Asphalt pavement construction uniformity evaluation method and system

Abstract

The invention discloses an asphalt pavement construction uniformity evaluation method and system. The method comprises the following steps: mounting a sensing and image acquisition unit on construction equipment; collecting a reference section data calibration base line; acoustics and vibration signals and road surface images are synchronously collected during construction; performing spectrum analysis and energy calculation on the signal to obtain an energy ratio, and generating an abnormal candidate region by combining a dynamic threshold and a space-time filtering rule after smoothing processing; and the acoustic anomaly index and the image texture index are fused, a comprehensive uniformity index is calculated to accurately evaluate the pavement uniformity, and grading treatment suggestions are generated and closed-loop retest is executed according to the comprehensive uniformity index. The system comprises corresponding sensing, image, processing and judging modules. The problems that in the prior art, internal defect detection blind areas exist, the false alarm rate is high, and information fusion is insufficient are solved, and full-dimension, high-reliability and intelligent closed-loop construction quality control is achieved.

Description

A method and system for evaluating the uniformity of asphalt pavement construction Technical Field

[0001] This invention relates to the field of road construction quality control technology, and in particular to a method and system for evaluating the uniformity of asphalt pavement construction. Background Technology

[0002] The uniformity of asphalt pavement construction is a core indicator that determines its long-term service performance and structural durability. Traditional quality control mainly relies on manual experience inspections and delayed destructive sampling (such as core sampling). This method is inefficient and, due to insufficient sampling density, is prone to missing hidden defects such as local cavities and delamination, thus creating hidden dangers for early road damage.

[0003] To improve inspection efficiency, non-destructive testing technologies have emerged. Among these, surface texture analysis methods based on image processing evaluate uniformity by analyzing the distribution of aggregates on the road surface. However, this method can only perceive surface features and is powerless to detect compaction defects within the structural layers, resulting in a limited evaluation dimension. Another type of technology, temperature difference detection based on infrared thermography, identifies defects by utilizing the difference in heat dissipation between defective and normal areas. However, its performance is greatly affected by ambient temperature, wind speed, and sunlight, exhibiting poor stability and a high false alarm rate under complex weather conditions, thus limiting its engineering applicability.

[0004] Furthermore, existing solutions attempting to integrate multiple detection technologies mostly remain at the level of simply listing and comparing test results afterward, failing to construct a closed-loop control system from real-time data acquisition and deep fusion analysis to intelligent judgment and handling suggestions. The various technical modules form "information silos," lacking collaborative verification and dynamic decision-making mechanisms under a unified spatiotemporal benchmark, resulting in a failure to achieve a qualitative leap in overall system performance. Therefore, there is an urgent need in this field for a method and system for evaluating the uniformity of asphalt pavement construction that can achieve deep multimodal integration, real-time accuracy, and intelligent closed-loop control capabilities. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] Therefore, to solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for evaluating the uniformity of asphalt pavement construction, comprising the following steps: S1: installing at least one sensing unit on the construction equipment, the sensing unit including a microphone and an accelerometer; simultaneously, installing an image acquisition unit on the construction equipment; S2: acquiring acoustic-vibration data in a reference section to obtain the baseline energy ratio. and its standard deviation S3: During construction, the sensing unit performs sampling at a preset rate. (The analog-to-digital conversion sampling frequency of the sensing unit) acquires acoustic and vibration signals, and processes them according to a preset window length. The process involves frame segmentation; synchronously, road surface images are acquired through the image acquisition unit, and a road surface texture uniformity index is calculated based on these images. S4: Perform spectral transformation on each frame of the signal and calculate the low-frequency energy. and high frequency band energy S5: Calculate the energy ratio ,in It is a small positive number, and for consecutive frames The value is smoothed to obtain S6: Based on baseline energy ratio and its standard deviation Calculate dynamic threshold ,in For a predefined coefficient (preferably 1.0–2.0), when consecutively exceeding When the frame is in time, it is marked as a temporal continuity anomaly; S7: within the spatial neighborhood radius and time window Within, spatial connectivity filtering is performed on temporally continuous anomalies; if at least... If there are consecutive anomalies in time, anomaly candidate regions are generated, and the average energy ratio of the anomaly candidate regions is calculated. S8: Average energy ratio based on the abnormal candidate region With the road surface texture uniformity index Calculate the comprehensive uniformity index Based on this, the pavement construction uniformity is evaluated and treatment recommendations are generated, so as to comprehensively evaluate the pavement uniformity and generate treatment recommendations; S9: Retesting is carried out according to the treatment recommendations, and the number of retests does not exceed If the anomaly persists after retesting, a final report recommending destructive testing will be output.

[0007] As a preferred embodiment of the asphalt pavement construction uniformity evaluation method of the present invention, the low frequency band has a frequency range of 100Hz to 300Hz, and the high frequency band has a frequency range of 300Hz to 1000Hz.

[0008] As a preferred embodiment of the asphalt pavement construction uniformity evaluation method of the present invention, wherein: the sampling rate The frequency range is 4kHz to 16kHz, preferably 8kHz; the window length The time is 0.5s, and the window overlap is 50% (balancing time resolution and frequency accuracy).

[0009] As a preferred embodiment of the asphalt pavement construction uniformity evaluation method of the present invention, wherein: in step S8, the comprehensive uniformity index , For the normalized energy ratio, , The preset fusion weights, This is the preset acceptable upper limit for energy ratio.

[0010] As a preferred embodiment of the asphalt pavement construction uniformity evaluation method of the present invention, the smoothing process adopts moving average or exponential smoothing, and the moving average window is 3 frames.

[0011] In a preferred embodiment of the asphalt pavement construction uniformity evaluation method described in this invention, the number of times the continuous sampling window exceeds the threshold is determined by triggering anomalies at certain times. The value is 3, indicating the number of system retests. The value is 2.

[0012] As a preferred embodiment of the asphalt pavement construction uniformity evaluation method of the present invention, wherein: the spatial neighborhood radius The time window is 0.5~2m. The At least 2.

[0013] As a preferred embodiment of the asphalt pavement construction uniformity evaluation method of the present invention, the method further includes a step of generating an evidence package, wherein the evidence package includes at least Original acoustic waveform of frame, corresponding spectrum, vibration channel waveform, The evidence package contains sequence, location information, timestamp, and corresponding road surface image and texture uniformity index D. The evidence package is then digitally hashed (using the SHA-256 algorithm) and stored locally and remotely.

[0014] An asphalt pavement construction uniformity evaluation system is provided for implementing the method described above. The system includes: several sensing units mounted on the construction equipment for acquiring acoustic and vibration signals; an image acquisition unit mounted on the construction equipment for acquiring pavement images; and an edge processor for preprocessing, spectral analysis, energy calculation, and... Calculate and use it to calculate the road surface texture uniformity index based on the road surface image. The judgment and evaluation module is used for dynamic threshold-based judgment. Temporal continuity anomaly detection and spatial connectivity filtering are performed to generate anomaly candidate regions, which are then used to execute the comprehensive uniformity index. The calculations are used to evaluate pavement uniformity and trigger remedial recommendations.

[0015] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described above.

[0016] The beneficial effects of this invention are as follows: 1. This invention, by jointly analyzing acoustic and vibration signals during the compaction process, innovatively uses the ratio of low-frequency band energy to high-frequency band energy as the core criterion, which can sensitively capture the resonance characteristics and compaction resistance changes caused by internal cavities or layering. By deeply integrating this acoustic index with image texture index reflecting the distribution of surface aggregates, a comprehensive uniformity index is constructed, thereby breaking through the bottleneck of existing technologies that can only detect surface defects or are insensitive to internal defects, and realizing a comprehensive evaluation of road surface quality "from the surface to the interior".

[0017] 2. This invention designs a dual filtering rule based on temporal continuity and spatial connectivity. The system not only requires that signal anomalies occur continuously for more than a preset number of times in time to filter out instantaneous interference, but also requires that these anomaly points form a connected cluster in physical space to confirm that they are meaningful defect areas. This mechanism, combined with dynamic threshold calibration, significantly improves the reliability of the judgment results and effectively overcomes the drawbacks of high false alarm rates in traditional acoustic vibration detection or thermal imaging methods under complex working conditions.

[0018] 3. This invention scientifically integrates the internal state information of acoustic perception and the surface texture information of image acquisition into a comprehensive index through normalization and weight allocation; the model can quantify the degree of influence of different defect types on the overall uniformity, so that the evaluation results take into account both internal density and appearance uniformity.

[0019] 4. This invention transforms quality control from passive and delayed post-event inspection to proactive, real-time management embedded in the construction process. The system can automatically generate graded treatment suggestions (such as additional compaction or core sampling) based on the anomaly confidence level, and drive a limited number of retests to verify the treatment effect. This closed-loop design not only significantly improves processing efficiency, but also ensures the traceability and closed-loop nature of quality issues, realizing an intelligent upgrade of construction quality management. Attached Figure Description

[0020] 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. Among them: Figure 1 is an overall flowchart of the present invention.

[0021] Figure 2 is a system architecture diagram of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Referring to Figures 1-2, an embodiment of the present invention provides an asphalt pavement construction uniformity evaluation system, which mainly includes: a sensing unit: In this embodiment, three sensing units are used, which are respectively installed about 20 centimeters behind the roller of the road roller (one on the left and one on the right) and at the center of the vehicle body (as a reference for environmental noise); each sensing unit includes a MEMS microphone and a triaxial accelerometer.

[0026] Image acquisition unit: In this embodiment, a high-definition industrial camera is installed at the height of the road roller frame, vertically downwards and aimed at the road surface that has just been compacted, to acquire images of the road surface texture.

[0027] Edge Processor: In this embodiment, an industrial-grade vehicle-mounted computer is used as the edge processor. It has multiple analog input channels and digital interfaces, which are connected to the sensing unit and image acquisition unit, respectively. This processor is responsible for running signal processing and data analysis algorithms.

[0028] Judgment and evaluation module: In this embodiment, this module is integrated into the edge processor in the form of a software program.

[0029] This embodiment also includes a touch screen and a large-capacity solid-state drive installed in the driver's cab; the screen is used to visualize alarms and display heat maps, and the solid-state drive is used to store evidence packages and test reports.

[0030] The data from the sensing unit and image acquisition unit is transmitted to the edge processor via cables. The edge processor processes the data in real time and sends the results and alarm information to the touch screen and the large-capacity solid-state drive.

[0031] This embodiment also provides a method for evaluating the uniformity of asphalt pavement construction based on the above system and related hardware equipment, specifically including the following steps: S101: Sensor unit installation and system initialization; First, the above sensor unit and image acquisition unit are firmly installed on the road roller according to the design position, and all cable connections are completed; The system is started, the edge processor performs a self-test to confirm that the power supply and communication of each unit are normal; The operator sets system parameters through the touch screen: sampling rate Window length The sampling time is 0.5s, and the window overlap is 50%. It is worth noting that in this embodiment, the sampling frequency is set to... This value ensures that data can be collected without distortion. The following target acoustic and vibration signals satisfy the requirements of the Nyquist sampling theorem; on the other hand, this rate is within the performance range of common industrial acquisition chips, ensuring the system's economy and feasibility; analysis window length The time frame is set to 0.5s, which strikes a good balance between time and frequency resolution. This is sufficient to spatially locate anomalous areas while providing adequate frequency resolution for accurate calculation of energy in each band. S102: Reference segment acquisition and baseline establishment; On a confirmed good road section (10 m long) with a compaction rate of 96%, a road roller passes through at a normal speed (approximately 3-5 km / h); The system acquires data in this section, collecting acoustic-vibration data, and the edge processor calculates the acoustic-vibration baseline energy ratio for this section. Its standard deviation Based on this, the dynamic threshold is calculated. It is worth noting that the coefficients in the dynamic threshold calculation... The value range is set to 1.0~2.0; this value is based on the principles of mathematical statistics; in a reference section with good quality, the energy ratio data usually fluctuates within a certain range; by setting the threshold at the baseline mean plus 1 to 2 times the standard deviation, it means that the system will identify normal road surface fluctuations as background with a high degree of statistical confidence (for example, corresponding to a coverage of about 68~95% under a normal distribution), while judging signals that deviate significantly from this range as abnormal, thereby ensuring the scientific and objective nature of the judgment criteria.

[0032] S103: Online data acquisition and preprocessing; during subsequent compaction, the system begins to synchronously acquire multimodal data; the sensing unit uses... Data is continuously collected at the sampling rate, according to the window length. The data is divided into frames with an overlap rate of 50%; the DC component is removed from each frame, a Hanning window is applied, and a bandpass filter of 50-1200Hz is performed; simultaneously, the image acquisition unit synchronously acquires road surface images at a rate of two frames per second; S104: Time-frequency analysis and energy calculation; the edge processor preprocesses each frame of acoustic vibration signal by removing DC, applying a Hanning window, and bandpass filtering (50 to 1200 Hz), and then performs a fast Fourier transform; the edge processor calculates the energy of the low-frequency band (100-300 Hz) respectively. and high frequency band (300-1000 Hz) energy It is worth noting that when there are cavities or delamination defects inside the asphalt pavement, a significant resonance effect will be triggered under the impact of the roller compaction, with the resonance frequency mainly distributed in the low-frequency range of 100-300 Hz; while the sound and vibration generated in the normal, dense area of ​​the pavement structure are more concentrated in the frequency band of 300-1000 Hz. Therefore, in this embodiment, 100-300 Hz is defined as the low-frequency band representing defects, and 300-1000 Hz is defined as the high-frequency band representing the normal state. By calculating the energy ratio of the two, an effective criterion that is highly sensitive to internal defects is constructed; S105: Energy ratio calculation and smoothing; calculate the energy ratio of each frame. For 3 consecutive frames The values ​​are averaged to obtain a smoothed energy ratio. It is worth noting that in the time continuity determination, the number of consecutive frames exceeding the threshold is set to 3. This design follows the principle of "multiple confirmations," which can effectively distinguish between real continuous anomalies and transient random noise, and is key to improving the system's anti-interference capability; S106: Time continuity anomaly determination; Edge processor smoothing energy ratio With dynamic threshold Comparison, when a certain location point is in 3 consecutive frames The values ​​are all greater than the dynamic threshold. When the value reaches 1.275, the system marks the point as a "temporal continuity anomaly" and records its timestamp and GPS location; S107: Spatial connectivity filtering and candidate region generation; The system checks the spatial neighborhood radius. Time window Within the range, are there at least two ( =2) Such time-continuous outliers; if satisfied, these outliers are aggregated to generate an "outlier candidate region", and the average energy ratio of the region is calculated. For example, if an abnormal candidate region is detected somewhere, its It is worth noting that in spatial connectivity filtering, the spatial neighborhood radius... The range is set to 0.5~2m (1m in this embodiment). This range is determined based on the typical physical dimensions of road surface defects in actual engineering, aiming to capture continuous defect areas of engineering significance. It requires at least two anomaly points in the neighborhood, further eliminating the possibility of misjudging isolated points from a spatial perspective, thus forming a double insurance against false alarms. S108: Uniformity evaluation and handling suggestions are generated. While simultaneously acquiring acoustic data in step S103, the camera acquires road surface images, and the texture uniformity index is calculated using an edge processor. (For example, by calculating the contrast of the image's gray-level co-occurrence matrix,) (The closer the value is to 1, the more uniform it is). After receiving the average smooth energy ratio and texture uniformity index of the abnormal candidate region, the judgment and evaluation module begins to perform fusion evaluation. First, the average energy ratio of the abnormal candidate region is normalized; assuming a preset acceptable upper limit for the energy ratio. ,but Then, with fusion weights Calculate the comprehensive uniformity index ;Should The value (0.72) compared to the original The value (0.85) decreased significantly, more comprehensively reflecting the non-uniformity of the area in terms of both texture and internal structure (cavity); the system immediately issued an audible and visual alarm via the touchscreen, displaying "Insufficient uniformity in area XX, additional compaction and retesting recommended," and highlighted the abnormal candidate area on the map interface; S109: Evidence package generation and closed-loop processing; the system automatically generated an evidence package for the abnormal candidate area, including the three frames of original acoustic waveforms that triggered the anomaly, vibration data, spectrum diagram, location information, timestamp, and road surface image and calculated indicators at the same time; the hash value and data package were stored together in the solid-state drive and uploaded to the cloud for backup; the operator performed an additional compaction on the area according to the alarm prompt; after compaction, the system automatically performed a retest after operator confirmation; if the retest is performed (maximum number of retests)... If the anomaly persists, the system will escalate the alarm and recommend "suspending construction and conducting core sampling for verification." It's worth noting that the system has a maximum limit of two retests. This design aims to balance quality control and construction efficiency; one retest can verify the initial alarm and check the effectiveness of preliminary measures; if the problem remains unresolved, the second retest serves as final confirmation; if the anomaly persists after two retests, it strongly suggests a serious quality defect requiring higher-level intervention. This mechanism effectively prevents the process from becoming an infinite loop, ensuring the orderly progress of construction.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating the uniformity of asphalt pavement construction, characterized in that: Includes the following steps: S1: Install several sensing units on the construction equipment, the sensing units including microphones and accelerometers; at the same time, install an image acquisition unit on the construction equipment; S2: Acquire acoustic and vibration data in the reference section to obtain the baseline energy ratio and its standard deviation; S3: During construction, acquire acoustic and vibration signals through the sensing unit at a preset sampling rate and divide the data into frames according to a preset window length; synchronously, acquire road surface images through the image acquisition unit and calculate the road surface texture uniformity index based on the images; S4: Perform spectral transformation on each frame signal to calculate the low-frequency band energy and high-frequency band energy; S5: Divide the low-frequency band energy by the sum of the high-frequency band energy and a very small positive number to obtain the energy ratio, and smooth the energy ratio of multiple consecutive frames to obtain the smoothed energy ratio; S6: Calculate a dynamic threshold based on the baseline energy ratio and its standard deviation; when the number of frames in which the smooth energy ratio continuously exceeds the dynamic threshold reaches a preset number, mark the position as a temporal continuity anomaly; S7: Within the set spatial neighborhood radius and time window, perform spatial connectivity filtering on time-continuous anomalies. If the number of time-continuous anomalies reaches the preset threshold, generate anomaly candidate regions and calculate the average energy ratio of the anomaly candidate regions. S8: Based on the average energy ratio of the abnormal candidate areas and the road surface texture uniformity index, calculate the comprehensive uniformity index, evaluate the road surface construction uniformity, and generate treatment suggestions accordingly. S9: Perform retesting according to the handling recommendations, with the number of retests not exceeding the preset limit; If the anomaly persists after retesting, a final report recommending destructive testing will be output.

2. The method for evaluating the uniformity of asphalt pavement construction as described in claim 1, characterized in that: The low-frequency band has a frequency range of 100Hz to 300Hz, and the high-frequency band has a frequency range of 300Hz to 1000Hz.

3. The method for evaluating the uniformity of asphalt pavement construction as described in claim 2, characterized in that: The sampling rate is 4kHz to 16kHz; the window length is 0.5s, and the window overlap is 50%.

4. The method for evaluating the uniformity of asphalt pavement construction as described in claim 3, characterized in that: The calculation method of the comprehensive uniformity index in step S8 is as follows: First, the average energy ratio is normalized and limited to between 0 and 1; then, the product of the preset fusion weight and the normalized energy ratio is subtracted from 1, and the result is multiplied by the road surface texture uniformity index to obtain the comprehensive uniformity index; the value range of the preset fusion weight is 0.1 to 0.

5.

5. The method for evaluating the uniformity of asphalt pavement construction as described in claim 4, characterized in that: The smoothing process employs either moving average or exponential smoothing, with the moving average window consisting of 3 frames.

6. The method for evaluating the uniformity of asphalt pavement construction as described in claim 5, characterized in that: The preset number of times for step S6 is 3, the preset number of times for step S7 is 2, and the preset maximum number of times for step S9 is 2.

7. The method for evaluating the uniformity of asphalt pavement construction as described in claim 6, characterized in that: In step S7, the spatial neighborhood radius is 0.5~2m, the time window is less than 5s, and the preset threshold number of time continuity anomalies is not less than 2.

8. The method for evaluating the uniformity of asphalt pavement construction as described in claim 7, characterized in that: It also includes the step of generating an evidence package, which includes at least the original acoustic waveform of the abnormal candidate region, the corresponding spectrogram, the vibration channel waveform, the energy ratio sequence, the location information, the timestamp, and the corresponding road surface image and texture uniformity index. The evidence package is then digitally hashed and stored in local and remote backups.

9. An asphalt pavement construction uniformity evaluation system, used to implement the method according to any one of claims 1 to 8, characterized in that, include: Several sensing units are installed on the construction equipment to collect acoustic and vibration signals; an image acquisition unit is installed on the construction equipment to collect road surface images; an edge processor is used to preprocess, perform spectrum analysis, energy calculation, and smooth energy ratio calculation on the collected acoustic and vibration signals, and to calculate the road surface texture uniformity index based on the road surface images; a judgment and evaluation module is used to perform temporal continuous anomaly judgment and spatial connectivity filtering based on dynamic thresholds to generate anomaly candidate areas, and to perform the calculation of the comprehensive uniformity index to evaluate road surface uniformity and trigger handling suggestions.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1 to 8.

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