A method for estimating equivalent load action frequency of pavement texture information adaptively

Abstract

The present application relates to the technical field of road surface detection, and particularly relates to a kind of road surface texture information adaptive equivalent load action frequency estimation method, comprising the following steps: S1, obtaining road surface texture data;S2, calculate the loss of roughness of road surface texture polishing;S3, establish the theoretical relationship between road surface texture roughness and road surface texture depth;S4, determine the equivalent load action frequency.The method provided by the present application realizes the self-adaptive low-cost estimation of road load accumulation without historical data, and provides a new technical approach for road load monitoring and performance evaluation;The method of the present application does not need complex monitoring equipment and destructive construction, and is suitable for large-scale road network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pavement detection, in particular to a road surface texture information adaptive equivalent load action frequency estimation method. BACKGROUND

[0002] The change of road surface texture polishing, wear and other morphological characteristics is the result of long-term interaction between vehicle tires and roads. It is essentially an intuitive external manifestation of cumulative damage of a large number of loads, and is directly related to the service performance and life of the road. Accurate estimation of equivalent load action frequency is the core basis for evaluating road structure performance, predicting remaining life and developing scientific maintenance strategies, which is of great significance for ensuring road safety and optimizing asset management.

[0003] At present, the detection and estimation methods for load action frequency mainly include traditional traffic survey method and sensor monitoring method. The traffic survey method includes manual counting, air pressure tube counter and geomagnetic coil detection, etc. They can directly obtain basic data such as traffic flow and vehicle type, and then indirectly estimate the equivalent single-axle load action frequency through axle load conversion. However, these methods have obvious limitations such as vertex monitoring, high installation and maintenance cost, etc. The sensor monitoring method directly measures the mechanical response of the road surface inside under the action of the wheel load by burying stress and strain sensors (such as fiber grating sensors, pressure cells, etc.) in the road structure layer. This method is accurate and can directly reflect the mechanical effect of the load. In theory, it is an ideal means to evaluate the load action. However, its shortcomings are also prominent: sensor burying is a destructive construction with complex process and huge initial investment. Moreover, sensors are easily damaged in harsh road environment, and long-term reliability and durability face serious challenges, making it difficult to be widely applied in large-scale road network.

[0004] With the maturity of high-precision three-dimensional laser scanning, photogrammetry and other image technologies, road surface texture has realized fast and accurate digital collection and analysis. How to use texture information to realize more rich engineering applications is the current development trend. The present application proposes an equivalent load action frequency estimation method based on road surface texture information, which can extend the application of texture information on the one hand, and realize adaptive low-cost estimation of road load accumulation without historical data on the other hand. The present application will provide a new technical approach for road load monitoring and performance evaluation, and has important theoretical value and broad application prospect. SUMMARY

[0005] In order to solve the limitations of existing equivalent load action frequency estimation methods and realize adaptive low-cost estimation of road load accumulation without historical data, the present application provides a road surface texture information adaptive equivalent load action frequency estimation method, which includes the following steps:

[0006] S1, acquiring road surface texture data: using laser scanning or photogrammetry to obtain road surface texture point cloud information of tire track band and non-tire track band, and calculating road surface texture roughness in a local scale range;

[0007] S2, calculating road surface texture polishing loss roughness: by calculating the difference between the roughness of the road surface texture of the tire track band and the non-tire track band in the micro scale range, the road surface texture polishing loss roughness is obtained;

[0008] S3, establishing a theoretical relationship between road surface texture roughness and road surface texture depth;

[0009] S4, determining the number of equivalent loads: determining the road surface wear, and substituting the wear into the wear model to finally determine the number of equivalent loads.

[0010] Further, the step S1 is specifically: according to the road surface texture data, the power spectral density C(q) of the tire track band and the non-tire track band texture is calculated respectively, and the calculation formula is:

[0011] (1);

[0012] In the formula, <…> represents the average operation, q is the frequency, z(x) is the road surface texture elevation at position x, and i is the imaginary unit;

[0013] The texture of a certain scale is taken for roughness calculation, and the road surface texture roughness h rms in a certain scale range can be obtained by integrating the power spectrum along the q axis, and the calculation formula is:

[0014] (2)。

[0015] Further, the step S2 is specifically: the original road surface texture is decomposed into micro texture A and macro texture B, the micro texture A and the macro texture B have roughness h rms (A) and h rms (B) respectively; the micro texture A and the macro texture B are two independent samples, and the roughness of the two independent samples A and B satisfies:

[0016] (3);

[0017] Therefore, the road surface texture polishing loss roughness is:

[0018] (4)。

[0019] Further, the micro texture A is a polishing component, and the macro texture B is a reserved component.

[0020] Further, the step S3 is specifically: assuming that the road surface texture height obeys normal distribution, then the road surface texture height z probability density function is:

[0021] (5) ;

[0022] For a surface with N independent microconvexities, the road surface texture depth h a satisfies:

[0023] (6) ;

[0024] By combining the above formulas (5) and (6), we obtain:

[0025] (7) ;

[0026] Define the ratio S of the road surface texture depth and the road surface texture roughness as:

[0027] (8) ;

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

[0029] (9) ;

[0030] wherein, is the road surface texture wear depth, is the road surface texture polishing loss roughness; at this time, the ratio of the road surface texture wear depth and the road surface texture polishing loss roughness satisfies the relationship as formula (10), and is approximately a constant;

[0031] (10).

[0032] Further, the step S4 is specifically: using the Archard wear model to obtain the theoretical relationship between the equivalent load action number M and the road surface texture wear depth :

[0033] (11) ;

[0034] In the formula, M is the equivalent load action number; K is a hardness parameter related to the material, p is the contact pressure, and v is the relative speed;

[0035] Finally, the equivalent load action number is obtained as:

[0036] (12).

[0037] The present application at least includes the following advantages:

[0038] By adopting the road surface texture wear analysis method, the accumulated road load is adaptively and low-cost estimated without historical data, a new technical approach is provided for road load monitoring and performance evaluation; compared with the traditional traffic survey method and sensor monitoring method, the present application avoids point monitoring, high installation and maintenance cost and destructive construction and other problems; through the extended application of road surface texture information, the equivalent load action frequency is accurately estimated; the present application method does not need complex equipment and destructive construction, and is suitable for large-scale road network. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0040] Figure 1 A road surface texture adaptive load action frequency estimation flowchart of the present application.

[0041] Figure 2 A non-trace band texture sample collected for the specific embodiment of the present application.

[0042] Figure 3 A trace band texture sample collected for the specific embodiment of the present application.

[0043] Figure 4 A trace band and non-trace band texture power spectral density result graph provided for the specific embodiment of the present application.

[0044] Figure 5 A roughness relationship between combined texture and single texture provided for the specific embodiment of the present application.

[0045] Figure 6 A h provided for the specific embodiment of the present application. a With An index calculation result schematic diagram.

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

[0047] Figure 8 A h of SMA-13 type road surface texture provided for the specific embodiment of the present application.a / Statistical distribution.

[0048] Figure 9 The AC-13 type road surface texture h provided by the embodiment of the present application a / Statistical distribution. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described in detail below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0050] The following embodiments will be described in detail below with reference to the drawings, and a road surface texture information adaptive equivalent load action frequency estimation method proposed in the present application will be described in detail.

[0051] Figure 1 A road surface texture adaptive load action frequency estimation flowchart provided in the embodiment of the present application is shown in Figure 1 , and includes the following steps:

[0052] S1, acquiring road surface texture data: acquiring tire track band and non-tire track band road surface texture point cloud information by using laser scanning or photogrammetry and the like, and calculating local scale range road surface texture roughness;

[0053] S2, calculating road surface texture polishing loss roughness: obtaining road surface texture polishing loss roughness by calculating the difference between the roughness of the tire track band and the non-tire track band road surface texture in the micro scale range;

[0054] S3, establishing a theoretical relationship between road surface texture roughness and road surface texture depth;

[0055] S4, determining the equivalent load action frequency: determining the road surface wear amount, and substituting the wear amount into the wear model to finally determine the equivalent load action frequency.

[0056] Further, the step S1 is specifically: calculating the power spectral density C(q) of the tire track band and the non-tire track band texture respectively according to the road surface texture data, and the calculation formula is:

[0057] (1) ;

[0058] In the formula, <…> represents the average operation, q is the frequency, z(x) is the road surface texture elevation at position x, and i is the imaginary unit;

[0059] Take a certain scale of texture for roughness calculation, and the road surface texture roughness h of a certain scale range can be obtained by integrating the power spectrum along the q axis rms , and the calculation formula is:

[0060] (2).

[0061] Further, the step S2 is specifically: the original road surface texture is decomposed into micro-texture A and macro-texture B, and the micro-texture A and the macro-texture B have roughnesses h rms (A) and h rms (B) respectively; the micro-texture A and the macro-texture B are two independent samples, and the roughnesses of the two independent samples A and B satisfy:

[0062] (3) ;

[0063] Therefore, the loss of roughness of road surface texture polishing is:

[0064] (4).

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

[0066] Further, the step S3 is specifically: assuming that the road surface texture height obeys a normal distribution, then the road surface texture elevation z probability density function is:

[0067] (5) ;

[0068] For a surface with N independent microconvex bodies, the road surface texture depth h a satisfies:

[0069] (6) ;

[0070] By combining the above formulas (5)-(6), we obtain:

[0071] (7) ;

[0072] Define the ratio S of the road surface texture depth to the road surface texture roughness as:

[0073] (8) ;

[0074] Theoretical analysis of formula (7) shows that the ratio S is not sensitive to the number N of microconvex bodies; since formula (7) is also applicable to the loss of roughness of road surface texture polishing, 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] The roughness of the road surface texture in a certain scale range can be obtained by integrating the power spectrum along the q axis. rms The calculation formula is:

[0090] (2);

[0091] In step S2, the polishing loss roughness of the road surface texture is obtained by calculating the difference between the roughness of the road surface texture in the track band and the non-track band in the micro-scale range. The original road surface texture is divided into micro-texture A and macro-texture B, and the micro-texture A and the macro-texture B have roughness h rms (A) and h rms (B), respectively. In a statistical sense, the micro-texture A and the macro-texture B are two independent samples, and the roughness of the two independent samples A and B satisfies:

[0092] (3);

[0093] Therefore, the polishing loss roughness of the road surface texture is:

[0094] (4);

[0095] According to formula (3) and formula (4), the roughness relationship between the combined texture and the single texture is obtained, as shown in Figure 5 .

[0096] In step S3, the theoretical relationship between the road surface texture roughness and the road surface texture depth is established by theoretical derivation. Assuming that the road surface texture height obeys the normal distribution, the road surface texture elevation z probability density function is:

[0097] (5);

[0098] For a surface with N micro-convex bodies, the road surface texture depth h a satisfies:

[0099] (6);

[0100] By combining the above formulas (5)-(6), we obtain:

[0101] (7);

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

[0103] (8);

[0104] AsFigure 6 h a with Index calculation results schematic diagram.

[0105] Further, set the number of micro convex body N = 10 6 At this time ; Set the number of micro convex body N = 10 4 , It can be seen that the ratio S of the road surface texture depth and the road surface texture roughness is not sensitive to the number of micro convex body N. Further, for the three common road surface textures (OGFC-13 type, SMA-13 type and AC-13 type asphalt mixture), 100 groups of texture profile data are taken respectively, and through statistical analysis of multiple groups of texture sample data, it is obtained that the value of S is more than 90% in 1~2, as shown in Figure 7-9 .

[0106] Since formula (7) is also applicable to the road surface texture polishing loss roughness, therefore:

[0107] (9);

[0108] wherein, is the road surface texture wear depth, is the road surface texture polishing loss roughness. At this time, the ratio of the road surface texture wear depth and the road surface texture polishing loss roughness satisfies the relationship as formula (10), and is approximately a constant.

[0109] (10);

[0110] Step S4, determining the road surface wear amount according to formula (10), and substituting the wear amount into the wear model to finally determine the equivalent load action frequency M;

[0111] Further, the Archard wear model is used, and the theoretical relationship between the equivalent load action frequency M and the road surface texture wear depth is as follows:

[0112] (11);

[0113] In the formula, M is the equivalent load action frequency; K is the hardness parameter related to the material, p is the contact pressure, and v is the relative speed.

[0114] Finally, the equivalent load action frequency is obtained:

[0115] (12);

[0116] Through the above steps, the texture wear degree of the road surface caused by the rolling of the vehicle can be accurately quantified, and the equivalent load action frequency can be accurately estimated, thereby providing a scientific basis for road maintenance and management. The method uses statistical principles to simplify the complex road surface texture changes into quantifiable parameters, and has the advantages of simple calculation and intuitive results.

[0117] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A pavement texture information adaptive equivalent load action frequency estimation method, characterized in that, The method comprises the following steps: S1, acquiring road surface texture data: acquiring road surface texture point cloud information of the wheel track and the non-wheel track by using a laser scanning or photogrammetry method, and calculating roughness of the road surface texture in a local scale range; S2, calculating polishing loss roughness of the road surface texture: calculating the difference between roughness of the road surface texture in the wheel track and the non-wheel track in a micro scale range to obtain the polishing loss roughness of the road surface texture; S3, establishing a theoretical relationship between the road surface texture roughness and the road surface texture depth; S4, determining the equivalent load action frequency: determining the road surface wear amount, substituting the wear amount into a wear model, and finally determining the equivalent load action frequency; The step S1 is specifically: calculating power spectral density C(q) of the wheel track and the non-wheel track texture according to the road surface texture data, and the calculation formula is: (1)； In the formula, <…> represents an average operation, q is a frequency, z(x) is the road surface texture elevation at the position x, and i is an imaginary unit; Take a certain scale of texture to calculate roughness, and the road surface texture roughness h in a certain scale range can be obtained by integrating the power spectrum along the q axis rms The calculation formula is: (2)； The step S2 is specifically: decomposing the original road surface texture into a micro-texture A and a macro-texture B, the micro-texture A and the macro-texture B respectively have a roughness h rms (A), h rms (B); the micro-texture A and the macro-texture B are two independent samples, and the roughness of the two independent samples A and B satisfies: (3)； Thus, the road surface texture is polished and loses roughness is: (4)； The step S3 is specifically: assuming that the road surface texture height obeys normal distribution, then the road surface texture elevation z probability density function is : (5)； For a surface with N independent asperities, the road texture depth h a satisfies: (6)； By combining the above formulas (5) and (6), the following formula is obtained: (7)； The ratio S of the road surface texture depth to the road surface texture roughness is defined as: (8)； Theoretical analysis of formula (7) shows that the ratio S is not sensitive to the number N of micro convex bodies; since formula (7) is also applicable to the polishing loss roughness of the road surface texture, the following formula is obtained: 　　　　　　　　(9)； wherein is the road surface texture wear depth, is the road surface texture polishing loss roughness; the ratio of the road surface texture wear depth to the road surface texture polishing loss roughness at this time satisfies a relationship as in Equation (10), and is approximately constant; (10)。 2. The method of claim 1, wherein The micro texture A is a polishing component, and the macro texture B is a reserved component.

3. The method of claim 1, wherein The step S4 specifically includes: obtaining a theoretical relationship between the equivalent load action times M and the road surface texture wear depth by using an Archard wear model. ​ (11)； In the formula, M is the equivalent load action frequency; K is a hardness parameter related to the material, p is the contact pressure, and v is the relative speed; Finally, the equivalent load action frequency is obtained as follows: (12)。

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