Roadbed ground penetrating radar image comparison monitoring method

Abstract

The invention discloses a roadbed ground penetrating radar image comparison monitoring method. The method comprises the following steps: carrying out road primary measurement by using a ground penetrating radar; synchronously recording the measuring line position of the ground penetrating radar, and establishing a block image archiving index; using the same ground penetrating radar to carry out regular rapid measurement; retrieving a corresponding archived image according to the measuring line position; performing self-positioning calibration on radar images at the same position; calculating the difference rate between the regular measurement image and the initial measurement image; calculating the depth offset between the regular measurement image and the initial measurement image; and carrying out roadbed settlement risk assessment by using the difference rate and the depth offset. According to the method, through initial calibration of positioning measurement and self-positioning calibration of the radar image, accurate matching of the regular measurement image and the initial measurement image of the road ground penetrating radar is achieved, evaluation quantities such as the image difference rate, the depth offset and the roadbed settlement risk value are provided, and an efficient method is provided for large-range road safety detection.

Description

Technical Field

[0001] The invention belongs to the field of geophysical detection, and in particular relates to a roadbed ground penetrating radar image comparison monitoring method. Background Art

[0002] Urban roads and highways are typical layered structures, with asphalt concrete surface layer, crushed stone base layer (or concrete base layer) and fill roadbed from top to bottom. The internal medium of each structural layer of a normal road is uniform, and the layers are tightly bonded. Due to the large absorption attenuation, the electromagnetic waves reflected by a normal road come from the interface between the air and the surface layer, the surface layer and the base layer, and the base layer and the roadbed. Its characteristics are continuous coinciding axes, relatively uniform amplitudes, and the amplitude of the reflected waves gradually weakens.

[0003] Ground penetrating radar is a device that uses electromagnetic waves to detect reflected signals from abnormal structures inside non-metallic objects. It has high-resolution imaging capabilities and strong penetration performance. It can quickly and non-destructively detect electromagnetic wave signals reflected by abnormal roadbeds such as cavities, cracks, and looseness. This technology plays an important role in road anomaly detection. For a long time, people have accumulated a lot of experience in ground penetrating radar roadbed anomaly maps by marking the polarity, strength, and image shape of the detected abnormal reflection signals of the roadbed, and drilling verification; and relying on this experience, they can select problems for new radar detection images, providing important guarantees for road safety. However, the internal conditions of the roadbed are very complicated. The above methods do not fully cover abnormal problems, are relatively inefficient, and are costly, making it difficult to conduct regular road surveys.

[0004] On the other hand, embankment anomalies such as cavities, cracks, and looseness are generally the result of long-term erosion accumulation, and the abnormal parts correspond to the relatively strong reflective parts in the GPR image. By regularly monitoring the changes in these anomalies (i.e., the relatively strong reflective parts of the image), possible risks can be evaluated or predicted, so that corresponding maintenance measures can be taken to ensure the safe use of the road. Due to the low efficiency of GPR measurement and positioning, similar methods that can be implemented in large-scale road surveys have not yet been retrieved. Summary of the invention

[0005] In order to solve the deficiencies in the above-mentioned prior art, the present invention provides a roadbed ground-penetrating radar image comparison monitoring method, which realizes the accurate matching of the road ground-penetrating radar periodic measurement image and the initial measurement image through positioning measurement initial calibration and radar image self-positioning calibration, and proposes evaluation quantities such as image difference rate, depth offset, and roadbed settlement risk value, providing a high-efficiency method for large-scale road safety detection.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] A roadbed ground penetrating radar image comparison monitoring method, the method comprising the following steps:

[0008] Step 1: Use ground penetrating radar to conduct the initial road measurement: During the measurement process, the lane information of the measured road is recorded, and the position of the ground penetrating radar measurement line is simultaneously recorded to keep the lane consistent in a single measurement;

[0009] Step 2: Establishing a block image archiving index according to the GPR line position recorded synchronously in step 1;

[0010] Step 3: Perform regular rapid measurement using the same ground penetrating radar as in step 1: During the regular measurement, the radar line position and lane information are recorded synchronously, and after data collection is completed, the regularly collected radar images are divided into blocks in the same manner as in step 2;

[0011] Step 4, retrieve the corresponding archived image according to the survey line position: for the selected radar image block measured regularly, according to its radar survey line position information, use the query index table established in step 2 to retrieve and obtain the archived image of the corresponding position;

[0012] Step 5: Self-positioning calibration of radar images at the same position: For the paired radar image blocks and archived images in step 4, since there is inevitably a certain deviation between the two measurement positioning, it is necessary to first perform overall translation calibration on the regularly measured radar image blocks, that is, set different overall offsets and calculate the correlation coefficient of the two images;

[0013] Step 6, calculating the difference rate between the periodic measurement image and the initial measurement image;

[0014] Step 7, calculating the depth offset between the periodic measurement image and the initial measurement image;

[0015] Step 8: Use the difference rate and depth deviation to conduct roadbed settlement risk assessment.

[0016] Furthermore, in step 1, the position of the ground penetrating radar survey line is synchronously recorded using a GPS module or an odometer. When an odometer is used, the starting position of the survey line also needs to be recorded.

[0017] Further, in step 2, the method for establishing the block image archiving index is as follows: for an open and unobstructed road, the GPS latitude and longitude synchronously collected by the GPS module are used to archive the radar image in blocks, which are recorded as I 0 (x i ,y i ,p),x i is the starting or ending longitude information of the block radar image, y i is the corresponding latitude information, p represents the recorded lane number; then the stored block radar image file name and longitude and latitude, lane position (x i,y i ,p) Establish a query index table, and subsequently obtain the corresponding radar image based on the location information;

[0018] For roads with weak GPS signals, the radar image is divided into blocks using the relative mileage positions collected synchronously by the odometer, denoted as I 0 (S i ,p),S i is the relative mileage position information of the block radar image; and the stored block radar image file name and mileage, lane position (S i ,p) Establish a query index table, and subsequently obtain the corresponding radar image based on the location information;

[0019] For different radar line position recording methods, the initial measurement archived block radar image is uniformly recorded as I 0,i,p , the jth column echo is recorded as I 0,i,p (j).

[0020] Furthermore, in step 3, when the odometer is used to record the radar line position, it is necessary to maintain the same starting point position or record the offset dS of the starting point position relative to the initial measurement. 0 ; If the starting point is the same, dS 0 After data collection is completed, the regularly collected radar images are divided into blocks according to step 2, and the position is recorded as I when GPS is used to record the position. n (x i ,y i ,p), when the odometer is used to record the position, it is recorded as I n (L i -dS 0 ,p), n is the number of periodic measurements, starting from 1; it will be uniformly recorded as I n,i,p , the jth column echo is recorded as I n,i,p (j).

[0021] Further, in step 5, the selected regularly measured radar image block is recorded as I n,i,p , retrieve the archived image at the corresponding location and record it as I 0,i,p , the overall offset is set to Δj 0 , then the correlation coefficient between the regularly measured radar image blocks and the archived images at the corresponding locations is:

[0022]

[0023] Among them, each column of echo is treated as a vector, I n,i,p (j+Δj 0 )·I 0,i,p (j) I n,i,p (j+Δj 0)·I n,i,p (j+Δj 0 ), I 0,i,p (j) I 0,i,p (j) are all vector dot products; and record c n,i,p (Δj 0 ) takes the maximum value, denoted as Δj 0max On this basis, the radar image I n Perform local translation calibration, that is, the jth 1 to 2 Column echo, set different local offset Δj, calculate the local correlation coefficient of the two images:

[0024]

[0025]

[0026] In the formula, the local offset Δj is expressed as Δj 0max Take a range value for the center to reduce calculation time and record c n,i,p The local offset when (j,Δj) takes the maximum value is denoted as Δj max (j), where On this basis, the overall correlation coefficient of the two images is calculated again:

[0027]

[0028] Further, in step 6, the method for calculating the difference rate between the periodic measurement image and the initial measurement image is:

[0029] First, the number of the acquisition point in each column of echo in the radar image is recorded as k, that is, I n,i,p (j,k) or I 0,i,p (j,k) represents the value of the kth sampling point of the jth column echo in the corresponding image. 1 to 2 The kth echo of the column 1 to k 2 Sampling points, calculate their correlation coefficient:

[0030]

[0031] and will and The larger value of is denoted as a n,i,p (j,k), represents the amplitude of this part of the image;

[0032] Then calculate the difference rate:

[0033]

[0034] Among them, H(a n,i,p (j,k)) and It can be a step function, linear or quadratic transition function, where H(a n,i,p (j,k)) is used to reduce the deviation caused by the small amplitude of the local image. Used to exclude evaluation failures caused by low overall image similarity;

[0035] For the function H(a), set its independent variable threshold a 0 , when taking the step function,

[0036]

[0037] When taking the linear transition function,

[0038]

[0039] When taking the quadratic curve transition function,

[0040]

[0041] Furthermore, in step 7, the method for calculating the depth offset between the periodic measurement image and the initial measurement image is as follows: 1 to 2 The kth echo of the column 1 to k 2 sampling points, select different depth offsets Δk, and first calculate the corresponding correlation coefficient:

[0042]

[0043] And record c n,i,p The depth offset when (j, k, Δk) takes the maximum value is denoted as Δk max (j,k); and and The larger value of n,i,p (j,k), and then calculate the depth offset between the periodic measurement image and the initial measurement image:

[0044] δ n,i,p (j,k)=Δk max (j,k)×H(b n,i,p (j,k))×H(c n,i,p (j,k,Δk max )),

[0045] In the formula, same as step 6, H(b n,i,p (j,k)) or H(c n,i,p (j,k,Δk max)) can be a step function, linear or quadratic transition function, where H(b n,i,p (j,k)) is used to reduce the deviation caused by the small amplitude of the local image, H(c n,i,p (j,k,Δk max )) is used to eliminate failures caused by low local image correlation coefficients.

[0046] Further, in step 8, the method for evaluating the roadbed settlement risk using the difference rate and the depth deviation includes the following steps:

[0047] Step 81, setting a weighted value α for the image difference rate 0 ;

[0048] Step 82: To eliminate the influence of the change of the road dielectric constant and the overall drift of the radar system, the entire image δ n,i,p (j, k), and then classify and weight the depth offset of the image by column: when the whole column δ n,i,p When it tends to positive values, it can be marked as Class 1, indicating that the road surface has local depressions relative to the initial measurement, and the weighted value is set to α 1 ;When the entire column δ n,i,p When it tends to negative values, it can be marked as Class 2, indicating that the road surface has local uplift relative to the initial measurement, and the weighted value is set to α 2 ; When δ near a certain depth n,i,p When it tends to negative values, the δ at other depths n,i,p When it approaches zero, it can be marked as Class 3, indicating that the roadbed is loose or empty and is developing towards the road surface. The weighted value is set to α 3 ;

[0049] Step 83, after classification and weighted processing, the formula for calculating the roadbed settlement risk based on ground penetrating radar image comparison monitoring is as follows:

[0050]

[0051] In the formula, α q (q=1,2,3,…) represents different classification weight values; is the entire image δ n,i,p The mean of (j,k); δ 0 It is the set depth deviation reference. When the risk amount is greater than 0.7, it indicates a higher risk. When the risk amount is less than 0.3, it indicates a lower risk.

[0052] Based on the above technical solution, the present invention has the following advantages compared with the prior art:

[0053] 1. A large-scale road subgrade anomaly comparison monitoring method based on the change in the strong reflection part of the ground penetrating radar image is proposed. This method does not require the accumulation of abnormal radar map experience in the early stage, and can greatly improve the efficiency and coverage of roadbed safety detection; 2. A ground penetrating radar image matching method based on the combination of positioning measurement and self-positioning calibration is proposed, which reduces the requirements for high-precision positioning measurement of ground penetrating radar;

[0054] 3. The calculation methods of GPR image difference rate, depth deviation and roadbed settlement risk are proposed, which can provide a reliable quantitative approach for roadbed safety assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 ,A flow chart of a roadbed ground penetrating radar image comparison monitoring method.

[0056] Figure 2 , the archived image of the initial measurement of the road at a certain location in Example 1.

[0057] Figure 3 , the road is regularly measured at the same location in Example 1.

[0058] Figure 4 , a graph showing the local offset of the self-positioning calibration in Example 1.

[0059] Figure 5 , distribution diagram of the difference rate between the regular and initial measurement images in Example 1.

[0060] Figure 6 , a distribution diagram of the depth offset between the periodic and initial measurement images in Example 1.

[0061] Figure 7 , distribution diagram of the roadbed settlement risk evaluated in Example 1.

[0062] Figure 8 , the archived image of the initial measurement of the road at a certain location in Example 2.

[0063] Fig. 9 , the road periodically measured images at the same location in Example 2.

[0064] Fig.10 , distribution diagram of the roadbed settlement risk evaluated in Example 2. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the present invention clearer, the specific embodiments shown below are used to describe the present invention. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0066] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0067] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms and should not be understood as indicating or implying relative importance. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0068] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or the internal connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0069] In order to better understand the technical solution of the present invention, the technical solution of the present invention is described in detail below in conjunction with specific embodiments. The embodiments described below are only used to explain the present invention and are not intended to limit the present invention. Specific experimental methods not mentioned in the embodiments are usually carried out according to conventional experimental methods. The equipment used without indicating the manufacturer is a conventional product that can be obtained commercially.

[0070] like Figure 1 As shown, a roadbed ground penetrating radar image comparison monitoring method, the method comprises the following steps:

[0071] Step 1: Use ground penetrating radar to conduct the initial road measurement: During the measurement process, the lane information of the measured road is recorded, and the position of the ground penetrating radar measurement line is simultaneously recorded to keep the lane consistent in a single measurement;

[0072] Step 2: Establishing a block image archiving index according to the GPR line position recorded synchronously in step 1;

[0073] Step 3: Perform regular rapid measurement using the same ground penetrating radar as in step 1: During the regular measurement, the radar line position and lane information are recorded synchronously, and after data collection is completed, the regularly collected radar images are divided into blocks in the same manner as in step 2;

[0074] Step 4, retrieve the corresponding archived image according to the survey line position: for the selected radar image block measured regularly, according to its radar survey line position information, use the query index table established in step 2 to retrieve and obtain the archived image of the corresponding position;

[0075] Step 5: Self-positioning calibration of radar images at the same position: For the paired radar image blocks and archived images in step 4, since there is inevitably a certain deviation between the two measurement positioning, it is necessary to first perform overall translation calibration on the regularly measured radar image blocks, that is, set different overall offsets and calculate the correlation coefficient of the two images;

[0076] Step 6, calculating the difference rate between the periodic measurement image and the initial measurement image;

[0077] Step 7, calculating the depth offset between the periodic measurement image and the initial measurement image;

[0078] Step 8: Use the difference rate and depth deviation to conduct roadbed settlement risk assessment.

[0079] Furthermore, in step 1, the position of the ground penetrating radar survey line is synchronously recorded using a GPS module or an odometer. When an odometer is used, the starting position of the survey line also needs to be recorded.

[0080] Further, in step 2, the method for establishing the block image archiving index is as follows: for an open and unobstructed road, the GPS latitude and longitude synchronously collected by the GPS module are used to archive the radar image in blocks, which are recorded as I 0 (x i ,y i ,p),x i is the starting or ending longitude information of the block radar image, y i is the corresponding latitude information, p represents the recorded lane number; then the stored block radar image file name and longitude and latitude, lane position (x i ,y i ,p) Establish a query index table, and subsequently obtain the corresponding radar image based on the location information;

[0081] For roads with weak GPS signals, the radar image is divided into blocks using the relative mileage positions collected synchronously by the odometer, denoted as I 0 (S i ,p),S i is the relative mileage position information of the block radar image; and the stored block radar image file name and mileage, lane position (Si ,p) Establish a query index table, and subsequently obtain the corresponding radar image based on the location information;

[0082] For different radar line position recording methods, the initial measurement archived block radar image is uniformly recorded as I 0,i,p , the jth column echo is recorded as I 0,i,p (j).

[0083] Furthermore, in step 3, when the odometer is used to record the radar line position, it is necessary to maintain the same starting point position or record the offset dS of the starting point position relative to the initial measurement. 0 ; If the starting point is the same, dS 0 After data collection is completed, the regularly collected radar images are divided into blocks according to step 2, and the position is recorded as I when GPS is used to record the position. n (x i ,y i ,p), when the odometer is used to record the position, it is recorded as I n (L i -dS 0 ,p), n is the number of periodic measurements, starting from 1; it will be uniformly recorded as I n,i,p , the jth column echo is recorded as I n,i,p (j).

[0084] Further, in step 5, the selected regularly measured radar image block is recorded as I n,i,p , retrieve the archived image at the corresponding location and record it as I 0,i,p , the overall offset is set to Δj 0 , then the correlation coefficient between the regularly measured radar image blocks and the archived images at the corresponding locations is:

[0085]

[0086] Among them, each column of echo is treated as a vector, I n,i,p (j+Δj 0 )·I 0,i,p (j) I n,i,p (j+Δj 0 )·I n,i,p (j+Δj 0 ), I 0,i,p (j) I 0,i,p (j) are all vector dot products; and record c n,i,p (Δj 0 ) takes the maximum value, denoted as Δj 0max ;

[0087] On this basis, the radar image I nPerform local translation calibration, that is, the jth 1 to 2 Column echo, set different local offset Δj, calculate the local correlation coefficient of the two images:

[0088]

[0089] In the formula, the local offset Δj is expressed as Δj 0max Take a range value for the center to reduce calculation time and record c n,i,p The local offset when (j,Δj) takes the maximum value is denoted as Δj max (j), where

[0090] On this basis, the overall correlation coefficient of the two images is calculated again:

[0091]

[0092] Further, in step 6, the method for calculating the difference rate between the periodic measurement image and the initial measurement image is:

[0093] First, the number of the acquisition point in each column of echo in the radar image is recorded as k, that is, I n,i,p (j,k) or I 0,i,p (j,k) represents the value of the kth sampling point of the jth column echo in the corresponding image. 1 to 2 The kth echo of the column 1 to k 2 Sampling points, calculate their correlation coefficient:

[0094]

[0095] and will and The larger value of is denoted as a n,i,p (j,k), represents the amplitude of this part of the image;

[0096] Then calculate the difference rate:

[0097]

[0098] Among them, H(a n,i,p (j,k)) and It can be a step function, linear or quadratic transition function, where H(a n,i,p (j,k)) is used to reduce the deviation caused by the small amplitude of the local image. Used to exclude evaluation failures caused by low overall image similarity;

[0099] For the function H(a), set its independent variable threshold a0 , when taking the step function,

[0100]

[0101] When taking the linear transition function,

[0102]

[0103] When taking the quadratic curve transition function,

[0104]

[0105] Furthermore, in step 7, the method for calculating the depth offset between the periodic measurement image and the initial measurement image is as follows: 1 to 2 The kth echo of the column 1 to k 2 sampling points, select different depth offsets Δk, and first calculate the corresponding correlation coefficient:

[0106]

[0107] And record c n,i,p The depth offset when (j, k, Δk) takes the maximum value is denoted as Δk max (j,k); and and The larger value of n,i,p (j,k), and then calculate the depth offset between the periodic measurement image and the initial measurement image:

[0108] δ n,i,p (j,k)=Δk max (j,k)×H(b n,i,p (j,k))×H(c n,i,p (j,k,Δk max )),

[0109] In the formula, same as step 6, H(b n,i,p (j,k)) or H(c n,i,p (j,k,Δk max )) can be a step function, linear or quadratic transition function, where H(b n,i,p (j,k)) is used to reduce the deviation caused by the small amplitude of the local image, H(c n,i,p (j,k,Δk max )) is used to eliminate failures caused by low local image correlation coefficients.

[0110] Further, in step 8, the method for evaluating the roadbed settlement risk using the difference rate and the depth deviation includes the following steps:

[0111] Step 81, setting a weighted value α for the image difference rate 0 ;

[0112] Step 82: To eliminate the influence of the change of the road dielectric constant and the overall drift of the radar system, the entire image δ n,i,p (j, k), and then classify and weight the depth offset of the image by column: when the whole column δ n,i,p When it tends to positive values, it can be marked as Class 1, indicating that the road surface has local depressions relative to the initial measurement, and the weighted value is set to α 1 ;When the entire column δ n,i,p When it tends to negative values, it can be marked as Class 2, indicating that the road surface has local uplift relative to the initial measurement, and the weighted value is set to α 2 ; When δ near a certain depth n,i,p When it tends to negative values, the δ at other depths n,i,p When it approaches zero, it can be marked as Class 3, indicating that the roadbed is loose or empty and is developing towards the road surface. The weighted value is set to α 3 ;

[0113] Step 83, after classification and weighted processing, the formula for calculating the roadbed settlement risk based on ground penetrating radar image comparison monitoring is as follows:

[0114]

[0115] In the formula, αp (q = 1, 2, 3, ...) represents different classification weight values; is the entire image δn ,i,p The mean of (j,k); δ 0 It is the set depth deviation reference. When the risk amount is greater than 0.7, it indicates a higher risk. When the risk amount is less than 0.3, it indicates a lower risk.

[0116] Embodiment 1

[0117] An air-coupled ground-penetrating radar was used to conduct perspective measurement of the highway subgrade, and GPS was used for measurement and positioning. The radar detection center frequency was about 300MHz, the air-coupled detection distance from the bottom of the radar antenna to the road surface was about 15cm, the center distance between the transmitting antenna and the receiving antenna was 65cm, it had a large dual-station angle, the total survey line length exceeded 100km, and the measured speed was about 90km / h.

[0118] The measured GPR images were divided into blocks of 0.001° according to GPS longitude and latitude, and the interval of the measurement line length of each column of data was 0.1m. The initial GPR image at the end point of 121.2850° east longitude and 31.0518° north latitude is as follows: Figure 2 As shown in the figure, the radar image measured again one month later is as follows Figure 3As shown, the overall horizontal offset Δj of the two images 0max =-7, local offset Δj max (j) Figure 4 As shown, the overall correlation coefficient after the two images are matched is 93%.

[0119] The calculated difference rate distribution between the regular and initial measurement images is shown in Figure 5 As shown; the calculated depth offset distribution between the regular and initial measurement images is shown in Figure 6 shown.

[0120] Set the weighted value of settlement risk assessment: α 0 =0.4,α 1 =0.15,α 2 =0.05,α 3 =0.4; and the depth offset reference is δ 0 =10; the distribution of roadbed settlement risk according to the weighted evaluation is as follows Figure 7 As shown, the maximum is about 0.12, which is a relatively small risk situation.

[0121] Embodiment 2

[0122] An air-coupled ground-penetrating radar was used to conduct perspective measurement of the highway subgrade, and GPS was used for measurement and positioning. The radar detection center frequency was about 350MHz, the air-coupled detection distance from the bottom of the radar antenna to the road surface was about 15cm, the center distance between the transmitting antenna and the receiving antenna was 13cm, the total measurement line length exceeded 100km, and the measured speed was about 90km / h.

[0123] The measured GPR images were divided into blocks of 0.001° according to GPS longitude and latitude, and the interval of the measurement line length of each column of data was 0.1m. The initial GPR image at the end point of 121.2830° east longitude and 31.0505° north latitude is as follows: Figure 8 As shown in the figure, the radar image measured again one month later is as follows Fig. 9 As shown, the overall horizontal offset Δj 0max =-1, the overall correlation coefficient after the two images are matched is 94%.

[0124] Set the weighted value of settlement risk assessment: α 0 =0.4,α 1 =0.15,α 2 =0.05,α 3 =0.4; and the depth offset reference is δ 0 =10; the distribution of roadbed settlement risk according to the weighted evaluation is as follows Fig.10 As shown, the maximum is about 0.13, which is a relatively small risk situation.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary technicians in the field should understand that the specific implementation methods of the invention can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solution of the present invention, which should be included in the scope of the technical solution for protection of the present invention.

Claims

1. A roadbed ground penetrating radar image comparison monitoring method, characterized in that: The method comprises the following steps: Step 1: Use ground penetrating radar to conduct the initial road measurement: During the measurement process, the lane information of the measured road is recorded, and the position of the ground penetrating radar measurement line is recorded simultaneously, and the lane is kept consistent in a single measurement; Step 2: Establishing a block image archiving index according to the GPR line position recorded synchronously in step 1; Step 3: Perform regular rapid measurement using the same ground penetrating radar as in step 1: During the regular measurement, the radar line position and lane information are recorded synchronously, and after data collection is completed, the regularly collected radar images are divided into blocks in the same manner as in step 2; Step 4, retrieve the corresponding archived image according to the survey line position: for the selected radar image block measured regularly, according to its radar survey line position information, use the query index table established in step 2 to retrieve and obtain the archived image of the corresponding position; Step 5: Self-positioning calibration of radar images at the same position: For the paired radar image blocks and archived images in step 4, since there is inevitably a certain deviation between the two measurement positioning, it is necessary to first perform overall translation calibration on the regularly measured radar image blocks, that is, set different overall offsets and calculate the correlation coefficient of the two images; Step 6, calculating the difference rate between the periodic measurement image and the initial measurement image; Step 7, calculating the depth offset between the periodic measurement image and the initial measurement image; Step 8: Use the difference rate and depth deviation to conduct roadbed settlement risk assessment.

2. A roadbed ground penetrating radar image comparison monitoring method according to claim 1, characterized in that: In step 1, the position of the ground penetrating radar survey line is synchronously recorded using a GPS module or an odometer. When an odometer is used, the starting position of the survey line also needs to be recorded.

3. A roadbed ground penetrating radar image comparison monitoring method according to claim 2, characterized in that: In step 2, the method for establishing the block image archiving index is as follows: for an open and unobstructed road, the GPS longitude and latitude synchronously collected by the GPS module are used to archive the radar image in blocks, which is recorded as I0(x i ,y i ,p),x i is the starting or ending longitude information of the block radar image, y i is the corresponding latitude information, p represents the recorded lane number; then the stored block radar image file name and latitude and longitude, lane position (x i ,y i ,p) Establish a query index table, and subsequently the corresponding radar image can be obtained based on the location information; For roads with weak GPS signals, the radar image is divided into blocks using the relative mileage positions collected synchronously by the odometer, denoted as I0(S i ,p),S i It is the relative mileage position information of the block radar image; The stored radar image file name, mileage, and lane position (S i , p) establishing a query index table, and subsequently obtaining the corresponding radar image based on the location information; For different radar line position recording methods, the initial measurement archived block radar image is uniformly recorded as I 0，i，p , the jth column echo is recorded as I 0，i，p (j).

4. A roadbed ground penetrating radar image comparison monitoring method according to claim 3, characterized in that: In step 3, when the odometer is used to record the position of the radar line, the same starting point position must be maintained or the offset dS0 of the starting point position relative to the initial measurement must be recorded; if the starting point position is the same, dS0 can be recorded as 0; after the data collection is completed, the regularly collected radar images are divided into blocks according to step 2, and when the GPS is used to record the position, it is recorded as I n (x i ,y i , p), when the odometer is used to record the position, it is recorded as I n (L i -dS0,p), n is the number of periodic measurements, starting from 1; it will be uniformly recorded as I n，i，p , the jth column echo is recorded as I n，i，p (j).

5. A roadbed ground penetrating radar image comparison monitoring method according to claim 4, characterized in that: In step 5, the selected regularly measured radar image block is denoted as I n，i，p , retrieve the archived image at the corresponding location and record it as I 0，i，p , the overall offset is set to Δj0, then the correlation coefficient between the regularly measured radar image blocks and the archived images at the corresponding positions is: Among them, each column of echo is treated as a vector, I n，i，p (j+Δj0)·I 0，i，p (j) I n，i，p (j+Δj0)·I n，i，p (j+Δj0), I 0，i，p (j) I 0，i，p (j) are all vector dot products; and record c n，i，p The overall offset when (Δj0) takes the maximum value is denoted as Δj 0max ; On this basis, the radar image I n Perform local translation calibration, that is, set different local offsets Δj for the j1th to j2th column echoes in the image, and calculate the local correlation coefficient of the two images: In the formula, the local offset Δj is expressed as Δj 0max Take a range value for the center to reduce calculation time and record c n，i，p The local offset when (j, Δj) takes the maximum value is denoted as Δj max (j), where On this basis, the overall correlation coefficient of the two images is calculated again:

6. A roadbed ground penetrating radar image comparison monitoring method according to claim 5, characterized in that: In step 6, the difference rate between the periodic measurement image and the initial measurement image is calculated as follows: First, the number of the acquisition point in each column of echo in the radar image is recorded as k, that is, I n，i，p (j, k) or I 0，i，p (j, k) represents the value of the kth sampling point of the jth column echo in the corresponding image, and the correlation coefficient is calculated for the k1 to k2th sampling points of the j1 to j2th column echo: and will and The larger value of is denoted as a n，i，p (j, k), represents the amplitude of this part of the image; Then calculate the difference rate: Among them, H(a n，i，p (j, k)) and It can be a step function, linear or quadratic transition function, where H(a n，i，p (j, k)) is used to reduce the deviation caused by the small amplitude of the local image. Used to exclude evaluation failures caused by low overall image similarity; For the function H(a), set its independent variable threshold a0, and take the step function, When taking the linear transition function, When taking the quadratic curve transition function, 7. A roadbed ground penetrating radar image comparison monitoring method according to claim 6, characterized in that: In step 7, the method for calculating the depth offset between the periodic measurement image and the initial measurement image is as follows: for the k1-k2 sampling points of the echoes in the j1-j2 columns of the image, different depth offsets Δk are selected, and the corresponding correlation coefficients are first calculated: And record c n，i，p The depth offset when (j, k, Δk) takes the maximum value is denoted as Δk max (j, k); and and The larger value of n，i，p (j, k), and then calculate the depth offset between the periodic measurement image and the initial measurement image: δ n，i，p (j,k)=Δk max (j,k)×H(b n，i，p (j,k))×H(c n，i，p (j, k, Δk max )), In the formula, same as step 6, H(b n，i，p (j, k)) or H(c n，i，p (j, k, Δk max )) can be a step function, linear or quadratic transition function, where H(b n，i，p (j, k)) is used to reduce the deviation caused by the small amplitude of the local image, H(c n，i，p (j, k, Δk max )) is used to eliminate failures caused by low local image correlation coefficients.

8. A roadbed ground penetrating radar image comparison monitoring method according to claim 7, characterized in that: In step 8, the method for evaluating the roadbed settlement risk using the difference rate and the depth deviation includes the following steps: Step 81, setting a weighted value α0 for the image difference rate; Step 82: To eliminate the influence of the change of the road dielectric constant and the overall drift of the radar system, the entire image δ n，i，p (j, k), and then classify and weight the depth offset of the image by column: when the whole column δ n，i，p When it tends to positive values, it can be marked as Class 1, indicating that the road surface has local depressions relative to the initial measurement, and the weight value is set to α1; n，i，p When it tends to negative values, it can be marked as Class 2, indicating that the road surface has local uplift relative to the initial measurement, and the weighting value is set to α2; when δ near a certain depth n，i，p When it tends to negative values, the δ at other depths n，i，p When it approaches zero, it can be marked as Class 3, indicating that the loose roadbed or void is developing toward the road surface, and the weighted value is set to α3; Step 83, after classification and weighted processing, the formula for calculating the roadbed settlement risk based on ground penetrating radar image comparison monitoring is as follows: In the formula, α q (q=1, 2, 3, ...) represents different classification weight values; is the entire image δ n，i，p δ0 is the mean of (j, k); δ0 is the set depth deviation reference. When the risk amount is greater than 0.7, it indicates a higher risk; when the risk amount is less than 0.3, it indicates a lower risk.

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