A complex road surface mechanical response monitoring device and method

Abstract

The application discloses a complex road surface mechanical response monitoring device and method, relates to the technical field of road surface detection, and can solve the technical problem that internal structure damage caused by two different mechanisms of aggregate bonding failure and pore filler increase cannot be effectively distinguished, and comprises the following steps: acquiring a source end impact response event set and a receiving end impact response event set; generating a potential propagation path parameter set according to the source end impact response event set and the receiving end impact response event set, and screening a candidate propagation path set from the potential propagation path parameter set according to initial propagation statistics; determining refined propagation statistics according to the candidate propagation path set, and determining an identified propagation path set from the potential propagation path parameter set according to the refined propagation statistics; determining a current state coordinate according to the identified propagation path set; and diagnosing a road surface disease type according to the direction of a displacement vector between the current state coordinate and a road surface reference coordinate.

Description

Technical Field

[0001] This invention relates to the field of pavement testing technology, and specifically to a device and method for monitoring the mechanical response of complex pavements. Background Technology

[0002] Open-graded friction course (OGFC) asphalt pavements possess excellent drainage and skid resistance due to their large-pore structure. However, during use, they are prone to two typical early-stage defects: early loosening due to decreased aggregate bonding caused by asphalt aging, and blockage caused by debris filling the pores. Currently, road maintenance often uses equipment such as falling weight deflectometers to assess the structural condition of the pavement by measuring macroscopic deflection, or relies on optical methods to analyze surface morphology to identify damage. However, these existing technologies often obtain the average response or apparent morphological characteristics of the pavement within a certain range, making it difficult to effectively capture and distinguish early, subtle internal structural changes caused by different microscopic deterioration mechanisms. This results in poor accuracy in diagnosing defects during the latent period and makes it difficult to provide a reliable basis for developing targeted maintenance measures. Summary of the Invention

[0003] To address the technical problem that current methods, which rely on macroscopic average response or surface morphology analysis, cannot effectively distinguish between internal structural damage caused by aggregate bonding failure and increased pore filling material in the early identification of complex pavements, the present invention aims to provide a device and method for monitoring the mechanical response of complex pavements. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a method for monitoring the mechanical response of complex pavements, comprising: acquiring a source-end impact response event set and a receiver-end impact response event set; wherein the source-end impact response event set and the receiver-end impact response event set include multiple impact response events extracted from acceleration signals, each impact response event being defined by an energy arrival time and amplitude; generating a potential propagation path parameter set based on the source-end impact response event set and the receiver-end impact response event set, and selecting a candidate propagation path set from the potential propagation path parameter set based on an initial propagation statistic; wherein the initial propagation statistic is used to characterize the propagation law based on all potential propagation paths; determining a refined propagation statistic based on the candidate propagation path set, and determining an identified propagation path set from the potential propagation path parameter set based on the refined propagation statistic; wherein the refined propagation statistic is used to characterize the propagation law based on the candidate propagation path set; determining the current state coordinates based on the identified propagation path set; wherein the current state coordinates are characteristic coordinates characterizing the current structural state of the pavement; diagnosing the pavement distress type based on the direction of the displacement vector between the current state coordinates and the pavement reference coordinates; wherein the pavement reference coordinates are characteristic coordinates characterizing the pavement health state.

[0004] In one possible implementation, a potential propagation path parameter set is generated based on the source-end impulse response event set and the receiver-end impulse response event set. Specifically, this includes: combining each event in the source-end impulse response event set with each event in the receiver-end impulse response event set to generate an event pairing set; selecting target event pairs from the event pairing set whose received event time is later than the source event time; and determining the potential propagation path parameter set based on the selected target event pairs.

[0005] In one possible implementation, a potential propagation path parameter set is determined based on the selected target event pairs. Specifically, this includes: determining the corresponding propagation parameters for each target event pair; wherein the propagation parameters include apparent wave velocity parameters and amplitude ratio parameters; and determining the potential propagation path parameter set based on the propagation parameters of each target event pair.

[0006] In one possible implementation, a set of candidate propagation paths is selected from the set of potential propagation path parameters based on the initial propagation statistics. Specifically, this includes: determining the initial propagation statistics based on the set of potential propagation path parameters; and selecting the set of candidate propagation paths from the set of potential propagation path parameters based on the initial propagation statistics.

[0007] In one possible implementation, a candidate propagation path set is selected from the potential propagation path parameter set based on the initial propagation statistics and the dynamic cost function. Specifically, this includes: calculating the propagation statistical deviation and the neighbor count of the same-origin path for each potential path in the potential propagation path parameter set; wherein the propagation statistical deviation characterizes the degree of deviation between the path parameters and the initial propagation statistics, and the neighbor count of the same-origin path characterizes the degree of clustering of the path within its group of same-origin paths; determining the proximity cost adjustment factor based on the neighbor count of the same-origin path, and determining the initial matching cost in conjunction with the propagation statistical deviation; and determining the candidate propagation path set based on the initial matching cost and a preset optimal matching algorithm.

[0008] In one possible implementation, the set of identified propagation paths is determined from the set of potential propagation path parameters based on refined propagation statistics. Specifically, this includes: determining the final matching cost based on refined propagation statistics; and filtering the set of identified propagation paths from the set of potential propagation path parameters based on the final matching cost and a preset optimal matching algorithm.

[0009] In one possible implementation, the current state coordinates are determined based on the identified propagation path set. Specifically, this includes: determining the weighted average propagation time and weighted average amplitude attenuation based on the received energy amplitude of each path in the identified propagation path set; and combining the weighted average propagation time and weighted average amplitude attenuation into a two-dimensional coordinate point as the current state coordinates.

[0010] In one possible implementation, the method further includes: querying the reference coordinates corresponding to the current road surface temperature from a pre-stored road surface health status reference database, and using them as road surface reference coordinates; wherein, the road surface health status reference database is obtained by measuring and storing the state coordinates multiple times under healthy road surface conditions.

[0011] In one possible implementation, acquiring the source-end impact response event set and the receiver-end impact response event set specifically includes: deploying a first accelerometer near the road impact point and a second accelerometer at a fixed distance from the first accelerometer, simultaneously acquiring the source-end raw acceleration sequence and the receiver raw acceleration sequence; preprocessing the source-end raw acceleration sequence and the receiver raw acceleration sequence to obtain the source-end energy envelope and the receiver energy envelope; identifying local maxima points in the source-end energy envelope and the receiver energy envelope to form the source-end impact response event set and the receiver impact response event set.

[0012] Secondly, the present invention provides a complex pavement mechanical response monitoring device, comprising: an acquisition unit and a processing unit; the acquisition unit is used to acquire a source-end impact response event set and a receiver-end impact response event set; wherein the source-end impact response event set and the receiver-end impact response event set include multiple impact response events extracted from acceleration signals, each impact response event being defined by an energy arrival time and amplitude; the processing unit is used to generate a potential propagation path parameter set based on the source-end impact response event set and the receiver-end impact response event set, and to filter a candidate propagation path set from the potential propagation path parameter set based on initial propagation statistics; wherein the initial propagation statistics... The processing unit is used to characterize the propagation patterns based on all potential propagation paths; it is also used to determine refined propagation statistics based on the candidate propagation path set, and to determine the identified propagation path set from the potential propagation path parameter set based on the refined propagation statistics; wherein, the refined propagation statistics are used to characterize the propagation patterns based on the candidate propagation path set; the processing unit is also used to determine the current state coordinates based on the identified propagation path set; wherein, the current state coordinates are characteristic coordinates characterizing the current structural state of the pavement; the processing unit is also used to diagnose the pavement distress type based on the direction of the displacement vector between the current state coordinates and the pavement reference coordinates; wherein, the pavement reference coordinates are characteristic coordinates characterizing the pavement health state.

[0013] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store one or more programs, the one or more programs including computer-executable instructions, and when the electronic device is running, the processor executes the computer-executable instructions stored in the memory to cause the electronic device to perform the complex pavement mechanical response monitoring method as described in the first aspect and any possible implementation thereof.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by an electronic device of the present invention, cause the electronic device to perform the complex pavement mechanical response monitoring method as described in the first aspect and any possible implementation thereof.

[0015] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the electronic device of the present invention to perform the complex pavement mechanical response monitoring method as described in the first aspect and any possible implementation thereof.

[0016] In a sixth aspect, the present invention provides a chip system applied to a complex pavement mechanical response monitoring device; the chip system includes one or more interface circuits and one or more processors. The interface circuits and the processors are interconnected via wiring; the interface circuits are used to receive signals from the memory of the complex pavement mechanical response monitoring device and send the signals to the processors, the signals including computer instructions stored in the memory. When the processor executes the computer instructions, the complex pavement mechanical response monitoring device performs the complex pavement mechanical response monitoring method as described in the first aspect and any possible design of the present invention.

[0017] The present invention has the following beneficial effects: by extracting discrete impact events from acceleration signals and performing two-stage path matching based on iterative statistical purification, the identified path set is finally transformed into diagnosable feature coordinates. Its technical effect is that it effectively suppresses noise interference and extracts physical features that can stably characterize the internal structural state of the road surface. Thus, it realizes the accurate differentiation and diagnosis of two types of defects, namely early loosening and pore blockage, which have similar macroscopic appearances but different microscopic mechanisms. It overcomes the defects of traditional methods in the latent period of defects, such as vague diagnosis and insufficient basis. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for monitoring the mechanical response of complex road surfaces according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating another method for monitoring the mechanical response of complex road surfaces, provided in one embodiment of the present invention. Figure 3 This is a flowchart illustrating another method for monitoring the mechanical response of complex road surfaces, provided in one embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of a complex road surface mechanical response monitoring device provided in one embodiment of the present invention; Figure 5 This is a schematic diagram of another complex road surface mechanical response monitoring device provided in one embodiment of the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] 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 this invention pertains.

[0022] For example, such as Figure 1 The diagram shown is a flowchart illustrating a method for monitoring the mechanical response of complex road surfaces according to an embodiment of the present invention, comprising the following steps: S101. Obtain the source impulse response event set and the receiver impulse response event set.

[0023] The source-end impact response event set and the receiver-end impact response event set include multiple impact response events extracted from the acceleration signal, each impact response event being defined by the energy arrival time and amplitude.

[0024] It should be noted that the executing entity of this embodiment of the invention is a complex pavement mechanical response monitoring device (hereinafter referred to as the monitoring device). For a detailed description of the monitoring device, please refer to the relevant information below. Figure 4 and Figure 5 The explanation will not be repeated here.

[0025] For example, the monitoring device acquires the source-end impact response event set and the receiver-end impact response event set, specifically including the following steps: (1) A first accelerometer is installed near the impact point on the road surface, and a second accelerometer is installed at a fixed distance from the first accelerometer to simultaneously collect the original acceleration sequence at the source end and the original acceleration sequence at the receiver end.

[0026] In this step, a flat impact point is selected in the area of ​​the road surface to be tested, and two vertical accelerometers (first accelerometer and second accelerometer) are set up along the radial direction of the impact point. The sensing direction of the two accelerometers is strictly perpendicular to the road surface to ensure that the stress wave response in the vertical direction inside the road surface is collected.

[0027] The first accelerometer is used to collect the original acceleration sequence at the source end. Its placement position should be as close as possible to the impact point (no more than 5cm away from the impact point) to reduce the energy loss of the impact signal in the early stage of propagation. The second accelerometer is used to collect the original acceleration sequence at the receiving end. Its horizontal distance from the first accelerometer is fixed at L. In this embodiment, L is 0.5m. This distance needs to be calibrated by a laser rangefinder to ensure that the error does not exceed ±1mm.

[0028] A standard impact source (a piezoelectric hammer with a range of 0-5kN) equipped with a force sensor and a trigger signal output interface is placed directly above the impact point. The trigger signal of this impact source needs to be connected to a multi-channel data acquisition system (sampling bit depth not less than 16 bits) to synchronously start the signal acquisition of the two accelerometers, avoiding time difference errors caused by asynchronous acquisition. Thus, the monitoring device synchronously acquires the original acceleration sequence at the source end and the original acceleration sequence at the receiver end, represented as follows: and .

[0029] (2) Preprocess the original acceleration sequence at the source end and the original acceleration sequence at the receiver end to obtain the energy envelope at the source end and the energy envelope at the receiver end.

[0030] In this step, the monitoring device first performs mean-removal processing on the two sets of original acceleration sequences to eliminate DC bias. Then, a bandpass filter is applied to each of the mean-removed signals to filter out low-frequency and high-frequency noise unrelated to the road surface structure response. In this embodiment, a fourth-order Butterworth bandpass filter with a passband range of 20Hz to 8000Hz can be used. This step yields a clean acceleration time-history signal. and .

[0031] To obtain the energy envelope of the signal, this step employs the Hilbert Transform method, a well-known technique in signal processing. Through the Hilbert Transform method, this step outputs two continuous energy envelope signals: the source-end energy envelope. and the energy envelope at the receiving end These two signals will serve as inputs to step (3).

[0032] (3) Identify local maxima in the energy envelope of the source end and the energy envelope of the receiver end to form the source end impulse response event set and the receiver end impulse response event set.

[0033] In this step, the energy envelope at the source end... and the energy envelope at the receiving end The peak finding algorithm is applied to identify all local maxima that meet preset conditions. These preset conditions include: 1. The peak amplitude must be greater than a minimum peak threshold. This condition is used to filter out minute fluctuations caused by residual background noise. An example value is 0.01 times the gravitational acceleration, and the rule for this value is: It is three times the maximum energy envelope value during the non-impact period, obtained through statistical analysis of non-impact signals during the preprocessing stage. 2. The time interval between any two adjacent peak values ​​must be greater than a minimum peak spacing. This condition is used to ensure that the identified peaks are independent energy arrival events, avoiding the identification of multiple small fluctuations in the same stress wave packet as multiple events.

[0034] Each identified peak point is defined as an "impact response event," described by two attributes: the time of its occurrence and the amplitude of its peak. Perform peak lookup to obtain These impact response events constitute the "source-end impact response event set". The set is represented as: in, An index for the source event. and The first The arrival time (hereinafter referred to as the source event time) and magnitude of each source event. This represents the total number of source events.

[0035] Similarly, for Perform peak lookup to obtain These impulse response events constitute the "receiver impulse response event set". The set is represented as: in, For the index of the receiving end event, and The first The arrival time (hereinafter referred to as the reception event time) and amplitude of each receiving event, This represents the total number of events received.

[0036] The final output of this step (3) is the source-end impact response event set. and receiver impulse response event set These two sets contain all the event information required for all subsequent analyses and serve as the basis for the discrete matching analysis in step S102.

[0037] S102. Based on the source-end impulse response event set and the receiver-end impulse response event set, generate a potential propagation path parameter set, and select a candidate propagation path set from the potential propagation path parameter set based on the initial propagation statistics.

[0038] The initial propagation statistics are used to characterize the propagation patterns based on all potential propagation paths. For example, the monitoring device generates a set of potential propagation path parameters based on the source-end impact response event set and the receiver-end impact response event set, and then selects a set of candidate propagation paths from the potential propagation path parameter set based on the initial propagation statistics, which involves the following two steps: (1) Generate a potential propagation path parameter set based on the source end impulse response event set and the receiver end impulse response event set.

[0039] In this step, the input data is the source-end impact response event set output by S101. and receiver impulse response event set .

[0040] It should be noted that since the stress wave generated by a single impact can propagate through various means such as direct contact between aggregates, multiple reflections between pore walls, and diffraction, in order to capture all possible energy transfer events, the monitoring device first calculates the potential propagation parameters corresponding to the pairing between each source event and each receiver event.

[0041] Specifically, the monitoring device traverses the set Each event in and set Each event in For each pair First, determine whether the received event time is later than the source event time. For all pairs that meet the aforementioned conditions, the monitoring device calculates their potential propagation parameters: 1. Potential apparent wave velocity ,in 1. The distance between the two accelerometers as defined in S101. 2. Amplitude ratio parameter All calculated The parameter pairs together constitute a conceptual set of potential propagation path parameters. The data from this set will serve as input for step (2) below. It should be noted that the specific process by which the monitoring device determines the potential propagation path parameter set according to the aforementioned steps is described in S201-S203 below, and will not be repeated here.

[0042] (2) Select a set of candidate propagation paths from the set of potential propagation path parameters based on the initial propagation statistics.

[0043] It is understandable that the potential propagation path parameter set generated by the above steps... Inevitably, the system contains pseudo-paths or deteriorated paths with extremely abnormal parameter values, caused by background noise from signals or early road surface deterioration. These extreme values ​​can severely affect conventional statistics based on the mean and standard deviation. Therefore, to establish a statistical benchmark that can robustly reflect the "typical" propagation behavior in this measurement and is unaffected by these extreme outliers, the monitoring device in this step uses statistics that are insensitive to outliers to construct the initial model.

[0044] Specifically, the monitoring device selects a set of candidate propagation paths from the potential propagation path parameter set based on the initial propagation statistics. This includes: determining the initial propagation statistics based on the potential propagation path parameter set; and then selecting the set of candidate propagation paths from the potential propagation path parameter set based on the initial propagation statistics and the dynamic cost function. It should be noted that the specific process by which the monitoring device determines the set of candidate propagation paths according to the aforementioned steps is detailed in S204-S205 below and will not be repeated here.

[0045] Thus, by conducting preliminary statistical screening of all possible propagation paths, the monitoring device effectively filters out a large number of pseudo-paths caused by noise and other factors, thereby obtaining a high-quality subset of candidate paths and laying a reliable data foundation for building a more accurate statistical model in the next stage.

[0046] S103. Determine the refined propagation statistics based on the candidate propagation path set, and determine the identified propagation path set from the potential propagation path parameter set based on the refined propagation statistics.

[0047] The refined propagation statistics are used to characterize the propagation patterns based on the candidate propagation path set. For example, the monitoring device determines the refined propagation statistics based on the candidate propagation path set, and then determines the identified propagation path set from the potential propagation path parameter set based on the refined propagation statistics, in the following two steps: (1) Determine the refined propagation statistics based on the candidate propagation path set.

[0048] In this step, the input data is a set of candidate propagation paths. .because The set represents a subset of paths that have been pre-selected and have higher confidence, so statistical models built on it can more accurately describe the main propagation mechanisms of the road surface.

[0049] Specifically, according to For all paths in the set, calculate the effective standard deviation of these parameters, and then use these effective standard deviations as a refined set of propagation statistics. The specific procedure for determining the refining and propagation statistics using the monitoring device is detailed in S301 below and will not be repeated here.

[0050] (2) Determine the set of identified propagation paths from the set of potential propagation path parameters based on the refined propagation statistics.

[0051] For example, the input data for this step is a set of potential propagation path parameters. and refined set of communication statistics Specifically, the monitoring device first calculates the final matching cost by combining refined propagation statistics; then, based on the final matching cost and a preset optimal matching algorithm, the monitoring device filters the set of identified propagation paths from the set of potential propagation path parameters. The specific process by which the monitoring device determines the set of identified propagation paths is detailed in S302-S303 below and will not be repeated here.

[0052] Therefore, the monitoring device uses the high-quality candidate path set selected in the previous stage to construct a more accurate statistical model that better reflects the actual propagation law of the road surface. Guided by this model, the device ultimately identifies the actual stress wave propagation path from all potential paths.

[0053] S104. Determine the current state coordinates based on the identified propagation path set.

[0054] Among them, the current state coordinates are the characteristic coordinates that represent the current structural state of the road surface.

[0055] For example, the monitoring device determines the current state coordinates based on the identified propagation path set, specifically including the following steps: (1) Determine the weighted average propagation time and weighted average amplitude attenuation based on the received energy amplitude of each path in the identified propagation path set.

[0056] In this step, the input data is the set of identified propagation paths obtained in step S103. ,in, Represents the propagation path set The propagation time of each path in the process. Represents the propagation path set The magnitude ratio parameter for each path in the equation. Represents the propagation path set The received energy amplitude for each path in the process. This represents the total number of paths identified. Because... The parameters of a single path are easily affected by local random factors, while the overall distribution of a scatter plot composed of all paths can more stably reflect the macroscopic state of the road surface's internal structure. To obtain a stable and clearly diagnostic quantitative indicator, this step calculates the energy-weighted mean that describes the distribution center of the scatter plot.

[0057] Specifically, the received energy amplitude of the corresponding path is used. As weights, to reflect the importance of high-energy paths in characterizing the mainstream state of the structure, the energy-weighted mean path coordinates are calculated. (That is, the current state coordinates). This coordinate is a two-dimensional coordinate point, and its calculation method is as follows: 1. Calculate the weighted average propagation time. 2. Calculate the weighted average amplitude decay. .

[0058] (2) Combine the weighted average propagation time and the weighted average amplitude attenuation into two-dimensional coordinate points, which are used as the current state coordinates.

[0059] For example, current state coordinates Defined as Based on this definition, the monitoring device determines the current state coordinates of the current measurement. .

[0060] S105. Diagnose the type of pavement distress based on the direction of the displacement vector between the current state coordinates and the pavement reference coordinates.

[0061] Among them, the road surface reference coordinates are the characteristic coordinates that characterize the health status of the road surface.

[0062] Understandably, due to The absolute value is affected by factors such as the original road design, material type and ambient temperature. Diagnosis must be based on the deviation of the current state characteristics from a known and reliable health state benchmark, so it is necessary to establish such a health state benchmark.

[0063] Optionally, the monitoring device queries a pre-stored pavement health status benchmark database for the benchmark coordinates corresponding to the current pavement temperature, and uses these coordinates as the pavement benchmark coordinates. The pavement health status benchmark database is obtained by measuring and storing state coordinates multiple times under healthy pavement conditions.

[0064] Specifically, when a road surface is newly constructed or otherwise confirmed to be in good health, the monitoring device performs measurements at multiple representative ambient temperatures based on the aforementioned S101-S103. For each measurement under good health conditions, its energy-weighted average path coordinates are calculated. These calculated reference coordinates, along with their corresponding measured temperatures, are stored in a database to form a road surface health status reference coordinate function or lookup table. Therefore, during routine inspections, the surface temperature of the current road surface is measured first. From the health status benchmark database, query or interpolate to obtain the value relative to the current temperature. Corresponding reference coordinates , used as the reference coordinates for the road surface.

[0065] Furthermore, the monitoring device analyzes... Compared to The offset can clearly distinguish the type of disease and assess its severity, specifically including: First, calculate the structural state displacement vector. : And further determine the displacement vector The direction of the displacement vector is used to diagnose the type of disease. This represents the current weighted average propagation time. This indicates that the current weighted average amplitude has decreased. This represents the weighted average transmission time under healthy conditions. This indicates the attenuation of the weighted average amplitude under healthy conditions. The specific diagnostic rules are as follows: Scenario 1: Diagnosed as early loosening: When the displacement vector When pointing to the lower right region of the (propagation time - amplitude attenuation) parameter space, it simultaneously satisfies the component... and components The mechanism lies in the failure of the inter-aggregate bonding interface due to asphalt aging. This interrupts the high-speed (low-weighted average propagation time), low-loss (high-weighted average amplitude attenuation) stress wave path that originally relied on tightly packed aggregate chains for transmission. Energy is forced to propagate through a more circuitous path with more scattering, resulting in a systematic increase in the propagation time of the mainstream path. Energy attenuation is also exacerbated by multiple interfacial interactions. It will inevitably shift in that direction.

[0066] Scenario 2: Diagnosed as pore blockage: When the displacement vector When the main direction is directly downwards, that is, the component. The values ​​are significantly negative, while the components... The change is small or even negative. The mechanism lies in the fact that fine aggregates, dust, and other impurities fill the large pores, introducing numerous solid-solid contact surfaces and highly damped media into the stress wave propagation path. The main effect of this change is to significantly increase energy absorption and dissipation during propagation, leading to a significant reduction in the received energy amplitude along all paths. In contrast, the change in propagation time is secondary and may even be slightly reduced due to the shortcut provided by the new filling medium. Therefore, The displacement will mainly manifest as a vertical downward movement.

[0067] Finally, the monitoring device uses the pavement structure damage index The severity of the disease is quantitatively assessed, and this index is calculated using the following formula: in, It represents the sample standard deviation of the weighted average propagation time obtained through multiple measurements under the baseline condition of a healthy pavement. Its unit is seconds, and it characterizes the inherent fluctuation range of a healthy pavement in this feature. It represents the sample standard deviation of the weighted average amplitude decay obtained through multiple measurements under the baseline condition of a healthy pavement. It is a dimensionless quantity that characterizes the inherent fluctuation range of a healthy pavement in this feature.

[0068] It is understandable that in the above formula and Both are dimensionless quantities, and their values ​​represent the propagation time and amplitude decay characteristics of the current state, respectively, and how many standard deviations they have deviated from their healthy baseline, quantifying the statistical significance of the deviation. Furthermore, the calculated... Defined as the Euclidean distance between the two normalized deviations mentioned above. Therefore, It is a dimensionless comprehensive index, and its physical meaning is the statistical distance from the current state to the center of the healthy state in a feature space normalized by standard deviation. Road surface structure damage index. The magnitude of the index objectively reflects the degree to which the current pavement structure deviates from its health benchmark; the larger the index, the more severe the pavement damage.

[0069] For example, in practical applications, the pavement structure damage index can be used for graded early warning. Its warning threshold is determined by analyzing the correlation between historical data and maintenance records. Specifically, this includes: collecting historical monitoring data and subsequent pavement maintenance records; setting the minimum damage index value corresponding to the maintenance record marked as "requiring preventative maintenance" as the first damage threshold θ1; and setting the minimum damage index value corresponding to the maintenance record marked as "requiring structural repair" as the second damage threshold θ2. Accordingly, When θ < θ1, the road surface is considered to be in a healthy state; when θ1 ≤ θ1, the road surface is considered to be in a healthy state. When ≤θ2, the road surface is judged to be in a state of minor damage; when When the value is ≥θ2, the road surface is considered to be in a state of severe damage.

[0070] Therefore, the monitoring device ultimately outputs a diagnosis of the type of pavement damage and a quantified pavement structure damage index. .

[0071] Based on the above technical solution, the embodiments of the present invention extract discrete impact events from acceleration signals and perform two-stage path matching based on iterative statistical purification. Finally, the identified path set is transformed into diagnosable feature coordinates. Its technical effect is that it effectively suppresses noise interference and extracts physical features that can stably characterize the internal structural state of the road surface. This enables accurate differentiation and diagnosis of two types of defects, namely early loosening and pore blockage, which have similar macroscopic appearances but different microscopic mechanisms. It overcomes the shortcomings of traditional methods in diagnosing defects during the latent period of the defects, such as fuzziness and insufficient evidence.

[0072] For example, in combination Figure 1 ,like Figure 2 The diagram shown illustrates a flowchart of another method for monitoring the mechanical response of complex pavements according to an embodiment of the present invention. In this method, a potential propagation path parameter set is generated based on the source-end impact response event set and the receiver-end impact response event set. Candidate propagation paths are then selected from the potential propagation path parameter set based on initial propagation statistics. Specifically, the method includes the following steps: S201. Combine each event in the source impulse response event set with each event in the receiver impulse response event set to generate an event pairing set.

[0073] Specifically, the monitoring device first performs a Cartesian product combination on the source event set and the receiver event set to generate all possible event pairs: Each event in and set Each event in Combine them one by one to construct a set of event pairs.

[0074] S202. From the event pairing set, filter out target event pairs whose receiving event time is later than the source event time.

[0075] Furthermore, the monitoring device filters valid pairs based on the physical laws governing the propagation time of stress waves: since stress waves travel a fixed distance L from the source to the receiver, a propagation time difference is inevitable. Therefore, pairs that satisfy the condition that the received event time is later than the source event time are selected, resulting in the target event pair set. After filtering, invalid pairs that are received earlier than the source event time due to noise misidentification are eliminated to ensure that all candidate pairs conform to the physical laws of propagation.

[0076] S203. Based on the selected target event pairings, determine the potential propagation path parameter set.

[0077] For example, the monitoring device determines a set of potential propagation path parameters based on the selected target event pairs, specifically including the following steps: (1) For each target event pair, determine the corresponding propagation parameters. Among them, the propagation parameters include the apparent wave velocity parameter and the amplitude ratio parameter.

[0078] Specifically, apparent wave velocity parameters Reflecting stress wave along The propagation speed along the corresponding path is calculated using the following formula: . In the formula, The apparent wave velocity parameter (unit: m / s) is positively correlated with the road surface structure stiffness (the denser the road surface, the higher the wave velocity); L represents the fixed horizontal distance between the first accelerometer and the second accelerometer. This represents the time it takes for the corresponding stress wave to propagate from the source event to the receiving event.

[0079] And, amplitude ratio parameter The formula reflects the energy loss of the stress wave during its propagation along the path, and is as follows: . In the formula, This represents the amplitude ratio parameter, which is a dimensionless ratio value. A value less than 1 indicates energy attenuation, and the smaller the value, the more severe the attenuation. Indicates the peak amplitude of the source event; This indicates the peak amplitude of the event received.

[0080] (2) Determine the potential propagation path parameter set based on the propagation parameters paired with each target event.

[0081] In this step, all the calculated The parameter pairs together constitute a conceptual set of potential propagation path parameters. .

[0082] S204. Determine the initial propagation statistics based on the potential propagation path parameter set.

[0083] It should be noted that, due to the generation determined in S203 Inevitably, the data contains pseudo-paths or deteriorated paths with extremely abnormal parameter values, caused by background noise from signals or early road surface deterioration. These extreme values ​​severely affect conventional statistics based on the mean and standard deviation. Therefore, to establish a statistical benchmark that robustly reflects the "typical" propagation behavior in this measurement and is unaffected by these extreme outliers, this step uses outlier-insensitive statistics to construct the initial model, specifically including: a. From Extract all potential apparent wave velocities Construct a set and calculate the median of that set, denoted as . .

[0084] b. Calculate the Median Absolute Deviation (MAD) for this set of wave velocities, and use the formula... Converted to effective standard deviation. In the formula, Indicates the absolute deviation of the median. This represents the effective standard deviation, where the coefficient 1.4826 ensures that the effective standard deviation is consistent with the estimate of the true standard deviation when the data follows a normal distribution.

[0085] c. Similarly, from Extract all amplitude ratio parameters Construct another set and calculate its median. and effective standard deviation .

[0086] Therefore, this step outputs an initial set of propagation statistics. This set will be used to determine the following candidate propagation path set.

[0087] S205. Based on the initial propagation statistics, select a set of candidate propagation paths from the set of potential propagation path parameters.

[0088] In this step, the data input is... and Specifically, the monitoring device constructs a dynamic cost function and solves for the optimal match to filter out high-confidence candidate paths from all potential paths.

[0089] For example, the monitoring device filters a set of candidate propagation paths from a set of potential propagation path parameters based on initial propagation statistics and a dynamic cost function, including the following steps: (1) Calculate the propagation statistical bias and the neighbor count of the same source path for each potential path in the potential propagation path parameter set.

[0090] Among them, propagation statistical bias is used to characterize the degree of deviation between path parameters and initial propagation statistics, and homologous path neighbor count is used to characterize the degree of clustering of paths in their homologous path groups.

[0091] For example, for Each potential path pairing The monitoring device calculates the propagation statistical deviation. The potential path pairing here refers to the pairing determined in step S203, which is based on the target event pairing (in which the event...). The time of receiving the event is later than the event The source event time and its corresponding propagation parameters ( The set of elements, consisting of these pairs, defines a potential stress wave propagation path for each pair. Optionally, propagation statistical bias... Specifically, it is calculated using the following formula: In the above formula, Indicates potential path pairing The statistical bias of the spread This indicates taking the larger value of the parameter within the parentheses. It should be noted that, to avoid the denominator being zero and the calculation becoming meaningless when the data dispersion is extremely low, the parentheses are added... Functions and parameter tuning coefficients , , , The possible value is 0.01.

[0092] After that, for Each potential path pairing The monitoring device calculates the proximity count of the same source path. This is used to quantify the degree of clustering of the current path within its group of source paths. Specifically, this includes: first, determining a normalized distance threshold. .in, As a pre-defined, dimensionless scaling factor, its value determines the leniency in determining the "proximity" of two paths in the normalized feature space: The smaller the value, the stricter the judgment; only highly clustered paths are counted as neighbors. The larger the value, the more lenient the judgment. This value needs to be calibrated based on experimental data to achieve a balance between sensitivity and robustness in path selection. In this embodiment, an exemplary value is... =0.5.

[0093] Finally, iterate through all... Other paths for the source (in ), and statistically analyze its parameters. With current path parameters Normalized Euclidean distance Less than the threshold The number of neighbors is the same as the number of paths from the same source. .in, Indicates events from the same source Starting from another receiving event The potential propagation path parameters are the endpoints. For example, the formula for calculating the normalized Euclidean distance is: in, Representing a path With the current path The normalized Euclidean distance between them.

[0094] (2) Determine the proximity cost adjustment factor based on the proximity count of the same source path, and determine the initial matching cost in combination with the propagation statistical bias.

[0095] In this step, the proximity cost adjustment factor is used to convert proximity counts into an adjustment coefficient for costs. This is achieved through the following steps: First, find the nearest neighbor count... Maximum neighbor count among all paths to the source .if Then the proximity cost adjustment factor .

[0096] And, if This indicates the source event. If no consistent path group is generated, then for all paths... For the path to the source, the proximity cost adjustment factor All are set to 1, meaning no cost adjustment is performed.

[0097] Therefore, the monitoring device determines the initial matching cost based on the parameters calculated above. The specific formula is as follows: , Indicates potential path pairing The statistical bias in the propagation.

[0098] (3) Determine the set of candidate propagation paths based on the initial matching cost and the preset optimal matching algorithm.

[0099] Furthermore, the monitoring device will calculate all the... Value constitutes a Cost matrix Then, a pre-defined optimal matching algorithm is applied to solve the cost matrix. For example, the pre-defined optimal matching algorithm could be the Hungarian algorithm. The Hungarian algorithm is a well-known algorithm in combinatorial optimization used to solve assignment problems. Its function is to find a one-to-one mapping scheme from source events to receiver events such that the total cost of the selected mapping is minimized globally.

[0100] Based on the above steps, the monitoring device constructs a candidate propagation path set by matching the path parameters corresponding to the index pairs output by the preset optimal matching algorithm. Specifically, the optimal matching algorithm outputs a set of source and receiver event index pairs that minimize the total matching cost. All its corresponding propagation parameters (including apparent wave velocity) With amplitude ratio parameter The set of candidate propagation paths constitutes the set of candidate propagation paths. .

[0101] Based on the above technical solution, this embodiment of the invention, through a systematic data processing flow, transforms the original, mixed acceleration signals into a high-quality set of candidate propagation paths, laying a reliable data foundation for subsequent accurate diagnosis. Specifically, by generating a potential path parameter set, all possible propagation modes are comprehensively preserved. Then, a screening mechanism based on the median and dynamic cost function effectively suppresses the interference of noise and abnormal paths, thereby obtaining a path subset that more realistically reflects the main propagation mechanisms of the road surface. This significantly improves the data quality for subsequent construction of refined statistical models and the accuracy of final defect identification.

[0102] For example, in combination Figure 1 ,like Figure 3 The diagram shown illustrates a flowchart of another method for monitoring the mechanical response of complex pavements according to an embodiment of the present invention. In this method, a refined propagation statistic is determined based on a set of candidate propagation paths, and an identified propagation path set is determined from a set of potential propagation path parameters based on the refined propagation statistic. Specifically, the method includes the following steps: S301. Determine the refined propagation statistics based on the candidate propagation path set.

[0103] Specifically, the input data for this step is a set of candidate propagation paths. ,because The set represents a subset of paths that have been pre-selected and have higher confidence, so statistical models built on it can more accurately describe the main propagation mechanisms of the road surface.

[0104] This step is only used The propagation parameters for all paths in the set are calculated using the same process as in S204. Specifically, the median of the apparent wave velocity parameters for the candidate paths is calculated. and its effective standard deviation And calculate the median of the candidate path magnitude ratio parameter. and its effective standard deviation Furthermore, the aforementioned four statistics constitute a refined set of communication statistics. This set will serve as the basis for data calculation in step S302.

[0105] S302. Determine the final matching cost based on the refined propagation statistics.

[0106] For example, the monitoring device uses a set of potential propagation path parameters. and refined set of communication statistics Repeat the calculation process in S205. It is important to note that throughout the calculations, the following steps are used: Statistical substitution in The corresponding values ​​in the matrix are used to generate a more accurate final matching cost matrix. .

[0107] S303. Based on the final matching cost and the preset optimal matching algorithm, select the set of identified propagation paths from the set of potential propagation path parameters.

[0108] In this step, the preset optimal matching algorithm is used again to refine the final matching cost matrix. Solve to obtain the final optimal matching index pair. For each final matching index pair... (in, The index of the finally identified path, This indicates the event index of the path in the source-end impact response event set. This represents the event index in the receiver's impulse response event set (where the path is represented by an event index), from which the corresponding propagation time, amplitude ratio parameter, and received energy amplitude are extracted: 1. Propagation time 2. Amplitude ratio parameter 3. Received energy amplitude .

[0109] Thus, via S303, the monitoring device identifies the set of propagation paths that have been identified. The set consists of triples: ,in This represents the total number of paths ultimately identified.

[0110] Based on the above technical solution, the embodiments of the present invention construct refined propagation statistics based on the candidate path set, and use this as a guide to complete the final matching from all potential paths. Its technical effect is to achieve high-precision identification of the real stress wave propagation path: it uses the high-quality data purified in the previous stage to establish a more reliable propagation model, and then optimizes the path allocation globally by calculating the final matching cost, so that the output identified path set can more completely and accurately represent the real state of the road surface internal structure, fundamentally improving the representativeness of the subsequent state coordinate calculation and the reliability of the disease diagnosis results.

[0111] In this embodiment of the invention, the complex pavement mechanical response monitoring device can be divided into functional modules or functional units according to the above method examples. For example, each function can be divided into its own functional modules or functional units, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or in software functional modules or functional units. The module or unit division in this embodiment is illustrative and represents only one logical functional division; in actual implementation, other division methods may be used.

[0112] For example, such as Figure 4 The diagram shown is a possible structural schematic of a complex pavement mechanical response monitoring device according to an embodiment of the present invention. The complex pavement mechanical response monitoring device 400 includes: an acquisition unit 401 and a processing unit 402.

[0113] Acquisition unit 401 is used to acquire the source-end impact response event set and the receiver-end impact response event set. The source-end impact response event set and the receiver-end impact response event set include multiple impact response events extracted from the acceleration signal, each impact response event being defined by the energy arrival time and amplitude.

[0114] Processing unit 402 is used to generate a potential propagation path parameter set based on the source-end impulse response event set and the receiver-end impulse response event set, and to filter a candidate propagation path set from the potential propagation path parameter set based on the initial propagation statistics. The initial propagation statistics are used to characterize the propagation pattern based on all potential propagation paths.

[0115] The processing unit 402 is further configured to determine refined propagation statistics based on the candidate propagation path set, and to determine the identified propagation path set from the potential propagation path parameter set based on the refined propagation statistics. The refined propagation statistics are used to characterize the propagation patterns based on the candidate propagation path set.

[0116] The processing unit 402 is further configured to determine the current state coordinates based on the identified propagation path set. The current state coordinates are characteristic coordinates representing the current structural state of the road surface.

[0117] The processing unit 402 is also used to diagnose the type of pavement distress based on the direction of the displacement vector between the current state coordinates and the pavement reference coordinates. The pavement reference coordinates are characteristic coordinates representing the health status of the pavement.

[0118] Optionally, the processing unit 402 is further configured to combine each event in the source-end impulse response event set with each event in the receiver-end impulse response event set to generate an event pairing set.

[0119] Optionally, the processing unit 402 is further configured to filter out target event pairs from the event pair set whose received event time is later than the source event time.

[0120] Optionally, the processing unit 402 is also configured to determine a set of potential propagation path parameters based on the selected target event pairings.

[0121] Optionally, the processing unit 402 is further configured to determine the corresponding propagation parameters for each target event pair. These propagation parameters include apparent wave velocity parameters and amplitude ratio parameters.

[0122] Optionally, the processing unit 402 is also configured to determine a set of potential propagation path parameters based on the propagation parameters paired with each target event.

[0123] Optionally, the processing unit 402 is also configured to determine the initial propagation statistics based on the potential propagation path parameter set.

[0124] Optionally, the processing unit 402 is also configured to select a set of candidate propagation paths from the set of potential propagation path parameters based on the initial propagation statistics.

[0125] Optionally, the processing unit 402 is further configured to calculate the propagation statistical deviation and the neighbor count of each potential path in the potential propagation path parameter set. The propagation statistical deviation characterizes the degree of deviation of the path parameters from the initial propagation statistics, and the neighbor count of the same path characterizes the degree of clustering of paths within their group of same-origin paths.

[0126] Optionally, the processing unit 402 is further configured to determine the proximity cost adjustment factor based on the proximity count of the same source path, and to determine the initial matching cost in conjunction with the propagation statistical bias.

[0127] Optionally, the processing unit 402 is also used to determine a set of candidate propagation paths based on the initial matching cost and the preset optimal matching algorithm.

[0128] Optionally, the processing unit 402 is also configured to determine the final matching cost based on the refining propagation statistics.

[0129] Optionally, the processing unit 402 is also configured to filter the set of identified propagation paths from the set of potential propagation path parameters based on the final matching cost and the preset optimal matching algorithm.

[0130] Optionally, the processing unit 402 is further configured to determine the weighted average propagation time and weighted average amplitude attenuation based on the received energy amplitude of each path in the identified propagation path set.

[0131] Optionally, the processing unit 402 is also used to combine the weighted average propagation time and the weighted average amplitude attenuation into two-dimensional coordinate points as the current state coordinates.

[0132] Optionally, the processing unit 402 is further configured to query the reference coordinates corresponding to the current pavement temperature from a pre-stored pavement health state reference database, and use them as pavement reference coordinates. The pavement health state reference database is obtained by measuring and storing state coordinates multiple times under healthy pavement conditions.

[0133] Optionally, the processing unit 402 is also used to deploy a first accelerometer near the impact point on the road surface and a second accelerometer at a fixed distance from the first accelerometer, and to simultaneously collect the original acceleration sequence from the source end and the original acceleration sequence from the receiver end.

[0134] Optionally, the processing unit 402 is further configured to preprocess the original acceleration sequence at the source end and the original acceleration sequence at the receiver end to obtain the energy envelope at the source end and the energy envelope at the receiver end.

[0135] Optionally, the processing unit 402 is also used to identify local maxima in the source energy envelope and the receiver energy envelope to form a source impulse response event set and a receiver impulse response event set.

[0136] Optionally, the complex pavement mechanical response monitoring device 400 may also include a storage unit ( Figure 4 (shown in dashed box) The storage unit stores a program or instruction. When the acquisition unit 401 and the processing unit 402 execute the program or instruction, the complex pavement mechanical response monitoring device can perform the complex pavement mechanical response monitoring method of the above method embodiment.

[0137] also, Figure 4 The technical effects of the complex road surface mechanical response monitoring device can be referred to the technical effects of the complex road surface mechanical response monitoring method in the above embodiments, and will not be repeated here.

[0138] For example, Figure 5 This is another possible structural schematic diagram of the complex road surface mechanical response monitoring device involved in the above embodiments. For example... Figure 5 As shown, the complex road surface mechanical response monitoring device 500 includes: processor 502.

[0139] The processor 502 is used to control and manage the operation of the complex road surface mechanical response monitoring device 400, for example, to execute the steps performed by the acquisition unit 401 and the processing unit 402 in the complex road surface mechanical response monitoring device 400, and / or to execute other processes of the technical solution described herein.

[0140] The processor 502 described above can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the present invention. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the present invention. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0141] Optionally, the complex pavement mechanical response monitoring device 500 may further include a communication interface 503, a memory 501, and a bus 504. The communication interface 503 supports communication between the complex pavement mechanical response monitoring device 500 and other network entities. The memory 501 stores the program code and data of the complex pavement mechanical response monitoring device.

[0142] The memory 501 may be a memory in a complex road surface mechanical response monitoring device. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; the memory may also include a combination of the above types of memory.

[0143] Bus 504 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 504 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0144] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and module described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0145] This invention provides a computer program product containing instructions that, when run on the electronic device of this invention, cause the computer to execute the complex road surface mechanical response monitoring method described in the above-described method embodiments.

[0146] This invention also provides a computer-readable storage medium storing instructions. When a computer executes these instructions, the electronic device of this invention performs each step of the complex road surface mechanical response monitoring device in the method flow shown in the above-described method embodiments.

[0147] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In embodiments of the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0148] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0149] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for monitoring the mechanical response of complex road surfaces, characterized in that, The method includes: Acquire the source-end impact response event set and the receiver-end impact response event set; wherein, the source-end impact response event set and the receiver-end impact response event set include multiple impact response events extracted from the acceleration signal, and each impact response event is defined by the energy arrival time and amplitude; Based on the source-end impulse response event set and the receiver-end impulse response event set, a potential propagation path parameter set is generated, and a candidate propagation path set is selected from the potential propagation path parameter set based on the initial propagation statistics; wherein, the initial propagation statistics are used to characterize the propagation pattern based on all potential propagation paths; A refined propagation statistic is determined based on the candidate propagation path set, and an identified propagation path set is determined from the potential propagation path parameter set based on the refined propagation statistic; wherein, the refined propagation statistic is used to characterize the propagation pattern based on the candidate propagation path set; Based on the identified propagation path set, the current state coordinates are determined; wherein, the current state coordinates are feature coordinates characterizing the current structural state of the road surface; The type of pavement distress is diagnosed based on the direction of the displacement vector between the current state coordinates and the pavement reference coordinates; wherein, the pavement reference coordinates are characteristic coordinates that characterize the health status of the pavement.

2. The method for monitoring the mechanical response of complex road surfaces according to claim 1, characterized in that, Based on the source-end impulse response event set and the receiver-end impulse response event set, a potential propagation path parameter set is generated, specifically including: Each event in the source-end impulse response event set is combined with each event in the receiver-end impulse response event set to generate an event pairing set; From the event pairing set, select target event pairs whose receiving event time is later than the source event time; Based on the selected target event pairings, the potential propagation path parameter set is determined.

3. The method for monitoring the mechanical response of complex road surfaces according to claim 2, characterized in that, Based on the selected target event pairings, the potential propagation path parameter set is determined, specifically including: For each target event pairing, the corresponding propagation parameters are determined; wherein, the propagation parameters include apparent wave velocity parameters and amplitude ratio parameters; Based on the propagation parameters paired with each target event, a set of potential propagation path parameters is determined.

4. The method for monitoring the mechanical response of complex road surfaces according to claim 1, characterized in that, Based on the initial propagation statistics, a set of candidate propagation paths is selected from the potential propagation path parameter set, specifically including: The initial propagation statistics are determined based on the set of potential propagation path parameters. Based on the initial propagation statistics, the candidate propagation path set is selected from the potential propagation path parameter set.

5. The method for monitoring the mechanical response of complex pavements according to claim 4, characterized in that, Based on the initial propagation statistics and dynamic cost function, the candidate propagation path set is selected from the potential propagation path parameter set, specifically including: Calculate the propagation statistical deviation and the same-origin path neighbor count for each potential path in the potential propagation path parameter set; wherein, the propagation statistical deviation is used to characterize the degree of deviation of the path parameters from the initial propagation statistics, and the same-origin path neighbor count is used to characterize the degree of clustering of the path in its same-origin path group; The proximity cost adjustment factor is determined based on the proximity count of the same source path, and the initial matching cost is determined in combination with the propagation statistical bias. The candidate propagation path set is determined based on the initial matching cost and the preset optimal matching algorithm.

6. The method for monitoring the mechanical response of complex road surfaces according to claim 1, characterized in that, The identified propagation path set is determined from the potential propagation path parameter set based on the refined propagation statistics, specifically including: The final matching cost is determined based on the refined propagation statistics. Based on the final matching cost and the preset optimal matching algorithm, the identified propagation path set is selected from the potential propagation path parameter set.

7. The method for monitoring the mechanical response of complex road surfaces according to claim 1, characterized in that, Based on the identified propagation path set, the current state coordinates are determined, specifically including: Based on the received energy amplitude of each path in the identified propagation path set, determine the weighted average propagation time and the weighted average amplitude attenuation; The weighted average propagation time and the weighted average amplitude attenuation are combined into two-dimensional coordinate points, which are used as the current state coordinates.

8. The method for monitoring the mechanical response of complex pavements according to claim 1, characterized in that, The method further includes: The reference coordinates corresponding to the current road surface temperature are retrieved from the pre-stored road surface health status reference database and used as the road surface reference coordinates; wherein, the road surface health status reference database is obtained by measuring and storing the state coordinates multiple times under healthy road surface conditions.

9. The method for monitoring the mechanical response of complex pavements according to any one of claims 1-8, characterized in that, Obtain the source-end impulse response event set and the receiver-end impulse response event set, specifically including: A first accelerometer is deployed near the impact point on the road surface, and a second accelerometer is deployed at a fixed distance from the first accelerometer to simultaneously collect the original acceleration sequence from the source end and the original acceleration sequence from the receiver end. The source-end raw acceleration sequence and the receiver-end raw acceleration sequence are preprocessed to obtain the source-end energy envelope and the receiver-end energy envelope; Local maxima in the source energy envelope and the receiver energy envelope are identified to form the source impulse response event set and the receiver impulse response event set.

10. A device for monitoring the mechanical response of complex road surfaces, characterized in that, The complex road surface mechanical response monitoring device includes: an acquisition unit and a processing unit; The acquisition unit is used to acquire a source-end impact response event set and a receiver-end impact response event set; wherein, the source-end impact response event set and the receiver-end impact response event set include multiple impact response events extracted from the acceleration signal, and each impact response event is defined by the energy arrival time and amplitude; The processing unit is configured to generate a potential propagation path parameter set based on the source-end impulse response event set and the receiver-end impulse response event set, and to select a candidate propagation path set from the potential propagation path parameter set based on the initial propagation statistics; wherein, the initial propagation statistics are used to characterize the propagation pattern based on all potential propagation paths; The processing unit is further configured to determine a refined propagation statistic based on the candidate propagation path set, and to determine an identified propagation path set from the potential propagation path parameter set based on the refined propagation statistic; wherein the refined propagation statistic is used to characterize the propagation pattern based on the candidate propagation path set; The processing unit is further configured to determine the current state coordinates based on the identified propagation path set; wherein the current state coordinates are feature coordinates characterizing the current structural state of the road surface; The processing unit is further configured to diagnose the type of pavement distress based on the direction of the displacement vector between the current state coordinates and the pavement reference coordinates; wherein the pavement reference coordinates are characteristic coordinates that characterize the health status of the pavement.

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