A complex road surface mechanical response monitoring device and method

Abstract

The application relates to the technical field of mechanical response monitoring, in particular to a complex road surface mechanical response monitoring device and method, which comprises the following steps: collecting sensor data of each road surface of a highway; constructing a graph structure by using the correlation among mechanics, heat and humidity among sensors; mapping the graph structure into a graph frequency domain feature vector by using graph Fourier transform to form a hypergraph space; constructing a target function based on the spatial proximity and time continuity of the graph frequency domain feature vector in the hypergraph space, so that the graph frequency domain feature vector is globally optimized by using a particle swarm optimization algorithm; inversely transforming the optimized graph frequency domain feature vector to a data space to generate a sensor signal coupling curve, and identifying road surface damage according to the abnormal change of the curve, so that real-time monitoring of the road surface health state is realized. The application aims to improve the accuracy and real-time performance of the road surface health state monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical response monitoring, in particular to a complex pavement mechanical response monitoring device and method. BACKGROUND

[0002] In modern highway construction, pavement mechanical response devices monitor the pavement state under the action of complex traffic load and environmental factors in real time by burying various sensors. These sensors can capture the mechanical response of different layers, such as the tensile deformation of the asphalt layer, the fatigue stress of the base layer, and the vertical pressure of the soil foundation, and analyze the changes in material properties in combination with temperature and humidity data. When the monitoring data is abnormal, the system can evaluate the health status of the pavement through multi-source data fusion technology, thereby providing a basis for maintenance decisions.

[0003] However, with the increase in the number of sensors and the expansion of the data size, precise monitoring faces significant challenges. On the one hand, the data of different sensors (such as strain gauges and pressure gauges) have complex correlations in space and time. On the other hand, the massive time series data accumulated through long-term monitoring makes it difficult for traditional analysis methods to effectively mine the implicit rules among the data. SUMMARY

[0004] To solve the above technical problems, the present application provides a complex pavement mechanical response monitoring device and method, and the technical solutions adopted are as follows:

[0005] In a first aspect, the embodiments of the present application provide a complex pavement mechanical response monitoring method, which comprises the following steps:

[0006] Multiple source sensors are buried at different layers of the highway, and mechanical response values, temperature values, and humidity values are collected in real time at each pavement section;

[0007] A graph structure is constructed with sensors as nodes and the data coupling correlation between mechanical-thermal-humidity of the nodes as edge weights, and a graph Fourier transform is used to map the graph structure into a graph frequency domain feature vector. The graph frequency domain feature vectors of all road sections at all collection times form a hypergraph space;

[0008] Based on the spatial proximity and temporal continuity of the graph frequency domain feature vectors in the hypergraph space, a target function is constructed containing data reconstruction error terms, spatial smoothing terms, and temporal smoothing terms, and a particle swarm optimization algorithm is used to globally optimize the graph frequency domain feature vectors;

[0009] The optimized graph frequency domain feature vectors are inversely transformed to the data space to generate a sensor signal coupling curve, and pavement damage is identified according to the abnormal changes in the curve, thereby realizing real-time monitoring of the pavement health status.

[0010] Preferably, the sensors include, but are not limited to, asphalt strain gauges, base strain gauges, pressure gauges, temperature sensors, and humidity sensors.

[0011] Preferably, the edge weight between nodes in the graph structure is determined by the following formula:

[0012]

[0013] wherein represents the edge weight between the first node and the second node of the first road section at the first time point, e is a natural constant, , , , , , , , , , , represents that there is a correlation between the data of the two nodes, if the data of the two nodes is positively correlated, 0 is taken; if the data of the two nodes is negatively correlated, 1 is taken; represents that there is no correlation between the data of the two nodes.

[0014] Preferably, the data collected by the asphalt strain gauges, the base strain gauges, and the pressure gauges are positively correlated, the data collected by the asphalt strain gauges and the temperature sensors are positively correlated, the data collected by the base strain gauges and the humidity sensors are positively correlated, the data collected by the temperature sensors and the humidity sensors are negatively correlated, and there is no correlation between the rest of the data.

[0015] Preferably, the construction method of the objective function is:

[0016] The spatial edge weight of the graph frequency domain feature vectors of any two road sections at the same time point is determined by the difference between the graph frequency domain feature vectors and the distance between any two road sections;

[0017] The time edge weight of the graph frequency domain feature vectors of any two time points under the same road section is determined by the difference between the graph frequency domain feature vectors;

[0018] The objective function is constructed in combination with the spatial edge weight and the time edge weight.

[0019] Preferably, the calculation method of the spatial edge weight is: ; wherein represents the distance between the first node and the second node of the first road section at the same time point , , Spatial edge weights of the graph frequency domain feature vectors of each road segment. , They represent in At this moment The, the The frequency domain feature vector of each road segment This indicates the calculation of the L2 norm between two vectors. Indicates in At this moment The road section and the first The Euclidean spatial distance between the center points of each road segment, where e is a natural constant. It is a preset spatial attenuation factor.

[0020] Preferably, the time edge weights are calculated as follows: ;in, Indicates in At this moment The frequency domain feature vector of each road segment It is a preset smoothing factor for the time edge weights.

[0021] Preferably, the objective function is calculated as follows:

[0022] Where T represents all times collected, and N represents all road segments collected. It is the first At this moment The diagram structure of each road segment, The corresponding value is obtained from the eigenvalue decomposition of the Laplace matrix during the graphical Fourier transform. The graph Fourier basis matrix.

[0023] Preferably, the particle swarm optimization algorithm uses each graph frequency domain feature vector as an individual particle, the fitness function as the objective function, the particle velocity update factor as 2, and the number of iterations as 100.

[0024] Secondly, embodiments of this application also provide a complex road surface mechanical response monitoring device, the device comprising:

[0025] The integrated housing houses an asphalt strain gauge, a base strain gauge, a pressure gauge, a temperature sensor, and a humidity sensor.

[0026] The wireless communication module is used to collect sensor data at 30-second sampling intervals and upload it to a remote system.

[0027] The data processing unit is used to execute the steps of the complex pavement mechanical response monitoring method described above.

[0028] This application has at least the following beneficial effects:

[0029] The application can simultaneously obtain three key physical quantities of mechanics-heat-moisture inside the pavement structure layer by one-time burying, eliminate the construction secondary excavation and sensor position deviation caused by layered arrangement, reduce the installation cost and improve the data space consistency. The application uses a graph structure to quantitatively express the physical coupling relationship between sensors, which can improve the data correlation dimension and reduce the subsequent calculation complexity without adding additional sensors compared with the traditional matrix splicing method. The application uses a space-time joint constraint objective function, so that the particle swarm optimization can converge to a globally better solution within a limited number of iterations, significantly reducing the interference of abnormal values on damage identification results and improving the robustness of the algorithm. The coupled curve after inverse transformation of the application restores the high-dimensional frequency domain features to the physical quantity curve which is easy to explain, which can improve the accuracy and real-time performance of the on-site staff to intuitively judge the damage location and degree. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0031] Figure 1 The flow chart of the steps of the complex pavement mechanical response monitoring method of the present application. DETAILED DESCRIPTION

[0032] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the specific embodiments, structures, features and effects of the complex pavement mechanical response monitoring device and method according to the present application are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0034] The specific scheme of the complex pavement mechanical response monitoring device and method provided by the present application is described in detail below with reference to the drawings.

[0035] An embodiment of the present application provides a complex pavement mechanical response monitoring device and method.

[0036] Specifically, a complex pavement mechanical response monitoring method is provided as follows, please refer to Figure 1The method comprises the following steps:

[0037] Step one: burying multiple source sensors at different layers of the highway, and collecting real-time mechanical response values, temperature values and humidity values at each section of the road surface.

[0038] The pavement mechanical response device is an integrated device of multiple sensors. According to the details of the highway project, corresponding sensors are installed at different depths of the device, including but not limited to asphalt strain gauges, base strain gauges, pressure gauges, temperature sensors and humidity sensors. Then the device is buried in the highway to ensure that the sensors of key layers such as asphalt layer, base layer and subbase layer can accurately measure the mechanical response sensor data of the corresponding layer.

[0039] Arranged longitudinally along the direction of the highway, a set of devices is buried at a predetermined interval, and each set of devices is used to capture various mechanical response sensor data of a section of the road in the highway. The interval of the device can be set by the implementer according to the implementation scene, and no special limitation is made here. In this embodiment, the interval between devices is 10 meters.

[0040] The sensor data in the device is wirelessly transmitted through the Internet of Things technology, and the data of the sensor in the device is sampled every 30 seconds and uploaded to the system for storage, which is used for subsequent long-term real-time monitoring of the pavement state. The sampling time can be set by the implementer according to the implementation scene, and no special limitation is made here. In this embodiment, the sampling interval is 30 seconds.

[0041] The sensor data is cleaned. For transient noise outliers, such as transient noise caused by construction interference in the maintenance section, the outliers can be removed by interpolation method. The inserted value can be obtained by taking the median value of the continuous sampling time period, The size can be set by the implementer according to the implementation scene, and in this embodiment, The size is set to 3. The interpolation method is a prior art, and its specific process will not be described here.

[0042] At this point, the pavement mechanical response device is installed, and the sensor data is acquired and preprocessed.

[0043] Step two: taking the sensor as the node and the data coupling correlation between the mechanical-thermal-humidity of the nodes as the edge weight to construct a graph structure, using graph Fourier transform to map the graph structure into a graph frequency domain feature vector, and constructing a hypergraph space with all the graph frequency domain feature vectors of all road sections at all collection times.

[0044] With the expansion of the construction scale of highways and other roads, the number of pavement state monitoring sensors and the scale of data are also expanding, and the traditional pavement mechanical response device is facing challenges in accurate monitoring.

[0045] First, the data of different sensors (such as strain gauges and pressure gauges) on the same section have complex correlations in space and time, for example, temperature changes will affect strain readings, and dynamic distribution of traffic loads will affect the response mode of pressure sensors.

[0046] From the long-term monitoring of all sensor data, a large amount of time series data is accumulated, which makes it difficult for traditional analysis methods to effectively mine the implicit rules between the data, and difficult to analyze the current road state by comprehensively analyzing the time series changes of a large amount of sensor data, thereby making it difficult to achieve accurate long-term monitoring.

[0047] In view of the above problems, the present application models and maps the pavement mechanical response value data to the graph signal space by constructing a graph structure, analyzes the relationship of the graph frequency domain feature vector in the time-space dimension to construct a target function, and uses a particle swarm optimization algorithm to optimize the graph frequency domain feature vector representation of each data, thereby improving the accuracy of complex pavement data monitoring.

[0048] Specifically, taking a single mechanical response device as a unit, the sensor data is a node, the weight value between the connection lines of two nodes is determined based on the correlation between the data collected by the two nodes, a graph structure of the sensor data is constructed, the mechanical response value data is mapped to the graph signal space using graph Fourier transform, the graph frequency domain feature vector hypergraph space of time-space is constructed by analyzing the characteristic performance of various pavement data in time-space, then a target constraint function is constructed, and finally a particle swarm optimization algorithm is used to optimize the graph frequency domain feature vector representation of each data, thereby improving the accuracy of complex pavement data monitoring.

[0049] a) Construct a graph structure with sensors as nodes and the data coupling correlation between mechanics-heat-moisture as edge weights, map the graph structure to graph frequency domain feature vectors using graph Fourier transform, and form a hypergraph space containing time-space relationships.

[0050] For a monitored highway, according to the interval of device embedding, it is assumed that devices are installed to detect the mechanical response data of each section of the road, and a total of time points of monitoring data of the entire highway are monitored. A graph structure is constructed based on the mechanical response device, and finally data graph structures are obtained.

[0051] Regarding the construction of a single data graph structure, it is assumed that the mechanical response device contains kinds of sensors, so that types of sensor data can be obtained. Taking sensor data as nodes, the weight value between the connection lines of two nodes is determined based on the correlation between the data collected by the two nodes, so that at the moment, the The graphical structure of the mechanical response data of a road segment can be represented as follows: ,in Indicates the first The first moment The first section of road surface Each node corresponds to data collected by the sensor. Indicates the first The first moment The first section of road surface The and the first The edge weights between nodes.

[0052] In this system, nodes represent a type of sensor data, and edge weights are calculated based on the correlation between the data collected by two nodes. The analysis and construction method is as follows:

[0053] For example, asphalt strain gauges and base layer strain gauges measure the micro-strain of asphalt layers and base layers by measuring the changes in strain gauges caused by pressure on the asphalt layer and base layer. and ,So and Pressure data measured by the pressure gauge These three factors are positively correlated. (Asphalt strain) With temperature Between these two conditions, excessively high temperatures lead to a decrease in asphalt modulus, while micro-strain increases under the same pressure; the two are positively correlated. (Base layer strain) With humidity Between these conditions, excessively high humidity, especially under freeze-thaw cycles, leads to a decrease in the base layer modulus and an increase in micro-strain; the two are positively correlated. Temperature With humidity There is a negative correlation between them. (Stress data) There is no relationship between temperature and humidity; asphalt strain is not related to humidity, and base layer strain is not related to temperature.

[0054] Based on the above correlation analysis between different types of data, the edge weights between nodes can be calculated using the following formula:

[0055]

[0056] in Indicates the first The first moment The first section of road surface The and the first The edge weights between nodes, where e is the natural constant. , They represent the first The first moment The first section of road surface The, the corresponding to the data collected by the sensor, represents that there is a correlation between the data of two nodes, if the data of two nodes is positively correlated, take 0; if the data of two nodes is negatively correlated, take 1; represent that there is no correlation between the data of two nodes, and the edge weight between the two nodes in the graph structure is 0.

[0057] By analyzing the correlation between different types of sensor data to construct a graph structure, the relationship between different sensor data under the mechanical response device can be clearly modeled. Modeling the monitoring data under the same section in the form of a graph structure not only records the numerical performance of the sensor data, but also quantifies the correlation between the sensor data, thereby reflecting the overall correlation of the monitoring data under the same section. The minimum data unit for subsequent analysis of the time-space relationship of the monitoring data of the entire road is determined.

[0058] At this point, the construction of the graph structure of the pavement mechanical response data is completed.

[0059] In order to facilitate subsequent analysis of the time-space relationship of the monitoring data, the graph structure is then mapped into a graph signal space using graph Fourier transform to obtain the graph frequency domain feature vector of , wherein represents the feature vector of the i-th node mapped into the graph signal space, and there are sensor nodes, reflecting the energy distribution of the sensor data signal on the graph structure. Graph Fourier transform is a prior art, and its specific process will not be repeated. Graph Fourier transform encodes data from graph node structure to frequency domain space, which can map the graph structure that is difficult to operate to a structured vector space, providing a basis for subsequent quantization of time-space relationship in hypergraph space and construction of target constraint function.

[0060] Mapping all the graph structures of the pavement mechanical response data in time-space into the graph signal space can obtain a hypergraph space of dimension

[0061] Each coordinate in the hypergraph space represents the graph frequency domain feature vector representation of the pavement mechanical response data of different road sections at different times:

[0062]

[0063] , wherein represents the data graph frequency domain feature vector of the first road section at the first time, ​​​a data graph frequency domain feature vector representing the first road segment at the first time point, a data graph frequency domain feature vector representing the first road segment at the first time point, a data graph frequency domain feature vector representing the first road segment at the first time point, a data graph frequency domain feature vector representing the first road segment at the first time point, a data graph frequency domain feature vector representing the first road segment at the first time point. Step three: based on the spatial proximity and time continuity of the graph frequency domain feature vectors in the hypergraph space, a target function containing a data reconstruction error term, a spatial smoothing term and a time smoothing term is constructed to globally optimize the graph frequency domain feature vectors using a particle swarm optimization algorithm.

[0064] In particular, each graph frequency domain feature vector in the hypergraph space is associated in a separate row or a separate column, i.e. in the spatial or temporal dimension, two graph frequency domain feature vectors at the same time or the same space have an edge weight relationship, which can be represented by and

[0065] to represent the edge weight value of the graph frequency domain feature vector in the hypergraph space: First, in the spatial dimension, assuming that a vehicle is driving on the road surface, the mechanical response data of the adjacent two road segments can be captured in a short time, and the graph frequency domain feature vectors of the two road segments are similar; while the mechanical response data of the road segments far apart have no correlation, and the similarity of the graph frequency domain feature vectors is relatively low.

[0066] In addition, assuming that the first road segment has road damage (such as depression, pothole), which will cause the monitoring value of strain and pressure in the mechanical response data to be higher, due to the influence of physical mechanics conduction and associated vibration, the mechanical response data at the road surface of the adjacent road segment will also fluctuate,

[0067] the mechanical response data at the road surface of the adjacent road segment will also fluctuate less than , so this abnormal relationship will produce a wave-like relationship with spatial decay, with the wave peak at the center abnormal value. According to the above analysis, the spatial edge weight value of the graph frequency domain feature vectors of the first and second road segments at the same time can be represented as:

[0068] wherein ,

[0069]

[0070] , respectively represent the first road segment at the first time point, ​​​​At this moment The, the The frequency domain feature vector of each road segment This indicates the calculation of the L2 norm between two vectors. Indicates in At this moment The road section and the first The Euclidean spatial distance between the center points of two road segments indicates that the correlation between their mechanical response data decreases as the distance between the two road segments increases. The smaller the value, the higher the correlation between the mechanical data of two adjacent road sections. Also bigger. This is the spatial decay term, where e is the natural constant. This is a preset spatial attenuation factor, set to 0.2 in this embodiment. When the distance between two road sections is large, the spatial attenuation term is smaller, further reducing... When two road sections are adjacent, the spatial attenuation term is slightly larger, and the spatial attenuation term is always less than 1. This setting of the spatial edge weights of the graph frequency domain feature vectors of the two road sections at the same time conforms to the spatial transmission properties of mechanics.

[0071] Next, from a temporal perspective, the mechanical response values ​​on the same road segment also exhibit similarities at two adjacent time points, and the... The moment of the first If the response data of the graph structure is abnormal, then in the first uninterrupted graph structure... The graph structure subsequently exhibited a high abnormal signal state for a continuous period of time, affecting the normal operation of the sensor. On the same road segment... Next The and the first The temporal side weights of the graph frequency domain feature vector at time t It can be represented as:

[0072]

[0073] Where e is the natural constant. Indicates in At this moment The frequency domain feature vector of each road segment Indicates in At this moment The frequency domain feature vector of each road segment It is a preset smoothing factor for the time edge weights, which is set to 0.5 in this embodiment.

[0074] Based on the above analysis of the time-space relationship of the graph frequency domain eigenvectors of mechanical response data in the hypergraph space, a concept regarding the hypergraph space can be obtained. The objective function, used as the objective function of the particle swarm optimization algorithm, optimizes the feature representation of pavement mechanical response data by minimizing the objective function, thereby improving the accuracy of pavement condition monitoring. The objective function includes the reconstruction error term, spatial smoothing term, and temporal smoothing term of the original mechanical response sensor data and the image frequency domain feature vector:

[0075]

[0076] Where T represents all times collected, and N represents all road segments collected. It is the first At this moment The diagram structure of each road segment, The corresponding value is obtained from the eigenvalue decomposition of the Laplace matrix during the graphical Fourier transform. The graph Fourier basis matrix, here This indicates that the frequency domain signal is inversely transformed to reconstruct the graph signal. This indicates the calculation of the L2 norm between two vectors. At the same moment in the hypergraph space Next The, the Spatial edge weights of the graph frequency domain feature vectors of each road segment For the same road segment in the hypergraph space Next The and the first The temporal edge weights of the graph frequency domain eigenvectors at each time step. These represent the frequency domain feature vectors of the graph at different times and road segments.

[0077] The objective function design ensures the fidelity of the reconstructed mechanical response data by reconstructing the error term, and prevents distortion caused by excessive smoothing of the spatial and temporal smoothing terms. The spatial smoothing term, combined with the spatial edge weight constraints in the hypergraph space, ensures that the mechanical response data on different road segments are combined with the physical and mechanical transmission characteristics. The temporal smoothing term, combined with the temporal edge weight constraints in the hypergraph space, ensures the consistency of the mechanical response data signal over time.

[0078] In the particle swarm optimization algorithm, each particle is set as a graph frequency domain feature vector. The fitness function is the objective function described above, the particle velocity update factor is set to 2, and the number of iterations is 100. The particle swarm optimization algorithm is a current technology, and its specific process will not be elaborated further.

[0079] This completes the optimization of the graph frequency domain feature vectors in the hypergraph space.

[0080] Step four: the optimized graph frequency domain feature vector is inverse transformed to the data space to generate a sensor signal coupling curve, and the road surface damage is identified according to the abnormal change of the curve to realize real-time monitoring of the road surface health state.

[0081] The graph Fourier basis matrix obtained by the graph Fourier transform is used to reversely map the graph frequency domain feature vector of the optimized road surface response data to the data space and to visualize, thereby improving the accuracy of long-term monitoring of the road surface state.

[0082] The method of the multi-axis line graph is used to draw a time sequence coupling curve of the road surface mechanical response data, and the road surface damage is identified according to the abnormal value of the curve to analyze the influence of pressure strain, temperature cycle, humidity penetration on the highway interlayer damage, and to realize real-time monitoring of the road surface health state.

[0083] In addition, the road surface mechanical response device is regularly checked to verify the sensor accuracy and to improve the reliability of long-term monitoring.

[0084] Based on the same inventive concept as the above-mentioned complex road surface mechanical response monitoring method, one embodiment of the present application provides a complex road surface mechanical response monitoring device, which comprises:

[0085] An integrated shell internally integrating an asphalt strain gauge, a base layer strain gauge, a pressure gauge, a temperature sensor and a humidity sensor;

[0086] A wireless communication module for collecting sensor data at a sampling interval of 30 seconds and uploading to a remote system;

[0087] A data processing unit for executing the steps of the above-mentioned complex road surface mechanical response monitoring method.

[0088] Each of the embodiments in the present application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

[0089] It should be noted that, unless otherwise specified and limited, terms such as "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such article or device. Without more limitation, the element limited by the statement "including a" does not exclude the presence of another same element in the article or device including the element. In addition, the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0090] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

[0091] It should be understood that the application is not limited to the precise construction and compositions described above and shown in the attached drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application.

Claims

1. A method for monitoring the mechanical response of complex road surfaces, characterized in that, The method includes the following steps: Multi-source sensors are buried at different layers of the highway to collect mechanical response values, temperature values ​​and humidity values ​​in real time for each section of the road surface; A graph structure is constructed using sensors as nodes and the data coupling correlation between nodes in terms of mechanics, heat, and humidity as edge weights. The graph structure is then mapped to graph frequency domain feature vectors using graph Fourier transform. The graph frequency domain feature vectors of all road segments at all acquisition times constitute a hypergraph space. Based on the spatial proximity and temporal continuity of the graph frequency domain feature vectors in the hypergraph space, an objective function is constructed that includes a data reconstruction error term, a spatial smoothing term, and a temporal smoothing term, so as to use the particle swarm optimization algorithm to perform global optimization on the graph frequency domain feature vectors. The optimized image frequency domain feature vector is inversely transformed to the data space to generate sensor signal coupling curves. Based on the abnormal changes in the curves, road surface damage is identified, thereby realizing real-time monitoring of the road surface health status. The edge weights between nodes in the graph structure are determined by the following formula: in Indicates the first The first moment The first section of road surface The and the first The edge weights between nodes, where e is the natural constant. , They represent the first The first moment The first section of road surface The, the Each node corresponds to data collected by the sensor. This indicates a correlation between the data of two nodes. If the data of two nodes are positively correlated, Set to 0; if the data of two nodes are negatively correlated, Take 1; This indicates that there is no correlation between the data of the two nodes.

2. The method for monitoring the mechanical response of complex road surfaces as described in claim 1, characterized in that, The sensors include, but are not limited to, asphalt strain gauges, base layer strain gauges, pressure gauges, temperature sensors, and humidity sensors.

3. The method for monitoring the mechanical response of complex pavements as described in claim 2, characterized in that, asphalt... The data collected from strain gauges, base layer strain gauges, and pressure gauges showed a positive correlation; the data collected from asphalt strain gauges and temperature sensors showed a positive correlation; the data collected from base layer strain gauges and humidity sensors showed a positive correlation; the data collected from temperature sensors and humidity sensors showed a negative correlation; and there was no correlation among the remaining data.

4. The method for monitoring the mechanical response of complex road surfaces as described in claim 1, characterized in that, The objective function is constructed as follows: By utilizing the differences between graph frequency domain feature vectors and the distance between any two road segments, the spatial edge weights of the graph frequency domain feature vectors of any two road segments at the same time can be determined. By utilizing the differences between graph frequency domain feature vectors, the temporal edge weights of graph frequency domain feature vectors at any two times under the same road segment can be determined; The objective function is constructed by combining spatial edge weights and temporal edge weights.

5. The method for monitoring the mechanical response of complex road surfaces as described in claim 4, characterized in that, The spatial edge weights are calculated as follows: ;in, Indicates the same moment Next The and the first Spatial edge weights of the graph frequency domain feature vectors of each road segment. , They represent in At this moment The, the The frequency domain feature vector of each road segment This indicates the calculation of the L2 norm between two vectors. Indicates in At this moment The road section and the first The Euclidean spatial distance between the center points of each road segment, where e is a natural constant. It is a preset spatial attenuation factor.

6. The method for monitoring the mechanical response of complex road surfaces as described in claim 5, characterized in that, The calculation method for the time edge weights is as follows: ;in, Indicates in At this moment The frequency domain feature vector of each road segment It is a preset smoothing factor for the time edge weights.

7. The method for monitoring the mechanical response of complex road surfaces as described in claim 6, characterized in that, The objective function is calculated as follows: ； Where T represents all times collected, and N represents all road segments collected. It is the first At this moment The diagram structure of each road segment, The corresponding value is obtained from the eigenvalue decomposition of the Laplace matrix during the graphical Fourier transform. The graph Fourier basis matrix.

8. The method for monitoring the mechanical response of complex road surfaces as described in claim 7, characterized in that, The particle swarm optimization algorithm uses each graph frequency domain feature vector as an individual particle, the fitness function as the objective function, the particle velocity update factor as 2, and the number of iterations as 100.

9. A device for monitoring the mechanical response of complex road surfaces, characterized in that, The device includes: The integrated housing houses an asphalt strain gauge, a base strain gauge, a pressure gauge, a temperature sensor, and a humidity sensor. The wireless communication module is used to collect sensor data at 30-second sampling intervals and upload it to a remote system. A data processing unit is used to perform the steps of the complex pavement mechanical response monitoring method according to any one of claims 1-8.

Images