A smart highway surface damage and water accumulation integrated detection method and system

Abstract

This application relates to the field of highway pavement inspection technology, and in particular to a smart highway pavement damage and water accumulation integrated detection method and system. The method includes: acquiring a pavement condition information set; based on the pavement condition information set, analyzing the coupling and temporal relationship between the vulnerable surrounding areas of expansion joints and the water accumulation distribution characteristics in detection and identification, to obtain an expansion joint anomaly information set; acquiring a traffic environment information set; based on the traffic environment information set, combined with the expansion joint anomaly information set, analyzing the correlation between damage formation, water accumulation induction, and external factors, to obtain a damage-causing information set; and based on the damage-causing information set, conducting pavement health status assessment and risk classification analysis, generating and outputting a highway pavement damage and water accumulation integrated detection log. This improves the intelligence and precision of pavement maintenance, ensures highway traffic safety and efficiency, and extends pavement service life.

Description

Technical Field

[0001] This application relates to the field of highway pavement inspection technology, and in particular to an intelligent highway pavement damage and water accumulation integrated detection method and system. Background Technology

[0002] In the process of inspecting expansion joints on highways, for damage detection, image recognition technology is mainly used to locate and classify surface defects such as cracks and potholes; for water accumulation detection, water depth and distribution are usually monitored through road surface water level sensors or remote sensing technology.

[0003] However, there are still significant shortcomings in the integrated detection of damage and water accumulation in the vicinity of expansion joints. On the one hand, the detection system lacks specific consideration for the connection characteristics of expansion joints. Existing technologies mostly treat expansion joints and the surrounding road surface as a whole for detection, without focusing on the structural special characteristics of expansion joints as road surface connections. Due to factors such as long-term concentrated impact from vehicle loads, uneven temperature stress transmission, and inconsistent deformation of the connection interface, the vicinity of expansion joints is more prone to damage such as transverse cracks, interlayer peeling, and edge chipping. Existing detection methods have not specifically designed targeted detection schemes for these damage-prone factors and still exhibit the characteristic of "separation of damage identification and water accumulation monitoring," failing to reflect the core logic of "damage first, then water accumulation," resulting in a lag in the risk assessment of water accumulation caused by damage in the connection area. Summary of the Invention

[0004] This application provides an integrated intelligent highway pavement damage and water accumulation detection method and system to solve the above problems.

[0005] Firstly, this application provides an integrated intelligent highway pavement damage and water accumulation detection method. The method includes: acquiring a pavement state information set; based on the pavement state information set, analyzing the coupling and temporal relationship between the vulnerable surrounding area of ​​expansion joints and the water accumulation distribution characteristics in detection and identification to obtain an expansion joint anomaly information set; acquiring a traffic environment information set; based on the traffic environment information set and combined with the expansion joint anomaly information set, analyzing the correlation between damage formation, water accumulation induction, and external factors to obtain a damage-causing information set; and performing pavement health status assessment and risk classification analysis based on the damage-causing information set, generating and outputting an integrated highway pavement damage and water accumulation detection log.

[0006] By exploring the coupling time sequence relationship between the vulnerable areas of expansion joints and the distribution of water accumulation, we can accurately identify expansion joint anomalies and locate the core damage factors. This breaks through the limitations of existing independent detection of damage and water accumulation, and realizes integrated full-process detection and analysis. It provides accurate and comprehensive reference for highway maintenance, improves the intelligence and refinement of road maintenance, ensures the safety and efficiency of highway traffic, and extends the service life of the road surface.

[0007] Optionally, the step of analyzing the coupling and temporal relationship between the vulnerable surrounding area of ​​the expansion joint and the distribution characteristics of water accumulation in detection and identification based on the road surface condition information set to obtain an expansion joint anomaly information set includes: the road surface condition information set includes an expansion joint structure information set and a road surface water information set; based on the expansion joint structure information set, analyzing the structural characteristics of the expansion joint itself and the continuity and strength change trend of the surrounding area materials to obtain a vulnerable area information set; based on the road surface water information set, analyzing the distribution range, depth changes, and retention time of road surface water to obtain dynamic water accumulation distribution information; based on the vulnerable area information set, combined with the dynamic water accumulation distribution information, analyzing the spatial overlap and interaction between water accumulation distribution and vulnerable areas to obtain spatial coupling information; based on the spatial coupling information, analyzing the change trend of water accumulation retention time in vulnerable areas and the cumulative impact process on regional damage development to obtain temporal evolution information; based on the temporal evolution information, analyzing early signs of damage in the area surrounding the expansion joint and dangerous sections where water accumulation accelerates damage to obtain the expansion joint anomaly information set.

[0008] Optionally, the process of constructing the spatial coupling information includes: based on the vulnerable area information set and combined with the dynamic distribution information of water accumulation, analyzing the intersection range and relative orientation of the water accumulation area and the vulnerable area in planar position to obtain spatial overlap information; based on the spatial overlap information, analyzing the hydrostatic ballast effect and infiltration soaking effect generated by water accumulation in the overlapping area to obtain water accumulation effect information; based on the water accumulation effect information, analyzing the degree of acceleration of the damage process of different vulnerable areas of the expansion joint by water accumulation effect to obtain the spatial coupling information.

[0009] Optionally, the process of constructing the temporal evolution information includes: based on the spatial coupling information, analyzing the fluctuation pattern of water retention time at each monitoring point in the vulnerable area under different water accumulation intensities to obtain water retention temporal information; based on the water retention temporal information, analyzing the progressive impact of water accumulation on the degree of damage to the vulnerable area under different retention time stages to obtain damage accumulation development information; based on the damage accumulation development information, analyzing the corresponding correlation between the change in water retention time and the damage development stage throughout the entire process from initial minor damage to increased damage in the vulnerable area to obtain the temporal evolution information.

[0010] Optionally, the step of analyzing early signs of damage and dangerous sections of water-accelerated damage in the area surrounding the expansion joint based on the temporal evolution information to obtain the expansion joint anomaly information set includes: based on the temporal evolution information, analyzing the different speeds and characteristics of damage morphology development from subtle surface changes to deep structural damage in vulnerable areas surrounding the expansion joint under different water retention duration patterns, obtaining damage mode differentiation information; based on the damage mode differentiation information, analyzing the specific spatial manifestations and evolution sequence of material peeling, crack propagation, and structural loosening in areas continuously affected by water, obtaining early signs of damage information; based on the early signs of damage information, combined with the dynamic distribution information of water accumulation, analyzing the immediate and potential threat levels to pavement structural safety in areas with different water depths, different retention durations, and clearly defined early signs, obtaining water-accelerated damage section information; and integrating the early signs of damage information and the water-accelerated damage section information to construct the expansion joint anomaly information set.

[0011] Optionally, based on the traffic environment information set and combined with the expansion joint anomaly information set, the correlation between damage formation, water accumulation, and external factors is analyzed to obtain a damage-causing information set, including: the traffic environment information set includes a traffic load information set and a traffic flow information set; based on the traffic load information set, the cumulative effect of vehicle axle load, axle type distribution, and traffic frequency on material fatigue damage and structural impact in the area surrounding the expansion joint is analyzed to obtain load-causing damage information; based on the traffic flow information set, the influence of vehicle speed, density, and vehicle type composition on water splashing, water flow disturbance, and drainage efficiency is analyzed to obtain traffic flow disturbance information. Based on the load-induced damage information and the expansion joint anomaly information set, the damage development rate and structural failure risk of the expansion joint anomaly area under different load characteristics are analyzed to obtain load-related damage information; based on the traffic flow disturbance information and the expansion joint anomaly information set, the retention and diffusion characteristics of water accumulation in the expansion joint anomaly area under different traffic flow states are analyzed to obtain traffic flow-related water accumulation information; based on the load-related damage information and the traffic flow-related water accumulation information, the interaction and enhancement relationship between damage formation and water accumulation induced by the combined action of load and traffic flow is analyzed to obtain the damage-induced information set.

[0012] Optionally, the process of constructing the traffic flow-related water accumulation information includes: based on the traffic flow disturbance information, analyzing the vehicle speed distribution and traffic density distribution corresponding to traffic congestion and smooth traffic conditions respectively to obtain traffic state characteristics; based on the traffic state characteristics, combined with the expansion joint anomaly information set, analyzing the repeated crushing and obstructing effect of low-speed, high-density traffic flow on the water accumulation in the water accumulation accelerated damage section under congestion conditions, and analyzing the stripping and dispersing effect of airflow generated by high-speed traffic flow on the surface water accumulation in the early damage sign area under smooth traffic conditions, to obtain a state-driven information set; based on the state-driven information set, analyzing the evolution process of water accumulation continuously infiltrating and stagnating in the deeper damage area under congestion conditions, and analyzing the spatial redistribution process of water accumulation spreading and migrating from the damaged area to the surrounding area under smooth traffic conditions, to obtain the water accumulation evolution path; integrating the state-driven information set and the water accumulation evolution path to construct the traffic flow-related water accumulation information that characterizes the dynamic correlation between traffic flow and water accumulation behavior.

[0013] Optionally, based on the load-related damage information and combined with the traffic flow-related water accumulation information, the analysis of the interaction and enhancement relationship between damage formation and water accumulation under the combined action of load and traffic flow to obtain the damage information set includes: based on the load-related damage information and combined with the traffic flow-related water accumulation information, analyzing the differential changes in damage efficiency caused by the softening of road surface materials due to water immersion in the water-accelerated damage section due to vehicle impact load, to obtain load damage enhancement information; based on the load damage enhancement information and combined with the state-driven information set, analyzing the heavy-load slow-moving vehicles under traffic congestion conditions. The flow, by extending the load application time and hindering the drainage of accumulated water, jointly induces the infiltration-compression composite damage characteristics in the early damage sign area, resulting in first interactive enhancement information. Based on the load damage enhancement information, combined with the state-driven information set, the combined damage characteristics of water splashing and material peeling jointly induced by high-speed traffic flow under smooth traffic conditions through additional airflow disturbances and load impacts in the early damage sign area are analyzed, resulting in second interactive enhancement information. The load damage enhancement information, the first interactive enhancement information, and the second interactive enhancement information are integrated to construct the damage information set.

[0014] Optionally, the step of conducting pavement health status assessment and risk classification analysis based on the damage information set, and generating and outputting an integrated detection log of highway pavement damage and water accumulation, includes: based on the damage information set, analyzing the speed and characteristic differences of pavement damage development under different interactive enhancement modes to obtain risk development mode information; based on the risk development mode information, combining the first interactive enhancement information and the second interactive enhancement information, analyzing the degradation path and critical state of pavement structural safety performance under different risk modes to obtain structural degradation path information; based on the structural degradation path information, analyzing the observable damage morphology, development speed, and immediate and potential threats to pavement safety corresponding to each risk mode to obtain risk level classification information; and based on the risk level classification information, generating a standard format report containing risk location, level, mode, and evolution trend, and outputting the integrated detection log of highway pavement damage and water accumulation.

[0015] Secondly, this application provides an integrated intelligent highway pavement damage and water accumulation detection system. The system includes: an anomaly analysis module, used to acquire a pavement state information set, and based on the pavement state information set, analyze the coupling and temporal relationship between the vulnerable surrounding area of ​​expansion joints and the water accumulation distribution characteristics in detection and identification, to obtain an expansion joint anomaly information set; an environmental damage module, used to acquire a traffic environment information set, and based on the traffic environment information set, combined with the expansion joint anomaly information set, analyze the correlation between damage formation, water accumulation induction, and external factors, to obtain a damage information set; and a graded assessment module, used to perform pavement health status assessment and risk grading analysis based on the damage information set, and generate and output an integrated highway pavement damage and water accumulation detection log. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application; Figure 2 A flowchart illustrating an integrated detection method for intelligent highway pavement damage and water accumulation, provided in one embodiment of this application; Figure 3 This is a schematic diagram of an integrated intelligent highway pavement damage and water accumulation detection system provided in one embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0020] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0021] During highway inspections, the integrated detection of damage and water accumulation near expansion joints has significant technical shortcomings: the detection system is not designed for the special characteristics of its connecting structure, and it does not focus on developing targeted solutions for vulnerable factors such as load impact and uneven stress; moreover, damage identification and water accumulation monitoring are separated, which violates the logic of damage first and then water accumulation, resulting in a serious lag in water accumulation risk assessment.

[0022] Based on this, this application provides an integrated detection method and system for intelligent highway pavement damage and water accumulation. It deeply explores the coupling and temporal correlation between the vulnerable areas of expansion joints and the distribution of water accumulation, accurately identifies expansion joint anomalies, locates the core damage factors, breaks through the limitations of independent detection of damage and water accumulation, realizes integrated full-process detection and analysis, provides accurate reference for highway maintenance, improves the level of intelligent and refined maintenance, ensures safe and efficient traffic, and extends the service life of the pavement.

[0023] Figure 1 This application provides an schematic diagram of an application scenario. In the process of detecting expansion joints on highways, the method provided in this application is used to analyze the coupling time sequence pattern between the vulnerable area of ​​the expansion joint and the distribution of water accumulation, accurately identify the defects, locate the core damage factors, break through the limitations of independent detection, and realize integrated detection and analysis. This result can provide precise support for highway maintenance, improve the level of intelligent and refined maintenance, ensure traffic safety, and extend the service life of the road surface.

[0024] Specifically, the method provided in this application can be applied to any server. The server interacts with road condition monitoring equipment and highway traffic platform to obtain the road surface status information set provided by the road condition monitoring equipment and the traffic environment information set provided by the highway traffic platform. It can also explore the coupling time sequence relationship between the vulnerable area of ​​expansion joints and the distribution of water accumulation, accurately identify expansion joint anomalies and locate the core damage factors, generate and output an integrated detection log of highway pavement damage and water accumulation to highway pavement maintenance personnel, and ensure the safety and efficiency of highway traffic.

[0025] For specific implementation details, please refer to the following examples.

[0026] Figure 2 This is a flowchart illustrating an integrated detection method for intelligent highway pavement damage and water accumulation, provided in one embodiment of this application. The method of this embodiment can be applied to servers in the above-mentioned scenarios. Figure 2 As shown, the method includes: S201. Obtain the road surface condition information set. Based on the road surface condition information set, analyze the coupling and temporal relationship between the vulnerable surrounding area of ​​the expansion joint and the water accumulation distribution characteristics in the detection and identification process to obtain the expansion joint anomaly information set.

[0027] The road surface condition information set can be a set of parameters that reflect the actual operating status of the road surface during the intelligent highway road surface detection process, with the road condition monitoring equipment as the data source.

[0028] The vulnerable area surrounding expansion joints can be a specific area on highways where cracks, potholes, and other damage are prone to occur. Water accumulation distribution characteristics can be the location, area, depth, and duration of water accumulation on the highway, characterizing the water accumulation state. Coupling can be the correspondence between the damage state of the vulnerable area surrounding expansion joints and the water accumulation distribution characteristics, showing mutual influence and correlation. Temporal relationship can be the sequential correlation between the development of damage in the vulnerable area surrounding expansion joints and the generation and dissipation of water accumulation over time. The expansion joint anomaly information set can be a collection of information that characterizes the abnormal states of damage and water accumulation associated with highway expansion joints and their vulnerable surrounding areas.

[0029] Specifically, in the detection of expansion joints in smart highways, expansion joints are considered weak links in the road structure. Expansion joint defects are not isolated; they are coupled with the degradation of surrounding road surfaces and the depth of water accumulation, forming a vicious cycle where damage induces water accumulation, and water accumulation exacerbates damage. Existing detection methods separate damage and water accumulation monitoring, resulting in delayed risk warnings and one-sided causal analysis. By collecting high spatiotemporal resolution road surface data, integrating multi-source data, and conducting time-series analysis, we can collaboratively monitor deformation, spectral characteristics, and water accumulation features, clarify the causal relationship between the two, and accurately extract coupled anomaly information.

[0030] S202. Obtain a traffic environment information set. Based on the traffic environment information set and combined with the expansion joint anomaly information set, analyze the correlation between damage formation, water accumulation, and external factors to obtain a damage information set.

[0031] Traffic environment information set can be a set of parameters that can reflect the traffic operation status and the surrounding natural environment status during the operation of smart highways, with the highway traffic platform as the data source.

[0032] Damage formation can refer to the generation and development process of various damage problems in the vicinity of expansion joints on highways. Water accumulation can refer to the causes and formation process of various water accumulation phenomena on highways. External factors can refer to traffic-related and environmental external influencing factors that affect the formation of road damage and the induction of water accumulation. Damage information set can be a set of information that can characterize the correlation between the formation of road damage, the induction of water accumulation, and various external factors.

[0033] Specifically, in the process of inspecting highway expansion joints, the evolution of road surface damage and water accumulation, in addition to its own coupling relationship, is driven by external factors such as traffic load and natural climate. Analyzing only the road surface condition itself cannot reveal the cause and it is difficult to carry out predictive maintenance. It is necessary to introduce traffic environment information sets, and through data mining, model the correlation between road surface anomalies and external factors, mine strong correlation rules, and transform the original anomalies into a damage information set containing the dominant damage factors and their intensity, so as to achieve accurate attribution.

[0034] S203. Based on the damage information set, conduct pavement health status assessment and risk classification analysis, and generate and output an integrated detection log of highway pavement damage and water accumulation.

[0035] Road surface health assessment can be a process of comprehensively judging the overall health level, severity of damage and water accumulation of smart highway pavements based on a set of damage information. Risk grading analysis can be based on the results of the road surface health assessment, combined with the degree of influence of the damage-causing factors. The integrated detection log of smart highway pavement damage and water accumulation can be a collection of log information including various detection data and analysis conclusions such as pavement condition monitoring, expansion joint anomaly analysis, damage factor identification, pavement health assessment, and risk grading results.

[0036] Specifically, during the inspection of highway expansion joints, maintenance decision-makers urgently need intuitive, quantifiable, and forward-looking decision support tools. Existing reports lack comprehensive assessment and risk ranking, which can easily lead to a loss of focus in the allocation of maintenance resources. By constructing a pavement health index model and a multi-dimensional risk matrix, deeply processing damage information, calculating structural and functional health indices, and combining the rate of disease development to complete risk classification, an integrated inspection log that integrates all dimensions of information can be automatically generated, thus breaking through the bottleneck of maintenance decision-making.

[0037] The method provided in this embodiment explores the temporal relationship between the vulnerable areas of expansion joints and the distribution of water accumulation, accurately identifies expansion joint anomalies and locates the core damage factors, breaks through the limitations of existing independent detection of damage and water accumulation, realizes integrated full-process detection and analysis, provides accurate and comprehensive reference for highway maintenance, improves the intelligence and refinement of road maintenance, ensures the safety and efficiency of highway traffic, and extends the service life of the road surface.

[0038] In some embodiments, the pavement condition information set includes an expansion joint structure information set and a pavement water information set. Based on the expansion joint structure information set, the structural characteristics of the expansion joint itself and the continuity and strength variation trends of the surrounding materials are analyzed to obtain a vulnerable area information set. Based on the pavement water information set, the distribution range, depth variation, and retention time of pavement water are analyzed to obtain dynamic water distribution information. Based on the vulnerable area information set, combined with the dynamic water distribution information, the spatial overlap and interaction between water distribution and vulnerable areas are analyzed to obtain spatial coupling information. Based on the spatial coupling information, the retention time variation trend of water in vulnerable areas and the cumulative impact process on regional damage development are analyzed to obtain temporal evolution information. Based on the temporal evolution information, early signs of damage and dangerous sections of accelerated damage caused by water accumulation in the area surrounding the expansion joint are analyzed to obtain an expansion joint anomaly information set.

[0039] The expansion joint structural information set can be a collection of information reflecting the structural characteristics of the expansion joint itself and the relevant properties of the pavement materials in its surrounding area. The pavement water information set can be a collection of information recording the relevant characteristics of pavement water accumulation, serving as the core basis for analyzing the dynamic distribution of water accumulation. The vulnerable area information set can be a collection of information reflecting the structural characteristics of the expansion joint itself and the continuity and strength variation trends of the surrounding materials, obtained by analyzing the expansion joint structural information set. Dynamic water accumulation distribution information can be information on the distribution range, depth changes, and retention time of pavement water accumulation, derived from the pavement water information set. Spatial coupling information can be information on the spatial overlap and interaction between water accumulation distribution and vulnerable areas, obtained by combining the vulnerable area information set and the dynamic water accumulation distribution information. Temporal evolution information can be information on the changing trend of water retention time in vulnerable areas and the cumulative impact on regional damage development, derived from the spatial coupling information.

[0040] Specifically, in analyzing the coupling and temporal relationship between expansion joints and water accumulation, the absence of this step makes it impossible to capture the spatial overlap and temporal accumulation of the two, easily overlooking early signs of damage, leading to misjudgments of damage risk, and accelerating the destruction of the deep pavement structure. To address these issues: First, high-precision sensor networks deployed on key road sections are used to collect raw data: Ground-penetrating radar and laser profilers are used to scan and obtain the geometric dimensions (such as joint width and depth) and dielectric constant and strength profile data of the surrounding materials constituting the expansion joint structural information set; millimeter-wave radar and video surveillance images are used to obtain the contour and depth information of the water accumulation range constituting the pavement water information set. Subsequently, feature extraction techniques are applied for analysis: For the structural data, edge detection and region growing algorithms are used to identify the physical boundaries of the expansion joints, and based on spatial interpolation and trend fitting of the strength profile data (e.g., using Kriging interpolation), areas with strength below a threshold (e.g., 30 MPa) are delineated. A vulnerable area information set is created. For water accumulation data, image segmentation techniques from computer vision (such as semantic segmentation based on U-Net networks) are used to accurately extract water accumulation pixel regions from surveillance videos. This data is then fused with radar water depth data (e.g., water depth of 0.5 cm at a certain point). Combined with continuous time frames, the duration of water accumulation in the same area is calculated to generate dynamic distribution information of water accumulation representing three-dimensional attributes: range, depth, and duration. Next, spatial coupling analysis is performed: the above two data sets are imported into a geographic information platform, and its spatial overlay analysis tool is used to calculate the intersection of the vulnerable area polygon and the water-covered area polygon to obtain "spatial overlap information." Based on this, a physical model (such as infiltration based on Darcy's law) is used to further analyze the data. A permeability model is used to simulate the infiltration process of water into the base material in the overlapping area, and to estimate the compaction effect of hydrostatic pressure on the loose material, quantifying the coupling strength and forming "spatial coupling information." Then, a temporal evolution analysis is performed: for the overlapping areas identified in the spatial coupling information, the long-term water retention time data is extracted, and the fluctuation law and trend are analyzed using a time-series prediction model (such as the ARIMA model) to obtain "water retention time-series information." Furthermore, this time-series information is used as input to drive a damage mechanics accumulation model (such as a modified model based on Miner's linear cumulative damage law) to simulate the decay of the material's elastic modulus and the propagation of microcracks under different continuous immersion-drying cycles. The process involves constructing "temporal evolution information" describing the relationship between "water accumulation duration and damage development." Ultimately, risk assessment is based on this evolution information: regions with significantly higher-than-average damage development rates are identified using pattern recognition algorithms (such as cluster analysis), and early signs (such as exposed aggregates and fine network cracks) confirmed by manual interpretation of high-resolution images or automated surface texture analysis are used to identify "early damage signs." By integrating water accumulation depth, retention trend, and damage rate, a risk matrix evaluation method is used to delineate "water accumulation accelerated damage sections" with high immediate risk or potential rapid deterioration risk. Together, these two elements form a precise and actionable "expansion joint anomaly information set."

[0041] The method provided in this embodiment can accurately detect early signs of damage around expansion joints, identify dangerous sections where water accumulation accelerates damage, and provide accurate abnormal data for subsequent damage analysis and risk classification, thereby enabling early detection and early warning of road damage and reducing blind maintenance.

[0042] In some embodiments, based on the vulnerable area information set and combined with the dynamic distribution information of water accumulation, the intersection range and relative orientation of the water accumulation area and the vulnerable area in planar position are analyzed to obtain spatial overlap information; based on the spatial overlap information, the hydrostatic ballast effect and infiltration soaking effect generated by water accumulation in the overlapping area are analyzed to obtain water accumulation effect information; based on the water accumulation effect information, the degree of acceleration of the damage process of different vulnerable areas of the expansion joint by water accumulation effect is analyzed to obtain spatial coupling information.

[0043] Spatial overlap information can be obtained by analyzing the planar positions of waterlogged areas and vulnerable areas, revealing their intersection range and relative orientation. The hydrostatic ballast effect refers to the load effect exerted on the pavement structure by the static pressure generated by water under its own weight within the overlapping area. The infiltration and soaking effect refers to the effect of water seeping through cracks and pores in the pavement structure within the overlapping area and continuously soaking the material. Water accumulation effect information can be derived from the spatial overlap information analysis, showing the hydrostatic ballast effect and the infiltration and soaking effect generated by water in the overlapping area. Different vulnerable areas of the expansion joint can be defined as different areas within the entire expansion joint area, differentiated by structural, material, and stress characteristics, exhibiting varying susceptibility and severity of damage. The acceleration of damage progression refers to the increase in the rate of damage development in each vulnerable area of ​​the expansion joint under water accumulation compared to a state without water accumulation.

[0044] Specifically, in analyzing the coupling relationship between the vulnerable area of ​​expansion joints and the distribution of accumulated water, the absence of this step makes it impossible to establish a spatial correlation between the two, clarify the physical effect of accumulated water on the vulnerable area, and provide spatial data support for subsequent temporal evolution analysis, leading to distorted identification of damage signs. To address the above issues: First, relying on the spatial analysis tools of a geographic information platform (such as ArcGIS or QGIS), the vector format "vulnerable area information set" (such as material loose polygons read from ground penetrating radar) and the raster format "dynamic distribution information of accumulated water" (such as information transmitted by liquid level) are combined. The water depth distribution map generated by sensor network interpolation is overlaid and intersected to accurately calculate the overlap area ratio and relative orientation of each vulnerable block and water accumulation area (e.g., whether the water completely covers the vulnerable area or only covers its downwind side), forming quantified "spatial overlap information." Subsequently, for each overlapping area, a mechanism model is established using hydrostatics and porous media seepage theory. For example, for the static ballast effect, the additional pressure generated is directly calculated based on the water depth value (e.g., 5 cm); for the infiltration and soaking effect, the additional pressure is calculated based on the road surface... The material database (containing parameters such as porosity and saturation of asphalt mixtures) and the duration of water retention in the area are used to estimate the depth of water infiltration and the extent of softening effect using a simplified model similar to Darcy's law. This comprehensively generates "water accumulation effect information" describing the intensity of the physicochemical effects of the water accumulation. Finally, a damage mechanics model is introduced, using the above "water accumulation effect information" as input parameters, and coupled with different vulnerability levels (such as slightly, moderately, and severely vulnerable areas based on deflection values) preset in the "vulnerable area information set" for analysis. For example, by establishing a machine learning regression model (such as a random forest model) based on finite element analysis or historical data, the acceleration factor (such as crack propagation rate or bearing capacity attenuation rate) of different vulnerability levels under specific water accumulation (such as 5 cm immersion for 10 hours) compared to the waterless state is evaluated (such as 1.5 times or 3 times acceleration). Finally, a "spatial coupling information" map that clearly maps "where water accumulation causes what degree of accelerated damage to what vulnerable areas" is output, providing an accurate initial action state for subsequent temporal evolution prediction.

[0045] The method provided in this embodiment accurately establishes the spatial relationship between vulnerable areas and water accumulation, clarifies the dual effect mechanism of water accumulation on vulnerable areas, provides reliable spatial data for subsequent time-series evolution analysis, improves the pertinence and scientific nature of damage detection, and lays a solid foundation for the construction of anomaly information sets.

[0046] In some embodiments, based on spatial coupling information, the fluctuation pattern of water retention time at each monitoring point in the vulnerable area under different water accumulation intensities is analyzed to obtain water retention time series information; based on the water retention time series information, the progressive impact of water accumulation on the degree of damage to the vulnerable area under different retention time stages is analyzed to obtain damage accumulation development information; based on the damage accumulation development information, the corresponding correlation between the change in water retention time and the damage development stage is analyzed throughout the entire process from initial minor damage to increased damage in the vulnerable area to obtain time series evolution information.

[0047] The intensity of water accumulation can be defined as the combined effect of hydrostatic ballast and infiltration immersion caused by water accumulation in vulnerable areas of expansion joints. Vulnerable areas are those where the continuity and strength of the expansion joint's structure and surrounding materials show changing trends, making them prone to damage. Monitoring points are fixed locations used to collect data on road surface water accumulation and damage. Water retention time is the duration of water accumulation at each monitoring point within the vulnerable area. Water retention time sequence information shows the fluctuation pattern of water retention time at each monitoring point in the vulnerable area over time under different water accumulation intensities. Retention time stages can be different intervals defined based on varying water retention time ranges. Damage severity refers to the degree of damage and deterioration of the pavement materials and structure in the vulnerable area of ​​the expansion joint caused by water accumulation. Damage accumulation and development information shows the progressive impact of water accumulation on the degree of damage in the vulnerable area under different water retention time stages. Initial minor damage can be a state of damage characterized by subtle surface changes in the vulnerable area of ​​an expansion joint, without resulting in deep structural failure. Increased damage can be a state where damage in the vulnerable area of ​​the expansion joint progresses from the surface to deeper layers, highlighting structural damage characteristics. Damage development stages can be the entire process from initial minor damage to increased damage in the vulnerable area, categorized into different evolutionary stages based on damage characteristics.

[0048] Specifically, after analyzing the spatial coupling information of expansion joints on highways, if the temporal analysis of water retention and damage development is lacking, it is impossible to grasp the cumulative damage pattern caused by water accumulation over time, easily overlooking early damage evolution, leading to delayed damage prediction, and allowing minor damage to develop into structural failure. To address these issues: First, a high-precision, high-frequency IoT monitoring network is relied upon. For example, millimeter-wave radar or optically based road surface water sensors (such as the LMS100 series laser rangefinder sensor from SICK in Germany, which can be used to invert water film thickness) are deployed around the expansion joints to continuously collect water depth data in vulnerable areas at a sampling frequency of several times per second, forming a raw time series. Then, time series analysis and data mining techniques are applied to process this massive amount of data: for the extraction of water retention temporal information, a sliding window statistical and threshold judgment method is used (e.g., setting a depth greater than 3 mm as valid water accumulation), automatically identifying the start and end times of each water accumulation event. The system calculates the duration of each water accumulation event. Then, using time series decomposition models (such as STL seasonal decomposition) or autoregressive integral moving average (ARIMA) models, it analyzes the trends, periodicity, and random fluctuations of these water accumulation duration sequences on daily, weekly, and monthly scales. For example, it finds that a small peak in water accumulation duration often occurs at a certain location after the morning rush hour due to reduced vehicle splashing. Regarding the construction of "damage accumulation and development information," a damage progression model based on physical mechanisms is established by combining knowledge of pavement materials engineering. Through laboratory calibration and inversion of historical field data, the performance degradation curves of different asphalt mixtures under short, medium, and long-term immersion in the corresponding "water accumulation time series information" are quantified (e.g., using the ultrasonic pulse velocity reduction rate to characterize the internal structural looseness). Finite element analysis software (such as ABAQUS) is applied to simulate the accelerating effect of water infiltration and traffic load coupling on microcrack propagation under the actual "water accumulation time series information" load spectrum obtained from monitoring. Finally, to establish the "correspondence relationship" between the two, a state transition model (such as a hidden Markov model) or association rule mining algorithm (such as the Apriori algorithm) in machine learning is used to train a big data association between the representative water retention patterns obtained from the aforementioned analysis (such as the combination of "high frequency short time - low frequency long time") and different levels of damage development stages (such as micro-cracks, crack propagation, and surface peeling) detected by high-definition cameras or ground penetrating radar. This generates a probabilistic model that can predict the damage evolution stage and speed based on real-time water retention time series information, which is the final time series evolution information.

[0049] The method provided in this embodiment accurately depicts the temporal correlation between the duration of water accumulation and damage development, captures key nodes in damage evolution, provides core temporal basis for identifying early signs of damage, realizes early warning of road damage, improves the detection and analysis system from a time dimension, and enhances the accuracy of damage prediction.

[0050] In some embodiments, based on temporal evolution information, the different speeds and characteristics of damage morphology development from subtle surface changes to deep structural damage in vulnerable areas around expansion joints under the influence of different water retention duration patterns are analyzed to obtain damage pattern differentiation information. Based on the damage pattern differentiation information, the specific spatial manifestations and evolution sequence of material peeling, crack propagation, and structural loosening in areas continuously affected by water accumulation are analyzed to obtain early damage sign information. Based on the early damage sign information, combined with dynamic water distribution information, the immediate and potential threat levels to pavement structural safety of areas with different water depths and retention durations and clear early signs are analyzed to obtain information on water-accelerated damage sections. The early damage sign information and water-accelerated damage section information are integrated to construct an expansion joint anomaly information set.

[0051] Damage pattern differentiation information can be information on the differences in the morphology and speed of damage development from the surface to the depth of the expansion joint under different water accumulation conditions. Early damage signs information can be information on minor damage such as material peeling, crack propagation, and structural loosening around the expansion joint. Information on water-accelerated damage zones can be information on the delineation of dangerous areas where the water depth and retention time have reached the threshold and early damage signs have appeared.

[0052] Specifically, in the process of analyzing time-series evolution information to deduce expansion joint anomaly information, if damage patterns are not analyzed, early signs of damage are not identified, and water accumulation damage sections are not delineated, risks cannot be accurately located, maintenance opportunities will be missed, damage will be aggravated, and subsequent damage analysis and health assessment will lack reliable basis. To address the above issues: First, a time series clustering algorithm (such as K-means clustering based on dynamic time warping) is used to perform pattern mining on the water retention duration sequence in the time-series evolution information, automatically classifying typical patterns such as short-term high-frequency fluctuations, long-term stable immersion, and intermittent changes, and associating each pattern with the other. Data collected by depth sensors and fiber optic strain sensors are used to quantify the changes in damage development rate (e.g., micro-strain growth rate) and surface material hardness (detected by a portable drop weight deflectometer) under each mode, thereby constructing damage mode differentiation information. For identified high-risk modes (e.g., long-term stable immersion), a deep learning-based image semantic segmentation model (e.g., U-Net network) is further deployed to process high-resolution road inspection images. The pre-trained model can accurately identify the visual features of early damage (e.g., by combining Canny edge detection from the OpenCV library with morphological operations). The system automatically outputs early damage signs, including fine cracks and texture anomalies in the material spalling area extracted using a ResNet backbone network. These signs are labeled with spatial locations, such as point-like spalling, network-like micro-cracks, and whitening softening areas, along with their confidence levels. Subsequently, it integrates spatial overlay analysis from Geographic Information System (GIS) to register and fuse these signs with real-time dynamic distribution raster data of accumulated water (sourced from an IoT water level sensor network, with a spatial resolution of up to 0.5 m × 0.5 m). A multi-criteria evaluation model based on the Analytic Hierarchy Process (AHP) is then embedded. This model considers the sign type, water depth (e.g., threshold), and other relevant factors. The system uses the values ​​(set to be greater than 5 mm) and retention time (e.g., threshold set to be more than 6 hours) as input variables, combined with expert experience weights (e.g., assigning higher risk scores to material spalling signs in long-term waterlogged areas), to automatically calculate the comprehensive threat index of each grid cell. Based on this, high-risk, medium-risk, and low-risk waterlogged accelerated damage sections are delineated. Finally, using spatial database technology (such as PostGIS), the vector point data of the signs, the raster boundaries of the risk sections, the associated time-series pattern labels, and quantitative parameters are structured, stored, and indexed to generate an expansion joint anomaly information set that can be directly used for decision support.

[0053] The method provided in this embodiment can accurately distinguish damage development patterns, promptly identify early signs of damage, scientifically delineate dangerous sections where water accumulation accelerates damage, accurately characterize and locate expansion joint anomalies, solidify the data foundation for subsequent analysis, and help prevent and control road structure risks in advance.

[0054] In some embodiments, the traffic environment information set includes a traffic load information set and a traffic flow information set. Based on the traffic load information set, the cumulative effects of vehicle axle load, axle type distribution, and traffic frequency on material fatigue damage and structural impact in the area surrounding the expansion joint are analyzed to obtain load-induced damage information. Based on the traffic flow information set, the influence of vehicle speed, density, and vehicle type composition on water splashing, water flow disturbance, and drainage efficiency is analyzed to obtain traffic flow disturbance information. Based on the load-induced damage information, combined with the expansion joint anomaly information set, the damage development rate and structural failure risk of the abnormal area of ​​the expansion joint under different load characteristics are analyzed to obtain load-related damage information. Based on the traffic flow disturbance information, combined with the expansion joint anomaly information set, the retention and diffusion characteristics of water in the abnormal area of ​​the expansion joint under different traffic flow states are analyzed to obtain traffic flow-related water accumulation information. Based on the load-related damage information, combined with the traffic flow-related water accumulation information, the interactive enhancement relationship between damage formation and water accumulation induced by the combined action of load and traffic flow is analyzed to obtain the damage information set.

[0055] Traffic load information sets can be sets of parameters reflecting vehicle axle load, axle type distribution, and traffic frequency, used to analyze pavement structural load damage. Traffic flow information sets can be sets of parameters reflecting vehicle speed, density, and vehicle type composition, used to analyze the impact of traffic flow disturbance on pavement water accumulation. Load-induced damage information can be obtained by analyzing the cumulative effects of traffic loads on material fatigue damage and structural impact in the area surrounding expansion joints. Traffic flow disturbance information can be obtained by analyzing the impact of traffic flow conditions on water splashing, water flow disturbance, and drainage efficiency. Load-related damage information can be obtained by analyzing the damage development and failure risk of abnormal areas of expansion joints under different load characteristics. Traffic flow-related water accumulation information can be obtained by analyzing the water retention and diffusion characteristics of abnormal areas of expansion joints under different traffic flow conditions.

[0056] Specifically, in analyzing the causes of pavement damage and water accumulation at highway expansion joints, skipping this step makes it impossible to clearly understand the correlation between traffic load and flow field on damage and water accumulation, easily leading to misjudgment of the root cause of damage. This results in subsequent health assessments and risk classifications lacking a scientific basis, and deviations in pavement maintenance measures. To address these issues, the approach begins with the synchronous collection and fusion of multi-source heterogeneous data. Existing dynamic weighing methods are used to collect vehicle axle loads (e.g., data on single axle loads up to 13 tons obtained through piezoelectric film sensors or bending plate sensors), axle type, and traffic frequency, forming a traffic load information set. Simultaneously, high-definition video vehicle detectors and microwave radar (such as RTMS radar detectors) are used to obtain the instantaneous speed, time occupancy, and flow of vehicles in each lane. This data is then combined with ETC gantry data to statistically analyze vehicle type composition, forming a traffic flow information set. In the analysis phase, for load-induced damage information, the material fatigue damage accumulation theory is adopted, specifically applying Miner's linear cumulative damage rule to establish a mathematical model. The collected axle load spectrum is converted into an equivalent fatigue damage degree for specific materials (such as asphalt concrete) around the expansion joint. The contribution rate of different load spectra (such as heavy-load high-frequency spectra) to the damage development of identified abnormal areas is quantified. For traffic flow disturbance information, computational fluid dynamics simulation is introduced, utilizing ANSYS. Commercial CFD software such as Fluent is used to establish a three-dimensional simulation model of vehicle-airflow-water accumulation. By setting different inlet boundary conditions (such as simulating traffic flow at speeds of 60 km / h and 100 km / h), the specific impact of traffic flow state changes on road surface water film splashing, migration, and drainage outlet efficiency can be intuitively analyzed. Then, through correlation analysis and multivariate regression modeling (such as using SPSS or Python's statsmodels library), the quantified load damage degree, traffic flow modulation parameters, and expansion joint anomaly information set (such as crack length and water accumulation depth) are spatiotemporally matched and statistically correlated to construct load-related damage information and traffic flow-related water accumulation information. Finally, to analyze the interaction enhancement effect, structural equation modeling or interaction term regression analysis is used to explicitly introduce interaction terms such as "load intensity × water accumulation depth" and "vehicle speed × congestion duration" into the model to quantitatively evaluate the nonlinear leap in damage development rate under different combinations of conditions (such as "heavy load + congestion + deep water accumulation"), thereby integrating and generating a damage information set that can characterize complex damage mechanisms.

[0057] The method provided in this embodiment accurately analyzes the correlation between traffic environment and expansion joint anomalies, clarifies the external damage logic of damage and water accumulation, and provides accurate basis for subsequent road health assessment and risk classification.

[0058] In some embodiments, based on traffic flow disturbance information, the vehicle speed distribution and traffic density distribution corresponding to traffic congestion and smooth traffic conditions are analyzed to obtain traffic state characteristics. Based on the traffic state characteristics, combined with the expansion joint anomaly information set, the repeated crushing and obstruction effect of low-speed, high-density traffic flow on water accumulation in the accelerated damage section under congestion conditions is analyzed, as well as the stripping and dispersing effect of airflow generated by high-speed traffic flow on surface water accumulation in the early damage sign area under smooth traffic conditions, to obtain a state-driven information set. Based on the state-driven information set, the evolution process of water accumulation continuously infiltrating and stagnating in the deeper layers of the damage under congestion conditions is analyzed, as well as the spatial redistribution process of water accumulation spreading and migrating from the damaged area to the surrounding areas under smooth traffic conditions, to obtain the water accumulation evolution path. The state-driven information set and the water accumulation evolution path are integrated to construct traffic flow-related water accumulation information that represents the dynamic correlation between traffic flow and water accumulation behavior.

[0059] Traffic state characteristics can be a collective term for the vehicle speed distribution and traffic flow density distribution characteristics corresponding to traffic congestion and smooth flow, respectively. Accelerated waterlogging damage zones can be areas with varying water depths and durations of retention, exhibiting clear early signs of damage, posing an immediate and potential threat to pavement structural safety. Areas showing early signs of damage can be the areas surrounding expansion joints where, under the continuous action of waterlogging, precursory damage such as material peeling, crack propagation, and structural loosening becomes apparent. The state-driven information set can be a collection of information related to the crushing, obstruction, peeling, and dispersion effects of traffic flow on waterlogging under different traffic states. The waterlogging evolution path can be the spatial movement and state change process of waterlogging undergoing deep infiltration, long-term retention, or diffusion migration under different traffic states.

[0060] Specifically, in the process of detecting highway expansion joints, during the stage of analyzing the correlation between traffic flow and water accumulation, if the impact of traffic flow on water accumulation is not analyzed in terms of traffic conditions, it will be impossible to accurately capture the dynamic correlation between the two. This will lead to deviations in subsequent analysis of damage caused by load and traffic flow interaction, and misjudgment of the causes and development trends of pavement damage. To address the above problems: First, using the raw traffic flow data collected by loop detectors or video vehicle detectors deployed on the roadside, pattern recognition is performed on historical and real-time data through statistical clustering methods (such as K-means clustering) or machine learning-based classification models (such as support vector machines, SVM), automatically classifying "congestion" and other traffic conditions. The system identifies two typical traffic states: "smooth" and "unobstructed." Representative vehicle speed distributions (e.g., average speed below 20 km / h and traffic density above 25 vehicles per kilometer) and traffic density characteristics are extracted for each state to construct quantifiable traffic state features. These features are then spatially overlaid with anomaly information sets from road surface sensor networks (such as humidity sensors and image detection units), containing geographic polygons of water-accelerated damage sections and coordinates of early signs of damage. For congested states, this approach applies a multiphase flow model from computational fluid dynamics (CFD) simulations or a simplified hydrostatic pressure-permeability model. The formula is verified to quantify the repeated crushing load and runoff obstruction effect of low-speed, high-density traffic flow on water accumulation in overlapping areas. The additional pressure and drainage delay time of water being squeezed into damaged gaps are calculated. For unobstructed conditions, boundary layer theory from aerodynamics is introduced. An airflow shear force calculation model is used to simulate the shear stripping force of near-surface airflow induced by high-speed traffic (e.g., speeds greater than 80 km / h) on surface water accumulation in early damaged areas, and the direction angle and migration distance of water dispersion are estimated. Based on the state-driven information set generated by the above physical driving analysis, path tracing and spatiotemporal extrapolation algorithms are further employed: for congestion-driven conditions, water infiltration is used... Permeation-diffusion models (such as modified models based on Darcy's law) simulate the infiltration path and residence time curve of water under continuous pressure into the deep pores of asphalt mixture. For smooth-flow-driven systems, particle systems or diffusion-convection models are used to simulate the spatial diffusion path of stripped water droplets or water flow under the action of road cross slope and wind. Finally, through the spatiotemporal data fusion engine of geographic information (GIS), the state-driven information set (driving mechanism) and the water evolution path (dynamic result) are integrated and visualized to generate a correlation map that can dynamically reflect how specific traffic conditions specifically change the water accumulation behavior of specific areas, i.e., high-fidelity traffic flow-related water accumulation information.

[0061] The method provided in this embodiment accurately distinguishes between congested and smooth traffic conditions to analyze the effect of traffic flow on water accumulation, clarifies the evolution pattern of water accumulation in the abnormal area of ​​expansion joints, provides accurate data for subsequent interactive damage analysis, and improves the scientific nature and detection accuracy of damage information analysis.

[0062] In some embodiments, based on load-related damage information and traffic flow-related water accumulation information, the differential changes in damage efficiency caused by the softening of pavement materials due to water immersion in the water-accelerated damage zone due to vehicle impact load are analyzed to obtain load damage enhancement information. Based on load damage enhancement information and state-driven information set, the infiltration-compression composite damage characteristics jointly induced by heavy-load slow traffic flow under traffic congestion conditions through prolonged load application time and obstruction of water discharge in the early damage sign area are analyzed to obtain first interactive enhancement information. Based on load damage enhancement information and state-driven information set, the water splash-material peeling composite damage characteristics jointly induced by high-speed traffic flow under smooth traffic conditions through additional airflow disturbance and load impact in the early damage sign area are analyzed to obtain second interactive enhancement information. The load damage enhancement information, first interactive enhancement information, and second interactive enhancement information are integrated to construct a damage information set.

[0063] Load-induced damage enhancement information can be related to the differential changes in damage efficiency caused by vehicle impact loads in water-accelerated damage zones due to the softening of pavement materials from water immersion. First-level interaction enhancement information can be related to how heavy-load, slow-moving traffic under congested conditions prolongs load application time and impedes water drainage, inducing infiltration-compression composite damage characteristics in early damage indicator areas. Second-level interaction enhancement information can be related to how high-speed traffic under smooth traffic conditions, through additional airflow disturbances and load impacts, induces water splashing-material delamination composite damage characteristics in early damage indicator areas.

[0064] Specifically, in the process of inspecting highway expansion joints, when analyzing the interaction between load and traffic flow causing damage, if the synergistic effect of the two is ignored, it is impossible to accurately identify the combined damage under different traffic scenarios of congestion and smooth flow, which can easily lead to misjudgment of pavement damage patterns and inaccurate analysis of damage causes. To address the above issues: First, regarding the quantification of "load damage enhancement information," vehicle-pavement coupled dynamics finite element analysis technology (such as using ABAQUS or ANSYS software) is applied to construct a constitutive model containing base course materials with different saturations. Simulations are then performed to model standard axle loads (such as BZZ-100) under both dry and water-soaked conditions. Under these conditions, for the dynamic loading process of the same microcrack region, the difference ratio of key parameters such as the rate of change of stress intensity factor at the crack tip and the cumulative efficiency of plastic strain energy are quantitatively extracted. This is used to numerically characterize the damage efficiency amplification factor caused by water immersion. Then, to obtain the "first interactive enhancement information", multiphysics coupling simulation technology is introduced. A high-fidelity model including the vehicle body, road surface water, and porous media pavement is established in computational fluid dynamics (CFD) software (such as STAR-CCM+) to simulate the passage of a heavy-duty truck platoon under congested conditions (such as a vehicle speed of 5 km / h and a vehicle spacing of 2 m). Through fluid-structure interaction calculations, the difference ratio of key parameters such as the rate of change of stress intensity factor at the crack tip and the cumulative efficiency of plastic strain energy is obtained. This study analyzes the transient pressure and velocity fields of water seeping into cracks under tire compression. Using a solid mechanics module, it calculates the combined stress distribution of the seepage pressure and the static load of the wheel around the crack. This allows for the extraction of a quantitative index for the seepage-compression failure mode characterized by a sudden increase in pore water pressure accompanied by shear stress concentration. For the "secondary interactive enhancement information," a discrete phase model (DPM) and dynamic mesh technology are used to simulate a high-speed (e.g., 100 km / h) single vehicle passing through a waterlogged area in the same CFD environment. The shear stripping effect of air turbulence generated by the vehicle body and chassis on the surface water film is calculated, and the impact of the entrained water droplets (discrete phase) on the failure is tracked. The impact energy at the edge of the damage is calculated. Simultaneously, transient dynamic analysis is coupled to calculate the additional impact load caused by road surface unevenness (caused by early damage) when the wheel passes. By analyzing the superposition effect of airflow stripping, water droplet erosion and impact load in the time series, a composite failure criterion related to material stripping rate, splash distance and peak impact force is defined. Finally, through a multi-criteria information fusion algorithm (such as based on fuzzy logic or evidence theory), the quantitative indicators obtained from the above simulation analysis are matched and verified with measured traffic state data (such as radar vehicle detection data), and the damage information set representing the interaction enhancement mechanism is dynamically constructed and updated.

[0065] The method provided in this embodiment accurately uncovers the interaction-enhanced damage patterns between load and traffic flow, clarifies the composite damage characteristics of pavement under different traffic conditions, and forms a complete damage information set, providing accurate and reliable core data support for pavement risk classification and health assessment.

[0066] In some embodiments, based on the damage information set, the speed and characteristic differences of pavement damage development under different interactive enhancement modes are analyzed to obtain risk development mode information; based on the risk development mode information, combined with the first and second interactive enhancement information, the degradation path and critical state of pavement structural safety performance under different risk modes are analyzed to obtain structural degradation path information; based on the structural degradation path information, the observable damage morphology, development speed, and immediate and potential threats to pavement safety corresponding to each risk mode are analyzed to obtain risk level classification information; based on the risk level classification information, a standard format report containing risk location, level, mode, and evolution trend is generated, and an integrated detection log of highway pavement damage and water accumulation is output.

[0067] Risk development pattern information can be a set of information formed by analyzing the differences in the development speed and characteristics of pavement damage under different interactive enhancement modes. Structural degradation path information can be a set of information formed by analyzing the degradation path and critical state of pavement structural safety performance under different risk modes. Risk level classification information can be pavement risk level classification information formed by analyzing the damage morphology, development speed and safety threat corresponding to each risk mode. Risk location can be information that accurately determines the specific road sections and locations on highways where there is a risk of pavement damage and water accumulation.

[0068] Specifically, during the inspection of highway expansion joints, if health assessment and risk classification are not carried out after pavement damage analysis, the critical state of structural degradation cannot be identified. This can easily lead to small damage developing into structural failure, with water accumulation and damage interacting and intensifying, causing safety accidents such as vehicle skidding and road collapse. It can also cause misallocation of maintenance resources. To address these issues: First, based on the damage information set, pattern recognition technology (such as fuzzy C-means clustering algorithm) is used to automatically cluster the damage development speed, morphological characteristics, and dominant factors. Similar interaction enhancement processes are categorized to extract concrete risk development pattern information. For example, the pattern dominated by heavy-load slow traffic flow and long-term water accumulation is identified as Type A infiltration-squeezing slow-progression. Then, for each type of pattern, combined with the physical process described by its corresponding first or second interaction enhancement information, a pre-set finite element mechanical analysis model is called to perform path deduction. Taking Type A pattern as an example, the model will simulate and calculate the infiltration of water into the base layer under repeated dynamic load compression based on parameters such as axle weight, water depth, and retention time. The rate of degradation and the subsequent softening of the base material modulus are used to deduce the complete structural degradation path from slight degradation of material properties to structural layer instability. A multi-index fusion classification decision model is constructed: This model sets quantitative criteria for key nodes in the path (such as the base water saturation exceeding the threshold, such as 25%, and the asphalt pavement flexural strain reaching the critical value, such as 150με). It also integrates the size of currently observable damage (such as crack width greater than 3 mm), development speed (such as monthly expansion length greater than 10 cm), and potential impact on driving safety (such as whether it is located in a heavy-load lane). Through a set of weighted scoring rules, the risk value is automatically calculated, and finally, risk level classification information is output, dividing the risk into high, medium, and low levels. Using a report generation engine (such as the ReportLab library based on JasperReports or Python), the above analysis results are automatically populated into a pre-designed XML or JSON template, and an integrated detection log containing charts and text descriptions is generated in a formatted manner, completing the closed loop from data to decision information.

[0069] The method provided in this embodiment can accurately determine the risk level and evolution trend of road damage, provide early warning of critical state of structural degradation, prevent further deterioration of damage, provide a basis for precise allocation of maintenance resources, reduce potential safety hazards on highways, and ensure the stability of road structure.

[0070] Figure 3 This application provides a schematic diagram of the structure of an integrated intelligent highway pavement damage and water accumulation detection system according to an embodiment of the present application. Figure 3 As shown, the intelligent highway pavement damage and water accumulation integrated detection system 300 of this embodiment includes: an anomaly analysis module 301, an environmental damage module 302, and a graded assessment module 303.

[0071] Anomaly analysis module 301 is used to acquire a road surface condition information set, and based on the road surface condition information set, analyze the coupling and temporal relationship between the vulnerable surrounding area of ​​the expansion joint and the water accumulation distribution characteristics in the detection and identification process to obtain an expansion joint anomaly information set; Environmental damage module 302 is used to acquire a traffic environment information set, and based on the traffic environment information set, combined with the expansion joint anomaly information set, analyze the correlation between damage formation, water accumulation induction and external factors to obtain a damage information set; Graded assessment module 303 is used to perform road surface health status assessment and risk graded analysis based on the damage information set, and generate and output an integrated detection log of highway road surface damage and water accumulation.

[0072] Optionally, when the anomaly analysis module 301 analyzes the coupling and temporal relationship between the vulnerable surrounding area of ​​the expansion joint and the distribution characteristics of water accumulation in the detection and identification based on the road surface condition information set to obtain the expansion joint anomaly information set, it is specifically used for: the road surface condition information set including the expansion joint structure information set and the road surface water information set; based on the expansion joint structure information set, analyzing the structural characteristics of the expansion joint itself and the continuity and strength change trend of the surrounding area materials to obtain the vulnerable area information set; based on the road surface water information set, analyzing the distribution range, depth change and retention time of road surface water to obtain the dynamic distribution information of water accumulation; based on the vulnerable area information set, combined with the dynamic distribution information of water accumulation, analyzing the overlap and interaction between the water accumulation distribution and the vulnerable area in spatial location to obtain spatial coupling information; based on the spatial coupling information, analyzing the change trend of the retention time of water accumulation in the vulnerable area and the cumulative impact process on the development of regional damage to obtain temporal evolution information; based on the temporal evolution information, analyzing the early signs of damage in the area surrounding the expansion joint and the dangerous sections where water accumulation accelerates damage to obtain the expansion joint anomaly information set.

[0073] Optionally, the anomaly analysis module 301, during the construction of the spatial coupling information, is specifically used to: based on the vulnerable area information set and combined with the dynamic distribution information of water accumulation, analyze the intersection range and relative orientation of the water accumulation area and the vulnerable area in planar position to obtain spatial overlap information; based on the spatial overlap information, analyze the hydrostatic ballast effect and infiltration soaking effect generated by water accumulation in the overlapping area to obtain water accumulation effect information; based on the water accumulation effect information, analyze the degree of acceleration of the damage process of different vulnerable areas of the expansion joint by water accumulation effect to obtain the spatial coupling information.

[0074] Optionally, the anomaly analysis module 301, during the construction of the temporal evolution information, is specifically used for: analyzing the fluctuation pattern of water retention time at each monitoring point in the vulnerable area under different water accumulation intensities based on the spatial coupling information, to obtain water retention temporal information; analyzing the progressive impact of water accumulation on the degree of damage to the vulnerable area under different retention time stages based on the water retention temporal information, to obtain damage accumulation development information; and analyzing the corresponding correlation between the change in water retention time and the damage development stage throughout the entire process from initial minor damage to increased damage in the vulnerable area, to obtain the temporal evolution information.

[0075] Optionally, when the anomaly analysis module 301 analyzes the early signs of damage and dangerous sections of water-accelerated damage in the area surrounding the expansion joint based on the temporal evolution information to obtain the expansion joint anomaly information set, it is specifically used for: analyzing the different speeds and characteristics of damage morphology development from subtle surface changes to deep structural damage in the vulnerable area surrounding the expansion joint under different water retention duration patterns based on the temporal evolution information, and obtaining damage mode differentiation information; analyzing the specific spatial manifestations and evolution sequence of material peeling, crack propagation, and structural loosening in the area of ​​continuous water accumulation, and obtaining early signs of damage information based on the early signs of damage information; combining the dynamic distribution information of water accumulation, analyzing the immediate and potential threat levels to pavement structure safety in areas with different water depths, different retention durations, and clear early signs of damage, and obtaining water-accelerated damage section information; and integrating the early signs of damage information and the water-accelerated damage section information to construct the expansion joint anomaly information set.

[0076] Optionally, when the environmental damage module 302 analyzes the correlation between damage formation, water accumulation, and external factors based on the traffic environment information set and the expansion joint anomaly information set to obtain a damage information set, it is specifically used for: the traffic environment information set including a traffic load information set and a traffic flow information set; based on the traffic load information set, analyzing the cumulative effect of vehicle axle load, axle type distribution, and traffic frequency on material fatigue damage and structural impact in the area surrounding the expansion joint to obtain load-induced damage information; based on the traffic flow information set, analyzing the influence of vehicle speed, density, and vehicle type composition on water splashing, water flow disturbance, and drainage efficiency. The process involves obtaining traffic flow disturbance information; based on the load-induced damage information and the expansion joint anomaly information set, analyzing the damage development rate and structural failure risk of the expansion joint anomaly area under different load characteristics to obtain load-related damage information; based on the traffic flow disturbance information and the expansion joint anomaly information set, analyzing the retention and diffusion characteristics of water accumulation in the expansion joint anomaly area under different traffic flow states to obtain traffic flow-related water accumulation information; and based on the load-related damage information and the traffic flow-related water accumulation information, analyzing the interactive enhancement relationship between damage formation and water accumulation induced by the combined action of load and traffic flow to obtain the damage information set.

[0077] Optionally, the environmental damage module 302, during the construction of the traffic flow-related water accumulation information, is specifically used for: analyzing the vehicle speed distribution and traffic density distribution corresponding to traffic congestion and smooth traffic conditions, respectively, based on the traffic flow disturbance information, to obtain traffic state characteristics; based on the traffic state characteristics, combined with the expansion joint anomaly information set, analyzing the repeated crushing and obstructing effect of low-speed, high-density traffic flow on the water accumulation in the water accumulation accelerated damage section under congestion conditions, and analyzing the stripping and dispersing effect of airflow generated by high-speed traffic flow on the surface water accumulation in the early damage sign area under smooth traffic conditions, to obtain a state-driven information set; based on the state-driven information set, analyzing the evolution process of water accumulation continuously infiltrating and stagnating in the deeper damage layer under congestion conditions, and analyzing the spatial redistribution process of water accumulation spreading and migrating from the damaged area to the surrounding area under smooth traffic conditions, to obtain the water accumulation evolution path; integrating the state-driven information set and the water accumulation evolution path to construct the traffic flow-related water accumulation information characterizing the dynamic correlation between traffic flow and water accumulation behavior.

[0078] Optionally, when the environmental damage module 302 analyzes the interaction and enhancement relationship between damage formation and water accumulation induced by the combined action of load and traffic flow under the load-related damage information and the traffic flow-related water accumulation information to obtain the damage information set, it is specifically used to: analyze the differential changes in damage efficiency caused by vehicle impact load in the water-accelerated damage section due to the softening of road surface materials by water immersion, based on the load-related damage information and the traffic flow-related water accumulation information, to obtain load damage enhancement information; and analyze the traffic congestion status based on the load damage enhancement information and the state-driven information set. The heavy-load, slow-moving traffic flow under certain conditions, by extending the load application time and hindering the drainage of accumulated water, jointly induces the infiltration-compression composite damage characteristics in the early damage sign area, resulting in first interactive enhancement information. Based on the load damage enhancement information and combined with the state-driven information set, the high-speed traffic flow under smooth traffic conditions, by causing additional airflow disturbances and load impacts, jointly induces the water splashing-material peeling composite damage characteristics in the early damage sign area, resulting in second interactive enhancement information. The load damage enhancement information, the first interactive enhancement information, and the second interactive enhancement information are integrated to construct the damage information set.

[0079] Optionally, when the graded assessment module 303 performs pavement health status assessment and risk grading analysis based on the damage information set, and generates and outputs the integrated detection log of highway pavement damage and water accumulation, it is specifically used for: analyzing the speed and characteristic differences of pavement damage development under different interactive enhancement modes based on the damage information set to obtain risk development mode information; analyzing the degradation path and critical state of pavement structural safety performance under different risk modes based on the risk development mode information, combined with the first interactive enhancement information and the second interactive enhancement information, to obtain structural degradation path information; analyzing the observable damage morphology, development speed, and immediate and potential threats to pavement safety corresponding to each risk mode based on the structural degradation path information to obtain risk level classification information; and generating a standard format report containing risk location, level, mode, and evolution trend based on the risk level classification information, and outputting the integrated detection log of highway pavement damage and water accumulation.

[0080] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A smart highway pavement damage and water accumulation integrated detection method, characterized in that, include: Obtain a road surface condition information set; based on the road surface condition information set, analyze the coupling and temporal relationship between the vulnerable surrounding area of ​​the expansion joint and the water accumulation distribution characteristics in the detection and identification process to obtain an expansion joint anomaly information set. Obtain a traffic environment information set; based on the traffic environment information set and the expansion joint anomaly information set, analyze the correlation between damage formation, water accumulation and external factors to obtain a damage information set. Based on the damage information set, a road surface health status assessment and risk classification analysis are performed, and an integrated detection log of highway road surface damage and water accumulation is generated and output.

2. The method according to claim 1, characterized in that, Based on the road surface condition information set, the coupling and temporal relationship between the vulnerable surrounding area of ​​the expansion joint and the water accumulation distribution characteristics in the detection and identification process are analyzed to obtain an expansion joint anomaly information set, including: The road surface condition information set includes an expansion joint structure information set and a road surface water information set; Based on the expansion joint structure information set, the structural characteristics of the expansion joint itself and the continuity and strength variation trend of the surrounding materials are analyzed to obtain the vulnerable area information set; Based on the road surface water information set, the distribution range, depth changes and retention time of road surface water are analyzed to obtain dynamic distribution information of water accumulation. Based on the vulnerable area information set and combined with the dynamic distribution information of water accumulation, the spatial overlap and interaction between water accumulation distribution and vulnerable areas are analyzed to obtain spatial coupling information; Based on the aforementioned spatial coupling information, the variation trend of water retention time in vulnerable areas and the cumulative impact on regional damage development are analyzed to obtain temporal evolution information. Based on the time-series evolution information, early signs of damage and dangerous sections where water accumulation accelerates damage in the area surrounding the expansion joint are analyzed to obtain the expansion joint anomaly information set.

3. The method according to claim 2, characterized in that, The process of constructing the spatial coupling information includes: Based on the vulnerable area information set and combined with the dynamic distribution information of water accumulation, the intersection range and relative orientation of the water accumulation area and the vulnerable area in planar position are analyzed to obtain spatial overlap information. Based on the spatial overlap information, the hydrostatic ballast effect and infiltration soaking effect generated by water accumulation in the overlapping area are analyzed to obtain information on the effects of water accumulation. Based on the information on the effect of water accumulation, the degree to which the effect of water accumulation accelerates the damage process in different vulnerable areas of the expansion joint is analyzed, and the spatial coupling information is obtained.

4. The method according to claim 3, characterized in that, The process of constructing the temporal evolution information includes: Based on the spatial coupling information, the fluctuation pattern of water retention time at each monitoring point in the vulnerable area under different water accumulation intensities is analyzed to obtain water retention time series information. Based on the water retention time sequence information, the progressive impact of water retention on the degree of damage to vulnerable areas under different retention time stages is analyzed to obtain information on the cumulative development of damage. Based on the information on the cumulative development of damage, the correlation between the change in the duration of water retention and the stage of damage development is analyzed throughout the entire process from initial minor damage to increased damage in the vulnerable area, thus obtaining the temporal evolution information.

5. The method according to claim 4, characterized in that, Based on the temporal evolution information, the early signs of damage and dangerous sections where water accumulation accelerates destruction in the area surrounding the expansion joint are analyzed to obtain the expansion joint anomaly information set, including: Based on the aforementioned temporal evolution information, the different speeds and characteristics of the damage morphology in the vulnerable area around the expansion joint, which develops from subtle surface changes to deep structural damage under the influence of different water retention duration patterns, are analyzed to obtain damage pattern differentiation information. Based on the damage mode differentiation information, the specific spatial manifestations and evolution sequence of material peeling, crack propagation and structural loosening in the area of ​​continuous water accumulation are analyzed to obtain early damage signs information. Based on the early signs of damage information and the dynamic distribution information of water accumulation, the immediate and potential threat levels to the road structure safety of areas with different water depths, different retention times and clear early signs are analyzed, and information on water-accelerated damage sections is obtained. By integrating the early signs of damage information and the information on the sections where water accumulation accelerates damage, an abnormal information set for the expansion joint is constructed.

6. The method according to claim 5, characterized in that, Based on the traffic environment information set and the expansion joint anomaly information set, the correlation between damage formation, water accumulation, and external factors is analyzed to obtain a damage information set, including: The traffic environment information set includes a traffic load information set and a traffic flow information set; Based on the traffic load information set, the cumulative effect of vehicle axle load, axle type distribution and traffic frequency on material fatigue damage and structural impact in the area surrounding the expansion joint is analyzed to obtain load-induced damage information. Based on the traffic flow information set, the influence of vehicle speed, density and vehicle type composition on water splashing, water flow disturbance and drainage efficiency is analyzed to obtain traffic flow disturbance information. Based on the load-induced damage information and combined with the expansion joint anomaly information set, the damage development rate and structural failure risk of the expansion joint anomaly area under different load characteristics are analyzed to obtain load-related damage information. Based on the traffic flow disturbance information and the expansion joint anomaly information set, the retention and diffusion characteristics of water accumulation in the expansion joint anomaly area under different traffic flow conditions are analyzed to obtain traffic flow-related water accumulation information. Based on the load-related damage information and the traffic flow-related water accumulation information, the interaction and enhancement relationship between damage formation and water accumulation under the combined action of load and traffic flow is analyzed to obtain the damage information set.

7. The method according to claim 6, characterized in that, The process of constructing the traffic flow-related water accumulation information includes: Based on the traffic flow disturbance information, the vehicle speed distribution and traffic density distribution corresponding to the traffic congestion state and the traffic smooth state are analyzed to obtain the traffic state characteristics. Based on the traffic state characteristics and the expansion joint anomaly information set, the analysis is conducted on the repeated crushing and obstruction of water accumulation in the water accumulation accelerated damage section under congested conditions, and the analysis is conducted on the stripping and dispersing effect of airflow generated by high-speed traffic on surface water accumulation in the early damage sign area under smooth conditions, thus obtaining the state-driven information set. Based on the state-driven information set, the evolution process of water accumulation continuously seeping into the deeper layers of the damage and remaining there for a long time under the congestion state is analyzed, as well as the spatial redistribution process of water accumulation spreading and migrating from the damaged area to the surrounding area under the unobstructed state is analyzed, thus obtaining the water accumulation evolution path. By integrating the state-driven information set and the water accumulation evolution path, traffic flow-related water accumulation information is constructed to represent the dynamic correlation between traffic flow and water accumulation behavior.

8. The method according to claim 7, characterized in that, Based on the load-related damage information and the traffic flow-related water accumulation information, the interaction and reinforcement relationship between damage formation and water accumulation induced by the combined action of load and traffic flow is analyzed to obtain the damage information set, including: Based on the load-related damage information and the traffic flow-related water accumulation information, the differential changes in the damage efficiency of vehicle impact load in the water-accelerated damage section due to the softening of road surface materials by water accumulation are analyzed to obtain load damage enhancement information. Based on the load damage enhancement information and combined with the state-driven information set, the infiltration-squeezing composite damage characteristics jointly triggered by the heavy-load slow traffic flow under traffic congestion by extending the load action time and hindering the discharge of accumulated water in the early damage sign area are analyzed to obtain the first interactive enhancement information. Based on the load damage enhancement information and combined with the state-driven information set, the composite damage characteristics of water splashing and material peeling caused by the high-speed traffic flow under smooth traffic conditions, which are jointly triggered by the additional airflow disturbance and load impact in the early damage sign area, are analyzed to obtain the second interactive enhancement information. The load damage enhancement information, the first interaction enhancement information, and the second interaction enhancement information are integrated to construct the damage information set.

9. The method according to claim 8, characterized in that, The process involves assessing the pavement health status and conducting risk classification analysis based on the damage information set, generating and outputting an integrated detection log for highway pavement damage and water accumulation, including: Based on the damage information set, the speed and characteristic differences of pavement damage development under different interactive enhancement modes are analyzed to obtain risk development mode information. Based on the risk development mode information, combined with the first interaction enhancement information and the second interaction enhancement information, the degradation path and critical state of pavement structure safety performance under different risk modes are analyzed to obtain structural degradation path information. Based on the structural degradation path information, the observable damage morphology, development speed and immediate and potential threats to road safety corresponding to each risk mode are analyzed to obtain risk level classification information. Based on the risk level classification information, a standard format report containing risk location, level, pattern and evolution trend is generated, and the integrated detection log of highway pavement damage and water accumulation is output.

10. A smart highway pavement damage and water accumulation integrated detection system, characterized in that, The method applied to any one of claims 1-9 includes: Anomaly analysis module is used to acquire road surface condition information set, and based on the road surface condition information set, analyze the coupling and temporal relationship between the vulnerable surrounding area of ​​expansion joint and water accumulation distribution characteristics in detection and identification, and obtain expansion joint anomaly information set; The environmental damage module is used to acquire a traffic environment information set, and based on the traffic environment information set and the expansion joint anomaly information set, analyze the correlation between damage formation, water accumulation and external factors to obtain the damage information set. The graded assessment module is used to assess the road surface health status and perform risk grading analysis based on the damage information set, and generate and output an integrated detection log of highway road surface damage and water accumulation.

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