Road surface texture information adaptive equivalent load action frequency estimation method

Abstract

The invention relates to the technical field of pavement detection, in particular to a road surface texture information adaptive equivalent load action frequency estimation method, which comprises the following steps of S1, acquiring road surface texture data; s2, the road surface texture polishing loss roughness is calculated; s3, establishing a theoretical relationship between the road surface texture roughness and the road surface texture depth; and S4, determining the number of times of equivalent load action. According to the method provided by the invention, self-adaptive low-cost estimation of the road surface load cumulant without historical data is realized, and a brand new technical approach is provided for road load monitoring and performance evaluation; the method does not need complex monitoring equipment and destructive construction, and is suitable for a large-scale road network.

Description

Technical Field

[0001] This invention relates to the field of road surface inspection technology, specifically to a method for estimating the number of equivalent load applications based on road surface texture information adaptively. Background Technology

[0002] Changes in the morphological characteristics of road surface texture, such as polishing and wear, are the result of long-term interaction between vehicle tires and the road. Essentially, they are the direct external manifestation of cumulative damage from a large amount of load, directly affecting the pavement's performance and lifespan. Accurately predicting the number of equivalent load applications is crucial for evaluating pavement structural performance, predicting remaining life, and developing scientific maintenance strategies. This is of great significance for ensuring road safety and optimizing asset management.

[0003] Currently, methods for detecting and predicting the number of load applications are mainly divided into traditional traffic survey methods and sensor monitoring methods. Traffic survey methods include manual counting, barometric counters, and geomagnetic coil detection. These methods can directly acquire basic data such as traffic flow and vehicle type, and then indirectly estimate the equivalent number of single-axle load applications through axle load conversion. However, these methods have significant limitations such as peak monitoring and high installation and maintenance costs. Sensor monitoring methods directly measure the mechanical response inside the pavement under wheel loads by embedding stress and strain sensors (such as fiber optic grating sensors and pressure cells) in the pavement structure layer. This method provides accurate data and can directly reflect the mechanical effects of the load, theoretically making it an ideal means of assessing load effects. However, its disadvantages are also prominent: sensor installation is a destructive construction process with complex technology and huge initial investment. Furthermore, sensors are easily damaged in harsh road environments, posing a serious challenge to long-term reliability and durability, making it difficult to promote and apply in large-scale road networks.

[0004] With the maturity of high-precision 3D laser scanning, photogrammetry, and other imaging technologies, road surface texture has achieved rapid and accurate digital acquisition and analysis. The current development trend is to utilize texture information for richer engineering applications. This invention proposes a method for estimating the equivalent load application frequency based on road surface texture information. On the one hand, it expands the application of texture information; on the other hand, it enables adaptive, low-cost prediction of road load accumulation without requiring historical data. This invention provides a novel technical approach for road load monitoring and performance evaluation, possessing significant theoretical value and broad application prospects. Summary of the Invention

[0005] To address the limitations of existing methods for estimating the number of equivalent load applications and to achieve adaptive, low-cost prediction of pavement load accumulation without requiring historical data, this invention provides a method for estimating the number of equivalent load applications that adapts to pavement texture information, comprising the following steps:

[0006] S1. Obtain road surface texture data: Use methods such as laser scanning or photogrammetry to obtain point cloud information of road surface texture in wheel track and non-wheel track areas, and calculate the road surface texture roughness in the local scale range.

[0007] S2. Calculate the surface texture polishing loss roughness: The surface texture polishing loss roughness is obtained by calculating the difference in roughness between the wheel track and non-wheel track surface textures at the microscale.

[0008] S3. Establish the theoretical relationship between road surface texture roughness and road surface texture depth;

[0009] S4. Determine the number of equivalent load applications: Determine the road surface wear amount and substitute it into the wear model to finally determine the number of equivalent load applications.

[0010] Further, step S1 specifically involves: calculating the power spectral density C(q) of the wheel track and non-wheel track textures based on the road surface texture data, using the following formula:

[0011] (1);

[0012] In the formula, <...> indicates averaging, q is the frequency, z(x) is the road surface texture elevation at position x, and i is the imaginary unit;

[0013] By taking a texture of a certain scale and calculating its roughness, the surface texture roughness h within a certain scale range can be obtained by integrating the power spectrum along the q-axis. rms The calculation formula is:

[0014] (2).

[0015] Furthermore, step S2 specifically involves: decomposing the original road surface texture into micro-texture A and macro-texture B, where micro-texture A and macro-texture B each have a roughness h. rms (A), h rms (B); Microtexture A and macrotexture B are two independent samples, and the roughness of the two independent samples A and B satisfies:

[0016] (3);

[0017] Therefore, surface texture grinding loses roughness. for:

[0018] (4).

[0019] Furthermore, the micro-texture A is a polishing component, and the macro-texture B is a retention component.

[0020] Furthermore, step S3 specifically involves: assuming the road surface texture height follows a normal distribution, then the probability density function of the road surface texture elevation z is... for:

[0021] (5);

[0022] For a surface with N independent micro-protrusions, the surface texture depth h a satisfy:

[0023] (6);

[0024] Combining the above formulas (5) and (6), we get:

[0025] (7);

[0026] The ratio S of road surface texture depth to road surface texture roughness is defined as:

[0027] (8);

[0028] Theoretical analysis of formula (7) shows that the ratio S is not sensitive to the number of micro-protrusions N; since formula (7) is also applicable to the surface texture grinding loss roughness, we have:

[0029] (9);

[0030] in, This refers to the wear depth of the road surface texture. This represents the surface roughness loss due to grinding; the ratio of surface wear depth to surface roughness loss due to grinding. Satisfying the relationship as in equation (10), and It is approximately a constant;

[0031] (10).

[0032] Furthermore, step S4 specifically involves: using the Archard wear model to obtain the equivalent load application number M and the road surface texture wear depth. Theoretical relationship:

[0033] (11);

[0034] In the formula, M is the number of equivalent load applications; K is the material-related hardness parameter; p is the contact pressure; and v is the relative velocity.

[0035] The final equivalent number of load applications is obtained as follows:

[0036] (12).

[0037] The present invention has at least the following beneficial effects:

[0038] By employing a road surface texture wear analysis method, this invention achieves adaptive and low-cost prediction of road load accumulation without requiring historical data, providing a novel technical approach for road load monitoring and performance evaluation. Compared with traditional traffic survey methods and sensor monitoring methods, this invention avoids problems such as high costs associated with point-based monitoring, installation and maintenance, and destructive construction. Through the extended application of road surface texture information, it enables accurate estimation of the number of equivalent load applications. This invention's method requires no complex equipment or destructive construction, making it suitable for large-scale road networks. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a road surface texture adaptive load application number estimation method according to the present invention.

[0041] Figure 2 The non-track texture sample provided for a specific embodiment of the present invention.

[0042] Figure 3 The collected wheel track texture sample is provided for a specific embodiment of the present invention.

[0043] Figure 4 The power spectral density results of the wheel track and non-wheel track textures are shown in the specific embodiment of the present invention.

[0044] Figure 5 The roughness relationship between combined textures and single textures is provided in a specific embodiment of the present invention.

[0045] Figure 6 h provided for specific embodiments of the present invention a and A diagram illustrating the calculation results of the indicators.

[0046] Figure 7 The OGFC-13 type road surface texture h provided in a specific embodiment of the present invention a / Statistical distribution.

[0047] Figure 8 The SMA-13 ​​type road surface texture h provided in a specific embodiment of the present inventiona / Statistical distribution.

[0048] Figure 9 AC-13 type road surface texture h provided for a specific embodiment of the present invention a / Statistical distribution. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] The following embodiments, in conjunction with the accompanying drawings, will provide a detailed explanation of the method for estimating the number of equivalent load actions based on adaptive road surface texture information proposed in this application.

[0051] Figure 1 This is a flowchart of a road surface texture adaptive load application number estimation method provided in an embodiment of the present invention, such as... Figure 1 As shown, it includes the following steps:

[0052] S1. Obtain road surface texture data: Use methods such as laser scanning or photogrammetry to obtain point cloud information of road surface texture in wheel track and non-wheel track areas, and calculate the road surface texture roughness in the local scale range.

[0053] S2. Calculate the surface texture polishing loss roughness: The surface texture polishing loss roughness is obtained by calculating the difference in roughness between the wheel track and non-wheel track surface textures at the microscale.

[0054] S3. Establish the theoretical relationship between road surface texture roughness and road surface texture depth;

[0055] S4. Determine the number of equivalent load applications: Determine the road surface wear amount and substitute it into the wear model to finally determine the number of equivalent load applications.

[0056] Further, step S1 specifically involves: calculating the power spectral density C(q) of the wheel track and non-wheel track textures based on the road surface texture data, using the following formula:

[0057] (1);

[0058] In the formula, <...> indicates averaging, q is the frequency, z(x) is the road surface texture elevation at position x, and i is the imaginary unit;

[0059] By taking a texture of a certain scale and calculating its roughness, the surface texture roughness h within a certain scale range can be obtained by integrating the power spectrum along the q-axis. rms The calculation formula is:

[0060] (2).

[0061] Furthermore, step S2 specifically involves: decomposing the original road surface texture into micro-texture A and macro-texture B, where micro-texture A and macro-texture B each have a roughness h. rms (A), h rms (B); Microtexture A and macrotexture B are two independent samples, and the roughness of the two independent samples A and B satisfies:

[0062] (3);

[0063] Therefore, surface texture grinding loses roughness. for:

[0064] (4).

[0065] Furthermore, the micro-texture A is a polishing component, and the macro-texture B is a retention component.

[0066] Furthermore, step S3 specifically involves: assuming the road surface texture height follows a normal distribution, then the probability density function of the road surface texture elevation z is... for:

[0067] (5);

[0068] For a surface with N independent micro-protrusions, the surface texture depth h a satisfy:

[0069] (6);

[0070] Combining the above formulas (5) and (6), we get:

[0071] (7);

[0072] The ratio S of road surface texture depth to road surface texture roughness is defined as:

[0073] (8);

[0074] Theoretical analysis of formula (7) shows that the ratio S is not sensitive to the number of micro-protrusions N; since formula (7) is also applicable to the surface texture grinding loss roughness, we have:

[0075] (9);

[0076] in, This refers to the wear depth of the road surface texture. This represents the surface roughness loss due to grinding; the ratio of surface wear depth to surface roughness loss due to grinding. Satisfying the relationship as in equation (10), and It is approximately a constant;

[0077] (10).

[0078] Furthermore, step S4 specifically involves: using the Archard wear model to obtain the equivalent load application number M and the road surface texture wear depth. Theoretical relationship:

[0079] (11);

[0080] In the formula, M is the number of equivalent load applications; K is the material-related hardness parameter; p is the contact pressure; and v is the relative velocity.

[0081] The final equivalent number of load applications is obtained as follows:

[0082] (12).

[0083] The following specific embodiments illustrate the implementation principle of the present invention:

[0084] Step S1: Acquire road surface texture data. Collect surface texture data of both wheel track and non-wheel track areas on the road surface to ensure that the collected data represents the true state of the road surface. Maintain consistent sampling intervals during data collection to ensure data comparability.

[0085] Specifically, a laser texture analyzer was used to collect the texture elevation of wheel tracks and non-wheel tracks, with the measured point cloud spacing being less than 0.1 mm and the actual length of the texture contour sample being 15 cm. Figure 2 The image shows a non-track texture sample. Figure 3 The image shown is a sample of wheel track texture.

[0086] Furthermore, the power spectral density C(q) of the wheel track and non-wheel track textures is calculated according to equation (1), and the results are as follows: Figure 4 As shown.

[0087] (1);

[0088] In the formula, <...> indicates averaging, q is the frequency, z(x) is the road surface texture elevation at position x, and i is the imaginary unit.

[0089] By taking a texture of a certain scale and calculating its roughness, the surface texture roughness h within a certain scale range can be obtained by integrating the power spectrum along the q-axis. rms The calculation formula is:

[0090] (2);

[0091] Step S2: By calculating the difference in roughness between the wheel track and non-wheel track surface textures at the microscale, the surface texture polishing loss roughness is obtained; the original surface texture is decomposed into micro-texture A and macro-texture B, with micro-texture A and macro-texture B each having a roughness h. rms (A), h rms (B). Statistically speaking, microtexture A and macrotexture B are two independent samples, and the roughness of the two independent samples A and B satisfies:

[0092] (3);

[0093] Therefore, surface texture grinding loses roughness. for:

[0094] (4);

[0095] The roughness relationship between combined textures and single textures is obtained based on equations (3) and (4), such as... Figure 5 As shown.

[0096] Step S3: Through theoretical derivation, establish the theoretical relationship between road surface texture roughness and road surface texture depth; assuming that the road surface texture height follows a normal distribution, then the probability density function of road surface texture elevation z is... for:

[0097] (5);

[0098] For a surface with N micro-protrusions, the surface texture depth h a satisfy:

[0099] (6);

[0100] Combining the above formulas (5) and (6), we get:

[0101] (7);

[0102] The ratio S of road surface texture depth to road surface texture roughness is defined as:

[0103] (8);

[0104] like Figure 6 The image shows h. a and A diagram illustrating the calculation results of the indicators.

[0105] Furthermore, let the number of micro-convexities be N=10. 6 ,at this time Let the number of micro-convexities be N=10. 4 , It can be seen that the ratio S of surface texture depth to surface texture roughness is not sensitive to the number of micro-protrusions N. Furthermore, for three common surface textures (OGFC-13, SMA-13, and AC-13 asphalt mixtures), 100 sets of texture contour data were taken for each. Through statistical analysis of multiple sets of texture sample data, it was found that the value of S is between 1 and 2 in more than 90% of cases. Figures 7-9 As shown.

[0106] Since equation (7) also applies to the surface texture grinding loss roughness, we have:

[0107] (9);

[0108] in, This refers to the wear depth of the road surface texture. This represents the surface roughness loss due to grinding. It is the ratio of the surface wear depth to the surface roughness loss due to grinding. Satisfying the relationship as in equation (10), and It is approximately a constant.

[0109] (10);

[0110] Step S4: Determine the road surface wear amount according to formula (10), and substitute the wear amount into the wear model to finally determine the number of equivalent load actions M;

[0111] Furthermore, using the Archard wear model, as shown in the following equation, the number of equivalent load applications M is related to the surface texture wear depth. Theoretical relationship:

[0112] (11);

[0113] In the formula, M is the number of equivalent load applications; K is the material-related hardness parameter; p is the contact pressure; and v is the relative velocity.

[0114] The final equivalent number of load applications is obtained as follows:

[0115] (12);

[0116] By following the steps above, the degree of texture wear on the road surface caused by vehicle traffic can be accurately quantified, thereby accurately predicting the number of equivalent load applications and providing a scientific basis for road maintenance and management. This method utilizes statistical principles to simplify complex road surface texture changes into quantifiable parameters, offering advantages such as ease of calculation and intuitive results.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the number of equivalent load applications adaptively based on road surface texture information, characterized in that, Includes the following steps: S1. Obtain road surface texture data: Use laser scanning or photogrammetry to obtain point cloud information of road surface texture in wheel track and non-wheel track areas, and calculate the road surface texture roughness in the local scale range. S2. Calculate the surface texture polishing loss roughness: The surface texture polishing loss roughness is obtained by calculating the difference in roughness between the wheel track and non-wheel track surface textures at the microscale. S3. Establish the theoretical relationship between road surface texture roughness and road surface texture depth; S4. Determine the number of equivalent load applications: Determine the road surface wear amount and substitute it into the wear model to finally determine the number of equivalent load applications.

2. The estimation method according to claim 1, characterized in that, Step S1 specifically involves: calculating the power spectral density C(q) of the wheel track and non-wheel track textures based on the road surface texture data, using the following formula: (1)； In the formula, <...> indicates averaging, q is the frequency, z(x) is the road surface texture elevation at position x, and i is the imaginary unit; By taking a texture of a certain scale and calculating its roughness, the surface texture roughness h within a certain scale range can be obtained by integrating the power spectrum along the q-axis. rms The calculation formula is: (2)。 3. The estimation method according to claim 2, characterized in that, Step S2 specifically involves: decomposing the original road surface texture into micro-texture A and macro-texture B, where micro-texture A and macro-texture B each have a roughness h. rms (A), h rms (B); Microtexture A and macrotexture B are two independent samples, and the roughness of the two independent samples A and B satisfies: (3)； Therefore, surface texture grinding loses roughness. for: (4)。 4. The estimation method according to claim 3, characterized in that, The micro-texture A is the polishing component, and the macro-texture B is the retention component.

5. The estimation method according to claim 4, characterized in that, Step S3 specifically involves: assuming the road surface texture height follows a normal distribution, then the probability density function of the road surface texture elevation z is... for: (5)； For a surface with N independent micro-protrusions, the surface texture depth h a satisfy: (6)； Combining the above formulas (5) and (6), we get: (7)； The ratio S of road surface texture depth to road surface texture roughness is defined as: (8)； Theoretical analysis of formula (7) shows that the ratio S is not sensitive to the number of micro-protrusions N; since formula (7) is also applicable to the surface texture grinding loss roughness, we have: 　　　　　　　　(9)； in, This refers to the wear depth of the road surface texture. This represents the surface roughness loss due to grinding; the ratio of surface wear depth to surface roughness loss due to grinding. Satisfying the relationship as in equation (10), and It is approximately a constant; (10)。 6. The estimation method according to claim 5, characterized in that, Step S4 specifically involves: using the Archard wear model to obtain the equivalent load application number M and the road surface texture wear depth. Theoretical relationship: (11)； In the formula, M is the number of equivalent load applications; K is the material-related hardness parameter; p is the contact pressure; and v is the relative velocity. The final equivalent number of load applications is obtained as follows: (12)。

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