Method for determining cutting depth of low-noise road surface based on grinding process

Abstract

The invention provides a method for determining the cutting depth of a low-noise road surface based on a grinding process, and belongs to the field of road surface noise reduction. Establishing a regression relation model of the road surface texture level and noise, and calculating the noise level and the structure depth of the road surface texture under different cutting depths according to the regression relation model; and determining a proper cutting depth or a cutting depth range according to the target construction depth and a preset noise level. According to the low-noise road surface cutting depth determination method based on the grinding process, the appropriate cutting depth is finally determined by collecting accurate road surface texture and noise data, establishing the regression relation model and calculating the noise level and the structure depth, so that noise pollution is effectively reduced, the road surface quality is improved, and the construction efficiency is improved; according to the method, estimation of the noise level based on the road surface texture is achieved, and balance of the road surface noise characteristic and the skid resistance during treatment of the road surface texture based on the grinding process is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of road surface noise reduction, and particularly to a method for determining the cutting depth of a low-noise road surface based on a grinding process. Background Art

[0002] During the use of a road surface, the increase in tire-road noise is caused by the abrasion and chipping of aggregates. The grinding process can reduce road surface noise to a certain extent. However, since the road surface is in direct contact with the vehicle tires, its texture characteristics will affect both the tire-road noise and the anti-slip characteristics of the road surface. Currently, there is no road surface texture treatment method that can balance the noise characteristics and anti-slip performance of the road surface. Therefore, the present invention aims to propose a method for determining the cutting depth of a low-noise road surface based on the grinding process, so as to accurately calculate the noise level and texture depth corresponding to different cutting depths before cutting and treating the road surface, thereby providing a technical means for the road surface texture treatment that balances the tire-road noise and anti-slip performance. Summary of the Invention

[0003] In view of this, the present invention proposes a method for determining the cutting depth of a low-noise road surface based on a grinding process to solve the problem that there is currently no road surface texture treatment method that can balance the noise characteristics and anti-slip performance of the road surface.

[0004] The technical solution of the present invention is realized as follows: The present invention provides a method for determining the cutting depth of a low-noise road surface based on a grinding process, including the following steps: S1, collecting the road surface texture and the corresponding noise level; S2, establishing a regression relationship model between the road surface texture level and the noise, and calculating the noise level and texture depth of the road surface texture at different cutting depths according to the regression relationship model; S3, determining a suitable cutting depth or a range of cutting depths according to the target texture depth and the preset noise level.

[0005] On the basis of the above technical solution, preferably, step S1 includes the following steps: S11, establishing a three-dimensional road surface texture through three-dimensional modeling technology and obtaining a number of texture collection points; S12, collecting and recording the tire-road noise level corresponding to the texture collection points.

[0006] On the basis of the above technical solution, preferably, the step of establishing a regression relationship model in step S2 includes: S21, calculating the texture level corresponding to different texture wavelengths using the road surface texture collected in step S1; S22, establishing a regression relationship model with the texture level as the input and the noise level as the output using a mathematical regression method.

[0007] More preferably, the regression relationship model is

[0008]

[0009] Wherein, ERNL is the estimated noise level, and L i represents the texture level corresponding to the wavelength i, and L j represents the texture level corresponding to the wavelength j, and β 0 and β i and β ij are the fitting parameters of the model.

[0010] More preferably, in step S2, first calculate the noise levels at different cutting depths according to the regression relationship model, and then calculate the average profile depth of the road surface texture at different cutting depths.

[0011] More preferably, the method for obtaining the road surface texture at different cutting depths is to assign the elevation value of the texture higher than the target cutting height in the original road surface texture to the elevation value of the target cutting height.

[0012] More preferably, step S3 includes the following steps: S31, draw the relationship curve between the noise levels and the average profile depths corresponding to different cutting depths; S32, determine the appropriate cutting depth or cutting depth range according to the target average profile depth and noise level.

[0013] More preferably, the cutting depth represents the difference between the highest point of the road surface texture and the target cutting height.

[0014] The method for determining the cutting depth of a low-noise road surface based on the grinding process of the present invention has the following beneficial effects compared with the prior art:

[0015] The method for determining the cutting depth of a low-noise road surface based on the grinding process of the present invention effectively reduces noise pollution, improves the road surface quality, and improves the construction efficiency by collecting accurate road surface texture and noise data, establishing a regression relationship model, calculating the noise level and the texture depth, and finally determining the appropriate cutting depth. It has strong adaptability and flexibility. This method realizes the estimation of the noise level based on the road surface texture, and realizes the balance of the road surface noise characteristics and the anti-skid performance during the treatment of the road surface texture based on the grinding process, providing a scientific basis and technical support for the construction and maintenance of low-noise roads, and having important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1Schematic diagram of the texture level of the road surface texture of the present invention at different texture wavelengths;

[0018] Figure 2 Schematic diagram of the texture change of the road surface of the present invention before and after cutting;

[0019] Figure 3 Schematic diagram of the change of the noise level and the average profile depth of the present invention with the cutting depth;

[0020] Figure 4 Schematic diagram of the calculation of the texture depth MPD of the present invention. Detailed implementation manners

[0021] Next, in combination with the implementation manners of the present invention, the technical solutions in the implementation manners of the present invention will be clearly and completely described. Obviously, the described implementation manners are only a part of the implementation manners of the present invention, rather than all of the implementation manners. Based on the implementation manners in the present invention, all other implementation manners obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0022] A method for determining the cutting depth of a low-noise road surface based on a grinding process according to the present invention includes the following steps.

[0023] S1. Collect the road surface texture and the corresponding noise level. By using three-dimensional modeling technology, detailed texture information of the road surface is obtained. These texture features are the main factors affecting the road surface noise level. The noise level of the road surface is usually measured at different positions by using a tire noise test device to establish the correlation between the road surface texture and the noise level. By accurately collecting the road surface texture and the noise level, data support can be provided for subsequent regression modeling. This process ensures the comprehensiveness and representativeness of the data and lays a foundation for accurately predicting the impact of the cutting depth on the noise level.

[0024] S2. Establish a regression relationship model between the road surface texture level and the noise, and calculate the noise level and the texture depth of the road surface texture at different cutting depths according to the regression relationship model. By using the collected road surface texture data, a relationship model between the road surface texture and the noise level is established by using a mathematical regression method (such as linear regression, non-linear regression, etc.). Specifically, the input of the model is the texture level corresponding to different texture wavelengths (i.e., information such as the height and shape of the surface features), and the output is the noise level. This step predicts the possible noise magnitude under different textures by calculating the noise level corresponding to different texture wavelengths. By establishing the regression model, the relationship between the road surface texture and the noise level can be quantified, thereby providing a scientific basis for subsequent calculation of the cutting depth.

[0025] The elevation undulations characterized by texture level and texture depth are different. The texture profile of the road surface is decomposed by Fourier transform to obtain the degree of road surface texture undulation corresponding to different wavelength bands, which belongs to the frequency domain expression. The center frequency of the wavelength band is determined according to the center frequency of 1 / 3 octave (or octave), and the corresponding texture level is 20 times the logarithm of the ratio of the amplitude corresponding to the wavelength band to the reference amplitude. Specifically, the calculation formula of the texture level is,

[0026]

[0027] where L tx,λ is the texture level of the wavelength band with a center frequency of λ, in dB, a λ is the average amplitude of the wavelength band, in m, a ref is the reference amplitude, usually taken as 10 -6 m.

[0028] The texture depth is obtained by the difference between the average elevation of the road surface texture profile per 100 mm and the average value of the maximum values on the contours 50 mm before and after. Its value characterizes the undulation degree of the road surface texture profile in elevation. The larger the value, the greater the elevation undulation and the rougher the road surface, and vice versa. Refer to Figure 4 the schematic diagram of the calculation of the texture depth MPD shown. Among them, h 1 and h 2 are the maximum elevations on the texture profiles 50 mm before and after respectively, the black line is the average value of the elevation of the entire 100 mm section, and the difference between the average value of h 1 and h 2 and the average value is MPD.

[0029] S3. Determine the appropriate cutting depth or cutting depth range according to the target texture depth and the preset noise level. According to the target noise level and the preset texture depth requirement, calculate the noise levels at different cutting depths through the regression model, and then determine the appropriate cutting depth. The determination of the cutting depth is based on the relationship curve between the noise level and the texture depth. By plotting this curve, the optimal cutting depth range can be found. By accurately calculating the noise levels at different cutting depths and comparing them with the target noise level, the cutting depth can be effectively optimized to achieve the effect of ensuring both the road surface quality and meeting the noise control requirements. This method can not only optimize the road surface noise, but also improve the smoothness and durability of the road.

[0030] Traditional noise control methods mostly rely on material selection or pavement structure design, making it difficult to precisely control specific noise levels. In contrast, the present invention can precisely control the noise level through optimizing the cutting depth based on the grinding process and optimize the cutting depth under different noise requirements. In practical applications, the depth selection of the pavement grinding process often relies on experience or tests, lacking a scientific basis. Through the method of the present invention, the optimal cutting depth can be accurately calculated according to the regression relationship model between the pavement texture and the noise level, thereby improving the grinding efficiency, avoiding excessive or insufficient cutting, saving resources and reducing costs. Since noise pollution is a common problem on urban roads and highways, especially in residential areas and traffic-intensive areas, the method of the present invention optimizes the tire noise characteristics by precisely controlling the depth of the pavement texture, thereby effectively reducing the road noise pollution and enhancing the comfort of road use. In addition, the method of the present invention can flexibly adjust the cutting depth according to different pavement types, different traffic environments and noise standards, with strong adaptability, and is applicable to various road construction and maintenance scenarios such as various highways and urban roads. Through the technical solution of the present invention, the pavement grinding depth can be accurately controlled, thereby effectively reducing the road noise pollution and improving the flatness and durability of the pavement. This method not only provides a quantitative calculation basis in theory, but also can effectively improve the efficiency of pavement construction and maintenance in practical applications, having significant technical advantages and practical value.

[0031] In a preferred embodiment based on the above embodiments, step S1 includes the following steps: S11, establish a three-dimensional pavement texture through three-dimensional modeling technology and obtain a number of texture acquisition points. The detailed pavement texture information obtained includes the microscopic morphology of the pavement (such as texture depth, wavelength and other characteristics); S12, collect and record the tire-road noise levels corresponding to the texture acquisition points. The tire noise is generated by the friction between the tire and the pavement, which is closely related to the morphology of the pavement texture. Through this data, the noise levels corresponding to different textures can be further analyzed.

[0032] In a preferred embodiment based on the above embodiments, the steps of establishing the regression relationship model in step S2 include: S21, calculate the texture levels corresponding to different texture wavelengths using the pavement textures collected in step S1 to quantify the pavement texture characteristics. Different texture wavelengths have different effects on the noise level. Shorter wavelengths tend to cause higher-frequency noise, while longer wavelengths may reduce high-frequency noise; S22, establish a regression relationship model with the texture level as the input and the noise level as the output using a mathematical regression method (such as linear regression or polynomial regression). This model can describe the variation law of the noise level under different texture levels, thereby providing a basis for calculating the noise levels at different cutting depths in the follow-up.

[0033] In a preferred embodiment based on the above embodiments, the regression relationship model is

[0034]

[0035] Among them, ERNL is the estimated noise level, and L i represents the texture level corresponding to the wavelength of i, and L j represents the texture level corresponding to the wavelength of j, and β 0 , β i and β ij are the fitting parameters of the model. When implementing specifically, the specific cutting depth determination method of this embodiment includes the following steps:

[0036] Step 1: Use a laser texture instrument to collect the three-dimensional texture information of the test points, and then use a tire road noise detection trailer to detect the noise level.

[0037] Step 2: Calculate the texture levels corresponding to different texture wavelengths. As Figure 1 shown, use the multiple linear regression method to establish a regression model with the texture level as the input and the noise level as the output as shown in the following formula.

[0038] ERNL = 0.5×L 80 −0.25×L 5 +60,

[0039] In the formula: ERNL is the estimated noise level, with the unit of dB; L 80 is the texture level corresponding to the wavelength of 80 mm; L 5 is the texture level corresponding to the wavelength of 5 mm.

[0040] Step 3: Use the regression model to calculate the noise levels at different cutting depths, calculate the mean profile depth (MPD) of the road surface texture at different cutting depths, and the road surface textures before and after cutting are as Figure 2 shown.

[0041] Step 4: Determine the appropriate cutting depth according to the target texture depth and the preset noise level, and draw the corresponding noise levels and MPDs at different cutting depths. As Figure 3 shown, when the target is to reduce the noise level by 3 dB, the cutting depth is 1.64 mm. At this cutting depth, the MPD is 0.186 mm, which is difficult to meet the anti-skid performance requirements. To meet the requirements of both anti-skid and noise reduction simultaneously, the final determined cutting depth is 1 mm. At this time, the noise level can be reduced by about 1 dB, and the MPD attenuation is less.

[0042] In a preferred embodiment based on the above embodiments, in step S2, first, calculate the noise levels at different cutting depths according to the regression relationship model. The change in the cutting depth will directly affect the elevation distribution of the road surface texture, thereby changing the noise level. Therefore, at different cutting depths, the change in the road surface noise level can be estimated through the regression model. Then, calculate the average profile depth of the road surface texture at different cutting depths. Specifically, calculate the average profile depth (MPD) of the road surface texture at different cutting depths. MPD is an important indicator for measuring the roughness of the road surface texture and directly affects the generation of noise and the road surface friction performance. By calculating MPD, the influence of different cutting depths on the roughness of the road surface texture can be better understood.

[0043] In a preferred embodiment based on the above embodiments, the method for obtaining road surface texture at different cutting depths is to assign the elevation value of the texture higher than the target cutting height in the original road surface texture to the elevation value of the target cutting height. The principle is to limit the elevation of the original road surface texture so that it does not exceed the target cutting height, thereby achieving the purpose of reducing the noise level. Specifically, first, the original road surface texture refers to the natural form and structure of the road surface before grinding or treatment, including the elevation values of various undulations. These undulating elevation values (i.e., the "height" of the texture) may have different degrees of influence on noise. In particular, higher texture peaks tend to generate greater noise. The target cutting height refers to the maximum height of the road surface texture that is desired to be achieved through grinding. This height is a preset value, which is usually determined according to noise control requirements and road surface flatness standards. Therefore, by "trimming" or "cutting down" the original road surface texture, the texture on the road surface will not exceed the target cutting height; specifically, when operating, traverse each point in the original road surface texture. If the elevation (i.e., the height of the texture) of a certain point is higher than the target cutting height, then adjust the elevation value of this point to the target cutting height; in other words, any high point exceeding the target cutting height will be "smoothed" to the target cutting height. For example: Suppose the elevation of the original road surface texture reaches 5 mm in some areas, and the target cutting height is 3 mm. Then, after cutting treatment, these texture points higher than 3 mm will be adjusted to 3 mm, that is, "smoothed" to the target height of 3 mm. This treatment will reduce the contribution of high peaks to noise, and at the same time maintain the roughness of the texture surface, enabling the road surface to maintain good friction performance and effectively reduce the noise level. Since high texture peaks usually increase the noise when the tire contacts the road surface, especially higher roughness bands may cause greater tire noise (especially high-frequency noise), by cutting down the texture elevation higher than the target cutting height, the irregularity of the road surface is reduced, thereby reducing traffic noise; at the same time, the setting of the target cutting height is usually at the balance point between noise control and road friction. By cutting down the texture exceeding this height, it is possible to avoid excessive noise caused by an overly rough surface, while maintaining a certain texture depth to ensure that the friction performance of the road surface will not decline excessively.

[0044] In a preferred embodiment based on the above embodiments, step S3 includes the following steps: S31, draw a relationship curve of the noise level and the average profile depth corresponding to different cutting depths. According to the preset noise level and the target profile depth (MPD), draw a relationship curve of the noise level and the MPD corresponding to different cutting depths. Through this curve, the balance between the noise level and the texture depth at different cutting depths can be intuitively seen;

[0045] S32. Determine an appropriate cutting depth or range of cutting depths based on the average profile depth and noise level of the target. To optimize the road surface design, it is necessary to set a target noise level (usually set according to road type, traffic volume, environmental requirements, etc.) and the target MPD value (set according to friction performance requirements and road surface durability requirements). These two target values will become the key indicators for determining the optimal cutting depth. The target noise level is usually preset. For example, it is necessary to control the road surface noise within a certain sound pressure level (dB). This target value is generally set based on the noise standards of the surrounding environment, traffic type, and noise source sensitivity. The target MPD value is usually set according to the usage function and safety requirements of the road surface. A larger MPD value helps to provide better friction performance but may result in higher noise, while a smaller MPD value helps to reduce noise but may affect the anti-skid performance of the road surface. Furthermore, by analyzing the plotted relationship curve between the noise level and MPD, we can find the cutting depth corresponding to the target noise level, and the MPD value at this depth is close to or equal to the target MPD value.

[0046] When conducting the analysis, if the intersection point of the target noise level and the target MPD value is very clear, then the cutting depth can be directly selected as the optimal cutting depth. If there is more than one intersection point, or the target value is between two intersection points, a range of cutting depths can be selected (for example, take the average or minimum value within the optimal depth range) to facilitate flexible adjustment during actual construction. For example, assume that the plotted noise level curve and MPD curve show that at a cutting depth of 3 mm, the noise level is 63 dB and the MPD value is 2.4 mm, while the target noise level is 65 dB and the target MPD value is 2.5 mm. Through analysis, it can be determined that the cutting depth range may be between 2.8 mm and 3.2 mm to ensure that the noise is controlled within a reasonable range and the roughness of the road surface meets the friction performance requirements. In addition, considering factors such as errors, it may also be necessary to consider the tolerance of the cutting depth during the construction process, that is, to determine a range of cutting depths that can be accepted within the error range.

[0047] In a preferred embodiment based on the above embodiments, the cutting depth represents the difference between the highest point of the road surface texture and the target cutting height. This definition more precisely reflects the "reduction" process of the road surface texture and provides more targeted parameters for the balance between noise control and road surface smoothness. This optimization step helps to better adjust the height distribution of the road surface texture, enabling it to meet the requirements of noise level control while ensuring frictional performance, comfort, and driving safety. The depth of the road surface texture directly affects the contact characteristics between the tire and the road surface, thereby influencing noise generation, friction, and driving comfort. To optimize noise control and road surface performance, the cutting depth, defined as the difference between the highest point of the texture and the target cutting height, can effectively fine-tune the higher texture parts on the road surface. Specifically, the higher texture peaks are the main cause of noise generation. Especially at high speeds, the "high points" on the road surface can cause violent impacts between the tire and the ground, resulting in significant noise. Therefore, controlling the height of these high texture peaks can significantly reduce traffic noise. By restricting the cutting depth and only reducing the texture above the target cutting height, the noise source can be effectively reduced. As a dynamic parameter, the cutting depth can be optimized according to the actual texture conditions of different road surfaces. For example, in the road surface texture, if the texture in some areas is too high, a deeper cut may be required to level these areas, thus achieving a more ideal road surface smoothness. If the texture is relatively uniform and already close to the target height, the cutting depth can be smaller to avoid excessive reduction of the texture and consequent decline in the road surface friction performance. By defining the cutting depth as the difference between the highest point of the road surface texture and the target cutting height, the height distribution of the road surface texture can be more precisely controlled, and the following optimization goals can be achieved: First is noise reduction. High texture peaks usually generate significant noise, especially when they are higher than the set target cutting height. By reducing these texture peaks, the noise source can be effectively reduced, especially at high speeds, which can significantly reduce the generation of tire noise and wind noise. Second is road condition adaptation. Since different road surfaces may have different texture distributions, a single cutting depth may not be suitable for all road surfaces. By adjusting the cutting depth and specifically reducing the locally higher texture peaks, various road surface conditions can be more flexibly adapted.

[0048] In actual implementation, the target cutting height is set by analyzing requirements such as the noise levels, road surface friction, and driving comfort of different roads. In areas where noise is more sensitive (such as urban streets, highways near residential areas, etc.), the target cutting height can be set lower to achieve better noise suppression effects. A method of dynamically adjusting the cutting depth can be adopted, and the cutting depth can be adjusted according to real-time monitoring data (such as noise levels, road surface friction coefficients, surface profiles, etc.); for example, during the grinding process, if it is found that the texture height in certain areas is significantly higher than the target height, the cutting depth can be increased; if the texture has reached the target height, the cutting depth can be reduced to reduce unnecessary material waste and costs. By cutting off the highest points of the texture, the noise level is reduced, especially at high vehicle speeds, and the noise reduction effect is significant. Although the high texture peaks are cut off, by reasonably controlling the cutting depth, it is possible to ensure the friction performance and driving safety of the road surface while maintaining an appropriate roughness. Compared with the traditional uniform cutting method, this cutting depth optimization method based on the difference in the local texture highest points can reduce unnecessary cutting, lower construction costs, reduce material waste, and has a more significant effect on optimizing the actual performance of the road surface.

[0049] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining the cutting depth of a low-noise road surface based on a grinding process, characterized in that: The following steps are included: S1, collects road surface texture and corresponding noise level; S2, establishing a regression relationship model between the road surface texture level and noise, and calculating the noise level and construction depth of the road surface texture at different cutting depths according to the regression relationship model; S3, determining a suitable cutting depth or cutting depth range according to a target structural depth and a preset noise level.

2. The method for determining the cutting depth of a low-noise road surface based on a grinding process according to claim 1, characterized in that: The step S1 comprises the following steps: S11, establishing a three-dimensional texture of the road surface through three-dimensional modeling technology, and obtaining a number of texture collection points; S12, collecting and recording tire-road noise levels corresponding to the texture collection points.

3. The method for determining the cutting depth of a low-noise road surface based on a grinding process according to claim 1, characterized in that: The step of establishing a regression relationship model in step S2 includes: S21, using the road surface texture collected in step S1 to calculate the texture levels corresponding to different texture wavelengths; S22, using a mathematical regression method to establish the regression relationship model with the texture level as input and the noise level as output.

4. The method for determining the cutting depth of a low-noise road surface based on a grinding process according to claim 3 is characterized in that: The regression relationship model is: Among them, ERNL is the estimated noise level, L i represents the texture level corresponding to wavelength i, L j represents the texture level corresponding to wavelength j, β0, β i and β ij are the fitting parameters of the model.

5. The method for determining the cutting depth of a low-noise road surface based on a grinding process according to claim 3, characterized in that: In the step S2, the noise level at different cutting depths is first calculated according to the regression relationship model, and then the average profile depth of the road surface texture at different cutting depths is calculated.

6. The method for determining the cutting depth of a low-noise road surface based on a grinding process according to claim 5, characterized in that: The method for obtaining the road surface texture at different cutting depths is to assign the elevation value of the texture above the target cutting height in the original road surface texture as the elevation value of the target cutting height.

7. The method for determining the cutting depth of a low-noise road surface based on a grinding process according to claim 5, characterized in that: The step S3 comprises the following steps: S31, plotting a relationship curve between the noise level and the average profile depth corresponding to different cutting depths; S32, determining a suitable cutting depth or cutting depth range according to an average profile depth and a noise level of the target.

8. The method for determining the cutting depth of a low-noise road surface based on a grinding process according to claim 7, characterized in that: The cutting depth represents the difference between the highest point of the road surface texture and the target cutting height.

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