A method for evaluating the skid resistance of asphalt pavement
The surface feature data of asphalt pavement is obtained through high-precision scanning equipment, combined with self-organization mapping and box dimension analysis data, and constructed a slip resistance evaluation model, which solves the problem that traditional methods are difficult to accurately evaluate the anti-slip performance of the pavement, and achieves a more scientific and reliable slip resistance evaluation.
Patent Information
- Application Number
- CN202411770414.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-04
- Publication Date
- 2025-04-01
AI Technical Summary
Traditional asphalt pavement anti-slip performance evaluation methods are difficult to accurately evaluate the anti-slip performance of the pavement, and cannot fully consider the microstructure characteristics and long-term use performance of the pavement, and the test results are easily affected by environmental factors.
High-precision scanning equipment is used to obtain the surface feature data of asphalt pavement, and by constructing the surface feature matrix and performing standardization, data is analyzed using self-organizing mapping algorithm and box dimension method, fractal dimensions and scale parameters are calculated, and anti-slip evaluation model is constructed based on these parameters, and the anti-slip value of the pavement is calculated and compared with the preset threshold.
It improves the accuracy and reliability of the evaluation of slip resistance, scientifically reflects the relationship between the microstructure of the road surface and the slip resistance, provides a clear basis for road surface maintenance and improvement, and can promptly determine whether the slip resistance of the road surface is qualified.
Smart Images
Figure CN119740007A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of anti-skid property judgment of asphalt pavement, and specifically relates to an anti-skid property evaluation method of asphalt pavement. Background Art
[0002] The anti-skid performance of asphalt pavement is one of the important factors to ensure road driving safety. Good anti-skid performance can effectively reduce the skidding phenomenon of vehicles during driving and reduce the incidence of traffic accidents. However, the traditional anti-skid evaluation method of asphalt pavement has some limitations and it is difficult to accurately evaluate the anti-skid performance of the pavement.
[0003] Traditional anti-skid evaluation methods mainly include friction coefficient test and texture depth measurement. Although these methods can reflect the anti-skid performance of the road surface to a certain extent, they can only provide local and instantaneous information and cannot fully consider the microstructural characteristics and long-term performance of the road surface. In addition, the test results of these methods are easily affected by test conditions and environmental factors, such as test speed, temperature, humidity, etc., which leads to certain limitations on the accuracy and reliability of the test results.
[0004] With the continuous development of road engineering technology, people have higher and higher requirements for the evaluation method of the skid resistance of asphalt pavement. In order to more accurately evaluate the skid resistance of the pavement, an evaluation method that can comprehensively consider the microstructural characteristics, long-term performance and environmental factors of the pavement is needed. In addition, with the development of intelligent transportation systems, real-time monitoring and evaluation of the skid resistance of pavement has become increasingly important, and traditional evaluation methods are difficult to meet this demand. Summary of the invention
[0005] In view of the shortcomings of the prior art, the present invention proposes a method for evaluating the skid resistance of asphalt pavement, which uses high-precision scanning equipment to obtain surface feature data. By constructing a surface feature matrix and performing standardization, the self-organizing map algorithm (SOM) and box dimension method are used to analyze the data, and the fractal dimension and scale parameters are calculated. Based on these parameters, a skid resistance evaluation model is constructed, and the actual data is input to calculate the pavement skid resistance value, which is compared with the preset threshold to evaluate the skid resistance of the pavement. This method effectively improves the accuracy of skid resistance evaluation, scientifically reflects the relationship between pavement microstructure and skid resistance, provides a clear basis for pavement maintenance and improvement, and comprehensively considers multiple factors, and the data processing method is scientific and reasonable.
[0006] The technical solution of the present invention is as follows:
[0007] A method for evaluating the skid resistance of an asphalt pavement, comprising:
[0008] Placing a number of molds on the asphalt pavement and pouring a silicone layer into the molds; peeling off the silicone layer to form a number of asphalt pavement simulation models, scanning the asphalt pavement simulation models, and obtaining surface characteristic data of the asphalt pavement to be evaluated;
[0009] A surface feature matrix is constructed based on the surface feature data of the asphalt pavement to be evaluated, and the surface feature matrix is subjected to homogeneous standardization to obtain a standardized surface feature matrix.
[0010] The standardized surface feature matrix is passed through self-organizing mapping to obtain the optimal surface feature row vector, and the optimal surface feature row vector is analyzed through the box dimension method to obtain the fractal dimension and scale parameter;
[0011] The skid resistance evaluation model defined herein obtains the pavement skid resistance value by inputting fractal dimension and scale parameter; sets the skid resistance threshold, and evaluates the skid resistance of the asphalt pavement by comparing the pavement skid resistance value with the skid resistance threshold.
[0012] The molds are placed in the selected road surface area in a horizontal and vertical array;
[0013] A 3D laser scanner is used to scan the asphalt pavement simulation model to obtain the surface characteristic data of the asphalt pavement.
[0014] The surface characteristic data of the asphalt pavement include: geometric characteristics and morphological characteristics;
[0015] Among them, the geometric features include: texture depth, texture height, texture spacing, texture width, and texture density; the morphological features include: porosity and connectivity.
[0016] Each row in the surface feature matrix represents an asphalt pavement simulation model, and each column represents a feature.
[0017] The formula for normalizing the surface feature matrix is:
[0018]
[0019] Among them, X STD is the standardized data, X is the original data, and X min is the minimum value in the data, X max is the maximum value in the data.
[0020] The step of obtaining the optimal surface feature row vector comprises:
[0021] Select U-MatrixSOM algorithm in SOM;
[0022] Determine the parameters of the SOM network and train the SOM network;
[0023] The standardized surface feature matrix is input into the trained SOM network, and the optimal surface feature row vector is output.
[0024] The calculation steps of the fractal dimension are:
[0025] The space is divided into a series of boxes of different sizes, and the optimal surface feature row vector is mapped to the box where the coordinates are located; wherein the dimension of the space is determined based on the number of elements of the optimal surface feature row vector;
[0026] For each box size, count the number of boxes N(ε) required to cover the optimal surface feature row vector;
[0027] The fractal dimension is calculated using the box dimension formula, as follows:
[0028]
[0029] Where D is the fractal dimension, N(ε) is the number of boxes required to cover the optimal surface feature row vector, and ε is the size of the box.
[0030] The scale parameter calculation formula is:
[0031] L=L0×(1 / E) D ;
[0032] Among them, L is the scale parameter, L0 is the reference scale parameter, E is the box size, and D is the fractal dimension.
[0033] The anti-skid evaluation model is:
[0034] S=k·D a ;
[0035] Among them, S is the pavement anti-skid value, D is the fractal dimension of the pavement, α is the scale parameter, which is related to the microstructural characteristics of the pavement, and k is a constant.
[0036] The beneficial effects of the present invention are as follows: by placing a mold on the asphalt pavement, pouring a silicone layer, peeling off the silicone layer to form a simulation model, and using a high-precision scanning device to scan, the surface feature data of the asphalt pavement to be evaluated can be accurately obtained, including geometric features and morphological features, providing a reliable data basis for subsequent anti-skid evaluation.
[0037] The surface feature matrix is processed and analyzed by methods such as convergent normalization and self-organizing mapping, which can effectively eliminate the influence of different feature dimensions and magnitudes, make the data comparable, and thus improve the accuracy of anti-slip evaluation.
[0038] The skid resistance evaluation model constructed based on fractal dimension and scale parameters can more accurately reflect the relationship between the microstructural characteristics of the pavement and its skid resistance, and provide a more scientific basis for evaluating the skid resistance of asphalt pavements.
[0039] By setting the anti-skid threshold and comparing the calculated road surface anti-skid value with the anti-skid threshold, it is possible to intuitively determine whether the road surface anti-skid performance is qualified, providing a clear basis for road surface maintenance and improvement, and helping to take timely measures to improve the road surface anti-skid performance and ensure driving safety.
[0040] In summary, the asphalt pavement skid resistance evaluation method provided by this material has many beneficial effects, can improve the accuracy and reliability of the evaluation, and provide strong guarantees for the development of road engineering and driving safety. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 The overall process of a method for evaluating the skid resistance of an asphalt pavement is shown;
[0042] Figure 2 A flow chart of an exemplary embodiment of S100 is shown;
[0043] Figure 3 A flow chart of an exemplary embodiment of S200 is shown;
[0044] Figure 4 A flow chart of an exemplary embodiment of S300 is shown;
[0045] Figure 5 An exemplary embodiment flow chart of S400 is shown. DETAILED DESCRIPTION
[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0047] See also Figure 1 , which shows a method for evaluating the skid resistance of an asphalt pavement provided by an embodiment of the present invention, comprising:
[0048] S100: placing a plurality of molds on the asphalt pavement, pouring a silicone layer in the molds; peeling off the silicone layer to form a plurality of asphalt pavement simulation models, scanning the asphalt pavement simulation models, and obtaining surface characteristic data of the asphalt pavement to be evaluated.
[0049] S200: constructing a surface feature matrix based on the surface feature data of the asphalt pavement to be evaluated, and performing homogeneous standardization processing on the surface feature matrix to obtain a standardized surface feature matrix.
[0050] S300: The standardized surface feature matrix is subjected to self-organizing mapping to obtain an optimal surface feature row vector, and the optimal surface feature row vector is analyzed by a box dimension method to obtain a fractal dimension and a scale parameter.
[0051] S400: A skid resistance evaluation model is defined, and a pavement skid resistance value is obtained by inputting a fractal dimension and a scale parameter; a skid resistance threshold is set, and the skid resistance of the asphalt pavement is evaluated by comparing the pavement skid resistance value with the skid resistance threshold.
[0052] When selecting an asphalt pavement area, S100 needs to consider factors such as its representativeness, flatness, age, and traffic volume. Then, several molds are set up in the area in a horizontal and vertical array. Before pouring the silicone layer, the asphalt pavement is soaked and poured to ensure that the silicone is evenly and densely laid. After the silicone layer is cured, it is carefully peeled off and the silicone model is marked and saved. The asphalt pavement simulation model is scanned using high-precision scanning equipment to obtain surface feature data including geometric features and morphological features. S100 can comprehensively and accurately evaluate the anti-skid performance of asphalt pavement, obtain a sufficient amount of sample data, ensure the accuracy of silicone laying effect and pavement feature replication, and provide rich data support.
[0053] Please refer to Figure 2 , which shows a flowchart of an exemplary asphalt pavement skid resistance evaluation method S100 of the present application, and its contents include:
[0054] S110: Select a suitable asphalt pavement area, arrange a plurality of molds in a horizontal and vertical array in the asphalt pavement area, and pour a silicone layer in the molds.
[0055] In one possible implementation, the following factors need to be considered when selecting the pavement area:
[0056] Select representative road surface areas, such as different locations on different lanes, to ensure the comprehensiveness and accuracy of the evaluation results.
[0057] Select a road surface area with better flatness to avoid the impact of uneven road surface on the simulation results.
[0058] Select pavement areas with different degrees of age, such as newly built pavement, pavement that has been used for a period of time, etc., to evaluate the skid resistance under different conditions.
[0059] Select pavement areas with different traffic volumes, such as main lanes, auxiliary lanes, etc., to evaluate skid resistance under different traffic conditions.
[0060] In this step, the mold spacing of the molds arranged in the horizontal and vertical arrays is determined according to the size of the road surface area to ensure that a sufficient amount of sample data can be obtained.
[0061] In this step, after the mold is placed, the asphalt pavement in the mold needs to be soaked and then poured using a pouring method to ensure that the silicone can be evenly and densely laid on the asphalt pavement.
[0062] S120: Wait for the silicone layer to solidify. The solidification time depends on the characteristics of the silicone and environmental conditions. After solidification, carefully peel off the silicone layer to separate it from the road surface to form several asphalt pavement simulation models. By waiting for the silicone layer to solidify, peeling off the silicone layer and forming asphalt pavement simulation models, the surface characteristics of the asphalt pavement can be effectively replicated, providing a reliable data basis for subsequent skid resistance evaluation.
[0063] The curing time should be strictly controlled according to the curing characteristics of the silicone material. Too short a curing time will result in insufficient silicone strength and inability to effectively replicate the road surface texture; too long a curing time will affect subsequent stripping operations.
[0064] In this step, the silicone layer is directly peeled off, and the peeling force is controlled to avoid damage to the silicone layer and ensure that the silicone layer can be completely peeled off. Each silicone model is marked, its source and collection location are recorded, and the peeled silicone model is stored to prevent it from being contaminated or damaged.
[0065] S130: Use high-precision scanning equipment to scan the asphalt pavement simulation model to obtain detailed surface feature data.
[0066] In a possible implementation, a three-dimensional laser scanner is used to scan the asphalt pavement simulation model to obtain surface feature data of the asphalt pavement.
[0067] The surface characteristic data of the asphalt pavement obtained include: geometric characteristics and morphological characteristics.
[0068] Geometric features include: texture depth, texture height, texture spacing, texture width, and texture density;
[0069] Morphological characteristics include: porosity and connectivity.
[0070] S200 constructs a surface feature matrix based on the surface feature data of the asphalt pavement to be evaluated, and then performs convergence standardization on the surface feature matrix, using a specific formula to convert each element in the matrix into a standardized value to achieve data standardization. By constructing the surface feature matrix, it is possible to comprehensively and systematically integrate various feature data of the asphalt pavement, providing a basis for subsequent analysis. Convergence standardization eliminates the influence of different feature dimensions and magnitudes, making the data comparable, facilitating effective data analysis and processing, and helping to improve the accuracy and reliability of skid resistance evaluation.
[0071] Please refer to Figure 3 , which shows a flowchart of an exemplary asphalt pavement skid resistance evaluation method S200 of the present application, and its contents include:
[0072] S210: Constructing a surface feature matrix based on the obtained surface feature data.
[0073] In one possible implementation, an asphalt pavement simulation model is constructed based on the selected pavement area. First, it is clear that each row represents an asphalt pavement simulation model, which means that if there are multiple simulation models, there will be multiple rows. Second, each column represents a feature. These features are determined based on the surface feature data obtained previously, such as texture depth, texture height, texture spacing, texture width, texture density, porosity, and connectivity.
[0074] Five asphalt pavement simulation models were scanned as an example, and four features were extracted: texture depth, texture height, texture spacing and texture density. The acquired data are shown in Table 1.
[0075] Table 1
[0076] Model Texture Depth Texture Height Texture Spacing Texture Density Model 1 0.2mm 0.5mm 1.0mm <![CDATA[20 per cm 2 > Model 2 0.3mm 0.6mm 0.9mm <![CDATA[25 per cm 2 > Model 3 0.4mm 0.7mm 0.8mm <![CDATA[30 per cm 2 > Model 4 0.5mm 0.8mm 0.7mm <![CDATA[35 per cm 2 > Model 5 0.6mm 0.9mm 0.6mm <![CDATA[40 per cm 2 >
[0077] The matrix constructed based on the data in Table 1 is as follows:
[0078]
[0079] S220: Performing homogeneous normalization processing on the surface feature matrix to obtain a normalized surface feature matrix.
[0080] Homogeneity normalization is a data preprocessing method that aims to eliminate the influence of different feature dimensions and magnitudes so that the data can be compared and analyzed on the same scale. Specifically, this process converts each element in the surface feature matrix into a standardized value.
[0081] In one possible implementation, the standardized formula used is:
[0082]
[0083] Among them, X STD is the standardized data, X is the original data, and X min is the minimum value in the data, X max is the maximum value in the data.
[0084] Through this formula, the original data is linearly mapped to the interval [0,1] to achieve data standardization.
[0085] Exemplarily, the matrix constructed by the data in Table 1 is formed by the convergence standardization process as follows:
[0086]
[0087] Through such standardization, data with different characteristics can have the same scale, which is convenient for subsequent data analysis and processing.
[0088] S300 trains the self-organizing map network, then inputs the standardized surface feature matrix into the trained SOM network and outputs the optimal surface feature row vector. Then the box dimension method is used to calculate the fractal dimension of the optimal surface feature row vector, including dividing the space into boxes of different sizes and mapping the optimal surface feature row vector, counting the number of boxes required for coverage, and finally using the box dimension formula to calculate the fractal dimension, and finally calculating the relevant scale parameters based on the fractal dimension. The whole process realizes the quantitative evaluation of the skid resistance of asphalt pavement, which is highly scientific and reliable.
[0089] Please refer to Figure 4 , which shows a flowchart of an exemplary asphalt pavement skid resistance evaluation method S300 of the present application, and its contents include:
[0090] S310: Select the U-MatrixSOM algorithm in the self-organizing map (SOM).
[0091] Self-organizing map (SOM) is an unsupervised learning algorithm that can map high-dimensional data to a low-dimensional space while maintaining the topological structure of the data. There are many variants of the SOM algorithm, including the standard SOM algorithm, U-MatrixSOM, and quantum SOM.
[0092] Since the skid resistance evaluation of asphalt pavement needs to consider the topological structure and similarity of the data, the U-MatrixSOM algorithm is selected. U-MatrixSOM can generate a U-Matrix matrix, clearly showing the similarities between the data and helping to better understand the characteristics of different types of pavements.
[0093] S320: Determine parameters of the SOM network and train the SOM network.
[0094] The parameters of the SOM network include: network size, learning rate, neighborhood function and number of iterations.
[0095] The standardized surface feature matrix is input into the SOM network for training. The training process includes the following steps:
[0096] Initialization: Randomly initialize the weight vector of the output layer neurons.
[0097] Select input sample: Randomly select a sample from the normalized surface feature matrix.
[0098] Find the best matching unit: Calculate the distance between the input sample and the weight vector of each neuron in the output layer. The neuron with the smallest distance is called the best matching unit (BMU).
[0099] Update weight vector: According to the weight vector of BMU and its neighboring neurons, update the weight vector of BMU and its neighboring neurons to make it closer to the input sample.
[0100] Iteration: Repeatedly select input samples, find the best matching unit, and update the weight vector until the network converges or reaches the preset number of training times.
[0101] Exemplarily, due to the characteristics of asphalt pavement data, the parameters of the SOM network are determined as follows: network size: 10x10, learning rate: 0.5, neighborhood function: Gaussian function, and number of iterations: 1000.
[0102] S330: Input the standardized surface feature matrix into the trained SOM network and output the optimal surface feature row vector.
[0103] By inputting the standardized surface feature matrix into the trained SOM network, an optimal neuron is selected from the output layer of the SOM network, that is, the optimal surface feature row vector, whose feature data can best represent the entire feature data set.
[0104] In the optimal surface feature row vector, each data represents representative data of the surface feature data of the asphalt pavement.
[0105] S340: Calculate the fractal dimension of the optimal surface feature row vector using the box dimension method.
[0106] The box dimension method is a method for calculating fractal dimension. The basic idea is to cover the research object on a series of boxes of different sizes, and then analyze the relationship between the number of boxes and the size of the boxes to obtain the fractal dimension.
[0107] In a possible implementation, the method steps of using the box dimension method to calculate the fractal dimension of the optimal surface feature row vector are:
[0108] S341: Divide the space into a series of boxes of different sizes, where the dimension of the space is determined based on the number of elements of the optimal surface feature row vector, and map the optimal surface feature row vector to these boxes.
[0109] The size of the box needs to be selected according to the specific conditions of the asphalt pavement. A box that is too large will lead to inaccurate calculation of the fractal dimension, and a box that is too small will lead to too much calculation. Usually, the box size can be from large to small, such as from 1cmx1cm to 0.1cmx0.1cm. The number of boxes needs to be sufficient to accurately describe the fractal characteristics of the research object. Usually, the number of boxes can be set to about 10.
[0110] Map the optimal surface feature row vector to the box where the coordinates lie.
[0111] S342: For each box size, count the number of boxes N(ε) required to cover the optimal surface feature row vector. For example, if the box size is 1cmx1cm, the number of boxes required to cover the optimal surface feature row vector is 1, then N(1)=1; if the box size is 0.5cmx0.5cm, the number of boxes required to cover the optimal surface feature row vector is 4, then N(0.5)=4.
[0112] S343: Calculate the fractal dimension. The fractal dimension is calculated using the box dimension formula, which is as follows:
[0113]
[0114] Where D is the fractal dimension, N(ε) is the number of boxes required to cover the optimal surface feature row vector, and ε is the size of the box.
[0115] For example, the optimal surface feature row vector output by the output layer of the SOM network is (0.5, 0.5, 0.15, 0.3). Since the optimal surface feature row vector is a four-dimensional vector, it is necessary to construct a box in the four-dimensional space. The box size is selected as 0.1, which means that the box size in each dimension is 0.1. The four-dimensional space is divided into a series of boxes of the same size, each box of size 0.1x0.1x0.1x0.1.
[0116] Map the optimal surface feature row vector (0.5, 0.5, 0.15, 0.3) to the box with coordinates (0.5, 0.5, 0.15, 0.3). Since the box size is 0.1, the vector is mapped to the box (5, 5, 1, 3).
[0117] For a box size of 0.1, the number of boxes required to cover the optimal surface feature row vector is 1, so N(0.1)=1.
[0118] The fractal dimension is calculated using the box dimension formula. Since only the box size of 0.1 is used, the fractal dimension cannot be calculated directly. A series of boxes of different sizes are needed, such as 0.1, 0.05, 0.025, ..., and the corresponding number of boxes N(ε) is calculated respectively. Then the relationship between logN(ε) and log(1 / ε) is plotted, and the slope is the fractal dimension D. The fractal dimension of the optimal surface feature row vector obtained by calculation is -3.
[0119] S350: Calculate a scale parameter related to the fractal dimension.
[0120] The scale parameter calculation formula is: L = L0 × (1 / E) D , where L is the scale parameter, L0 is the reference scale parameter, E is the box size, and D is the fractal dimension.
[0121] S400 determines the mathematical model of the skid resistance evaluation model based on the relationship between the fractal dimension and the scale parameter. The fractal dimension and scale parameter of the road surface to be evaluated are input into the skid resistance evaluation model to obtain the skid resistance value of the road surface. The critical value of the skid resistance of the asphalt pavement, that is, the skid resistance threshold, is determined according to relevant standards and actual needs. The calculated skid resistance value of the road surface is compared with the skid resistance threshold to evaluate the skid resistance of the asphalt pavement. This method constructs a skid resistance evaluation model based on fractal dimension and scale parameters, which can more accurately reflect the relationship between the microstructure characteristics of the road surface and the skid resistance performance; by comparing with the skid resistance threshold, it can intuitively judge whether the skid resistance of the road surface is qualified, which provides a clear basis for road surface maintenance and improvement; the whole process is scientific and systematic, and can improve the accuracy and reliability of the skid resistance evaluation of asphalt pavements.
[0122] Please refer to Figure 5 , which shows a flowchart of an exemplary asphalt pavement skid resistance evaluation method S400 of the present application, and its contents include:
[0123] S410: Determine a mathematical model of the anti-slip evaluation model based on the relationship between the fractal dimension and the scale parameter.
[0124] Considering the characteristics of the anti-skid property of asphalt pavement, namely, the anti-skid property increases with the increase of fractal dimension, that is, the larger the fractal dimension, the rougher the pavement, and the better the anti-skid property. The anti-skid property increases with the increase of scale parameter, that is, the larger the scale parameter, the rougher the pavement, and the better the anti-skid property. The mathematical model of the constructed anti-skid property evaluation model is:
[0125] S=k·D a
[0126] Among them, S is the anti-skid value of the road surface, D is the fractal dimension of the road surface, α is a scale parameter related to the microstructural characteristics of the road surface, and k is a constant used to adjust the range of the anti-skid value to meet the actual evaluation criteria.
[0127] S420: Input the fractal dimension and scale parameter of the road surface to be evaluated into a skid resistance evaluation model to obtain a road surface skid resistance value.
[0128] S430: Determine the critical value of the skid resistance of the asphalt pavement, i.e., the skid resistance threshold, according to relevant standards and actual needs.
[0129] Determining the anti-skid threshold is a key step in the asphalt pavement anti-skid evaluation model, which directly affects the accuracy and reliability of the evaluation results. The anti-skid threshold is understood as the minimum standard for the anti-skid performance of asphalt pavements. When the pavement anti-skid value is lower than the threshold, the pavement anti-skid performance is considered unqualified and measures need to be taken to improve it. According to road safety standards and relevant empirical formulas, the anti-skid thresholds for different types of pavements and different use conditions are determined. Alternatively, a large amount of asphalt pavement anti-skid data can be collected and the anti-skid threshold can be determined through statistical analysis methods.
[0130] S440: Compare the calculated pavement anti-skid value with the anti-skid threshold. Determine whether the pavement anti-skid value reaches or exceeds the anti-skid threshold. Evaluate the anti-skid property of the asphalt pavement based on the comparison result. If the pavement anti-skid value is greater than or equal to the anti-skid threshold, the asphalt pavement anti-skid property is considered to be good; otherwise, the asphalt pavement anti-skid property is considered to be insufficient and corresponding measures need to be taken to improve it.
[0131] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, rather than limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.
[0132] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.
[0133] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.
[0134] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present invention.
[0135] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.
Claims
1. A method for evaluating the skid resistance of an asphalt pavement, characterized in that: include: Place several molds on the asphalt pavement and pour a layer of silicone inside the molds; The silicone layer is peeled off to form a number of asphalt pavement simulation models, and the asphalt pavement simulation models are scanned to obtain surface characteristic data of the asphalt pavement to be evaluated; Based on the surface characteristic data of the asphalt pavement to be evaluated, a surface characteristic matrix is constructed, and the surface characteristic matrix is subjected to homogeneous standardization processing to obtain a standardized surface characteristic matrix; The standardized surface feature matrix is passed through self-organizing mapping to obtain the optimal surface feature row vector, and the optimal surface feature row vector is analyzed through the box dimension method to obtain the fractal dimension and scale parameter; The skid resistance evaluation model defined is used to obtain the pavement skid resistance value by inputting fractal dimension and scale parameter; The anti-skid threshold is set, and the anti-skid performance of the asphalt pavement is evaluated by comparing the pavement anti-skid value with the anti-skid threshold.
2. The method for evaluating the skid resistance of an asphalt pavement according to claim 1, characterized in that: The molds are placed in the selected road surface area in a horizontal and vertical array; A 3D laser scanner is used to scan the asphalt pavement simulation model to obtain the surface characteristic data of the asphalt pavement.
3. The method for evaluating the skid resistance of an asphalt pavement according to claim 2, characterized in that: The surface characteristic data of the asphalt pavement include: geometric characteristics and morphological characteristics; Among them, the geometric features include: texture depth, texture height, texture spacing, texture width, and texture density; the morphological features include: porosity and connectivity.
4. The method for evaluating the skid resistance of an asphalt pavement according to claim 1, characterized in that: Each row in the surface feature matrix represents an asphalt pavement simulation model, and each column represents a feature.
5. The method for evaluating the skid resistance of an asphalt pavement according to claim 4, characterized in that: The formula for normalizing the surface feature matrix is: Among them, X STD is the standardized data, X is the original data, and X min is the minimum value in the data, X max is the maximum value in the data.
6. The method for evaluating the skid resistance of an asphalt pavement according to claim 1, characterized in that: The step of obtaining the optimal surface feature row vector comprises: Select U-MatrixSOM algorithm in SOM; Determine the parameters of the SOM network and train the SOM network; The standardized surface feature matrix is input into the trained SOM network, and the optimal surface feature row vector is output.
7. The method for evaluating the skid resistance of an asphalt pavement according to claim 1, characterized in that: The calculation steps of the fractal dimension are: The space is divided into a series of boxes of different sizes, and the optimal surface feature row vector is mapped to the box where the coordinates are located; wherein the dimension of the space is determined based on the number of elements of the optimal surface feature row vector; For each box size, count the number of boxes N(ε) required to cover the optimal surface feature row vector; The fractal dimension is calculated using the box dimension formula, as follows: Where D is the fractal dimension, N(ε) is the number of boxes required to cover the optimal surface feature row vector, and ε is the size of the box.
8. The method for evaluating the skid resistance of an asphalt pavement according to claim 7, characterized in that: The scale parameter calculation formula is: L=L0×(1 / E) D ; Among them, L is the scale parameter, L0 is the reference scale parameter, E is the box size, and D is the fractal dimension.
9. The method for evaluating the skid resistance of an asphalt pavement according to claim 1, characterized in that: The anti-skid evaluation model is: S=k·D a ; Among them, S is the pavement anti-skid value, D is the fractal dimension of the pavement, α is the scale parameter, which is related to the microstructural characteristics of the pavement, and k is a constant.