first rut depth, and the second lateral distance between the centerline position of the longitudinal construction joint area and the position corresponding to the second rut depth. If the first lateral distance is less than the second lateral distance and the old road surface area is in the initial rutting abnormal state, the confirmation unit (203) confirms that the target road has a local deterioration state of the old road surface caused by the construction joint; if the first lateral distance is greater than the second lateral distance and the new road surface area is in the initial rutting abnormal state, the confirmation unit (203) confirms that the target road has a local deterioration state of the new road surface caused by the construction joint.

9. An electronic device, characterized in that, The device includes a processor (301), a memory (302), a user interface (303), and a network interface (304). The memory (302) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (302) to cause the electronic device (300) to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

Images