Method for extracting and reconstructing morphological characteristics of concrete interface

Abstract

The invention provides a concrete interface morphology feature extraction and reconstruction method. The method mainly comprises the following steps: (1) obtaining a representative profile curve of a concrete interface morphology; (2) decomposing the obtained representative profile curve into three components, namely a macroscopic shape profile, a waviness profile and a roughness profile; (3) carrying out statistical analysis on three components of a macroscopic shape contour, a waviness contour and a roughness contour obtained by decomposition; and (4) reconstructing the concrete rough interface morphology based on the statistical law of the three components of the macroscopic shape contour, the waviness contour and the roughness contour. The method disclosed by the invention is reasonable in conception, not only breaks through the limitation of single representation by a'roughness' index traditionally, but also fully excavates the multi-scale information of the surface appearance of the concrete, and provides a basis for the reconstruction of interface characteristics and the analysis of the adhesive property.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of civil engineering, and particularly relates to a method for extracting and reconstructing concrete interface morphology characteristics. BACKGROUND

[0002] Reliable bonding of new and old concrete interfaces is the core key to realize efficient reinforcement of deteriorated concrete structures, wet installation of fabricated concrete structures and other technologies. Studies have shown that interface morphology characteristics are one of the main factors affecting the bonding performance of new and old concrete. With the increase of interface roughness, the bonding area of new and old concrete increases, and the mechanical interlocking effect is enhanced, which can effectively improve the interface strength. In actual engineering, roughening treatment is a common method of roughening the surface of concrete. Different toothed plates with different tooth pitches can be used to roughen the surface of concrete. Accurate characterization of the surface morphology characteristics of the treated substrate concrete is crucial for analyzing and evaluating the bonding performance of the interface.

[0003] In existing engineering practice, probe method, sand laying method or 3D scanning method are usually used to obtain the roughness characteristics of the concrete surface. For example, the probe method obtains the corresponding linear roughness index by directional scanning along the rough surface; the 3D scanning obtains the roughness index such as arithmetic mean height by obtaining the surface point cloud data. However, studies have shown that even if the roughness index (such as average height) is the same, the geometric morphology characteristics of the interface may still be significantly different, resulting in different interface bonding performance. Therefore, extracting interface morphology characteristics and reconstructing representative interface morphology model is the key to accurately analyze and evaluate the interface bonding performance.

[0004] In terms of surface reconstruction methods, some scholars (Chinese patent CN202311791204.7, "Material ablation surface morphology characterization method and system based on wavelet transform") proposed to extract the three-dimensional profile of the scanned surface into a number of one-dimensional curves, and extract the high-frequency curve as the characteristic curve of the material surface roughness through wavelet transform. However, this method only uses roughness index to represent the surface morphology, which is too single, and does not fully consider the inherent geometric unevenness of the concrete substrate surface and the periodic fluctuation characteristics introduced by the toothed plate roughening treatment.

[0005] A test method for reconstructing the three-dimensional roughness of a concrete surface based on 3D scanning is disclosed in Chinese Patent CN201911065766.7. The method involves creating a mirror model of the concrete surface, conducting a 3D scanning test on the mirror model in the laboratory, importing the obtained rough surface scanning data into COMSOL Multiphysics software for analysis and post-processing, and calculating the three-dimensional roughness index values of the surface in multiple steps. However, the limitation of this patent technology is that only roughness index parameters are obtained, and further reconstruction of the representative topography of the rough surface is not achieved, making it difficult to meet the needs of subsequent numerical simulation and interface bonding behavior analysis.

[0006] In another Chinese patent CN202311791204.7, a material ablation surface topography characterization method and system based on wavelet transform is disclosed. For the material ablation surface, a surface point cloud profile model is obtained by three-dimensional scanning imaging, a number of one-dimensional profile curves are extracted, and wavelet transform is used for decomposition to obtain low-frequency curves and high-frequency curves. In the decomposition process, whether to stop decomposition is judged according to the change of the low-frequency curve, and finally the high-frequency curve is extracted as the characteristic curve of the material surface roughness, and the material surface roughness is calculated through the curve to characterize the surface topography. However, the limitation of this patent technology in the application of concrete surface topography characterization is: (1) single characterization: only the roughness characteristic curve and its related parameters are used to characterize the concrete surface topography, which is difficult to fully describe the complex features of the concrete surface; (2) insufficient engineering applicability: in engineering practice, the concrete surface is often not an ideal plane, but usually has certain geometric irregularity; in addition, when the concrete surface is roughened by toothed plate scratching, etc., in addition to the fine roughness features represented by the high-frequency curve, periodic fluctuation components related to the geometric shape of the processing tool will also be introduced, which cannot be effectively characterized or reconstructed in this technology.

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

[0008] (1) Single characterization index, unable to fully reflect the interface topography characteristics: existing technologies (such as patent number 201911065766.7) only use roughness indicators (such as arithmetic mean height) to characterize the concrete surface topography, and fail to capture the geometric characteristics and complex fluctuation characteristics of the surface, resulting in a single interface topography description that cannot fully reflect the true characteristics of the interface.

[0009] (2) Lack of representative topography reconstruction capability: existing technologies only stop at the extraction stage of roughness parameters and do not realize the reconstruction of representative three-dimensional topography of rough surfaces, which cannot meet the subsequent computational simulation needs and limits its application in interface bonding behavior analysis.

[0010] (3) Interface topography feature information loss: On the one hand, the concrete surface is usually not an ideal plane, but has certain geometric unevenness; on the other hand, when the concrete surface is treated by toothed plate roughening and other methods, periodic fluctuation characteristics related to the geometric shape of the treatment tool are introduced into the interface topography. The prior art does not fully consider the above characteristics, and part of the concrete interface topography feature information is lost.

[0011] In summary, it is necessary to further innovate the prior art. SUMMARY

[0012] In view of the technical problems in the above background art, the present application provides a concrete interface topography feature extraction and reconstruction method, which has reasonable concept and can not only accurately restore the multi-scale topography features of the concrete interface, but also flexibly adjust the generated interface features according to different toothed plate parameters, realize multi-dimensional high-precision representation and reconstruction of the concrete interface topography, and is suitable for interface topography analysis and bonding performance evaluation in various engineering scenarios.

[0013] In order to solve the above problems, the present application provides a concrete interface topography feature extraction and reconstruction method, which mainly includes the following steps:

[0014] (1) Obtain a representative profile curve of the concrete interface topography;

[0015] (2) Decompose the obtained representative profile curve into three components of macroscopic shape profile, waviness profile and roughness profile;

[0016] (3) Perform statistical analysis on the three components of macroscopic shape profile, waviness profile and roughness profile obtained by decomposition;

[0017] (4) Based on the statistical law of the three components of macroscopic shape profile, waviness profile and roughness profile, reconstruct the rough interface topography of the concrete.

[0018] The concrete interface topography feature extraction and reconstruction method, wherein the specific process of step (1) is: using a white light scanning method to perform three-dimensional imaging on the surface of the roughened concrete sample, and obtaining high-precision 3D point cloud data of the rough surface; in the point cloud data, along the tangent direction perpendicular to the waviness direction, a plurality of representative profile curves are extracted at equal intervals as the basic data for representing the concrete surface topography.

[0019] The concrete interface topography feature extraction and reconstruction method, wherein the specific process of step (2) for decomposing the representative profile curve is as follows:

[0020] The representative profile curve f(x) is decomposed by using wavelet transform theory to extract the topographic features of different wavelength scales; and the expression of the wavelet transform of a representative profile curve f(x) is:

[0021]

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

[0023]

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

[0025] Based on the wavelet transform theory, the representative profile curve of the concrete rough surface is decomposed into three components of macro shape profile, waviness profile and roughness profile by taking the Daubechies wavelet base, i.e. "db8", as the mother wavelet; and in the decomposition process of the representative profile curve, a high-pass filter and a low-pass filter are used to extract the high-frequency part and the low-frequency part of the representative profile curve respectively, and the low-frequency part is further decomposed; the cut-off layer number of wavelet decomposition is determined by analyzing the variance trend of the low-frequency component and taking the time of its sudden change as the criterion.

[0026] The concrete interface topographic feature extraction and reconstruction method, wherein the specific process of decomposing the representative profile curve of the concrete rough surface is as follows: first, the representative profile curve is taken as an input signal, and it is decomposed at different layers; the second sudden change point of the low-frequency component variance curve is taken as the cut-off layer number, and the low-frequency component is separated as the macro shape profile component; then, the data after removing the macro shape profile is taken as a new input signal, and it is decomposed at different layers; the first sudden change point of the low-frequency component variance curve is taken as the cut-off layer number, and the low-frequency component is extracted as the waviness profile component, and the high-frequency component is extracted as the roughness profile component.

[0027] The concrete interface topographic feature extraction and reconstruction method, wherein: the macro shape profile refers to the overall geometric shape feature of the concrete surface, which is used to reflect the geometric unevenness of the base concrete surface deviating from the ideal plane due to construction errors and other reasons in engineering practice;

[0028] The waviness profile refers to a periodic fluctuation component introduced by a toothed plate used in the concrete surface roughening process; the wavelength of the periodic fluctuation component is between the macro shape profile and the micro roughness profile, the wavelength is moderate in size, and the characteristics are closely related to the geometry and arrangement of the toothed plate used; the waviness profile is used to reflect the mesoscale topographic features generated in the artificial process;

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

[0030] The extraction and reconstruction method of the concrete interface topographic features, wherein the statistical analysis process of the three components of the macro shape profile, the waviness profile and the roughness profile in step (3) is:

[0031] (3.1) respectively, the profile height probability distribution of the macro profile shape component and the roughness profile component is found to be a Gaussian distribution with an expected value of 0, and the profile height standard deviation R q and the minimum autocorrelation length Sal are respectively calculated;

[0032] (3.2) the main period distance component of the waviness profile is determined by Fourier transform and power spectral density calculation, the average amplitude is calculated, and the relationship between the period distance and the amplitude is analyzed.

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

[0034] (4.1) generation of the waviness profile component

[0035] Fast Fourier transform and power spectral density calculation are performed on the decomposed waviness profile; 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 pitch x, the average amplitude A(x) decreases linearly with the increase of the pitch x, and the relationship is as follows:

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

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

[0038] According to the above relationship (3)-(4), a sinusoidal curve is generated to generate the waviness component curve after roughening treatment of the toothed plate with any pitch;

[0039] (4.2) generation of the macro shape profile component and the roughness profile component

[0040] The macro shape profile and the roughness profile are both subject to Gaussian distribution with an expected value of 0, and can be regarded as Gaussian random processes, according to the standard deviation R q and the minimum autocorrelation length Sal to generate data of the macro shape profile and the roughness profile;

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

[0042] With the technical scheme, the present application has the following beneficial effects:

[0043] The extraction and reconstruction method of the concrete interface morphology feature has reasonable concept, can accurately restore the multi-scale morphology feature of the concrete interface, can flexibly adjust the generated interface feature according to different tooth plate parameters, realizes multi-dimensional high-precision characterization and reconstruction of the concrete interface morphology, is suitable for interface morphology analysis and bonding performance evaluation in various engineering scenes, and can overcome the problems of single surface morphology index and insufficient reconstruction precision in the prior art.

[0044] The present application decomposes the one-dimensional representative profile curve of the concrete surface morphology into three components with physical meaning, namely the macro shape profile, the waviness profile and the roughness profile, adjusts and optimizes the waviness profile in combination with the actual tool geometry, can realize the reconstruction of the morphology feature of the surface of the tooth plate with any size, breaks through the limitation of the traditional single characterization of the roughness index, fully excavates the multi-scale information of the concrete surface morphology, and provides a basis for the reconstruction of the interface feature and the analysis of the bonding performance.

[0045] Compared with the prior art, the present application also has the following advantages and characteristics:

[0046] (1) Multi-scale accurate characterization of the concrete interface morphology: based on wavelet transform, the concrete surface morphology is decomposed into three components with physical meaning, namely the macro shape profile, the waviness profile and the roughness profile, which can comprehensively reflect the different scale features of the interface morphology, overcome the limitation of the prior art which only relies on a single roughness morphology index for characterization, 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 tooth plate parameters, the reconstruction method can accurately restore the morphology of the concrete surface after the hair pulling treatment, and provides a reliable geometric model for the subsequent bonding performance analysis and numerical simulation.

[0048] (3) Enhanced flexibility in bond performance evaluation and application: The present application can be flexibly adjusted according to different concrete processing processes and tooth plate parameters, suitable for various engineering scenarios, especially in the fields of concrete reinforcement, prefabricated buildings and structure repair, etc., with wide application value and practical significance. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0050] Figure 1 The schematic diagram for obtaining high-precision 3D point cloud data of the rough surface by the white light scanning method and extracting the representative profile curve in step S100 of the concrete interface morphology feature extraction and reconstruction method of the present application;

[0051] Figure 2 The principle schematic diagram of wavelet change decomposition and extraction of different scale signals in step S200 of the concrete interface morphology feature extraction and reconstruction method of the present application;

[0052] Figure 3 The schematic diagram of the mutation point and the cut-off layer number of the decomposition step in step S200 of the concrete interface morphology feature extraction and reconstruction method of the present application;

[0053] Figure 4 The schematic diagram of the decomposition result, i.e., the representative profile curve is decomposed into three components (taking a tooth pitch of 3.5 mm as an example) in step S200 of the concrete interface morphology feature extraction and reconstruction method of the present application;

[0054] Figure 5 The schematic diagram of the main period wave distance component of the corrugation component extracted by the fast Fourier transform and the power spectral density in step S400 of the concrete interface morphology feature extraction and reconstruction method of the present application (taking a tooth pitch of 3.5 mm, 5 mm, and 7 mm as an example);

[0055] Figure 6 The relationship diagram of the main wave distance component and the average amplitude analyzed in step S400 of the concrete interface morphology feature extraction and reconstruction method of the present application;

[0056] Figure 7 The schematic diagram of the three components (taking a tooth pitch of 3.5 mm as an example) reconstructed and generated in step S400 of the concrete interface morphology feature extraction and reconstruction method of the present application. DETAILED DESCRIPTION

[0057] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall 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 concrete interface morphology features, which mainly includes the following steps:

[0060] S100, obtain representative contour curve of concrete interface morphology

[0061] like Figure 1 As shown in the figure, the surface of the roughened concrete sample was scanned using 3D white light scanning technology 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 , which is used as a measure of surface texture uniformity.

[0062] Through calculation and analysis, it is found that the rough surface profile S obtained by roughening any tooth plate tr The values ​​are all close to 0, which indicates that the roughened concrete surface has strong directional characteristics. Therefore, the representative contour curve can be used to characterize the morphological characteristics of the concrete surface.

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

[0064] S200, decomposing the obtained representative profile curve into three components: macro shape profile, waviness profile, and roughness profile; the specific process is as follows:

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

[0066] For a representative contour curve f(x), the mathematical expression of its wavelet transform 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 the mother wavelet ψ(x) through scaling and translation:

[0069]

[0070] In the above equation (2), a is a scale factor, which determines the compression or expansion of the wavelet; b is a translation factor, which controls the position of the wavelet in the signal; the factor a -1 / 2 The energy normalization across scales is implemented. The choice of mother wavelet depends on the application requirements. In this case, the Daubechies’ wavelet “db8” is used to optimally decompose the profile features of the roughened concrete surface.

[0071] In actual operation, the discrete wavelet transform (DWT) is used to reduce redundant calculations, and the decomposition is performed step by step. As shown in Figure 2 In each level of decomposition, the input signal is decomposed into two components of different frequency scales, i.e., the approximate component (low-frequency component, A i ) of larger wavelength and the detail component (high-frequency component, D i ) of smaller wavelength. The decomposition process is iterated step by step, and the approximate component (A i ) of each level of decomposition will be used as the input for the next level of decomposition until the preset number of cutoff levels is reached. As the number of decomposition levels increases, the discrimination wavelength gradually increases, the discrimination frequency gradually decreases, and the non-stationary information contained in the approximate component decreases, while the detail component reflects more large-scale behavior.

[0072] Low-frequency component (A1, A2,...): reflects the macroscopic features of larger wavelength;

[0073] High-frequency component (D1, D2,...): reflects the detailed features of smaller wavelength.

[0074] Based on the wavelet transform theory, the representative profile curve of the rough concrete surface is decomposed into three components of macroscopic shape profile (trend term), waviness profile (waviness term), and roughness profile (roughness term) using the Daubechies wavelet basis “db8” as the mother wavelet, which correspond to the large, medium, and small wavelength components in the wavelet decomposition, respectively. During the decomposition process, the high-pass filter and the low-pass filter are used to extract the high-frequency part and the low-frequency part of the signal, respectively, and the low-frequency part is further decomposed. By analyzing the variance trend of the low-frequency component, the time of sudden change is taken as the criterion to determine the number of cutoff levels for wavelet decomposition.

[0075] The specific process of the decomposition of the representative profile curve of the rough surface of the concrete is as follows: taking the representative profile curve as an input signal, decomposing the input signal into different layers, taking the layer corresponding to the second mutation point of the low-frequency component variance curve as the cutoff layer, separating the low-frequency component as the macro shape profile component; taking the data after removing the macro shape profile as a new input signal, decomposing the new input signal into different layers, taking the layer corresponding to the first mutation point of the low-frequency component variance curve as the cutoff layer, extracting the low-frequency component as the waviness profile component, and the high-frequency component as the roughness profile component.

[0076] Roughness profile (roughness term): describing the micro roughness characteristics, referring to the random small 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 small wavelength and conforms to the white noise characteristics of Gaussian distribution, and the expectation value is close to 0 and the variance is small; the variance value does not change significantly with the data amount, and reflects the random micro unevenness of the concrete surface;

[0077] Macro shape profile (trend term): describing the overall macro trend, that is, the overall geometric shape characteristics of the concrete surface, conforming to the Gaussian distribution law and having a large variance; being a low-frequency feature caused by construction errors or geometric unevenness; used to reflect the geometric unevenness of the substrate concrete surface deviating from the ideal plane due to construction errors and the like in engineering practice;

[0078] Waviness profile (waviness term): describing the periodic characteristics of medium wavelength, which is closely related to the geometric shape of the tooth plate; the main period of the waviness term is close to the tooth pitch of the tooth plate, reflecting the periodic fluctuation characteristics of the manual brushing process; specifically, the waviness profile refers to the periodic fluctuation component introduced by the tooth plate used in the manual brushing process of the concrete surface (that is, using a tool with a fixed geometric shape and periodic structure); the wavelength of the periodic fluctuation component is between the macro shape profile and the micro roughness profile, and the wavelength is moderate, and its characteristics are closely related to the geometric structure and arrangement mode of the tooth plate used; the waviness profile is used to reflect the mesoscale topographic characteristics generated in the manual processing process;

[0079] It is found through research that the roughness profile component conforms to the Gaussian distribution law and can be regarded as white noise. If the signal only contains the component of the roughness profile, the variance value is very small; when the waviness profile is introduced into the signal, the variance will mutate; similarly, when the macro shape profile is introduced into the signal, the variance will mutate again. Therefore, by analyzing the variance of the signal, the components corresponding to different decomposition stages can be accurately extracted.

[0080] As Figure 3As shown, in this embodiment, a two-step decomposition scheme is adopted for the three components of macro shape profile, waviness profile and roughness profile: in the first step, the macro profile component representing the profile curve is extracted, and in the second step, the waviness component and the roughness component of the profile curve are extracted; in the decomposition process, the decomposition cutoff layer number is determined by analyzing the variance trend of the approximation component A i

[0081] In the first step, the representative profile curve is taken as the input signal, and 1-8 layer decomposition is performed, and the variances of the approximation components A1-A8 are calculated; the second mutation point of the variance curve corresponds to the layer number as the cutoff layer number m. A m characterizes the macro profile component;

[0082] In the second step, the sum of D1-D m is taken as the input signal, and 1-8 layer decomposition is performed, and the variances of the approximation components A m,1 -A m,8 are calculated; the first mutation point of the variance curve corresponds to the layer number as the cutoff layer number n. A m,n characterizes the waviness component, and the sum of D m,1 -D m,n characterizes the roughness component.

[0083] Taking the tooth plate pull-out data with a tooth pitch of 3.5 mm as an example, the decomposition results of the three components are obtained through two-step decomposition, as shown in Figure 4 .

[0084] S300, statistical analysis is performed on the macro shape profile, waviness profile and roughness profile obtained by decomposition, and the specific process is as follows:

[0085] S310, the profile height probability distribution of the macro profile shape component and the roughness profile component is respectively calculated, and it is found that the distribution conforms to the Gaussian distribution with an expected value of 0, and the profile height standard deviation R q and the minimum autocorrelation length Sal of the macro profile shape component and the roughness profile component are respectively calculated;

[0086] S320, the waviness profile has significant periodicity, and through fast Fourier transform and power spectral density calculation, the main period component of the wave distance is determined, the average amplitude is calculated, and the relationship between the wave distance and the amplitude is analyzed;

[0087] Among them, the statistical analysis of the macro shape profile, the waviness profile and the roughness profile three components finds that:

[0088] The roughness profile (roughness term) and the macro shape profile (trend term) both conform to the Gaussian random process with an expected value close to 0, indicating that these two parts have randomness and are not directly related to the artificial pull-out process; ​

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

[0090] Further analysis found that the average amplitude decreases linearly with the increase of the pitch, indicating that the pitch has an important influence on the amplitude characteristics of the waviness morphology.

[0091] S400, based on the statistical law of the macro shape profile, the waviness profile and the roughness profile, the concrete rough interface morphology is reconstructed; the specific steps are as follows:

[0092] S410, generation of waviness profile component

[0093] The decomposition obtained waviness term is subjected to fast Fourier transform (FFT) and power spectral density (PSD) calculation. The abscissa corresponding to the maximum power value is the main period of the waviness component, and the corresponding main period is as shown in Figure 5 The main period P(x) of the waviness component is close to the pitch x, and the average amplitude A(x) decreases linearly with the increase of the pitch x, which conforms to the following relationship (as shown in Figure 6 ):

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

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

[0096] According to the above relationship (3)-(4), the sine curve is approximately generated, and the waviness component curve after the tooth plate roughening treatment with any pitch is generated;

[0097] S420, generation of macro shape profile component and roughness profile component

[0098] The trend term and the roughness term both obey the Gaussian distribution with the expected value of 0, and can be regarded as Gaussian random process; the trend term and the roughness term data are generated according to the standard deviation (R q ) and the minimum autocorrelation length (Sal);

[0099] S430, the height data of the waviness profile generated in the above step S410 and the macro shape profile and the roughness profile generated in the above step S420 are superimposed to generate a complete concrete surface profile curve. As shown in Figure 7 The reconstructed profile curve can truly reflect the concrete surface morphology after the tooth plate treatment, and the concrete surface profile curve under different pitches can be generated, which has good engineering applicability.

[0100] The present application concept is reasonable, not only breaks through the traditional single representation of "roughness" index limitation, also fully excavates the multi-scale information of concrete surface morphology, and provides a basis for interface feature reconstruction and bonding performance analysis.

[0101] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not limited to them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for extracting and reconstructing concrete interface morphology features, characterized in that , mainly including the following steps: (1) Obtain representative contour curves of concrete interface morphology; (2) Decomposing the obtained representative profile curve into three components: macro shape profile, waviness profile, and roughness profile; (3) Statistical analysis is performed on the three components of the decomposed macro shape profile, waviness profile, and roughness profile; (4) Based on the statistical laws of the three components of macro shape profile, waviness profile and roughness profile, the concrete rough interface morphology is reconstructed.

2. The method for extracting and reconstructing the concrete interface morphology characteristics according to claim 1 is characterized in that The specific process of step (1) is as follows: a white light scanning method is used 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, several representative contour curves are extracted at equal intervals along the tangent direction perpendicular to the corrugation direction as basic data for characterizing the concrete surface morphology.

3. The method for extracting and reconstructing the concrete interface morphology characteristics according to claim 1, characterized in that: The specific process of decomposing the representative contour curve in step (2) is as follows: The representative contour curve f(x) is decomposed using wavelet transform theory to extract the morphological features of different wavelength scales. For a representative contour curve f(x), the wavelet transform expression is: Here, f(x) is decomposed into a set of wavelet functions ψ a,b (x); ψ a,b (x) is generated by the mother wavelet ψ(x) through scaling and translation: In the above formula (2), a is the scale 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 cross-scale energy normalization; Based on wavelet transform theory, the Daubechies wavelet basis (db8) is used as the mother wavelet to decompose the representative contour curve of the concrete rough surface into three components: macro-shape contour, waviness contour, and roughness contour. During the decomposition process, high-pass and low-pass filters are used to extract the high-frequency and low-frequency components of the representative contour curve, respectively. The low-frequency component is further decomposed. By analyzing the variance variation trend of the low-frequency component and taking the moment of sudden change as the criterion, the cutoff number of wavelet decomposition levels is determined.

4. The method for extracting and reconstructing the concrete interface morphology characteristics according to claim 3, characterized in that: The specific process of decomposing the representative contour curve of the concrete rough surface is as follows: first, the representative contour curve is used as an input signal, and decomposed into different layers, and the layer corresponding to the second mutation point of the low-frequency component variance curve is used as the cutoff layer, and the low-frequency component is separated as the macro shape contour component; The data after removing the macro-shape contour is then used as a new input signal and decomposed into different layers. The layer corresponding to the first mutation point of the low-frequency component variance curve is used as the cutoff layer. The low-frequency component is extracted as the waviness contour component, and the high-frequency component is extracted as the roughness contour component.

5. The method for extracting and reconstructing the concrete interface morphology characteristics according to claim 1 or 3, characterized in that The macro 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 roughening teeth used in the roughening treatment of the concrete surface. The wavelength of this periodic fluctuation component is between the macroscopic shape profile and the microscopic roughness profile, and its wavelength is moderate. Its characteristics are closely related to the geometric structure and arrangement of the teeth used. The waviness profile is used to reflect the mesoscale morphological characteristics generated during the artificial treatment process. The roughness profile refers to random minor unevenness caused by the microscopic structural characteristics of the concrete material itself and the construction process; the roughness profile is a high-frequency random distribution with a small wavelength, and is used to reflect the microscopic roughness characteristics of the concrete surface.

6. The method for extracting and reconstructing the concrete interface morphology characteristics according to claim 1, characterized in that: The process of statistically analyzing the three components of the macro shape profile, the waviness profile and the roughness profile in step (3) is as follows: (3.1) The probability distribution of the contour height of the macro contour shape component and the roughness contour component is statistically analyzed. It is found that the distribution conforms to the Gaussian distribution with an expected value of 0. The standard deviation R of the contour height of the macro contour shape component and the roughness contour component is statistically analyzed. q and minimum autocorrelation length Sal; (3.2) Through Fourier transform and power spectrum density calculation, the main periodic pitch components of the waviness profile are determined, their average amplitude is calculated, and the relationship between pitch and amplitude is analyzed.

7. The method for extracting and reconstructing the concrete interface morphology characteristics according to claim 6, characterized in that: The specific steps of reconstructing the representative morphology curve of the concrete rough interface in step (4) are as follows: (4.1) Generation of waviness profile components The decomposed corrugation profile is subjected to fast Fourier transform and power spectral density calculation. The abscissa corresponding to the maximum power value is the main period of the corrugation component. The main period P(x) of the corrugation component is close to the tooth pitch x. The average amplitude A(x) decreases linearly with the increase of the tooth pitch x, which conforms to the following relationship: P(x)=x (3); A(x)=-0.10776x+0.95439 (4); According to the above relationship (3)-(4), a sine curve is used to approximate the waviness component curve of the tooth plate after roughening treatment with arbitrary tooth pitch; (4.2) Generation of macroscopic shape contour components and roughness contour components The macro shape profile and roughness profile both obey the Gaussian distribution with an expected value of 0 and can be regarded as a Gaussian random process. q and minimum autocorrelation length Sal to generate data of macro shape profile and roughness profile; (4.3) The waviness profile generated in the above step (4.1) and the height data of the macro shape profile and roughness profile generated in the above step (4.2) are superimposed to generate a complete concrete surface profile curve.

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