A Method for Detecting the Compactness of Adhesive Joints in Segmental Precast Box Girder Bridges Based on Multi-Source Data

Abstract

This invention belongs to the field of bridge inspection technology, specifically disclosing a method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data. Infrared thermal imagers are used to scan the adhesive joint area to identify potential defect zones, and ultrasonic time-of-flight data is collected for these zones to divide them into normal and defective areas. A time-of-flight-range benchmark model is then established for both defective and normal areas. Defect depth assessment values ​​at each measuring point within the defective area are calculated, generating a density distribution map. This method integrates rapid screening with infrared thermal imaging and quantitative ultrasonic diagnosis, achieving non-destructive and quantitative assessment of adhesive joint density. Simultaneously, core samples are drilled at the locations with the greatest defect depth in the density distribution map. The actual defect morphology of the core sample is compared with the defect characteristics inferred by ultrasonic testing, allowing for direct verification of ultrasonic detection conclusions with minimal local damage. This effectively reduces the risk of misjudgment and missed detection while avoiding structural damage caused by comprehensive core drilling.

Description

Technical Field

[0001] This invention belongs to the field of bridge inspection technology, and specifically discloses a method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data. Background Technology

[0002] Segmental precast assembly construction has been widely used in modern bridge construction due to its advantages such as fast construction speed and controllable quality. This method divides the main beam into several precast segments. These segments are joined by applying epoxy resin adhesive and prestressing to form adhesive joints. The density of these adhesive joints directly affects the overall performance of the bridge. Defects such as voids or incomplete compaction within the adhesive joints can lead to stress concentration, affecting the structure's load-bearing capacity and even causing serious safety problems.

[0003] Currently, the main methods for testing the density of adhesive joints in precast segmental box girder bridges are as follows: Core sampling: Core samples are drilled at the adhesive joint location, and the filling and bonding status of the adhesive are directly observed. This method provides intuitive and reliable results, but it is a destructive test that can cause irreversible damage to the structure. Furthermore, the number of sampling points is limited, making it difficult to represent the density distribution of the entire adhesive joint and thus unable to achieve large-area surveys.

[0004] Ultrasonic testing: This method infers internal defects by utilizing changes in parameters such as the speed and amplitude of ultrasonic waves propagating in a medium. However, in field applications, this method is easily affected by environmental noise and internal steel reinforcement, and is highly dependent on the experience of the operators.

[0005] In summary, existing single-modal detection methods cannot effectively isolate interference and fully display defects, resulting in uncertainty in the detection results. Summary of the Invention

[0006] To solve the above-mentioned technical problems, or at least partially solve them, the present invention provides a method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data.

[0007] The objective of this invention can be achieved through the following technical solution: a method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data, comprising: delineating the adhesive joint area to be detected based on the design drawings and on-site survey of the precast segmental box girder bridge.

[0008] Thermal imaging scans were performed on the adhesive joint area. Temperature distribution curves were extracted from the thermal images to identify areas of temperature anomalies. By combining this with spatial overlay analysis of the prestressed ducts and steel reinforcement areas in the design drawings, potential defect areas were determined.

[0009] Ultrasonic time data were collected for potential defect areas using the single-sided flat measurement method and compared with the reference time value measured in the same adhesive joint area without apparent defects to divide the normal area and defect area.

[0010] Establish a time-range acoustic benchmark model for the defect area and the normal area, and calculate the defect depth assessment value of each measuring point in the defect area based on the model to generate a density distribution map.

[0011] Core samples are drilled at the location with the greatest defect depth in the density distribution map. The actual defect morphology of the core sample is compared with the defect characteristics inferred by ultrasound. The decision on whether to re-inspect is made based on the comparison results.

[0012] Combining all the above technical solutions, the positive effects of this invention are as follows: 1. This invention uses an infrared thermal imager to scan and determine potential defect areas in the adhesive joint area, and uses a non-metallic ultrasonic detector to collect ultrasonic time data for the potential defect areas. Measurement points whose measured time exceeds the reference time value are classified as defect areas, and those that do not exceed it are classified as normal areas. Then, an acoustic time-range reference model for defect areas and normal areas is established, and the defect depth assessment value of each measurement point in the defect area is calculated to generate a density distribution map. By integrating the rapid screening of infrared thermal imaging and the quantitative diagnosis of ultrasound, a non-destructive, quantitative, and visual assessment of the density of adhesive joints is achieved on the basis of eliminating interference factors.

[0013] 2. This invention drills a core sample at the location with the greatest defect depth in the density distribution map, compares the actual defect morphology of the core sample with the defect characteristics inferred by ultrasound, and decides whether to re-inspect based on the comparison results. This allows for direct verification of the ultrasonic test conclusion with minimal local damage, and adaptively adjusts the detection strategy based on the verification results. This effectively reduces the risk of misjudgment and missed judgment while avoiding structural damage caused by full core drilling. Attached Figure Description

[0014] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0015] Figure 1 This is a diagram illustrating the implementation steps of the method of the present invention;

[0016] Figure 2 This is a schematic diagram of the ultrasonic testing point arrangement in this invention;

[0017] Figure 3 This is a schematic diagram of the right-angled triangle geometric path relationship of the edge of the ultrasonic diffraction defect in this invention;

[0018] Figure 4 This is a density distribution diagram in this invention.

[0019] Attached reference numerals: 1. Box girder segment, 2. Adhesive joint, 3. Layout of measuring points, 4. Area of ​​loose joint, 5. Diffraction path, 6. Ultrasonic transmitter, 7. Receiving transducer. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] See Figure 1 As shown, the present invention proposes a method for detecting the density of adhesive joints in segmental precast box girder bridges based on multi-source data, including the following steps: S1, Determining the detection area: Based on the design drawings of the segmental precast box girder bridge and on-site survey, the area of ​​adhesive joints to be detected is delineated.

[0022] Given the large number and wide distribution of adhesive joints in precast segmental box girder bridges, and the differences in stress state and accessibility of joints at different locations, it is not feasible to perform indiscriminate testing on all adhesive joints of the entire bridge. Instead, it is necessary to delineate testing areas and conduct density testing within these designated areas. The specific implementation process is as follows: S11. Determine the longitudinal boundary: Using the centerline of the pier top of the precast segmental box girder bridge as the longitudinal starting point and the centerline of the expansion joint or closure section as the longitudinal ending point, the section between the starting and ending points is used as the longitudinal boundary for testing. This is because the stress on the pier top area of ​​the precast box girder bridge is complex, and the adhesive joints bear significant shear stress, making them prone to defects. Near the expansion joint or closure section, due to construction errors or temperature deformation, the adhesive joints are also prone to becoming loose. Using these two boundaries allows for concentrated testing of stress-bearing sections, avoiding indiscriminate scanning of all segments of the entire bridge and improving testing efficiency.

[0023] S12. Determine the lateral and vertical boundaries: Since the adhesive joint exists in the bottom plate, web plate and top plate of the box girder, its lateral range is the width between the two web plates, and its vertical range is from the lower surface of the bottom plate to the upper surface of the top plate. Therefore, the outer edge of the web plate of the box girder is used as the lateral boundary, and the lower surface of the bottom plate and the upper surface of the top plate are used as the vertical boundaries to form a three-dimensional detection range, which can ensure that the detection space completely covers the area where the adhesive joint is located.

[0024] S13. Calculate the geometric parameters of the adhesive joint: Read the design three-dimensional coordinates (X, Y, Z) of the four corner points of each precast segment end face from the bridge design drawings, where X is the longitudinal coordinate, Y is the transverse coordinate, and Z is the vertical elevation coordinate. The splicing interface between two adjacent segment end faces is the theoretical adhesive joint plane, and the spatial position of this plane is determined by the design coordinates of the corner points of the end faces of the two segments.

[0025] (1) Calculate the coordinates of the start and end points of the adhesive joint plane.

[0026] Intersect the theoretical adhesive joint plane with the longitudinal starting point plane and the longitudinal ending point plane determined by S11 respectively, and calculate the coordinates of the starting and ending points of the adhesive joint at the longitudinal starting point and the longitudinal ending point.

[0027] (2) Calculate the normal direction vector of the adhesive joint plane.

[0028] Select three non-collinear corner points on the theoretical adhesive joint plane. Specifically, select one end from two adjacent segment end faces and take the upper left, upper right, and lower left corner points of that end face.

[0029] The normal direction vector is calculated as follows: First, calculate the two non-collinear edge vectors on the plane. Then, calculate the cross product of these two vectors to obtain the normal vector. After expansion, the normal vector has three components. Finally, the vector composed of the three components is the normal direction vector perpendicular to the adhesive joint plane.

[0030] In the above operations, the start and end point coordinates are used to determine the specific locations of both ends of the adhesive joint on site, and the normal direction is used to determine the direction of the adhesive joint. Only by obtaining both the location and direction can the spatial orientation of the adhesive joint in the design drawings be reproduced on the solid structure, ensuring the accuracy of subsequent layout and measurement point arrangement.

[0031] S14. On-site layout and positioning: Considering that the coordinates in the design drawings are theoretical values, while the actual bridge structure may have slight deviations after construction, and there are no directly visible adhesive joint boundary lines on site, the theoretical coordinates can be reproduced on the physical structure by using a total station for layout, forming a visual marker line. Specifically, using a total station on site, based on the start and end point coordinates and normal direction vector calculated in S13, the spatial position of the adhesive joint is laid out on the bridge physical structure. Specifically, the positions of the two ends of the adhesive joint are determined by the start and end point coordinates, the direction of the adhesive joint is determined by the normal direction, and the actual inspection boundary of the adhesive joint is marked on the surface of the box girder using the marker line.

[0032] S15. Generate the adhesive joint area: Based on the actual detection boundary obtained from the on-site layout in S14, combined with the lateral and vertical boundaries determined in S12, the adhesive joint plane area defined by the actual detection boundary is taken as the adhesive joint area within the three-dimensional detection range.

[0033] S2. Preliminary screening: Perform thermal imaging scans on the adhesive joint area, extract temperature distribution curves from the thermal images to identify temperature anomaly areas, and combine this with spatial overlay analysis of the prestressed pipes and steel reinforcement areas in the design drawings to determine potential defect areas.

[0034] Considering that internal defects in the adhesive joints of precast segmental box girder bridges can cause temperature anomalies under environmental heat radiation conditions, and that while ultrasonic testing is accurate, it is time-consuming and unsuitable for detailed inspection of all adhesive joints across the bridge, infrared thermal imaging was first used for rapid scanning after the adhesive joint area was identified, before conducting detailed ultrasonic testing, to pinpoint potential defect areas.

[0035] In an optional embodiment of the invention, S21, the thermal imaging scan can be performed as follows: Given that the surface temperature above the defect area will exhibit an abnormal distribution under environmental thermal radiation conditions such as sunlight, an infrared thermal imager can detect and record this temperature difference to form a thermal image. This allows for selection of a period of stable sunlight for inspection, ensuring that the bridge surface receives uniform solar radiation.

[0036] Set the surface emissivity parameter of the infrared thermal imager to a value that matches the concrete and structural adhesive materials, typically 0.92 to 0.95, and adjust the temperature range and temperature difference sensitivity to an appropriate level.

[0037] Move the camera at a constant speed along the direction of the adhesive joint, keeping the lens basically perpendicular to the detection surface and the distance between the lens and the detection surface constant, preferably 1.5m to 2.0m, and continuously acquire thermal image sequences covering the entire adhesive joint area.

[0038] S22. The specific process of extracting temperature distribution curves from thermal images to identify temperature anomaly areas is as follows: S22-1. The scanned thermal image sequence is stitched together to form a temperature field distribution map of the adhesive joint, and the surface temperature distribution curve along the extension direction of the adhesive joint is extracted.

[0039] S22-2. Taking each point on the surface temperature distribution curve as the center, calculate the average temperature within a preset length range before and after it as the local reference temperature. For example, the preset length range before and after is 0.2m. If the measured temperature of a certain point is lower than the local reference temperature, it is marked as a temperature anomaly point.

[0040] S22-3. After identifying temperature anomalies, since temperature difference can only determine whether a point is colder or hotter than the surrounding area, but cannot distinguish whether the anomaly is a real defect edge or background noise, a temperature gradient is introduced. By capturing the location of abrupt temperature changes, the defect boundary can be located, improving the accuracy of anomaly identification. Specifically, the temperature gradient along the adhesive joint direction is calculated from the temperature anomaly point. In one example, the temperature values ​​of two adjacent points before and after the temperature anomaly point are taken, and the temperature gradient G is calculated using the central difference formula. Where Δx is the distance between adjacent measuring points, and the actual sampling distance of the thermal imager in the direction of the adhesive joint is taken, for example, one temperature value per centimeter. The measured temperature at position x represents the temperature gradient, which reflects the degree of temperature change along the seam direction. If the temperature gradient exceeds the gradient threshold, the point is identified as a high-confidence outlier.

[0041] S22-4. A section with consecutive high-confidence anomalies of a certain length is identified as a temperature anomaly zone. The certain length is usually taken as 0.3m, because isolated anomalies with a length of less than 0.3m may be caused by sensor noise.

[0042] S23. Based on the spatial superposition analysis of the prestressed duct and rebar area in the design drawings, the implementation process for determining the potential defect area is as follows: S23-1. Perform spatial superposition analysis of the boundary coordinates of the temperature anomaly area with the coordinates of the prestressed duct and rebar area in the design drawings. Since the grouting inside the prestressed duct is not dense, it will also cause abnormal surface temperature. The rebar area may also produce false positives in thermal imaging due to the fast thermal conductivity of metal. Therefore, it is necessary to remove the areas that overlap with the spatial position of the prestressed duct and rebar area and take the remaining area as the candidate potential defect area. This can effectively isolate the temperature anomaly caused by non-adhesive joint defect factors, avoid misjudging the interference of the duct or rebar as adhesive joint defects, and improve the targeting of subsequent ultrasonic testing.

[0043] S23-2. Given that the temperature field during a single sunshine period may be affected by the solar incident angle, resulting in time-dependent identification results, the above steps are repeated for at least two different sunshine periods to obtain a set of candidate potential defect areas for each period.

[0044] S23-3. Calculate the spatial intersection and union of candidate potential defect areas set at different time periods. The intersection reflects areas identified as abnormal in all time periods, indicating that the temperature in that area is abnormally stable. The union reflects areas identified as abnormal in at least one time period, encompassing all possible abnormal areas. The ratio of the intersection area to the union area, i.e., the overlap rate, reflects the consistency of identification results across different time periods: a higher overlap rate indicates more stable temperature anomalies and less interference; a lower overlap rate indicates that temperature anomalies are greatly affected by time periods and may result in false alarms. If the ratio of the intersection area to the union area exceeds the consistency limit, typically set at 70%, the intersection is taken as the final potential defect area. This is because a high overlap rate means that the area exhibits abnormalities in multiple time periods, and using the intersection ensures the reliability of the detection results. Conversely, the union is taken as the final potential defect area because a low overlap rate may stem from defect features being masked by environmental factors at a certain time period. Taking the union ensures that no real defects are missed, allowing for further identification through subsequent ultrasonic fine-tuning.

[0045] When applied to the above steps, in the defect-free normal adhesive joint area, the surface temperature distribution is relatively gentle, and the absolute value of the temperature gradient is usually less than 0.5℃ / m; while at the edge of the defect area, due to the abrupt change in heat conduction, the temperature gradient can reach more than 1.0℃ / m. Therefore, the gradient threshold can be set to 0.8℃ / m to distinguish between the normal area and the defect edge.

[0046] S3. Fine inspection: Collect ultrasonic time data for potential defect areas using the single-sided flat measurement method, and compare it with the reference time value measured in the same adhesive joint area without apparent defects to divide the normal area and defect area.

[0047] After identifying potential defect areas through rapid screening using infrared thermal imaging, ultrasonic waves can be used for detailed defect detection. The specific detection principle is as follows: When defects such as debonding or voids exist inside the adhesive joint, the defect area is usually filled with air. Because the acoustic impedance of air is much lower than that of concrete or structural adhesive solid materials, impedance mismatch occurs when ultrasonic waves propagate to the adhesive defect, causing most of the sound wave energy to be reflected and difficult to penetrate directly. In this case, the ultrasonic waves mainly rely on diffraction paths to propagate, that is, bypassing the defect edge to reach the receiving probe, resulting in a longer sound wave propagation path and a significantly increased acoustic duration. Therefore, by collecting ultrasonic time data within the potential defect area, ultrasonic signals reflecting the internal medium condition of the adhesive joint can be obtained.

[0048] As one implementable method of this invention, the specific process of acquiring ultrasonic time data using the single-sided planar measurement method is as follows: See Figure 2 As shown, within the potential defect area, grid-like measuring points are arranged at preset fixed intervals on the surface of the box girder along both sides of the adhesive joint. The center line of each measuring point is marked with a marker. The arranged measuring points provide fixed positions for ultrasonic wave transmission and reception, making the detection repeatable and spatially locatable.

[0049] The transmitting and receiving transducers of the ultrasonic testing instrument are coupled to the corresponding measuring points on both sides of the adhesive joint. The coupling agent is a special ultrasonic coupling medium used to eliminate air between the probe and the concrete surface, ensuring that the ultrasonic waves can effectively enter the tested object. At the same time, it ensures that the contact pressure is stable within the preset range, usually 0.2MPa to 0.3MPa. Stable contact pressure can reduce human operation error and ensure the repeatability of acoustic time data.

[0050] Set the excitation voltage, pulse width, and gain of the ultrasonic detector to an appropriate level, such as an excitation voltage of 500V to 1000V, a pulse width of 0.5μs to 1.0μs, and a gain of 60dB to 80dB. Collect the first wave acoustic time data for each measuring point. Repeat the data collection multiple times for each measuring point and take the arithmetic mean as the measured acoustic time for that measuring point.

[0051] Considering that the potential defect area is merely a temperature anomaly zone identified by infrared imaging, and not a true internal defect area of ​​the adhesive joint, not all areas will show defects during ultrasonic testing. The ultrasonic time interval can be used to filter out the true defect area from the potential defect area, with the rest considered normal areas. After obtaining the defect area, the defect depth needs to be obtained to quantitatively assess the severity of the defect. Since the propagation path of ultrasound within the defect area is diffracted, the defect depth cannot be directly measured. Therefore, a mathematical model between acoustic time interval and ranging needs to be established, using the acoustic time difference between the defect area and the normal area, combined with geometric relationships, to inversely calculate the defect depth. In this case, using ultrasonic time interval to partition the potential defect area provides the necessary data foundation for subsequent modeling.

[0052] In a preferred embodiment of the present invention, the specific partitioning process is as follows: On the same adhesive joint 1.0m to 1.5m outside the edge of the potential defect area, multiple measuring points without apparent defects and with stable ultrasonic signals are selected, and their acoustic time values ​​are measured and averaged as the reference acoustic time value. This reference acoustic time represents the normal acoustic time of the adhesive joint in a dense state and is the reference standard for subsequent defect judgment.

[0053] In a dense adhesive joint, ultrasonic waves propagate in a straight or near-straight line with stable acoustic time. When defects exist, ultrasonic waves need to diffract, and the extended path leads to an increase in acoustic time. Therefore, the measured acoustic time of each measuring point in the potential defect area is compared with the reference acoustic time value. If the measured acoustic time exceeds the reference acoustic time value by a preset proportional threshold (for example, the proportional threshold is set to 5%), then the measuring point is determined to have a defect and is classified into the defect area; otherwise, the measuring point is classified into the normal area.

[0054] The set of measurement points that are classified into the defect area constitutes the defect area, and the set of measurement points that are classified into the normal area constitutes the normal area.

[0055] S4. Output Results: Establish a time-range acoustic benchmark model for the defect area and the normal area, calculate the defect depth assessment value of each measuring point in the defect area based on the model, and generate a density distribution map.

[0056] After dividing the defect area and the normal area, their respective acoustic time-range reference models can be constructed for subsequent inversion calculations of defect depth. The specific modeling and calculation process is as follows: S41, Establish the acoustic time-range reference model.

[0057] S41-1. Select multiple measuring points with different horizontal measurement distances within the defect area and measure their acoustic time. The horizontal measurement distance refers to the straight-line distance between the ultrasonic transmitting probe and the receiving probe. In the single-sided horizontal measurement method, both probes are arranged on the same side surface of the adhesive joint, and the distance between them is the horizontal measurement distance. This is because the acoustic time increases with the increase of the propagation distance. By changing the horizontal measurement distance, multiple sets of (acoustic time, distance) data points can be obtained, and then the linear relationship between acoustic time and distance can be fitted. Specifically, with acoustic time as the abscissa and horizontal measurement distance as the ordinate, perform linear regression on the data points within the defect area to obtain the baseline straight line equation of the defect area.

[0058] In the formula, t represents the sound time. The regression coefficient represents the apparent sound velocity in the defect area. is the regression constant for the defect region, and represents the line intercept.

[0059] S41-2. Select multiple measuring points with different horizontal measurement distances within the normal zone, and perform linear regression with acoustic time as the abscissa and horizontal measurement distance as the ordinate to obtain the baseline equation of the normal zone.

[0060] In the formula, t represents the sound time. The regression coefficient represents the apparent sound velocity in the normal region. is the regression constant for the normal region, representing the intercept of the line.

[0061] S41-3. Calculate the Pearson correlation coefficients for the regression equations of the defective area and the normal area respectively. The correlation coefficient reflects the degree of fit between the data points and the regression line. The closer the absolute value of the Pearson correlation coefficient is to 1, the stronger the linear relationship. If any correlation coefficient is small, it indicates that the selected measurement point data has large dispersion, which may be due to insufficient representativeness of the measurement points. At this time, it is necessary to increase the number of measurement points and re-regress until the correlation coefficient reaches the preset threshold, such as 0.95, to ensure the reliability of the benchmark model.

[0062] S42. Calculate the defect depth assessment value of each measuring point in the defect area based on the benchmark model.

[0063] S42-1. In actual testing, the measured acoustic time of the measuring point in the defect area is known. If a defect exists at the measuring point, the actual propagation path of the ultrasonic wave is diffraction, and its acoustic time is longer than the straight-line propagation acoustic time when there is no defect at the same horizontal measurement distance. Substituting the measured acoustic time of the measuring point into the normal area reference equation, we obtain the horizontal measurement distance required to reach this acoustic time under the assumption of no defect; substituting it into the defect area reference equation, we obtain the horizontal measurement distance corresponding to this acoustic time under the defect area model. The difference between the two reflects the extra length traveled by the diffraction path compared to the straight-line path, thus allowing us to inversely determine the defect depth. Therefore, for any measuring point in the defect area, substituting its measured acoustic time into the defect area reference straight-line equation and the normal area reference straight-line equation respectively, we can calculate the corresponding equivalent horizontal measurement distance.

[0064] S42-2. When the defect is located inside the adhesive joint, the ultrasonic wave cannot penetrate directly and can only bypass it from the edge of the defect. For example... Figure 3 As shown, the following right-angled triangle geometric model is established: Let L be the actual horizontal distance between the transmitting probe and the receiving probe on the adhesive joint surface, that is, the distance between the centers of the two probes.

[0065] Let the vertical distance from the top of the defect, i.e. the diffraction point, to the surface of the adhesive joint be the defect depth h.

[0066] The ultrasonic wave originates from the transmitting probe, propagates in a straight line to the diffraction point at the edge of the defect, and then propagates in a straight line to the receiving probe. The length of the diffraction path is denoted as S.

[0067] Since the transmitting and receiving probes are symmetrically arranged on both sides of the defect, the diffraction path can be decomposed into two congruent right triangles.

[0068] The length of the base of each right triangle is The height is h, and the length of the hypotenuse is... .

[0069] According to the Pythagorean theorem, we have .

[0070] The defect depth can be calculated using the following formula: .

[0071] The defect depth calculation formula in this invention is as follows: ,in This represents the defect depth at the i-th measuring point, where i represents the measuring point number within the defect area. , These represent the equivalent horizontal measurement distances calculated from the measured acoustic time at the i-th measurement point using the reference straight line equations of the defect area and the normal area. The absolute value operation in the formula ensures that the values ​​within the square root are non-negative, so that an effective defect depth value can be obtained regardless of the relationship between the two equivalent horizontal measurement distances.

[0072] S43. Generate a density distribution map.

[0073] See Figure 4 As shown, the depth distribution of defects at each measuring point is plotted using contour lines along the direction of the adhesive joint, serving as a density distribution map. This map visually displays the location, depth, and extent of defects within the adhesive joint, providing a direct basis for subsequent core drilling verification.

[0074] S5. Result verification: Drill core samples at the location with the largest defect depth in the density distribution map, compare the actual defect morphology of the core sample with the defect characteristics inferred by ultrasonication, and decide whether to re-inspect based on the comparison results.

[0075] After detecting the defect depth of the adhesive joint, considering that infrared and ultrasonic are both non-destructive testing methods, there may be errors due to environmental interference, so verification is required. However, core drilling is a destructive test and is not suitable for comprehensive testing. Under these circumstances, this invention performs targeted core drilling verification to confirm the accuracy of the non-destructive testing results with minimal damage.

[0076] In the innovative implementation of this invention, the result verification process is as follows: S51, Core sampling: In the density distribution map, the location with the largest defect depth assessment value is selected as the core sampling point. This is because this location represents the most severe defect in the detection area and has extreme representativeness. If the ultrasonically inferred defect morphology at this location highly matches the actual morphology of the core sample, it can be inferred that other locations with lighter defects also have high credibility. Conversely, if the verification at the largest defect location fails, it indicates that the entire model may have deviations and needs adjustment. At the core sampling point, a drilling rig is used to vertically drill a hole for core sampling. The drilling depth should penetrate the adhesive joint and enter at least 50mm into the concrete on both sides to obtain a columnar sample containing the complete defect interface.

[0077] S52. Sample Analysis: After removing the core sample, clean the surface and cut along the adhesive joint interface. Observe and record the following actual defect morphologies using a stereomicroscope: the continuous distribution of the adhesive on the joint interface, the bonding state between the adhesive and the concrete on both sides, and whether there are visible voids or separation gaps inside the joint. These morphologies are direct physical characteristics of the adhesive joint density. Continuous adhesive distribution indicates full filling, good interface bonding indicates no debonding, and the absence of voids or separation gaps indicates density. Recording these characteristics can serve as a comparison standard for inferring results from ultrasonic testing.

[0078] S53. Verification and Comparison: Obtain the acoustic time characteristics used in the defect depth assessment as the defect characteristics inferred by ultrasonic testing. These include: the extent of the measured acoustic time exceeding the reference acoustic time value, and the difference between the equivalent horizontal distance between the defect area and the normal area. The extent of the acoustic time exceeding the reference value directly corresponds to the extension of the ultrasonic diffraction path, while the difference in the equivalent horizontal distance is positively correlated with the defect depth. Together, they constitute indirect evidence of colloid loss or interface separation by ultrasonic testing. Compare the inferred defect characteristics with the actual defect morphology observed in S52. If both indicate the presence of colloid discontinuity or interface separation, for example, if the ultrasonic test shows a large extent of the acoustic time exceeding the reference value and a large difference in the equivalent distance, while the core sample shows colloid discontinuity or interface debonding, thus directly confirming the reliability of the previous non-destructive testing results at the physical level, the test results are deemed valid. Conversely, if the ultrasonic test infers a serious defect but the core sample colloid is intact, the verification is deemed unsuccessful.

[0079] S54. Comparison and processing: When the verification fails, it indicates that the current ultrasonic testing model has misjudged the location. Possible reasons include that the spacing between the measuring points is too large and the defect details are missed. Ultrasonic testing measuring points can be densified around the measuring points that failed the verification with a smaller spacing to re-test, so as to improve the spatial resolution, capture more subtle changes in the defect boundary, and perform core drilling verification again.

[0080] Specifically, if the encrypted retest still fails, the area will be marked as having questionable results, and third-party testing is recommended.

[0081] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0082] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0084] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0085] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting the compactness of adhesive joints in precast segmental box girder bridges based on multi-source data, characterized in that, include: Based on the design drawings and site survey of the segmental precast box girder bridge, the area of ​​adhesive joints to be inspected was delineated; Thermal imaging scans were performed on the adhesive joint area, and temperature distribution curves were extracted from the thermal images to identify areas of abnormal temperature. Combined with spatial overlay analysis of the prestressed ducts and steel reinforcement areas in the design drawings, potential defect areas were determined. Ultrasonic time data were collected for potential defect areas using the single-sided flat measurement method and compared with the reference time value measured in the same adhesive joint area without apparent defects to divide the normal area and defect area. Establish a time-range acoustic benchmark model for the defect area and the normal area, and calculate the defect depth assessment value of each measuring point in the defect area based on the model to generate a density distribution map. Core samples are drilled at the location with the greatest defect depth in the density distribution map. The actual defect morphology of the core sample is compared with the defect characteristics inferred by ultrasound. The decision on whether to re-inspect is made based on the comparison results.

2. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 1, characterized in that: The process of defining the adhesive joint area to be inspected is as follows: The longitudinal starting point is the centerline of the pier top of the segmental precast box girder bridge, the longitudinal ending point is the centerline of the expansion joint or closure section, and the longitudinal boundary of the inspection is the section between the starting point and the ending point. The three-dimensional detection range is enclosed by the outer edge of the web of the box girder, the lower surface of the bottom plate, and the upper surface of the top plate as the lateral and vertical boundaries; Based on the design coordinates of the corner points of the end faces of adjacent segments in the design drawings, determine the spatial position of the theoretical adhesive joint plane and its starting and ending point coordinates and normal direction on the longitudinal boundary; On-site, a total station was used to lay out the coordinates of the start and end points of the adhesive joint and the normal direction onto the bridge structure, and the actual inspection boundary of the adhesive joint was marked on the surface of the box girder with marking lines. The adhesive joint area is delineated within the three-dimensional detection range by combining the actual detection boundary with the horizontal and vertical boundaries.

3. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 1, characterized in that: The specific process of extracting temperature distribution curves from thermal images to identify temperature anomaly areas is as follows: The scanned thermal image sequence was stitched together to form a temperature field distribution map of the adhesive joint, and the surface temperature distribution curve along the extension direction of the adhesive joint was extracted. Taking each point on the surface temperature distribution curve as the center, calculate the average temperature within a preset length range before and after it as the local reference temperature. If the measured temperature at a certain point is lower than the local reference temperature, it is marked as a temperature anomaly point. Calculate the temperature gradient along the adhesive joint direction at the temperature anomaly point. If the gradient exceeds the gradient threshold, it is determined to be a high-confidence anomaly point. A segment that continuously contains high-confidence outliers and reaches a certain length is identified as a temperature anomaly zone.

4. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 1, characterized in that: The process for identifying potential defect areas is as follows: Spatial overlay analysis was performed between the boundary coordinates of the temperature anomaly zone and the coordinates of the prestressed duct and the steel reinforcement zone in the design drawings. Areas that overlapped with the spatial positions of the prestressed duct and the steel reinforcement zone were eliminated, and the remaining areas were taken as candidate potential defect areas. Repeat the above steps at least twice during different periods of daylight to obtain a set of candidate potential defect areas for each period. Calculate the spatial intersection and union of candidate potential defect area sets at different time periods. If the ratio of the intersection area to the union area exceeds the consistency limit, the intersection is taken as the final potential defect area; otherwise, the union is taken as the final potential defect area.

5. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 1, characterized in that: The specific process for acquiring ultrasonic time data is as follows: Within the potential defect area, grid-like measuring points are laid out along the surface of the box girder on both sides of the adhesive joint at a preset fixed interval; The transmitting and receiving transducers of the ultrasonic testing instrument are coupled to the corresponding measuring points on both sides of the adhesive joint. Set the excitation voltage, pulse width, and gain of the ultrasonic testing instrument to an appropriate level, and collect the measured acoustic time data for each measuring point.

6. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 1, characterized in that: The division of the normal area and the defective area is described below: On the same adhesive joint at a preset distance outside the edge of the potential defect area, select multiple measuring points with no apparent defects and stable ultrasonic signals, measure the acoustic time, and take the average as the reference acoustic time value. The measured sound time of each measuring point in the potential defect area is compared with the reference sound time value. If the measured sound time exceeds the reference sound time value by a preset proportional threshold, the measuring point is determined to have a defect and is classified into the defect area; otherwise, the measuring point is classified into the normal area.

7. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 6, characterized in that: The establishment of the acoustic time-range benchmark model for the defective region and the normal region is described below: Multiple measuring points with different horizontal measurement distances are selected within the defect area, and their acoustic time is measured. Linear regression is performed with acoustic time as the abscissa and horizontal measurement distance as the ordinate to obtain the baseline equation of the defect area. Multiple measuring points with different horizontal measurement distances were selected within the normal zone, and linear regression was performed to obtain the baseline equation for the normal zone. Calculate the correlation coefficients of the baseline equations for the defective and normal areas respectively. If any correlation coefficient is lower than the preset threshold, increase the number of measurement points until the correlation coefficient requirement is met.

8. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 1, characterized in that: The specific process of calculating the defect depth assessment value of each measuring point in the defect area based on the model and generating the density distribution map is as follows: For any measuring point within the defect area, substitute the measured acoustic time into the reference straight line equation of the defect area and the reference straight line equation of the normal area respectively, and calculate the corresponding equivalent horizontal measurement distance. Based on the right-angled triangle geometric path relationship of the ultrasonic diffraction defect edge, the defect depth at the measuring point is calculated using the following formula: ,in This represents the defect depth at the i-th measuring point, where i represents the measuring point number within the defect area. , These represent the equivalent horizontal distances calculated from the measured acoustic time at the i-th measurement point through the reference straight line equations of the defect area and the normal area; Along the direction of the adhesive joint, the defect depth distribution at each measuring point is plotted using contour lines to serve as a density distribution map.

9. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 8, characterized in that: The core samples obtained include the following: In the density distribution map, the location with the largest defect depth assessment value is selected as the core sampling point, and a vertical hole is drilled at the core sampling point to extract the core. After removing the core sample, clean the surface and cut along the joint interface to record the following actual defect morphologies: the continuous distribution of the adhesive on the joint interface, the bonding state between the adhesive and the concrete on both sides, and whether there are voids or separation gaps inside the joint.

10. The method for detecting the density of adhesive joints in precast segmental box girder bridges based on multi-source data as described in claim 9, characterized in that: The decision on whether to re-test based on the comparison results includes the following: The acoustic time characteristics used in the assessment of defect depth are used as the defect characteristics inferred by ultrasound. The inferred defect features are compared with the observed actual defect morphology: if both indicate the presence of colloidal continuity interruption or interface separation, the test result is deemed valid; otherwise, the verification is deemed unsuccessful. Around the test points that failed the verification, test points were set up with a smaller spacing and retested.

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