Reinforcing steel bar corrosion nondestructive testing method integrating ultrasonic wave and electrochemical signals

Abstract

The invention relates to the technical field of reinforcement corrosion detection, in particular to a reinforcement corrosion nondestructive testing method integrating ultrasonic and electrochemical signals, and the method comprises the following steps: calculating an environmental interference coefficient and an environmental influence evaluation value through an environmental noise signal and concrete surface impedance, and constructing an initial evaluation model; collecting and processing ultrasonic and electrochemical signals in real time to obtain characteristic parameters, and calculating an initial corrosion risk coefficient through an initial evaluation model; generating a comprehensive decision coefficient in combination with the environmental influence evaluation value, determining a feature weight distribution strategy, and generating a fusion diagnosis index; adaptively adjusting the detection point grid spacing according to the fusion diagnosis index; the diagnosis reliability is verified through the time-space consistency coefficient of the ultrasonic waves and the electrochemical active region, and a fusion diagnosis deviation value is calculated; and carrying out collaborative optimization on the detection period and the evaluation model based on the cumulative fusion diagnosis deviation value. According to the invention, the problem that a single detection means is insufficient in precision and cannot be adaptively adjusted in a complex environment is solved.

Description

Technical Field

[0001] This invention relates to the field of steel reinforcement corrosion detection technology, and in particular to a non-destructive testing method for steel reinforcement corrosion that integrates ultrasonic and electrochemical signals. Background Technology

[0002] Steel corrosion is a core issue affecting the durability and safety of concrete structures, making the development of efficient and accurate non-destructive testing (NDT) technologies crucial. Existing testing methods are mainly divided into electrochemical and physical methods. While electrochemical methods (such as the half-cell potential method) are sensitive to corrosion activity, they are easily affected by environmental factors and struggle to locate and differentiate corrosion types. Among physical methods, ultrasonic technology is sensitive to internal defects but insensitive to early-stage corrosion, failing to provide early warning.

[0003] Existing fusion technologies mostly remain at the level of simple data comparison, lacking in-depth feature-level integration. Furthermore, their detection strategies are fixed and cannot adaptively adjust according to the corrosion stage, leading to early missed detections or misjudgments at severe stages. In addition, existing methods lack self-validation and continuous optimization capabilities; the models cannot evolve through engineering practice, limiting their long-term reliability.

[0004] Therefore, there is an urgent need for an intelligent detection method that can deeply integrate multi-physics information, has an adaptive detection strategy, and can continuously self-optimize.

[0005] Chinese Patent Publication No. CN109668953A discloses a method for detecting the corrosion of internal reinforcing steel bars in grounded concrete. Based on the half-cell potential method, the method includes the following steps in sequence: first, preparing a reinforced concrete specimen and subjecting it to corrosion treatment; second, detecting the potential difference of the corroded specimen before and after grounding; third, performing potential detection on the grounded reinforced concrete to be tested; and finally, calculating the potential of the grounded reinforced concrete to be tested, and judging the corrosion status of the internal reinforcing steel bars based on the magnitude of the potential. This invention improves the accuracy of judging the corrosion status of internal reinforcing steel bars in grounded reinforced concrete and realizes non-destructive testing, applicable to the field of concrete reinforcing steel corrosion detection technology.

[0006] Therefore, the method for detecting the corrosion of internal reinforcing steel bars in grounded concrete has the following problems: 1. The detection methods for steel reinforcement corrosion are limited and do not combine electrochemical and physical detection methods. Electrochemical detection is only sensitive to passivation and early corrosion, resulting in low accuracy in detecting steel reinforcement corrosion under complex conditions.

[0007] 2. The testing method was not adapted to the actual corrosion of the steel bars after the test, which led to the accumulation of errors and reduced the accuracy of subsequent steel bar corrosion testing. Summary of the Invention

[0008] Therefore, this invention provides a non-destructive testing method for steel reinforcement corrosion that integrates ultrasonic and electrochemical signals, in order to overcome the problems of single detection methods and low detection accuracy in complex engineering environments in the prior art, as well as the inability to make adaptive adjustments.

[0009] To achieve the above objectives, the present invention provides a non-destructive testing method for steel reinforcement corrosion that integrates ultrasonic and electrochemical signals, comprising: Acquire historical steel corrosion sample data and environmental parameters of the steel bars within the preset monitoring period. Determine the degree of environmental influence on the detection of steel corrosion by ultrasonic and electrochemical methods based on the environmental noise signal and the surface impedance of concrete. Calculate the environmental impact assessment value. An initial assessment model for steel corrosion status is constructed based on the historical steel corrosion sample database using multimodal signal fusion. The ultrasonic and electrochemical signals of the steel bar to be tested are collected in real time and processed in real time to obtain the ultrasonic time-frequency domain characteristics and electrochemical characteristics. The initial corrosion risk coefficient of the current test point is calculated according to the initial evaluation model. The weighting of ultrasonic time-frequency domain characteristics and electrochemical characteristics is determined by the comprehensive decision coefficient obtained from the environmental impact assessment value and the initial corrosion risk coefficient, and a fusion diagnostic index is generated. Based on the fusion diagnostic index, the mesh spacing of ultrasonic detection points is increased and the mesh spacing of electrochemical detection points is decreased to locate and identify the type of steel reinforcement corrosion. The reliability of a single diagnosis is determined based on the spatiotemporal consistency coefficient between the ultrasonic positioning results and the electrochemically active area. When the reliability meets the standard, the fusion diagnosis deviation value is calculated based on the accuracy deviation of the corrosion state classification, the accuracy deviation of the corrosion area positioning, and the estimation error of the corrosion degree. Based on the cumulative fusion diagnostic deviation value, the preset monitoring period and the training samples or weights of the initial evaluation model are adjusted adaptively and the initial evaluation model is retrained.

[0010] Furthermore, the process of determining the degree of environmental influence on the detection of steel reinforcement corrosion by ultrasonic and electrochemical methods based on the environmental interference coefficient and calculating the environmental impact assessment value includes, Acquire the environmental noise signal of the environment where the reinforcing steel is located and the surface impedance of the concrete layer; Calculate the environmental noise interference component and the concrete impedance interference component; The environmental interference coefficient is obtained by calculating the weighted sum of the environmental noise interference component and the concrete impedance interference component. The environmental interference coefficient is compared with the preset environmental interference coefficient; The environmental interference coefficient is determined to be less than or equal to a preset environmental interference coefficient, indicating a low level of environmental impact on the detection. The ratio of the environmental interference coefficient to the preset environmental interference coefficient is then used as the first environmental impact assessment value.

[0011] Furthermore, the process of determining the degree of environmental influence on the detection of steel reinforcement corrosion by ultrasonic and electrochemical methods based on the environmental interference coefficient and calculating the environmental impact assessment value also includes, Based on the fact that the environmental interference coefficient is greater than the preset environmental interference coefficient, the degree of influence of the environment on the detection is determined to be high, and the ratio of the environmental interference coefficient to the preset environmental interference coefficient is used to determine the second environmental impact assessment value.

[0012] Furthermore, the process of determining the weight allocation of ultrasonic time-frequency domain characteristics and electrochemical characteristics and generating a fusion diagnostic index based on the comprehensive decision coefficient calculated according to the environmental impact assessment value and the initial corrosion risk coefficient includes, Obtain the environmental impact assessment value and calculate the environmental impact compensation coefficient; The comprehensive decision coefficient is obtained by multiplying the initial corrosion risk coefficient and the environmental impact compensation coefficient. The comprehensive decision coefficient is compared with the first preset comprehensive decision coefficient and the second preset comprehensive decision coefficient; Based on the fact that the comprehensive decision coefficient is less than or equal to the first preset comprehensive decision coefficient, a conservative weight allocation strategy is adopted. Based on the fact that the comprehensive decision coefficient is greater than the first preset comprehensive decision coefficient and less than or equal to the second preset comprehensive decision coefficient, a balanced weight allocation strategy is adopted. Based on the fact that the comprehensive decision coefficient is greater than the second preset comprehensive decision coefficient, a sensitive weight allocation strategy is adopted.

[0013] Furthermore, the process of determining to increase the mesh spacing of ultrasonic detection points and decrease the mesh spacing of electrochemical detection points based on the fusion diagnostic index to locate and identify the type of steel reinforcement corrosion includes, Obtain the percentage of ultrasonic energy and wavelet energy entropy in the time-frequency domain features of the ultrasonic wave; Obtain the corrosion current density and polarization resistance from the electrochemical characteristics; The ultrasonic energy percentage, the wavelet energy entropy, the corrosion current density, and the polarization resistance were normalized. The fusion diagnostic index was calculated. The fusion diagnostic index is compared with the first preset fusion diagnostic index and the second preset fusion diagnostic index; Based on the fusion diagnostic index being less than or equal to the first preset fusion diagnostic index, the algorithm determines to increase the mesh spacing of the ultrasonic detection points and decrease the mesh spacing of the electrochemical detection points.

[0014] Furthermore, the process of determining to increase the mesh spacing of ultrasonic detection points and decrease the mesh spacing of electrochemical detection points based on the fusion diagnostic index for locating steel corrosion and identifying the type of steel corrosion also includes, Based on the fusion diagnostic index being greater than the first preset fusion diagnostic index and less than or equal to the second preset fusion diagnostic index, it is determined that ultrasonic and electrochemical synergistic detection will be performed.

[0015] Furthermore, the process of determining to increase the mesh spacing of ultrasonic detection points and decrease the mesh spacing of electrochemical detection points based on the fusion diagnostic index for locating steel corrosion and identifying the type of steel corrosion also includes, Based on the fact that the fusion diagnostic index is greater than the second preset fusion diagnostic index, it is determined to increase the grid spacing of the electrochemical detection points and decrease the grid spacing of the ultrasonic detection points.

[0016] Furthermore, the process of determining the reliability of a single diagnosis based on the spatiotemporal consistency coefficient between the ultrasonic positioning results and the electrochemically active area, and calculating the fusion diagnosis deviation value based on the corrosion state classification accuracy deviation, corrosion area positioning accuracy deviation, and corrosion degree estimation error when the reliability meets the standard, includes the following steps: Extract all identified corroded areas from the ultrasonic testing data to form a set of spatial coordinates; The spatial coordinates of all highly active regions are derived from the electrochemical detection data. Calculate the number of coordinate points in the intersection and union of two sets of spatial coordinates; The spatiotemporal consistency coefficient is obtained by calculating the ratio of the number of coordinate points in the intersection set to the number of coordinate points in the union set. The reliability of the fusion diagnosis result is determined based on the spatiotemporal consistency coefficient being greater than or equal to the preset coefficient. At this point, the fusion diagnosis deviation value is calculated.

[0017] Furthermore, the process of determining the duration of the preset monitoring period and adaptively adjusting the training samples or weights of the initial evaluation model based on the cumulative fusion diagnostic deviation value, and then retraining the initial evaluation model, includes the following steps: The cumulative fusion diagnostic deviation value is obtained by calculating the arithmetic mean of several historical fusion diagnostic deviation values. The cumulative fusion diagnostic deviation value is compared with the preset cumulative fusion diagnostic deviation value; Based on the cumulative fusion diagnostic deviation value being less than or equal to a preset cumulative fusion diagnostic deviation value, the duration of the preset monitoring period and the training samples of the initial evaluation model are determined to be increased synchronously.

[0018] Furthermore, the process of determining the duration of the preset monitoring period and adaptively adjusting the training samples or weights of the initial evaluation model based on the cumulative fusion diagnostic deviation value, and then retraining the initial evaluation model, also includes... Based on the fact that the cumulative fusion diagnostic deviation value is greater than the preset cumulative fusion diagnostic deviation value, the weights are adaptively adjusted and the initial evaluation model is retrained.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: by constructing a complete technical system encompassing environmental perception, multimodal fusion, adaptive decision-making, and self-optimization, this invention achieves intelligent diagnosis of steel reinforcement corrosion throughout its entire lifecycle, from early warning to precise quantitative analysis. First, by quantifying the impact of environmental noise and concrete impedance, an environmental interference coefficient and evaluation value are introduced, solving the industry problem of environmental factor interference in detection accuracy. Second, by deeply integrating environmental assessment results with a multimodal signal fusion model based on random forests, and dynamically adjusting the weight allocation of ultrasonic and electrochemical features through comprehensive decision coefficients, intelligent adaptation of the detection strategy is achieved. Furthermore, a three-level refined detection strategy based on a fusion diagnostic index can adaptively adjust the grid scanning density, effectively balancing detection efficiency and accuracy, and achieving seamless switching from macroscopic screening to microscopic localization. Finally, spatiotemporal consistency verification ensures the reliability of single-diagnosis, and cumulative deviation analysis is used to collaboratively optimize the environmental assessment model and the corrosion diagnosis model, enabling the system to continuously evolve. This method effectively overcomes the limitations of single detection technologies, enabling non-destructive and accurate diagnosis and early warning of steel corrosion throughout its entire lifecycle, from early initiation to severe development. It significantly improves the detection accuracy, robustness, and practicality in complex engineering environments, providing a reliable technical means for the durability assessment of concrete structures.

[0020] Furthermore, by comprehensively considering the environmental noise signal-to-noise ratio and the relative deviation of concrete impedance, this invention transforms ambiguous environmental factors into accurately calculable objective indicators, providing a reliable data foundation for detection decisions. This significantly improves the system's environmental robustness and adaptability, enabling it to automatically identify environmental conditions and trigger corresponding detection strategies based on quantitative assessment results. This effectively reduces misjudgments and missed detections caused by environmental factors, ensuring the consistency and reliability of detection results under different working conditions. It provides key inputs for accurate correction and optimization of detection results, improves feature quality from the data source, lays a solid foundation for generating accurate corrosion risk assessments, and ultimately achieves a leap in overall detection accuracy.

[0021] Furthermore, this invention constructs an initial evaluation model based on random forest multimodal signal fusion and utilizes the time-frequency domain sensitivity characteristics of ultrasound combined with electrochemical activity parameters to achieve extremely high detection sensitivity for the passivation state of steel bars and extremely weak early uniform corrosion, thus solving the technical problem of the insensitivity of traditional ultrasonic methods to early corrosion.

[0022] Furthermore, this invention, through a dynamic weight allocation strategy based on the initial corrosion risk coefficient, can intelligently adjust the contribution of ultrasonic and electrochemical signals at different corrosion stages. It relies on electrochemical detection in low-risk stages and on ultrasonic detection in high-risk stages, which not only ensures diagnostic accuracy but also achieves optimal allocation of detection resources, avoids unnecessary redundant detection, and thus significantly improves overall detection efficiency.

[0023] Furthermore, this invention, through a refined detection strategy based on a fusion diagnostic index, can adaptively adjust the grid scanning density, achieving a seamless switch from macroscopic large-scale screening to precise microscopic damage localization. It can also effectively distinguish between different types of corrosion, such as uniform corrosion, localized corrosion, and pitting corrosion, providing more accurate data support for structural durability assessment and repair decisions.

[0024] Furthermore, by calculating the spatiotemporal consistency coefficient between the ultrasonic positioning results and the electrochemically active region, and by quantitatively evaluating the diagnostic reliability, this invention establishes an effective internal cross-validation mechanism that can automatically identify and filter out unreliable diagnostic results, ensuring the data quality for subsequent model optimization and decision analysis, and greatly improving the robustness of the system.

[0025] Furthermore, through a model optimization mechanism based on cumulative fusion diagnostic deviation values, this invention enables the initial evaluation model to be periodically optimized using continuously accumulated and validated field data during long-term use. This allows the diagnostic system to have the ability to learn on its own and continuously improve its performance, adapt to different engineering environments and material properties, and extend the effective life cycle of the technology. Attached Figure Description

[0026] Figure 1 This is a step diagram of the non-destructive testing method for steel corrosion integrating ultrasonic and electrochemical signals according to an embodiment of the present invention; Figure 2 This is a logic block diagram of an embodiment of the present invention, which determines the degree of influence of the environment on the detection of steel corrosion by ultrasonic and electrochemical methods based on the environmental interference coefficient. Figure 3 This is a logic block diagram of the weighting strategy for ultrasonic time-frequency domain features and electrochemical features based on the initial corrosion risk coefficient in an embodiment of the present invention. Figure 4This is a logic block diagram of how the present invention determines a refined detection strategy based on a fusion diagnostic index. Figure 5 This is a logic block diagram illustrating how the reliability of the current diagnosis is determined based on the spatiotemporal consistency coefficient in an embodiment of the present invention. Figure 6 This is a block diagram illustrating the logic for optimizing the initial evaluation model based on the cumulative fusion diagnostic deviation value, as described in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0030] Please see Figure 1 The diagram shown illustrates the steps of a non-destructive testing method for steel reinforcement corrosion that integrates ultrasonic and electrochemical signals, as described in an embodiment of the present invention.

[0031] This invention provides a non-destructive testing method for steel reinforcement corrosion that integrates ultrasonic and electrochemical signals, comprising: Step S1: Obtain historical steel corrosion sample data and environmental parameters of the steel bars within the preset monitoring period. Determine the degree of influence of the environment on the detection of steel corrosion by ultrasonic and electrochemical methods based on the environmental interference coefficient obtained from the environmental noise signal and the concrete surface impedance, and calculate the environmental impact assessment value. Step S2: Construct an initial assessment model for steel corrosion status based on the historical steel corrosion sample database using multimodal signal fusion; Step S3: Real-time synchronous acquisition of the current ultrasonic and electrochemical signals of the steel bar to be tested, and real-time processing to obtain the ultrasonic time-frequency domain characteristics and electrochemical characteristics. The initial corrosion risk coefficient of the current test point is calculated according to the initial evaluation model. Step S4: Generate a comprehensive decision coefficient based on the environmental impact assessment value and the initial corrosion risk coefficient; determine the weighting strategy for ultrasonic time-frequency domain characteristics and electrochemical characteristics based on the comprehensive decision coefficient and generate a fusion diagnostic index. Step S5: Based on the fusion diagnostic index, determine to increase the mesh spacing of ultrasonic detection points and decrease the mesh spacing of electrochemical detection points to locate steel corrosion and identify the type of steel corrosion. Step S6: Determine the reliability of a single diagnosis based on the spatiotemporal consistency coefficient between the ultrasonic positioning results and the electrochemically active area. When the reliability meets the standard, calculate the fusion diagnosis deviation value based on the accuracy deviation of the corrosion state classification, the positioning accuracy deviation of the corrosion area, and the estimation error of the corrosion degree. Step S7: Based on the cumulative fusion diagnostic deviation value, determine the synchronous increase of the preset monitoring period and the training samples or weights of the initial evaluation model, adaptively adjust them, and retrain the initial evaluation model.

[0032] Specifically, this invention achieves intelligent diagnosis of steel reinforcement corrosion throughout its entire lifecycle, from early warning to precise quantitative analysis, by constructing a complete technical system encompassing environmental perception, multimodal fusion, adaptive decision-making, and self-optimization. First, by quantifying the impact of environmental noise and concrete impedance, an environmental interference coefficient and evaluation value are introduced, solving the industry challenge of accurate detection due to environmental interference. Second, the environmental assessment results are deeply integrated with a multimodal signal fusion model based on random forests. By dynamically adjusting the weight allocation of ultrasonic and electrochemical features through comprehensive decision coefficients, intelligent adaptation of the detection strategy is achieved. Furthermore, a three-level refined detection strategy based on a fusion diagnostic index can adaptively adjust the grid scanning density, effectively balancing detection efficiency and accuracy, and achieving seamless switching from macroscopic screening to microscopic localization. Finally, spatiotemporal consistency verification ensures the reliability of single-diagnosis, and cumulative deviation analysis is used to collaboratively optimize the environmental assessment model and the corrosion diagnosis model, enabling the system to continuously evolve. This method effectively overcomes the limitations of single detection technologies, enabling non-destructive and accurate diagnosis and early warning of steel corrosion throughout its entire lifecycle, from early initiation to severe development. It significantly improves the detection accuracy, robustness, and practicality in complex engineering environments, providing a reliable technical means for the durability assessment of concrete structures.

[0033] In this embodiment of the invention, the environmental parameters obtained include environmental noise signals and concrete surface impedance.

[0034] In this embodiment of the invention, the preset monitoring period is 5 min-15 min, and preferably 10 min.

[0035] Please see Figure 2As shown, it is a logic block diagram of an embodiment of the present invention for determining the degree of influence of the environment on the detection of steel corrosion by ultrasonic and electrochemical methods based on the environmental interference coefficient.

[0036] Specifically, the environmental interference coefficient is calculated based on the environmental noise signal and the surface impedance of the concrete layer. The degree of environmental influence on ultrasonic and electrochemical detection is determined by comparing the environmental interference coefficient with a preset interference coefficient, and an environmental impact assessment value is calculated. If the environmental interference coefficient is less than or equal to the preset environmental interference coefficient, then the degree of influence of the environment on the detection is determined to be low, and the first environmental impact assessment value is determined at this time. If the environmental interference coefficient is greater than the preset environmental interference coefficient, then the degree of influence of the environment on the detection is determined to be high, and at this time, the second environmental impact assessment value is determined.

[0037] In this embodiment of the invention, the environmental interference coefficient is calculated by weighting the environmental noise interference component and the concrete impedance interference component. The environmental noise interference component is obtained by the reciprocal of the signal-to-noise ratio of the environmental noise signal collected on site, and the concrete impedance interference component is obtained by the relative deviation between the measured value of the concrete surface impedance and the reference impedance value.

[0038] The method for acquiring the environmental noise signal is as follows: multiple acoustic sensors are arranged around the detection site, and a continuous environmental noise time-domain signal is acquired without active ultrasonic excitation. The signal is then subjected to a fast Fourier transform to obtain its frequency domain distribution. The ratio of the signal power in the main ultrasonic working frequency band to the signal power in the entire frequency band is calculated, and the reciprocal of this ratio is taken as the environmental noise interference component.

[0039] The method for measuring the surface impedance of concrete is as follows: using the four-electrode method, four equally spaced electrodes are arranged on the concrete surface. A constant alternating current signal is applied through the two outer electrodes, and the voltage drop generated is measured through the two inner electrodes. The surface impedance value of the concrete is calculated according to Ohm's law. The relative deviation is obtained by first calculating the absolute difference between the measured impedance value and the reference impedance value, and then calculating the ratio of the absolute difference to the reference impedance value.

[0040] In this embodiment of the invention, the reference impedance value is the impedance value measured by concrete specimens of the same mix proportion under standard curing conditions in the laboratory at the same age. Its value range is 50Ω·m-200Ω·m. The preferred value in this invention is 100Ω·m. The preferred value range and preferred value can be determined according to the actual situation and are not specifically limited here.

[0041] In this embodiment of the invention, the weighting coefficient of the environmental noise interference component ranges from 0.5 to 0.7, and the weighting coefficient of the concrete impedance interference component ranges from 0.3 to 0.5, with the sum of the two weighting coefficients being 1. Preferably, the weight of the environmental noise interference component is 0.6, and the weight of the concrete impedance interference component is 0.4. The preferred range and values ​​can be determined based on actual conditions and are not specifically limited here.

[0042] In this embodiment of the invention, the preset environmental interference coefficient ranges from 0.3 to 0.5, and the preferred value is 0.4. The preferred range and preferred value can be determined according to the actual detection accuracy requirements, and are not specifically limited here.

[0043] In this embodiment of the invention, the first environmental impact assessment value is the ratio of the first environmental interference coefficient to the preset environmental interference coefficient under the condition that the environmental interference coefficient is less than or equal to the preset environmental interference coefficient. In this embodiment of the invention, the second environmental impact assessment value is the ratio of the second environmental interference coefficient to the preset interference coefficient under the condition that the environmental interference coefficient is greater than the preset environmental interference coefficient.

[0044] Specifically, this invention transforms ambiguous environmental factors into precisely calculable objective indicators by comprehensively considering the signal-to-noise ratio of environmental noise and the relative deviation of concrete impedance. This provides a reliable data foundation for detection decisions, significantly improving the system's environmental robustness and adaptability. It can automatically identify environmental conditions and trigger corresponding detection strategies based on quantitative assessment results, thereby effectively reducing misjudgments and missed detections caused by environmental factors. It ensures the consistency and reliability of detection results under different working conditions, providing key inputs for accurate correction and optimization of detection results. It improves feature quality from the data source, lays a solid foundation for generating accurate corrosion risk assessments, and ultimately achieves a leap in overall detection accuracy.

[0045] In this embodiment of the invention, step S2 constructs an initial assessment model for the corrosion state of steel bars based on a historical steel bar corrosion sample database, which is composed of ultrasonic time-frequency domain features and electrochemical features of steel bars in known corrosion states, as well as corresponding actual corrosion state labels. The actual corrosion state labels are three corrosion states: passivation, initial corrosion, and active corrosion. In practical applications, a random forest classifier is selected as the initial assessment model. The ultrasonic time-frequency domain features and electrochemical features of each sample in the historical steel bar corrosion sample database are used as the training set and input into the random forest classifier for training. The corresponding output labels are used as the training targets, and the output results are the probability distributions of the three corrosion states.

[0046] In this embodiment of the invention, after constructing an initial assessment model for the corrosion state of steel bars based on multimodal signal fusion, the current ultrasonic and electrochemical signals of the part to be tested are acquired synchronously in real time. The ultrasonic signals are preprocessed and then decomposed into four layers of wavelet packets. The energy percentage and wavelet energy entropy of the selected frequency band are calculated as the ultrasonic time-frequency domain features. The corrosion current density and polarization resistance in the electrochemical signal are extracted as the electrochemical features. The real-time extracted ultrasonic time-frequency domain features and electrochemical features are input into the initial assessment model. The initial assessment model outputs the probability distribution of three corrosion states: passivation, initial corrosion, and active corrosion. The sum of the probabilities of initial corrosion and active corrosion is used as the initial corrosion risk coefficient.

[0047] Specifically, this invention constructs an initial evaluation model based on random forest multimodal signal fusion and utilizes the time-frequency domain sensitivity characteristics of ultrasound combined with electrochemical activity parameters to achieve extremely high detection sensitivity for the passivation state of steel bars and extremely weak early uniform corrosion, thus solving the technical problem of the insensitivity of traditional ultrasonic methods to early corrosion.

[0048] Please see Figure 3 As shown, it is a logic block diagram of the weighting strategy for ultrasonic time-frequency domain features and electrochemical features based on the initial corrosion risk coefficient in an embodiment of the present invention.

[0049] Specifically, given the calculated initial corrosion risk coefficient, a comprehensive decision coefficient is calculated by combining the environmental impact assessment value with the initial corrosion risk coefficient. Based on the comparison between the comprehensive decision coefficient and the first and second preset comprehensive decision coefficients, a weighting strategy for the ultrasonic time-frequency domain characteristics and electrochemical characteristics is determined. If the comprehensive decision coefficient is less than or equal to the first preset comprehensive decision coefficient, then a conservative weight allocation strategy is adopted, that is, the ultrasonic time-frequency domain feature weight is 0.2 and the electrochemical feature weight is 0.8. If the comprehensive decision coefficient is greater than the first preset comprehensive decision coefficient and less than or equal to the second preset comprehensive decision coefficient, then a balanced weight allocation strategy is adopted, that is, the ultrasonic time-frequency domain feature weight is 0.5 and the electrochemical feature weight is 0.5. If the comprehensive decision coefficient is greater than the second preset comprehensive decision coefficient, then a sensitive weight allocation strategy is adopted, that is, the ultrasonic time-frequency domain feature weight is 0.7 and the electrochemical feature weight is 0.3.

[0050] In this embodiment of the invention, the comprehensive decision coefficient is calculated by multiplying the initial corrosion risk coefficient and the environmental impact compensation coefficient, wherein the environmental impact compensation coefficient is calculated by the inverse function of the environmental impact assessment value.

[0051] The calculation method of the environmental impact compensation coefficient is as follows: when the environmental impact assessment value is the first environmental impact assessment value, the environmental impact compensation coefficient is 1; when the environmental impact assessment value is the second environmental impact assessment value, the environmental compensation coefficient is obtained by first calculating the sum of the value 1 and the second environmental impact assessment value, and then calculating the ratio of the value 1 to the value 2.

[0052] In this embodiment of the invention, the value range of the first preset comprehensive decision coefficient is 0.2-0.4, and the preferred value is 0.3. The value range of the second preset comprehensive decision coefficient is 0.5-0.7, and the preferred value is 0.6. The preferred value range and the preferred value can be determined according to the actual detection accuracy requirements, and are not specifically limited here.

[0053] In this embodiment of the invention, the conservative weight allocation strategy is used when the risk of steel corrosion is low and / or environmental interference is strong. At this stage, the corrosion signal is weak, and ultrasound is insensitive to early corrosion and microscopic changes at the interface, while electrochemical methods are inherently sensitive to corrosion activity. Simultaneously, strong environmental interference further reduces the reliability of the ultrasound signal. Therefore, the weight of the electrochemical characteristics is significantly increased to 0.8 to prioritize capturing early trends in corrosion activity and ensure the system's early warning sensitivity.

[0054] In this embodiment of the invention, the balanced allocation strategy is employed when the steel reinforcement corrosion has entered a defined development stage and environmental conditions are relatively stable. At this point, corrosion has triggered changes in physical properties that can be detected by ultrasound, while electrochemical activity continues to increase. Therefore, assigning equal weights to both features aims to achieve a more comprehensive assessment and more precise localization of the corrosion state through information complementarity and cross-validation, balancing the sensitivity and specificity of detection.

[0055] In this embodiment of the invention, the sensitive allocation strategy is used when the risk of steel reinforcement corrosion is high. At this stage, the steel reinforcement may have already suffered cross-sectional loss or macroscopic cracks. Ultrasonic methods have extremely high identification accuracy and spatial localization capabilities for such physical damage. Although the ultrasonic method is highly electrochemically active, its signal is easily saturated and has low spatial resolution. Therefore, increasing the ultrasonic feature weight to 0.7 aims to accurately quantify the degree of corrosion, locate the damaged area, and identify serious defects such as cracks, providing a crucial basis for structural safety assessment.

[0056] In this embodiment of the invention, after determining the weighting strategy for the ultrasonic time-frequency domain features and electrochemical features, a fusion diagnostic index is calculated. In practical applications, the ultrasonic energy percentage, wavelet energy entropy, corrosion current density, and polarization resistance are normalized by the difference between the eigenvalue and the minimum value and the ratio of the maximum value to the minimum value. The fusion diagnostic index is then calculated according to the following formula.

[0057] In the formula, These are the time-frequency domain feature weighting coefficients for ultrasound. This represents the normalized percentage of ultrasonic energy. Here, V is the normalized wavelet energy entropy, and V is the electrochemical feature weighting coefficient. This is the normalized corrosion current density. The normalized polarization resistance. , , , It is the eigenvalue contribution weight.

[0058] In this embodiment of the invention, the preferred values ​​of the feature value contribution weights α, β, γ, and δ are 0.6, 0.4, 0.7, and 0.3, respectively. The preferred values ​​of the contribution weights can be adjusted according to the actual detection effect, and are not specifically limited here.

[0059] Specifically, this invention uses a dynamic weighting strategy based on the initial corrosion risk coefficient to intelligently adjust the contribution of ultrasonic and electrochemical signals at different corrosion stages. It relies on electrochemical detection in low-risk stages and on ultrasonic detection in high-risk stages. This not only ensures diagnostic accuracy but also achieves optimal allocation of detection resources, avoids unnecessary redundant detection, and thus significantly improves overall detection efficiency.

[0060] Please see Figure 4 As shown, it is a logic block diagram of an embodiment of the present invention for determining a refined detection strategy based on a fusion diagnostic index.

[0061] Specifically, after determining the fusion diagnostic index, a refined detection strategy for locating rebar corrosion and identifying rebar corrosion types is determined based on the comparison results between the fusion diagnostic index and the first preset fusion diagnostic index and the second preset fusion diagnostic index. If the fusion diagnostic index is less than or equal to the first preset fusion diagnostic index, then the first refined detection strategy is determined; If the fusion diagnostic index is greater than the first preset fusion diagnostic index and less than or equal to the second preset fusion diagnostic index, then the second refined detection strategy is determined. If the fusion diagnostic index is greater than the second preset fusion diagnostic index, then a third refined detection strategy is determined.

[0062] In this embodiment of the invention, the value range of the first preset fusion diagnostic index is 0.3-0.5, and the preferred value is 0.4. The preferred value range and preferred value of the first preset fusion diagnostic index can be determined according to the actual situation, and are not specifically limited here.

[0063] In this embodiment of the invention, the value range of the second preset fusion diagnostic index is 0.6-0.8, and the preferred value is 0.7. The preferred value range and preferred value of the second preset fusion diagnostic index can be determined according to the actual situation, and are not specifically limited here.

[0064] In this embodiment of the invention, when the fusion diagnostic index is less than or equal to the first preset fusion diagnostic index, it indicates that the steel bar is in a passivated state or only has extremely weak uniform corrosion. Ultrasonic detection cannot detect subtle corrosion changes. At this time, the first refined detection strategy is to increase the grid spacing of ultrasonic detection points and decrease the grid spacing of electrochemical detection points. The grid spacing of ultrasonic detection points is increased to 1.5-1.7 times the original detection spacing, preferably 1.6 times, to reduce the ultrasonic detection density. The grid spacing of electrochemical detection points is decreased to 0.5-0.7 times the original detection spacing, preferably 0.6 times, to increase the electrochemical detection density. Under this strategy, the corrosion status is mainly assessed based on the electrochemical detection results. The range of abnormal steel bar corrosion activity is initially delineated by the potential contour map of the electrochemical detection points to locate the corrosion, and the corrosion type is initially determined based on the uniformity of the potential drop.

[0065] In this embodiment of the invention, when the fusion diagnostic index is greater than the first preset fusion diagnostic index and less than or equal to the second preset fusion diagnostic index, it indicates that the steel bar is in the initial to middle stage of local corrosion development. At this stage, corrosion has caused local interface peeling and changes in physical properties of the steel bar. At this time, the second refined detection strategy is an ultrasonic and electrochemical collaborative detection strategy. The grid spacing of ultrasonic detection points and the grid spacing of electrochemical detection points are both set to the standard detection spacing, that is, kept at 1.0 times the original detection spacing. The detection area is subjected to equal density and equal precision collaborative scanning and data fusion analysis. Under this strategy, the detection results of ultrasonic and electrochemical detection are comprehensively combined for joint diagnosis. The corrosion area is located by spatial superposition of the abnormal wave velocity area identified by ultrasonic and the high activity area identified by electrochemical. The corrosion type is initially distinguished according to the ultrasonic echo morphology and electrochemical activity intensity.

[0066] In this embodiment of the invention, when the fusion diagnostic index is greater than the second preset fusion diagnostic index, it indicates that the reinforcing steel is in an active and severely corroded state and may have pitting corrosion or macroscopic cracks. At this stage, the cross-section of the reinforcing steel has suffered significant loss and may be accompanied by crack propagation. Ultrasonic detection is highly sensitive to the physical morphological changes of the reinforcing steel at this stage. At this time, the third refined detection strategy is to increase the grid spacing of electrochemical detection points and decrease the grid spacing of ultrasonic detection points. The grid spacing of ultrasonic detection points is reduced to 0.5-0.7 times the original detection spacing, preferably 0.6 times, to increase the ultrasonic detection density. The grid spacing of electrochemical detection points is increased to 1.5-1.7 times the original detection spacing, preferably 1.6 times, to reduce the electrochemical detection density. Under this strategy, diagnosis is mainly based on the ultrasonic detection results. Sparse electrochemical detection points are used to verify the activity of high-risk areas. Through high-density ultrasonic scanning, the boundaries and spatial morphology of the corroded areas and crack extensions are accurately depicted, achieving precise positioning at the microscale and identifying the type of severe corrosion based on signal attenuation, spectral changes, and imaging morphology features.

[0067] Specifically, this invention, through a refined detection strategy based on a fusion diagnostic index, can adaptively adjust the grid scanning density, achieving a seamless switch from macroscopic large-scale screening to precise microscopic damage localization. It can also effectively distinguish between different types of corrosion, such as uniform corrosion, localized corrosion, and pitting corrosion, providing more accurate data support for structural durability assessment and repair decisions.

[0068] Please see Figure 5 As shown, it is a logic block diagram of determining the reliability of this diagnosis based on the spatiotemporal consistency coefficient in an embodiment of the present invention.

[0069] Specifically, after determining a refined detection strategy for locating and identifying the type of rebar corrosion, the spatiotemporal consistency coefficient between the ultrasonic positioning results and the electrochemically active area is calculated. The reliability of the diagnosis is determined based on the comparison between this spatiotemporal consistency coefficient and a preset coefficient. When the reliability meets the standard, a fusion diagnosis deviation value is calculated based on the accuracy deviation of the corrosion state classification, the accuracy deviation of the corrosion area positioning, and the estimation error of the corrosion degree. This value is denoted as the fusion diagnosis deviation value for this diagnosis. If the spatiotemporal consistency coefficient is less than the preset coefficient, the result of this fusion diagnosis is determined to be unreliable. If the spatiotemporal consistency coefficient is greater than or equal to the preset coefficient, the fusion diagnosis result is determined to be reliable. At this time, the deviation between the fusion diagnosis result and the actual situation is calculated.

[0070] In this embodiment of the invention, the preset coefficient ranges from 0.5 to 0.7, and the preferred value is 0.6. The preferred range and preferred value of the preset coefficient can be determined according to the actual situation, and are not specifically limited here.

[0071] In this embodiment of the invention, the spatial consistency coefficient is obtained by calculating the spatial overlap between the ultrasonic corrosion area and the electrochemically active area. In practical applications, all areas identified as corrosion are extracted from the ultrasonic detection data to form a spatial coordinate set, denoted as A. Each coordinate point represents a corrosion location detected by the ultrasonic wave. All highly active area points are retrieved from the electrochemical detection data to form a spatial coordinate set, denoted as B. Each coordinate point represents an abnormal corrosion activity location detected by the electrochemical wave. Subsequently, the intersection and union of coordinate sets A and B are calculated, and the number of coordinate points in the intersection and the number of coordinate points in the union are counted. Finally, the ratio of the number of coordinate points in the intersection to the number of coordinate points in the union is calculated as the spatial consistency coefficient.

[0072] In this embodiment of the invention, the deviation value of the current fusion diagnosis is obtained by comprehensively evaluating the accuracy of the corrosion status classification, the accuracy of the corrosion area positioning, and the estimation error of the corrosion degree. In practical applications, the detection area is first divided into several independent evaluation units. The fusion diagnosis result of each unit is compared with the actual corrosion state, and the number of correctly diagnosed units is counted. Then, the ratio of the number of correctly diagnosed units to the total number of units is calculated. Finally, the ratio is subtracted from 1 to obtain the corrosion state classification accuracy deviation. Next, for each correctly diagnosed corrosion unit, the Euclidean distance between the predicted corrosion area center point and the actual corrosion area center point is calculated. The Euclidean distances of all correctly diagnosed units are counted and their average value is calculated to obtain the corrosion area positioning accuracy deviation. At the same time, for each correctly diagnosed corrosion unit, the predicted corrosion degree and the actual corrosion degree are obtained, and the absolute value of the difference between the two is calculated. The absolute values ​​of all correctly diagnosed units are counted and their average value is calculated to obtain the corrosion degree estimation error. Finally, the corrosion state classification accuracy deviation, corrosion area positioning accuracy deviation, and corrosion degree estimation error are multiplied by their respective weighting coefficients and summed. The summation result is used as the fusion diagnosis deviation value.

[0073] In this embodiment of the invention, the sum of each weight coefficient is 1. It is preferred to use an equal weight setting, that is, the three weight coefficients are all one-third.

[0074] Specifically, this invention establishes an effective internal cross-validation mechanism by calculating the spatiotemporal consistency coefficient between ultrasonic positioning results and electrochemically active regions, and by quantitatively evaluating diagnostic reliability. This mechanism can automatically identify and filter out unreliable diagnostic results, ensuring the data quality for subsequent model optimization and decision analysis, and greatly improving the robustness of the system.

[0075] Please see Figure 6 As shown, it is a logic block diagram for determining the optimization of the initial evaluation model based on the cumulative fusion diagnostic deviation value in an embodiment of the present invention.

[0076] Specifically, after determining the current fusion diagnostic deviation value, the calculation process for the environmental impact coefficient is determined based on the comparison between the cumulative fusion diagnostic deviation value from several historical tests and the preset cumulative fusion diagnostic deviation value. This process is then collaboratively optimized with the initial assessment model. If the cumulative fusion diagnostic deviation value is less than or equal to the preset cumulative fusion diagnostic deviation value, then the first collaborative optimization strategy is determined to be executed. If the cumulative fusion diagnostic deviation value is greater than the preset cumulative fusion diagnostic deviation value, then the second collaborative optimization strategy is determined.

[0077] In this embodiment of the invention, the preset cumulative fusion diagnostic deviation value is obtained by extracting the cumulative fusion diagnostic deviation values ​​calculated during the stable performance phase of the system within at least 50 historical detection cycles, and taking the median of all values ​​as the preset cumulative fusion diagnostic deviation value. The preferred value range is 0.15-0.25, and the preferred value in this invention is 0.2. The preferred value range and preferred value of the preset cumulative fusion diagnostic deviation value can be determined according to the actual situation, and are not specifically limited here.

[0078] In this embodiment of the invention, the cumulative fusion diagnostic deviation value is calculated by statistically analyzing several consecutive diagnostic results of steel reinforcement corrosion in history, and each fusion diagnostic result involved in the calculation is reliable. In practical application, firstly, several current fusion diagnostic deviation values ​​corresponding to several consecutive reliable fusion diagnostic results are obtained. Then, the arithmetic mean of several current fusion diagnostic deviation values ​​is calculated, and the arithmetic mean is used as the cumulative fusion diagnostic deviation value.

[0079] In this embodiment of the invention, the first collaborative optimization strategy is to simultaneously increase the duration of the preset monitoring period and the training samples of the initial evaluation model. The duration of the preset monitoring period is increased from the original 10 min to 15 min, reducing the impact of random fluctuations in environmental noise signals and improving the stability of concrete surface impedance measurements, thereby improving the accuracy of environmental interference coefficient calculation. Several new detection cases used to calculate the cumulative fusion diagnostic deviation value and their corresponding actual corrosion status labels are used as new training samples to perform an incremental training on the initial evaluation model with a small learning rate to fine-tune the model parameters, thereby improving the accuracy of the initial corrosion risk coefficient obtained through the initial evaluation model and further making the weight allocation strategy of ultrasonic time-frequency domain features and electrochemical features more precise.

[0080] In this embodiment of the invention, the second collaborative optimization strategy involves adaptively adjusting the weights and retraining the initial evaluation model. Historical detection data is analyzed, and the correlation coefficients between the environmental noise interference component, the concrete impedance interference component, and the current fusion diagnostic deviation value are calculated. Based on the relative magnitude of these correlation coefficients, their weights in the calculation of the environmental interference coefficient are adjusted, giving greater weight to the interference component with higher correlation to the diagnostic deviation. This ensures that the environmental interference coefficient more effectively reflects the actual impact on diagnostic accuracy. Simultaneously, the historical training database is merged with several new detection cases used to calculate the cumulative fusion diagnostic deviation value to form a completely new expanded training set. This expanded training set is then used to completely retrain the initial evaluation model to generate a new evaluation model.

[0081] The correlation coefficients are obtained by first calculating the covariance between the environmental noise interference component and the fusion diagnostic deviation value, and the covariance between the concrete impedance interference component and the fusion diagnostic deviation value, respectively. Then, the standard deviations of the environmental noise interference component, the concrete impedance interference component, and the fusion diagnostic deviation value are calculated, respectively. Finally, the correlation coefficient of the environmental noise interference component is obtained by dividing the covariance of the environmental noise interference component and the fusion diagnostic deviation value by the product of the standard deviations of these two parameters. Similarly, the correlation coefficient of the concrete impedance interference component is obtained by dividing the covariance of the concrete impedance interference component and the fusion diagnostic deviation value by the product of the standard deviations of these two parameters. Finally, two correlation coefficients are obtained, which respectively characterize the degree of influence of environmental noise and concrete impedance on the diagnostic deviation.

[0082] Specifically, this invention, through a model optimization mechanism based on cumulative fusion diagnostic deviation values, can periodically optimize the initial evaluation model using continuously accumulated and validated field data during long-term use. This enables the diagnostic system to have the ability to learn on its own and continuously improve its performance, adapt to different engineering environments and material properties, and extend the effective life cycle of the technology.

[0083] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A non-destructive testing method for steel reinforcement corrosion integrating ultrasonic and electrochemical signals, characterized in that, include: Acquire historical steel corrosion sample data and environmental parameters of the steel bars within the preset monitoring period. Determine the degree of environmental influence on the detection of steel corrosion by ultrasonic and electrochemical methods based on the environmental noise signal and the surface impedance of concrete. Calculate the environmental impact assessment value. An initial assessment model for steel corrosion status is constructed based on the historical steel corrosion sample data, using multimodal signal fusion. The ultrasonic and electrochemical signals of the steel bar to be tested are collected in real time and processed in real time to obtain the ultrasonic time-frequency domain characteristics and electrochemical characteristics. The initial corrosion risk coefficient of the current test point is calculated according to the initial evaluation model. The weighting of ultrasonic time-frequency domain characteristics and electrochemical characteristics is determined by the comprehensive decision coefficient obtained from the environmental impact assessment value and the initial corrosion risk coefficient, and a fusion diagnostic index is generated. Based on the fusion diagnostic index, the mesh spacing of ultrasonic detection points is increased and the mesh spacing of electrochemical detection points is decreased to locate and identify the type of steel reinforcement corrosion. The reliability of a single diagnosis is determined based on the spatiotemporal consistency coefficient between the ultrasonic positioning results and the electrochemically active area. When the reliability meets the standard, the fusion diagnosis deviation value is calculated based on the accuracy deviation of the corrosion state classification, the accuracy deviation of the corrosion area positioning, and the estimation error of the corrosion degree. Based on the cumulative fusion diagnostic deviation value, the preset monitoring period and the training samples or weights of the initial evaluation model are adjusted adaptively and the initial evaluation model is retrained.

2. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 1, characterized in that, The process of determining the degree of environmental influence on the detection of steel reinforcement corrosion by ultrasonic and electrochemical methods based on the environmental interference coefficient and calculating the environmental impact assessment value includes the following steps: Acquire the environmental noise signal of the environment where the reinforcing steel is located and the surface impedance of the concrete layer; Calculate the environmental noise interference component and the concrete impedance interference component; The environmental interference coefficient is obtained by calculating the weighted sum of the environmental noise interference component and the concrete impedance interference component. The environmental interference coefficient is compared with the preset environmental interference coefficient; The environmental interference coefficient is determined to be less than or equal to a preset environmental interference coefficient, indicating a low level of environmental impact on the detection. The ratio of the environmental interference coefficient to the preset environmental interference coefficient is then used as the first environmental impact assessment value.

3. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 2, characterized in that, The process of determining the degree of influence of the environment on the detection of steel corrosion by ultrasonic and electrochemical methods based on the environmental interference coefficient and calculating the environmental impact assessment value also includes... Based on the fact that the environmental interference coefficient is greater than the preset environmental interference coefficient, the degree of influence of the environment on the detection is determined to be high, and the ratio of the environmental interference coefficient to the preset environmental interference coefficient is used to determine the second environmental impact assessment value.

4. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 3, characterized in that, The process of determining the weight allocation of ultrasonic time-frequency domain characteristics and electrochemical characteristics and generating a fusion diagnostic index based on the comprehensive decision coefficient calculated according to the environmental impact assessment value and the initial corrosion risk coefficient includes: Obtain the environmental impact assessment value and calculate the environmental impact compensation coefficient; The comprehensive decision coefficient is obtained by multiplying the initial corrosion risk coefficient and the environmental impact compensation coefficient. The comprehensive decision coefficient is compared with the first preset comprehensive decision coefficient and the second preset comprehensive decision coefficient; Based on the fact that the comprehensive decision coefficient is less than or equal to the first preset comprehensive decision coefficient, a conservative weight allocation strategy is adopted. Based on the fact that the comprehensive decision coefficient is greater than the first preset comprehensive decision coefficient and less than or equal to the second preset comprehensive decision coefficient, a balanced weight allocation strategy is adopted. Based on the fact that the comprehensive decision coefficient is greater than the second preset comprehensive decision coefficient, a sensitive weight allocation strategy is adopted.

5. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 4, characterized in that, The process of determining, based on the fusion diagnostic index, to increase the mesh spacing of ultrasonic detection points and decrease the mesh spacing of electrochemical detection points for locating and identifying the type of steel reinforcement corrosion includes: Obtain the percentage of ultrasonic energy and wavelet energy entropy in the time-frequency domain features of the ultrasonic wave; Obtain the corrosion current density and polarization resistance from the electrochemical characteristics; The ultrasonic energy percentage, the wavelet energy entropy, the corrosion current density, and the polarization resistance were normalized. The fusion diagnostic index was calculated. The fusion diagnostic index is compared with the first preset fusion diagnostic index and the second preset fusion diagnostic index; Based on the fusion diagnostic index being less than or equal to the first preset fusion diagnostic index, the algorithm determines to increase the mesh spacing of the ultrasonic detection points and decrease the mesh spacing of the electrochemical detection points.

6. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 5, characterized in that, The process of determining the increase of ultrasonic detection point grid spacing and the decrease of electrochemical detection point grid spacing based on the fusion diagnostic index for locating steel corrosion and identifying steel corrosion type also includes... Based on the fusion diagnostic index being greater than the first preset fusion diagnostic index and less than or equal to the second preset fusion diagnostic index, it is determined that ultrasonic and electrochemical synergistic detection will be performed.

7. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 6, characterized in that, The process of determining the increase of ultrasonic detection point grid spacing and the decrease of electrochemical detection point grid spacing based on the fusion diagnostic index for locating steel corrosion and identifying steel corrosion type also includes... Based on the fact that the fusion diagnostic index is greater than the second preset fusion diagnostic index, it is determined to increase the grid spacing of the electrochemical detection points and decrease the grid spacing of the ultrasonic detection points.

8. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 7, characterized in that, The process of determining the reliability of a single diagnosis based on the spatiotemporal consistency coefficient between the ultrasonic positioning results and the electrochemically active area, and calculating the fusion diagnosis deviation value based on the corrosion state classification accuracy deviation, corrosion area positioning accuracy deviation, and corrosion degree estimation error when the reliability meets the standard, includes the following steps: Extract all identified corroded areas from the ultrasonic testing data to form a set of spatial coordinates; The spatial coordinates of all highly active regions are derived from the electrochemical detection data. Calculate the number of coordinate points in the intersection and union of two sets of spatial coordinates; The spatiotemporal consistency coefficient is obtained by calculating the ratio of the number of coordinate points in the intersection set to the number of coordinate points in the union set. The reliability of the fusion diagnosis result is determined based on the spatiotemporal consistency coefficient being greater than or equal to the preset coefficient. At this point, the fusion diagnosis deviation value is calculated.

9. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 8, characterized in that, The process of determining the duration of the preset monitoring period and adaptively adjusting the training samples or weights of the initial evaluation model based on the cumulative fusion diagnostic deviation value, and then retraining the initial evaluation model, includes... The cumulative fusion diagnostic deviation value is obtained by calculating the arithmetic mean of several historical fusion diagnostic deviation values. The cumulative fusion diagnostic deviation value is compared with the preset cumulative fusion diagnostic deviation value; Based on the cumulative fusion diagnostic deviation value being less than or equal to a preset cumulative fusion diagnostic deviation value, the duration of the preset monitoring period and the training samples of the initial evaluation model are determined to be increased synchronously.

10. The non-destructive testing method for reinforcing steel corrosion integrating ultrasonic and electrochemical signals according to claim 9, characterized in that, The process of determining the duration of the preset monitoring period and adaptively adjusting the training samples or weights of the initial evaluation model based on the cumulative fusion diagnostic deviation value, and then retraining the initial evaluation model, further includes... Based on the fact that the cumulative fusion diagnostic deviation value is greater than the preset cumulative fusion diagnostic deviation value, the weights are adaptively adjusted and the initial evaluation model is retrained.

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