Quantitative detection method and system for corrosion of steel bar in concrete

Abstract

The invention provides a quantitative detection method and system for corrosion of steel bars in concrete, and belongs to the technical field of nondestructive testing and health monitoring of civil engineering structures. According to the method, an alternating weak magnetic field is applied to a reinforced concrete structure through a controllable magnetic field excitation device; a superconducting quantum interference magnetic sensor array is adopted to collect magnetic field response signals, and a space magnetic field gradient data set is obtained; performing wavelet packet decomposition and feature extraction on the data set to obtain a corrosion feature frequency band signal; based on a magnetic diffusion-corrosion coupling physical model, inversely calculating steel bar section corrosion rate distribution; and generating and outputting a three-dimensional corrosion distribution cloud picture and a quantitative evaluation report. The system comprises a controllable magnetic field excitation device, a superconducting quantum interference magnetic sensor array, a data processing and inversion calculation device and a result output device which are sequentially connected and cooperatively work. According to the invention, nondestructive, accurate and quantitative detection of corrosion of steel bars in concrete is realized, and the method has the advantages of strong deep penetration ability, high sensitivity to micro-corrosion, good field applicability and the like.

Description

Technical Field

[0001] This invention relates to the field of non-destructive testing and health monitoring technology for civil engineering structures, and in particular to a method and system for quantitative detection of steel reinforcement corrosion in concrete. Background Technology

[0002] Steel reinforcement corrosion is a significant threat to the service life of concrete structures, and its economic and safety consequences are increasingly prominent. The volume expansion of corrosion products can reach 2-6 times that of the original steel reinforcement, leading to cracking and spalling of the concrete cover. Simultaneously, corrosion directly weakens the effective cross-sectional area and material strength of the steel reinforcement. Therefore, accurate quantitative detection of internal steel reinforcement corrosion has become a crucial technical problem that urgently needs to be solved in order to achieve pre-assessment and improvement of the service life of reinforced concrete structures.

[0003] Currently, the main methods for detecting steel corrosion in concrete include electrochemical methods, ultrasonic methods, fiber optic sensing methods, and X-ray tomography. However, these methods all have certain limitations: electrochemical and fiber optic sensing methods usually require pre-embedded electrodes or sensors, making them destructive tests and difficult to apply to existing structures that have already been completed and put into service; ultrasonic methods have limited detection accuracy, and the error increases with the depth of steel reinforcement embedment; X-ray tomography has strict requirements on specimen size and environment, mainly limiting it to laboratory experimental research and making it difficult to apply in the field.

[0004] Overall, existing technologies struggle to simultaneously meet the comprehensive demands of engineering practice, including non-destructive testing, ease of operation, strong field applicability, and reliable accuracy. Therefore, there is an urgent need to develop an innovative non-destructive testing method to achieve accurate quantitative identification and assessment of steel reinforcement corrosion within concrete, especially micro-corrosion beneath thick protective layers. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for quantitative detection of steel reinforcement corrosion in concrete, so as to solve the problem that existing detection methods are difficult to balance in terms of non-destructiveness, quantitative accuracy, detection depth and field applicability, and to achieve non-destructive, high-precision and quantitative assessment of steel reinforcement corrosion inside concrete.

[0006] To achieve the above objectives, the present invention provides a method for quantitative detection of steel reinforcement corrosion in concrete, comprising the following steps: Step S1: Apply an alternating weak magnetic field to the reinforced concrete structure using a controllable magnetic field excitation device; Step S2: Use a superconducting quantum interference magnetic sensor array to collect the magnetic field response signal generated by the steel bar and obtain a spatial magnetic field gradient dataset; Step S3: Perform wavelet packet decomposition and feature extraction on the obtained spatial magnetic field gradient dataset to obtain the characteristic frequency band signal representing steel corrosion; Step S4: Based on the extracted characteristic frequency band signals, the corrosion rate distribution of the steel bar cross section is reconstructed by inversion calculation using the magnetic diffusion-corrosion coupled physical model. Step S5: Based on the obtained corrosion rate distribution, generate and output a three-dimensional corrosion distribution cloud map and an evaluation report containing quantitative corrosion rate.

[0007] Preferably, in step S1: The frequency range of the applied alternating weak magnetic field is 0.1Hz-10Hz; Magnetic field strength depends on the thickness of the concrete cover. Adaptive adjustment: when When ≤50cm, the value is 1μT-3μT; when 50< When ≤70cm, it is 5μT-7μT. >70cm, the value is 8μT-10μT; The magnetic field direction is parallel to the axial direction of the reinforcing bar, and the positioning error is ≤5°; A uniform magnetic field is generated in space by a Helmholtz coil array, with a non-uniformity of <5%; The duration of the applied magnetic field is 60s~120s, of which the single-frequency point detection time is ≥10 cycles and the total duration of multi-frequency scanning is ≤120s.

[0008] Preferably, in step S2: The probes of the superconducting quantum interference magnetic sensor array are arranged in a cross topology, with the spacing between adjacent probes... satisfy ≤0.2 ,in The diameter of the target reinforcing bar; During the data acquisition process, dynamic baseline correction is performed on the measured data to eliminate interference from the geomagnetic field and sensor temperature drift. ; ; ; in, This represents the corrected magnetic flux density vector. This represents the magnetic induction vector measured by the sensor. Represents the geomagnetic field vector. This represents the magnetic field vector of the sensor's temperature drift. Indicates the geomagnetic compensation coefficient. This represents the temperature drift attenuation coefficient. Indicates the detection time. Represents the time constant. It represents the base of the natural logarithm.

[0009] Preferably, in step S3, the Morlet wavelet basis function is used to perform at least 6 layers of wavelet packet decomposition on the spatial magnetic field gradient data, and the 8Hz-25Hz frequency band is identified and extracted from the decomposed signal as the characteristic frequency band signal for characterizing steel corrosion. Corrosion Feature Frequency Band Identification Standard for: ; in, These are wavelet coefficients. For scale parameters, These are the translation parameters.

[0010] Preferably, in step S4, the magnetic diffusion-corrosion coupling equation is: ; in, Indicates magnetic field strength. Indicates the electrical conductivity of the steel reinforcement. Represents the permeability function. Represents the curl operator; The inversion equation is solved iteratively using the finite element-adjoint optimization algorithm, and the inversion objective function is: ; in, Describe the objective function. Represents the regularization parameter. Indicates the corrosion rate distribution. This represents the corrosion rate distribution based on the current estimate. The predicted magnetic field strength obtained by numerical solution using the finite element method The gradient representing the corrosion rate distribution; gradient of objective function for: ; in, Indicates transpose; The corrosion rate distribution was updated using the Barzilai-Borwein step size: ; in, Indicates the iteration count index. Indicates the first The estimated corrosion rate distribution after the next iteration Indicates the first The new estimate of the corrosion rate distribution obtained from the next iteration of optimization. Indicates the iteration step size. Indicates the first The gradient value of the objective function in the next iteration.

[0011] Preferably, in step S5: The quantitative corrosion rate was calculated using the layered integration method, and its 95% confidence interval was given: ; ; in, Indicates the volume of the reinforcing steel. Indicates the number of three-dimensional sampling points. express Distribution critical value, Indicates the average corrosion rate. This represents the spatial distribution function of the corrosion rate. Represents the three-dimensional integration region. This represents the estimation error of the average corrosion rate. The variance represents the corrosion rate; 3D Corrosion Distribution Cloud Map Generate according to the following rules: .

[0012] Preferably, the controllable magnetic field excitation device used in step S1 consists of three sets of orthogonally arranged Helmholtz coils. The current of each set of coils can be independently adjusted, and a current feedback module is configured to achieve precise control of the magnetic field strength.

[0013] Preferably, the superconducting quantum interference magnetic sensor array used in step S2 comprises nine probes arranged in an axial and radial combination, and has a built-in drift compensation module to improve signal stability. ; in, This represents the total drift of the output signal of the superconducting quantum interference magnetic sensor probe. This represents the temperature sensitivity coefficient of the sensor. This represents the change in real-time temperature at the location of the sensor probe relative to a reference temperature baseline. Indicates the intrinsic relaxation drift coefficient. Indicates the detection time. Represents the time constant. It represents the base of the natural logarithm.

[0014] Preferably, during the detection process, an electromagnetic barrier structure is used to shield the superconducting quantum interference magnetic sensor array and the detection area. The electromagnetic barrier structure is composed of a 0.3mm thick permalloy inner layer and an outer copper mesh with a mesh size greater than 200.

[0015] This invention also provides a quantitative detection system for steel reinforcement corrosion in concrete, comprising: A controllable magnetic field excitation device is used to apply a controllable alternating weak magnetic field to the reinforced concrete structure under test. A superconducting quantum interference magnetic sensor array is used to acquire magnetic field response signals generated by a controllable magnetic field excitation device. Electromagnetic barrier structure, used to shield external electromagnetic interference during the detection process; The data processing and inversion calculation device is connected to the superconducting quantum interference magnetic sensor array. It is used to perform wavelet packet decomposition and feature extraction on the acquired signals, and to invert and calculate the corrosion rate distribution based on the magnetic diffusion-corrosion coupling model. It also generates and displays a three-dimensional corrosion distribution cloud map and a quantitative assessment report. Among them, the controllable magnetic field excitation device, the superconducting quantum interference magnetic sensor array, and the data processing and inversion calculation device are connected in sequence and work together to realize non-destructive quantitative detection of steel corrosion.

[0016] Therefore, the present invention employs the above-mentioned method and system for quantitative detection of steel reinforcement corrosion in concrete, and the beneficial technical effects are as follows: (1) This invention achieves non-destructive, accurate, and quantitative detection of steel reinforcement corrosion inside concrete. This method utilizes an externally controllable magnetic field for active excitation and employs a superconducting quantum interference magnetic sensor array for non-contact signal acquisition. It eliminates the need for pre-embedded components or structural damage, overcoming the defects of existing electrochemical and fiber optic sensing methods that require destructive installation. Furthermore, by establishing a physical inversion model coupling magnetic diffusion and corrosion, the corrosion rate distribution of the steel reinforcement cross-section can be quantitatively calculated and a three-dimensional cloud map can be generated, solving the problems of insufficient accuracy and increasing error with burial depth in non-destructive methods such as ultrasonic methods.

[0017] (2) This invention possesses strong deep penetration capability and high sensitivity to micro-corrosion. By employing a low-frequency variable-frequency weak magnetic field for excitation, combined with ultra-high sensitivity quantum sensing technology and signal extraction methods targeting specific corrosion characteristic frequency bands, it can effectively capture the weak magnetic signal disturbances generated by early micro-corrosion of steel bars inside deep or thick protective layers of concrete. This enables the method to effectively identify and quantitatively assess early corrosion of steel bars inside various concrete structures, solving the industry problem of insufficient detection capability of existing methods for deep internal micro-corrosion.

[0018] (3) The present invention has a high degree of system integration, is easy to operate, and has strong field applicability. The detection system integrates adaptive excitation, high-sensitivity sensing, intelligent shielding, and edge computing platform. By optimizing the scanning process and building a fast processing model, it can complete the detection in a short time on site and directly output a visual report. This feature enables the method to be used directly for efficient detection in complex engineering sites such as tunnels and bridges, overcoming the limitations of laboratory methods such as X-ray tomography, which have strict requirements on the environment and samples and cannot be applied on site. Attached Figure Description

[0019] Figure 1 This is a flowchart of a quantitative detection method for steel reinforcement corrosion in concrete according to the present invention; Figure 2 This is a structural diagram of a quantitative detection system for steel reinforcement corrosion in concrete according to the present invention; Figure 3 This is a graph showing the relationship between the magnetic field signal and the scanning position. Figure 4 This is a three-dimensional corrosion cloud map. Detailed Implementation

[0020] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0022] Example 1 The method and system proposed in this invention are particularly suitable for on-site inspection and long-term monitoring of major infrastructure projects with stringent structural safety requirements, complex testing environments, and a need for early identification of hidden defects. Typical application scenarios include highway tunnels and railway tunnels (especially those with shotcrete lining structures). The complex water and vapor environment behind tunnel linings makes steel reinforcement corrosion one of the main causes of lining spalling and even structural collapse. This system is highly integrated, easy to operate, and can be mounted on a mobile inspection platform to quickly scan lining areas such as the arch and sidewalls during limited time periods, such as tunnel operation windows. With advanced signal processing capabilities, the system can effectively isolate interference from complex background magnetic fields such as the supporting steel arch frame and steel mesh, directly outputting a three-dimensional distribution cloud map of steel reinforcement corrosion rate, enabling rapid and intuitive assessment of the service status of tunnel lining structures, thereby providing key data support for preventive maintenance decisions.

[0023] 1. Detection system composition and layout.

[0024] like Figure 2 As shown, the detection system used in this embodiment mainly includes: The controllable magnetic field excitation device consists of three sets of orthogonally arranged Helmholtz coils, fixed on a movable support. A current feedback module is configured to achieve a magnetic field strength accuracy of ±0.1μT. The current feedback module is a high-precision closed-loop control system, comprising: a high-sensitivity fluxgate magnetometer (as a magnetic field monitoring probe), a digital PID controller, and a multi-channel programmable precision current source. The magnetometer is fixed in the common central area of ​​the three Helmholtz coils for real-time, in-situ monitoring of the actual strength of the synthesized magnetic field. The PID controller receives the real-time magnetic field strength signal monitored by the magnetometer and performs high-speed comparison and calculation with the user-set target value. The current source dynamically and independently adjusts the magnitude and direction of the current flowing into the three coils based on the correction signal output by the PID controller. Its working process is as follows: when the actual magnetic field strength deviates from the set value, the PID controller will immediately generate a control signal related to the magnitude, integral and derivative of the deviation. This signal drives the current source to fine-tune the current of each coil, thereby offsetting the influence of environmental interference or coil thermal drift, forming a fast and automatic negative feedback adjustment loop, and finally stabilizing the magnetic field strength of the target area within the accuracy range of ±0.1μT of the set value.

[0025] Superconducting quantum interference magnetic sensor array: Contains 9 probes (5 axial, 4 radial), mounted in a cross-shaped topology within an electromagnetic barrier structure, with a built-in drift compensation module. ; in, This represents the total drift of the output signal of the superconducting quantum interference magnetic sensor probe. This represents the temperature sensitivity coefficient of the sensor, which is set in this embodiment. , This represents the change in real-time temperature at the location of the sensor probe relative to a reference temperature baseline. Nanoteslas represent one degree Celsius. Indicates the intrinsic relaxation drift coefficient. Indicates the detection time. This represents the time constant, which is set in this implementation. , It represents the base of the natural logarithm.

[0026] Electromagnetic barrier structure: A shielding cover composed of a 0.3mm permalloy inner layer and a copper mesh outer layer with a mesh size greater than 200 is used to suppress electromagnetic interference on site.

[0027] The data processing and inversion calculation device is connected to the superconducting quantum interference magnetic sensor array. It is used to perform wavelet packet decomposition and feature extraction on the acquired signals, and to invert and calculate the corrosion rate distribution based on the magnetic diffusion-corrosion coupling model. It also generates and displays a three-dimensional corrosion distribution cloud map and a quantitative assessment report.

[0028] Among them, the controllable magnetic field excitation device, the superconducting quantum interference magnetic sensor array, and the data processing and inversion calculation device are connected in sequence and work together to realize non-destructive quantitative detection of steel corrosion.

[0029] 2. Detection methods and procedures.

[0030] like Figure 1 As shown, the specific testing steps are as follows: Step S1: Apply an alternating weak magnetic field excitation.

[0031] Align the controllable magnetic field excitation device with the preset detection area, ensuring that the direction of the magnetic field generated by the device is parallel to the axial direction of the reinforcing bar (error controlled within 5°). Based on the concrete thickness ( =5cm~50cm), with the excitation magnetic field strength set to 1μT~100μT. The excitation frequency is scanned in the range of 0.1Hz-10Hz with a resolution of 0.1Hz. The Helmholtz coil current is precisely controlled through a current feedback module to ensure that the spatial magnetic field non-uniformity is less than 5%. The total duration of single-point excitation is 300 seconds, with each frequency point lasting for at least 10 cycles.

[0032] Step S2: Acquire the magnetic field response signal.

[0033] Activate the superconducting quantum interference magnetic sensor array (sensitivity ≥ The system simultaneously acquires the space magnetic field gradient response signal induced by the excitation magnetic field. During the acquisition process, the system executes a dynamic baseline correction algorithm in real time to deduct interference introduced by the geomagnetic field and the sensor's own temperature drift, obtaining a clean dataset of the steel reinforcement response signal. The correction formula is as follows: ; ; ; in, This represents the corrected magnetic flux density vector. This represents the magnetic induction vector measured by the sensor. Represents the geomagnetic field vector. This represents the magnetic field vector of the sensor's temperature drift. Indicates the geomagnetic compensation coefficient. This represents the temperature drift attenuation coefficient.

[0034] The typical signal morphology of the space magnetic field gradient dataset obtained after acquisition and correction is as follows: Figure 3 As shown.

[0035] Step S3: Signal processing and feature extraction.

[0036] The collected spatial magnetic field gradient dataset was transmitted to an edge computing platform. A 6-level wavelet packet decomposition was performed using Morlet wavelet basis functions.

[0037] ; in, Let be the Morlet wavelet basis function, representing the time... The wavelet waveform at that location, Represents pi (π). It represents the imaginary unit.

[0038] The wavelet coefficient energy ratio is calculated based on the pre-calibrated corrosion characteristics.

[0039] Corrosion Feature Frequency Band Identification Standard for: ; in, Represents wavelet coefficients, Indicates the scale parameter. This represents the translation parameter.

[0040] extract The signal components corresponding to >0.35, i.e. the 8-25Hz frequency band signals, are used as quantitative characteristics of steel corrosion.

[0041] Step S4: Physical inversion calculation of corrosion rate distribution.

[0042] Based on the extracted corrosion feature signals, a magnetic diffusion-corrosion coupled inversion model was established and solved: ; in, Indicates magnetic field strength. Indicates the electrical conductivity of the steel reinforcement. Represents the permeability function. This represents the curl operator.

[0043] The inversion equation is solved iteratively using the finite element-adjoint optimization algorithm, and the inversion objective function is: ; in, Represents the regularization parameter. , Describe the objective function. Indicates the corrosion rate distribution. This represents the corrosion rate distribution based on the current estimate. The predicted magnetic field strength obtained by numerical solution using the finite element method This represents the gradient of the corrosion rate distribution.

[0044] gradient of objective function for: ; in, This indicates transpose.

[0045] The corrosion rate distribution was updated using the Barzilai-Borwein step size: ; in, Indicates the iteration count index. Indicates the first The estimated corrosion rate distribution after the next iteration Indicates the first The new estimate of the corrosion rate distribution obtained from the next iteration of optimization. Indicates the iteration step size. Indicates the first The gradient value of the objective function in the next iteration.

[0046] Step S5: Generate and output the detection results.

[0047] After the inversion calculation is completed, the system automatically performs post-processing: The quantitative corrosion rate was calculated using the layered integration method, and its 95% confidence interval was given: ; ; in, Indicates the volume of the reinforcing steel. Indicates the number of three-dimensional sampling points. express Distribution critical value, Indicates the average corrosion rate. This represents the spatial distribution function of the corrosion rate. Represents the three-dimensional integration region. This represents the estimation error of the average corrosion rate. The variance represents the corrosion rate.

[0048] 3D Corrosion Distribution Cloud Map Generate according to the following rules: .

[0049] Report output: Displays a cloud map on the screen of the results output device and generates a text report containing key data.

[0050] 3. Test results.

[0051] The shotcrete in the aforementioned tunnel was tested using this system and method, and the following quantitative results were obtained: The average corrosion rate of steel bars in this area is 13.28% ± 0.14% (95% confidence interval).

[0052] The system successfully identified two rust hotspots with spatial coordinates of (X=78.253mm, Y=75.782mm, Z=0.415mm) and (X=142.256m, Y=60.781m, Z=0.420m).

[0053] 3D rust cloud map ( Figure 4 This visually demonstrates the distribution of corrosion along the length and depth of the reinforcing bars.

[0054] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0055] Therefore, the present invention employs the above-mentioned quantitative detection method and system for steel reinforcement corrosion in concrete, which can perform non-destructive and accurate quantitative detection of the corrosion state of steel reinforcement inside concrete. It effectively solves the industry problem of difficulty in identifying and assessing early micro-corrosion under thick protective layers, and provides a reliable technical means for durability assessment and preventive maintenance of civil engineering structures.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for quantitative detection of steel bar corrosion in concrete, characterized in that, The method comprises the following steps: Step S1, applying an alternating weak magnetic field to the reinforced concrete structure by a controllable magnetic field excitation device; Step S2, collecting the magnetic field response signal generated by the steel bar by using a superconducting quantum interference magnetic sensor array to obtain a spatial magnetic field gradient data set; Step S3, wavelet packet decomposition and feature extraction are performed on the obtained spatial magnetic field gradient data set to obtain characteristic frequency band signals representing the corrosion of the steel bar; Step S4, based on the extracted characteristic frequency band signals, an inversion calculation is performed using a magnetic diffusion-corrosion coupling physical model to reconstruct the corrosion rate distribution of the steel bar cross section; Step S5, according to the obtained corrosion rate distribution, a three-dimensional corrosion distribution cloud map and an evaluation report containing quantitative corrosion rate are generated and output.

2. The method for quantitatively detecting corrosion of steel bars in concrete according to claim 1, characterized in that, In step S1: The frequency range of the applied alternating weak magnetic field is 0.1 Hz-10 Hz; Magnetic field strength according to concrete cover thickness Adaptive adjustment: When 1 μT - 3 μT when ≤ 50 cm, 5 μT - 7 μT when 50 < ≤ 70 cm, 8 μT - 10 μT when > 70 cm. The magnetic field direction is parallel to the steel bar axial direction, and the positioning error is ≤5°; A spatial uniform magnetic field is generated by a Helmholtz coil set, and the non-uniformity is <5%; The magnetic field application time is 60s~120s, wherein the single frequency point detection time is ≥10 cycles, and the total multi-frequency scanning time is ≤120s.

3. The method for quantitatively detecting corrosion of steel bars in concrete according to claim 1, characterized in that, In step S2: The probes of the superconducting quantum interference magnetic sensor array are arranged in a cross topology manner, and the distance between adjacent probes is satisfies ≤0.2 wherein is the diameter of the target reinforcing bar; During the collection process, dynamic baseline correction is performed on the measured data to eliminate the interference of the geomagnetic field and sensor temperature drift: ； ； ； wherein, denotes the corrected magnetic induction vector, denotes the sensor measured magnetic induction vector, denotes the geomagnetic field vector, denotes the sensor temperature drift magnetic field vector, denotes the geomagnetic compensation coefficient, denotes the temperature drift decay coefficient, denotes the detection time, denotes the time constant, denotes the base of the natural logarithm.

4. The method for quantitatively detecting corrosion of steel bars in concrete according to claim 1, characterized in that, In step S3, the Morlet wavelet basis function is used to perform at least 6 layers of wavelet packet decomposition on the spatial magnetic field gradient data, and the 8Hz-25Hz frequency band is identified and extracted from the decomposed signal as the characteristic frequency band signal representing the corrosion of the steel bar; Rust characteristic frequency band identification criteria To: ； wherein, is a wavelet coefficient, is a scale parameter, is a translation parameter.

5. The method for quantitatively detecting corrosion of steel bars in concrete according to claim 1, characterized in that, In step S4, the magnetic diffusion-corrosion coupling equation is: ； wherein denotes the magnetic field strength, denotes the steel reinforcement electrical conductivity, denotes the magnetic permeability function, denotes the curl operator; The finite element-companion optimization algorithm is used to iteratively solve the inversion equation, and the inversion objective function is: ； wherein, denotes the objective function, denotes a regularization parameter, denotes the corrosion rate distribution, denotes the corrosion rate distribution based on the current estimate the magnetic field strength prediction value obtained by numerical solution of the finite element method, denotes the gradient of the corrosion rate distribution; Objective function gradient is: ； wherein denotes the transpose; And the Barzilai-Borwein step is used to update the corrosion rate distribution: ； wherein, denotes an iteration number index, denotes the corrosion rate distribution estimate after the first iteration, denotes the new corrosion rate distribution estimate obtained after the first iteration optimization, denotes an iteration step size, denotes the gradient value of the objective function for the first iteration.

6. The method for quantitatively detecting corrosion of steel bars in concrete according to claim 1, characterized in that, In step S5: The quantitative corrosion rate is calculated by the layer integration method, and the 95% confidence interval is given: ； ； wherein, denotes the steel volume, denotes the number of three-dimensional sampling points, denotes the distribution threshold value, denotes the average corrosion rate, denotes the spatial distribution function of the corrosion rate, denotes the three-dimensional integration region, denotes the estimation error of the average corrosion rate, denotes the variance of the corrosion rate; Three-dimensional corrosion distribution cloud Generated according to the following rules: 。 7. The method for quantitatively detecting corrosion of steel bars in concrete according to claim 1, characterized in that, The controllable magnetic field excitation device used in step S1 is composed of three groups of orthogonally arranged Helmholtz coils, and the current of each coil can be independently controlled and configured with a current feedback module to realize precise control of the magnetic field strength.

8. The method for quantitatively detecting corrosion of steel bars in concrete according to claim 1, characterized in that, The superconducting quantum interference magnetic sensor array used in step S2 includes 9 probes and is arranged in an axial and radial combination, and has a built-in drift compensation module to improve signal stability: ； wherein, represents the total drift of the superconducting quantum interference magnetometer probe output signal, represents the temperature sensitivity coefficient of the sensor, represents the change amount of the real-time temperature of the sensor probe and the reference temperature base point, represents the intrinsic relaxation drift coefficient, represents the detection time, represents the time constant, represents the base number of the natural logarithm.

9. The method for quantitatively detecting corrosion of steel bars in concrete according to claim 1, characterized in that, During the detection process, an electromagnetic barrier structure is used to shield the superconducting quantum interference magnetic sensor array and the detection area, and the electromagnetic barrier structure is composed of a 0.3mm thick permalloy inner layer and a more than 200 mesh copper mesh outer layer.

10. A system for quantitative detection of steel reinforcement corrosion in concrete, characterized in that, It comprises: A controllable magnetic field excitation device for applying a controllable alternating weak magnetic field to the measured reinforced concrete structure; A superconducting quantum interference magnetic sensor array for collecting the magnetic field response signal generated by the controllable magnetic field excitation device; An electromagnetic barrier structure for shielding external electromagnetic interference during detection; A data processing and inversion calculation device connected to the superconducting quantum interference magnetic sensor array, for wavelet packet decomposition and feature extraction of the collected signal, and inversion calculation of the corrosion rate distribution based on the magnetic diffusion-corrosion coupling model, and generation and display of a three-dimensional corrosion distribution cloud map and a quantitative evaluation report; The controllable magnetic field excitation device, the superconducting quantum interference magnetic sensor array and the data processing and inversion calculation device are sequentially connected and cooperatively work to realize nondestructive quantitative detection of steel bar corrosion.

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