Method, device and equipment for detecting grouting compactness of bridge prestressed pipeline

Abstract

The invention discloses a bridge prestressed pipeline grouting compactness detection method, device and equipment, and relates to the technical field of bridge construction. Initial slowness vectors are obtained and arranged into an initial slowness matrix; limiting and constraining a concrete area grid and a pipeline area grid in the initial slowness matrix by adopting a to-be-detected concrete area wave velocity range and a pipeline area grouting material wave velocity range, iteratively acquiring a final slowness vector by utilizing an iterative reconstruction method SIRT, forming a final slowness matrix, imaging, and judging the grouting compactness; according to the process, firstly, the positions of a concrete area and a pipeline area are obtained, known information serves as prior information in iterative inversion of an iterative reconstruction method SIRT, the concrete area and the pipeline area in the iterative inversion are limited and restrained, the position of a grouting pipeline is highlighted, the phenomenon that wave velocity change does not correspond to a defect area in the SIRT inversion at the present stage is counteracted, and the defect area of the grouting pipeline is overcome. Therefore, the grouting compactness of the bridge prestressed pipeline can be accurately judged.

Description

Technical Field

[0001] The invention relates to the technical field of bridge construction, and in particular to a method, device, equipment and medium for detecting the grouting density of a prestressed pipe of a bridge. Background Art

[0002] With the rapid development of transportation construction fields such as roads and railways, post-tensioned prestressed concrete beams have been widely used in bridge construction due to their advantages such as long span, light weight and good integrity. Nowadays, more than 95% of newly built bridges are prestressed concrete bridges; cracks, voids and loose concrete pouring will affect the strength and life of the beam body, but the biggest impact on the beam body is the loose grouting defect in the prestressed pipe. The prestressed pipe is the most important component of the prestressed beam. The prestressed steel strands in the pipe can offset the pressure of vehicles and pedestrians on the bridge deck, and the quality of its grouting determines the life of the bridge; the role of grouting in the prestressed pipe is to protect the steel strands from rust. According to statistics, the loose grouting of the corrugated pipe causes the steel strands to rust and break, which in turn causes the premature loss of prestress, which will eventually shorten the service life of the bridge to one tenth of the design life.

[0003] In order to ensure the prestressing effect of the beam body and the durability of the structure, and to prevent moisture from invading and corroding the prestressed steel strands, the prestressed pipe must be filled with cement slurry. However, due to the grouting process (due to the presence of seepage and residual air, large holes are prone to appear near the inlet, outlet, upper convex section of the curved pipe and the exhaust hole of the grouting pipe) and certain human factors, the grouting of the prestressed pipe is often not full, resulting in corrosion of the prestressed steel strands under the action of air and water, leading to the loss of prestress in the beam body, greatly reducing the bearing capacity and durability of the concrete structural components, and thus causing structural failure or collapse; therefore, it is particularly important to detect the density of the grouting of the prestressed pipes of bridges.

[0004] At present, the commonly used detection technologies include infrared thermal imaging detection technology, geological radar detection technology, impact echo detection technology and ultrasonic detection technology. However, due to the strong scattering and reflection of waves in concrete, the wave energy decays quickly inside the concrete structure. At the same time, due to the influence of strong reflection of steel strands in pipelines, this type of technology is not very effective in detecting defects. In response to this phenomenon, elastic wave CT technology came into being. This technology uses a large amount of elastic wave information for inversion calculation to obtain the elastic wave velocity distribution law inside the object being measured, thereby understanding the situation inside the object being measured.

[0005] At present, the main elastic wave CT inversion imaging algorithm is the joint iterative reconstruction algorithm (SIRT). However, in the image results of SIRT inversion, there will be some areas with low wave velocity but not defects at the edges and diagonals. At the same time, there will be areas with discrete and discontinuous wave velocity in some positions, making it difficult to accurately judge the grouting density of the bridge prestressed pipe. Summary of the invention

[0006] The embodiments of the present invention provide a method, device and equipment for detecting the density of grouting of prestressed pipes of bridges, which can solve the problem in the prior art that in the image results of SIRT inversion, some areas with low wave velocity but not defects will appear at the edges and diagonals, and areas with discrete and discontinuous wave velocity will appear in some positions, making it difficult to accurately judge the density of grouting of prestressed pipes of bridges.

[0007] The embodiment of the present invention provides a method for detecting the density of grouting of a prestressed pipe of a bridge, comprising the following steps:

[0008] Divide the cross section of the prestressed pipe of the bridge to be tested into multiple discretized grids, and divide the discretized grids into concrete areas and pipe areas according to the annotation information on the bridge engineering drawing;

[0009] The elastic wave CT inversion imaging method is used to obtain the wave velocity range of the concrete area of ​​the prestressed pipe to be tested, and the concrete slowness range is formed;

[0010] The test block is built with the same material as the pipeline area grouting, and the wave velocity range of the test block is obtained by using the elastic wave CT inversion imaging method to obtain the wave velocity range of the pipeline area grouting material and form the grouting material slowness range;

[0011] According to the slowness range of concrete and grouting materials, as well as the ray propagation time of elastic waves, the initial slowness vector is obtained by adopting the iterative reconstruction method SIRT, and the initial slowness vector is arranged into an initial slowness matrix according to multiple discretized grids divided by the cross section of the prestressed pipe of the bridge to be tested;

[0012] The concrete area grid in the initial slowness matrix is ​​constrained by the concrete area velocity range, and the pipeline area grid is constrained by the pipeline area grouting material velocity range; and based on the initial slowness vector, the iterative reconstruction method SIRT is used for iterative inversion to obtain the final slowness vector and form the final slowness matrix;

[0013] The slowness values ​​in the final slowness matrix are imaged to determine the grouting density of the prestressed pipe of the bridge to be tested.

[0014] Preferably, obtaining the wave velocity range of the prestressed pipe concrete area to be tested includes:

[0015] Obtain the cross-sectional dimensions of the prestressed pipe of the bridge to be tested, divide the cross-sectional dimensions into n×n discretized grids, and divide the discretized grids into a concrete area and a pipe area according to the bridge engineering drawing;

[0016] A numerical model of the prestressed pipe of the bridge to be tested was established in the finite element numerical simulation software ABAQUS, and multiple transmitting points and receiving points were arranged on both sides of the cross section for simulation. The concrete wave velocity was tested multiple times to obtain the wave velocity range of the concrete area.

[0017] Preferably, obtaining the wave velocity range of the grouting material in the pipeline area includes:

[0018] The same material as the grouting material in the pipeline area was used to cast a cubic test block. A numerical model of the test block was established in ABAQUS software. The transmitting point and the receiving point were set. The wave velocity of the test block was measured multiple times to obtain the maximum value of the wave velocity of the grouting material and the maximum value of the wave velocity of the test block.

[0019] If there is an empty scene in the pipeline area, the minimum value of the pipeline area is set to 0, and the maximum value of the wave velocity of the grouting material in the pipeline area is set to the maximum value of the wave velocity of the test block, so as to obtain the wave velocity range of the grouting material in the pipeline area.

[0020] Preferably, the acquisition of the slowness range of the concrete and the slowness range of the grouting material, and the acquisition of the ray propagation time of the elastic wave include:

[0021] The wave velocity range of the prestressed pipe concrete area and the wave velocity range of the grouting material in the pipe area are calculated inversely to obtain the slowness range of the concrete and the slowness range of the grouting material;

[0022] A numerical model identical to that of the prestressed pipe of the bridge to be tested was established in ABAQUS software. The transmitting point and receiving point were set, and the CT inversion imaging method was used for simulation. The elastic wave waveform signal of the receiving point corresponding to each transmitting point was obtained, and the first arrival time of the waveform signal was extracted to obtain the ray propagation time of the elastic wave.

[0023] Preferably, forming the final slowness matrix comprises:

[0024] The concrete area grid in the initial slowness matrix is ​​constrained by the concrete area velocity range, and the pipeline area grid is constrained by the pipeline area grouting material velocity range. Based on the initial slowness vector, iterative inversion is performed using the iterative reconstruction method SIRT.

[0025] During the iteration, the difference between the slowness value of each iteration and the slowness value of the previous iteration is set to be less than the set threshold of 1×10 -7 When , the iteration is stopped, the slowness vector at this moment is obtained, and the slowness vector at this moment is set as the final slowness vector;

[0026] The final slowness vector is arranged into a final slowness matrix according to multiple discretized grids divided by the cross section of the prestressed pipe of the bridge to be tested.

[0027] Preferably, the step of determining the grouting density of the prestressed pipe of the bridge to be tested includes:

[0028] Perform two-dimensional bilinear interpolation on the final slowness matrix to 1000×1000, obtain the slowness value of each grid in the final slowness matrix, and use the imagesc function of Matlab software to image the slowness value to obtain the pipeline image;

[0029] The grouting density of the prestressed pipe of the bridge to be tested is determined according to the colors displayed in different areas of the pipe image.

[0030] The embodiment of the present invention further provides a device for detecting the density of grouting of a prestressed pipe of a bridge, comprising:

[0031] A test module is used to divide the cross section of the prestressed pipe of the bridge to be tested into multiple discretized grids, and divide the discretized grids into concrete areas and pipe areas according to the annotation information on the bridge engineering drawing;

[0032] The elastic wave CT inversion imaging method is used to obtain the wave velocity range of the concrete area of ​​the prestressed pipe to be tested, and the concrete slowness range is formed;

[0033] The test module is used to establish a test block using the same material as the pipeline area grouting, and use the elastic wave CT inversion imaging method to obtain the wave velocity range of the test block, so as to obtain the wave velocity range of the pipeline area grouting material and form the slowness range of the grouting material;

[0034] A numerical module is used to obtain an initial slowness vector according to the slowness range of concrete and the slowness range of grouting materials, as well as the ray propagation time of elastic waves, and adopt an iterative reconstruction method SIRT, and arrange the initial slowness vector into an initial slowness matrix according to multiple discretized grids divided by the cross section of the prestressed pipe of the bridge to be tested;

[0035] The inversion module is used to restrict the concrete area grid in the initial slowness matrix by the concrete area velocity range, and restrict the pipeline area grid by the pipeline area grouting material velocity range; and based on the initial slowness vector, the iterative reconstruction method SIRT is used for iterative inversion to obtain the final slowness vector and form the final slowness matrix;

[0036] The image module is used to image the slowness values ​​in the final slowness matrix to determine the grouting density of the prestressed pipe of the bridge to be tested.

[0037] An embodiment of the present invention further provides an electronic device, including a memory and a processor;

[0038] The memory is used to store computer programs;

[0039] The processor is used to implement the steps of the above-mentioned method for detecting the grouting density of prestressed pipes of bridges when executing the computer program stored in the memory.

[0040] An embodiment of the present invention further provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for detecting the density of grouting of a prestressed pipe of a bridge.

[0041] The embodiment of the present invention provides a method, device and equipment for detecting the density of grouting of a prestressed pipe of a bridge. Compared with the prior art, the beneficial effects thereof are as follows:

[0042] The present invention first obtains the velocity range of the concrete area of ​​the prestressed pipe to be tested through the engineering drawing of the bridge to be tested, and then establishes a bridge prestressed pipe test block with the same material as the grouting in the pipe area, and performs numerical simulation test on the test block to obtain the velocity range of the grouting material in the pipe area; uses the iterative reconstruction method SIRT to obtain the initial slowness vector, and arranges the initial slowness vector into an initial slowness matrix according to multiple discretized grids divided by the cross-section of the prestressed pipe of the bridge to be tested, and restricts the concrete area grid in the initial slowness matrix by the concrete area velocity range, and restricts the pipe area grid by the pipe area grouting material velocity range, and based on the initial slowness vector, uses the iterative reconstruction method SIRT to iteratively obtain the final slowness vector and form the final slowness matrix, and finally performs imaging based on the final slowness matrix to judge the grouting density of the prestressed pipe of the bridge to be tested. The process first obtains the positions of the concrete area and the pipe area from the bridge engineering drawing, and uses this known information as prior information in the iterative inversion of the iterative reconstruction method SIRT. Restrictions are imposed on the concrete area and the pipe area in the iterative inversion, highlighting the position of the grouting pipe, and offsetting the phenomenon that the wave velocity change in the current SIRT inversion does not correspond to the defective area, thereby making an accurate judgment on the grouting density of the bridge prestressed pipe. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of a method for detecting the density of grouting of a prestressed pipe of a bridge provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of the cross-sectional dimensions and grid division of a prestressed pipe in a method for detecting the grouting density of a bridge prestressed pipe provided in an embodiment of the present invention;

[0045] Figure 3 A schematic diagram of the arrangement of transmitting points and receiving points on a grid in a method for detecting the density of grouting of prestressed pipes of a bridge provided by an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a prestressed corrugated pipe and defect positions in a method for detecting the grouting density of a prestressed pipe of a bridge provided by an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of the grid division of the concrete area and the pipeline area of ​​a method for detecting the density of grouting of a prestressed pipeline of a bridge provided by an embodiment of the present invention;

[0048] Figure 6 A schematic diagram of wave field distribution at the time of 4.8e-5s under elastic wave propagation in a numerical model of a method for detecting the density of grouting of a prestressed pipe of a bridge provided by an embodiment of the present invention;

[0049] Figure 7 A schematic diagram of wave field distribution at the time of 9e-5s under elastic wave propagation in a numerical model of a method for detecting the density of grouting of a prestressed pipe of a bridge provided by an embodiment of the present invention;

[0050] Figure 8 A schematic diagram of elastic waveform and first arrival time extraction of a method for detecting the density of grouting of a prestressed pipe of a bridge provided by an embodiment of the present invention;

[0051] Fig. 9 A schematic diagram of the preliminary iterative slowness imaging effect of a method for detecting the density of grouting of a prestressed pipe of a bridge provided by an embodiment of the present invention;

[0052] Fig.10 A schematic diagram of the imaging effect of constrained iterative calculation of a method for detecting the density of grouting of a prestressed pipe of a bridge provided by an embodiment of the present invention;

[0053] Fig.11 A schematic diagram of constrained pre-interpolation imaging effect of a method for detecting the density of grouting of prestressed pipes in a bridge provided by an embodiment of the present invention;

[0054] Fig.12 A schematic diagram of constrained post-interpolation imaging effect of a method for detecting the density of grouting of prestressed pipes in a bridge provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0056] See also Figure 1The embodiment of the present invention provides a method for detecting the density of grouting of prestressed pipes of bridges. At present, the commonly used means for detecting the density of grouting of prestressed pipes of bridges include infrared thermal imaging detection technology, geological radar detection technology, impact echo detection technology and ultrasonic detection technology.

[0057] Infrared thermal imaging detection technology: As an emerging non-destructive testing technology, this technology mainly uses the difference in temperature and emissivity of the material surface to form a visible thermal image, thereby detecting the structural state and defects of the material surface, and judging the material properties and internal defects; when there is a certain defect inside the object, the heat conduction mode of the object will change, and a certain temperature distribution will be formed on the surface of the object. This distribution reflects the difference in thermal conductivity between the surface layer of the object and the material or structure below. This phenomenon can be used to detect defects such as concrete cracks and voids, and can effectively detect the position and shape of metal parts inside the concrete; Maierhofer et al. Through experimental research, it was found that infrared thermal imaging is very suitable for detecting shallow cavity defects in concrete structures, with a measurement depth of about 10 cm. It can also be used to detect cavities in prestressed grouting pipes. A significant advantage of this method is visualization, without the need for post-data processing. During the detection process, it is possible to directly determine whether there are defects inside the concrete from the image. However, it takes a long time (about 1.5 hours) to detect prestressed pipes, and when the defects are large, the heating time is also long. Therefore, this method is not convenient for application in actual detection, and can only be used for model tests in the laboratory. In addition, the detection depth is limited, so it is currently difficult to apply in practice.

[0058] Geological radar detection technology: Geological radar detection technology (GPR) is currently the most commonly used method for detecting the density of grouting of plastic corrugated pipes; Robert et al. pointed out that the voids in plastic corrugated pipes can be detected by geological radar; Antonios et al. applied geological radar to the detection of voids in plastic corrugated pipes of prestressed beams, and pointed out that the detection effect is best when the radar antenna is perpendicular to the direction of the corrugated pipe; Xin Gongfeng et al. used geological radar to detect the density of grouting of prestressed pipes through indoor tests and on-site tests; Zhou Xianyan et al. found through experiments that GPR can be used to detect the position and direction of the pipeline, but due to the shielding effect of metal bellows on electromagnetic waves, it is difficult for GPR to detect the grouting defects of metal bellows; because GPR has a high detection accuracy for plastic bellows, it cannot be used to detect metal bellows, and there are many steel bars arranged vertically and horizontally inside the beam body, which will also produce strong reflections on electromagnetic waves and interfere with the identification of abnormalities in the bellows.

[0059] Impact echo detection technology: This technology is a method mainly suitable for detecting internal defects of planar plate structures. This method was proposed by the National Institute of Standards and Technology (NIST) in the 1980s. Sansalone et al. of Cornell University in the United States further studied and improved this method. Cheng et al. used the impact echo method to detect concrete slabs with delamination defects. Lin et al. used the impact echo method to evaluate the bonding quality of different concrete interfaces. Ghomi et al. used the impact echo method to measure the thickness of concrete slabs. Kee et al. used the impact echo test method to detect the delamination of the bridge deck of a serving bridge structure, and compared the test results with the non-destructive test results based on infrared imaging. Comparison; Nicholas et al. first carried out experimental work on the impact echo detection of grouting defects in prestressed pipes; these researchers studied the influence of steel bars on the detection effect, as well as the similarities and differences in the detection of plastic bellows and metal bellows. The study found that the impact echo method can better detect the grouting defects in metal bellows, but it is impossible to accurately detect plastic bellows; although the impact echo method can make a preliminary judgment on the grouting quality of metal bellows to a certain extent, it is difficult to quantitatively calculate the defects; the use of the impact echo method for detection has the problems of low efficiency, low accuracy, and difficulty in detecting double-layer pipes, and it is found in actual detection that the frequency spectrum obtained by the impact echo method is sometimes more complex, and simply relying on the frequency spectrum characteristics cannot accurately determine the location, size and other information of the pipeline grouting defects.

[0060] Ultrasonic detection technology: This technology has good directivity. The higher the frequency, the better the directivity. Ultrasonic waves have large propagation energy and strong penetration into various materials. Ultrasonic waves have characteristics such as amplitude, frequency and phase change, which provide rich information for ultrasonic detection. Jiang Alan proved through engineering practice that it is feasible to use ultrasonic waves to detect the grouting quality of reserved concrete pipes, and pointed out that the amplitude value is very sensitive to the gap reaction, which is the main basis for judging and analyzing the density of pipe grouting. Yang Tianchun et al. used the ultrasonic transmission method to conduct experimental detection of the grouting quality of T-beams, and successfully predicted the grouting quality in the pipe by combining neural networks. Langenberg et al. conducted forward simulation and imaging method research on elastic wave nondestructive testing of prestressed pipes, and used the synthetic aperture imaging algorithm based on Fourier transform (FT-SAFT) to detect the grouting quality of T-beams. ), inverted the ultrasonic detection simulation data and measured data, and proved that elastic waves can be used to detect defects in prestressed pipes; Schickert and Krause et al. from the BAM Research Center used the finite integral method based on elastic dynamics (EFIT) to study the propagation and attenuation characteristics of ultrasonic waves in concrete, and also discussed the detection parameters such as the frequency and arrangement spacing of ultrasonic transducers; the research results show that concrete aggregates have strong scattering and reflection effects on ultrasonic waves, resulting in rapid attenuation of ultrasonic energy inside the concrete structure. To reduce the attenuation of ultrasonic energy, low-frequency ultrasonic waves need to be used, but such a low frequency will also lead to a decrease in resolution. In addition, due to the attenuation of ultrasonic amplitude and the influence of strong reflection of steel strands in the pipeline, the imaging effect of defects is not easy to meet the requirements.

[0061] The emergence of elastic wave CT technology has solved the problems in infrared thermal imaging detection technology, geological radar detection technology, impact echo detection technology and ultrasonic detection technology. Elastic wave CT technology is a type of seismic CT and one of the means of geophysical exploration. This technology uses a large amount of elastic wave information for inversion calculation to obtain the elastic wave velocity distribution law inside the object being measured, thereby knowing the internal situation of the object being measured. Elastic wave CT technology was first developed and applied in the medical field. X-ray tomography technology (X-CT), one of the CT technologies, has the characteristics of high scanning accuracy and fast imaging speed, which has brought about subversive changes in medical imaging. It also prompted the rapid development of CT technology. In the field of civil engineering inspection, elastic wave CT technology mainly uses elastic waves as a medium to judge the quality of underground rock and soil or internal defects of concrete through tomography. Shi Yadong used cross-hole elastic wave survey technology to test the karst development section of Nanjing Metro Line 4 and established a set of rock quality classification methods specifically for karst development areas. Gong Siyuan used the elastic wave CT inversion method to carry out concrete sample testing of voids and successfully verified the applicability of the elastic wave CT inversion method to detect voids. Prestressed duct grouting defects are anomalies in concrete, and the use of elastic wave CT for focused detection has good results.

[0062] At present, the elastic wave CT inversion imaging algorithm is mainly the joint iterative reconstruction algorithm (SIRT). However, in the image results of SIRT inversion, some areas with low wave velocity but not defects will appear at the edges and diagonals, and areas with discrete and discontinuous wave velocity will appear in some positions. To address this phenomenon, the present invention proposes a new method for improving imaging quality based on elastic wave CT technology.

[0063] The essence of elastic wave CT technology is to study the velocity distribution v(x, y) or slowness S(x, y) of vibration waves in the region; assuming that the propagation path of the i-th vibration wave is L i , whose propagation time is T i , then the propagation time can be expressed as:

[0064]

[0065] Where: ds represents the arc length; L i represents the propagation path from the ith source to the probe. The propagation path is usually a curve, but when the velocity field does not change much, the path can be approximately regarded as a straight line; T i It represents the travel time of the vibration wave and can be obtained through numerical simulation or experimental test.

[0066] Discretize the inversion area into n×n grids, the number of grids is n 2 , and the slowness of each cell is a constant S j (j=1,2,...,n), then the propagation time of the i-th ray can be expressed as:

[0067]

[0068] Where: d ij Represents the length of the i-th ray passing through the j-th grid.

[0069] When there are a large number of rays (such as m rays) passing through the inversion region, there are m equations about unknown numbers (j=1, 2, ..., n):

[0070] T1=d 11 S1+d 12 S2+…+d 1n S n .

[0071] T2=d 21 S1+d 22 S2+…+d 2n S n .

[0072] T i =di1 S1+d i2 S2+...+d in S n .

[0073] T m =d m1 S1+d m2 S2+...+d mn S n .

[0074] Convert the above formula into matrix form:

[0075] DS=T.

[0076] Where: D represents the ray distance matrix; T represents the elastic wave travel time vector; S represents the slowness vector.

[0077] By solving the determined S matrix, the discrete slowness distribution can be obtained, thereby realizing the velocity field inversion imaging of the study area; in the process of solving, D is usually a large irregular sparse ill-conditioned matrix, which cannot be directly inverted, and a joint iterative reconstruction algorithm (SIRT) is used to iteratively solve S; after solving S, the present invention constrains the distribution range of slowness or velocity on the grid through known prior information, and further iterates to improve the imaging quality; the specific steps include:

[0078] Step 1: Obtain the test section size, select the appropriate number of grids, and divide it into n×n discretized grids; arrange the transmitting points at the midpoints of all grids on one side of the section and the receiving points at the midpoints of all grids on the other side to calculate the ray distance matrix D.

[0079] Step 2: According to the bridge design drawings, obtain the center coordinates and radius of the prestressed pipe.

[0080] Step 3: Divide the grid into concrete area and pipe area according to the prestressed pipe location information obtained in step 2.

[0081] Step 4: Measure the concrete wave velocity multiple times at the position where the prestressed channel is not passed, and calculate its probability distribution f (using the statistical distribution to constrain the concrete slowness during inversion), and the concrete wave velocity range is obtained as v1~v2.

[0082] Step 5: Use the same grouting material to cast a cubic test block, arrange elastic wave measuring points on the grouting material test block, measure the wave velocity multiple times, and measure the maximum value v3.

[0083] Step 6: Considering the possibility that the channel may be empty (i.e. defective), set the wave velocity range of the channel area to 0~v3.

[0084] Step 7: Based on the wave speed range, find the reciprocal to get the slowness range.

[0085] Step 8: Obtain the ray travel time T through numerical simulation calculation or actual experimental test.

[0086] Step 9: Use SIRT iterative calculation to obtain the initial slowness vector S, and arrange it into a slowness matrix according to the divided grids.

[0087] Step 10: Based on the concrete area grid and pipeline area grid divided in step 3, the v1~v2 constraint is applied to the concrete area grid in the slowness matrix, that is, the wave velocity range of the concrete area is limited to v1~v2; the 0~v3 constraint is applied to the pipeline area in the slowness matrix, that is, the wave velocity range of the pipeline area is limited to 0~v3.

[0088] Step 11: Use the SIRT iterative algorithm with different constraints. Based on the initial S, carry out SIRT iterations and set the convergence standard that the difference between the slowness value of each iteration and the value of the previous iteration is less than a minimum value ε to obtain the final S.

[0089] Step 12: Perform bilinear interpolation on the slowness matrix to make its numerical changes transition smoothly.

[0090] Step 13: Image the slowness value and determine the density of the grouting at the pipeline location.

[0091] Specific experiments:

[0092] Step 1: If Figure 2 As shown in the figure, a square section with a width of 30 cm and a height of 50 cm is set, and the test section is divided into 20×20 grids with a total of 400 grids; the test scheme is left-transmitting and right-receiving, that is, 20 elastic wave transmitting points are arranged at the midpoints of the 20 grids on the left side of the section, and 20 signal receiving points are arranged at the midpoints of the 20 grids on the right side of the section, and the ray distance matrix D is calculated; the transmitting points, receiving points and some rays are arranged as shown in the figure. Figure 3 shown.

[0093] Step 2: Set up a metal corrugated pipe with a diameter of 8 cm at a distance of 9 cm from the right edge and 15 cm from the upper edge, and the pipe is completely empty. Figure 4 shown.

[0094] Step 3: If Figure 5 As shown, the grid is divided into a concrete area and a pipe area; the red grid is the concrete area grid, and the blue grid is the pipe area grid.

[0095] Step 4: Establish a numerical model of concrete material in the finite element analysis software ABAQUS, set the transmitting point and receiving point, test the concrete wave velocity multiple times, and obtain that the concrete wave velocity range is 3590m / s to 4000m / s.

[0096] Step 5: Establish a numerical model of the grouting material in the finite element analysis software ABAQUS, set the transmitting point and the receiving point, measure the wave velocity of the grouting material multiple times, and obtain the maximum wave velocity of the grouting material to be 4000m / s.

[0097] Step 6: According to step 5, set the wave velocity range of the pipeline area to 0-4000m / s.

[0098] Step 7: According to Step 4 and Step 5, the slowness range of concrete is 2.5e-4 (4000m / s) and 2.78e-4 (3590m / s), and the slowness range of grouting material is below 2.5e-4 (4000m / s).

[0099] Step 8: A numerical model with the same design dimensions is established in the finite element analysis software ABAQUS, and simulation is performed to derive the waveform signals of 20 receiving points corresponding to each transmitting point, a total of 400 receiving point elastic waveform signals, and extract the first arrival time (take-off time) of the waveform signal; the wave field distribution of elastic wave propagation in the numerical simulation is as follows: Figure 6 He Ru Figure 7 As shown, the first arrival time of the waveform signal is extracted as Figure 8 shown.

[0100] Step 9: Use the SIRT iterative algorithm to iteratively calculate the initial slowness vector S, and arrange it into a slowness matrix according to the divided grid. Use Matlab's imagesc function to image. The imaging effect is as follows: Fig. 9 As shown; it can be seen that there are more red areas at the defect location in the figure, and the color is darker, indicating that the elastic wave CT detection method successfully detects the existence of defects, but there are still more red areas at the edge of the cross section. This phenomenon is an artifact calculated during the iteration process of the SIRT algorithm.

[0101] Step 10:

[0102] For the grids in the pipeline area, the slowness values ​​of grids 92 to 97, 112 to 117, 132 to 137, and 152 to 157 are constrained to be below 2.5e-4 (4000 m / s).

[0103] For the grids in the concrete area, the slowness values ​​of grids 1 to 91, 98 to 111, 118 to 131, 138 to 151, and 158 to 400 are constrained between 2.78e-4 (3590 m / s) and 2.5e-4 (4000 m / s).

[0104] Step 11: After the constraint, further iteration is performed, and the iteration is stopped when the difference between the slowness value of each iteration and the value of the previous iteration is set to be less than 1e-7; the slowness vector S calculated finally is arranged into a slowness matrix according to the divided grid, and the imagesc function of Matlab is used for imaging; the iterative imaging effect after the constraint is as follows Fig.10 As shown, it can be seen that the artifact area at its edge has been eliminated.

[0105] Step 12: Perform two-dimensional bilinear interpolation on the slowness matrix to 1000×1000 to make the value change smoother.

[0106] Step 13: Fig.11 and Fig.12 The figure shows the comparison of the imaging effects of interpolation before and after the constraint. It can be seen from the figure that there is a large red area at the position of the prestressed pipe, that is, the low-speed area, which can be judged that there is a defect in the pipeline position.

[0107] Since the prestressed pipe of the bridge is filled with cement slurry, the defective cross-section is mainly composed of concrete, grouting material, corrugated pipe wrapped in the grouting material and air. The position and diameter of the prestressed pipe (corrugated pipe) of the bridge have been marked on the design drawings of the bridge. The present invention uses this known information as prior information in the inversion process, constrains the area of ​​the inversion section, highlights the position of the grouting pipe, and thus improves the imaging quality.

[0108] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for detecting the density of grouting of prestressed pipes in bridges, characterized in that: The following steps are involved: Divide the cross section of the prestressed pipe of the bridge to be tested into multiple discretized grids, and divide the discretized grids into concrete areas and pipe areas according to the annotation information on the bridge engineering drawing; The elastic wave CT inversion imaging method is used to obtain the wave velocity range of the concrete area of ​​the prestressed pipe to be tested, and the concrete slowness range is formed; The test block is built with the same material as the pipeline area grouting, and the wave velocity range of the test block is obtained by using the elastic wave CT inversion imaging method to obtain the wave velocity range of the pipeline area grouting material and form the grouting material slowness range; According to the slowness range of concrete and grouting materials, as well as the ray propagation time of elastic waves, the initial slowness vector is obtained by adopting the iterative reconstruction method SIRT, and the initial slowness vector is arranged into an initial slowness matrix according to multiple discretized grids divided by the cross section of the prestressed pipe of the bridge to be tested; The concrete area grid in the initial slowness matrix is ​​constrained by the concrete area velocity range, and the pipeline area grid is constrained by the pipeline area grouting material velocity range; and based on the initial slowness vector, the iterative reconstruction method SIRT is used for iterative inversion to obtain the final slowness vector and form the final slowness matrix; The slowness values ​​in the final slowness matrix are imaged to determine the grouting density of the prestressed pipe of the bridge to be tested.

2. A method for detecting the density of grouting of a prestressed pipe of a bridge according to claim 1, characterized in that: The step of obtaining the wave velocity range of the concrete area of ​​the prestressed pipe to be tested comprises: Obtain the cross-sectional dimensions of the prestressed pipe of the bridge to be tested, divide the cross-sectional dimensions into n×n discretized grids, and divide the discretized grids into a concrete area and a pipe area according to the bridge engineering drawing; A numerical model of the prestressed pipe of the bridge to be tested was established in the finite element numerical simulation software ABAQUS, and multiple transmitting points and receiving points were arranged on both sides of the cross section for simulation. The concrete wave velocity was tested multiple times to obtain the wave velocity range of the concrete area.

3. A method for detecting the density of grouting of a prestressed pipe of a bridge according to claim 1, characterized in that: The step of obtaining the wave velocity range of the grouting material in the pipeline area includes: The same material as the grouting material in the pipeline area was used to cast a cubic test block. A numerical model of the test block was established in ABAQUS software. The transmitting point and the receiving point were set. The wave velocity of the test block was measured multiple times to obtain the maximum value of the wave velocity of the grouting material and the maximum value of the wave velocity of the test block. If there is an empty scene in the pipeline area, the minimum value of the pipeline area is set to 0, and the maximum value of the wave velocity of the grouting material in the pipeline area is set to the maximum value of the wave velocity of the test block, so as to obtain the wave velocity range of the grouting material in the pipeline area.

4. A method for detecting the density of grouting of a prestressed pipe of a bridge according to claim 1, characterized in that: The acquisition of the slowness range of the concrete and the slowness range of the grouting material, and the acquisition of the ray propagation time of the elastic wave, include: The wave velocity range of the prestressed pipe concrete area and the wave velocity range of the grouting material in the pipe area are calculated inversely to obtain the slowness range of the concrete and the slowness range of the grouting material; A numerical model identical to that of the prestressed pipe of the bridge to be tested was established in ABAQUS software. The transmitting point and receiving point were set, and the CT inversion imaging method was used for simulation. The elastic wave waveform signal of the receiving point corresponding to each transmitting point was obtained, and the first arrival time of the waveform signal was extracted to obtain the ray propagation time of the elastic wave.

5. A method for detecting the density of grouting of a prestressed pipe of a bridge according to claim 1, characterized in that: The forming of the final slowness matrix comprises: The concrete area grid in the initial slowness matrix is ​​constrained by the concrete area velocity range, and the pipeline area grid is constrained by the pipeline area grouting material velocity range. Based on the initial slowness vector, iterative inversion is performed using the iterative reconstruction method SIRT. During the iteration, the difference between the slowness value of each iteration and the slowness value of the previous iteration is set to be less than the set threshold of 1×10 -7 When , the iteration is stopped, the slowness vector at this moment is obtained, and the slowness vector at this moment is set as the final slowness vector; The final slowness vector is arranged into a final slowness matrix according to multiple discretized grids divided by the cross section of the prestressed pipe of the bridge to be tested.

6. A method for detecting the density of grouting of a prestressed pipe of a bridge according to claim 1, characterized in that: The method of judging the grouting density of the prestressed pipe of the bridge to be tested comprises: Perform two-dimensional bilinear interpolation on the final slowness matrix to 1000×1000, obtain the slowness value of each grid in the final slowness matrix, and use the imagesc function of Matlab software to image the slowness value to obtain the pipeline image; The grouting density of the prestressed pipe of the bridge to be tested is determined according to the colors displayed in different areas of the pipe image.

7. A device for detecting the density of grouting of prestressed pipes in bridges, characterized in that: include: A test module is used to divide the cross section of the prestressed pipe of the bridge to be tested into multiple discretized grids, and divide the discretized grids into concrete areas and pipe areas according to the annotation information on the bridge engineering drawing; The elastic wave CT inversion imaging method is used to obtain the wave velocity range of the concrete area of ​​the prestressed pipe to be tested, and the concrete slowness range is formed; The test module is used to establish a test block using the same material as the pipeline area grouting, and use the elastic wave CT inversion imaging method to obtain the wave velocity range of the test block, so as to obtain the wave velocity range of the pipeline area grouting material and form the slowness range of the grouting material; A numerical module is used to obtain an initial slowness vector according to the slowness range of concrete and the slowness range of grouting materials, as well as the ray propagation time of elastic waves, and adopt an iterative reconstruction method SIRT, and arrange the initial slowness vector into an initial slowness matrix according to multiple discretized grids divided by the cross section of the prestressed pipe of the bridge to be tested; The inversion module is used to restrict the concrete area grid in the initial slowness matrix by the concrete area velocity range, and restrict the pipeline area grid by the pipeline area grouting material velocity range; and based on the initial slowness vector, the iterative reconstruction method SIRT is used for iterative inversion to obtain the final slowness vector and form the final slowness matrix; The image module is used to image the slowness values ​​in the final slowness matrix to determine the grouting density of the prestressed pipe of the bridge to be tested.

8. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is used to implement the steps of a method for detecting the density of grouting of a prestressed pipe of a bridge as described in any one of claims 1 to 6 when executing the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the steps of a method for detecting the density of grouting of a prestressed pipe of a bridge as described in any one of claims 1 to 6.

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