Method and device for identifying the polishing resistance of a surface layer mortar of concrete

Abstract

This application relates to a method and apparatus for identifying the abrasion resistance of concrete surface mortar. The method includes: acquiring three-dimensional texture data of the concrete surface mortar, and preprocessing the three-dimensional texture data to obtain target three-dimensional data of the concrete surface mortar; based on the target three-dimensional data, identifying the abrasion resistance degree of the concrete surface mortar at various scales using an abrasion resistance evaluation strategy; and identifying the abrasion resistance evaluation result of the concrete surface mortar based on the abrasion resistance degree at each scale. This method can improve the accuracy of abrasion resistance evaluation for cement concrete pavement surface mortar.

Description

Technical Field

[0001] This application relates to the field of road engineering technology, and in particular to a method and apparatus for identifying the abrasion resistance of concrete surface mortar. Background Technology

[0002] Concrete pavement, as an important form of road paving, is widely used in transportation infrastructure such as highways, urban roads, and airport runways. The anti-skid performance of a pavement is one of the key factors ensuring driving safety, and the pavement texture and polishing degree directly affect its anti-skid performance. The anti-skid performance of a pavement mainly depends on its surface texture structure; its geometric shape and distribution characteristics jointly determine the pavement's frictional properties. Under the combined effects of long-term traffic loads, tire wear, and environmental factors (such as rainwater and gravel), the surface of concrete pavement gradually undergoes polishing. Polishing smooths the texture, alters the geometric shape and distribution characteristics of the texture at various scales, leading to a decline in the pavement's anti-skid performance over time and increasing the risk of traffic accidents on slippery roads. Therefore, how to improve the accurate assessment of the anti-polishing performance of concrete surface mortar is a current research focus.

[0003] Existing technologies rely on power spectral density (PSD) analysis of texture across the entire elevation range to analyze the texture of concrete surface mortar and identify its abrasion resistance. However, concrete surface mortar is essentially a two-phase composite material composed of hardened cement paste and fine aggregates (such as sand). These two phases exhibit significant differences in hardness and abrasion resistance, making it difficult to capture the differential wear caused by these material compositional differences and the resulting texture evolution characteristics. Consequently, the accuracy of identifying the abrasion resistance of concrete surface mortar is relatively low. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for identifying the abrasion resistance of concrete surface mortar in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for identifying the abrasion resistance of concrete surface mortar, including:

[0006] The three-dimensional texture data of the concrete surface mortar is obtained, and the three-dimensional texture data is preprocessed to obtain the target three-dimensional data of the concrete surface mortar.

[0007] Based on the target three-dimensional data, the degree of texture polishing at each scale of the concrete surface mortar is identified through an anti-polishing performance evaluation strategy.

[0008] Based on the degree of texture polishing at each scale, the results of the anti-polishing performance evaluation of the concrete surface mortar are identified.

[0009] Optionally, the step of preprocessing the three-dimensional texture data to obtain the target three-dimensional data of the concrete surface mortar includes:

[0010] The three-dimensional texture data is subjected to anomaly removal processing to obtain optimized three-dimensional data;

[0011] The optimized 3D image data is subjected to tilt correction processing to obtain the initial target 3D data;

[0012] The initial target 3D data is processed by a data interpolation strategy to obtain 3D elevation matrix data, which is then used as the target 3D data for the concrete surface mortar.

[0013] Optionally, before identifying the degree of texture polishing at various scales of the concrete surface mortar based on the target three-dimensional data and using an anti-polishing performance evaluation strategy, the method further includes:

[0014] Based on the target three-dimensional data, identify the texture wavelength distribution information of the concrete surface mortar;

[0015] Based on the texture wavelength distribution information, and according to the preset wavelength threshold, the sub-target three-dimensional data corresponding to each texture analysis type of the concrete surface mortar are identified.

[0016] Optionally, the texture analysis type includes analysis types above and below the wavelength threshold, and the step of identifying the texture polishing degree of the concrete surface mortar at various scales based on the target three-dimensional data and using an anti-polishing performance evaluation strategy includes:

[0017] Identify the range of elevation data values ​​for the three-dimensional data of the sub-target corresponding to the analysis type greater than the wavelength threshold, and calculate the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold based on the range of elevation data values ​​using a power spectral density algorithm;

[0018] For the sub-target 3D data corresponding to the less-than-wavelength threshold analysis type, identify the elevation data range of the surface top region corresponding to the sub-target 3D data, and calculate the power spectral density distribution information corresponding to the less-than-wavelength threshold analysis type based on the elevation data range of the surface top region;

[0019] Based on the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold and the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold, the texture polishing degree of the concrete surface mortar is identified at each scale through a texture polishing degree evaluation strategy.

[0020] Optionally, calculating the power spectral density distribution information corresponding to the less-than-wavelength threshold analysis type based on the elevation data range of the top region of the surface includes:

[0021] Based on the elevation data range of the top region of the surface, the cumulative distribution function of the top region of the surface is calculated, and based on the cumulative distribution function, the support area curve of the top region of the surface is identified.

[0022] Obtain the support area ratio threshold and identify the elevation data value threshold corresponding to the support area ratio threshold;

[0023] Based on the elevation data value threshold, a valid elevation data range is selected from the elevation data range of the top region of the surface, and based on the valid elevation data value range, the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold is calculated using the power spectral density algorithm.

[0024] Optionally, the assessment result of the abrasion resistance performance of the concrete surface mortar based on the abrasion degree of each scale includes:

[0025] Based on the degree of texture polishing at each scale, the root mean square roughness of the surface texture of the concrete surface mortar is identified.

[0026] Based on the root mean square roughness of the surface texture, the abrasion resistance evaluation results of the concrete surface mortar are identified through a texture abrasion resistance evaluation strategy.

[0027] Secondly, this application also provides a device for identifying the abrasion resistance of concrete surface mortar, comprising:

[0028] The acquisition module is used to acquire the three-dimensional texture data of the concrete surface mortar and perform data preprocessing on the three-dimensional texture data to obtain the target three-dimensional data of the concrete surface mortar.

[0029] The first identification module is used to identify the degree of texture polishing at various scales of the concrete surface mortar based on the target three-dimensional data and through an anti-polishing performance evaluation strategy.

[0030] The second identification module is used to identify the anti-polishing performance evaluation results of the concrete surface mortar based on the polishing degree of each scale texture.

[0031] Optionally, the acquisition module is specifically used for:

[0032] The three-dimensional texture data is subjected to anomaly removal processing to obtain optimized three-dimensional data;

[0033] The optimized 3D image data is subjected to tilt correction processing to obtain the initial target 3D data;

[0034] The initial target 3D data is processed by a data interpolation strategy to obtain 3D elevation matrix data, which is then used as the target 3D data for the concrete surface mortar.

[0035] Optionally, the device further includes:

[0036] The third identification module is used to identify the texture wavelength distribution information of the concrete surface mortar based on the target three-dimensional data;

[0037] The fourth identification module is used to identify the sub-target three-dimensional data corresponding to each texture analysis type of the concrete surface mortar based on the texture wavelength distribution information and according to a preset wavelength threshold.

[0038] Optionally, the first identification module is specifically used for:

[0039] Identify the range of elevation data values ​​for the three-dimensional data of the sub-target corresponding to the analysis type greater than the wavelength threshold, and calculate the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold based on the range of elevation data values ​​using a power spectral density algorithm;

[0040] For the sub-target 3D data corresponding to the less-than-wavelength threshold analysis type, identify the elevation data range of the surface top region corresponding to the sub-target 3D data, and calculate the power spectral density distribution information corresponding to the less-than-wavelength threshold analysis type based on the elevation data range of the surface top region;

[0041] Based on the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold and the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold, the texture polishing degree of the concrete surface mortar is identified at each scale through a texture polishing degree evaluation strategy.

[0042] Optionally, the first identification module is specifically used for:

[0043] Based on the elevation data range of the top region of the surface, the cumulative distribution function of the top region of the surface is calculated, and based on the cumulative distribution function, the support area curve of the top region of the surface is identified.

[0044] Obtain the support area ratio threshold and identify the elevation data value threshold corresponding to the support area ratio threshold;

[0045] Based on the elevation data value threshold, a valid elevation data range is selected from the elevation data range of the top region of the surface, and based on the valid elevation data value range, the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold is calculated using the power spectral density algorithm.

[0046] Optionally, the second identification module is specifically used for:

[0047] Based on the degree of texture polishing at each scale, the root mean square roughness of the surface texture of the concrete surface mortar is identified.

[0048] Based on the root mean square roughness of the surface texture, the abrasion resistance evaluation results of the concrete surface mortar are identified through a texture abrasion resistance evaluation strategy.

[0049] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0050] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0051] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0052] The aforementioned method and apparatus for identifying the abrasion resistance of concrete surface mortar involves acquiring three-dimensional texture data of the concrete surface mortar and preprocessing the three-dimensional texture data to obtain target three-dimensional data of the concrete surface mortar. Based on the target three-dimensional data, an abrasion resistance evaluation strategy is used to identify the abrasion degree of the texture at each scale of the concrete surface mortar. Based on the abrasion degree of the texture at each scale, the abrasion resistance evaluation result of the concrete surface mortar is identified. This scheme effectively excludes "invalid" micro-texture information located in deep depressions on the mortar surface, far from the actual contact interface, and contributing little or no to anti-skid performance by considering only the top elevation data (the area where the tire and road surface make primary contact) when calculating the micro-texture PSD. This significantly improves the accuracy of evaluating the abrasion state of the surface micro-texture that truly participates in friction, overcoming the defect of overestimating the contribution of bottom micro-textures in full-elevation PSD analysis. Furthermore, this scheme proposes a frequency threshold λ-based approach. T A method for calculating the mixed power spectral density (PSD). This is achieved by setting a frequency threshold λ. T For wavelengths λ > λ T Long-wavelength textures were analyzed using full elevation data via PSD, while short-wavelength textures (λ > λ) were analyzed. TThe microstructure of the surface was analyzed using PSD data from the top region, and the two were combined to introduce a mixed power spectral density C(q). m This more realistically reflects the multi-scale evolution of effective textures contributing to anti-slip properties during the polishing process. Finally, this scheme proposes a method based on mixed power spectral density C(q). m The modified RMS calculation method, used as an indicator for anti-abrasion performance evaluation, more realistically reflects the actual abrasion state of the surface texture that is in effective contact with the tire. It overcomes the problem that traditional MTD indicators and full-elevation PSD analysis methods may overestimate the role of micro-texture due to the inclusion of invalid information, leading to deviations in roughness evaluation results, and significantly improves the accuracy of anti-abrasion performance evaluation. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a method for identifying the abrasion resistance of concrete surface mortar in one embodiment.

[0055] Figure 2 This is a schematic diagram of the process for evaluating the abrasion resistance of concrete surface mortar in one embodiment.

[0056] Figure 3 This is an example diagram illustrating the identification of Pearson correlation coefficients in one embodiment;

[0057] Figure 4 Example diagram of Pearson correlation coefficient identification in another embodiment;

[0058] Figure 5 This is a flowchart illustrating an example of identifying the abrasion resistance of concrete surface mortar in one embodiment.

[0059] Figure 6 This is a structural block diagram of a device for identifying the abrasion resistance of concrete surface mortar in one embodiment.

[0060] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] The method for identifying the abrasion resistance of concrete surface mortar provided in this application embodiment can be applied to various environments for identifying the abrasion resistance of concrete surface mortar. This method can be applied to a terminal, a server, or a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. The terminal effectively excludes "invalid" micro-texture information located in deep depressions on the mortar surface, far from the actual contact interface, and contributing little or no to anti-skid performance by considering only the top elevation data (the area where the tire and road surface make primary contact) when calculating the micro-texture PSD. This significantly improves the accuracy of assessing the abrasion state of surface micro-textures that truly participate in friction, overcoming the defect of overestimating the contribution of bottom micro-textures in full-elevation PSD analysis. Then, this scheme proposes a method based on a frequency threshold λ. T A method for calculating the mixed power spectral density (PSD). This is achieved by setting a frequency threshold λ. T For wavelengths λ > λ T Long-wavelength textures were analyzed using full elevation data via PSD, while short-wavelength textures (λ > λ) were analyzed. T The microstructure of the surface was analyzed using PSD data from the top region, and the two were combined to introduce a mixed power spectral density C(q). m This more realistically reflects the multi-scale evolution of effective textures contributing to anti-slip properties during the polishing process. Finally, this scheme proposes a method based on mixed power spectral density C(q). m The modified RMS calculation method, used as an indicator for anti-abrasion performance evaluation, more realistically reflects the actual abrasion state of the surface texture that is in effective contact with the tire. It overcomes the problem that traditional MTD indicators and full-elevation PSD analysis methods may overestimate the role of micro-texture due to the inclusion of invalid information, leading to deviations in roughness evaluation results, and significantly improves the accuracy of anti-abrasion performance evaluation.

[0063] In one exemplary embodiment, such as Figure 1 As shown, a method for identifying the abrasion resistance of concrete surface mortar is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein:

[0064] Step S101: Obtain the three-dimensional texture data of the concrete surface mortar, and perform data preprocessing on the three-dimensional data of the surface mortar to obtain the target three-dimensional data of the concrete surface mortar.

[0065] In this embodiment, the terminal acquires the three-dimensional surface topography data of the concrete surface mortar sample or pavement area to be evaluated by receiving high-precision three-dimensional measurement equipment. Optional equipment includes, but is not limited to, 3D laser scanners, structured light scanners, white light interferometric profilometers, confocal microscopes, or contact high-precision profilometers. The sampling range is determined by the texture scale to be evaluated. For ungrooved concrete pavement surface mortar, the sampling range is no less than 5 mm × 5 mm. For grooved / roughened concrete pavement surface mortar, the sampling range is no less than 100 mm × 100 mm. The measurement data is stored in three-dimensional coordinate form (x, y, z), where z represents the elevation value. Then, the terminal preprocesses the three-dimensional texture data through outlier processing, tilt correction, and data interpolation to obtain the target three-dimensional data of the concrete surface mortar. The specific preprocessing process will be described in detail later.

[0066] Step S102: Based on the target three-dimensional data, the degree of texture polishing of the concrete surface mortar at various scales is identified through an anti-polishing performance evaluation strategy.

[0067] In this embodiment, the terminal identifies the degree of texture polishing at various scales of the concrete surface mortar based on the target three-dimensional data and through an anti-polishing performance evaluation strategy. This anti-polishing performance evaluation strategy is a wavelength-differentiated hybrid power spectral density (PSD) calculation and anti-polishing performance evaluation strategy, wherein the strategy is based on a frequency threshold λ. T A method for calculating the mixed power spectral density (PSD). This is achieved by setting a frequency threshold λ. T For wavelengths λ > λ T The long-wavelength texture was analyzed using full elevation data (i.e., C(q)). full For short wavelengths (λ > λ) T The microstructure of the surface was analyzed using PSD (i.e., C(q)) data from the top region of the surface. top ), and then combine the two to introduce a mixed power spectral density C(q). m This more realistically reflects the multi-scale evolution of effective textures that contribute to anti-slip properties during the polishing process. The specific identification process will be explained in detail later.

[0068] Step S103: Based on the degree of texture polishing at each scale, identify the results of the anti-polishing performance evaluation of the concrete surface mortar.

[0069] In this embodiment, the terminal identifies the abrasion resistance assessment results of the concrete surface mortar based on the degree of texture polishing at various scales. Specifically, the terminal uses the mixed power spectral density C(q) as a basis. mThe modified RMS calculation method, used as an indicator for evaluating abrasion resistance, more accurately reflects the actual abrasion state of the surface texture that is in effective contact with the tire. It overcomes the problem that traditional MTD indicators and full-elevation PSD analysis methods may overestimate the role of micro-texture due to the inclusion of invalid information, leading to biased roughness assessment results. This significantly improves the accuracy of abrasion resistance evaluation. The specific identification process will be explained in detail later.

[0070] Based on the above scheme, by considering only the top elevation data (the area where the tire and road surface make primary contact) when calculating the micro-texture PSD, "invalid" micro-texture information located in deep depressions on the mortar surface, far from the actual contact interface, and contributing little or no to anti-skid performance is effectively eliminated. This significantly improves the accuracy of assessing the polishing state of surface micro-textures that truly participate in friction, overcoming the defect of overestimating the contribution of bottom micro-textures in full-elevation PSD analysis. Then, this scheme proposes a frequency threshold λ-based approach... T A method for calculating the mixed power spectral density (PSD). This is achieved by setting a frequency threshold λ. T For wavelengths λ > λ T The long-wavelength texture was analyzed using full elevation data (i.e., C(q)). full For short wavelengths (λ > λ) T The microstructure of the surface was analyzed using PSD (i.e., C(q)) data from the top region of the surface. top ), and then combine the two to introduce a mixed power spectral density C(q). m This more realistically reflects the multi-scale evolution of effective textures contributing to anti-slip properties during the polishing process. Finally, this scheme proposes a method based on mixed power spectral density C(q). m The modified RMS calculation method, used as an indicator for anti-abrasion performance evaluation, more realistically reflects the actual abrasion state of the surface texture that is in effective contact with the tire. It overcomes the problem that traditional MTD indicators and full-elevation PSD analysis methods may overestimate the role of micro-texture due to the inclusion of invalid information, leading to deviations in roughness evaluation results, and significantly improves the accuracy of anti-abrasion performance evaluation.

[0071] Optionally, the three-dimensional texture data is preprocessed to obtain the target three-dimensional data of the concrete surface mortar, including: removing outliers from the three-dimensional texture data to obtain optimized three-dimensional data; performing tilt correction on the optimized three-dimensional image data to obtain initial target three-dimensional data; and performing interpolation on the initial target three-dimensional data using a data interpolation strategy to obtain three-dimensional elevation matrix data, and using the three-dimensional elevation matrix data as the target three-dimensional data of the concrete surface mortar.

[0072] In this embodiment, the terminal performs outlier removal processing on the 3D texture data to obtain optimized 3D data. Specifically, the terminal uses median filtering to remove outlier data points in the 3D texture data that significantly deviate from the normal elevation distribution due to measurement noise, bad pixels, etc., to obtain optimized 3D data. Then, the terminal performs tilt correction processing on the optimized 3D image data to obtain the initial target 3D data. Specifically, the terminal uses the least squares method to fit the planes or low-order surfaces in the optimized 3D image data and removes the image data corresponding to the planes or low-order surfaces, thereby eliminating data errors caused by device installation tilt.

[0073] Finally, the terminal uses a data interpolation strategy to perform interpolation processing on the initial target 3D data to obtain 3D elevation matrix data, which is then used as the target 3D data for the concrete surface mortar. The original data points are irregularly distributed or contain a small number of missing points; interpolation processing can be performed to generate a regularly gridded 2D elevation matrix Z(x,y), facilitating subsequent 2D PSD analysis.

[0074] Based on the above scheme, the original data is optimized by outlier processing, tilt correction, and data interpolation, thereby eliminating interference data, error data, and outlier data, thus improving the accuracy of the target three-dimensional data of concrete surface mortar.

[0075] Optionally, before identifying the degree of texture polishing at various scales of concrete surface mortar based on the target 3D data and through an anti-polishing performance evaluation strategy, the method further includes: identifying the texture wavelength distribution information of the concrete surface mortar based on the target 3D data; and identifying the sub-target 3D data corresponding to each texture analysis type of the concrete surface mortar based on the texture wavelength distribution information and according to a preset wavelength threshold.

[0076] In this embodiment, the terminal identifies the texture wavelength distribution information of the concrete surface mortar based on the target 3D data. Based on the target 3D data, the terminal uses linear recognition technology to identify the wavelength data between various points in the target 3D data, obtaining the texture wavelength corresponding to each point. The texture wavelengths corresponding to all points are then distributed and arranged according to the position information of each point to obtain the texture wavelength distribution information of the concrete surface mortar. The texture wavelength is identified by λ. The linear recognition technology can be a high autocorrelation function of the surface texture, and the formula for calculating the high autocorrelation function is:

[0077] Where z(0,0) is the height of the reference position, and L represents the size of the measurement range.

[0078] ;

[0079] Based on texture wavelength distribution information, the terminal identifies the 3D sub-target data corresponding to the texture analysis types at various scales of concrete surface mortar according to preset wavelength thresholds. For example, the terminal sets a wavelength threshold λ based on the tire-road interface contact mechanism. T This is used to differentiate the calculation range of the power density spectrum. For the surface mortar of concrete pavement, λ can be set. T = 0.25 mm. Then, the texture wavelength λ at the terminal screening location point is greater than λ. T The texture is used as the location point for the wavelength threshold analysis type, and the range of all location points greater than the wavelength threshold analysis type is used as the sub-target 3D data for the wavelength threshold analysis type. Then, the terminal filters the texture wavelength λ < λ of the location points. T The texture is used as the location point of the less than long threshold analysis type, and the range of all location points less than the wavelength threshold analysis type is used as the sub-target 3D data of the less than wavelength threshold analysis type.

[0080] Based on the above scheme, a frequency threshold λ is proposed. T A method for calculating the mixed power spectral density (PSD). This is achieved by setting a frequency threshold λ. T For wavelengths λ > λ T The long-wavelength texture was analyzed using full elevation data (i.e., C(q)). full For short wavelengths (λ > λ) T The microstructure of the surface was analyzed using PSD (i.e., C(q)) data from the top region of the surface. top ), and then combine the two to introduce a mixed power spectral density C(q). m This more realistically reflects the multi-scale evolution of effective textures that contribute to anti-slip properties during the polishing process.

[0081] Optionally, the texture analysis types include those greater than the wavelength threshold and those less than the wavelength threshold. Based on the target 3D data, an anti-wear performance evaluation strategy is used to identify the texture wear degree of the concrete surface mortar at various scales. This includes: identifying the elevation data value range of the sub-target 3D data corresponding to the greater than the wavelength threshold analysis type, and calculating the power spectral density distribution information corresponding to the greater than the wavelength threshold analysis type based on the elevation data value range using a power spectral density algorithm; for the sub-target 3D data corresponding to the less than the wavelength threshold analysis type, identifying the elevation data range of the top surface region corresponding to the sub-target 3D data, and calculating the power spectral density distribution information corresponding to the less than the wavelength threshold analysis type based on the elevation data range of the top surface region; and based on the power spectral density distribution information corresponding to both the greater than and less than the wavelength threshold analysis types, an anti-wear performance evaluation strategy is used to identify the texture wear degree of the concrete surface mortar at various scales.

[0082] In this embodiment, as Figure 2 The diagram shows the process of evaluating the abrasion resistance of concrete surface mortar. The terminal identifies the range of elevation data values ​​of the three-dimensional data of the sub-target corresponding to the analysis type with wavelength threshold values ​​greater than the wavelength threshold, and calculates the power spectral density distribution information corresponding to the analysis type with wavelength threshold values ​​greater than the wavelength threshold based on the range of elevation data values ​​using the power spectral density algorithm.

[0083] The calculation formula for the power spectral density algorithm is as follows:

[0084] ;

[0085] In the above formula, q represents the spatial angular frequency, and C(q) is the distribution function of the surface texture frequency domain energy.

[0086] For the 3D data of sub-targets corresponding to the "less than wavelength threshold analysis type," the terminal identifies the elevation data range of the top surface region corresponding to the 3D data of the sub-target and calculates the power spectral density distribution information corresponding to the "less than wavelength threshold analysis type" based on the elevation data range of the top surface region. The calculation process for this power spectral density distribution information will be explained in detail later.

[0087] Finally, based on the power spectral density distribution information corresponding to analysis types above and below the wavelength threshold, the terminal identifies the texture polishing degree of the concrete surface mortar at various scales using a texture polishing degree evaluation strategy. Here, the texture polishing degree at each scale is represented by the power spectral density distribution information labeled with the texture analysis type identifier.

[0088] The texture polishing degree evaluation strategy includes a scale-based texture polishing degree evaluation algorithm, the calculation formula of which is:

[0089] ;

[0090] In the above formula, C(q) m C(q) represents the combination of texture polishing levels at various scales. full The scale texture polishing degree, C(q), is identified based on the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold. m The degree of scale texture polishing is identified based on the power spectral density distribution information corresponding to the analysis type below the wavelength threshold.

[0091] Based on the above scheme, a frequency threshold λ is proposed. T A method for calculating the mixed power spectral density (PSD). This is achieved by setting a frequency threshold λ. T For wavelengths λ > λ TLong-wavelength textures were analyzed using full elevation data via PSD, while short-wavelength textures (λ > λ) were analyzed. T The microstructure of the surface was analyzed using PSD data from the top region, and the two were combined to introduce a mixed power spectral density C(q). m This more realistically reflects the multi-scale evolution of effective textures that contribute to anti-slip properties during the polishing process.

[0092] Optionally, based on the elevation data range of the top region of the surface, the power spectral density distribution information corresponding to the analysis type below the wavelength threshold is calculated, including: based on the elevation data range of the top region of the surface, calculating the cumulative distribution function of the top region of the surface, and based on the cumulative distribution function, identifying the support area curve of the top region of the surface; obtaining the support area ratio threshold, and identifying the elevation data value threshold corresponding to the support area ratio threshold; based on the elevation data value threshold, filtering the effective elevation data range in the elevation data range of the top region of the surface, and based on the effective elevation data value range, calculating the power spectral density distribution information corresponding to the analysis type below the wavelength threshold using a power spectral density algorithm.

[0093] In this embodiment, the terminal calculates the cumulative distribution function of the top surface region based on the elevation data range of the top surface region, and identifies the support area curve of the top surface region based on the cumulative distribution function. The cumulative distribution function is calculated using a cumulative distribution function algorithm. Specifically, as follows... Figure 2 As shown, the support area curve of the top region of the surface is calculated using the support area curve algorithm in the hybrid power spectral density (PSD) technique.

[0094] Then, the terminal obtains the support area ratio threshold. This support area ratio threshold is typically set between 10% and 30%. Next, the terminal identifies the corresponding elevation data value threshold. This elevation data value threshold is the elevation value h. top

[0095] Finally, based on an elevation data value threshold, the terminal filters out the valid elevation data range within the elevation data range of the top region of the surface. Then, based on this valid elevation data range, it calculates the power spectral density distribution information corresponding to the analysis type below the wavelength threshold using a power spectral density algorithm. Specifically, the terminal uses the identified top range data h... top Remove Z < h top The data points are used to construct a new elevation data subset Z. top (x,y). And based on Z top For (x, y), calculate C(q) using the power spectral density algorithm. top The power spectral density distribution information corresponding to the analysis type below the wavelength threshold is obtained.

[0096] Based on the above scheme, by considering only the top elevation data (the area where the tire and road surface make the main contact) when calculating the micro-texture PSD, "invalid" micro-texture information located in deep depressions on the mortar surface, far from the actual contact interface, and contributing little or no to anti-skid performance is effectively eliminated. This significantly improves the accuracy of assessing the polishing state of surface micro-textures that truly participate in friction, overcoming the defect of overestimating the contribution of bottom micro-textures in full-elevation PSD analysis.

[0097] Optionally, based on the degree of texture polishing at each scale, the abrasion resistance performance evaluation results of the concrete surface mortar are identified, including: based on the degree of texture polishing at each scale, identifying the root mean square roughness of the surface texture of the concrete surface mortar; and based on the root mean square roughness of the surface texture, identifying the abrasion resistance performance evaluation results of the concrete surface mortar through a texture abrasion resistance performance evaluation strategy.

[0098] In this embodiment, the terminal identifies the root mean square roughness of the surface texture of the concrete surface mortar based on the degree of texture polishing at various scales. The formula for calculating this root mean square roughness is as follows:

[0099] ;

[0100] In the above formula, RMS is the root mean square roughness of the surface texture, and C(q) is the surface roughness. m q represents the degree of texture polishing at each scale, and q represents the target three-dimensional data of each location point on the concrete surface.

[0101] Then, based on the root mean square roughness of the surface texture, the terminal identifies the abrasion resistance evaluation result of the concrete surface mortar through a texture abrasion resistance evaluation strategy. This strategy includes the range of surface texture root mean square roughness values ​​corresponding to each abrasion resistance evaluation value. The terminal identifies the abrasion resistance evaluation value of the concrete surface mortar based on this range of surface texture root mean square roughness values ​​through range adaptation, and uses this abrasion resistance evaluation value as the abrasion resistance evaluation result for the concrete surface mortar.

[0102] Twenty sets of tests were conducted on the anti-skid performance and surface texture of the surface mortar of actual cement concrete pavement at different polishing time periods, and its full-elevation power spectral density C(q) was calculated. full With the mixed power spectral density C(q) m The results showed that:

[0103] like Figure 3 As shown, the anti-skid performance index BPN value is related to the power spectral density C(q) based on the full elevation. full Calculated RMS full The Pearson correlation coefficient was 0.83.

[0104] like Figure 4 As shown, the anti-skid performance index BPN value is related to the mixed power spectral density C(q). m Calculated RMS m The Pearson correlation coefficient was 0.91. Test results on the anti-skid performance and surface texture of actual cement concrete pavement surface mortar demonstrate that this method more effectively establishes the relationship between the surface texture characteristics and anti-skid performance of cement concrete pavement surface mortar, achieving accurate identification of its polishing degree and condition.

[0105] Based on the above scheme, a method based on hybrid power spectral density C(q) is proposed. m The modified RMS calculation method, used as an indicator for anti-abrasion performance evaluation, more realistically reflects the actual abrasion state of the surface texture that is in effective contact with the tire. It overcomes the problem that traditional MTD indicators and full-elevation PSD analysis methods may overestimate the role of micro-texture due to the inclusion of invalid information, leading to deviations in roughness evaluation results, and significantly improves the accuracy of anti-abrasion performance evaluation.

[0106] This application also provides an example of analyzing and summarizing multidisciplinary diagnostic information, such as... Figure 5 As shown, the specific processing procedure includes the following steps:

[0107] Step S501: Obtain the three-dimensional texture data of the concrete surface mortar.

[0108] Step S502: Perform outlier removal processing on the 3D texture data to obtain optimized 3D data.

[0109] Step S503: Perform tilt correction processing on the optimized 3D image data to obtain the initial target 3D data.

[0110] Step S504: The initial target three-dimensional data is processed by the data difference strategy to obtain the three-dimensional elevation matrix data, and the three-dimensional elevation matrix data is used as the target three-dimensional data of the concrete surface mortar.

[0111] Step S505: Based on the target three-dimensional data, identify the texture wavelength distribution information of the concrete surface mortar.

[0112] Step S506: Based on the texture wavelength distribution information, identify the sub-target 3D data corresponding to each texture analysis type of the concrete surface mortar according to the preset wavelength threshold.

[0113] Step S507: Identify the range of elevation data values ​​of the three-dimensional data of the sub-target corresponding to the analysis type with wavelength threshold greater than the wavelength threshold, and calculate the power spectral density distribution information corresponding to the analysis type with wavelength threshold greater than the wavelength threshold based on the range of elevation data values ​​using the power spectral density algorithm.

[0114] Step S508: For the sub-target 3D data corresponding to the analysis type below the wavelength threshold, identify the elevation data range of the surface top region corresponding to the sub-target 3D data.

[0115] Step S509: Based on the elevation data range of the top region of the surface, calculate the cumulative distribution function of the top region of the surface, and based on the cumulative distribution function, identify the support area curve of the top region of the surface.

[0116] Step S510: Obtain the support area ratio threshold and identify the elevation data value threshold corresponding to the support area ratio threshold.

[0117] Step S511: Based on the elevation data value threshold, select the effective elevation data range in the elevation data range of the top area of ​​the surface, and calculate the power spectral density distribution information corresponding to the analysis type below the wavelength threshold using the power spectral density algorithm based on the effective elevation data value range.

[0118] Step S512: Based on the power spectral density distribution information corresponding to the analysis type above the wavelength threshold and the power spectral density distribution information corresponding to the analysis type below the wavelength threshold, the texture polishing degree of the concrete surface mortar is identified at each scale through a texture polishing degree evaluation strategy.

[0119] Step S513: Based on the degree of texture polishing at each scale, identify the root mean square roughness of the surface texture of the concrete surface mortar.

[0120] Step S514: Based on the root mean square roughness of the surface texture, the abrasion resistance performance evaluation results of the concrete surface mortar are identified through the texture abrasion resistance performance evaluation strategy.

[0121] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0122] Based on the same inventive concept, this application also provides a device for identifying the abrasion resistance of concrete surface mortar, used to implement the above-described method for identifying the abrasion resistance of concrete mortar. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for identifying the abrasion resistance of concrete surface mortar provided below can be found in the limitations of the method for identifying the abrasion resistance of concrete surface mortar described above, and will not be repeated here.

[0123] In one exemplary embodiment, such as Figure 6 As shown, a device for identifying the abrasion resistance of concrete surface mortar is provided, comprising: an acquisition module 610, a first identification module 620, and a second identification module 630, wherein:

[0124] The acquisition module 610 is used to acquire the three-dimensional texture data of the concrete surface mortar and perform data preprocessing on the three-dimensional texture data to obtain the target three-dimensional data of the concrete surface mortar.

[0125] The first identification module 620 is used to identify the degree of texture polishing at various scales of the concrete surface mortar based on the target three-dimensional data and through an anti-polishing performance evaluation strategy.

[0126] The second identification module 630 is used to identify the anti-polishing performance evaluation result of the concrete surface mortar based on the polishing degree of the texture at each scale.

[0127] Optionally, the acquisition module 610 is specifically used for:

[0128] The three-dimensional texture data is subjected to anomaly removal processing to obtain optimized three-dimensional data;

[0129] The optimized 3D image data is subjected to tilt correction processing to obtain the initial target 3D data;

[0130] The initial target 3D data is processed by a data interpolation strategy to obtain 3D elevation matrix data, which is then used as the target 3D data for the concrete surface mortar.

[0131] Optionally, the device further includes:

[0132] The third identification module is used to identify the texture wavelength distribution information of the concrete surface mortar based on the target three-dimensional data;

[0133] The fourth identification module is used to identify the sub-target three-dimensional data corresponding to each texture analysis type of the concrete surface mortar based on the texture wavelength distribution information and according to a preset wavelength threshold.

[0134] Optionally, the first identification module 620 is specifically used for:

[0135] Identify the range of elevation data values ​​for the three-dimensional data of the sub-target corresponding to the analysis type greater than the wavelength threshold, and calculate the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold based on the range of elevation data values ​​using a power spectral density algorithm;

[0136] For the sub-target 3D data corresponding to the less-than-wavelength threshold analysis type, identify the elevation data range of the surface top region corresponding to the sub-target 3D data, and calculate the power spectral density distribution information corresponding to the less-than-wavelength threshold analysis type based on the elevation data range of the surface top region;

[0137] Based on the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold and the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold, the texture polishing degree of the concrete surface mortar is identified at each scale through a texture polishing degree evaluation strategy.

[0138] Optionally, the first identification module 620 is specifically used for:

[0139] Based on the elevation data range of the top region of the surface, the cumulative distribution function of the top region of the surface is calculated, and based on the cumulative distribution function, the support area curve of the top region of the surface is identified.

[0140] Obtain the support area ratio threshold and identify the elevation data value threshold corresponding to the support area ratio threshold;

[0141] Based on the elevation data value threshold, a valid elevation data range is selected from the elevation data range of the top region of the surface, and based on the valid elevation data value range, the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold is calculated using the power spectral density algorithm.

[0142] Optionally, the second identification module 630 is specifically used for:

[0143] Based on the degree of texture polishing at each scale, the root mean square roughness of the surface texture of the concrete surface mortar is identified.

[0144] Based on the root mean square roughness of the surface texture, the abrasion resistance evaluation results of the concrete surface mortar are identified through a texture abrasion resistance evaluation strategy.

[0145] Each module in the aforementioned concrete surface mortar abrasion resistance identification device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0146] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for identifying the abrasion resistance of concrete surface mortar. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0147] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0148] In one exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps corresponding to the method for identifying the abrasion resistance of concrete surface mortar.

[0149] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps corresponding to the method for identifying the abrasion resistance of concrete surface mortar.

[0150] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps corresponding to the method for identifying the abrasion resistance of concrete surface mortar.

[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0152] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0153] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for identifying the abrasion resistance of concrete surface mortar, characterized in that, The method includes: The three-dimensional texture data of the concrete surface mortar is obtained, and the three-dimensional texture data is preprocessed to obtain the target three-dimensional data of the concrete surface mortar. Based on the target three-dimensional data, identify the texture wavelength distribution information of the concrete surface mortar; Based on the texture wavelength distribution information, according to the preset wavelength threshold, the three-dimensional data of the sub-targets corresponding to each texture analysis type of the concrete surface mortar are identified; the texture analysis type includes analysis types greater than the wavelength threshold and analysis types less than the wavelength threshold. Identify the range of elevation data values ​​for the three-dimensional data of the sub-target corresponding to the analysis type greater than the wavelength threshold, and calculate the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold based on the range of elevation data values ​​using a power spectral density algorithm; For the sub-target three-dimensional data corresponding to the less-than-wavelength threshold analysis type, the elevation data range of the surface top region corresponding to the sub-target three-dimensional data is identified, and based on the elevation data range of the surface top region, the power spectral density distribution information corresponding to the less-than-wavelength threshold analysis type is calculated; Based on the power spectral density distribution information corresponding to the analysis type greater than the wavelength threshold and the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold, the texture polishing degree of the concrete surface mortar is identified at each scale through a texture polishing degree evaluation strategy. Based on the degree of texture polishing at each scale, the results of the anti-polishing performance evaluation of the concrete surface mortar are identified.

2. The method according to claim 1, characterized in that, The step of preprocessing the three-dimensional texture data to obtain the target three-dimensional data of the concrete surface mortar includes: The three-dimensional texture data is subjected to anomaly removal processing to obtain optimized three-dimensional data; The optimized 3D data is subjected to tilt correction processing to obtain the initial target 3D data; The initial target 3D data is processed by a data interpolation strategy to obtain 3D elevation matrix data, which is then used as the target 3D data for the concrete surface mortar.

3. The method according to claim 1, characterized in that, The calculation of the power spectral density distribution information corresponding to the less-than-wavelength threshold analysis type based on the elevation data range of the top region of the surface includes: Based on the elevation data range of the top region of the surface, the cumulative distribution function of the top region of the surface is calculated, and based on the cumulative distribution function, the support area curve of the top region of the surface is identified. Obtain the support area ratio threshold and identify the elevation data value threshold corresponding to the support area ratio threshold; Based on the elevation data value threshold, a valid elevation data range is selected from the elevation data range of the top region of the surface, and based on the valid elevation data range, the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold is calculated using the power spectral density algorithm.

4. The method according to claim 1, characterized in that, The assessment result of the anti-polishing performance of the concrete surface mortar based on the polishing degree of each scale includes: Based on the degree of texture polishing at each scale, the root mean square roughness of the surface texture of the concrete surface mortar is identified. Based on the root mean square roughness of the surface texture, the abrasion resistance evaluation results of the concrete surface mortar are identified through a texture abrasion resistance evaluation strategy.

5. A device for identifying the abrasion resistance of concrete surface mortar, characterized in that, The device includes: The acquisition module is used to acquire the three-dimensional texture data of the concrete surface mortar and perform data preprocessing on the three-dimensional texture data to obtain the target three-dimensional data of the concrete surface mortar. The third identification module is used to identify the texture wavelength distribution information of the concrete surface mortar based on the target three-dimensional data; The fourth identification module is used to identify the three-dimensional data of sub-targets corresponding to each texture analysis type of the concrete surface mortar based on the texture wavelength distribution information and according to a preset wavelength threshold; the texture analysis type includes analysis types greater than the wavelength threshold and analysis types less than the wavelength threshold; The first identification module is used to identify the elevation data value range of the three-dimensional data of the sub-target corresponding to the "greater than wavelength threshold" analysis type, and calculate the power spectral density distribution information corresponding to the "greater than wavelength threshold" analysis type based on the elevation data value range using a power spectral density algorithm; for the three-dimensional data of the sub-target corresponding to the "less than wavelength threshold" analysis type, it identifies the elevation data range of the top surface region corresponding to the three-dimensional data of the sub-target, and calculates the power spectral density distribution information corresponding to the "less than wavelength threshold" analysis type based on the elevation data range of the top surface region; based on the power spectral density distribution information corresponding to the "greater than wavelength threshold" analysis type and the power spectral density distribution information corresponding to the "less than wavelength threshold" analysis type, it identifies the texture polishing degree of the concrete surface mortar at each scale using a texture polishing degree evaluation strategy; The second identification module is used to identify the anti-polishing performance evaluation results of the concrete surface mortar based on the polishing degree of each scale texture.

6. The apparatus according to claim 5, characterized in that, The acquisition module is specifically used for: Based on the elevation data range of the top region of the surface, the cumulative distribution function of the top region of the surface is calculated, and based on the cumulative distribution function, the support area curve of the top region of the surface is identified. Obtain the support area ratio threshold and identify the elevation data value threshold corresponding to the support area ratio threshold; Based on the elevation data value threshold, a valid elevation data range is selected from the elevation data range of the top region of the surface, and based on the valid elevation data range, the power spectral density distribution information corresponding to the analysis type less than the wavelength threshold is calculated using the power spectral density algorithm.

7. The apparatus according to claim 5, characterized in that, The second identification module is used today for: Based on the degree of texture polishing at each scale, the root mean square roughness of the surface texture of the concrete surface mortar is identified. Based on the root mean square roughness of the surface texture, the abrasion resistance evaluation results of the concrete surface mortar are identified through a texture abrasion resistance evaluation strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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