Comprehensive evaluation method and system for skid resistance of asphalt pavement

Abstract

The invention relates to the technical field of road engineering, in particular to a comprehensive evaluation method and system for the skid resistance of an asphalt pavement, and the method comprises the steps: obtaining MTD and SFC data of the asphalt pavement, and carrying out the preprocessing; monitoring the temperature and humidity conditions of the asphalt pavement in real time, and constructing a temperature and humidity correction model for correction; analyzing and extracting microscopic and macroscopic texture features of the pavement on the basis of the obtained MTD and SFC data of the asphalt pavement; and based on the corrected MTD and SFC data and in combination with microscopic and macroscopic texture features, calculating a comprehensive evaluation index of the anti-skid performance of the road surface. By means of the method, the problems that in an existing method, a single index is limited, temperature and humidity influences are not corrected, and comprehensive texture feature analysis is lacked are effectively solved, and the accuracy and comprehensiveness of asphalt pavement skid resistance evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering, and in particular to a method and system for comprehensively evaluating the anti-skid performance of an asphalt pavement. Background Art

[0002] As the mileage of highways in my country increases rapidly, asphalt pavement has become a commonly used structural form in highway construction. As the number of vehicles increases and the service life of the pavement increases, the anti-skid performance of the asphalt pavement gradually decreases, reducing the safety of highway driving. As the aging of highways intensifies, the lack of anti-skid performance of asphalt pavement has become a pain point for many highway maintenance units.

[0003] At present, the detection methods of the anti-skid performance of asphalt pavement include the pendulum instrument method, the sand spreading method, the vehicle-mounted laser structure depth meter and the single-wheel / double-wheel lateral force coefficient test system. Among them, the vehicle-mounted laser structure depth meter and the lateral force coefficient test system can realize continuous and rapid detection, and the commonly used indicators are the structure depth (MTD) and the lateral force coefficient (SFC). MTD mainly reflects the macro texture of the pavement, while SFC is related to the micro texture. However, in actual detection, the phenomenon of large MTD and small SFC is more common, indicating that the correlation between the two is low. Since the anti-skid performance of asphalt pavement is affected by both macro and micro textures, a single indicator is difficult to comprehensively and scientifically evaluate the anti-skid performance of the pavement. Therefore, a single evaluation indicator is difficult to comprehensively evaluate the macro texture and micro texture of the road surface, and it is difficult to achieve a scientific evaluation of the anti-skid performance of asphalt pavement. Summary of the invention

[0004] The present invention provides a method and system for comprehensively evaluating the anti-skid performance of an asphalt pavement, thereby effectively solving the problems pointed out in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A comprehensive evaluation method for the anti-skid performance of an asphalt pavement, comprising:

[0007] Obtain asphalt pavement MTD and SFC data and perform preprocessing;

[0008] Real-time monitoring of the temperature and humidity of the asphalt pavement, and building a temperature and humidity correction model for correction;

[0009] Based on the acquired MTD and SFC data of the asphalt pavement, analyzing and extracting the microscopic and macroscopic texture features of the pavement;

[0010] Based on the corrected MTD and SFC data and combined with the micro- and macro-texture characteristics, the comprehensive evaluation index of the pavement anti-skid performance was calculated to obtain the comprehensive evaluation results.

[0011] Furthermore, the calculation formula of the comprehensive evaluation index is:

[0012] RSP=10.1640×exp(0.00035×v×SFC corrected )+9.9679×MTD corrected -21.7772,

[0013] Among them, SFC corrected and MTD corrected are the structural depths of the lateral force coefficient after correction for temperature and humidity, and v is the vehicle speed.

[0014] Furthermore, the preprocessing includes:

[0015] Performing sliding average processing on the MTD and SFC data of the asphalt pavement;

[0016] Based on the 3σ principle, outliers in the MTD and SFC data of the asphalt pavement are eliminated.

[0017] Furthermore, the temperature and humidity of the asphalt pavement are monitored in real time, and a temperature and humidity correction model is constructed for correction, including:

[0018] Obtain real-time temperature and humidity data of the environment where the asphalt pavement is located;

[0019] Based on data fusion technology, the real-time temperature and humidity data are aligned and fused with the MTD and SFC data of the asphalt pavement in time and space;

[0020] Based on historical test data, the impact of temperature changes on SFC and MTD was analyzed to build a temperature correction model. Based on the impact of humidity on SFC, a slip correction model was built.

[0021] Combining real-time temperature and humidity data with historical data, a dynamic update mechanism is used to optimize the temperature correction coefficient and slippery correction coefficient in the correction model.

[0022] Furthermore, the mathematical relationship of the temperature correction coefficient is determined by linear regression, and the formula is:

[0023] SFC corrected =SFC×(1+k t ×(T-T0))

[0024] MTD corrected =MTD×(1+k m ×(T-T0))

[0025] Among them, SFC and MTD are the uncorrected original detection values, k t and k mare temperature correction coefficients, which are used for correction of SFC and MTD respectively. T is the ambient temperature monitored in real time, and T0 is the reference temperature.

[0026] Furthermore, the mathematical relationship of the slipperiness correction coefficient is:

[0027] SFC corrected =SFC dry ×(1-h w ×H)

[0028] Among them, SFC dry is the SFC reference value under dry conditions, h w is the slippery correction coefficient, and H is the slippery degree percentage monitored in real time. Further, the determination of the temperature correction coefficient includes:

[0029] Select multiple different temperature conditions, repeat the measurement of the same road section multiple times under each temperature condition, and record the MTD and SFC data under each temperature condition;

[0030] The measured data under each temperature condition is compared with the reference temperature, the influence of temperature change on MTD and SFC is calculated, and the correction coefficient is determined by regression analysis. The regression formula is: Among them, ΔSFC and ΔMTD represent the changes of SFC and MTD caused by temperature change, respectively, and ΔT is the temperature deviation;

[0031] The correction coefficient is used to correct the data under each temperature condition and compared with the data under the reference temperature to obtain the verification result. Furthermore, based on the obtained MTD and SFC data of the asphalt pavement, the microscopic and macroscopic texture characteristics of the pavement are analyzed and extracted, including:

[0032] Based on MTD data, the macroscopic pavement texture is analyzed, the pavement structure depth is modeled, the macroscopic texture characteristics of the asphalt pavement are extracted, and the average structure depth, peak-to-valley difference and texture depth distribution of the pavement are calculated;

[0033] Perform frequency domain analysis on SFC data, extract high-frequency components in SFC data, analyze the frequency distribution and power spectrum density of road surface micro-texture characteristics, and characterize road surface micro-roughness;

[0034] The extracted macro and micro texture features are comprehensively analyzed, and the multi-scale texture characteristics of the pavement are quantitatively evaluated by combining MTD and SFC data, and a correlation model between pavement texture and anti-skid performance is established.

[0035] A comprehensive evaluation system for the anti-skid performance of an asphalt pavement, the system comprising:

[0036] Data acquisition module, which acquires MTD and SFC data of asphalt pavement and performs preprocessing;

[0037] A data correction module monitors the temperature and humidity of the asphalt pavement in real time and constructs a temperature and humidity correction model for correction;

[0038] A feature analysis module, based on the acquired MTD and SFC data of the asphalt pavement, analyzes and extracts the microscopic and macroscopic texture features of the pavement;

[0039] The index calculation module calculates the comprehensive evaluation index of the pavement's anti-skid performance based on the corrected MTD and SFC data and combines the micro- and macro-texture characteristics to obtain a comprehensive evaluation result.

[0040] Furthermore, the feature analysis module includes:

[0041] The macro analysis unit analyzes the macroscopic pavement texture based on the MTD data, models the pavement's structural depth, extracts the macroscopic texture features of the asphalt pavement, and calculates the average structural depth, peak-to-valley difference, and texture depth distribution of the pavement.

[0042] The micro-analysis unit performs frequency domain analysis on the SFC data, extracts the high-frequency components in the SFC data, analyzes the frequency distribution and power spectrum density of the micro-texture characteristics of the road surface, and characterizes the micro-roughness of the road surface;

[0043] The comprehensive analysis unit comprehensively analyzes the extracted macro and micro texture features, quantitatively evaluates the multi-scale texture features of the road surface by combining MTD and SFC data, and establishes a correlation model between road surface texture and anti-skid performance.

[0044] The technical solution of the present invention can achieve the following technical effects:

[0045] It effectively solves the problems of single indicator limitation, uncorrected temperature and humidity influence, and lack of comprehensive texture feature analysis in existing methods, and improves the accuracy and comprehensiveness of the evaluation of the anti-skid performance of asphalt pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a flow chart of a comprehensive evaluation method for the anti-skid performance of asphalt pavement;

[0048] Figure 2 is a schematic diagram of the process of the pretreatment step;

[0049] Figure 3 A schematic diagram of the process of making corrections for building a temperature and humidity correction model;

[0050] Figure 4 Schematic diagram of the process for analyzing and extracting pavement micro- and macro-texture features. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0053] Embodiment 1

[0054] like Figure 1 As shown, the present invention provides a method for comprehensively evaluating the anti-skid performance of an asphalt pavement, the method comprising:

[0055] S1: Obtain MTD and SFC data of asphalt pavement and perform preprocessing;

[0056] Specifically, MTD, or mean texture depth, is an important indicator for measuring the macro-texture of the road surface. It reflects the roughness and unevenness of the road surface and is usually measured by sand-laying method or laser texture depth meter. A larger MTD value usually indicates that the road surface is relatively rough, with good drainage performance and strong anti-skid performance. MTD is mainly related to the macro-texture of the road surface, and plays an important role in the contact and drainage between the wheel and the road surface, thereby affecting the vehicle's anti-skid ability during braking and turning; SFC, or lateral force coefficient, is an important indicator reflecting the micro-texture of the road surface. It indicates the lateral friction force generated by the road surface on the tire at a certain sideslip speed of the vehicle. It is usually measured using a single-wheel or double-wheel lateral force coefficient vehicle. The higher the SFC value, the greater the friction between the road surface and the tire, and the better the anti-skid performance. SFC is mainly related to the micro-texture of the road surface. The micro-texture can enhance the friction between the tire and the road surface, improve the vehicle's grip, and thus enhance the anti-skid ability.

[0057] S2: Real-time monitoring of the temperature and humidity of the asphalt pavement, and building a temperature and humidity correction model for correction;

[0058] Specifically, changes in temperature will cause changes in the softness or hardness of asphalt materials, thereby affecting the measurement results of the lateral force coefficient (SFC); humidity directly affects the slippery state of the road surface and changes the friction. By real-time monitoring of temperature and humidity and using the correction model to correct the data, the errors caused by changes in environmental factors can be eliminated, making the data under different conditions comparable. The corrected data can more accurately reflect the true anti-skid performance of the road surface, improve the scientific nature of the comprehensive evaluation index of anti-skid performance, and ensure the consistency of test results under different seasons and climate conditions.

[0059] S3: Analyze and extract the microscopic and macroscopic texture features of the pavement based on the acquired MTD and SFC data of the asphalt pavement;

[0060] Specifically, the macro texture (described by MTD) mainly affects the drainage and large-scale contact characteristics of the road surface, while the micro texture (reflected by SFC) determines the friction between the tire and the road surface. Through methods such as three-dimensional reconstruction and spectrum analysis, in-depth analysis of the macro texture and frequency feature extraction of the micro texture can be performed to fully understand the roughness and contact performance of the road surface. The extracted texture features provide data support for the comprehensive evaluation of anti-skid performance, ensuring that the comprehensive evaluation is more scientific and comprehensive.

[0061] S4: Based on the corrected MTD and SFC data, and combined with the micro- and macro-texture characteristics, the comprehensive evaluation index of the pavement anti-skid performance is calculated to obtain the comprehensive evaluation results.

[0062] Specifically, a scientific comprehensive evaluation model for anti-skid performance is constructed by combining the corrected MTD and SFC data with the micro- and macro-texture characteristics of the pavement, thereby comprehensively reflecting the anti-skid ability of the pavement. This can eliminate the influence of environmental conditions, improve the comparability and consistency of data under different climatic conditions, overcome the limitations of a single indicator, quantify the anti-skid performance of the pavement, and provide a reliable basis for road safety management. The present invention effectively solves the problems of single indicator limitations, uncorrected temperature and humidity effects, and lack of comprehensive texture feature analysis in existing methods, thereby improving the accuracy and comprehensiveness of the anti-skid performance evaluation of asphalt pavements.

[0063] As a preferred embodiment of the above embodiment, the calculation formula of the comprehensive evaluation index is:

[0064] RSP=10.1640×exp(0.00035×v×SFC corrected )+9.9679×MTD corrected -21.7772,

[0065] Among them, SFC corrected and MTD correctedare the structural depths of the lateral force coefficients after temperature and humidity correction, and v is the vehicle speed. Specifically, SFC corrected It represents the lateral force coefficient after temperature and humidity correction, reflecting the micro-texture characteristics of the road surface; MTD corrected It represents the corrected structural depth, reflecting the macro texture characteristics of the road surface; the first exponential term in the formula 10.1640×exp(0.00035×v×SFC corrected ) reflects the nonlinear effect between SFC and vehicle speed, indicating the contribution of SFC to the anti-skid performance of the road surface under different vehicle speed conditions. The exponential function is introduced to better describe the influence of vehicle speed on friction; the second term 9.9679×MTD corrected It represents the direct effect of structural depth on the anti-skid performance of the pavement, and MTD reflects the macro-roughness of the pavement and its contribution to the drainage performance. Finally, the constant term -21.7772 is used for benchmark calibration to ensure the rationality and comparability of the calculation results of the formula.

[0066] Overall, the formula scientifically quantifies the anti-skid performance of the road surface by comprehensively considering the corrected SFC and MTD data and their relationship with the vehicle speed. Each item in the formula is obtained through experimental fitting, which can comprehensively reflect the influence of the micro- and macro-characteristics of the road surface on the anti-skid ability. By setting appropriate evaluation standards (such as RSP≥15), the quality of the anti-skid performance of the road surface can be intuitively judged, providing a quantitative basis for road maintenance and safety management.

[0067] As a preferred embodiment of the above, Figure 2 As shown, the preprocessing includes:

[0068] A10: Perform sliding average processing on MTD and SFC data of asphalt pavement;

[0069] A20: Based on the 3σ principle, outliers in the MTD and SFC data of asphalt pavement are eliminated.

[0070] Specifically, the MTD (tectonic depth) and SFC (lateral force coefficient) data of the asphalt pavement are smoothed by the sliding average method. The purpose of the sliding average processing is to eliminate short-term fluctuations and noise in the data and improve the stability and continuity of the data. The MTD and SFC data are calculated by sliding average with a certain window size (for example, 5 or 7 consecutive sampling points) to obtain the smoothed data sequence. In this way, the random errors of the measuring equipment and the data fluctuations caused by the local unevenness of the road surface can be effectively reduced; for the smoothed MTD and SFC data, outliers are identified and eliminated according to the 3σ principle (i.e., 3 times the standard deviation range in the normal distribution). Specifically, the mean and standard deviation of the smoothed data sequence are first calculated, and then each data point is determined whether it exceeds the range of 3 times the standard deviation. If the deviation of a data point exceeds this range, it is regarded as an outlier and eliminated. The purpose of this step is to eliminate abnormal data caused by equipment failure, sudden environmental changes or extreme road conditions to ensure the accuracy of subsequent calculations.

[0071] As a preferred embodiment of the above, Figure 3 As shown, step S2, real-time monitoring of the temperature and humidity of the asphalt pavement, and building a temperature and humidity correction model for correction, including:

[0072] S21: Acquire real-time temperature and humidity data of the environment where the asphalt road surface is located;

[0073] S22: Based on data fusion technology, the real-time temperature and humidity data are aligned and fused with the MTD and SFC data of the asphalt pavement in time and space;

[0074] S23: Based on historical test data, the impact of temperature changes on SFC and MTD is analyzed to build a temperature correction model. Based on the impact of humidity on SFC, a slippery correction model is built.

[0075] S24: Combining real-time temperature and humidity data with historical data, a dynamic update mechanism is used to optimize the temperature correction coefficient and slippery correction coefficient in the correction model.

[0076] Specifically, firstly, the temperature and humidity information of the road environment is monitored in real time through the multi-source sensors arranged on the detection vehicle, and the instantaneous temperature and humidity data of the detection point are obtained by using high-precision temperature sensors and humidity sensors, and combined with the location records of the detection equipment to ensure the accuracy of the subsequent correction model; then, the real-time monitored temperature and humidity data are aligned with the MTD and SFC data of the asphalt pavement using data fusion technology, and each temperature and humidity data is synchronized with the corresponding MTD and SFC data through timestamps and high-precision GPS location information, while achieving accurate spatial correspondence of the detection point data, which can eliminate the deviation caused by differences in sensor sampling frequency or position information errors, thereby ensuring data consistency; next, through the analysis Analyze a large amount of historical experimental data, study the changing laws of SFC and MTD under different temperature conditions, use regression analysis or other statistical methods to build a temperature correction model, and determine the temperature correction coefficient. For the slippery correction model, use the experimental data of the influence of humidity on SFC to establish a slippery correction coefficient model to adjust the SFC data under slippery conditions. This model can reflect the influence of different temperature and humidity conditions on the pavement anti-skid performance data; finally, during use, the temperature and humidity data obtained in real time are compared with the historical data, and the temperature correction coefficient and slippery correction coefficient are adjusted through a dynamic update mechanism, so that the correction model can adapt to seasonal changes and regional differences, and the correction model is continuously optimized through machine learning algorithms or data-driven methods to improve the accuracy and adaptability of the correction.

[0077] As a preferred embodiment of the above, the mathematical relationship of the temperature correction coefficient is determined by linear regression, and the formula is:

[0078] SFC corrected =SFC×(1+k t ×(T-T0)),

[0079] MTD corrected =MTD×(1+k m ×(T-T0)),

[0080] Among them, SFC and MTD are the uncorrected original detection values, k t and k m are temperature correction coefficients, which are used for correction of SFC and MTD respectively. T is the ambient temperature monitored in real time, and T0 is the reference temperature.

[0081] Specifically, SFC corrected By the correction term (1+k t ×(T-T0)) to adjust the SFC value. The purpose of this formula is to correct the measurement error of the lateral force coefficient caused by temperature deviation so that the SFC data under different temperature conditions remain consistent. Temperature correction coefficient k tDetermined by experiment, used to reflect the degree of influence of temperature change on SFC; MTD corrected It is used to correct the influence of ambient temperature change on the MTD data. The correction term 1+k in the formula is m ×(T-T0) represents the adjustment of MTD by temperature deviation, and the temperature correction coefficient k m Determined through experimental analysis, it reflects the degree of influence of temperature change on MTD. When the ambient temperature is higher than the reference temperature, the MTD data may be amplified due to the thermal expansion of the material, and vice versa. Through correction, the influence of temperature change on the MTD measurement results can be eliminated to ensure the comparability of the data. The overall goal of these two formulas is to correct the SFC and MTD data by considering the influence of temperature changes, so that the measurement data under different ambient temperature conditions are more accurate and consistent, thereby providing a reliable data basis for the scientific evaluation of the anti-skid performance of the road surface.

[0082] As a preferred embodiment of the above embodiment, the mathematical relationship of the slipperiness correction coefficient is:

[0083] SFC corrected =SFC dry ×(1-h w ×H)

[0084] Among them, SFC dry is the SFC reference value under dry conditions, h w is the slippery correction coefficient, and H is the percentage of slippery degree monitored in real time. Specifically, the formula considers the slippery degree H under slippery conditions and the slippery correction coefficient h w , the SFC data is corrected so that it can reflect the actual anti-skid performance of the road surface in a slippery state. The correction term 1-h in the formula w ×H indicates that the higher the slipperiness, the greater the negative impact on the road surface's anti-skid performance. The corrected SFC data can more realistically reflect the change in friction on a slippery road surface, thereby improving the accuracy of the evaluation. Through this formula, after actually monitoring the slipperiness percentage H under slippery conditions, the slippery correction factor h obtained based on experiments can be used to calculate the slippery correction factor h. w , the SFC reference value under dry conditions is corrected to the SFC value under slippery conditions. This correction method ensures the consistency and comparability of the data measured under different slippery conditions, and provides a reliable data basis for further evaluation of the road surface anti-skid performance. In order to accurately correct the impact of slippery conditions on the road lateral force coefficient (SFC), it is necessary to determine the slippery correction factor h through experimental measurement. wFirst, different slippery conditions (such as dry, slightly wet, moderately slippery, etc.) are set on a representative asphalt road surface, and a humidity sensor is used to record the slippery percentage H. Under each slippery condition, the lateral force coefficient (SFC) is measured multiple times, and the SFC value under dry conditions is selected as the benchmark; then, the slippery correction factor h is calculated by analyzing the relationship between SFC and slippery degree under each slippery condition. w , whose formula is SFC dry is the lateral force coefficient under dry conditions, SFC wet is the lateral force coefficient under slippery conditions; finally, the optimal correction coefficient is determined through regression analysis and its accuracy is verified. This coefficient can correct the effect of slipperiness on SFC and ensure that the SFC data obtained under slippery conditions accurately reflects the actual anti-skid performance.

[0085] As a preferred embodiment of the above, the determination of the temperature correction coefficient includes:

[0086] B10: Select multiple different temperature conditions, repeat the measurement of the same road section multiple times under each temperature condition, and record the MTD and SFC data under each temperature condition;

[0087] B20: Compare the measured data under each temperature condition with the reference temperature, calculate the impact of temperature change on MTD and SFC, and determine the correction coefficient through regression analysis. The regression formula is: Among them, ΔSFC and ΔMTD represent the changes of SFC and MTD caused by temperature change, respectively, and ΔT is the temperature deviation;

[0088] B30: Use the correction coefficient to correct the data under each temperature condition and compare it with the data under the reference temperature to obtain the verification result.

[0089] Specifically, select a number of different temperature conditions (such as low temperature, medium temperature and high temperature conditions), and perform repeated measurements on the same road section under these temperature conditions. Use a vehicle-mounted laser structural depth meter and a single-wheel lateral force coefficient test vehicle to obtain the MTD (structural depth) and SFC (lateral force coefficient) data under each temperature condition. Each temperature condition should be measured at least three times to reduce the impact of random errors. Through this experimental setup, the changes in the anti-skid performance of the road surface under different temperature conditions can be fully observed; the MTD and SFC data under each temperature condition are compared with the data under the reference temperature (usually 20°C) to analyze the impact of temperature changes on MTD and SFC. Calculate the changes (ΔSFC and ΔMTD) compared to the reference temperature under each temperature condition, and calculate the correction coefficient based on the temperature deviation ΔT (i.e., the difference between the detection temperature and the reference temperature). Use the correction coefficient k obtained by regression analysis.t and k m , correct the MTD and SFC data under each temperature condition. After correction, compare the data under each temperature condition with the data under the reference temperature to check whether the corrected data is close to the data under the reference temperature to verify the accuracy of the correction coefficient. If the corrected data is consistent with the data under the reference temperature, it means that the correction coefficient is reasonable and effective; if the difference is large, the correction coefficient needs to be further adjusted.

[0090] As a preferred embodiment of the above, Figure 4 As shown, step S3, based on the acquired MTD and SFC data of the asphalt pavement, analyze and extract the microscopic and macroscopic texture features of the pavement, including:

[0091] S31: Analyze the macroscopic pavement texture based on MTD data, model the pavement's structural depth, extract the macroscopic texture features of the asphalt pavement, and calculate the average structural depth, peak-to-valley difference, and texture depth distribution of the pavement;

[0092] S32: Perform frequency domain analysis on the SFC data, extract the high-frequency components in the SFC data, analyze the frequency distribution and power spectrum density of the micro-texture characteristics of the road surface, and characterize the micro-roughness of the road surface;

[0093] S33: The extracted macro and micro texture features are comprehensively analyzed, and the multi-scale texture characteristics of the road surface are quantitatively evaluated by combining MTD and SFC data to establish a correlation model between road surface texture and anti-skid performance.

[0094] Specifically, firstly, the pavement structure depth is three-dimensionally modeled and reconstructed through data modeling technology, the macro-characteristic parameters of the pavement surface are obtained, and the average structure depth, peak-to-valley difference and texture depth distribution of the pavement are calculated. The average structure depth reflects the roughness of the pavement surface, the peak-to-valley difference shows the difference in pavement undulations, and the texture depth distribution can quantify the macro-texture changes at different locations of the pavement. These macro-texture features directly affect the drainage of the vehicle and the contact characteristics between the tire and the pavement. Then, the acquired SFC data is subjected to frequency domain analysis, and spectrum analysis techniques such as Fourier transform, short-time Fourier transform or wavelet transform are used to convert the SFC data from the time domain to the frequency domain. Through frequency domain analysis, the high-frequency components in the SFC data can be extracted, thereby revealing The frequency characteristics and energy distribution of the pavement microtexture are shown. The high-frequency components are related to the details of the pavement microroughness, and the power spectral density can represent the energy distribution of each frequency component. By analyzing these characteristics, the detailed changes in the pavement microtexture and the surface roughness can be characterized. Finally, the macro-texture features and micro-texture features extracted in the first two steps are comprehensively analyzed. The specific method is to combine the characteristic parameters of the MTD data and the SFC data to evaluate the multi-scale texture characteristics of the pavement. By quantifying the texture characteristics of the pavement at different scales, an overall evaluation model including macro- and micro-texture characteristics is constructed. Furthermore, through statistical analysis or machine learning methods, a correlation model between pavement texture characteristics and anti-skid performance is established to reveal the specific influence of texture characteristics on the anti-skid performance of the pavement.

[0095] Embodiment 2

[0096] Based on the same inventive concept as the method for comprehensively evaluating the anti-skid performance of an asphalt pavement in the aforementioned embodiment, the present invention further provides a comprehensive evaluation system for the anti-skid performance of an asphalt pavement, the system comprising:

[0097] Data acquisition module, which acquires MTD and SFC data of asphalt pavement and performs preprocessing;

[0098] The data correction module monitors the temperature and humidity of the asphalt pavement in real time, and builds a temperature and humidity correction model for correction;

[0099] Feature analysis module, which analyzes and extracts the microscopic and macroscopic texture features of the pavement based on the acquired MTD and SFC data of the asphalt pavement;

[0100] The index calculation module calculates the comprehensive evaluation index of the pavement's anti-skid performance based on the corrected MTD and SFC data and combines the micro- and macro-texture characteristics to obtain a comprehensive evaluation result.

[0101] The above-mentioned evaluation system in the present invention can effectively realize the comprehensive evaluation method of the anti-skid performance of asphalt pavement, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0102] As a preferred embodiment of the above, the feature analysis module includes:

[0103] The macro analysis unit analyzes the macroscopic pavement texture based on the MTD data, models the pavement's structural depth, extracts the macroscopic texture features of the asphalt pavement, and calculates the average structural depth, peak-to-valley difference, and texture depth distribution of the pavement.

[0104] The micro-analysis unit performs frequency domain analysis on the SFC data, extracts the high-frequency components in the SFC data, analyzes the frequency distribution and power spectrum density of the micro-texture characteristics of the road surface, and characterizes the micro-roughness of the road surface;

[0105] The comprehensive analysis unit comprehensively analyzes the extracted macro and micro texture features, quantitatively evaluates the multi-scale texture features of the road surface by combining MTD and SFC data, and establishes a correlation model between road surface texture and anti-skid performance.

[0106] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0107] Although the present application has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined herein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application.

[0108] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these modifications and variations.

Claims

1. A comprehensive evaluation method for the anti-skid performance of an asphalt pavement, characterized in that: include: Obtain asphalt pavement MTD and SFC data and perform preprocessing; Real-time monitoring of the temperature and humidity of the asphalt pavement, and building a temperature and humidity correction model for correction; Based on the acquired MTD and SFC data of the asphalt pavement, analyzing and extracting the microscopic and macroscopic texture features of the pavement; Based on the corrected MTD and SFC data and combined with the micro- and macro-texture characteristics, the comprehensive evaluation index of the pavement anti-skid performance was calculated to obtain the comprehensive evaluation results.

2. The method for comprehensive evaluation of the anti-skid performance of asphalt pavement according to claim 1, characterized in that: The calculation formula of the comprehensive evaluation index is: RSP=10.1640×exp(0.00035×v×SFC corrected )+9.9679×MTD corrected -21.7772, Among them, SFC corrected and MTD corrected are the structural depths of the lateral force coefficient after correction for temperature and humidity, and v is the vehicle speed.

3. The method for comprehensive evaluation of the anti-skid performance of asphalt pavement according to claim 1, characterized in that: The pre-processing comprises: Performing sliding average processing on the MTD and SFC data of the asphalt pavement; Based on 3 σ In principle, outliers in the MTD and SFC data of the asphalt pavement are eliminated.

4. The method for comprehensive evaluation of the anti-skid performance of asphalt pavement according to claim 1, characterized in that: Real-time monitoring of the temperature and humidity of the asphalt pavement, and building a temperature and humidity correction model for correction, including: Obtain real-time temperature and humidity data of the environment where the asphalt pavement is located; Based on data fusion technology, the real-time temperature and humidity data are aligned and fused with the MTD and SFC data of the asphalt pavement in time and space; Based on historical test data, the impact of temperature changes on SFC and MTD was analyzed to build a temperature correction model. Based on the impact of humidity on SFC, a slip correction model was built. Combining real-time temperature and humidity data with historical data, a dynamic update mechanism is used to optimize the temperature correction coefficient and slippery correction coefficient in the correction model.

5. The method for comprehensive evaluation of the anti-skid performance of asphalt pavement according to claim 4, characterized in that: Through linear regression, the mathematical relationship of the temperature correction coefficient is determined as follows: SFC corrected =SFC×(1+k t ×(T-T0)) MTD corrected =MTD×(1+k m ×(T-T0)) Among them, SFC and MTD are the uncorrected original detection values, k t and k m are temperature correction coefficients, which are used for correction of SFC and MTD respectively. T is the ambient temperature monitored in real time, and T0 is the reference temperature.

6. The method for comprehensive evaluation of the anti-skid performance of asphalt pavement according to claim 4, characterized in that: The mathematical relationship of the slipperiness correction coefficient is: SFC corrected =SFC dry ×(1-h w ×H) Among them, SFC dry is the SFC reference value under dry conditions, h w is the slippery correction coefficient, and H is the slippery degree percentage monitored in real time.

7. The method for comprehensive evaluation of the anti-skid performance of asphalt pavement according to claim 5, characterized in that: The determination of the temperature correction coefficient includes: Select multiple different temperature conditions, repeat the measurement of the same road section multiple times under each temperature condition, and record the MTD and SFC data under each temperature condition; The measured data under each temperature condition is compared with the reference temperature, the influence of temperature change on MTD and SFC is calculated, and the correction coefficient is determined by regression analysis. The regression formula is: Among them, ΔSFC and ΔMTD represent the changes of SFC and MTD caused by temperature change, respectively, and ΔT is the temperature deviation; The data under each temperature condition are corrected using the correction coefficient and compared with the data under the reference temperature to obtain the verification results.

8. The method for comprehensive evaluation of the anti-skid performance of asphalt pavement according to claim 1, characterized in that: Based on the obtained MTD and SFC data of the asphalt pavement, the microscopic and macroscopic texture features of the pavement are analyzed and extracted, including: Based on MTD data, the macroscopic pavement texture is analyzed, the pavement structure depth is modeled, the macroscopic texture characteristics of the asphalt pavement are extracted, and the average structure depth, peak-to-valley difference and texture depth distribution of the pavement are calculated; Perform frequency domain analysis on SFC data, extract high-frequency components in SFC data, analyze the frequency distribution and power spectrum density of road surface micro-texture characteristics, and characterize road surface micro-roughness; The extracted macro and micro texture features are comprehensively analyzed, and the multi-scale texture characteristics of the pavement are quantitatively evaluated by combining MTD and SFC data, and a correlation model between pavement texture and anti-skid performance is established.

9. A comprehensive evaluation system for the anti-skid performance of asphalt pavement, characterized in that: The system comprises: Data acquisition module, which acquires MTD and SFC data of asphalt pavement and performs preprocessing; A data correction module monitors the temperature and humidity of the asphalt pavement in real time and constructs a temperature and humidity correction model for correction; A feature analysis module, based on the acquired MTD and SFC data of the asphalt pavement, analyzes and extracts the microscopic and macroscopic texture features of the pavement; The index calculation module calculates the comprehensive evaluation index of the pavement's anti-skid performance based on the corrected MTD and SFC data and combines the micro- and macro-texture characteristics to obtain a comprehensive evaluation result.

10. The asphalt pavement anti-skid performance comprehensive evaluation system according to claim 9, characterized in that: The feature analysis module includes: a macro analysis unit, which performs macroscopic pavement texture analysis based on MTD data, models the pavement's structural depth, extracts macroscopic texture features of the asphalt pavement, and calculates the average structural depth, peak-to-valley difference, and texture depth distribution of the pavement; The micro-analysis unit performs frequency domain analysis on the SFC data, extracts the high-frequency components in the SFC data, analyzes the frequency distribution and power spectrum density of the micro-texture characteristics of the road surface, and characterizes the micro-roughness of the road surface; The comprehensive analysis unit comprehensively analyzes the extracted macro and micro texture features, quantitatively evaluates the multi-scale texture features of the road surface by combining MTD and SFC data, and establishes a correlation model between road surface texture and anti-skid performance.

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