A method and system for detecting defects of a reinforced concrete structure

Abstract

The present application relates to the technical field of nondestructive testing and evaluation of reinforced concrete structures, and discloses a method and system for detecting defects of reinforced concrete structures. The method ensures sufficient wetting by pretreating the concrete surface, identifies crack features using three-dimensional point cloud technology and adaptively adjusts the spacing between measurement points, and simultaneously performs potential gradient and linear polarization measurement to obtain internal data of the concrete. The system integrates environmental sensors to compensate for the effects of temperature and humidity in real time, uses a multi-source information fusion algorithm to comprehensively evaluate the degree of reinforcement corrosion, and divides the corrosion level. The system improves detection accuracy and adaptability, provides a reliable basis for structure maintenance, and effectively prolongs the service life of the structure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of non-destructive testing and evaluation of reinforced concrete structures, and particularly relates to a reinforced concrete structure disease defect detection method and system. BACKGROUND

[0002] Traditional reinforced concrete structure disease detection mainly relies on manual visual inspection or single parameter measurement, which has the problems of low detection efficiency and limited coverage.

[0003] For early micro-disease or structural damage in complex environment, manual detection is difficult to find, and single parameter measurement (such as only through potential gradient or corrosion current density) is easily disturbed by environmental temperature and humidity, concrete surface state and other factors, resulting in large data fluctuation and insufficient reliability. In addition, the fixed interval of the measurement points in the traditional method cannot be dynamically adjusted according to the crack distribution characteristics, which may miss detection in the dense crack area and waste detection resources in the sparse crack area, and the overall adaptability is poor. SUMMARY

[0004] In view of the above technical deficiencies, the purpose of the present application is to provide a reinforced concrete structure disease defect detection method and system, which solves the problems of low detection accuracy, poor adaptability and large environmental interference of the existing reinforced concrete disease detection.

[0005] To solve the above technical problems, the present application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a reinforced concrete structure disease defect detection method, which comprises:

[0007] Pretreating the surface of the concrete structure member to be measured, uniformly spraying water and standing for a predetermined time to make the surface of the member fully wet and free of free water;

[0008] Based on the crack characteristics of the surface of the concrete structure member, an adaptive algorithm is used to adjust the spacing of the measurement points;

[0009] Placing a multi-electrode array at the measurement points, and connecting the electrodes to the detection host through a cable, and simultaneously performing potential gradient measurement and linear polarization measurement to obtain the potential gradient and corrosion current density data inside the concrete;

[0010] During the measurement process, the integrated environmental sensor monitors the environmental temperature and humidity in real time, and automatically compensates for the influence of the environmental temperature and humidity on the potential gradient and corrosion current density;

[0011] Based on the compensated potential gradient and corrosion current density data, a multi-source information fusion algorithm is used to output the reinforced corrosion degree evaluation result.

[0012] Preferably, in a possible implementation form of the first aspect, the adaptive algorithm obtains point cloud data of the surface of the concrete structural member based on a three-dimensional point cloud detection technology, identifies surface crack features including crack density and crack length by a point cloud processing algorithm, and adaptively adjusts the measuring point spacing within a preset measuring point spacing range according to the surface crack features.

[0013] calculating the crack distribution index based on the surface crack features , and the formula is:

[0014]

[0015] wherein is the crack density, is the average crack length, is the crack length standard deviation, is a smoothing coefficient;

[0016] calculating the spacing based on the crack distribution index , and the formula is:

[0017]

[0018] wherein is the initial measuring point spacing, is an adjustment coefficient;

[0019] the preset measuring point spacing range is , when the calculated spacing is less than , the measuring point spacing is , when the calculated spacing is greater than or equal to and less than or equal to , the measuring point spacing is , and when the calculated spacing is greater than , the measuring point spacing is .

[0020] Preferably, in a possible implementation form of the first aspect, the potential gradient measurement specifically includes:

[0021] synchronously collecting steady-state potential values of each electrode inside the concrete by a multi-electrode array, each electrode in the multi-electrode array is annularly distributed, and the distance between adjacent electrodes is equal;

[0022] calculating the potential difference between adjacent electrode pairs , and the calculation formula is , wherein electrode and electrode are adjacent electrodes, Electrode The steady-state potential value, Electrode The steady-state potential value is obtained, and the average potential difference of the multi-electrode array is calculated based on the potential difference between all adjacent electrode pairs. ;

[0023] Calculate the potential gradient based on the average potential difference. Its formula is:

[0024]

[0025] in, For potential gradient, The average potential difference This represents the distance between adjacent electrodes.

[0026] Preferably, in one possible implementation of the first aspect, the linear polarization measurement applies a polarization voltage at the measurement point. Measure the change in the generated current Calculate polarization resistance :

[0027]

[0028] Then, the corrosion current density is calculated according to the Stern-Geary formula. :

[0029]

[0030] in It is a constant and depends on the steel reinforcement material.

[0031] Preferably, in one possible implementation of the first aspect, the automatic compensation monitors environmental parameters in real time by integrating temperature and humidity sensors, and dynamically corrects the potential gradient and corrosion current density data obtained from potential gradient measurement and linear polarization measurement using a pre-stored calibration database to compensate for measurement drift caused by temperature and humidity changes.

[0032] The automatic compensation of the potential gradient adopts a compensation algorithm based on multivariate nonlinear regression, and the compensation formula is as follows:

[0033]

[0034] in The compensated potential gradient, The current temperature. For reference temperature, The current humidity. For reference humidity, , , is the temperature and humidity compensation coefficient;

[0035] The automatic compensation of the corrosion current density adopts a compensation algorithm based on multi-parameter correction, and the compensation formula is:

[0036]

[0037] wherein is the corrected corrosion current density, and is a correction coefficient.

[0038] Preferably, in a possible implementation form of the first aspect, the multi-source information fusion algorithm comprises calculating the corrosion degree of the steel bar:

[0039] According to the compensated potential gradient and the corrosion current density , the corrosion degree index is calculated:

[0040]

[0041] wherein is the total number of measurement points, is the compensated potential gradient of the th measurement point, is the compensated corrosion current density of the th measurement point, and is a weight coefficient, and is a reference value;

[0042] According to the corrosion degree index , the loss amount per unit area of the steel bar is calculated , and the calculation formula is:

[0043]

[0044] wherein, and are corrosion kinetic constants, is the service time of the structure, is a time constant.

[0045] Preferably, in a possible implementation form of the first aspect, the multi-source information fusion algorithm further comprises fitting the corrosion probability density function of the corrosion depth , and the corrosion probability density function adopts a generalized extreme value distribution, and its form is:

[0046]

[0047] wherein is a position parameter, is a scale parameter, is a shape parameter, and , , and are determined by maximum likelihood estimation method from compensated potential gradient and corrosion current density data.

[0048] Preferably, in a possible implementation form of the first aspect, the corrosion risk index is calculated according to the steel loss per unit area and the corrosion probability density function , and the calculation formula is:

[0049]

[0050] wherein is a risk coefficient, is a step function, is a critical corrosion depth, when , otherwise .

[0051] Preferably, in a possible implementation form of the first aspect, the steel corrosion degree evaluation result is based on the corrosion risk index to divide corrosion grades.

[0052] The corrosion risk index is divided into multiple continuous intervals, respectively corresponding to no corrosion, mild corrosion, moderate corrosion and severe corrosion corrosion grades.

[0053] In a second aspect, the present application provides a reinforced concrete structure disease defect detection system, which is used to realize the reinforced concrete structure disease defect detection method as described in the first aspect, and comprises:

[0054] A pretreatment module is configured to pretreat the surface of the concrete structure member to be measured, so that the surface of the member is in a fully wet state and free of free water by uniformly spraying water and standing for a predetermined time.

[0055] A measurement point adjustment module is configured to adjust the distance between measurement points based on the crack characteristics of the surface of the concrete structure member by using an adaptive algorithm.

[0056] A data acquisition module is configured to place a multi-electrode array at the measurement points, connect the electrodes to a detection host computer through a cable, and synchronously perform potential gradient measurement and linear polarization measurement to obtain potential gradient and corrosion current density data inside the concrete.

[0057] ​An environmental compensation module measures the environmental temperature and humidity in real time during the process by integrating environmental sensors and automatically compensates for the influence of the environmental temperature and humidity on the potential gradient and the corrosion current density.

[0058] An information fusion module outputs the steel bar corrosion degree evaluation result by using a multi-source information fusion algorithm based on the compensated potential gradient and corrosion current density data.

[0059] The present application has the beneficial effects that the present application ensures that the concrete surface is fully moistened by the pretreatment module, reduces the interference of the surface moisture on the conductivity, uses the three-dimensional point cloud to identify the crack features and adaptively adjusts the measuring point spacing, improves the detection accuracy of the dense crack area, and optimizes the efficiency of the sparse area.

[0060] The integrated temperature and humidity sensor compensates for the environmental influence in real time, eliminates the temperature and humidity drift, and improves the data stability. The multi-source information fusion algorithm comprehensively considers the potential gradient and the corrosion current density, quantifies the corrosion degree, the loss amount and the risk index, and realizes the accurate division of the corrosion grade.

[0061] The system has high adaptability, anti-interference and analysis and evaluation capabilities, provides a reliable basis for structure maintenance, and prolongs the service life of the structure. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0063] Figure 1 A steel reinforced concrete structure disease defect detection method flow chart is provided for the present application.

[0064] Figure 2 A steel reinforced concrete structure disease defect detection system structure diagram is provided for the present application.

[0065] Reference signs: 1-preprocessing module, 2-measuring point adjustment module, 3-data acquisition module, 4-environmental compensation module, 5-information fusion module. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0067] Embodiment One: As shown, the present application provides a method for detecting defects in a reinforced concrete structure, comprising: Figure 1

[0068] The surface of the concrete structure to be tested is pretreated by uniformly spraying water and standing for a predetermined time, so that the surface of the component reaches a fully wet state and there is no free water.

[0069] In this embodiment, the purpose of pretreating the surface of the concrete structure to be tested is to ensure that the concrete surface reaches a uniform and stable wet state. The effect of pretreatment affects the accuracy of potential gradient measurement and linear polarization measurement, because the water content of the concrete surface will significantly affect the electrical conductivity and polarization behavior. If the surface is too dry, the measurement signal may be weak or unstable; if there is free water on the surface, it will cause current short circuit or measurement drift.

[0070] Specifically, the pretreatment first cleans the concrete surface to remove dust, oil or other contaminants, avoiding impurities interfering with the uniform penetration of water. After cleaning, a water spraying device is used to uniformly spray water. In this embodiment, the water spraying device includes a spray system with a precision nozzle to spray deionized water or distilled water at a constant pressure and water flow rate, ensuring that water molecules uniformly cover the entire surface to be tested. The amount of water sprayed is adjusted according to the porosity of the concrete, environmental temperature and humidity, etc., and is controlled between 100-300 milliliters per square meter. During the spraying process, the nozzle is kept about 30 centimeters away from the surface and moves at a uniform speed to ensure consistency in each area.

[0071] After spraying, the water is allowed to fully penetrate the concrete surface layer for a certain period of time, which is set to 2 minutes in this embodiment. In order to confirm that the surface reaches a fully wet state and there is no free water, a state check is required. The fully wet state means that the water in the concrete surface layer below about 1-2 millimeters in depth reaches saturation, but there is no visible water film or droplets on the surface. The inspection method includes visual observation and touch test. Visually, the surface should present a uniform dark luster without bright spots or dry areas; when touched, the surface should feel moist but not sticky with water. If local free water or insufficient wetness is found, additional spraying or wiping adjustment is required until the requirements are met.

[0072] Based on the characteristics of the cracks on the surface of the concrete structure component, an adaptive algorithm is used to adjust the spacing between the measurement points.

[0073] In this embodiment, the adaptive algorithm is realized based on three-dimensional point cloud detection technology, which uses a three-dimensional laser scanner to non-contact scan the surface of the concrete structure component and obtain detailed point cloud data. During the scanning process, the resolution of the scanner is set to 1 millimeter to capture the fine cracks on the surface, and the point cloud data contains three-dimensional coordinate information, which is preprocessed to remove noise and abnormal points.​

[0074] The point cloud processing algorithm uses a region growing-based segmentation method to identify surface crack features, including crack density and average crack length . Crack density is defined as the total length of cracks per unit area, obtained by dividing the sum of the lengths of all cracks in the scanned area by the area of the region. The average crack length L_avg is calculated by the arithmetic mean of the lengths of all identified cracks, while the standard deviation of crack length evaluates the degree of dispersion of crack size.

[0075] Based on the surface crack features, the crack distribution index is calculated, with the formula where ε is a smoothing coefficient, with a value of 0.01 in this embodiment, used to prevent the denominator from being too small and causing unstable calculations. The larger the crack distribution index value, the more complex the crack distribution, and a denser layout of measurement points is required to improve detection accuracy.

[0076] According to the crack distribution index , the spacing is calculated, with the formula where is the initial measurement point spacing, set according to the size of the component and detection requirements; is an adjustment coefficient, calibrated through experiments, ranging from 0.1 to 0.5, used to control the sensitivity of the spacing to . This formula shows that in areas with dense cracks, the measurement point spacing is automatically reduced to enhance detection resolution, while in areas with sparse cracks, the spacing is increased to improve detection efficiency.

[0077] The preset measurement point spacing range is , and the algorithm automatically compares the calculated spacing with the measurement point spacing range: when is less than , the measurement point spacing is taken as ; when is greater than or equal to and less than or equal to , the calculated value is taken; when is greater than , the value is taken. This dynamic adjustment mechanism optimizes the distribution of measurement points, making the detection process more targeted and reliable, and is suitable for concrete structures under various crack conditions.

[0078] A multi-electrode array is placed at the measurement point, and the electrodes are connected to the detection host through a cable, and the potential gradient measurement and linear polarization measurement are performed synchronously to obtain the potential gradient and corrosion current density data inside the concrete.

[0079] In this example, the multi-electrode array adopts a ring-shaped distribution design, consisting of eight electrodes with equal distance between electrodes, constant electrode spacing of 40 mm. The cable adopts a shielded twisted pair, connected to the input channel of the detection host, reducing external electromagnetic interference.

[0080] The potential gradient measurement is achieved by synchronously collecting the steady-state potential values of each electrode inside the concrete through the multi-electrode array. Before measurement, the system performs initial calibration to ensure that the potential references of all electrodes are consistent. During the collection of steady-state potential values, the detection host controls the multi-channel switch to activate each electrode in turn and records its potential value relative to the reference electrode. The electrode and the electrode are adjacent electrodes, and the potential difference between the adjacent electrode pair is calculated , the formula of which is , where is the steady-state potential value of the electrode , and is the steady-state potential value of the electrode .

[0081] According to the potential differences between all adjacent electrode pairs, the average potential difference of the multi-electrode array is calculated , the formula of which is , where is the number of sequentially adjacent electrode pairs in the ring-shaped array, which is in this example. Based on the average potential difference, the potential gradient is calculated , the formula of which is , where is the potential gradient, is the average potential difference, is the adjacent electrode spacing. The potential gradient reflects the spatial variation of the potential inside the concrete.

[0082] The linear polarization measurement is performed synchronously with the potential gradient measurement, by applying a small amplitude polarization voltage at the measurement point, and measuring the resulting current change . The polarization resistance is calculated, the formula of which is . Then the corrosion current density is calculated according to the Stern-Geary formula, the formula of which is , where is a constant, which depends on the steel material and environmental conditions, and is 26 mV in this example. The corrosion current density directly quantifies the corrosion rate of the steel bar, and the higher the value, the more serious the corrosion.

[0083] During the measurement, the integrated environmental sensors monitor the temperature and humidity in real time and automatically compensate for their effects on the potential gradient and corrosion current density.

[0084] In this embodiment, the electrical measurement signals of the concrete structure are easily disturbed by changes in temperature and humidity, leading to drift in the potential gradient and corrosion current density data. The system integrates temperature and humidity sensors to collect environmental parameters in real time. The temperature sensor uses a platinum resistance PT100, with a measurement range of -20°C to 80°C and an accuracy of ±0.1°C. The humidity sensor uses a capacitive relative humidity sensor, with a measurement range of 0% to 100% RH and an accuracy of ±2% RH. The sensors are placed near the multi-electrode array and connected to the detection host through a cable. The temperature and humidity values are recorded synchronously during the measurement of the multi-electrode array.

[0085] The real-time monitoring data stream is processed by an embedded system. The system has a pre-stored calibration database that stores compensation coefficients for various typical concrete mixtures and environmental conditions, including reference temperatures set to 25°C and reference humidity set to 50% RH as the baseline environment. During detection, the system automatically matches the current concrete type and environmental state and calls the corresponding compensation parameters for dynamic correction.

[0086] For potential gradient measurement, automatic compensation uses a compensation algorithm based on multivariate nonlinear regression. This algorithm considers the individual effects of temperature and humidity and their interaction. The compensation formula is where is the compensated potential gradient, is the original measured potential gradient, is the current temperature, is the reference temperature, is the current humidity, is the reference humidity, , , are the temperature and humidity compensation coefficients, determined by the calibration database. The compensation calculation is performed in real time by the digital signal processor of the detection host, ensuring that the potential gradient data eliminates environmental drift.

[0087] For corrosion current density measurement, automatic compensation uses a compensation algorithm based on multi-parameter correction. This algorithm combines linear and nonlinear correction for the temperature sensitivity and humidity dependence of the corrosion current. The compensation formula is where is the corrected corrosion current density, is the original calculated corrosion current density, and are the correction coefficients, obtained from the calibration database.

[0088] At the start of each measurement cycle, the environmental sensor collects the current temperature and humidity, the system queries the calibration database to obtain the latest coefficients, and applies the compensation formula to adjust the potential gradient and corrosion current density data in real time. To verify the compensation effect, the system includes a self-test module that periodically simulates signal drift through a standard resistor network to check the compensation accuracy.

[0089] Based on the compensated potential gradient and corrosion current density data, the evaluation results of the degree of steel corrosion are output using a multi-source information fusion algorithm.

[0090] In this embodiment, the corrosion degree index is first calculated to reflect the overall degree of corrosion activity; then the unit area loss of steel bars is derived to quantify the material loss caused by corrosion; further, the probability distribution of corrosion depth is fitted to describe the uncertainty of corrosion; finally, the corrosion risk index is calculated, and the corrosion level is classified based on this, thereby outputting an intuitive evaluation result.

[0091] The multi-source information fusion algorithm first calculates the corrosion degree index. This index is used to characterize the overall degree of corrosion activity within the measurement area. Calculation is based on the compensated potential gradient. and corrosion current density The data comes from multiple measurement points. Specifically, for each measurement point... The compensated potential gradient is denoted as The compensated corrosion current density is denoted as Corrosion degree index Calculation formula ,in The total number of measurement points represents the number of measurement points laid out on the surface of the component. In this embodiment... The distance between measuring points is determined based on the component dimensions and the adaptively adjusted spacing. and The weighting coefficients are used to balance the contributions of potential gradient and corrosion current density to corrosion assessment. These coefficients are determined experimentally in this embodiment. Take 0.6, Take 0.4. and This is a reference value, representing the baseline value under rust-free conditions. Based on experimental data of the potential gradient of fresh concrete, The corrosion degree index is set based on the corrosion current density under the passivated state of the reinforcing steel. The higher the value, the more severe the corrosion.

[0092] Next, the algorithm uses a corrosion degree index. Calculate the unit area loss of reinforcing steel This quantity is used to quantify the quality loss of reinforcing steel caused by corrosion, directly reflecting the cumulative effect of corrosion. The calculation formula is as follows: ,in and The corrosion kinetic constant is determined through accelerated corrosion tests in the laboratory. In this embodiment... Take 0.05, The value of 1.2 represents the nonlinear characteristics of corrosion growth. The structural service life is obtained from the system database. The time constant reflects the decay rate of corrosion over time and is set according to the environmental category; the exponential term... This value is used to correct for the impact of service time on corrosion accumulation. It can be used to predict the remaining load-bearing capacity of reinforcing steel, providing a basis for structural safety assessment. The algorithm also includes a verification module to ensure... The value is within a reasonable range; if an abnormal value is found, a remeasurement will be triggered.

[0093] While calculating the unit area loss of reinforcing steel, the algorithm also fits the corrosion depth. Corrosion probability density function This describes the statistical distribution characteristics of corrosion depth, thereby assessing the uncertainty and variability of corrosion. The corrosion probability density function adopts a generalized extreme value distribution, which has the form: ,in This is a location parameter, representing the central tendency of the distribution; The scale parameter controls the degree of dispersion of the distribution; The shape parameter determines the tail characteristics of the distribution. and These parameters were determined using the maximum likelihood estimation method based on the compensated potential gradient and corrosion current density data. In practice, the system collects data from all measurement points. and Data is collected to construct a sample set, and then an iterative optimization algorithm is used to solve for the maximum likelihood function to obtain the result. , and The estimated value. In this embodiment, the maximum likelihood estimation process is executed by the processor of the detection host. First, the parameter values ​​are initialized, then the gradient of the likelihood function is calculated, and the process is iterated step by step until convergence, with a tolerance of 0.001. Corrosion depth The unit is millimeters, and its range is set to 0 to 10 mm based on historical data. After fitting, The function is used to calculate the probability density of any corrosion depth, thereby assessing the risk of corrosion depth exceeding a threshold.

[0094] Subsequently, based on the unit area loss of steel reinforcement and corrosion probability density function Calculate the corrosion risk index This index comprehensively quantifies the severity and risk level of corrosion. The formula is: ,in The risk coefficient, determined through reliability analysis, is set to 0.1 in this embodiment for adjustment. Contribution to risk. It is a step function. The critical corrosion depth is set based on the rebar diameter and concrete cover thickness. The step function is defined as when... hour ,otherwise This indicates that corrosion only contributes to risk when the corrosion depth exceeds a critical value. Corrosion Risk Index It reflects the probability of structural failure due to corrosion, providing support for maintenance decisions.

[0095] Finally, the assessment results of the degree of steel corrosion are based on the corrosion risk index. Classify corrosion levels and assign corrosion risk indices. The system is divided into multiple continuous intervals, corresponding to no corrosion, light corrosion, moderate corrosion, and heavy corrosion levels, respectively. In this embodiment, the interval division is as follows: When When the value is zero, it is considered a no-rust level, indicating that the risk of rust is negligible; when... At this stage, it is classified as light corrosion, indicating that corrosion has begun and requires regular monitoring; when At this stage, the corrosion level is moderate, indicating significant corrosion, and repair measures are recommended; when At this point, it is classified as severe corrosion, indicating serious rusting requiring immediate intervention. The classification results are output through the display interface of the detection host and generated in the form of a report, including the corrosion index value, level description, and recommended actions. The system also supports historical data comparison to track the evolution of rust.

[0096] Example 2: Figure 2 As shown, the present invention provides a defect detection system for reinforced concrete structures, comprising:

[0097] Pretreatment module 1 pretreatments the surface of the concrete structural member to be tested by uniformly spraying water and letting it stand for a predetermined time to make the surface of the member fully wet and free of free water.

[0098] Measuring point adjustment module 2 adjusts the spacing of measuring points based on the surface crack characteristics of concrete structural components using an adaptive algorithm;

[0099] Data acquisition module 3 places a multi-electrode array at the measuring point. The electrodes are connected to the detection host via cables to simultaneously perform potential gradient measurement and linear polarization measurement to obtain potential gradient and corrosion current density data inside the concrete.

[0100] Environmental compensation module 4 integrates environmental sensors to monitor ambient temperature and humidity in real time during the measurement process and automatically compensates for their effects on potential gradient and corrosion current density.

[0101] Information fusion module 5, based on the compensated potential gradient and corrosion current density data, uses a multi-source information fusion algorithm to output the assessment result of the degree of steel corrosion.

[0102] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for detecting defects in reinforced concrete structures, characterized in that, The method includes: The surface of the concrete structural member to be tested is pretreated by uniformly spraying water and letting it stand for a predetermined time to ensure that the surface of the member is fully wetted and free of free water. Based on the surface crack characteristics of concrete structural members, an adaptive algorithm is used to adjust the spacing of measuring points; A multi-electrode array is placed at the measuring point, and the electrodes are connected to the detection host via cables to simultaneously perform potential gradient measurement and linear polarization measurement to obtain potential gradient and corrosion current density data inside the concrete. The potential gradient measurement specifically includes: The steady-state potential values ​​of each electrode inside the concrete are collected synchronously by a multi-electrode array. The electrodes in the multi-electrode array are distributed in a ring, and the distance between adjacent electrodes is equal. Calculate the potential difference between adjacent electrode pairs Its calculation formula is Among them, electrodes and electrodes For adjacent electrodes, Electrode The steady-state potential value, Electrode The steady-state potential value is obtained, and the average potential difference of the multi-electrode array is calculated based on the potential difference between all adjacent electrode pairs. ; Calculate the potential gradient based on the average potential difference. Its formula is: in, For potential gradient, The average potential difference, The distance between adjacent electrodes; The linear polarization measurement applies a polarization voltage at the measuring point. Measure the change in the generated current Calculate polarization resistance : Then, the corrosion current density is calculated according to the Stern-Geary formula. : in It is a constant, depending on the steel reinforcement material; During the measurement process, the integrated environmental sensor monitors the ambient temperature and humidity in real time and automatically compensates for their effects on the potential gradient and corrosion current density. The automatic compensation monitors environmental parameters in real time by integrating temperature and humidity sensors, and uses a pre-stored calibration database to dynamically correct the potential gradient and corrosion current density data obtained from potential gradient measurement and linear polarization measurement, thereby compensating for measurement drift caused by temperature and humidity changes. The automatic compensation of the potential gradient adopts a compensation algorithm based on multivariate nonlinear regression, and the compensation formula is as follows: in The compensated potential gradient, The current temperature. For reference temperature, The current humidity. For reference humidity, , , This refers to the temperature and humidity compensation coefficient. The automatic compensation for the corrosion current density adopts a compensation algorithm based on multi-parameter correction, and the compensation formula is as follows: in The corrected corrosion current density, and This is a correction factor; Based on the compensated potential gradient and corrosion current density data, the evaluation results of the degree of steel corrosion are output using a multi-source information fusion algorithm.

2. A system for detecting defects in reinforced concrete structures, characterized in that, The system is used to implement the method for detecting defects in reinforced concrete structures as described in claim 1, including: The pretreatment module pretreats the surface of the concrete structural member to be tested by uniformly spraying water and letting it stand for a predetermined time to make the surface of the member fully wetted and free of free water. The measuring point adjustment module uses an adaptive algorithm to adjust the spacing of measuring points based on the surface crack characteristics of concrete structural components. The data acquisition module places a multi-electrode array at the measuring point, and the electrodes are connected to the detection host via cables to simultaneously perform potential gradient measurement and linear polarization measurement to obtain potential gradient and corrosion current density data inside the concrete. The environmental compensation module integrates environmental sensors to monitor ambient temperature and humidity in real time during the measurement process and automatically compensates for their effects on potential gradient and corrosion current density. The information fusion module, based on the compensated potential gradient and corrosion current density data, uses a multi-source information fusion algorithm to output the assessment results of the degree of steel corrosion.

Images