Corrosion detection method for reinforcing steel bar in bridge prestressed duct

Abstract

The invention relates to a method for detecting corrosion of a steel bar in a bridge prestressed duct. The method comprises the following steps: acquiring diameter parameters of the steel bar, calibrating magnetic field intensity, acquiring magnetic field characteristics of an uncorroded steel bar material, setting a zero baseline, and acquiring a 2D magnetic flux leakage image of the uncorroded steel bar material through AI inversion; scanning sensors with corresponding channel numbers are selected according to steel bar diameter parameters and precision required by scanning; defining a detection direction on the to-be-detected concrete box girder member, and starting detection by using the array probe; acquiring magnetic induction intensity of steel bars in the concrete box girder member through a magnetoresistive sensor, performing inversion to obtain a corrosion rate of a scanning section, and generating a 2D magnetic flux leakage image of a detection section; and acquiring an abnormal region in the 2D magnetic flux leakage image of the detection section. The prestressed reinforcement is scanned and detected by combining the active additional magnetic field, the array scanning and the AI data inversion technology, the concrete structure does not need to be damaged, the corrosion position can be accurately positioned, and the maintenance cost is effectively saved under the condition that safe operation of a bridge is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of bridge engineering inspection technology, specifically to a method for detecting corrosion of reinforcing steel bars in prestressed ducts of bridges. Background Technology

[0002] Prestressed bridge technology is crucial for ensuring the stable bearing capacity of bridge beams against traffic loads. Whether the prestress in the prestressed steel bars or strands inside the prestressed ducts is relaxed or ineffective is a significant cause of reduced bridge load-bearing capacity and beam deformation and deflection. Corrosion of prestressed steel bars or strands due to inadequate grouting in the ducts or water intrusion through concrete cracks directly threatens the operational safety of the bridge. Therefore, it is necessary to conduct necessary prestressed duct inspections to promptly assess the corrosion status of the prestressed steel bars or strands.

[0003] Existing testing technologies are divided into non-destructive testing (NDT) and micro-destructive testing (MDT). NDT primarily measures prestress, which can reveal the prestress loss in bridges, but it cannot accurately pinpoint the location of corrosion or the extent of corrosion in the reinforcing bars or strands at the site of damage. It also cannot accurately determine whether the prestressed reinforcing bars or strands need to be directly replaced or re-tensioned. MDT mainly relies on borehole video inspection, but it cannot be used alone and needs to be combined with other NDT techniques as an auxiliary method. Furthermore, drilling can damage the prestressing ducts, potentially leading to further damage or even destroying intact sections, resulting in water seepage and corrosion. For example, patent CN119985672A uses spontaneous magnetic leakage technology for corrosion equivalent assessment, but this method suffers from a weak magnetic field, easily affected by the magnetizing magnetic field strength of the bridge beam's reinforcing mesh, leading to inaccurate corrosion measurements. Patent CN118443565A involves resistance measurement. The detection process requires connecting to exposed steel bars or drilling holes in the concrete protective layer, which damages the structure and makes it easy for moisture to penetrate and damage the structure. Furthermore, resistance detection can only detect overall corrosion and cannot accurately locate the location and extent of corrosion.

[0004] Therefore, it is necessary to propose new measures to overcome the above-mentioned shortcomings. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting corrosion of steel bars in prestressed ducts of bridges, so as to at least solve the problems of inaccurate location of defects and the further development of defects caused by drilling in the existing technology.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for detecting corrosion of reinforcing steel bars in prestressed ducts of bridges is provided, the method comprising:

[0008] S1: Obtain the diameter parameters of the steel bar, calibrate the magnetic field strength of the same material, obtain the magnetic field characteristics of the non-rusted steel bar material, set a zero baseline, and use AI to invert and obtain a 2D magnetic leakage image of the non-rusted steel bar material.

[0009] S2: Select a magnetoresistive sensor with the appropriate number of channels based on the rebar diameter parameters and the required scanning accuracy;

[0010] S3: Mark the detection direction on the concrete box girder component to be tested, and start the detection using an array probe containing a magnetoresistive sensor;

[0011] S4: The magnetic induction intensity of the steel bars in the concrete box girder is collected by the magnetoresistive sensor, and the corrosion rate of the scanned section is obtained by AI inversion, generating a 2D magnetic leakage image of the detection profile.

[0012] S5: Obtain the abnormal area in the 2D magnetic flux leakage image of the detection profile, determine the length of the corrosion location, judge the degree of corrosion, and establish the relationship function between the change in magnetic induction intensity and the degree of corrosion.

[0013] Furthermore, in S1, AI inversion acquires 2D magnetic flux leakage images of the uncorroded steel reinforcement material, including:

[0014] Establish a polynomial function relationship between magnetic induction intensity and corrosion rate on the detection section, and obtain the optimal polynomial coefficients by data fitting.

[0015] The AI ​​agent is trained by iterating through gradient descent, selecting the polynomial function with the best fit for data inversion, and obtaining the optimal solution by using the method of finding the extremum of multivariate functions. The corrosion rate in the uncorroded state is then used as the benchmark value.

[0016] The image is visualized based on grayscale values ​​to obtain a 2D magnetic flux leakage image of the uncorroded steel reinforcement material.

[0017] Furthermore, in S3, the array probes are operated manually by hand or mounted on a drone.

[0018] Furthermore, in S4, the magnetic induction intensity of the reinforcing steel bars within the concrete box girder is collected using a magnetoresistive sensor. AI then inverts this data to determine the corrosion rate of the scanned section, generating a 2D magnetic flux leakage image of the inspection profile, including:

[0019] Using the collected magnetic induction intensity, AI inversion is performed through the polynomial function with the best fit to obtain the corrosion rate of the scanned section;

[0020] By comparing the value with the uncorroded baseline, the comparison value is visualized and output based on grayscale, resulting in a 2D magnetic flux leakage image of the detection profile.

[0021] Furthermore, in S5, abnormal regions are acquired in the 2D magnetic flux leakage image of the detection profile, including:

[0022] In the 2D magnetic flux leakage image of the inspection profile, the grayscale brightness of the color reflects the difference between the defect data and the baseline data. The larger the difference, the more obvious the image contrast, which indicates an abnormal area.

[0023] Furthermore, in S5, the length of the rust location is determined, including:

[0024] The distance traveled by the array probe is the length of corrosion of the steel bar or steel strand, starting from the location of the image anomaly observed at the beginning of the detection process and ending at the location of the anomaly.

[0025] Furthermore, in S5, the determination of the degree of corrosion includes:

[0026] The 2D magnetic flux leakage image of the test section is compared with the theoretical cross-sectional area of ​​the steel bar diameter to obtain the corrosion ratio of the steel bar cross section. The corrosion is then classified according to the "Technical Standard for Field Testing of Concrete Structures" GB / T 50784 to obtain the degree of corrosion.

[0027] Furthermore, in S5, a function relating the change in magnetic induction intensity to the degree of corrosion is established, including:

[0028] A polynomial function relationship between the change in magnetic induction intensity and the degree of corrosion was established, and the optimal polynomial coefficients were obtained through data fitting.

[0029] Further, determine whether the reinforcing bars need to be replaced or locally treated for rust prevention, including:

[0030] If the corrosion rate of the steel reinforcement section is less than 5%, the corrosion level is considered intact, and there is no prestress loss, then no repair is required;

[0031] If the corrosion rate of the steel reinforcement section is 5%-10%, the corrosion degree is moderate, and the prestress loss is still within the design allowable range, then local repair and rust prevention treatment should be carried out.

[0032] If the corrosion rate of the reinforcing steel section is greater than 10%, or if the prestressing loss exceeds the allowable loss in the design, the prestressed components or the entire beam in the concrete beam shall be replaced.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] This invention provides a method for detecting corrosion of prestressed steel bars in bridge prestressed ducts. Combining active magnetic flux leakage, array scanning, and AI data inversion technologies, it scans prestressed steel bars or strands in concrete box girders without damaging the concrete structure. It accurately locates the corrosion sites of prestressed steel bars or strands inside the concrete box girder, determines the length of the corrosion site by identifying abnormal magnetic induction areas in the corroded portion, and judges the degree of corrosion by the change in magnetic induction intensity. A relationship function between the change in magnetic induction intensity and the degree of corrosion is established, allowing bridge maintenance technicians to accurately determine whether prestressed steel bars or strands need replacement or localized rust prevention treatment. This avoids premature replacement of prestressed steel bars or strands with low corrosion levels, which would increase maintenance costs and lead to redundant material safety performance. It effectively saves maintenance costs while ensuring the safe operation of the bridge. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0036] Figure 1 This is a flowchart of the detection method of the present invention.

[0037] Figure 2 This is a schematic diagram of the detection method of the present invention.

[0038] Figure 3 It is a 2D magnetic flux leakage image of the detection profile. Detailed Implementation

[0039] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0040] It should be noted that although the order of steps is mentioned in the method description, in some cases, they may be performed in a different order than that described here, and this should not be interpreted as a restriction on the order of steps.

[0041] This invention provides a method for detecting corrosion of reinforcing bars in prestressed ducts of bridges. It can also be used for detecting corrosion of steel strands. It is a non-destructive testing method that can detect the corrosion status of reinforcing bars or steel strands inside prestressed ducts.

[0042] like Figure 1The detection method specifically includes:

[0043] S1: Obtain the diameter parameters of the reinforcing bars, find materials with the same prestressed reinforcing bars reserved on site for magnetic field strength calibration, obtain the magnetic field characteristics of the non-rusted reinforcing bar material and set a zero baseline, and use AI inversion to obtain a 2D magnetic leakage image of the non-rusted reinforcing bar material.

[0044] In this step, AI inversion obtains a 2D magnetic flux leakage image of the uncorroded steel reinforcement material, including:

[0045] S11: Establish a polynomial function relationship between magnetic induction intensity and corrosion rate on the detection section, and obtain the optimal polynomial coefficients by data fitting;

[0046] S12: Train the AI ​​agent, select the polynomial function with the best fit through gradient descent iteration, perform data inversion, obtain the optimal solution by using the method of finding the extremum of multivariate functions, and obtain the corrosion rate of the non-corroded state as the benchmark value.

[0047] S13: Based on grayscale, perform image visualization output to obtain a 2D magnetic flux leakage image of the uncorroded steel reinforcement material.

[0048] S2: Select a magnetoresistive sensor with the appropriate number of channels based on the diameter parameters of the reinforcing bar and the required scanning accuracy.

[0049] When the diameter of the reinforcing bar increases, in order to ensure that enough magnetic field lines pass through the cross section of the target body, a magnetoresistive sensor array probe with 64 channels can be selected. If it is necessary to detect larger diameter or smaller defects, it is necessary to ensure that there is enough saturated magnetic field covering the target body. Theoretically, the number of channels of the magnetoresistive sensor array probe can be continuously increased when the external magnetic field strength is sufficient, thereby capturing more subtle changes in the magnetic field.

[0050] Before the formal testing, it is also necessary to mark the position of the steel bar section according to the as-built drawings, clean the dust or other surface dirt that may affect the magnetization effect of the scanning section, and remove obstacles in the scanning path.

[0051] S3: Mark the detection direction on the concrete box girder component to be tested, and start the detection using an array probe containing a magnetoresistive sensor. The array probe of the magnetoresistive sensor can be operated manually or mounted on a drone.

[0052] During operation, the detection distance is marked according to the magnetoresistive feedback, and the detection position is corrected in real time to avoid deviation. The detection speed should be based on the completeness and accuracy of the magnetoresistive data acquisition and should not exceed 0.5m / s.

[0053] S4: The magnetic induction intensity of the steel bars in the concrete box girder is collected by a magnetoresistive sensor, and the corrosion rate of the scanned section is obtained by AI inversion, generating a 2D magnetic leakage image of the detection profile.

[0054] At the start of the test, the magnetization array acts on the concrete surface outside the prestressed duct. The magnetization array generates a saturated strong magnetic field, causing the tested steel bar to generate an induced magnetic field due to external magnetic excitation. The magnetic field lines generated by the magnetization array are always orthogonal to the extension direction of the tested steel bar.

[0055] In this step, the magnetic induction intensity of the reinforcing steel bars within the concrete box girder is collected using a magnetoresistive sensor. AI then inverts the corrosion rate of the scanned section, generating a 2D magnetic flux leakage image of the inspection profile, including:

[0056] S41: Using the collected magnetic induction intensity, AI inversion is performed through the polynomial function with the best fit to obtain the corrosion rate of the scanned section;

[0057] S42: Compare with the uncorroded baseline value, visualize the comparison value based on grayscale, and obtain a 2D magnetic flux leakage image of the detection profile.

[0058] During the inspection, the magnetic excitation time is set according to the principle of clear imaging. The device moves along the direction of the prestressed duct. When moving discontinuously, the device must be marked and the inspection section must have a 10% overlap in length so that the magnetization characteristics of the prestressed steel bars can be continuously detected during scanning, avoiding missed detections or poor imaging due to excessive walking speed.

[0059] S5: Obtain the abnormal area in the 2D magnetic flux leakage image of the detection profile, determine the length of the corrosion location, judge the degree of corrosion, and establish the relationship function between the change in magnetic induction intensity and the degree of corrosion.

[0060] Specifically:

[0061] S501: Identify abnormal areas in the 2D magnetic flux leakage image of the inspection profile. In the 2D magnetic flux leakage image of the inspection profile, the brightness and darkness of the grayscale reflect the difference between the defect data and the baseline data. The larger the difference, the more obvious the image contrast, which indicates an abnormal area.

[0062] S502: Determine the length of the corrosion location. The starting point is the abnormal position observed in the image at the beginning of the detection process, and the ending point is the abnormal position. The distance traveled by the array probe is the length of the corrosion of the steel bar or steel strand.

[0063] S503: The degree of corrosion is determined by the change in magnetic induction intensity. The 2D magnetic flux leakage image of the test section is compared with the theoretical cross-sectional area of ​​the steel bar diameter to obtain the corrosion ratio of the steel bar cross section. The corrosion is graded according to the "Technical Standard for On-site Testing of Concrete Structures" GB / T 50784 to obtain the degree of corrosion.

[0064] S504: Establish the relationship function between the change in magnetic induction intensity and the degree of corrosion, including:

[0065] A polynomial function relationship between the change in magnetic induction intensity and the degree of corrosion was established, and the optimal polynomial coefficients were obtained through data fitting.

[0066] Then, based on the relationship between the change in magnetic induction intensity and the degree of corrosion, it can be determined whether the reinforcing steel needs to be replaced or subjected to localized rust prevention treatment.

[0067] If the corrosion rate of the steel reinforcement section is less than 5%, the corrosion level is considered intact, and there is no prestress loss, then no repair is required;

[0068] If the corrosion rate of the steel reinforcement section is 5%-10%, the corrosion degree is moderate, and the prestress loss is still within the design allowable range, then local repair and rust prevention treatment should be carried out.

[0069] If the corrosion rate of the reinforcing steel section is greater than 10%, or if the prestressing loss exceeds the allowable loss in the design, the prestressed components or the entire beam in the concrete beam shall be replaced.

[0070] In some embodiments, an infrared thermal radiation scanning module can be added to capture changes in thermal radiation and detect cracks at the location of rust. When the detection depth is shallow, the thermal excitation effect will highlight the thermodynamic characteristics of the rusted area, which will help to further analyze the location of rust defects and the quantitative analysis of rust.

[0071] The electromagnetic thermal excitation module can be used as a supplementary detection technology of this invention for non-destructive verification of the degree of corrosion. When local corrosion is detected but there is doubt, an alternating current coil is used to electromagnetically excite the target steel bar. The heat can be captured by an infrared scanning module to capture the changes in the heating and cooling process of the steel bar. The difference between the temperature conduction rate at the defect location and the temperature conduction rate between the defective material and the non-defective material is used to evaluate whether the suspected location has rusted. The degree of corrosion can be obtained by using AI inversion data to estimate the corrosion ratio of the cross section. Furthermore, the presence of concrete surface cracks at the location can be identified based on the infrared spectrum, thereby further determining whether the steel bar corrosion is severe.

[0072] The aforementioned method employs active magnetic excitation scanning technology for steel reinforcement corrosion detection, causing no damage to the concrete beam structure of the bridge and effectively avoiding the structural damage caused by micro-destructive testing methods. Simultaneously, it utilizes saturated magnetic flux leakage detection technology to directionally amplify the defect characteristics at the defect location, avoiding detection distortion caused by interference from the concrete protective layer and internal metal components, as is common with conventional electromagnetic wave or elastic wave detection methods. Furthermore, it can effectively distinguish the electromagnetic characteristics of corroded and non-corroded locations based on the magnetic permeability of the tested component, using this as a basis for judging the degree of corrosion. Computer inversion can directly yield a visual 2D cross-sectional image of the component showing corrosion status, and when a comprehensive defect evaluation of the component is required, a CT scan corrosion model of the component can be generated.

[0073] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for detecting corrosion of reinforcing bars in prestressed ducts of bridges, characterized in that: The detection method includes: S1: Obtain the diameter parameters of the steel bar, calibrate the magnetic field strength of the same material, obtain the magnetic field characteristics of the non-rusted steel bar material, set a zero baseline, and use AI to invert and obtain a 2D magnetic leakage image of the non-rusted steel bar material. S2: Select a magnetoresistive sensor with the appropriate number of channels based on the rebar diameter parameters and the required scanning accuracy; S3: Mark the detection direction on the concrete box girder component to be tested, and start the detection using an array probe containing a magnetoresistive sensor; S4: The magnetic induction intensity of the steel bars in the concrete box girder is collected by the magnetoresistive sensor, and the corrosion rate of the scanned section is obtained by AI inversion, generating a 2D magnetic leakage image of the detection profile. S5: Obtain the abnormal area in the 2D magnetic flux leakage image of the detection profile, determine the length of the corrosion location, judge the degree of corrosion, and establish the relationship function between the change in magnetic induction intensity and the degree of corrosion.

2. The method for detecting corrosion of reinforcing bars in prestressed ducts of bridges according to claim 1, characterized in that: In S1, AI inversion obtains 2D magnetic flux leakage images of the uncorroded steel reinforcement material, including: Establish a polynomial function relationship between magnetic induction intensity and corrosion rate on the detection section, and obtain the optimal polynomial coefficients by data fitting. The AI ​​agent is trained by iterating through gradient descent, selecting the polynomial function with the best fit for data inversion, and obtaining the optimal solution by using the method of finding the extremum of multivariate functions. The corrosion rate in the uncorroded state is then used as the benchmark value. The image is visualized based on grayscale values ​​to obtain a 2D magnetic flux leakage image of the uncorroded steel reinforcement material.

3. The method for detecting corrosion of reinforcing bars in prestressed ducts of bridges according to claim 2, characterized in that: In S3, the array probes are operated either by hand or mounted on a drone.

4. The method for detecting corrosion of reinforcing bars in prestressed ducts of bridges according to claim 3, characterized in that: In S4, the magnetic induction intensity of the reinforcing steel bars within the concrete box girder is collected using a magnetoresistive sensor. AI then inverts this data to determine the corrosion rate of the scanned section, generating a 2D magnetic flux leakage image of the inspection profile, including: Using the collected magnetic induction intensity, AI inversion is performed through the polynomial function with the best fit to obtain the corrosion rate of the scanned section; By comparing the value with the uncorroded baseline, the comparison value is visualized and output based on grayscale, resulting in a 2D magnetic flux leakage image of the detection profile.

5. The method for detecting corrosion of reinforcing bars in prestressed ducts of bridges according to claim 4, characterized in that: In S5, abnormal regions are obtained from the 2D magnetic flux leakage image of the detection profile, including: In the 2D magnetic flux leakage image of the inspection profile, the grayscale brightness of the color reflects the difference between the defect data and the baseline data. The larger the difference, the more obvious the image contrast, which indicates an abnormal area.

6. The method for detecting corrosion of reinforcing bars in prestressed ducts of bridges according to claim 5, characterized in that: In S5, the length of the rust location is determined, including: The distance traveled by the array probe is the length of corrosion of the steel bar or steel strand, starting from the location of the image anomaly observed at the beginning of the detection process and ending at the location of the anomaly.

7. The method for detecting corrosion of reinforcing bars in prestressed ducts of bridges according to claim 6, characterized in that: In S5, determining the degree of corrosion includes: The 2D magnetic flux leakage image of the test section is compared with the theoretical cross-sectional area of ​​the steel bar diameter to obtain the corrosion ratio of the steel bar cross section. The corrosion is then classified according to the "Technical Standard for Field Testing of Concrete Structures" GB / T 50784 to obtain the degree of corrosion.

8. The method for detecting corrosion of reinforcing bars in prestressed ducts of bridges according to claim 7, characterized in that: In S5, a function relating the change in magnetic induction intensity to the degree of corrosion is established, including: A polynomial function relationship between the change in magnetic induction intensity and the degree of corrosion was established, and the optimal polynomial coefficients were obtained through data fitting.

9. The method for detecting corrosion of reinforcing bars in prestressed ducts of bridges according to claim 8, characterized in that: Determining whether reinforcing bars need replacement or localized rust prevention treatment includes: If the corrosion rate of the steel reinforcement section is less than 5%, the corrosion level is considered intact, and there is no prestress loss, then no repair is required; If the corrosion rate of the steel reinforcement section is 5%-10%, the corrosion degree is moderate, and the prestress loss is still within the design allowable range, then local repair and rust prevention treatment should be carried out. If the corrosion rate of the reinforcing steel section is greater than 10%, or if the prestressing loss exceeds the allowable loss in the design, the prestressed components or the entire beam in the concrete beam shall be replaced.

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