CN116266215B - Geometric feature extraction method for pavement recessive disease area - Google Patents
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- 201000010099 disease Diseases 0.000 title claims abstract description 64
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 64
- 238000000605 extraction Methods 0.000 title claims abstract description 17
- 238000001228 spectrum Methods 0.000 claims abstract description 38
- 239000011800 void material Substances 0.000 claims abstract description 31
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B15/00—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/885—Radar or analogous systems specially adapted for specific applications for ground probing
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a cement pavement void geometric feature extraction algorithm based on a CWT energy spectrum. 1. Data acquisition, forward and backward modeling is carried out to obtain original data; 2. preprocessing the original data; 3. performing continuous wavelet transformation processing on the preprocessed signals to reconstruct an energy spectrum; 4. performing dimension reduction on the energy spectrum, performing discrete wavelet decomposition on the dimension reduction data, and removing interference; 5. solving an energy function probability density function of a disease area, and solving an expected value of the function; 6. setting a threshold value by using an expected value, filtering out extreme points generated by interference, and solving two nearest minimum value points on the left and right sides of the maximum value point to determine the boundary width of the disease; 7. and calculating an improved generalized S transformation spectrum of the disease area, and solving a maximum value with minimum index in a dimension reduction function after dimension reduction, so as to calculate the depth. The problem that the existing radar map identification cannot accurately position a disease area and cannot determine the geometric characteristics of a void disease area is solved, and a basis is provided for pavement maintenance.
Description
Technical Field
The invention belongs to the field of pavement maintenance, and particularly relates to a method for reconstructing and extracting geometric features of a cement pavement void disease area by utilizing a ground penetrating radar signal continuous wavelet transformation energy spectrum.
Background
Cement roads are important in the traffic network in China, and have the advantages of high bearing capacity, good stability and low maintenance cost, and are widely applied to highway pavement structures of main roads in provinces and counties. Due to uneven construction, uneven roadbed settlement, influence of vehicle load and temperature stress and the like, a certain local gap is generated between the panel and the base layer. After the caulking materials between the plates fall off, rainwater easily enters the void area. The width of the water-containing void area can be further enlarged under the action of alternating load, the influence on the bearing capacity of a road is larger along with the enlargement of the void area, the void causes problems of settlement and dislocation of cement slabs, and finally, the slab breakage occurs, so that the traffic capacity is seriously influenced.
The cement pavement void disease detection method mainly comprises a pulse response technology, a sound vibration method, a vibration sensing and Ground Penetrating Radar (GPR) method and the like, wherein the ground penetrating radar is the most effective nondestructive detection technology in pavement void detection, and has been widely applied to pavement diseases and thickness of structural layers. The A-Scan signal of GPR is the minimum information unit of GPR, and also is the basic element formed by B-Scan map, and contains information such as depth, medium characteristics, thickness dimension and the like, so that if disease identification can be performed by using the GPR signal, the disease boundary position can be accurately positioned. However, a large amount of data generated during detection needs experienced professionals to process the data to judge whether diseases and positions of the diseases exist or not, so that the working efficiency is low and the accuracy is low; in addition, the existing radar spectrum identification method cannot accurately position the disease area and cannot determine the geometric characteristics of the void disease area. Therefore, it is highly desirable to determine the geometry of the void area at the bottom of the slab to evaluate the load bearing capacity of the pavement slab, maintain the pavement before breaking the slab, and extend the life of the pavement.
Echo signals of Ground Penetrating Radar (GPR) are transient non-stationary signals, and the distribution of different frequency components in the signals on a time axis cannot be revealed by the traditional Fourier transform due to the characteristics of a basis function. Whereas wavelet transform (CWT) is a time-frequency localized analysis with a fixed window size and a variable shape, capturing local and global features of signals by different scales. The method is often used for signal time-frequency analysis or accurate signal transient positioning, especially for signals with abrupt transient frequency changes. Therefore, the continuous wavelet transformation provides a new thought and method for identifying the ground penetrating radar map, positioning the disease area and determining the geometric characteristics of the disease area.
The applicant starts with continuous wavelet transformation to extract geometric feature parameters of a disease area, and the invention provides a cement pavement void geometric feature extraction method based on reconstructed CWT energy spectrum.
Disclosure of Invention
In order to solve the problems that the existing radar spectrum identification method cannot accurately position a disease area and cannot determine the geometric characteristics of a void disease area, the existing ground penetrating radar signal interpretation relies on manual experience to be time-consuming and labor-consuming, the subjectivity is strong and the like, a cement pavement void geometric characteristic extraction method based on reconstructed CWT energy spectrum is provided.
The invention is realized by the following steps.
And step 1, data acquisition, namely simulating a model by using the gprMax, or performing actual data acquisition by using the GPR to obtain original GPR data.
And 2, preprocessing the original GPR data (static correction, gain, background removal and F-K offset) to obtain a preprocessed signal.
And 3, performing Continuous Wavelet Transform (CWT) processing on the preprocessed signals, and then constructing a reconstructed energy spectrum. Compared with the original B-Scan, the reconstructed energy spectrum can more effectively reveal the transverse and longitudinal distribution of different disease areas, and can judge the medium difference of different areas.
And 4, performing dimension reduction on the energy spectrum to obtain a dimension reduction curve, performing discrete wavelet decomposition on the dimension reduction curve by adopting a sym4 wavelet basis function, removing background noise, and improving the smoothness of the curve.
And 5, obtaining an energy function probability density function of the dimension reduction curve, and using an expected value of the function as a threshold setting condition for obtaining an extreme point and a confirmation width.
And 6, extracting the void width, setting a threshold value by using the expected values in the steps 4 and 5, filtering out extreme points generated by interference, solving two nearest minimum value points on the left and right of the maximum value point to determine the boundary width of the disease, and storing the widths of all disease areas in a vector L.
And 7, obtaining depth according to the principle of improving the maximum value of the generalized S transformation spectrum. Calculating the improved S-transform spectrum Gs of the ith channel A-Scan data in the disease area (the area corresponding to the non-zero element) by the non-zero element value in the width vector i (f, t) for which spectrum Gs i Dimension reduction is carried out to obtain a (time-energy) function g i (t), function g i (t) is a reduced-dimension energy function that improves the S-transform; solving for a function g i And (3) the extreme point m with the smallest index in the largest n values in the (t) is the depth information.
g i (t n )＝max(Gs i (f,t n )),n＝1,2,…,N (3)
g i (t)＝[g i (t 1 ),…,g i (t N )] (4)
Where N is the total sampling point number of the ith track A-Scan data, g i (t n ) Is Gs i At t n Maximum value of time.
Preferably, when discrete wavelet decomposition is performed in the step 4, the principle of decomposing the layer number is that when the number of extreme points of which the function is lower than the average value after dimension reduction is less than half of the total number of extreme points, the layer number is 3, and conversely, the dimension reduction layer number is 4.
Preferably, when two minimum value points closest to the maximum value point are obtained in the step 6 to determine the boundary width of the disease, since the radar waves similar to the disease have overlapping phenomenon, the hyperbolic characteristic of the disease is intersected, and further, part of the minimum value points cannot reflect the actual width of the disease, so that a relative height factor alpha, namely, the height difference of the value of the minimum value point relative to the minimum value in the two maximum values of the left and right of the minimum value point is introduced. When the relative height exceeds a set threshold, it is necessary to filter out interference from multiple local minima points.
α＝min(M 1 ,M 2 )-y min (5)
Wherein y is min Is minimum value, M 1 ,M 2 Is two maxima around the minimum.
Preferably, in step 6, the number of tracks is used as a constraint for the case where there is no minimum point at the maximum point boundary.
Preferably, in step 7, the maximum energy is not the original Ricker sub-peak due to superposition of the sub-waves, and therefore, the extreme point with the smallest index out of the n maximum values in the function is obtained as the depth calculation.
Preferably, in step 7, the disease depth is calculated asWhere t=m·dt, ε is the dielectric constant of the concrete.
The invention has the beneficial effects that
(1) According to the invention, by proposing a reconstructed CWT energy spectrum and combining the dimension reduction idea, the energy maximum value point of the void disease is extracted, and the disease width is calculated by utilizing the local minimum value point near the maximum value point. The depth is calculated according to the principle of improving the maximum value of the generalized S transformation spectrum, so that the geometrical characteristics (depth and width) of the void diseases of the cement pavement can be extracted, and a basis is provided for evaluating the bearing capacity of the pavement slab.
(2) The geometric feature extraction method for the cement pavement void disease area provided by the invention has the advantages of better width calculation for the void area with circular characteristics, small error and high positioning precision.
(3) The geometric feature extraction method for the cement pavement void disease area can reduce the participation of manpower in void identification and reduce the interference of the artificial experience on the identification result.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a forward modeling model of void removal;
FIG. 3 is a schematic diagram of a process of reconstructing an energy spectrum;
FIG. 4 is a reconstructed energy spectrum of a model;
FIG. 5 is a dimension reduction signal DWT decomposition of model B;
FIG. 6 is a flow chart of a disease point extraction algorithm;
FIG. 7 is a schematic diagram of void width determination;
FIG. 8 is a maximum value schematic diagram with minimum index;
FIG. 9 is a flow chart for determining depth of disease;
FIG. 10 is a depth and width extraction result of a model;
FIG. 11 is a histogram of width extraction errors for a model;
FIG. 12 is an inverted indoor model;
FIG. 13 is an energy spectrum of an indoor model reconstruction;
FIG. 14 is a graph showing the width and depth extraction results of an indoor model;
fig. 15 is a histogram of width extraction errors of an indoor model.
Detailed Description
Step 1, constructing two forward models, simulating different depths and different shapes of a cement pavement void disease area, and simulating the models by using gprMax to obtain original GPR data, as shown in fig. 2.
And 2, preprocessing the original GPR data, wherein the preprocessing mainly comprises extracting an average channel, static correction, gain, background removal, band-pass filtering, and then weakening the hyperbolic characteristic of the isolated body through F-K offset processing to obtain a GPR preprocessing signal.
Step 3, reconstructing an energy spectrum for an A-scan signal x of the preprocessed GPR signal i (t) performing Continuous Wavelet Transform (CWT) processing with wavelet coefficient spectrum of G i (f, t) determining the maximum energy of CWTThe corresponding frequency is used as the main frequency of the signal>Then find +.>Corresponding time-energy signal y i And (t) traversing all A-scan data of the preprocessed signals to obtain a reconstructed energy spectrum B (x, t) (shown in fig. 4), wherein the specific operation flow is shown in fig. 3.
And 4, in order to obtain the disease size parameter, the dimension of the two-dimensional matrix B (x, t) is required to be reduced, so that an energy function y (x) is obtained, at the moment, the local convex peak of the disease can become a local maximum point of the curve y, and the information of the maximum point can be further used for determining the transverse position of the disease, wherein the dimension reduction process is shown in a formula (6).
y(x i )＝B max (x i ,t)i＝1,2,3…N (6)
Wherein y (x) i ) At x for energy spectrum B i Maximum value at, N is the maximum sampling channel number, y (x i ) Is the i-th component constituting the vector y (x). The method comprises the following steps:
y(x)＝[y(x 1 ),y(x 2 ),…,y(x N )] (7)
due to the introduction of background noise caused by the dimension reduction, the smoothness of the interference curve is achieved by applying a sym4 wavelet basis function with periodic boundary processing to the signal y (x) and performing 4-layer discrete wavelet decomposition (fig. 5).
Wherein, c i For high frequency wavelet coefficients, cA n Is a low frequency wavelet coefficient. n is the number of decomposition layers, the smaller n is, the lower the frequency band resolution is, and on the contrary, the higher the frequency band resolution is. The decomposed approximation gamma (x) signal does not affect the main maximum point position of the curve y (x), and each sample in the function gamma (x) is valued gamma (x) i ) The energy range of the sample is divided, and the sample energy distribution is approximately subjected to lognormal distribution through actual statistical analysis and combination of the positive and negative properties of the sample skewness. Therefore, the concentration of the sample energy distribution is described based on the expected value E of the sample energy distribution, and the obtained concentration can be used as a threshold condition for obtaining the extreme point and the confirmation width.
And 5, extracting the void width.
(1) Knowing the B (x, T) time domain energy profile T (T), reconstructing the energy to obtain a reconstructed energy spectrum.
(2) Estimating each sample gamma (x) in an energy function gamma (x) of the reconstructed energy spectrum i ) Log-normal division of (2)The expected value of the cloth is denoted as K.
(3) Solving all maximum points of the function gamma (x) and the values thereof to form a vector A 1×N If A 1×N Element a of (2) i <A th Then a (i) =0, threshold a th Let K be K.
(4) Considering the interference between disease areas, the method is required to further reject the product of A 1×N Local minimum points of the function phi (x) reconstructed by the medium maximum points are recorded as H by the average value of the relative heights of all the minimum points of phi (x) mean If phi (x) i )>H mean Let ψ (i) =0. The vector psi (m) stores the extreme point index in A, and m is the number of all the extreme points.
(5) Let vector L 1×N =0, and the boundary width is determined by calculating two nearest minimum points on the left and right of the maximum point in the function y (x) from the index of the extremum point. And for the situation that the boundary has no minimum value point, the number of tracks is required to be used as a limiting condition, and a disease point identification algorithm flow chart is shown in fig. 6.
(6) The input parameter disease point is the result of the algorithm calculation of FIG. 6, wherein the function P min ，R min The left and right minimum value points (boundary points) on both sides of the maximum value point (disease point) are obtained. The return value is an index of a disease boundary point or False, K is an energy drop threshold, and the width is obtained so as to prevent overlapping of left and right extreme points of adjacent disease points. f (f) 1 ，f 2 The calculated left and right boundaries of the lesion area are respectively, the vector L stores the widths of all the lesion areas, and a lesion width extraction algorithm flow chart is shown in fig. 7.
And 6, extracting the emptying depth.
Knowing the calculated width vector L 1×N And pre-processed data B (x, t), calculating an improved S-transformed spectrum Gs of the ith track A-Scan data in the disease area (area corresponding to non-zero element) according to the non-zero element value in the width vector i (f, t) for which spectrum Gs i Dimension reduction is carried out to obtain a (time-energy) function g i (t), function g i (t) is a reduced-dimension energy function that improves the S-transform; solving for a function g i And (3) the extreme point m with the smallest index in the largest n values in the (t) is the depth information. Due to the sonThe superposition of the waves results in that the maximum energy is not the original Ricker sub-peak, therefore, the extreme point with the minimum index in the extreme points is obtained to be used as depth calculation, for example, the extreme point with the minimum index in three values with the maximum extreme point number 2 is recorded as the depth of the channel A-Scan, the principle of the maximum value with the minimum index is shown in figure 8, and the emptying depth extraction algorithm flow chart is shown in figure 9.
Fig. 10 and 11 are depth and width extraction results and error histograms of models a and B. It can be seen that the information of the basic disease points is effectively identified, but misjudgment occurs. The misjudgment at the position 2 in the model A is caused by noise interference caused by pretreatment and F-K treatment; and the model B has no misjudgment, and all disease points are effectively identified, so that the algorithm can be effectively used for identifying the void disease. In the width error bar graph, detection is the calculated value of the proposed algorithm. The width errors of the different shapes in the graph (a) are obvious, and the targets (3-5) containing triangles are larger in width errors and smaller in circular targets (1, 2). The reason is that the edge acute angle characteristics of the right triangle, the isosceles triangle and the isosceles trapezoid are that the longitudinal resolution at the acute angle is poor, so that the reflected echo energy is small, and the effective width is reduced; the middle area of the triangle is larger in longitudinal dimension, so that the reflected energy is stronger and the error is smaller, but the calculation error for the upper edge width of the isosceles trapezium (6, 7) is smaller. Compared with a triangle, under the same width, the effective width of the circular middle area is larger due to the special structure of the round hole, the boundary of the round hole is gentle, and therefore, the error is smaller than that of a triangle. The graph (b) is a rectangular disease area with an error of about 5% and a circular error of about 10%, the overall is smaller than a circle, and the disease error is larger at a deeper position. And the shape of the actual void area is many rectangular and circular. Thus, the algorithm presented herein can effectively perform better width calculations for regions containing rectangular and circular features.
And 7, in order to further verify the correctness and the accuracy of the method provided by the invention, constructing indoor emptying models with the same shape, different sizes and different depths. As shown in fig. 12, the model length x width x thickness is 2.07 x 0.4 x 0.5m, 10 void round holes with 15cm intervals are designed, the diameters are sequentially 100, 90-30, 25 and 16mm, the positions of all circle centers are positioned on a central line of 20cm, the model material is cement concrete, the test is performed by adopting an US radar 1G antenna, the sampling frequency is 16GHz, the time window is 11.675ns, the total channel number is 207, and the original GPR data are obtained.
FIG. 13 is a three-dimensional visualization of the reconstructed energy spectrum of an indoor build model. From the figure, it can be seen that 1-9 are voids, the energy of which increases progressively in sequence, and the energy of the deeper voids is weaker. Fig. 14 shows the calculation result of the width and depth processing algorithm, except that the energy of the cavity 10 is too small to be resolved from the background threshold, the rest is correctly identified, the energy is consistent with the trend of fig. 13, the depth position is distributed at the lower edge of the "black dot", and the actual depth is based on the center of the "black dot", so that the proposed algorithm can effectively estimate the depth position of each cavity. FIG. 15 is a bar graph of the width error of the void disease, the Detection is a calculated value of the proposed algorithm, the hole error is larger with deeper depth, but the error basically shows a decreasing trend with decreasing depth. Therefore, the method can effectively solve the problem of calculation of the width and estimation of the depth of the void area and judge the safety of the pavement structure.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (8)
1. The geometrical feature extraction method of the pavement recessive disease area is characterized by comprising the following steps of:
step one, data acquisition, simulating a model by using gprMax or performing actual data acquisition by using GPR to obtain original GPR data;
preprocessing the original GPR data (static correction, gain, background removal and F-K offset) to obtain a preprocessed signal;
step three, continuous Wavelet Transform (CWT) processing is carried out on the preprocessed signals, and then a reconstructed energy spectrum is constructed, so that compared with the original B-Scan, the reconstructed energy spectrum can more effectively reveal the transverse and longitudinal distribution of different disease areas, and can judge the medium difference of different areas;
step four, reducing the dimension of the energy spectrum to obtain a dimension-reduced curve, performing discrete wavelet decomposition on the dimension-reduced curve by adopting a sym4 wavelet basis function, removing background noise and improving the smoothness of the curve;
step five, solving an energy function probability density function of the dimension reduction curve, and utilizing an expected value of the function as a threshold setting condition for solving an extreme point and a confirmation width;
where μ, σ are the mean and variance, respectively, of the reduced dimension data matrix
Step six, extracting the void width, setting a threshold value by using the expected value in the step four and the step five, filtering out extreme points generated by interference, solving two nearest minimum value points on the left and right of the maximum value point to determine the boundary width of the disease, and storing the widths of all disease areas in a vector L;
seventh, according to the maximum principle of the improved generalized S transformation spectrum, the depth and the non-zero element value in the width vector are obtained, and the improved S transformation spectrum Gs of the ith channel A-Scan data in the disease area (the area corresponding to the non-zero element) is calculated i (f, t) for which spectrum Gs i Dimension reduction is carried out to obtain a (time-energy) function g i (t), function g i (t) is a reduced-dimension energy function that improves the S-transform; solving for a function g i The extreme point m with the smallest index in the largest n values in the (t) is the depth information;
g i (t n )＝max(Gs i (f,t n )),n＝1,2,…,N
g i (t)＝[g i (t 1 ),…,g i (t N )]
wherein N isTotal sampling point number, g, of the ith track A-Scan data i (t n ) Is Gs i At t n Maximum value of time.
2. The method of claim 1, wherein the preprocessing of the raw GPR data in the second step mainly comprises extracting an average channel, static correction, gain, background removal, bandpass filtering, and then weakening the isolated hyperbolic characteristic by F-K offset processing.
3. The method for extracting geometric features of a pavement recessive disease area according to claim 1, wherein the method for dimension reduction of the energy spectrum in the fourth step is to project the energy spectrum onto a train Number-Magnitude plane, so that a peak value in the energy spectrum is converted into an extreme point on a plane view.
4. The method for extracting geometric features of a pavement recessive disease area according to claim 1, wherein the sym wavelet basis function adopted in the fourth step is subjected to 4 times of discrete wavelet decomposition to remove background noise, wherein the principle of the number of decomposition layers is that when the number of extreme points of the function lower than the average value after dimension reduction is less than half of the number of total extreme points, the number of layers is 3, and conversely, the number of dimension reduction layers is 4.
5. The method for extracting geometric features of a pavement recessive disease area according to claim 1, wherein the expected value of a probability density function of an energy function of the disease area is obtained in the fifth step as a condition for filtering interference generated by two adjacent disease signals.
6. The geometric feature extraction method of a pavement recessive defect region according to claim 1, wherein in the step six, when the defect region width is obtained, the defect point is first identified, then the defect width is determined by obtaining minimum value points on both sides of the maximum value point, and the number of tracks is required as a limiting condition for the case that the boundary has no minimum value point.
7. The method for extracting geometric features of a pavement hidden defect area according to claim 1, wherein when two minimum value points closest to a maximum value point are obtained in the sixth step to determine the boundary width of the defect, a relative height factor α is required to be introduced, that is, a height difference between the value of the minimum value point and the minimum value of the two maximum values, and when the relative height exceeds a set threshold value, interference of a plurality of local minimum value points is required to be filtered.
8. The method for extracting geometric features of a pavement recessive defect region according to claim 1, wherein in the step seven, when the defect depth is obtained, the maximum energy is not the original Ricker sub-peak due to superposition of sub-waves, so that the extreme point with the minimum index in the maximum n values in the function is obtained as the depth calculation.
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| CN101303802A (en) * | 2008-06-26 | 2008-11-12 | 吉林大学 | Method and system for on-line automatically beforehand judgment of overloading wagon |
| CN101751782A (en) * | 2009-12-30 | 2010-06-23 | 北京大学深圳研究生院 | Crossroad traffic event automatic detection system based on multi-source information fusion |
| CN102509291A (en) * | 2011-10-31 | 2012-06-20 | 东南大学 | Pavement disease detecting and recognizing method based on wireless online video sensor |
| CN111749092A (en) * | 2020-06-02 | 2020-10-09 | 交通运输部公路科学研究所 | Porous asphalt pavement flying disease detection method based on noise signal identification |
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| CN101303802A (en) * | 2008-06-26 | 2008-11-12 | 吉林大学 | Method and system for on-line automatically beforehand judgment of overloading wagon |
| CN101751782A (en) * | 2009-12-30 | 2010-06-23 | 北京大学深圳研究生院 | Crossroad traffic event automatic detection system based on multi-source information fusion |
| CN102509291A (en) * | 2011-10-31 | 2012-06-20 | 东南大学 | Pavement disease detecting and recognizing method based on wireless online video sensor |
| CN111749092A (en) * | 2020-06-02 | 2020-10-09 | 交通运输部公路科学研究所 | Porous asphalt pavement flying disease detection method based on noise signal identification |
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