Quantitative detection method for corrosion rate of steel bar of concrete structure based on spontaneous magnetic flux leakage

Abstract

The invention discloses a spontaneous magnetic flux leakage-based concrete structure steel bar corrosion rate quantitative detection method, which is applied to the technical field of civil engineering. Comprising the following steps: acquiring spontaneous leakage magnetic field intensity data of a concrete structure before and after corrosion, and extracting spontaneous leakage magnetic field change information; determining the corrosion position and range of the concrete structure; extracting a spontaneous leakage magnetic field variable rate index for quantitatively representing the corrosion rate of the steel bar and correcting the spontaneous leakage magnetic field variable rate index; determining a steel bar corrosion rate pre-test information model, and performing quantitative probabilistic estimation on the corrosion rate of the section of the corroded steel bar by using the corrected spontaneous leakage magnetic field variable rate index; and calculating a quantitative estimation value of the steel bar corrosion rate based on an estimation result. The method does not need to pre-embed a sensor, does not depend on long-term monitoring, has no damage to the concrete structure, and has no influence on the service performance of the concrete structure. The detection method is convenient, low in cost, high in precision and stable in effect.

Description

Technical Field

[0001] The invention relates to the technical field of civil engineering, and more particularly to a quantitative detection method for the corrosion rate of steel bars in a concrete structure based on spontaneous magnetic leakage. Background Art

[0002] Steel bars are a key part of concrete structures and determine their bearing capacity and durability. Due to the erosion of the external environment, the steel bars inside the concrete structure will inevitably corrode, resulting in degradation of the steel-concrete interface bonding performance, loss of steel bar cross-section, and reduced strength, which ultimately reduces the structural bearing capacity and durability. In order to ensure the bearing capacity and durability of concrete structures, it is necessary to accurately detect the corrosion of internal steel bars. However, the current situation of steel bar corrosion in in-service concrete structures is unclear, and there is a lack of corresponding detection systems, making it difficult to accurately and effectively detect steel bar corrosion characteristics.

[0003] At present, the corrosion detection technology of reinforced concrete structures is mainly divided into two categories: destructive testing and non-destructive testing. Destructive testing usually includes sampling methods, which will cause damage to the concrete structure and is difficult to be used for continuous detection of in-service concrete structures. Non-destructive testing methods are mostly based on physical properties such as sound, light, and electricity, such as ultrasonic testing and electromagnetic technology. Although ultrasonic testing does not require the destruction of the structure, its results are greatly affected by operating skills and its application is limited in complex structures. Detection methods based on electromagnetic principles, such as the magnetoresistance method, rely on pre-embedded sensors and are difficult to implement for in-service structures without pre-embedded sensors or damaged sensors. In addition, acoustic-based detection technology also requires pre-embedded sensors, and has poor adaptability to the complex environment of steel corrosion inside concrete. The interference and diffraction of sound waves will cause the received signal to be complex, making it difficult to accurately measure steel corrosion.

[0004] Most existing corrosion detection methods are limited by the coordination between the structure and the sensor, or by the special composite system of steel bars and concrete, making it difficult to accurately detect the corrosion characteristics of in-service concrete structures. Therefore, how to provide a quantitative detection method for the corrosion rate of steel bars in concrete structures based on spontaneous magnetic leakage is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a quantitative detection method for the corrosion rate of steel bars in a concrete structure based on spontaneous magnetic flux leakage, which non-destructively detects the corrosion characteristics of steel bars inside reinforced concrete through spontaneous magnetic flux leakage technology.

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

[0007] A method for quantitatively detecting the corrosion rate of steel bars in a concrete structure based on spontaneous magnetic leakage comprises the following steps:

[0008] S1. Obtain the spontaneous leakage magnetic field strength data of the concrete structure before and after corrosion, and extract the spontaneous leakage magnetic field variation information;

[0009] S2. Determine the corrosion position and range of the concrete structure based on the spontaneous leakage magnetic field variation information;

[0010] S3, extracting and correcting the spontaneous leakage magnetic field variation rate index that quantitatively represents the steel bar corrosion rate;

[0011] S4. Determine the prior information model of steel bar corrosion, and use the modified spontaneous leakage magnetic field change rate index to make a quantitative probabilistic estimate of the corrosion rate of the corroded steel bar section;

[0012] S5. Calculate the quantitative estimate of the steel bar corrosion rate based on the quantitative probabilistic estimate of the corrosion rate of the corroded steel bar section.

[0013] Optionally, S1 is:

[0014] S101, determining a spontaneous magnetic leakage scanning path of the concrete structure in an initial non-corroded state, and performing scanning to obtain initial spontaneous magnetic leakage magnetic field intensity data;

[0015] S102, after the concrete structure is corroded, scanning is performed based on the same path to obtain the corrosion spontaneous leakage magnetic field intensity data;

[0016] S103, calculating the spontaneous leakage magnetic field variation information caused by corrosion according to the initial spontaneous leakage magnetic field strength data and the corrosion spontaneous leakage magnetic field strength data.

[0017] Optionally, the spontaneous leakage magnetic field variable information caused by corrosion is calculated in S103 as follows:

[0018]

[0019] In the formula, H Sx and H Sz are the tangential and normal components of the spontaneous leakage magnetic field variation, H Ix and H Iz are the tangential and normal components of the initial spontaneous leakage magnetic field, H x and H z They are the tangential component and normal component of the spontaneous leakage magnetic field intensity due to corrosion, respectively.

[0020] Optionally, S2 is specifically: according to the spontaneous leakage magnetic field H Sx and H Sz The numerical variation section of the information directly determines the location and scope of steel corrosion inside the concrete.

[0021] Optional, S3 is:

[0022] S301, determine the quantitative spontaneous leakage magnetic field change rate index NH x :

[0023]

[0024] In the formula, H I-av is the total magnetic field strength H of the initial spontaneous magnetic leakage of steel bars within the corrosion range I The average value, H Sx is the tangential component of the spontaneous leakage magnetic field variation;

[0025] S302, spontaneous leakage magnetic field change rate index NH x To make corrections:

[0026]

[0027] In the formula, NH xm is the corrected spontaneous leakage magnetic field change rate index value, NH x (1.5) represents the spontaneous leakage magnetic field change rate index value NH at the midpoint of a standard corroded steel bar with a radius of 7 mm and a length of 1.5 m x Theoretical value, NH x (2l) is the spontaneous leakage magnetic field change rate index value NH at the midpoint of the measured corroded steel bar with a length of 2l x Theoretical value.

[0028] Optionally, S4 is specifically:

[0029] S401. Constructing a Bayesian model for evaluating the steel bar corrosion rate as a priori information model of the steel bar corrosion rate:

[0030]

[0031] In the formula, NH xm is the modified spontaneous leakage magnetic field change rate index value, η is the steel bar section corrosion rate, π(η|NH xm ) is the posterior distribution, f(NH xm |η) is the likelihood function, π(η) is the prior distribution, and Θ is the parameter space;

[0032] S402, determining a priori distribution curve of the corrosion rate according to a priori information model of the steel bar corrosion rate;

[0033] S403, determining a likelihood function;

[0034]

[0035] Where λ is the scale parameter and k is the shape parameter;

[0036] S404. Establish the posterior distribution π(η|NH xm )’s computational model;

[0037] S405. Calculate the posterior distribution π(η|NH xm ) is solved and the quantitative probabilistic estimation result of the corrosion rate of the corroded steel bar section is obtained in the form of probability density distribution.

[0038] Optionally, S5 is specifically:

[0039] S501, using the steel bar cross-section corrosion rate probability density distribution estimation sample data to calculate the cross-section corrosion rate mean and a quantitative point estimate with a preset confidence level;

[0040] S502. Calculate the standard error based on the confidence level to obtain the uncertainty range of the quantitative point estimate of the steel bar cross-section corrosion rate.

[0041] It can be seen from the above technical solution that, compared with the prior art, the present invention provides a quantitative detection method for the corrosion rate of steel bars in concrete structures based on spontaneous magnetic leakage, which has the following beneficial effects:

[0042] The present invention can non-destructively detect the corrosion characteristics of steel bars inside reinforced concrete through spontaneous magnetic leakage technology, does not need to pre-embed sensors, does not rely on long-term monitoring, does not cause any damage to the concrete structure, and does not have any impact on the service performance of the concrete structure. A spontaneous magnetic leakage scan of a fixed path is directly performed on the surface of the concrete structure to obtain spontaneous magnetic leakage field information before and after the steel bars are corroded. According to the spontaneous magnetic leakage field variation information obtained by the scan, corrosion characteristics such as the steel bar corrosion position and cross-section corrosion rate can be measured. The detection method is convenient, low-cost, high-precision, and stable in effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0044] Figure 1 It is a flow chart of the quantitative detection method of steel bar corrosion rate of concrete structure of the present invention;

[0045] Figure 2 It is a schematic diagram of the spontaneous magnetic flux leakage scanning of the concrete structure of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The embodiment of the present invention discloses a method for quantitatively detecting the corrosion rate of steel bars in a concrete structure based on spontaneous magnetic leakage. Figure 1 As shown, the following steps are included:

[0048] S1. Obtain the spontaneous leakage magnetic field strength data of the concrete structure before and after corrosion, and extract the spontaneous leakage magnetic field variation information;

[0049] S2. Determine the corrosion position and range of the concrete structure based on the spontaneous leakage magnetic field variation information;

[0050] S3, extracting and correcting the spontaneous leakage magnetic field variation rate index that quantitatively represents the steel bar corrosion rate;

[0051] S4. Determine the prior information model of steel bar corrosion, and use the modified spontaneous leakage magnetic field change rate index to make a quantitative probabilistic estimate of the corrosion rate of the corroded steel bar section;

[0052] S5. Calculate the quantitative estimate of the steel bar corrosion rate based on the quantitative probabilistic estimate of the corrosion rate of the corroded steel bar section.

[0053] Furthermore, S1 is specifically:

[0054] S101, such as Figure 2 As shown, the spontaneous magnetic leakage scanning path of the concrete structure in the initial non-corroded state is determined, and the initial spontaneous magnetic leakage magnetic field intensity data is obtained by scanning;

[0055] S102, after the concrete structure is corroded, scanning is performed based on the same path to obtain the corrosion spontaneous leakage magnetic field intensity data;

[0056] S103, calculating the spontaneous leakage magnetic field variation information caused by corrosion according to the initial spontaneous leakage magnetic field strength data and the corrosion spontaneous leakage magnetic field strength data.

[0057] Furthermore, the spontaneous leakage magnetic field variable information caused by corrosion is calculated in S103 as follows:

[0058]

[0059] In the formula, H Sx and H Sz are the tangential and normal components of the spontaneous leakage magnetic field variation, H Ix and H Izare the tangential and normal components of the initial spontaneous leakage magnetic field, H x and H z They are the tangential component and normal component of the spontaneous leakage magnetic field intensity due to corrosion, respectively.

[0060] Further, S2 is specifically: according to the spontaneous leakage magnetic field H Sx and H Sz The numerical variation section of the information directly determines the location and scope of steel corrosion inside the concrete.

[0061] In the embodiment of the present invention, the spontaneous leakage magnetic field becomes H Sx and H Sz A value of 0 indicates no corrosion, and a value other than 0 indicates a mutation.

[0062] Furthermore, S3 is specifically:

[0063] S301, determine the quantitative spontaneous leakage magnetic field change rate index NH x :

[0064]

[0065] In the formula, H I-av is the total magnetic field strength H of the initial spontaneous magnetic leakage of steel bars within the corrosion range I The average value, H Sx is the tangential component of the spontaneous leakage magnetic field variation;

[0066] S302, spontaneous leakage magnetic field change rate index NH x To make corrections:

[0067]

[0068] In the formula, NH xm is the corrected spontaneous leakage magnetic field change rate index value, NH x (1.5) represents the spontaneous leakage magnetic field change rate index value NH at the midpoint of a standard corroded steel bar with a radius of 7 mm and a length of 1.5 m x Theoretical value, NH x (2l) is the spontaneous leakage magnetic field change rate index value NH at the midpoint of the measured corroded steel bar with a length of 2l x Theoretical value.

[0069] Furthermore, S4 is specifically:

[0070] S401. Constructing a Bayesian model for evaluating the steel bar corrosion rate as a priori information model of the steel bar corrosion rate:

[0071]

[0072] In the formula, NH xmis the modified spontaneous leakage magnetic field change rate index value, η is the steel bar section corrosion rate, π(η|NH xm ) is the posterior distribution, f(NH xm |η) is the likelihood function, π(η) is the prior distribution, and Θ is the parameter space (the range of η);

[0073] S402, determining a priori distribution curve of the corrosion rate according to a priori information model of the steel bar corrosion rate;

[0074] S403, determining a likelihood function;

[0075]

[0076] Where λ is the scale parameter and k is the shape parameter;

[0077] S404. Establish the posterior distribution π(η|NH xm )’s computational model;

[0078] S405. Calculate the posterior distribution π(η|NH xm ) is solved and the quantitative probabilistic estimation result of the corrosion rate of the corroded steel bar section is obtained in the form of probability density distribution.

[0079] Furthermore, S5 is specifically:

[0080] S501, using the steel bar cross-section corrosion rate probability density distribution estimation sample data to calculate the cross-section corrosion rate mean and a quantitative point estimate with a preset confidence level;

[0081] S502. Calculate the standard error based on the confidence level to obtain the uncertainty range of the quantitative point estimate of the steel bar cross-section corrosion rate.

[0082] In the embodiment of the present invention, the preset confidence level is 95% or 99%.

[0083] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0084] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A quantitative detection method for the corrosion rate of steel bars in concrete structures based on spontaneous magnetic flux leakage, characterized in that: The following steps are involved: S1. Obtain the spontaneous leakage magnetic field strength data of the concrete structure before and after corrosion, and extract the spontaneous leakage magnetic field variation information; S2. Determine the corrosion position and range of the concrete structure based on the spontaneous leakage magnetic field variation information; S3, extracting and correcting the spontaneous leakage magnetic field variation rate index that quantitatively represents the steel bar corrosion rate; S4. Determine the prior information model of steel bar corrosion, and use the modified spontaneous leakage magnetic field change rate index to make a quantitative probabilistic estimate of the corrosion rate of the corroded steel bar section; S5. Calculate the quantitative estimate of the steel bar corrosion rate based on the quantitative probabilistic estimate of the corrosion rate of the corroded steel bar section.

2. A quantitative detection method for steel bar corrosion rate of concrete structure based on spontaneous magnetic flux leakage according to claim 1, characterized in that: S1 is specifically: S101, determining a spontaneous magnetic leakage scanning path of the concrete structure in an initial non-corroded state, and performing scanning to obtain initial spontaneous magnetic leakage magnetic field intensity data; S102, after the concrete structure is corroded, scanning is performed based on the same path to obtain the corrosion spontaneous leakage magnetic field intensity data; S103, calculating the spontaneous leakage magnetic field variation information caused by corrosion according to the initial spontaneous leakage magnetic field strength data and the corrosion spontaneous leakage magnetic field strength data.

3. A quantitative detection method for steel bar corrosion rate of concrete structure based on spontaneous magnetic flux leakage according to claim 2, characterized in that: The spontaneous leakage magnetic field variation information caused by corrosion is calculated in S103 as follows: In the formula, H Sx and H Sz are the tangential and normal components of the spontaneous leakage magnetic field variation, H Ix and H Iz are the tangential and normal components of the initial spontaneous leakage magnetic field, H x and H z They are the tangential component and normal component of the spontaneous leakage magnetic field intensity due to corrosion, respectively.

4. The method for quantitatively detecting the corrosion rate of steel bars in concrete structures based on spontaneous magnetic flux leakage according to claim 1 is characterized in that: S2 is specifically: according to the spontaneous leakage magnetic field H Sx and H Sz The numerical variation section of the information directly determines the location and scope of steel corrosion inside the concrete.

5. The method for quantitatively detecting the corrosion rate of steel bars in concrete structures based on spontaneous magnetic flux leakage according to claim 1 is characterized in that: S3 is specifically: S301, determine the quantitative spontaneous leakage magnetic field change rate index NH x : In the formula, H I-av is the total magnetic field strength H of the initial spontaneous magnetic leakage of steel bars within the corrosion range I The average value, H Sx is the tangential component of the spontaneous leakage magnetic field variation; S302, spontaneous leakage magnetic field change rate index NH x To make corrections: In the formula, NH xm is the corrected spontaneous leakage magnetic field change rate index value, NH x (1.5) represents the spontaneous leakage magnetic field change rate index value NH at the midpoint of a standard corroded steel bar with a radius of 7 mm and a length of 1.5 m x Theoretical value, NH x (2l) is the spontaneous leakage magnetic field change rate index value NH at the midpoint of the measured corroded steel bar with a length of 2l x Theoretical value.

6. The method for quantitatively detecting the corrosion rate of steel bars in concrete structures based on spontaneous magnetic flux leakage according to claim 1, characterized in that: S4 is specifically: S401. Constructing a Bayesian model for evaluating the steel bar corrosion rate as a priori information model of the steel bar corrosion rate: In the formula, NH xm is the modified spontaneous leakage magnetic field change rate index value, η is the steel bar section corrosion rate, π(η|NH xm ) is the posterior distribution, f(NH xm |η) is the likelihood function, π(η) is the prior distribution, and Θ is the parameter space; S402, determining a priori distribution curve of the corrosion rate according to a priori information model of the steel bar corrosion rate; S403, determining a likelihood function; Where λ is the scale parameter and k is the shape parameter; S404. Establish the posterior distribution π(η|NH xm )’s computational model; S405. Calculate the posterior distribution π(η|NH xm ) is solved and the quantitative probabilistic estimation result of the corrosion rate of the corroded steel bar section is obtained in the form of probability density distribution.

7. The method for quantitatively detecting the corrosion rate of steel bars in concrete structures based on spontaneous magnetic flux leakage according to claim 1, characterized in that: S5 is as follows: S501, using the steel bar cross-section corrosion rate probability density distribution estimation sample data to calculate the cross-section corrosion rate mean and a quantitative point estimate with a preset confidence level; S502. Calculate the standard error based on the confidence level to obtain the uncertainty range of the quantitative point estimate of the steel bar cross-section corrosion rate.

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