Asphalt pavement paving thickness monitoring method based on ground penetrating radar

Abstract

The invention discloses an asphalt pavement paving thickness monitoring method based on a ground penetrating radar, and relates to the field of asphalt pavement paving thickness monitoring. The method comprises the following steps: extracting a paving thickness sequence, identifying a paving thickness change area, and combining active adjustment and instability events of screed parameters; analyzing the goodness of fit between the thickness anomaly and the parameter anomaly in time; the method comprises the following steps: mapping a paving thickness change region to a corresponding paving road section, and carrying out correlation analysis by integrating an opportunity goodness of fit and asphalt mixture distribution uniformity; according to the method, the leading reason of the thickness anomaly is judged according to the relevance and the distribution uniformity of the mixture, the thickness deviation degree is analyzed based on the reason type, the corresponding parameter adjustment range and direction are determined, diagnosis of the root of the thickness anomaly is achieved, quantitative adjustment guidance can be output, and the speed of monitoring the paving thickness of the asphalt pavement in real time is increased.

Description

Technical Field

[0001] This invention belongs to the field of asphalt pavement paving thickness monitoring technology, and relates to a method for monitoring asphalt pavement paving thickness based on ground penetrating radar. Background Technology

[0002] Asphalt pavement paving is a process in road construction and maintenance, and the uniformity of its paving thickness is a key indicator of the pavement's structural strength and long-term service performance. Traditional paving thickness monitoring methods struggle to achieve objective monitoring throughout the entire process. With the development of non-destructive testing technologies such as ground-penetrating radar, these technologies are being applied to thickness measurement after paving or during construction, providing a new technical approach to improving quality control.

[0003] Currently, existing ground-penetrating radar-based asphalt pavement paving thickness monitoring technologies have the following shortcomings: First, existing technologies mainly focus on whether the paving thickness measurement value itself exceeds the limit, lacking in-depth analysis of thickness fluctuations and trends. It is difficult to distinguish between random fluctuations caused by uneven instantaneous distribution of asphalt mixture and systematic thickness deviations caused by improper screed working parameters, resulting in vague judgment of the cause of anomalies and failing to provide a clear direction for adjustment.

[0004] Secondly, existing technologies process diverse information such as paving thickness, mechanical parameters, and material condition in a relatively isolated manner. Although multiple data can be collected simultaneously, they fail to analyze the real-time correlation between thickness changes and specific mechanical actions or material condition changes, making it difficult to trace the root cause of thickness problems and to automatically determine whether the responsible link is mechanical control or material supply.

[0005] In addition, the control logic of existing technologies is mostly open-loop or simple closed-loop, with each subsystem operating relatively independently. When there is a deviation in paving thickness, there is a lack of decision-making mechanism to comprehensively evaluate and adjust instructions, resulting in delayed and untargeted adjustment behavior. This may cause repeated oscillations of parameters, making it difficult to achieve stable control of the construction process, thereby restricting the improvement of paving quality. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background art, the present invention provides a method for monitoring the asphalt pavement paving thickness based on ground penetrating radar.

[0007] The objective of this invention can be achieved through the following technical solution: a method for monitoring the paving thickness of asphalt pavement based on ground penetrating radar, comprising: S1, acquiring the paving thickness obtained by ground penetrating radar scanning, and identifying the paving thickness variation area.

[0008] S2. Divide the paving work surface into multiple paving sections according to the location of the paver, and map the paving thickness variation area to the corresponding paving section.

[0009] S3. Analyze the timing of the occurrence of paving thickness variation areas, obtain the timing of active adjustment events and instability events of screed working parameters in the corresponding time period for each paving section, calculate the timing coincidence between thickness variation timing and parameter anomaly timing, and analyze the correlation based on the timing coincidence.

[0010] S4. Analyze the waveform and amplitude characteristics of the ground-penetrating radar reflection signal within the paved section to determine the uniformity of asphalt mixture distribution.

[0011] S5. Based on the correlation and the uniformity of asphalt mixture distribution, determine the dominant cause of thickness anomalies. Based on the dominant cause of thickness anomalies, analyze the degree of thickness deviation of each paved section and determine the corresponding parameter adjustment range.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention extracts the paving thickness sequence and identifies the paving thickness variation area, and combines the active adjustment and instability events of the screed parameters to analyze the temporal consistency between thickness anomalies and parameter anomalies. This overcomes the problem that the prior art only determines whether the thickness exceeds the limit and is difficult to distinguish between random fluctuations and systematic deviations, and realizes the identification of thickness anomaly types, providing a clear direction for construction parameter adjustment.

[0013] (2) This invention maps the paving thickness variation area to the corresponding paving section and performs correlation analysis by combining the timing of the correlation with the uniformity of asphalt mixture distribution. This overcomes the problem of isolated multi-information processing in the prior art, which cannot correlate thickness changes with specific mechanical actions or material state changes in real time. It realizes the tracing of the root cause of thickness abnormality and can automatically determine whether the problem is due to abnormal mechanical control or abnormal material supply, thereby improving the accuracy of problem location in the construction process.

[0014] (3) This invention determines the dominant cause of thickness abnormality based on correlation and uniformity of mixture distribution, and analyzes the degree of thickness deviation based on cause type, and determines the adjustment range and direction of corresponding parameters. This realizes a differentiated control strategy for different abnormal causes, avoids the lag and blindness of adjustment behavior, reduces repeated parameter oscillations, and helps to improve the consistency of paving quality. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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.

[0016] Figure 1 This is a diagram illustrating the implementation steps of the method of the present invention.

[0017] Figure 2 This is a flowchart of the timing-match correlation analysis for the present invention.

[0018] Figure 3 This is a flowchart for judging the uniformity of mixture distribution in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 As shown, the present invention provides a method for monitoring the paving thickness of asphalt pavement based on ground penetrating radar, including: S100, acquiring the paving thickness obtained by ground penetrating radar scanning.

[0021] Considering that paving thickness is an indicator of asphalt pavement quality, it is necessary to obtain continuous thickness data to monitor the thickness status at all times during the paving process and realize process-oriented quality control. Specifically, the steps for obtaining the paving thickness from ground-penetrating radar scanning are as follows: First, a ground-penetrating radar device is fixedly installed behind the paver. During the paver's movement, the ground-penetrating radar scanning direction is perpendicular to the road surface and towards the paving layer surface. The ground-penetrating radar is triggered to perform a scan every 0.1 meters when the paver's travel encoder moves forward, and the original radar reflection waveform data at each location is obtained.

[0022] Then, the raw radar reflection waveform data is preprocessed, including removing DC offset, bandpass filtering, and gain control, to suppress noise and enhance the effective reflection signal. Before paving, the average relative permittivity ε of the current batch of asphalt mixture is obtained through experimental measurement. The preprocessed reflection signal is then used to apply the formula... Convert to the actual depth axis d, where c is the speed of light in a vacuum, and t is the time it takes for the electromagnetic wave to travel from emission to reflection at the bottom of the paving layer and then be received, i.e., two-way travel time.

[0023] Finally, the reflection peaks of the bottom surface of the paving layer and the surface of the base layer are identified in the depth axis domain signal, and their depth difference is calculated as the paving thickness at each point, thus obtaining the continuous paving thickness.

[0024] S101. Identify areas of varying paving thickness.

[0025] Because the paving thickness data acquired by ground penetrating radar is a continuous spatial sequence, it includes not only asphalt pavement paving thickness information, but also thickness variation characteristics of construction process fluctuations and material uniformity. Pavement quality defects always appear in the form of local thickness anomalies, such as gradual thickness changes caused by improper adjustment of the screed.

[0026] Based on this, the identification of paving thickness variation areas includes: extracting paving thickness points along the paver's travel direction to form a thickness variation sequence.

[0027] Set a fixed-length sliding window along the thickness variation sequence, calculate the variance ratio of the thickness data of each window to the previous adjacent window, and accumulate the ratio to obtain the cumulative variance sum sequence.

[0028] Because the thickness anomaly region has uncertainty in spatial scale, it may manifest as a sudden change in a short distance or a gradual change in a long distance. In order to detect possible thickness changes at different locations, this invention uses a sliding window of fixed length to traverse and analyze the thickness sequence. Usually, the number of sampling points is set to 1 to 2 meters corresponding to the physical length, based on the paving speed and radar sampling rate. This can magnify and highlight the sections where the thickness characteristics change continuously.

[0029] Identify continuously rising and falling segments in the variance cumulative sum sequence and define them as candidate thickness variation segments.

[0030] When the cumulative variance sequence shows a continuous upward trend, it indicates that the dispersion of the thickness data is increasing in that direction, which may correspond to the initial region of process instability; conversely, a continuous downward trend indicates that the dispersion is decreasing, and the process may be recovering to stability.

[0031] Specifically, a continuous rise and fall interval is defined as an interval of continuous data points that satisfies the condition of monotonically increasing or monotonically decreasing, and contains at least 3 continuous data points.

[0032] For each candidate thickness variation segment, construct a multi-scale smooth thickness signal, calculate the first-order gradient of the smooth thickness signal at each scale, and record the gradient points where the gradient direction is consistent across multiple scales and the gradient magnitude is greater than the average gradient magnitude at the current scale as consistent gradient points.

[0033] The construction of multi-scale smoothed thickness signals for each candidate thickness variation segment specifically involves smoothing the thickness signals using three Gaussian kernels of different scales to obtain smoothed thickness signals at three scales. Example settings for the scale parameters of the three Gaussian kernels are 0.3 meters, 0.6 meters, and 1.2 meters, respectively. Smaller scales preserve local details and rapid fluctuations in thickness variation, medium scales effectively suppress high-frequency noise while reflecting trend changes within a medium range, and larger scales are used to extract the overall trend and gradually changing background of thickness variation within the segment.

[0034] Within each candidate thickness variation zone, find a set of continuously distributed, consistent gradient points. The two points with the largest gradient magnitude in the set are taken as the start and end boundary points of the thickness change and defined as the paving thickness variation area.

[0035] The actual paving thickness anomalies are spatially continuous, with the most dramatic thickness changes occurring at their boundaries. Within the candidate section, a continuously distributed set of consistent gradient points identifies the intervals where substantial thickness changes occur. Within these intervals, the two points with the largest gradient amplitudes correspond to the locations of the steepest initial and final transitions during the thickness change process, respectively, thus capturing the actual spatial start and end points of the thickness anomaly region.

[0036] S200: The paving work surface is divided into multiple paving sections according to the location of the paver.

[0037] Because the evaluation of paving thickness and material uniformity quality requires a sample of considerable size, road sections are divided according to fixed physical lengths. This ensures that the paving area represented by each analysis unit is similar, thus making the thickness data and anomaly characteristics between different road sections comparable.

[0038] Based on this, dividing the paving work surface into multiple paving sections according to the position of the paver includes: continuously acquiring the real-time planar coordinate position of the paver during the paving operation to form a continuous sequence of paver travel trajectories.

[0039] To achieve accurate road segmentation of the paving work surface, the method for continuously obtaining the real-time plane coordinate position of the paver is as follows: using a reflective target installed on the paver and an automatic tracking total station set up in the construction area, the plane coordinates of the target are calculated in real time by automatically measuring the angle and distance of the target by the total station, which is the position of the paver.

[0040] Starting from the first trajectory point, calculate the cumulative travel distance along the direction of travel.

[0041] It should be noted that the cumulative travel distance refers to the distance between the current trajectory point and the starting point of the road segment.

[0042] When the cumulative travel distance reaches the standard road segment length for the first time, mark the current trajectory point as the end point of the current paving segment and the starting point of the next paving segment. Repeat the above steps until the paving operation is completed.

[0043] The standard road segment length ensures that each segment contains a sufficient number of thickness sampling points, making the calculated statistical characteristics and trend analysis representative and avoiding misjudgments due to insufficient data. It can be determined in the following way: First, based on the response time of the paver screed and the travel speed of the paving operation, calculate the minimum travel distance required for the screed to complete one full action response as the lower limit of the length; then, combined with the conventional inspection interval length specified in the construction quality acceptance specification, finally select a value that is both greater than the lower limit of the length and is an integer multiple of the conventional inspection interval length as the standard road segment length.

[0044] S201. Map the paving thickness variation area to the corresponding paving section.

[0045] Since the thickness variation areas detected by ground-penetrating radar only provide spatial location information of the anomaly, and the time-series data of the paver's working parameters only record the state information that changes over time, the two are isolated from each other at the data level. Therefore, it is necessary to establish a clear causal relationship between thickness quality anomalies and the state of the construction process.

[0046] Therefore, mapping the paving thickness variation area to the corresponding paving section includes: recording the timestamps of the paver to the start and end positions of each paving section, forming a time window for each section.

[0047] Obtain the time points corresponding to the paving thickness variation areas, match them with the time windows of each road segment, and initially map the paving thickness variation areas to the paved road segments whose timestamps fall within their time windows.

[0048] The real-time position of the paver is obtained when the thickness change area is detected. If the real-time position is within the range of the previous road segment of the initially mapped road segment, the mapping result is corrected to the previous road segment adjacent to the current road segment.

[0049] If it is located within the next road segment, then it should be corrected to the next road segment adjacent to this road segment.

[0050] If a change in thickness is detected and the paver's real-time position has entered the section preceding the initially mapped section, it indicates that the paver's main body is likely located further ahead when it is working on the anomaly point, and therefore the mapping should be corrected forward. Similarly, if the real-time position has entered the section following the initial mapping, it indicates that the paver may be located further behind when it is working, and therefore the mapping should be corrected backward.

[0051] S300. Analyze the timing of the occurrence of paving thickness variation areas, obtain the timing of active adjustment events and instability events of screed working parameters in the corresponding time period for each paving section, and calculate the timing coincidence between the thickness variation timing and the parameter anomaly timing.

[0052] The timing coincidence between the calculated thickness change timing and the parameter anomaly timing includes: extracting all paving thickness change areas mapped to the current paved section, and recording the time point when each paving thickness change area is first detected by ground penetrating radar as the thickness anomaly timing sequence.

[0053] Retrieve the timing data of the screed working parameters during the current paving section's construction period, identify proactive adjustment events triggered by the control system, and identify instability events where the actual parameter values ​​continuously exceed the stable range.

[0054] The implementation steps for identifying proactive adjustment events triggered by the control system are as follows: When the ironing board control system issues a control command to change the format of the ironing board's working parameters, the timestamp of the command is immediately recorded and marked as a proactive adjustment event.

[0055] The implementation steps for an instability event where the actual value of a parameter continuously exceeds the stable range are as follows: First, based on the construction specifications, set the allowable stable range for each working parameter of the screed; second, monitor the actual measured value of the parameter in real time. If the actual value of a parameter is found to continuously exceed its corresponding stable range, an instability event is determined to have occurred, and the moment when the parameter first begins to continuously exceed the limit is taken as the event timestamp.

[0056] The trigger points of active adjustments and instability events are denoted as the parameter anomaly timing sequence.

[0057] The number of time point pairs in the sequence of thickness anomalies and parameter anomalies within the matching time window that have a time difference less than the width of the matching time window is counted as the timing match degree.

[0058] Based on temporal causal analysis, two truly related events exhibit proximity in their occurrence time. The number of successful pairing points within the statistical matching time window is the frequency of synchronous occurrence of thickness anomaly events and parameter anomaly events on the time axis. This number of points reflects the tightness of coupling between the two types of events in the time dimension. It can tolerate the inherent delay and detection error of the system, avoid missing true correlations, and filter out temporally irrelevant random events.

[0059] The matching time window is determined as follows: the ratio of the fixed horizontal distance between the ground-penetrating radar and the screed to the standard operating speed of the paver is calculated to obtain the theoretical shortest time delay from the generation of thickness information to its detection; then, this theoretical delay is multiplied by an empirical coefficient ranging from 1.5 to 3.0 to obtain the final matching time window width. This avoids missing true correlations due to failure to include the most basic information transmission time, and also prevents incorrect matching of accidental events that are too far apart in time.

[0060] S301. Analyze the correlation based on the timing coincidence.

[0061] Abnormal paving thickness can be caused by a variety of factors, the most important being abnormal screed operation and inhomogeneity of the asphalt mixture itself. Thickness changes caused by screed operation should exhibit a high degree of synchronization with the occurrence of abnormal parameter events, while thickness changes caused by material inhomogeneity usually show no regular temporal correlation with abnormal parameter events. By pointing the dominant cause of thickness anomalies to material-related issues, this effectively distinguishes it from thickness anomalies caused by mechanical factors.

[0062] See Figure 2 As shown, the correlation analysis based on timing coincidence includes: statistically analyzing the timing coincidence of all analyzed paved road sections within the current paving operation construction section, and calculating their arithmetic mean as the benchmark coincidence.

[0063] Within the same continuous construction section, material properties, environmental conditions, and equipment status are relatively stable. The arithmetic mean of the timing of the calculation represents the average level of the accidental temporal correlation between thickness anomalies and parameter anomalies caused by random factors under the current construction conditions.

[0064] For each paved section, if the timing match is greater than or equal to the baseline match, it is determined that there is a strong correlation between the paving thickness change and the working state of the screed; otherwise, it is determined to be a weak correlation, until all paved sections have been analyzed.

[0065] The baseline consistency represents the level of random temporal correlation between thickness anomalies and parameter anomalies under current construction conditions. If the timing consistency of a road segment is greater than or equal to the baseline consistency, it indicates that the temporal coupling strength between the two types of events within that segment has significantly exceeded the general level of random correlation. Therefore, it can be determined that there is a strong correlation beyond chance between the thickness change and the working state of the screed. Conversely, if it is lower than the baseline consistency, it indicates that the correlation strength has not deviated from the range of general random fluctuations and is insufficient to prove the existence of a specific mechanical cause; therefore, it is determined to be a weak correlation.

[0066] S400: Analyze the waveform and amplitude characteristics of ground-penetrating radar reflection signals within the paved section to determine the uniformity of asphalt mixture distribution.

[0067] See Figure 3 As shown, the determination of the uniformity of asphalt mixture distribution includes: sequentially acquiring the reflection signals of each measuring point of the ground penetrating radar in the current paved section, performing Hilbert transform on the reflection signals, and obtaining the analytical signal.

[0068] The specific steps for performing a Hilbert transform on the reflected signal to obtain an analytic signal are as follows: First, perform a Hilbert transform on the ground-penetrating radar reflected signal sequence to generate a corresponding orthogonal sequence. This transform delays the phase of the positive frequency component of the original signal by -90 degrees and advances the phase of the negative frequency component by +90 degrees in the frequency domain. Next, take the original ground-penetrating radar reflected signal sequence as the real part and its corresponding Hilbert transform result sequence as the imaginary part. The two together constitute a complex sequence, which is the analytic signal.

[0069] The magnitude of the analytical signal is calculated as the signal envelope. The sum of the squares of all discrete values ​​of the signal envelope is then used to obtain the envelope energy value of each measurement point of the ground penetrating radar.

[0070] The envelope energy values ​​corresponding to each paved road segment are arranged in the order of all paved road segments to form an envelope energy sequence.

[0071] Calculate the coefficient of variation of the envelope energy spatial sequence. If the coefficient of variation is less than or equal to the uniformity judgment threshold, the asphalt mixture distribution of the current road section is determined to be uniform; otherwise, it is determined to be non-uniform.

[0072] The coefficient of variation refers to the ratio of the standard deviation of the envelope energy spatial sequence to its arithmetic mean, representing the relative spatial dispersion of energy values.

[0073] The uniformity of asphalt mixture distribution determines the spatial consistency of dielectric constant distribution. When the mixture is uniform, the spatial variation of dielectric constant is small, and the energy reflected by radar waves at the same depth tends to be consistent at all points in space, that is, the dispersion of the energy sequence is low, which means the coefficient of variation of the envelope energy spatial sequence is lower. When the mixture is non-uniform, the dielectric constant changes abruptly in space, resulting in significant differences in reflected energy at each point, and the dispersion of the energy sequence is high, that is, the coefficient of variation of the envelope energy spatial sequence is higher.

[0074] The uniformity threshold is determined as follows: First, in completed projects using the same equipment and materials, a standard road section that has been tested and verified to have a uniform mixture distribution is selected; second, on the standard road section, the envelope energy spatial sequence and its coefficient of variation of each analysis sub-section are collected and calculated according to the method of the present invention; finally, the high percentile value of the statistical distribution of the coefficient of variation of all sub-sections is taken as the uniformity threshold.

[0075] S500. Determine the primary cause of thickness anomalies based on correlation and the uniformity of asphalt mixture distribution.

[0076] Considering that abnormal paving thickness is mainly caused by two different physical mechanisms: improper screed working parameters and uneven distribution of asphalt mixture.

[0077] Therefore, the main reasons for judging thickness anomalies based on correlation and asphalt mixture distribution uniformity include: if the correlation is strong and the asphalt mixture distribution is uniform, the main reason is abnormal working parameters of the screed.

[0078] The strong correlation indicates that the thickness variation and the abnormal screed parameters are highly consistent in time, which meets the necessary conditions for mechanical cause; while the uniform distribution of asphalt mixture rules out material inhomogeneity as the main cause of this thickness anomaly.

[0079] As the actuator that directly controls the paving thickness, the sudden change or instability of the working parameters of the screed will immediately change the paving thickness. If the material input itself is uniform, the change in thickness will be purely caused by the change in mechanical state.

[0080] If the correlation is weak and the asphalt mixture is unevenly distributed, the dominant cause is the abnormal distribution of the asphalt mixture.

[0081] The weak correlation indicates that the thickness change and the abnormal screed parameters lack a stable synchronous relationship in terms of occurrence time, thus ruling out that the abnormal working condition of the screed was the direct or main cause of this thickness change; while the uneven distribution of the asphalt mixture confirms that there is an unevenness problem in the material itself.

[0082] In other cases, the dominant cause is a combination of factors.

[0083] S501. Based on the main causes of thickness anomalies, analyze the degree of thickness deviation in each paved section.

[0084] Specifically, if the main cause is abnormal working parameters of the screed, the absolute value of the slope of the linear trend of the thickness change sequence is calculated as the thickness deviation value of that section.

[0085] The impact of abnormal screed working parameters on paving thickness typically manifests as a continuous trend along the paving direction, such as gradual thickening or thinning, rather than random fluctuations. The slope of the linear trend quantifies the rate of spatial shift in the thickness of this road segment; the larger its absolute value, the more severe the systematic thickness deviation caused by abnormal mechanical parameters. Therefore, using the absolute value of the linear trend slope as a measure of the degree of thickness deviation can reflect the persistence of thickness anomalies caused by such mechanical factors.

[0086] If the dominant cause is abnormal distribution of asphalt mixture, the thickness variation sequence is detrended, and the standard deviation of the detrended sequence is calculated as the thickness deviation value of the road section.

[0087] The detrending process for the thickness variation sequence specifically includes: First, using the least squares method to perform a first-order linear fit on the thickness variation sequence to obtain the fitted line equation; then, based on the fitted line equation, calculating the trend component corresponding to each thickness data point in the thickness variation sequence; finally, subtracting the corresponding trend component from the original value of each thickness data point in the thickness variation sequence to obtain the detrended residual sequence. This process removes trend components that may be introduced by other factors, such as slow basalt undulations or very slight mechanical drift.

[0088] The impact of asphalt mixture distribution anomalies on paving thickness typically manifests as random fluctuations without a specific direction at a given thickness level, rather than a continuous trend. Calculating the standard deviation of the detrended sequence quantifies the magnitude of this random fluctuation; a larger standard deviation indicates greater instability in the localized random thickness deviation caused by material distribution anomalies. Therefore, using the standard deviation of the detrended sequence as a value for the degree of thickness deviation can characterize the irregularity of thickness anomalies caused by such material factors.

[0089] If the dominant cause is a composite cause, then calculate the absolute value of the linear trend slope and the standard deviation of the detrended sequence, and take the maximum value of the two as the thickness deviation value of the road segment.

[0090] Using the maximum of the absolute value of the linear trend slope and the standard deviation of the detrended sequence as the thickness deviation value for that road segment indicates that the most severe deviation feature exhibited by the current road segment is selected to represent its overall deviation degree. The advantage of this approach is that it ensures that the subsequently determined parameter adjustment range is sufficient to cover the most prominent deviation impact, thereby avoiding underestimation of the deviation degree leading to insufficient adjustment.

[0091] S502. Determine the adjustment range of the corresponding parameters.

[0092] Specifically, if the primary cause is abnormal ironing plate operating parameters, then the parameter to be adjusted is the ironing plate elevation angle, and the adjustment direction is opposite in sign to the linear trend slope of the thickness change sequence.

[0093] During paver operation, the change in the screed elevation angle is positively correlated with the change in thickness. The sign of the linear trend slope of the thickness change sequence indicates the direction of the systematic deviation of the thickness within the road section. A positive sign indicates that the thickness continues to increase along the paving direction, such as from thin to thick, while a negative sign indicates that it continues to decrease, such as from thick to thin.

[0094] If the current paving thickness continues to increase (positive slope), then the elevation angle is reduced to suppress the thickening; if the current paving thickness continues to decrease (negative slope), then the elevation angle is increased to compensate for the thickness loss, so that the subsequent paving thickness returns to the target value.

[0095] If the primary cause is abnormal distribution of asphalt mixture, then the adjustment parameter is determined to be the vibration frequency of the screed, and the adjustment direction is to increase it.

[0096] The vibration frequency of the screed affects the rearrangement and secondary distribution of aggregate particles: increasing the vibration frequency enhances the vibration compaction and homogenization of the mixture, helps reduce uneven distribution, and thus improves the material uniformity of the paving layer, indirectly correcting the thickness fluctuations caused by it. The adjustment direction is determined to increase the vibration frequency to improve homogenization, aiming to compensate for and correct material distribution defects by actively increasing the vibration energy of the screed.

[0097] Collect statistics on all completed paving sections within the current continuous construction batch that are similar to the current dominant cause, obtain the set of absolute values ​​of thickness deviation, and select the maximum value in the set of absolute values ​​as the maximum deviation value.

[0098] Calculate the ratio of the absolute value of the thickness deviation of the current road segment to the maximum deviation value, and multiply this ratio by the basic adjustment unit to obtain the parameter adjustment step size.

[0099] The basic adjustment unit is determined as follows: First, the minimum adjustable increments of the elevation angle and vibration frequency are obtained based on the technical parameters of the screed control system; second, the recommended single adjustment amount for the above parameters is determined according to the requirements for thickness and flatness in the construction quality specifications; finally, the larger value between the minimum adjustable increment and the recommended single adjustment amount is used as the basic adjustment unit for the elevation angle and vibration frequency, respectively. This ensures that the basic adjustment unit simultaneously meets the requirements of equipment adjustment accuracy and construction quality control.

[0100] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0101] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0104] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A ground penetrating radar based method of asphalt pavement paving thickness monitoring, characterized by: include: Obtain the paving thickness obtained from ground-penetrating radar scans and identify areas of varying paving thickness; The paving surface is divided into multiple paving sections based on the location of the paver, and the areas of varying paving thickness are mapped to the corresponding paving sections. Analyze the timing of the occurrence of paving thickness variation areas, obtain the timing of active adjustment events and instability events of screed working parameters in the corresponding time period for each paved road section, calculate the timing coincidence between thickness variation timing and parameter anomaly timing, and analyze the correlation between paving thickness variation and screed working parameters based on the timing coincidence. Analyze the waveform and amplitude characteristics of ground-penetrating radar reflection signals within the paved section to determine the uniformity of asphalt mixture distribution; Based on the correlation and the uniformity of asphalt mixture distribution, the dominant cause of thickness anomalies is determined. Based on the dominant cause of thickness anomalies, the degree of thickness deviation of each paved section is analyzed, and the corresponding parameter adjustment range is determined.

2. The asphalt pavement paving thickness monitoring method based on ground penetrating radar according to claim 1, characterized in that: The areas where the paving thickness varies are identified include: Extract paving thickness points along the paver's travel direction to form a thickness variation sequence; A fixed-length sliding window is set along the thickness variation sequence. The variance ratio of the thickness data of each window to the previous adjacent window is calculated. The cumulative variance sum sequence is obtained by accumulating the ratios. Identify continuously rising and falling segments in the cumulative variance sum sequence and define them as candidate thickness variation segments; For each candidate thickness variation segment, construct a multi-scale smooth thickness signal, calculate the first-order gradient of the smooth thickness signal at each scale, and record the gradient points with consistent gradient directions and gradient magnitudes greater than the average gradient magnitude at the current scale as consistent gradient points. Within each candidate thickness variation zone, find a set of continuously distributed, consistent gradient points. The two points with the largest gradient magnitude in the set are taken as the start and end boundary points of the thickness change and defined as the paving thickness variation area.

3. The asphalt pavement paving thickness monitoring method based on ground penetrating radar according to claim 1, characterized in that: The division of the paving work surface into multiple paving sections based on the location of the paver includes: During the paving operation, the real-time planar coordinate position of the paver is continuously acquired to form a continuous sequence of paver travel trajectories; Starting from the first trajectory point, calculate the cumulative travel distance along the direction of travel; When the cumulative travel distance reaches the standard road segment length for the first time, mark the current trajectory point as the end point of the current paving segment and the starting point of the next paving segment. Repeat the above steps until the paving operation is completed.

4. The asphalt pavement paving thickness monitoring method based on ground penetrating radar according to claim 1, characterized in that: The process of mapping areas of varying paving thickness to corresponding paved road sections includes: Record the timestamps of the paver's arrival and departure points at each paving section to form time windows for each section; Obtain the time points corresponding to the paving thickness variation areas, match them with the time windows of each road segment, and initially map the paving thickness variation areas to the paved road segments whose timestamps fall within their time windows. The real-time position of the paver is obtained when the thickness change area is detected. If the real-time position is within the range of the previous road segment of the initially mapped road segment, the mapping result is corrected to the previous road segment adjacent to the current road segment. If it is located within the range of the next road segment, then it is corrected to the road segment adjacent to this road segment.

5. The method for monitoring the thickness of asphalt pavement paving based on ground penetrating radar according to claim 1, characterized in that: The timing of the calculated thickness change and the timing of parameter anomalies include: Extract all paving thickness variation areas mapped to the current paved section, and record the time point when each paving thickness variation area is first detected by ground penetrating radar as the thickness anomaly timing sequence; Retrieve the time sequence data of the screed working parameters during the current paving section construction period, identify the active adjustment events triggered by the control system, and the instability events where the actual parameter values ​​continuously exceed the stable range; The triggering points of active adjustments and instability events are denoted as the parameter anomaly timing sequence; The number of time point pairs in the sequence of thickness anomalies and parameter anomalies within the matching time window that have a time difference less than the width of the matching time window is counted as the timing match degree.

6. The method for monitoring asphalt pavement paving thickness based on ground-penetrating radar according to claim 1, characterized in that: The correlation analysis based on timing coincidence includes: The timing match of all analyzed paved sections within the current paving operation section is statistically analyzed, and their arithmetic mean is calculated as the baseline match. For each paved section, if the timing match is greater than or equal to the baseline match, it is determined that there is a strong correlation between the paving thickness change and the working state of the screed; otherwise, it is determined to be a weak correlation, until all paved sections have been analyzed.

7. The method for monitoring asphalt pavement paving thickness based on ground-penetrating radar according to claim 1, characterized in that: The determination of the uniformity of asphalt mixture distribution includes: The reflected signals from each measuring point of the ground penetrating radar within the current paved section are sequentially acquired, and the reflected signals are subjected to Hilbert transform to obtain the analytical signal; The magnitude of the analytical signal is calculated as the signal envelope. The sum of the squares of all discrete values ​​of the signal envelope is then obtained to obtain the envelope energy value of each measurement point of the ground penetrating radar. The envelope energy values ​​corresponding to each paved road segment are arranged in the order of all paved road segments to form an envelope energy sequence; Calculate the coefficient of variation of the envelope energy spatial sequence. If the coefficient of variation is less than or equal to the uniformity judgment threshold, the asphalt mixture distribution of the current road section is determined to be uniform; otherwise, it is determined to be non-uniform.

8. The method for monitoring asphalt pavement paving thickness based on ground-penetrating radar according to claim 1, characterized in that: The main causes of thickness anomalies determined based on correlation and asphalt mixture distribution uniformity include: If the correlation is strong and the asphalt mixture is evenly distributed, the main cause is abnormal working parameters of the screed. If the correlation is weak and the asphalt mixture is unevenly distributed, the dominant cause is the abnormal distribution of the asphalt mixture. In other cases, the dominant cause is a combination of factors.

9. The method for monitoring asphalt pavement paving thickness based on ground-penetrating radar according to claim 8, characterized in that: The analysis of the thickness deviation of each paved section includes: If the main cause is abnormal working parameters of the screed, then calculate the absolute value of the slope of the linear trend of the thickness change sequence as the thickness deviation value of the section. If the main cause is abnormal distribution of asphalt mixture, the thickness change sequence is detrended and the standard deviation of the detrended sequence is calculated as the thickness deviation value of the road section. If the dominant cause is a composite cause, then calculate the absolute value of the linear trend slope and the standard deviation of the detrended sequence, and take the maximum value of the two as the thickness deviation value of the road segment.

10. The method for monitoring asphalt pavement paving thickness based on ground-penetrating radar according to claim 9, characterized in that: Determining the adjustment range of the corresponding parameters includes: If the primary cause is abnormal ironing plate operating parameters, then the parameter to be adjusted is the ironing plate elevation angle, and the adjustment direction should be opposite to the sign of the slope of the linear trend of the thickness change sequence. If the primary cause is abnormal distribution of asphalt mixture, then the adjustment parameter is determined to be the vibration frequency of the screed, and the adjustment direction is to increase it. Collect statistics on all completed paved sections within the current continuous construction batch that are similar to the current dominant cause, obtain the set of absolute values ​​of thickness deviation, and select the maximum value in the set of absolute values ​​as the maximum deviation value; Calculate the ratio of the absolute value of the thickness deviation of the current road segment to the maximum deviation value, and multiply this ratio by the basic adjustment unit to obtain the parameter adjustment step size.

Images