A method for extracting and reconstructing concrete interface topography

Abstract

The application provides a concrete interface morphology feature extraction and reconstruction method, which mainly comprises the following steps: (1) obtaining a representative profile curve of the concrete interface morphology; (2) decomposing the obtained representative profile curve into three components of a macro shape profile, a waviness profile and a roughness profile; (3) performing statistical analysis on the three components of the macro shape profile, the waviness profile and the roughness profile; (4) reconstructing the concrete rough interface morphology based on the statistical rules of the three components of the macro shape profile, the waviness profile and the roughness profile. The application has reasonable concept, breaks through the limitation of traditional single representation with a roughness index, fully excavates the multi-scale information of the concrete surface morphology, and provides a basis for the reconstruction of the interface features and the analysis of the bonding performance.

Description

Technical Field

[0001] This invention relates to the field of civil engineering technology, specifically to a method for extracting and reconstructing the morphological features of concrete interfaces. Background Technology

[0002] Reliable bonding between new and old concrete is crucial for achieving efficient reinforcement of degraded concrete structures and wet installation of prefabricated concrete structures. Research indicates that interface morphology is one of the most significant factors influencing the bond performance between new and old concrete. Increased interface roughness leads to a larger bonding area and enhanced mechanical interlocking, effectively improving interface strength. In practical engineering, roughening is a common method for concrete surface treatment, achieved by using toothed plates with varying tooth pitches. Accurately characterizing the surface morphology of the treated matrix concrete is essential for analyzing and evaluating interfacial bonding performance.

[0003] In current engineering practice, techniques such as probe methods, sand-spreading methods, or 3D scanning are commonly used to obtain the roughness characteristics of concrete surfaces. For example, probe methods obtain corresponding linear roughness indices by directional scanning along a rough surface; 3D scanning acquires surface point cloud data and calculates roughness indices such as the arithmetic mean height. However, research shows that even with the same roughness indices (such as the mean height), the geometric morphology of the interface can still differ significantly, leading to variations in interfacial bonding performance. Therefore, extracting interfacial morphology features and reconstructing a representative interfacial morphology model is crucial for accurately analyzing and evaluating interfacial bonding performance.

[0004] Regarding surface reconstruction methods, some scholars (Chinese Patent CN202311791204.7, "A Method and System for Characterizing the Morphology of Material Ablation Surface Based on Wavelet Transform") have proposed extracting the three-dimensional contour of the surface obtained by scanning into several one-dimensional curves, and using wavelet transform to extract high-frequency curves as characteristic curves of material surface roughness. However, this method, which only uses roughness indices to characterize the surface morphology, is too simplistic and fails to fully consider the inherent geometric irregularities of the concrete substrate surface and the periodic fluctuation characteristics introduced by the roughening treatment of the toothed plate.

[0005] Chinese patent CN201911065766.7 discloses a testing method for the three-dimensional roughness of concrete surfaces based on 3D scanning reconstruction. This method involves creating a mirror model of the concrete surface, conducting 3D scanning experiments on the model in a laboratory, and importing the obtained roughness scan data into COMSOL Multiphysics software for analysis and post-processing. The three-dimensional roughness index values ​​of the surface are calculated in multiple steps. However, this patented technology is limited in that it only obtains roughness index parameters and does not further reconstruct the representative morphology of the rough surface, making it difficult to meet the needs of subsequent numerical simulations and analysis of interfacial bonding behavior.

[0006] Another Chinese patent, CN202311791204.7, discloses a method and system for characterizing the morphology of ablated material surfaces based on wavelet transform. This method targets ablated material surfaces, acquiring a surface point cloud contour model through three-dimensional scanning imaging, extracting several one-dimensional contour curves, and decomposing them using wavelet transform to obtain low-frequency and high-frequency curves. During the decomposition process, the method determines whether to stop the decomposition based on changes in the low-frequency curves. Finally, the high-frequency curve is extracted as a characteristic curve for the material surface roughness, and the surface roughness is calculated using this curve to characterize the surface morphology. However, the limitations of this patented technology in characterizing the surface morphology of concrete are: (1) limited characterization: it is difficult to fully depict the complex features of the concrete surface by only characterizing the surface morphology through roughness characteristic curves and related parameters; (2) insufficient engineering applicability: in actual engineering, the concrete surface is often not an ideal plane and usually has a certain degree of geometric unevenness; in addition, when the concrete surface is roughened by methods such as toothed plate roughening, in addition to the fine roughness features characterized by high frequency curves, periodic fluctuation components related to the geometry of the processing tool will also be introduced. These features have not been effectively characterized or reconstructed in this technology.

[0007] The existing technology mainly has the following technical problems:

[0008] (1) The characterization index is too simple and cannot fully reflect the interface morphology characteristics: Existing technologies (such as patent number 201911065766.7) only characterize the surface morphology of concrete by calculating roughness index (such as arithmetic mean height), which fails to capture the geometric characteristics and complex fluctuation characteristics of the surface, resulting in an overly simple description of the interface morphology and making it difficult to fully reflect the true characteristics of the interface.

[0009] (2) Lack of representative morphology reconstruction capability: Existing technology only stays at the stage of roughness parameter extraction and has not achieved representative three-dimensional morphology reconstruction of rough surfaces, which cannot meet the needs of subsequent calculation simulation and limits its application in the analysis of interface bonding behavior.

[0010] (3) Loss of interface morphology features: On the one hand, concrete surfaces are usually not ideal planes, but have a certain degree of geometric unevenness; on the other hand, when using methods such as toothed plate roughening to treat concrete surfaces, periodic fluctuation features related to the geometry of the treatment tool will be introduced into the interface morphology. Existing technologies do not fully consider the above features, resulting in the loss of some concrete interface morphology features.

[0011] In conclusion, it is necessary to further innovate existing technologies. Summary of the Invention

[0012] To address the technical problems existing in the background art, this invention proposes a method for extracting and reconstructing the morphological features of concrete interfaces. Its concept is reasonable, and it can not only accurately restore the multi-scale morphological features of concrete interfaces, but also flexibly adjust the generated interface features according to different tooth plate parameters, so as to realize multi-dimensional high-precision characterization and reconstruction of concrete interface morphology. It is applicable to interface morphology analysis and bonding performance evaluation in various engineering scenarios.

[0013] To address the aforementioned problems, this invention proposes a method for extracting and reconstructing the morphological features of concrete interfaces, which mainly includes the following steps:

[0014] (1) Obtain representative contour curves of concrete interface morphology;

[0015] (2) The obtained representative contour curve is decomposed into three components: macroscopic shape contour, waviness contour, and roughness contour.

[0016] (3) Statistical analysis was performed on the three components obtained from the decomposition: macroscopic shape profile, waviness profile, and roughness profile.

[0017] (4) Based on the statistical laws of the three components of macroscopic shape profile, waviness profile and roughness profile, the morphology of the rough interface of concrete is reconstructed.

[0018] The method for extracting and reconstructing the morphological features of the concrete interface, wherein the specific process of step (1) is as follows: using white light scanning method to perform three-dimensional imaging on the surface of the roughened concrete sample to obtain high-precision 3D point cloud data of the rough surface; in the point cloud data, along the tangent direction perpendicular to the corrugation direction, extract several representative contour curves at equal intervals as the basic data for the characterization of the concrete surface morphology.

[0019] The method for extracting and reconstructing the morphological features of the concrete interface, wherein the specific process of decomposing the representative contour curve in step (2) is as follows:

[0020] Wavelet transform theory is used to decompose the representative contour curve f(x) to extract morphological features at different wavelength scales; and for a certain representative contour curve f(x), its wavelet transform expression is:

[0021]

[0022] Here, f(x) is decomposed into a set of wavelet functions ψ a,b (x); ψ a,b (x) is generated by scaling and translation of the mother wavelet ψ(x):

[0023]

[0024] In equation (2) above, a is the scaling factor, which determines the compression or expansion of the wavelet; b is the translation factor, which controls the position of the wavelet in the signal; factor a -1 / 2 Used to achieve energy normalization across scales;

[0025] Based on wavelet transform theory, using the Daubechies wavelet basis (db8) as the mother wavelet, the representative contour curve of the rough concrete surface is decomposed into three components: macroscopic shape contour, waviness contour, and roughness contour. During the decomposition of the representative contour curve, high-pass and low-pass filters are used to extract the high-frequency and low-frequency components of the representative contour curve, respectively, and the low-frequency component is further decomposed. By analyzing the variance variation trend of the low-frequency component, the cutoff level of the wavelet decomposition is determined by using the moment of abrupt change as the criterion.

[0026] The method for extracting and reconstructing the morphological features of the concrete interface, wherein the specific process of decomposing the representative contour curve of the rough concrete surface is as follows: First, the representative contour curve is used as the input signal and decomposed into different layers. The layer number corresponding to the second abrupt change point of the low-frequency component variance curve is used as the cutoff layer number to separate the low-frequency component as the macroscopic shape contour component. Then, the data after removing the macroscopic shape contour is used as the new input signal and decomposed into different layers. The layer number corresponding to the first abrupt change point of the low-frequency component variance curve is used as the cutoff layer number to extract the low-frequency component as the waviness contour component and the high-frequency component as the roughness contour component.

[0027] The method for extracting and reconstructing the morphological features of the concrete interface, wherein: the macroscopic shape profile refers to the overall geometric shape features of the concrete surface, which is used to reflect the geometric unevenness of the base concrete surface that deviates from the ideal plane due to construction errors and other reasons in engineering practice.

[0028] The waviness profile refers to the periodic fluctuation component introduced by the use of a roughening toothed plate in the roughening process of concrete surface; the wavelength of this periodic fluctuation component is between the macroscopic shape profile and the microscopic roughness profile, and the wavelength is moderate. Its characteristics are closely related to the geometry and arrangement of the toothed plate used; the waviness profile is used to reflect the mesoscale morphological features generated during the artificial processing.

[0029] The roughness profile refers to the random, minute unevenness caused by the microstructure characteristics of the concrete material itself and the construction process; the roughness profile is a high-frequency random distribution with a small wavelength, used to reflect the micro-roughness characteristics of the concrete surface.

[0030] The method for extracting and reconstructing the morphological features of the concrete interface, wherein the statistical analysis of the three components—macroscopic shape profile, waviness profile, and roughness profile—in step (3) is as follows:

[0031] (3.1) The probability distributions of the profile heights of the macroscopic profile shape component and the roughness profile component were statistically analyzed. It was found that the distributions conformed to a Gaussian distribution with an expected value of 0. The standard deviations R of the profile heights of the macroscopic profile shape component and the roughness profile component were statistically analyzed. q and minimum autocorrelation length Sal;

[0032] (3.2) By Fourier transform and power spectral density calculation, the main periodic wavelength components of the waviness profile are determined, their average amplitude is statistically analyzed, and the relationship between wavelength and amplitude is analyzed.

[0033] The method for extracting and reconstructing the morphological features of the concrete interface, wherein the specific steps for reconstructing the representative morphological curve of the rough concrete interface in step (4) are as follows:

[0034] (4.1) Generation of waviness profile components

[0035] Fast Fourier Transform and power spectral density calculations were performed on the waviness profile obtained from the decomposition. The abscissa corresponding to the maximum power value is the main period of the waviness component. The main period P(x) of the waviness component is close to the tooth pitch x. The average amplitude A(x) decreases linearly with the tooth pitch x, which conforms to the following relationship:

[0036] P(x) = x (3);

[0037] A(x)=-0.10776x+0.95439 (4);

[0038] Based on the above relationships (3)-(4), a sine curve is approximately used to generate the waviness component curve after roughening treatment of tooth plates with arbitrary tooth pitch.

[0039] (4.2) Generation of macroscopic shape profile components and roughness profile components

[0040] Both the macroscopic shape profile and the roughness profile follow a Gaussian distribution with an expected value of 0, and can be regarded as a Gaussian random process, based on its standard deviation R. q Data for macroscopic shape profiles and roughness profiles are generated using the minimum autocorrelation length Sal.

[0041] (4.3) The height data of the waviness profile generated in step (4.1) and the macroscopic shape profile and roughness profile generated in step (4.2) are superimposed to generate a complete concrete surface profile curve.

[0042] By adopting the above technical solution, the present invention has the following beneficial effects:

[0043] The method for extracting and reconstructing the morphological features of concrete interfaces in this invention is well-conceived. It can not only accurately restore the multi-scale morphological features of concrete interfaces, but also flexibly adjust the generated interface features according to different tooth plate parameters, so as to achieve multi-dimensional high-precision characterization and reconstruction of concrete interface morphology. It is applicable to interface morphology analysis and bonding performance evaluation in various engineering scenarios, and can overcome the problems of single surface morphology index and insufficient reconstruction accuracy in the existing technology.

[0044] This invention decomposes the one-dimensional representative contour curve of concrete surface morphology into three physically meaningful components: macroscopic shape contour, waviness contour, and roughness contour. By combining the geometry of the actual processing tool, the waviness contour is adjusted and optimized, enabling the reconstruction of the morphological features of surfaces treated with toothed plates of any size. This not only breaks through the limitations of the traditional single characterization by the "roughness" index, but also fully explores the multi-scale information of concrete surface morphology, providing a foundation for the reconstruction of interface features and the analysis of bonding performance.

[0045] Compared with the prior art, the present invention has the following advantages and features:

[0046] (1) Multi-scale accurate characterization of concrete interface morphology: Based on wavelet transform, the concrete surface morphology is decomposed into three physically meaningful components: macroscopic shape profile, waviness profile and roughness profile. This can comprehensively reflect the different scale characteristics of the interface morphology, overcome the limitation of existing technologies that rely only on a single roughness morphology index, and provide a more accurate characterization method.

[0047] (2) High-precision interface morphology reconstruction capability: Through the analysis of the characteristics of each component and the parameters of the toothed plate, the proposed reconstruction method can accurately restore the surface morphology of the concrete after roughening treatment, providing a reliable geometric model for subsequent bonding performance analysis and numerical simulation.

[0048] (3) Enhanced flexibility in bonding performance evaluation and application: This invention can be flexibly adjusted according to different concrete treatment processes and tooth plate parameters, and is applicable to a variety of engineering scenarios, especially in the fields of concrete reinforcement, prefabricated buildings and structural repair, and has broad application value and practical significance. Attached Figure Description

[0049] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of step S100 in the method for extracting and reconstructing the morphological features of concrete interface of the present invention, in which high-precision 3D point cloud data of rough surface is obtained by white light scanning method and representative contour curves are extracted.

[0051] Figure 2 This is a schematic diagram illustrating the principle of wavelet transform decomposition and extraction of signals at different scales in step S200 of the concrete interface morphology feature extraction and reconstruction method of the present invention.

[0052] Figure 3 This is a schematic diagram of the abrupt change point and cutoff layer number in step S200 of the decomposition step in the method for extracting and reconstructing the morphological features of concrete interface of the present invention.

[0053] Figure 4 This is a schematic diagram of the decomposition result in step S200 of the concrete interface morphology feature extraction and reconstruction method of the present invention, that is, the representative contour curve is decomposed into 3 components (taking a tooth pitch of 3.5 mm as an example).

[0054] Figure 5 This is a schematic diagram of step S400 in the method for extracting and reconstructing the morphological features of concrete interface of the present invention, in which the main periodic wave pitch components of the waviness component are extracted by fast Fourier transform and power spectral density (taking tooth pitch of 3.5mm, 5mm, and 7mm as examples).

[0055] Figure 6 In step S400 of the method for extracting and reconstructing the morphological features of concrete interface of the present invention, the relationship between the main wave pitch components and the average amplitude is analyzed and obtained.

[0056] Figure 7 This is a schematic diagram of the three components (taking a tooth pitch of 3.5 mm) reconstructed and generated in step S400 of the concrete interface morphology feature extraction and reconstruction method of the present invention. Detailed Implementation

[0057] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] The present invention will be further explained below with reference to specific embodiments.

[0059] This embodiment provides a method for extracting and reconstructing the morphological features of concrete interfaces, which mainly includes the following steps:

[0060] S100, Obtain representative contour curves of concrete interface morphology.

[0061] like Figure 1 As shown, a three-dimensional white light scanning technique was used to scan the surface of the roughened concrete sample to obtain high-precision 3D point cloud data of the rough surface. Based on the point cloud data, the aspect ratio S of the concrete surface texture was calculated. tr This is used as a metric for the uniformity of surface texture.

[0062] Through calculation and analysis, it was found that the rough surface profile S obtained by roughening arbitrary toothed plates is... tr The values ​​are all close to 0, indicating that the roughened concrete surface has strong directional characteristics. Therefore, a representative profile curve can be used to characterize the morphological features of the concrete surface.

[0063] To extract representative contour curves, several one-dimensional contour curves are extracted at equal intervals along the tangent direction perpendicular to the ripple direction as representative data.

[0064] S200. The acquired representative contour curve is decomposed into three components: macroscopic shape contour, waviness contour, and roughness contour; the specific process is as follows:

[0065] Wavelet transform theory is used to decompose the obtained representative contour curve f(x) and extract the morphological features at different wavelength scales;

[0066] For a representative contour curve f(x), its wavelet transform mathematical expression is:

[0067]

[0068] Here, f(x) is decomposed into a set of wavelet functions ψ a,b (x); ψ a,b (x) is generated by scaling and translation of the mother wavelet ψ(x):

[0069]

[0070] In equation (2) above, a is the scaling factor, which determines the compression or expansion of the wavelet; b is the translation factor, which controls the position of the wavelet in the signal; factor a -1 / 2 This is used to achieve energy normalization across scales. The choice of the mother wavelet depends on the application requirements. Comparison shows that, in this example, the "db8" Daubechies' wavelet can optimally decompose the contour features of the rough concrete surface.

[0071] In practice, Discrete Wavelet Transform (DWT) is used to reduce redundant calculations, and the decomposition is performed step by step. For example... Figure 2 As shown, in each level of decomposition, the input signal is decomposed into two components at different frequency scales, namely the approximate component with a larger wavelength (low-frequency component, A). i ) and lower wavelength detail components (high frequency components, D i The decomposition process iterates step by step, and the approximate components (A) of each decomposition level are... i This will be used as the input for the next decomposition layer until a preset cutoff number of layers is reached. As the number of decomposition layers increases, the discrimination wavelength gradually increases, the discrimination frequency gradually decreases, the non-stationary information contained in the approximate components decreases, and the detail components reflect more large-scale behavior.

[0072] Low-frequency components (A1, A2, ...): reflect macroscopic characteristics of larger wavelengths;

[0073] High-frequency components (D1, D2, ...): reflect the detailed features at smaller wavelengths.

[0074] Based on wavelet transform theory, using the Daubechies wavelet basis (db8) as the mother wavelet, the representative contour curve of a rough concrete surface is decomposed into three components: macroscopic shape contour (trend term), waviness contour (waviness term), and roughness contour (roughness term), corresponding to the large, medium, and small wavelength fluctuation components in wavelet decomposition, respectively. During the decomposition of the representative contour curve, high-pass and low-pass filters are used to extract the high-frequency and low-frequency components of the signal, respectively, and the low-frequency components are further decomposed. By analyzing the variance variation trend of the low-frequency components, the time of abrupt change is used as the criterion to determine the cutoff level of the wavelet decomposition.

[0075] The specific process of decomposing the representative contour curve of the rough concrete surface is as follows: First, the representative contour curve is used as the input signal and decomposed into different layers. The layer number corresponding to the second abrupt change point of the low-frequency component variance curve is used as the cutoff layer number to separate the low-frequency component as the macroscopic shape contour component. Then, the data after removing the macroscopic shape contour is used as the new input signal and decomposed into different layers. The layer number corresponding to the first abrupt change point of the low-frequency component variance curve is used as the cutoff layer number to extract the low-frequency component as the waviness contour component and the high-frequency component as the roughness contour component.

[0076] Roughness profile (roughness term): describes the micro-roughness characteristics, referring to the random micro-unevenness caused by the micro-structure characteristics of the concrete material itself and the construction process; the roughness profile is a high-frequency random distribution with a small wavelength, conforming to the white noise characteristics of Gaussian distribution, with an expected value close to 0 and a small variance; the variance value does not change significantly with the amount of data, reflecting the random micro-unevenness of the concrete surface.

[0077] Macroscopic shape profile (trend item): describes the overall macroscopic trend, that is, the overall geometric shape characteristics of the concrete surface, which conforms to the Gaussian distribution law and has a large variance; it is a low-frequency feature caused by construction errors or geometric unevenness; it is used to reflect the geometric unevenness of the base concrete surface that deviates from the ideal plane due to construction errors and other reasons in engineering practice.

[0078] Wrinkle profile (wrinkle term): describes a periodic characteristic of medium wavelength, closely related to the geometry of the toothed plate; the main period of the wrinkle term is close to the tooth pitch of the toothed plate, reflecting the periodic fluctuation characteristics of the artificial roughening process; specifically, the wrinkle profile refers to the periodic fluctuation component introduced by the use of roughening toothed plates (i.e., tools such as toothed plates with fixed geometric shapes and periodic structures) in the roughening process of concrete surfaces; the wavelength of this periodic fluctuation component is between the macroscopic shape profile and the microscopic roughness profile, with a moderate wavelength, and its characteristics are closely related to the geometry and arrangement of the toothed plates used; the wrinkle profile is used to reflect the mesoscale morphological characteristics generated during the artificial processing.

[0079] Research has shown that the roughness profile components follow a Gaussian distribution and can be considered as white noise. If the signal contains only the roughness profile components, its variance is extremely small; when waviness profiles are introduced into the signal, the variance changes abruptly; similarly, when macroscopic shape profiles are introduced into the signal, the variance changes significantly again. Therefore, by analyzing the changes in signal variance, the components corresponding to different decomposition stages can be accurately extracted.

[0080] like Figure 3As shown, this embodiment employs a two-step decomposition scheme for the three components: macroscopic shape profile, waviness profile, and roughness profile. The first step decomposes and extracts the macroscopic profile component of the representative profile curve, and the second step decomposes and extracts the waviness and roughness components of the profile curve. During the decomposition process, approximate component A is analyzed. i The variance trend is used to determine the decomposition cutoff level.

[0081] The first step of decomposition involves using a representative profile curve as the input signal and performing 1 to 8 levels of decomposition, calculating the variance of the approximate components A1 to A8; the level corresponding to the second abrupt change point of the variance curve is taken as the cutoff level m. Using A... m Characterizing macroscopic contour components;

[0082] The second step is decomposition, using D1 to D2. m The sum is used as the input signal, and it is decomposed into 1 to 8 levels respectively, and the approximate component A is calculated. m,1 ~A m,8 The variance; the number of layers corresponding to the first abrupt change point of the variance curve is taken as the cutoff layer number n. Using A... m,n Characterizing the waviness component, with D m,1 ~D m,n The sum represents the roughness component.

[0083] Taking the roughening data of a toothed plate with a tooth pitch of 3.5mm as an example, after two-step decomposition, the decomposition results of three components are obtained, such as... Figure 4 As shown.

[0084] S300. Statistical analysis is performed on the three components obtained from the decomposition: macroscopic shape profile, waviness profile, and roughness profile. The specific process is as follows:

[0085] S310. Statistically analyze the probability distributions of the profile heights of the macroscopic profile shape component and the roughness profile component, respectively. The distributions are found to conform to a Gaussian distribution with an expected value of 0. Calculate the standard deviation R of the profile heights of the macroscopic profile shape component and the roughness profile component, respectively. q and minimum autocorrelation length Sal;

[0086] The S320 wave profile exhibits significant periodicity. By using fast Fourier transform and power spectral density calculation, its main periodic wavelength components are determined, its average amplitude is statistically analyzed, and the relationship between wavelength and amplitude is examined.

[0087] Among them, statistical analysis of the three components—macroscopic shape profile, waviness profile, and roughness profile—revealed the following:

[0088] Both the roughness profile (roughness term) and the macroscopic shape profile (trend term) conform to a Gaussian random process with an expected value of approximately 0, indicating that these two parts are random and have no direct correlation with the artificial roughening process.

[0089] The main periodic characteristics of the waviness profile (waviness term) are close to the tooth pitch of the toothed plate used, indicating that the waviness term is determined by the geometric characteristics of the toothed plate.

[0090] Further analysis revealed that the average amplitude decreased linearly with the increase of tooth pitch, indicating that tooth pitch has a significant impact on the amplitude characteristics of the corrugation morphology.

[0091] S400. Based on the statistical laws of the three components—macroscopic shape profile, waviness profile, and roughness profile—the morphology of the rough concrete interface is reconstructed. The specific steps are as follows:

[0092] S410, Generation of Waviness Profile Components

[0093] The ripple term obtained from the decomposition is calculated using Fast Fourier Transform (FFT) and Power Spectral Density (PSD). The abscissa corresponding to the maximum power value is the principal period of the ripple component, and the corresponding principal periods are as follows: Figure 5 As shown, the dominant period P(x) of the waviness component is close to the tooth pitch x, and the average amplitude A(x) decreases linearly with increasing tooth pitch x, conforming to the following relationship (e.g. Figure 6 As shown):

[0094] P(x) = x (3);

[0095] A(x)=-0.10776x+0.95439 (4);

[0096] Based on the above relationships (3)-(4), a sine curve is approximately used to generate the waviness component curve after roughening treatment of tooth plates with arbitrary tooth pitch.

[0097] S420. Generation of macroscopic shape profile components and roughness profile components.

[0098] Both the trend term and the roughness term follow a Gaussian distribution with an expected value of 0, and can be regarded as a Gaussian random process; according to their standard deviation (R²), q The trend term and roughness term data are generated using the minimum autocorrelation length (Sal).

[0099] S430. The height data of the waviness profile generated in step S410 and the macroscopic shape profile and roughness profile generated in step S420 are superimposed to generate a complete concrete surface profile curve. For example... Figure 7 As shown, the reconstructed contour curve can accurately reflect the surface morphology of the concrete after the tooth plate treatment, and can generate concrete surface contour curves with different tooth pitches, thus having good engineering applicability.

[0100] This invention is well-conceived and not only breaks through the limitations of the traditional single-index characterization of "roughness", but also fully explores the multi-scale information of concrete surface morphology, providing a foundation for the reconstruction of interface features and the analysis of bonding performance.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for extracting and reconstructing the morphological features of concrete interfaces, characterized in that... It mainly includes the following steps: (1) Obtain representative contour curves of concrete interface morphology; (2) The obtained representative contour curve is decomposed into three components: macroscopic shape contour, waviness contour, and roughness contour; the specific process of decomposing the representative contour curve is as follows: Using wavelet transform theory to analyze representative contour curves The process involves decomposition to extract morphological features at different wavelength scales; and for a representative contour curve... Its wavelet transform expression is: （1）； in, Decomposed into a set of wavelet functions ; Mother wavelet Generated by scaling and translation: （2）； In the above formula (2), The scaling factor determines whether the wavelet is compressed or expanded. The translation factor controls the position of the wavelet within the signal; factor Used to achieve energy normalization across scales; Based on wavelet transform theory, using the Daubechies wavelet basis (db8) as the mother wavelet, the representative contour curve of the rough concrete surface is decomposed into three components: macroscopic shape contour, waviness contour, and roughness contour. During the decomposition of the representative contour curve, high-pass and low-pass filters are used to extract the high-frequency and low-frequency components of the representative contour curve, respectively, and the low-frequency component is further decomposed. By analyzing the variance variation trend of the low-frequency component, the cutoff level of the wavelet decomposition is determined by using the moment of abrupt change as the criterion. (3) Perform statistical analysis on the three components obtained from the decomposition: macroscopic shape profile, waviness profile, and roughness profile; (4) Based on the statistical laws of the three components of macroscopic shape profile, waviness profile and roughness profile, the morphology of the rough interface of concrete is reconstructed.

2. The method for extracting and reconstructing the morphological features of concrete interfaces as described in claim 1, characterized in that... The specific process of step (1) is as follows: the surface of the roughened concrete sample is three-dimensionally imaged using the white light scanning method to obtain high-precision 3D point cloud data of the rough surface; in the point cloud data, several representative contour curves are extracted at equal intervals along the tangent direction perpendicular to the corrugation direction as the basic data for the characterization of the concrete surface morphology.

3. The method for extracting and reconstructing the morphological features of concrete interfaces as described in claim 1, characterized in that, The specific process of decomposing the representative contour curve of the rough concrete surface is as follows: First, the representative contour curve is used as the input signal and decomposed into different layers. The layer corresponding to the second abrupt change point of the low-frequency component variance curve is used as the cutoff layer to separate the low-frequency component as the macroscopic shape contour component. The data after removing the macroscopic shape contour is then used as a new input signal and decomposed into different levels. The first abrupt change point of the variance curve of the low-frequency component is used as the cutoff level to extract the low-frequency component as the waviness contour component and the high-frequency component as the roughness contour component.

4. The method for extracting and reconstructing the morphological features of concrete interfaces as described in claim 1, characterized in that... The macroscopic shape profile refers to the overall geometric shape characteristics of the concrete surface, which is used to reflect the geometric unevenness of the base concrete surface that deviates from the ideal plane due to construction errors and other reasons in engineering practice. The waviness profile refers to the periodic fluctuation component introduced by the use of a roughening toothed plate in the roughening process of concrete surface; the wavelength of this periodic fluctuation component is between the macroscopic shape profile and the microscopic roughness profile, and the wavelength is moderate. Its characteristics are closely related to the geometry and arrangement of the toothed plate used; the waviness profile is used to reflect the mesoscale morphological features generated during the artificial processing. The roughness profile refers to the random, minute unevenness caused by the microstructure characteristics of the concrete material itself and the construction process; the roughness profile is a high-frequency random distribution with a small wavelength, used to reflect the micro-roughness characteristics of the concrete surface.

5. The method for extracting and reconstructing the morphological features of concrete interfaces as described in claim 1, characterized in that, The statistical analysis of the three components—macroscopic shape profile, waviness profile, and roughness profile—in step (3) is as follows: (3.1) The probability distributions of the profile heights of the macroscopic profile shape component and the roughness profile component were statistically analyzed. It was found that the distributions conformed to a Gaussian distribution with an expected value of 0. The standard deviations of the profile heights of the macroscopic profile shape component and the roughness profile component were statistically analyzed. and minimum autocorrelation length ; (3.2) By Fourier transform and power spectral density calculation, the main periodic wavelength components of the waviness profile are determined, their average amplitude is statistically analyzed, and the relationship between wavelength and amplitude is analyzed.

6. The method for extracting and reconstructing the morphological features of concrete interfaces as described in claim 5, characterized in that, The specific steps for reconstructing the representative morphology curve of the rough concrete interface in step (4) are as follows: (4.1) Generation of waviness profile components Fast Fourier Transform and power spectral density calculations are performed on the decomposed waviness profile; the abscissa corresponding to the maximum power value is the principal period of the waviness component. With tooth pitch Approximate, average amplitude With tooth pitch It increases and decreases linearly, conforming to the following relationship: （3）； （4）； Based on the above relationships (3)-(4), a sine curve is approximately used to generate the waviness component curve after roughening treatment of tooth plates with arbitrary tooth pitch. (4.2) Generation of macroscopic shape profile components and roughness profile components Both the macroscopic shape profile and the roughness profile follow a Gaussian distribution with an expected value of 0, and can be regarded as a Gaussian random process, based on its standard deviation. and minimum autocorrelation length Generate data for macroscopic shape profiles and roughness profiles; (4.3) The height data of the waviness profile generated in step (4.1) and the macroscopic shape profile and roughness profile generated in step (4.2) are superimposed to generate a complete concrete surface profile curve.

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