阅读说明：本技术 一种路面表面纹理构造波长分布的评价方法 (Evaluation method for road surface texture structure wavelength distribution ) 是由 林江涛 樊亮 张瀚坤 李永振 张岩 毕飞 魏慧� 梁皓 周圣杰 侯佳林 姜峰 于 2021-08-31 设计创作，主要内容包括：本发明公开一种路面表面纹理构造波长分布的评价方法,涉及道路工程技术领域,采用技术方案包括：采用激光纹理扫描仪采集路面表面纹理构造；采用倍频程分析,设置低通滤波方式,获取不同波长的路面功率谱密度PSD；采用Gaussmod函数模型,对不同波长的路面功率谱密度PSD进行曲线拟合,得到波长-功率谱密度PSD拟合曲线；对波长-功率谱密度PSD拟合曲线进行面积积分,获取任意段波长面积占比,以某段波长面积占比为指标,对路面纹理构造波长的分布情况进行评价。本发明的评价方法,可以稳定获取路面表面纹理构造波长分布函数,进而用于路面抗滑、噪音等因素分析,有效指导道路工程应用生产。(The invention discloses an evaluation method of road surface texture structure wavelength distribution, which relates to the technical field of road engineering and adopts the technical scheme that the evaluation method comprises the following steps: collecting the texture structure of the surface of the road surface by adopting a laser texture scanner; adopting octave analysis and setting a low-pass filtering mode to obtain the power spectral density PSD of the pavement with different wavelengths; performing curve fitting on the road surface power spectrum density PSD with different wavelengths by adopting a Gaussmod function model to obtain a wavelength-power spectrum density PSD fitting curve; and performing area integration on the wavelength-power spectral density PSD fitting curve to obtain the area ratio of any wavelength, and evaluating the distribution condition of the wavelengths of the pavement texture structure by taking the area ratio of a certain wavelength as an index. The evaluation method can stably obtain the wavelength distribution function of the texture structure on the surface of the pavement, is further used for analyzing factors such as skid resistance, noise and the like of the pavement, and effectively guides the application and production of road engineering.)

1. The method for evaluating the wavelength distribution of the texture structure on the surface of the pavement is characterized by comprising the following implementation processes of:

s1, collecting the texture structure of the surface of the road surface by using a laser texture scanner;

s2, acquiring the power spectral density PSD of the pavement with different wavelengths by adopting octave analysis and setting a low-pass filtering mode;

step S3, adopting a Gaussmod function model to perform curve fitting on the road surface power spectrum density PSD with different wavelengths to obtain a wavelength-power spectrum density PSD fitting curve;

and S4, performing area integration on the PSD fitting curve of the wavelength-power spectral density to obtain the area ratio of any wavelength, and evaluating the distribution condition of the wavelengths of the pavement texture structure by taking the area ratio of a certain wavelength as an index.

2. The method for evaluating the wavelength distribution of the texture on the surface of the road surface as claimed in claim 1, wherein the step S1 is performed by collecting the texture on the surface of the road surface with at least three sections of the same length by the laser texture scanner, then performing the steps S2-S4, and averaging the output results of the step S4.

3. The method for evaluating the wavelength distribution of the texture structure on the surface of the road surface according to claim 1, wherein a low-pass filtering mode between 0.5mm and 1mm is set to obtain the PSD of the power spectral density of the road surface with different wavelengths when step S2 is executed.

4. The method for evaluating the wavelength distribution of the texture structure on the surface of the road surface according to claim 1, wherein when step S3 is executed, curve fitting is performed on the PSD of the power spectral density of the road surface at different wavelengths, and the specific operation process is as follows:

s3.1, opening 0rigin 8.0 software, calling a Gaussmod function model of the 0rigin 8.0 software, and performing curve fitting on the PSD (power spectral density) of the road surface with different wavelengths;

step S3.2, an expression of the gauss-mod function model is determined by formula (1) and formula (2), wherein formula (1) is a function expression, and formula (2) is a precondition that formula (1) is satisfied:

wherein x represents an independent variable, i.e. a wavelength,

y is expressed as a dependent variable, namely power spectral density,

y₀、A、x_c、w、t₀representing fitting parameters, obtained by the following processes respectively: directly carrying out iterative calculation according to the formula (1) and the formula (2), wherein the iterative calculation mode adopts an orthogonal distance regression algorithm or a Levenberg Marquardt optimization algorithm until the fitting calculation is converged, and determining a fitting parameter y in the formula (1)₀、A、x_c、w、t₀A value of (d);

step S3.3, according to the determined fitting parameter y₀、A、x_c、w、t₀And obtaining a specific Gaussmod function model, and obtaining a wavelength-Power Spectral Density (PSD) fitting curve according to the Gaussmod function model.

5. The method for evaluating the wavelength distribution of the texture structure on the surface of the road surface according to claim 1, wherein in the step S4, the area integral of the PSD curve is first performed, the area integral is an absolute area, the integral baseline is a constant Y-0, and then the wavelength area ratio of 10mm or more is obtained to evaluate the wavelength distribution of the texture structure on the road surface.

Technical Field

The invention relates to the technical field of road engineering, in particular to an evaluation method for the wavelength distribution of a texture structure on the surface of a road surface.

Background

The world road association divides the road texture structure into: aggregate surface microstructure, pavement macro texture structure, pavement giant texture structure and pavement unevenness. The wavelength of the aggregate micro texture is between 1 mu m and 0.5mm, the micro structure provides enough anti-skid capability for vehicles running on dry pavements and wet pavements with the vehicle speed lower than 80km/h, and the influence of the micro texture on pavement noise is small; the wavelength of the macro texture is between 0.5mm and 50mm, and the wavelength of the macro texture provides skid resistance for the pavement. Meanwhile, the macro texture is a main cause of tire road noise; the wavelength of the giant texture is 50-500 mm, which affects the noise in the vehicle and the driving comfort; the coarse texture has a wavelength greater than 500mm, which affects vehicle dynamics, ride comfort and drainage performance.

Therefore, the micro texture structure wavelength and the macro texture structure wavelength of the road surface have close relation with the noise, the skid resistance, the drainage performance and the like of the road surface/tire, the wavelength distribution of the obtained texture structure of the road surface has very important practical application value, and the application of road engineering can be effectively guided.

However, the wavelength distribution of the road texture structure is mainly used for obtaining giant textures and rough textures at present, the wavelength distribution is mainly used for evaluating the flatness of the whole road surface structure, and the distribution research of micro texture structures and macro texture structures which have the largest influence on performances such as skid resistance and noise of the road surface is few.

Disclosure of Invention

Aiming at the requirements and the defects of the prior art development, the invention provides the method for evaluating the wavelength distribution of the texture structure on the surface of the pavement so as to more scientifically guide the practical problems related to factors such as skid resistance, noise and the like in road construction.

The invention discloses an evaluation method for the wavelength distribution of a pavement surface texture structure, which adopts the following technical scheme for solving the technical problems:

a method for evaluating the wavelength distribution of the texture structure on the surface of a pavement comprises the following steps:

s1, collecting the texture structure of the surface of the road surface by using a laser texture scanner;

s2, acquiring the power spectral density PSD of the pavement with different wavelengths by adopting octave analysis and setting a low-pass filtering mode;

step S3, adopting a Gaussmod function model to perform curve fitting on the road surface power spectrum density PSD with different wavelengths to obtain a wavelength-power spectrum density PSD fitting curve;

and S4, performing area integration on the PSD fitting curve of the wavelength-power spectral density to obtain the area ratio of any wavelength, and evaluating the distribution condition of the wavelengths of the pavement texture structure by taking the area ratio of a certain wavelength as an index.

In step S1, the laser texture scanner collects at least three sections of surface texture structures of the road surface with the same length, and then performs steps S2-S4, respectively, and averages the results output in step S4.

And step S2 is executed, a low-pass filtering mode between 0.5mm and 1mm is set, and the road surface power spectral density PSD with different wavelengths is obtained.

When step S3 is executed, curve fitting is performed on the road surface power spectral density PSD with different wavelengths, and the specific operation process is as follows:

s3.1, opening 0rigin 8.0 software, calling a Gaussmod function model of the 0rigin 8.0 software, and performing curve fitting on the PSD (power spectral density) of the road surface with different wavelengths;

step S3.2, an expression of the gauss-mod function model is determined by formula (1) and formula (2), wherein formula (1) is a function expression, and formula (2) is a precondition that formula (1) is satisfied:

wherein x represents an independent variable, i.e. a wavelength,

y is expressed as a dependent variable, namely power spectral density,

y₀、A、x_c、w、t₀representing fitting parameters, obtained by the following processes respectively: directly carrying out iterative calculation according to the formula (1) and the formula (2), wherein the iterative calculation mode adopts an orthogonal distance regression algorithm or a Levenberg Marquardt optimization algorithm until the fitting calculation is converged, and determining a fitting parameter y in the formula (1)₀、A、x_c、w、t₀A value of (d);

step S3.3, according to the determined fitting parameter y₀、A、x_c、w、t₀And obtaining a specific Gaussmod function model, and obtaining a wavelength-Power Spectral Density (PSD) fitting curve according to the Gaussmod function model.

When step S4 is executed, first, area integration is performed on the wavelength-power spectral density PSD fitted curve, the area integration is an absolute area, the integral baseline is a constant Y equal to 0, then, the wavelength area ratio of 10mm or more is obtained, and the distribution of the road texture structure wavelength is evaluated.

Compared with the prior art, the evaluation method for the wavelength distribution of the texture structure on the surface of the pavement has the beneficial effects that:

(1) the method can stably obtain the wavelength distribution function of the texture structure on the surface of the pavement, has high function fitting degree, has the characteristics of science, high accuracy, strong practicability and the like, and has high popularization value;

(2) the method can stably obtain the wavelength distribution function of the texture structure on the surface of the pavement, is further used for analyzing factors such as skid resistance and noise of the pavement and effectively guides the application and production of road engineering.

Drawings

FIG. 1 is a surface texture map of a pavement A collected by a laser texture scanner according to an embodiment;

FIG. 2 is a surface texture map of a pavement B collected by a laser texture scanner according to an embodiment;

FIG. 3 is a surface texture map of a pavement C collected by a laser texture scanner according to one embodiment;

FIG. 4 is a road surface power spectral density PSD and a fitting curve of a road surface A under different wavelengths in the first embodiment;

FIG. 5 is a road surface power spectral density PSD and a fitting curve of a road surface B under different wavelengths in the first embodiment;

FIG. 6 is a road surface power spectral density PSD and a fitting curve of a road surface C under different wavelengths in the first embodiment;

FIG. 7 is a schematic diagram showing the area integrals of the full wavelength band and the wavelength band of 10mm or more for the wavelength distribution function of the road surface A in the first embodiment;

FIG. 8 is a schematic diagram showing the area integrals of the full wavelength band and the wavelength band of 10mm or more with respect to the wavelength distribution function of the road surface B in the first embodiment;

FIG. 9 is a schematic diagram showing the area integrals of the full wavelength band and the wavelength band of 10mm or more for the wavelength distribution function of the road surface C in the first embodiment.

Detailed Description

In order to make the technical scheme, the technical problems to be solved and the technical effects of the present invention more clearly apparent, the following technical scheme of the present invention is clearly and completely described with reference to the specific embodiments.

The first embodiment is as follows:

with reference to fig. 1 to 9, this embodiment provides a method for evaluating wavelength distribution of a texture structure on a surface of a road, which includes:

and step S1, collecting the surface texture structures of the road surface A, the road surface B and the road surface C by adopting a laser texture scanner. Referring to fig. 1, 2 and 3, the lengths of the road surface a, the road surface B and the road surface C are the same.

And step S2, acquiring the power spectral density PSD of the road surface with different wavelengths by adopting octave analysis and setting a 0.5mm low-pass filtering mode.

The road surface power spectral density PSDs of the road surface a, the road surface B and the road surface C under different wavelengths are specifically shown in table 1:

TABLE 1

Wavelength (mm)	Road surface A	Road surface B	Road surface C
				146.0857	0.001642	0.010298	0.003714
73.04284	0.006483	0.026788	0.009165
				36.52142	0.099109	0.270001	0.089972
18.26071	0.288007	0.798305	0.334179
				9.130355	0.574403	2.1791	0.794623
4.565177	0.771608	2.986646	1.322842
				2.282589	0.876176	3.387237	1.6262
1.141294	0.650635	2.235971	1.163571
				0.570647	0.026896	0.080377	0.039816
0.285324	1.41E-05	3.81E-05	1.97E-05
				0.142662	2.37E-09	8.41E-09	2.75E-09

And step S3, adopting a Gaussmod function model to perform curve fitting on the road surface power spectrum density PSD with different wavelengths to obtain a wavelength-power spectrum density PSD fitting curve.

Performing curve fitting on the road surface power spectral density PSD with different wavelengths, wherein the specific operation process comprises the following steps:

s3.1, opening 0rigin 8.0 software, calling a Gaussmod function model of the 0rigin 8.0 software, and performing curve fitting on the PSD (power spectral density) of the road surface with different wavelengths;

step S3.2, an expression of the gauss-mod function model is determined by formula (1) and formula (2), wherein formula (1) is a function expression, and formula (2) is a precondition that formula (1) is satisfied:

wherein x represents an independent variable, i.e. a wavelength,

y is expressed as a dependent variable, namely power spectral density,

y₀、A、x_c、w、t₀representing fitting parameters, obtained by the following processes respectively: directly carrying out iterative calculation according to the formula (1) and the formula (2), wherein the iterative calculation mode adopts an orthogonal distance regression algorithm or a Levenberg Marquardt optimization algorithm until the fitting calculation is converged, and determining a fitting parameter y in the formula (1)₀、A、x_c、w、t₀The values of (a) as in table 2;

step S3.3, according to the determined fitting parameter y₀、A、x_c、w、t₀And obtaining a specific Gaussmod function model, and obtaining a wavelength-Power Spectral Density (PSD) fitting curve according to the Gaussmod function model.

In this step, parameters of the gaussian smod function model when curve fitting is performed are shown in table 2 specifically for the road surface power spectral density PSDs of the road surface a, the road surface B, and the road surface C under different wavelengths, and the obtained fitting curves refer to fig. 4, 5, and 6.

TABLE 2

And step S4, performing area integration on the wavelength-power spectral density PSD fitting curve, wherein the area integration is absolute area, an integral base line is constant Y which is 0, then obtaining the wavelength area ratio of more than 10mm, and evaluating the distribution condition of the road texture structure wavelength.

In this step, with reference to fig. 7, 8, and 9, the specific area integration results of the road surface a, the road surface B, and the road surface C are shown in table 3:

TABLE 3

Item	Road surface A	Road surface B	Road surface C
				Total area of	14.48	48.40	19.98
Area of 10mm or more wavelength	7.97	23.88	9.18
				Ratio (%)	55.0	49.3	45.9

Based on the area integration results of table 3, the in-vehicle noise situation can be further evaluated.

In conclusion, by adopting the method for evaluating the wavelength distribution of the texture structure on the surface of the road, the wavelength distribution function of the texture structure on the surface of the road can be stably obtained, and the method can be further used for analyzing factors such as skid resistance and noise of the road and effectively guiding the application and production of road engineering.

The principles and embodiments of the present invention have been described in detail using specific examples, which are provided only to aid in understanding the core technical content of the present invention. Based on the above embodiments of the present invention, those skilled in the art should make any improvements and modifications to the present invention without departing from the principle of the present invention, and therefore, the present invention should fall into the protection scope of the present invention.