Intelligent environment-adaptive steel bar corrosion detection system and method

Abstract

The invention discloses an intelligent environment-adaptive steel bar corrosion detection system and method.The intelligent environment-adaptive steel bar corrosion detection system is provided with a sensor module, and the sensor module comprises an electrochemical impedance spectroscopy unit, an ultrasonic detection unit and a temperature and humidity sensing unit; the electrochemical impedance spectroscopy unit adopts a three-electrode system, a working electrode is a platinum wire electrode, a reference electrode is a saturated calomel electrode, and an auxiliary electrode is a graphite electrode; the signal processing module is in signal transmission connection with the sensor module and comprises an analog-to-digital converter, a digital signal processor and a memory; a wavelet transform module, a principal component analysis module and a deep learning reasoning module are arranged in the digital signal processor. The method quantitatively evaluates the corrosion rate of the steel bar, qualitatively analyzes the interface stripping condition and predicts the development trend, is suitable for health monitoring of infrastructures such as bridges and tunnels, can improve the detection precision and environmental adaptability under complex working conditions, and provides an efficient solution for safety and durability evaluation of a reinforced concrete structure.

Description

Technical Field

[0001] This invention belongs to the field of building engineering and intelligent detection technology, specifically an intelligent environmentally adaptable steel corrosion detection system and method. Background Technology

[0002] With the widespread application of reinforced concrete structures in infrastructure, steel corrosion has become a significant factor affecting structural safety and durability. To address this issue, existing technologies have proposed various methods and devices for steel corrosion detection, aiming to achieve efficient and accurate non-destructive testing. However, these technologies still exhibit certain limitations in terms of environmental adaptability, intelligence level, and detection accuracy, making it difficult to fully meet the testing needs under complex working conditions.

[0003] A search revealed a patent document, CN109884178B, which discloses a device for collecting information on steel reinforcement corrosion in concrete structures and a method for detecting steel reinforcement corrosion, published on September 2, 2022. This patent utilizes a combination of magnetic sensor components and displacement components to detect the corrosion of steel reinforcement inside concrete, and the use of two magnetic sensors improves the detection effect and accuracy. However, this technical solution exhibits weak adaptability in complex environments (such as humid, underwater, or areas with strong electromagnetic interference), and environmental factors may lead to data distortion or equipment malfunction. Furthermore, the device lacks intelligent data processing capabilities, making it difficult to achieve real-time analysis and feedback of detection results, thus limiting its application in modern detection scenarios.

[0004] A search revealed a patent document, CN113740342B, which discloses an intelligent detection device for rebar corrosion rate, published on July 2, 2024. This patent combines a camera with a classification convolutional neural network to achieve rapid determination of rebar corrosion rate, possessing high accuracy and intelligence. However, this technical solution relies heavily on image recognition technology, requiring high visibility of the rebar surface. When the rebar is covered by concrete or heavily contaminated, the detection effect may significantly decrease. Furthermore, the device is sensitive to lighting conditions and shooting angles, potentially leading to unstable detection results, especially in complex environments where consistent accuracy is difficult to guarantee.

[0005] The aforementioned problems indicate that existing steel reinforcement corrosion detection technologies still have room for improvement in terms of adaptability to complex environments, universality of detection scenarios, and level of intelligence. Therefore, this invention provides an intelligent, environmentally adaptable steel reinforcement corrosion detection system. This system aims to achieve comprehensive and accurate detection of steel reinforcement corrosion by integrating multi-source sensor data, optimizing environmental adaptability design, and introducing advanced intelligent algorithms, thereby better meeting the demand for efficient and reliable detection systems under complex working conditions. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to build an intelligent environmentally adaptable steel corrosion detection system and method by integrating multi-source sensor data, optimizing environmental adaptability design and introducing advanced intelligent algorithms, so as to overcome the shortcomings of the existing technology in terms of environmental adaptability, universality of detection scenarios and level of intelligence.

[0007] To solve the above technical problems, the present invention provides the following technical solution: A smart, environmentally adaptable steel reinforcement corrosion detection system, comprising: The sensor module includes an electrochemical impedance spectroscopy unit, an ultrasonic detection unit, and a temperature and humidity sensing unit. The electrochemical impedance spectroscopy unit adopts a three-electrode system, wherein the working electrode is a platinum wire electrode, the reference electrode is a saturated calomel electrode, and the auxiliary electrode is a graphite electrode. A signal processing module connected to the sensor module for signal transmission includes an analog-to-digital converter, a digital signal processor, and a memory; the digital signal processor has a built-in wavelet transform module, a principal component analysis module, and a deep learning inference module.

[0008] Optionally, the electrochemical impedance spectroscopy unit, ultrasonic detection unit, and temperature and humidity sensing unit in the sensor module are connected via a flexible circuit board. The probe of the ultrasonic detection unit is made of piezoelectric ceramic material with a center frequency of 500kHz; the sensitive element of the temperature and humidity sensing unit is based on MEMS technology.

[0009] Optionally, the analog-to-digital converter has a resolution of 24 bits and a sampling rate of 100kSPS; the core chip of the digital signal processor is a microcontroller based on the ARM Cortex-M7 architecture; and the memory uses industrial-grade eMMC memory chips. The input terminal of the analog-to-digital converter is connected to the output terminal of the sensor module via a low-noise amplifier.

[0010] A portable detector integrates any of the intelligent environmentally adaptable steel corrosion detection systems described in this invention, and also includes a power management module, a display screen, and a user interface.

[0011] Optionally, the power management module is powered by a lithium-ion battery, the display screen is a 3.5-inch TFT LCD screen, and the user interface supports touch operation. The portable detector has a waterproof and dustproof casing.

[0012] A method for assessing steel corrosion using any of the intelligent environmentally adaptable steel corrosion detection systems described in this invention includes the following steps: The sensor module was fixed to the surface of the reinforced concrete structure to be tested. After the system was started, electrochemical impedance spectroscopy data, ultrasonic reflection data and ambient temperature and humidity data were collected in sequence. After data acquisition, the data is preprocessed, including removing high-frequency noise, correcting temperature drift, and normalizing. Then, key characteristic parameters are extracted, including the low-frequency impedance value of the electrochemical impedance spectrum, the peak position of the ultrasonic reflection signal, and the fluctuation range of temperature and humidity. Finally, based on a deep learning model, the feature parameters are comprehensively analyzed to generate a quantitative assessment result of the steel corrosion rate and a prediction report of the corrosion development trend.

[0013] Optionally, the sensor module is fixed to the reinforced concrete surface by magnetic attraction and sealed with waterproof tape; The sensor module is connected to the remote monitoring terminal via a wireless communication protocol.

[0014] Optionally, the processing of the electrochemical impedance spectroscopy data includes extracting the charge transfer resistance using a Randle equivalent circuit model. Unit: Ω·cm 2 The corrosion current density was calculated based on the Stern-Geary formula, in A / cm². 2 : ; Where B is the Stern-Geary constant, with a value of 0.026V; The processing of the ultrasonic reflection data includes calculating the acoustic impedance and reflection coefficient; Acoustic impedance, in Pa·s / m: ; in, This refers to the density of concrete, expressed in kg / m³. 3 Obtained through pre-calibration; The propagation speed of ultrasonic waves in concrete is expressed in m / s and is calculated from the probe spacing and flight time. Reflection coefficient, dimensionless: ; in, The unit for acoustic impedance of concrete is Pa·s / m. The unit for acoustic impedance of reinforcing steel is Pa·s / m; when At that time, the system determined that there was significant peeling at the reinforced concrete interface.

[0015] Optionally, it also includes generating a spatial distribution map of corrosion rate based on the Kriging interpolation algorithm: ; in, The weighting coefficients are determined by fitting a variogram model to ensure the unbiasedness and optimality of spatial prediction. 'i' represents the i-th measurement point, and 'n' represents the number of known measurement points. and All of these are spatial coordinates, and none of them are dimensionless.

[0016] Optionally, it also includes calculating the corrosion index of multi-source data fusion. : ; Where C is the temperature-humidity coupled corrosion index, and its calculation formula is: ; The unit of charge transfer resistance after temperature correction is Ω·cm. 2 , The unit of initial charge transfer resistance is Ω·cm. 2 R is the ultrasonic wave reflection coefficient, α, β, and γ are weighting coefficients, and satisfy the following condition: H represents the relative humidity of the environment, in percentages. The unit for humidity is %, and T is the unit for ambient temperature in Kelvin. The reference temperature unit is K, and m and k are empirical coefficients for the influence of temperature and humidity. The The basic feature parameters are input into a deep learning model for comprehensive corrosion assessment and trend prediction.

[0017] The beneficial effects of this invention are: This invention integrates an electrochemical impedance spectroscopy unit, an ultrasonic detection unit, and a temperature and humidity sensing unit to construct a multi-source data fusion system for detecting steel reinforcement corrosion, comprehensively reflecting the physicochemical characteristics of steel reinforcement corrosion. The signal processing module of this invention employs a high-performance digital signal processor and deep learning algorithms, improving data processing efficiency and accuracy, and solving the problem of traditional methods being susceptible to interference in complex environments. Furthermore, the portable detector and distributed monitoring nodes of this invention are flexibly designed, suitable for both rapid on-site detection and long-term online monitoring, significantly improving the applicability and reliability of the detection system. This invention has broad application prospects in the field of infrastructure health monitoring, providing an efficient and accurate solution for assessing the safety and durability of reinforced concrete structures. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1This is a block diagram of the overall structure of the intelligent environmentally adaptable steel corrosion detection system of the present invention; Figure 2 This is a block diagram showing the structural composition of the sensor module; Figure 3 This is a block diagram of the internal structure of the signal processing module; Figure 4 This is a block diagram of the portable detector. Figure 5 A flowchart for the method of assessing steel reinforcement corrosion; Figure 6 This is a schematic diagram of a bridge monitoring application scenario. Detailed Implementation

[0019] The intelligent environmentally adaptable steel corrosion detection system of the present invention achieves comprehensive monitoring of the steel corrosion status through specific hardware structure and operating principle.

[0020] The first aspect of this invention provides a sensor module for an intelligent environmentally adaptable steel reinforcement corrosion detection system. The sensor module includes an electrochemical impedance spectroscopy (EIS) unit, an ultrasonic detection unit, and a temperature and humidity sensing unit. The EIS unit is used to acquire changes in the electrochemical properties of the steel reinforcement surface. Its core component is a three-electrode system, where the working electrode is a platinum wire electrode, the reference electrode is a saturated calomel electrode, and the auxiliary electrode is a graphite electrode. The ultrasonic detection unit is used to detect changes in the physical state of the steel reinforcement-concrete interface. Its probe is made of piezoelectric ceramic material with a center frequency of 500 kHz. The temperature and humidity sensing unit is used to monitor the ambient temperature and humidity in real time. Its sensing element is a thin-film temperature and humidity sensor based on MEMS technology. These three units are connected via a flexible circuit board to form an integrated sensor module.

[0021] The second aspect of this invention provides a signal processing module for an intelligent environmentally adaptable rebar corrosion detection system. The signal processing module consists of an analog-to-digital converter (ADC), a digital signal processor (DSP), and a memory. The ADC converts the analog signal output from the sensor module into a digital signal with a resolution of 24 bits and a sampling rate of 100 kSPS. The DSP performs filtering, feature extraction, and preliminary analysis on the digital signal; its core chip is a microcontroller based on the ARM Cortex-M7 architecture. The memory stores the acquired raw data and processed results, with a capacity of 8GB and support power-off data protection. The signal processing module is connected to the sensor module via a high-speed serial interface to ensure stable and real-time data transmission.

[0022] In this invention, the input terminal of the analog-to-digital converter is connected to the output terminal of the sensor module through a low-noise amplifier to improve the signal quality and stability.

[0023] In this invention, the digital signal processor incorporates multiple algorithm modules, including a wavelet transform module, a principal component analysis module, and a deep learning inference module, which are used for signal denoising, feature dimensionality reduction, and corrosion degree prediction, respectively.

[0024] In this invention, the memory uses an industrial-grade eMMC memory chip, which has the characteristics of being resistant to high and low temperatures, vibration, and electromagnetic interference, and can operate reliably for a long time in complex environments.

[0025] The third aspect of this invention provides a multi-source data fusion corrosion index (MCFI) that includes a temperature-humidity coupling factor, directly integrating environmental corrosion assessment into the comprehensive corrosion index. MCFI constructs an environmentally adaptive intelligent assessment system by weighted fusion of temperature-corrected electrochemical parameters, ultrasonic parameters, and the temperature-humidity coupling index C, significantly improving detection accuracy in complex environments.

[0026] The fourth aspect of this invention provides the application of the above-mentioned intelligent environmentally adaptable steel corrosion detection system in steel corrosion assessment, including quantitative determination of steel corrosion rate, qualitative analysis of steel-concrete interface peeling, and prediction of corrosion development trend.

[0027] The fifth aspect of this invention provides the application of the above-mentioned intelligent environmentally adaptable steel corrosion detection system in infrastructure health monitoring, including safety assessment and durability prediction of reinforced concrete structures such as bridges, tunnels and high-rise buildings.

[0028] The following applications also fall within the scope of protection of this invention: The above-mentioned sensor module-related hardware devices are used in the detection of steel corrosion. The hardware devices include portable detectors containing sensor modules, distributed monitoring nodes, and remote monitoring terminals. The above-mentioned embedded system related to the signal processing module is used in the analysis of steel corrosion data. The embedded system includes a data acquisition box containing the signal processing module, an edge computing gateway, and a cloud server.

[0029] A portable testing instrument is a portable device that integrates a sensor module and a signal processing module. The device includes a power management module, a display screen, and a user interface. The power management module is powered by a lithium-ion battery, the display screen is a 3.5-inch TFT LCD screen, and the user interface supports touch operation.

[0030] The sixth aspect of this invention provides a method for assessing steel corrosion using the aforementioned intelligent environmentally adaptable steel corrosion detection system. The specific steps are as follows: The sensor module is fixed to the surface of the reinforced concrete structure to be tested. After starting the system, electrochemical impedance spectroscopy data, ultrasonic reflection data, and ambient temperature and humidity data are collected sequentially. After data acquisition, the signal processing module preprocesses the data, including removing high-frequency noise, correcting temperature drift, and normalizing the data. To eliminate the influence of temperature on the electrochemical measurement results, a modified Arrhenius equation is used to correct the charge transfer resistance. ; in, Resistance (Ω·cm) after temperature correction 2 , Charge transfer resistance Ω·cm 2 , The activation energy is given by (typically 50 kJ / mol), and U is the gas constant. The reference temperature unit is K. The current ambient temperature is expressed in Kelvin (K).

[0031] Subsequently, key characteristic parameters were extracted, such as the low-frequency impedance value of the electrochemical impedance spectroscopy, the peak position of the ultrasonic reflection signal, and the fluctuation range of temperature and humidity; the multi-source data fusion corrosion index MCFI, which includes the temperature-humidity coupling factor, was calculated. ; The temperature-corrected charge transfer resistance in Ω·cm 2 , The initial charge transfer resistance is Ω·cm 2 R is the ultrasonic wave reflection coefficient, α, β, and γ are weighting coefficients, and satisfy the following condition: H represents the ambient relative humidity (%). The humidity is % for reference, and T is the ambient temperature in K. The reference temperature is K, m and k are empirical coefficients for the influence of temperature and humidity, and C is the temperature-humidity coupled corrosion index, which is dimensionless.

[0032] Finally, based on a deep learning model, a comprehensive analysis of the feature parameters and MCFI is performed to generate a quantitative assessment result of the steel reinforcement corrosion rate and a prediction report of the corrosion development trend. The deep learning model is a known mature model, based on a physically constrained multimodal temporal fusion neural network. It comprehensively analyzes electrochemical impedance, ion concentration, acoustic response, temperature and humidity, and image recognition features, combined with corrosion mechanism feature parameters, to output a quantitative assessment of the steel reinforcement corrosion rate and a prediction result of its development trend.

[0033] In one embodiment of the invention, technicians conducted a steel reinforcement corrosion assessment on a bridge that had been in service for ten years. First, multiple sensor modules were deployed at key locations on the bridge. Each module was magnetically attached to the reinforced concrete surface and sealed with waterproof tape to prevent external interference. Subsequently, the sensor modules were connected to a remote monitoring terminal via a wireless communication protocol, enabling real-time data upload and centralized management. After one month of continuous monitoring, the system generated a detailed corrosion assessment report, including a spatial distribution map of the steel reinforcement corrosion rate, a three-dimensional reconstruction map of the interface peeling area, and a corrosion development trend prediction curve for the next five years.

[0034] The following is in conjunction with the appendix Figures 1 to 6 This document details the specific implementation method of the system.

[0035] Example 1: exist Figure 1 The diagram illustrates the overall structure of the invention, where the sensor module and signal processing module are connected via a high-speed serial interface. The sensor module includes an electrochemical impedance spectroscopy (EIS) unit, an ultrasonic detection unit, and a temperature and humidity sensing unit. These units are physically connected and integrated into a single design via a flexible circuit board. The core component of the EIS unit is a three-electrode system consisting of a working electrode, a reference electrode, and an auxiliary electrode. The working electrode is a platinum wire electrode, the reference electrode is a saturated calomel electrode, and the auxiliary electrode is a graphite electrode. The working end face of the platinum wire electrode faces the reinforced concrete surface to be measured and is connected to the analog-to-digital converter in the signal processing module via a wire. The reference electrode and auxiliary electrode are located on opposite sides of the platinum wire electrode, and the distance between them is optimized to ensure the stability and sensitivity of the electrochemical measurement. The ultrasonic detection unit's probe is made of piezoelectric ceramic material with a center frequency of 500 kHz. The transmitter and receiver ends of the probe are connected in parallel to the output of the EIS unit via a flexible circuit board. The temperature and humidity sensing unit's sensing element is based on MEMS technology; its thin-film sensor is mounted at the edge of the flexible circuit board and connected in series with the signal output of the ultrasonic detection unit. This layout effectively avoids signal interference and facilitates the overall installation and maintenance of the module.

[0036] The internal structure of the signal processing module is as follows: Figure 3As shown, it mainly consists of an analog-to-digital converter (ADC), a digital signal processor (DSP), and a memory. The input of the ADC is connected to the output of the sensor module via a low-noise amplifier (LNA). The LNA amplifies the weak signals acquired by the sensor module, thereby improving signal quality and stability. The ADC has a 24-bit resolution and a sampling rate of 100kSPS, which meets the requirements for synchronous acquisition of multi-source data. The core chip of the DSP is a microcontroller based on the ARM Cortex-M7 architecture, which integrates various algorithm modules, including wavelet transform, principal component analysis, and deep learning inference modules. These modules are connected to the ADC via a dedicated data bus, and the data flow path is optimized to reduce latency and power consumption. The memory uses industrial-grade eMMC storage chips with a capacity of 8GB and supports power-off data protection. It communicates with the DSP via an SPI interface to store the acquired raw data and processed results. The memory's resistance to high and low temperatures, vibration, and electromagnetic interference enables it to operate reliably in complex environments for extended periods.

[0037] The specific structure of the portable detector is as follows: Figure 4 As shown, it integrates a sensor module and a signal processing module. The portable detector includes a power management module, a display screen, and a user interface. The power management module is powered by a lithium-ion battery and provides a stable voltage output to each functional module through a voltage regulator circuit. The display screen is a 3.5-inch TFT LCD screen, installed in the center of the front of the portable detector, used to display the collected data and analysis results in real time. The user interface supports touch operation, and its button area is distributed below the display screen and connected to the input terminal of the signal processing module 2. The portable detector's casing is waterproof and dustproof, suitable for rapid on-site testing.

[0038] The specific process of steel reinforcement corrosion assessment method is as follows: Figure 5As shown, sensor module 1 is first fixed to the surface of the reinforced concrete structure to be tested. After the system is started, electrochemical impedance spectroscopy data, ultrasonic reflection data, and ambient temperature and humidity data are collected sequentially. The electrochemical impedance spectroscopy data acquisition process is as follows: a platinum wire electrode applies an excitation signal to the steel bar to be tested, and the reference electrode and auxiliary electrode record the response signals and transmit the data to the analog-to-digital converter 6. The probe of the ultrasonic detection unit 4 emits high-frequency sound waves and receives the reflected signals, which are transmitted to the analog-to-digital converter through a flexible circuit board. The temperature and humidity sensing unit monitors the ambient temperature and humidity in real time and transmits the data to the analog-to-digital converter through a flexible circuit board. After the acquisition is completed, the signal processing module preprocesses the data. First, wavelet transform threshold denoising is used to remove high-frequency noise: the Symlets 8 wavelet basis is selected to decompose the original signal, and the obtained high-frequency detail coefficients are processed by an adaptive threshold λ based on unbiased risk estimation. Finally, the denoised clean signal is reconstructed through wavelet inverse transform. Subsequently, feature extraction and correction are performed on the denoised signal. The first step is to process and correct the electrochemical signal, and the charge transfer resistance R is extracted by fitting the Randles equivalent circuit model. ct To eliminate the effects of temperature drift, temperature and humidity data are used to correct the values ​​to those at the standard reference temperature using the Arrhenius equation: ; Among them, R ct,corr Resistance (Ω·cm) after temperature correction 2 R ct Charge transfer resistance Ω·cm 2 , The activation energy is given by (typically 50 kJ / mol), and U is the gas constant. The reference temperature unit is K. The current ambient temperature is expressed in Kelvin (K).

[0039] Then, the corrosion current density was calculated using the Stern-Geary formula: ; Where B is 0.026V, Resistance is the charge transfer resistance, in Ω·cm 2 , The corrosion current density is expressed in A / cm². 2 : The second step is to calculate the acoustic impedance of the concrete in Pa·s / m based on the ultrasonic reflection data. ; in, The density of concrete is kg / m³. 3 Obtained through pre-calibration; The propagation speed of ultrasonic waves in concrete, in m / s, is calculated from the probe spacing and flight time. Based on the principle of acoustic reflection, the reflection coefficient at the interface between the steel reinforcement and concrete is calculated; it is dimensionless. ; in, The acoustic impedance of concrete is Pa·s / m. The acoustic impedance of the reinforcing steel is Pa·s / m; when At that time, the system determined that there was significant peeling at the reinforced concrete interface.

[0040] The third step is to calculate the multi-source data fusion corrosion index MCFI, which includes temperature-humidity coupling factors: ; The C-index is calculated as follows: ; in, The temperature-corrected charge transfer resistance in Ω·cm 2 , The initial charge transfer resistance is Ω·cm 2 R is the ultrasonic wave reflection coefficient, α, β, and γ are weighting coefficients, and satisfy the following condition: H represents the ambient relative humidity (%). The humidity is % for reference, and T is the ambient temperature in K. The reference temperature is K, and m and k are empirical coefficients for the influence of temperature and humidity.

[0041] The fourth step involves constructing a feature vector F by combining the MCFI with the basic feature parameters. Finally, a comprehensive analysis of the feature parameters is performed based on a deep learning model to generate a quantitative assessment result of the steel reinforcement corrosion rate and a prediction report of the corrosion development trend.

[0042] Bridge monitoring application scenarios such as Figure 6 As shown, multiple sensor modules 1 were deployed on a bridge that had been in service for ten years. Each module was magnetically attached to the reinforced concrete surface and sealed with waterproof tape to prevent external interference. Sensor modules 1 connected to a remote monitoring terminal via a wireless communication protocol, enabling real-time data upload and centralized management. After one month of continuous monitoring, the system generated a detailed corrosion assessment report, including a spatial distribution map of the steel reinforcement corrosion rate, a 3D reconstruction map of the interface peeling area, and a corrosion development trend prediction curve for the next five years. The placement of sensor modules 1 was determined based on key parts of the bridge structure, such as piers, bridge decks, and beams, to ensure the comprehensiveness and representativeness of the monitoring data.

[0043] The operating principle and process of this system are as follows: When the sensor module is fixed to the surface of the reinforced concrete structure to be tested, the electrochemical impedance spectroscopy unit acquires the changes in electrochemical characteristics of the steel surface through a three-electrode system; the ultrasonic detection unit detects the changes in the physical state of the interface between the steel and concrete through a probe; and the temperature and humidity sensing unit monitors changes in ambient temperature and humidity in real time. The analog signals collected by the sensor module are transmitted to the signal processing module through a flexible circuit board. The analog-to-digital converter converts the analog signals into digital signals and transmits them to the digital signal processor. The digital signal processor filters, extracts features, and performs preliminary analysis on the digital signals, extracting key feature parameters and storing them in the memory. The data in the memory can be transmitted to a remote monitoring terminal via wired or wireless means to generate a steel corrosion assessment report. Throughout the operation of the entire system, the data flow paths between the modules are optimized to ensure the stability and real-time performance of data transmission.

[0044] The hardware of this system includes a portable detector with sensor modules, distributed monitoring nodes, and a remote monitoring terminal. The portable detector is suitable for rapid on-site detection, the distributed monitoring nodes are suitable for long-term online monitoring, and the remote monitoring terminal is used for centralized data management and analysis. The embedded system includes a data acquisition box with signal processing modules, an edge computing gateway, and a cloud server. These devices achieve efficient data transmission and processing through high-speed communication protocols.

[0045] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0046] In practical applications of bridge steel reinforcement corrosion detection, sensor module 1 is first fixed to the reinforced concrete surface of key parts of the bridge using magnetic attraction. For example... Figure 6 As shown, the sensor modules are positioned in areas including bridge piers, bridge decks, and beams. For example, the bridge piers, bridge decks, and beams shown in the figure correspond to sensor module 1, sensor module 2, and sensor module 3, respectively, to ensure the comprehensiveness and representativeness of the monitoring data. To prevent external interference, the outside of the sensor modules is sealed with waterproof tape and connected to a remote monitoring terminal via a wireless communication protocol to upload the collected data in real time.

[0047] Step 1: During electrochemical impedance spectroscopy (EIS) data acquisition, a platinum wire electrode in the EIS unit applies an excitation signal to the steel bar under test, while the reference electrode and auxiliary electrode record the response signals. The excitation signal acts on the surface of the steel bar through a three-electrode system, reflecting changes in the steel bar's electrochemical properties. The high stability and corrosion resistance of the platinum wire electrode ensure the reliability of the measurement results. A saturated calomel electrode is used as the reference electrode to provide a stable reference potential; a graphite electrode is used as the auxiliary electrode to complete the current loop. Optimized spacing between the three electrodes ensures both sensitivity and stability of the measurement. The acquired analog signal is transmitted to an analog-to-digital converter via a flexible circuit board, where a low-noise amplifier initially amplifies the weak signal to improve signal quality.

[0048] Step 2: During ultrasonic reflection data acquisition, the ultrasonic detection unit's probe emits a high-frequency sound wave with a center frequency of 500kHz. This sound wave penetrates the concrete and reflects off the steel reinforcement interface. The reflected signal is received by the probe and transmitted to the analog-to-digital converter via a flexible circuit board. Because the probe is made of piezoelectric ceramic material, it has high sensitivity and anti-interference capabilities, enabling stable operation in complex environments. The ultrasonic detection unit analyzes the peak position and intensity of the reflected signal to determine changes in the physical state of the steel reinforcement-concrete interface, such as the presence of peeling or cracks.

[0049] Step 3: During temperature and humidity data acquisition, the temperature and humidity sensing unit uses a thin-film sensor based on MEMS technology to monitor changes in ambient temperature and humidity in real time. The sensor is mounted at the edge of the flexible circuit board to avoid interference with signals from other units. The temperature and humidity data is transmitted to an analog-to-digital converter via the flexible circuit board to correct measurement deviations caused by environmental factors. For example, temperature drift may affect the accuracy of electrochemical impedance spectroscopy data, thus requiring compensation using temperature and humidity data.

[0050] In step four, data processing and feature extraction, the analog-to-digital converter (ADC) converts the acquired analog signals into digital signals and transmits them to the digital signal processor (DSP). The DSP's built-in wavelet transform module denoises the signal, removing the influence of high-frequency noise. The principal component analysis (PCA) module performs dimensionality reduction on the multi-source data, extracting key feature parameters such as the low-frequency impedance value of the electrochemical impedance spectroscopy, the peak position of the ultrasonic reflection signal, and the fluctuation range of temperature and humidity. These feature parameters reflect different aspects of steel reinforcement corrosion, providing a foundation for subsequent comprehensive analysis.

[0051] Step 5: During deep learning model analysis, the feature parameters in the memory are input into the deep learning inference module for comprehensive analysis. By training on a large amount of historical data, the deep learning model can accurately predict the steel reinforcement corrosion rate and its development trend. For example, the changing trend of low-frequency impedance values ​​can be used to quantitatively assess the degree of steel reinforcement corrosion, while abnormal peaks in ultrasonic reflection signals may indicate the delamination of the steel reinforcement-concrete interface. Finally, the system generates a spatial distribution map of the steel reinforcement corrosion rate, a 3D reconstruction map of the interface delamination area, and a corrosion development trend prediction curve for the next five years.

[0052] In step six, "Result Feedback and Decision Support," the processing results in the memory are transmitted to the remote monitoring terminal via wired or wireless means. The remote monitoring terminal centrally manages the data and generates a detailed corrosion assessment report. Technicians develop maintenance plans based on the report, such as localized repairs or anti-corrosion measures for severely corroded areas. Furthermore, the system supports long-term online monitoring and regularly updates the corrosion assessment results, providing dynamic support for the safety and durability assessment of the bridge.

[0053] Throughout the operation, the data flow paths between modules have been optimized to ensure the stability and real-time performance of data transmission. For example, the flexible circuit board layout effectively avoids signal interference, and the high and low temperature resistance, vibration resistance, and electromagnetic interference resistance of the industrial-grade eMMC memory chips ensure the long-term reliable operation of the system. The waterproof and dustproof design of the portable testing instrument makes it suitable for rapid on-site testing, while the combination of distributed monitoring nodes and remote monitoring terminals enables long-term online monitoring and centralized management.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent environmentally adaptable steel reinforcement corrosion detection system, characterized in that, set up: The sensor module includes an electrochemical impedance spectroscopy unit, an ultrasonic detection unit, and a temperature and humidity sensing unit. The electrochemical impedance spectroscopy unit adopts a three-electrode system, wherein the working electrode is a platinum wire electrode, the reference electrode is a saturated calomel electrode, and the auxiliary electrode is a graphite electrode. A signal processing module connected to the sensor module for signal transmission includes an analog-to-digital converter, a digital signal processor, and a memory; the digital signal processor has a built-in wavelet transform module, a principal component analysis module, and a deep learning inference module.

2. The intelligent environmentally adaptable steel corrosion detection system according to claim 1, characterized in that, The electrochemical impedance spectroscopy unit, ultrasonic detection unit, and temperature and humidity sensing unit in the sensor module are connected by a flexible circuit board. The probe of the ultrasonic detection unit is made of piezoelectric ceramic material with a center frequency of 500kHz; the sensitive element of the temperature and humidity sensing unit is based on MEMS technology.

3. The intelligent environmentally adaptable steel corrosion detection system according to claim 1 or 2, characterized in that, The analog-to-digital converter has a resolution of 24 bits and a sampling rate of 100kSPS; the core chip of the digital signal processor is a microcontroller based on the ARM Cortex-M7 architecture; the memory uses industrial-grade eMMC memory chips. The input terminal of the analog-to-digital converter is connected to the output terminal of the sensor module via a low-noise amplifier.

4. A portable detector, characterized in that, The system integrates the intelligent environmentally adaptable steel corrosion detection system according to any one of claims 1-3, and further includes a power management module, a display screen, and a user interface.

5. The portable detector according to claim 4, characterized in that, The power management module is powered by a lithium-ion battery, the display screen is a 3.5-inch TFT LCD screen, and the user interface supports touch operation. The portable detector has a waterproof and dustproof casing.

6. A method for assessing steel reinforcement corrosion using the intelligent environmentally adaptable steel reinforcement corrosion detection system according to any one of claims 1-3, characterized in that, Includes the following steps: The sensor module was fixed to the surface of the reinforced concrete structure to be tested. After the system was started, electrochemical impedance spectroscopy data, ultrasonic reflection data and ambient temperature and humidity data were collected in sequence. After data acquisition, the data is preprocessed, including removing high-frequency noise, correcting temperature drift, and normalizing. Then, key characteristic parameters are extracted, including the low-frequency impedance value of the electrochemical impedance spectrum, the peak position of the ultrasonic reflection signal, and the fluctuation range of temperature and humidity. Finally, based on a deep learning model, the feature parameters are comprehensively analyzed to generate a quantitative assessment result of the steel corrosion rate and a prediction report of the corrosion development trend.

7. The method for assessing steel reinforcement corrosion according to claim 6, characterized in that, The sensor module is fixed to the reinforced concrete surface by magnetic attraction and sealed with waterproof tape. The sensor module is connected to the remote monitoring terminal via a wireless communication protocol.

8. The method for assessing steel reinforcement corrosion according to claim 6 or 7, characterized in that, The processing of the electrochemical impedance spectroscopy data includes extracting charge transfer resistance using a Randle equivalent circuit model. Unit: Ω·cm 2 The corrosion current density was calculated based on the Stern-Geary formula, in A / cm². 2 : ； Where B is the Stern-Geary constant, with a value of 0.026V; The processing of the ultrasonic reflection data includes calculating the acoustic impedance and reflection coefficient; Acoustic impedance, in Pa·s / m: ； in, This refers to the density of concrete, expressed in kg / m³. 3 Obtained through pre-calibration; The propagation speed of ultrasonic waves in concrete is expressed in m / s and is calculated from the probe spacing and flight time. Reflection coefficient, dimensionless: ； in, The unit for acoustic impedance of concrete is Pa·s / m. The unit for acoustic impedance of reinforcing steel is Pa·s / m; when At that time, the system determined that there was significant peeling at the reinforced concrete interface.

9. The method for assessing steel reinforcement corrosion according to claim 6 or 7, characterized in that, It also includes generating a spatial distribution map of corrosion rate based on the Kriging interpolation algorithm: ； in, The weighting coefficients are determined by fitting a variogram model to ensure the unbiasedness and optimality of spatial prediction. 'i' represents the i-th measurement point, and 'n' represents the number of known measurement points. and All of these are spatial coordinates, and none of them are dimensionless.

10. The method for assessing steel reinforcement corrosion according to claim 6 or 7, characterized in that, It also includes calculating the corrosion index of multi-source data fusion. : ； Where C is the temperature-humidity coupled corrosion index, and its calculation formula is: ； The unit of charge transfer resistance after temperature correction is Ω·cm. 2 , The unit of initial charge transfer resistance is Ω·cm. 2 R is the ultrasonic wave reflection coefficient, α, β, and γ are weighting coefficients, and satisfy the following condition: H represents the relative humidity of the environment, in percentages. The unit for humidity is %, and T is the unit for ambient temperature in Kelvin. The reference temperature unit is K, and m and k are empirical coefficients for the influence of temperature and humidity. The The basic feature parameters are input into a deep learning model for comprehensive corrosion assessment and trend prediction.

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