A method, device and equipment for detecting grouting compactness of bridge prestressed ducts

Abstract

The application discloses a bridge prestressed pipe grouting compactness detection method, device and equipment, relates to the bridge construction technical field, and obtains the initial slowness vector, and arranges the initial slowness vector into the initial slowness matrix, and the concrete area grid and the pipe area grid in the initial slowness matrix are limited and constrained by the wave speed range of the concrete area to be detected and the wave speed range of the pipe area grouting material, the final slowness vector is obtained by using the iterative reconstruction method SIRT iteration, and the final slowness matrix is formed and imaged to judge the grouting compactness;The process firstly obtains the position of the concrete area and the pipe area, uses the known information as the prior information in the iterative reconstruction method SIRT iteration inversion, limits and constrains the concrete area and the pipe area in the iterative inversion, highlights the position of the grouting pipe, offsets the phenomenon that the wave speed change does not correspond to the defect area in the present stage SIRT inversion, and thus accurately judges the bridge prestressed pipe grouting compactness.

Description

Technical Field

[0001] This invention relates to the field of bridge construction technology, and in particular to a method, apparatus, equipment and medium for detecting the density of grouting in prestressed ducts of bridges. Background Technology

[0002] With the rapid development of transportation construction such as highways 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. Today, more than 95% of newly built bridges are prestressed concrete bridges. Cracks, voids, and incomplete concrete pouring can all affect the strength and life of the beam, but the most significant impact is the defect of incomplete grouting in the prestressing duct. The prestressing duct is the most important component of the prestressed beam. The prestress of the steel strands in the duct 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 function of prestressing duct grouting is to protect the steel strands from corrosion. According to statistics, incomplete grouting of the corrugated pipe leads to corrosion and breakage of the steel strands, resulting in premature loss of prestress, which can ultimately shorten the service life of the bridge to one-tenth of its design life.

[0003] To ensure the prestressing effect and structural durability of the beam and prevent moisture intrusion that corrodes the prestressed steel strands, the prestressing ducts must be filled with cement grout. However, due to the grouting process (the presence of seepage water and residual air makes it easy for large voids to appear at the inlet, outlet, upper convex section of curved ducts, and near the vent holes) and certain human factors, the grouting of the prestressed ducts is often incomplete. This leads to corrosion of the prestressed steel strands under the influence of air and water, resulting in the loss of prestress in the beam and greatly reducing the load-bearing capacity and durability of the concrete structural members, thus causing structural failure or collapse. Therefore, the detection of the grouting density of bridge prestressed ducts is particularly important.

[0004] Currently, commonly used detection technologies include infrared thermal imaging, ground-penetrating radar, impact echo detection, and ultrasonic testing. However, due to the strong scattering and reflection of waves in concrete, wave energy decays rapidly within the concrete structure. Furthermore, the strong reflection from steel strands inside pipes further hinders the effectiveness of these technologies in detecting defects. To address this issue, elastic wave CT technology has emerged. This technology utilizes a large amount of elastic wave information for inversion calculations to obtain the distribution pattern of elastic wave velocity within the tested object, thereby revealing the internal condition of the object.

[0005] At present, the main elastic wave CT inversion imaging algorithm is the Joint Iterative Reconstruction (SIRT) algorithm. However, in the image results of SIRT inversion, there are some areas with low wave velocity at the edges and diagonals that are not defects. At the same time, there are some areas with discrete and discontinuous wave velocity, making it difficult to accurately judge the grout density of bridge prestressed ducts. Summary of the Invention

[0006] This invention provides a method, apparatus, and equipment for detecting the grout density of prestressed ducts in bridges. It can solve the problem in the prior art that in the SIRT inversion image results, there are some areas with low wave velocity at the edges and diagonals that are not defects, and there are also some areas with discrete and discontinuous wave velocity, making it difficult to accurately judge the grout density of prestressed ducts in bridges.

[0007] This invention provides a method for detecting the grout density of prestressed ducts in bridges, comprising the following steps:

[0008] The prestressed duct section of the bridge under test is divided into multiple discretized grids, and the discretized grids are divided into concrete zone and duct zone according to the annotation information on the bridge engineering drawing;

[0009] The wave velocity range of the prestressed duct concrete zone under test was obtained by using the elastic wave CT inversion imaging method, and the concrete slowness range was formed.

[0010] Test blocks were constructed using the same materials as those used for grouting in the pipeline area. Elastic wave CT inversion imaging was used to obtain the wave velocity range of the test blocks in order to obtain the wave velocity range of the grouting material in the pipeline area and to form the grouting material slowness range.

[0011] Based on the slowness range of concrete and grout, as well as the ray propagation time of elastic waves, the initial slowness vector is obtained by using the iterative reconstruction method SIRT. The initial slowness vector is then arranged into an initial slowness matrix according to multiple discretized grids divided by the prestressed duct section of the bridge under test.

[0012] The concrete zone mesh in the initial slowness matrix is ​​constrained by the wave velocity range of the concrete zone, and the pipe zone mesh is constrained by the wave velocity range of the grouting material in the pipe zone. Based on the initial slowness vector, the final slowness vector and the final slowness matrix are obtained by iterative inversion using the SIRT iterative reconstruction method.

[0013] The slowness values ​​in the final slowness matrix are imaged to determine the grouting density of the prestressed ducts in the bridge under test.

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

[0015] Obtain the cross-sectional dimensions of the prestressed duct of the bridge to be tested, divide the cross-section into an n×n discretized grid, and divide the discretized grid into a concrete zone and a duct zone according to the bridge engineering drawings;

[0016] A numerical model of the prestressed duct of the bridge under test was established in the finite element numerical simulation software ABAQUS. Multiple emission 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 in the concrete area.

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

[0018] Using the same material as the grouting material in the pipeline area, a cubic test block was cast. A numerical model of the test block was established in ABAQUS software, and the emission point and 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 area in the pipeline zone, the minimum value of the pipeline zone is set to 0, and the maximum value of the wave velocity of the grouting material in the pipeline zone is 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 zone.

[0020] Preferably, obtaining the slowness range of the concrete and the slowness range of the grout, as well as obtaining the propagation time of the elastic wave, includes:

[0021] The reciprocals of the wave velocity range of the prestressed duct concrete zone and the wave velocity range of the grouting material in the duct zone are respectively obtained to obtain the slowness range of the concrete and the slowness range of the grouting material.

[0022] A numerical model identical to the prestressed duct of the bridge under test was established in ABAQUS software. The emission point and the receiving point were set, and the CT inversion imaging method was used for simulation to obtain the elastic wave waveform signal of the receiving point corresponding to each emission point. 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 includes:

[0024] The concrete zone mesh in the initial slowness matrix is ​​constrained by the wave velocity range of the concrete zone, and the pipe zone mesh is constrained by the wave velocity range of the grouting material in the pipe zone. Based on the initial slowness vector, the SIRT iterative reconstruction method is used for iterative inversion.

[0025] During iteration, the difference between the slowness value of the slowness vector in each iteration and the value in the previous iteration is set to be less than a set threshold of 1 × 10. -7 When the iteration stops, the current slowness vector is obtained and set as the final slowness vector.

[0026] The final slowness vector is arranged into the final slowness matrix by dividing the prestressed duct section of the bridge under test into multiple discretized grids.

[0027] Preferably, determining the grout density of the prestressed duct of the bridge under test includes:

[0028] The final slowness matrix is ​​interpolated to 1000×1000 using two-dimensional bilinear interpolation to obtain the slowness value of each grid in the final slowness matrix. The slowness values ​​are then imaged using the imagesc function of Matlab software to obtain the pipeline image.

[0029] The grouting density of the prestressed duct of the bridge under test can be determined by the colors displayed in different areas of the duct image.

[0030] This invention also provides a device for detecting the grout density of prestressed duct grouting in bridges, comprising:

[0031] The testing module is used to divide the prestressed duct section of the bridge under test into multiple discretized grids, and divide the discretized grids into concrete area and duct area according to the annotation information on the bridge engineering drawing;

[0032] The wave velocity range of the prestressed duct concrete zone under test was obtained by using the elastic wave CT inversion imaging method, and the concrete slowness range was formed.

[0033] The test module is used to build a test block using the same material as the grouting material in the pipeline area. 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 grouting material in the pipeline area and form the grouting material slowness range.

[0034] The numerical module is used to obtain the initial slowness vector based on the slowness range of concrete and grout, as well as the ray propagation time of elastic waves, and the iterative reconstruction method SIRT. The initial slowness vector is then arranged into an initial slowness matrix according to multiple discretized grids divided by the prestressed duct section of the bridge under test.

[0035] The inversion module is used to constrain the concrete zone mesh in the initial slowness matrix by applying the concrete zone wave velocity range and the pipe zone mesh by applying the pipe zone grouting material wave velocity range. Based on the initial slowness vector, the final slowness vector is obtained by iterative inversion using the SIRT iterative reconstruction method, and the final slowness matrix is ​​formed.

[0036] The image module is used to image the slowness values ​​in the final slowness matrix to determine the grouting density of the prestressed ducts of the bridge under test.

[0037] This invention also provides an electronic device, including a memory and a processor;

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

[0039] When the processor executes the computer program stored in the memory, it implements the steps of the method for detecting the grout density of prestressed duct grouting in bridges as described above.

[0040] This invention also provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of a method for detecting the grout density of prestressed duct grouting in bridges as described above.

[0041] This invention provides a method, apparatus, and equipment for detecting the grout density of prestressed ducts in bridges. Compared with the prior art, its advantages are as follows:

[0042] This invention first obtains the wave velocity range of the concrete zone of the prestressed duct under test using engineering drawings of the bridge to be tested. Then, a test block of the prestressed duct of the bridge is built using the same material as the grout in the duct zone, and numerical simulation tests are performed on the test block to obtain the wave velocity range of the grout in the duct zone. The initial slowness vector is obtained using the SIRT iterative reconstruction method, 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 duct of the bridge to be tested. The concrete zone grid in the initial slowness matrix is ​​constrained by the wave velocity range of the concrete zone, and the duct zone grid is constrained by the wave velocity range of the grout in the duct zone. Based on the initial slowness vector, the SIRT iterative reconstruction method is used to obtain the final slowness vector and form the final slowness matrix. Finally, imaging is performed based on the final slowness matrix to determine the grout density of the prestressed duct of the bridge to be tested. The process first obtains the locations of the concrete zone and the duct zone from the bridge engineering drawings. This known information is used as prior information in the SIRT iterative inversion method to impose constraints on the concrete zone and the duct zone in the iterative inversion, highlighting the location of the grouting duct and offsetting the phenomenon that wave velocity changes do not correspond to the defect area in the current SIRT inversion. This allows for an accurate judgment on the grouting density of the bridge prestressed duct. Attached Figure Description

[0043] Figure 1 A schematic diagram illustrating a method for detecting the grout density of prestressed duct grouting in bridges, provided in an embodiment of the present invention;

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

[0045] Figure 3 A schematic diagram of the arrangement of the transmitting and receiving points on the grid for a method for detecting the grout density of prestressed duct grouting in bridges, provided in an embodiment of the present invention.

[0046] Figure 4 A schematic diagram of a prestressed corrugated duct and defect location, provided for an embodiment of the present invention, regarding a method for detecting the grout density of prestressed ducts in bridges.

[0047] Figure 5 A schematic diagram of the grid division of the concrete zone and the duct zone in a method for detecting the grout density of prestressed duct grouting in bridges, provided in an embodiment of the present invention.

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

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

[0050] Figure 8 A schematic diagram of the elastic waveform and first-to-last time extraction of a method for detecting the density of prestressed duct grouting in bridges, provided in an embodiment of the present invention;

[0051] Figure 9 A schematic diagram of the preliminary iterative slow-motion imaging effect of a method for detecting the density of prestressed duct grouting in bridges, provided in an embodiment of the present invention;

[0052] Figure 10 A schematic diagram of the constrained iterative calculation imaging effect of a method for detecting the grout density of prestressed duct grouting in bridges, provided in an embodiment of the present invention;

[0053] Figure 11 A schematic diagram of the constrained pre-interpolation imaging effect of a method for detecting the grout density of prestressed duct grouting in bridges, provided in an embodiment of the present invention.

[0054] Figure 12 This is a schematic diagram of the constrained post-interpolation imaging effect of a method for detecting the grout density of prestressed duct grouting in bridges, provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] See Figure 1This invention provides a method for detecting the grout density of prestressed ducts in bridges. Currently, commonly used methods for detecting the grout density of prestressed ducts in bridges include infrared thermal imaging detection technology, ground-penetrating 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 utilizes the differences in temperature and emissivity of material surfaces to form visible thermal images, thereby detecting the structural state and defects of the material surface, and thus determining the material properties and internal defects. When a defect exists inside an object, the heat conduction mode of the object will change, and a certain temperature distribution will form on the object's surface. This distribution reflects the difference in thermal conductivity between the surface layer and the underlying material or structure. This phenomenon can be used to detect defects such as cracks and voids in concrete, and can effectively detect the location and shape of metal components inside concrete; Maierhofer et al. Experimental studies have shown that infrared thermal imaging is highly suitable for detecting shallow voids and defects in concrete structures, with a measurement depth of approximately 10 cm. It can also be used to detect voids within prestressed grouting ducts. A significant advantage of this method is its visualization capability; no post-processing is required, and the presence of defects within the concrete can be directly determined from the images during the inspection process. However, the inspection of prestressed ducts is time-consuming (approximately 1.5 hours), and the heating time is also prolonged when the defects are large. Therefore, this method is not suitable for practical application and can only be used for model tests in the laboratory. Furthermore, the detection depth is limited, making practical application currently difficult.

[0058] Ground-penetrating radar (GPR) detection technology is currently the most commonly used method for detecting the grouting density of plastic corrugated pipes. Robert et al. pointed out that GPR can detect voids inside plastic corrugated pipes. Antonios et al. applied GPR to detect 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 GPR to detect the grouting density of prestressed pipes through indoor experiments and field tests. Zhou Xianyan et al. found through experiments that GPR can detect the location and direction of the pipe, but due to the shielding effect of the metal corrugated pipe on electromagnetic waves, GPR is difficult to detect grouting defects in the metal corrugated pipe. Although GPR has high detection accuracy for plastic corrugated pipes, it cannot be used to detect metal corrugated pipes. In addition, the many longitudinal and transverse steel bars inside the beam will also produce strong reflections of electromagnetic waves, interfering with the identification of anomalies inside the corrugated pipe.

[0059] Impact-echo testing technology: This technology is primarily applicable to the detection of internal defects in planar slab structures. It was proposed in the 1980s by the National Institute of Standards and Technology (NIST) in the United States. Sansalone et al. at Cornell University further researched and refined the method. Cheng et al. used the impact-echo method to detect delamination defects in concrete slabs; Lin et al. used it to evaluate the bond quality of different concrete interfaces; Gomi et al. used it to measure the thickness of concrete slabs; Kee et al. used the impact-echo testing method to detect delamination in the bridge deck of a service bridge structure and compared the results with those from infrared imaging-based non-destructive testing. In comparison, Nicholas et al. first conducted experimental work on the detection of grouting defects in prestressed pipes using impact echo. These researchers studied the influence of reinforcing steel on the detection effect, as well as the similarities and differences between the detection of plastic corrugated pipes and metal corrugated pipes. The study found that the impact echo method can detect grouting defects in metal corrugated pipes well, but cannot accurately detect them in plastic corrugated pipes. Although the impact echo method can make a preliminary judgment on the grouting quality of metal corrugated pipes to a certain extent, it is difficult to quantitatively calculate the defects. The impact echo method has problems such as low efficiency, low accuracy, and difficulty in detecting double-layer pipes. In actual detection, it was found that the spectrum obtained by the impact echo method is sometimes quite complex. Simply relying on the spectral characteristics cannot accurately determine the location, size, and other information of the grouting defects in the pipe.

[0060] Ultrasonic testing technology: This technology has good directivity, and the higher the frequency, the better the directivity; ultrasonic waves have high propagation energy and strong penetrating power through various materials; the amplitude, frequency, and phase changes of ultrasonic waves 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 pre-reserved concrete pipes, and pointed out that the amplitude value is very sensitive to the void response, which is the main basis for judging and analyzing the grouting density of pipes; Yang Tianchun et al. conducted experimental research on the grouting quality of T-beams using ultrasonic transmission method, and successfully predicted the grouting quality inside the pipes by combining neural networks; Langenberg et al. conducted research on forward modeling and imaging methods for non-destructive testing of prestressed pipes using elastic waves, and used the Fourier transform-based synthetic aperture imaging algorithm (FT-SAFT). The study inverted simulated and measured data from ultrasonic testing, proving that elastic waves can be used to detect defects in prestressed ducts. Schickert and Krause et al. from the BAM Research Center used the Finite Integration Method (EFIT) based on elastic dynamics to study the propagation and attenuation characteristics of ultrasonic waves in concrete, and also explored detection parameters such as the frequency and spacing of ultrasonic transducers. The results showed that concrete aggregates have a strong scattering and reflection effect on ultrasonic waves, leading to rapid attenuation of ultrasonic energy within the concrete structure. To reduce ultrasonic energy attenuation, low-frequency ultrasonic waves are needed; however, such low frequencies also reduce resolution. Furthermore, due to the attenuation of ultrasonic amplitude and the strong reflection from the steel strands inside the duct, the imaging effect of defects is not easily achieved.

[0061] The emergence of elastic wave CT technology has solved problems encountered in infrared thermal imaging, ground-penetrating radar, shock echo detection, and ultrasonic detection technologies. Elastic wave CT is a type of seismic CT and a geophysical exploration method. This technology utilizes a large amount of elastic wave information for inversion calculations to obtain the distribution of elastic wave velocities within the measured object, thereby revealing the internal conditions of the object. Elastic wave CT technology was first developed and applied in the medical field. X-ray tomography (X-CT), a type of CT technology, has the characteristics of high scanning accuracy and fast imaging speed, which has revolutionized medical imaging. This has also spurred the rapid development of CT technology. In the field of civil engineering testing, elastic wave CT technology mainly uses elastic waves as a medium to determine the quality of underground rock and soil or the internal defects of concrete through tomographic imaging. Shi Yadong used cross-hole elastic wave survey technology to test the karst-developed section of Nanjing Metro Line 4 and established a set of rock mass quality classification methods specifically for karst-developed areas. Gong Siyuan used the elastic wave CT inversion method to conduct tests on concrete samples with voids, successfully verifying the applicability of the elastic wave CT inversion method for detecting voids. Prestressed duct grouting defects, as anomalies in concrete, can be effectively detected by focusing elastic wave CT.

[0062] Currently, the main elastic wave CT inversion imaging algorithm is the Joint Iterative Reconstruction (SIRT) algorithm. However, in the SIRT inversion image results, there are some areas with low wave velocity but not defects at the edges and diagonals, and there are also some areas with discrete and discontinuous wave velocity. In order to address this phenomenon, this 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 within a region; assuming the propagation path of the i-th vibration wave is L i Its propagation time is T i Then the propagation time can be expressed as:

[0064]

[0065] Where: ds represents the arc length infinitesimal element; L i This represents the propagation path from the i-th source to the probe. The propagation path is usually a curve, but when the velocity field does not change significantly, the path can be approximated as a straight line; T i The travel time of a vibration wave can be obtained through numerical simulation or experimental testing.

[0066] The inversion region is discretized into n×n grids, with n being the number of grids. 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 This represents the length of the i-th ray as it passes through the j-th grid.

[0069] When a large number of rays (e.g., m rays) pass through the inversion region, there are m equations concerning the unknowns (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] The above expression can be converted into matrix form as follows:

[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. During the solution process, D is usually a large, irregular, sparse, ill-conditioned matrix that cannot be directly inverted. Therefore, the Joint Iterative Reconstruction Algorithm (SIRT) is used to iteratively solve S. After solving S, this invention uses known prior information to constrain the distribution range of slowness or velocity on the grid, and further iterates to improve the imaging quality. The specific steps include:

[0078] Step 1: Obtain the test cross-section dimensions, select an appropriate number of grids, and divide the cross-section into n×n discretized grids; place emission points at the midpoints of all grids on one side of the cross-section and receive points at the midpoints of all grids on the other side, and calculate the ray distance matrix D accordingly.

[0079] Step 2: Based on the bridge design drawings, obtain the coordinates of the center position of the prestressed duct and the duct radius.

[0080] Step 3: Based on the prestressed duct location information obtained in Step 2, divide the grid into concrete zone and duct zone.

[0081] Step 4: Measure the concrete wave velocity multiple times at locations not passing through prestressed ducts, and statistically analyze its probability distribution f (using this statistical distribution to constrain the concrete slowness during inversion), obtaining the concrete wave velocity range as v1~v2.

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

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

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

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

[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 grid.

[0087] Step 10: Based on the concrete zone mesh and pipe zone mesh divided in Step 3, apply v1~v2 constraints to the concrete zone mesh in the slowness matrix, that is, restrict the wave velocity range of the concrete zone to v1~v2; apply 0~v3 constraints to the pipe zone in the slowness matrix, that is, restrict the wave velocity range of the pipe zone to v3.

[0088] Step 11: Using the SIRT iterative algorithm with different constraints, SIRT iterations are carried out based on the initial S, and the convergence criterion is set as 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 smooth the numerical transition.

[0090] Step 13: Image the slowness value to determine the grout density at the pipeline location.

[0091] Specific experiment:

[0092] Step 1: As Figure 2 As shown, a square cross-section with a width of 30cm and a height of 50cm is set up, and the test cross-section is divided into 20×20 grids, totaling 400 grids. The test scheme is left-to-right transmission and right-to-reception, that is, 20 elastic wave emission points are arranged at the midpoints of the 20 grids on the left side of the cross-section, and 20 signal receiving points are arranged at the midpoints of the 20 grids on the right side of the cross-section. The ray distance matrix D is calculated. The arrangement of emission points, receiving points, and some rays is as follows. Figure 3 As shown.

[0093] Step Two: Install a corrugated metal pipe with a diameter of 8cm at a distance of 9cm from the right edge and 15cm from the top edge. The pipe should be completely hollow. Figure 4 As shown.

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

[0095] Step 4: Establish a numerical model of the concrete material in the finite element analysis software ABAQUS, set the emission point and the receiving point, and test the concrete wave velocity multiple times to obtain the concrete wave velocity range of 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 emission point and the receiving point, and measure the wave velocity of the grouting material multiple times. The maximum wave velocity of the grouting material is found to be 4000m / s.

[0097] Step Six: Based on Step Five, set the wave velocity range in the pipeline area to 0–4000 m / s.

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

[0099] Step 8: In the finite element analysis software ABAQUS, establish a numerical model with the same dimensions as the design, perform simulation, and export the waveform signals of 20 receiving points corresponding to each transmitting point, for a total of 400 elastic waveform signals from receiving points. Extract the first-arrival time (take-off time) of the waveform signals; the wavefield distribution of the elastic wave propagation in the numerical simulation is as follows: Figure 6 He Ru Figure 7 As shown, the first-to-last time extraction of the waveform signal is as follows: Figure 8 As shown.

[0100] Step 9: Using the SIRT iterative algorithm, iteratively calculate the initial slowness vector S, arrange it into a slowness matrix according to the divided grid, and use the imagesc function in Matlab to create the image. The imaging effect is as follows. Figure 9 As shown in the figure, there are many red areas at the defect location, and the color is darker, indicating that the elastic wave CT detection method successfully detected the presence of the defect. However, there are still many red areas at the edge of the cross section. This phenomenon is an artifact calculated during the SIRT algorithm iteration process.

[0101] Step 10:

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

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

[0104] Step 11: After constraint, further iterations are performed, and iteration is stopped when the difference between the slowness value of each iteration and the value of the previous iteration is less than 1e-7; the final calculated slowness vector S is arranged into a slowness matrix according to its grid division, and imaged using the imagesc function in Matlab; the iterative imaging effect after constraint is as follows. Figure 10 As shown, it can be seen that the artifact regions at its edges have been eliminated.

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

[0106] Step Thirteen: As Figure 11 and Figure 12 The image shows a comparison of the imaging effects before and after the constraint interpolation. As can be seen from the figure, there is a large red area at the location of the prestressed duct, which is the low-speed area. This indicates that there is a defect at the location of the duct.

[0107] Since the prestressed ducts of bridges are filled with cement grout, the defective cross-sections mainly consist of concrete, grout, corrugated pipes encasing the grout, and air. The location and diameter of the prestressed ducts (corrugated pipes) of the bridge are already marked on the bridge design drawings. This invention uses this known information as prior information in the inversion process to constrain the area of ​​the inversion section, highlight the location of the grouting ducts, and thus improve the imaging quality.

[0108] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for detecting the grout density of prestressed ducts in bridges, characterized in that, Includes the following steps: The prestressed duct section of the bridge under test is divided into multiple discretized grids, and the discretized grids are divided into concrete zone and duct zone according to the annotation information on the bridge engineering drawing; The wave velocity range of the prestressed duct concrete zone under test was obtained by using the elastic wave CT inversion imaging method, and the concrete slowness range was formed. Test blocks were constructed using the same materials as those used for grouting in the pipeline area. Elastic wave CT inversion imaging was used to obtain the wave velocity range of the test blocks in order to obtain the wave velocity range of the grouting material in the pipeline area and to form the grouting material slowness range. Based on the slowness range of concrete and grout, as well as the ray propagation time of elastic waves, the initial slowness vector is obtained by using the iterative reconstruction method SIRT. The initial slowness vector is then arranged into an initial slowness matrix according to multiple discretized grids divided by the prestressed duct section of the bridge under test. The concrete zone mesh in the initial slowness matrix is ​​constrained by the wave velocity range of the concrete zone, and the pipe zone mesh is constrained by the wave velocity range of the grouting material in the pipe zone. Based on the initial slowness vector, the final slowness vector and the final slowness matrix are obtained by iterative inversion using the SIRT iterative reconstruction method. The slowness values ​​in the final slowness matrix are imaged to determine the grouting density of the prestressed ducts in the bridge under test.

2. The method for detecting the grout density of prestressed ducts in bridges according to claim 1, characterized in that, The process of obtaining the wave velocity range in the concrete zone of the prestressed duct to be tested includes: Obtain the cross-sectional dimensions of the prestressed duct of the bridge to be tested, divide the cross-section into an n×n discretized grid, and divide the discretized grid into a concrete zone and a duct zone according to the bridge engineering drawings; A numerical model of the prestressed duct of the bridge under test was established in the finite element numerical simulation software ABAQUS. Multiple emission 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 in the concrete area.

3. The method for detecting the grout density of prestressed ducts in bridges according to claim 1, characterized in that, The obtained grouting material wave velocity range in the pipeline area includes: Using the same material as the grouting material in the pipeline area, a cubic test block was cast. A numerical model of the test block was established in ABAQUS software, and the emission point and 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 area in the pipeline zone, the minimum value of the pipeline zone is set to 0, and the maximum value of the wave velocity of the grouting material in the pipeline zone is 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 zone.

4. The method for detecting the grout density of prestressed duct grouting in bridges according to claim 1, characterized in that, The acquisition of the slowness range of concrete and the slowness range of grout, as well as the acquisition of the ray propagation time of elastic waves, includes: The reciprocals of the wave velocity range of the prestressed duct concrete zone and the wave velocity range of the grouting material in the duct zone are respectively obtained to obtain the slowness range of the concrete and the slowness range of the grouting material. A numerical model identical to the prestressed duct of the bridge under test was established in ABAQUS software. The emission point and the receiving point were set, and the CT inversion imaging method was used for simulation to obtain the elastic wave waveform signal of the receiving point corresponding to each emission point. The first arrival time of the waveform signal was extracted to obtain the ray propagation time of the elastic wave.

5. The method for detecting the grout density of prestressed ducts in bridges according to claim 1, characterized in that, The formation of the final slowness matrix includes: The concrete zone mesh in the initial slowness matrix is ​​constrained by the wave velocity range of the concrete zone, and the pipe zone mesh is constrained by the wave velocity range of the grouting material in the pipe zone. Based on the initial slowness vector, the SIRT iterative reconstruction method is used for iterative inversion. During iteration, the difference between the slowness value of the slowness vector in each iteration and the value in the previous iteration is set to be less than a set threshold of 1 × 10. -7 When the iteration stops, the current slowness vector is obtained and set as the final slowness vector. The final slowness vector is arranged into the final slowness matrix by dividing the prestressed duct section of the bridge under test into multiple discretized grids.

6. The method for detecting the grout density of prestressed ducts in bridges according to claim 1, characterized in that, The determination of the grouting density of the prestressed ducts in the bridge under test includes: The final slowness matrix is ​​interpolated to 1000×1000 using two-dimensional bilinear interpolation to obtain the slowness value of each grid in the final slowness matrix. The slowness values ​​are then imaged using the imagesc function of Matlab software to obtain the pipeline image. The grouting density of the prestressed duct of the bridge under test can be determined by the colors displayed in different areas of the duct image.

7. A device for detecting the grout density of prestressed ducts in bridges, characterized in that, include: The testing module is used to divide the prestressed duct section of the bridge under test into multiple discretized grids, and divide the discretized grids into concrete area and duct area according to the annotation information on the bridge engineering drawing; The wave velocity range of the prestressed duct concrete zone under test was obtained by using the elastic wave CT inversion imaging method, and the concrete slowness range was formed. The test module is used to build a test block using the same material as the grouting material in the pipeline area. 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 grouting material in the pipeline area and form the grouting material slowness range. The numerical module is used to obtain the initial slowness vector based on the slowness range of concrete and grout, as well as the ray propagation time of elastic waves, and the iterative reconstruction method SIRT. The initial slowness vector is then arranged into an initial slowness matrix according to multiple discretized grids divided by the prestressed duct section of the bridge under test. The inversion module is used to constrain the concrete zone mesh in the initial slowness matrix by applying the concrete zone wave velocity range and the pipe zone mesh by applying the pipe zone grouting material wave velocity range. Based on the initial slowness vector, the final slowness vector is obtained by iterative inversion using the SIRT iterative reconstruction method, and the final slowness matrix is ​​formed. The image module is used to image the slowness values ​​in the final slowness matrix to determine the grouting density of the prestressed ducts of the bridge under test.

8. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the steps of the method for detecting the grout density of prestressed duct grouting in bridges as described in any one of claims 1 to 6.

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 in prestressed ducts of bridges as described in any one of claims 1 to 6.

Images