Bridge support corrosion current field imaging monitoring method and system

Abstract

The invention relates to the technical field of bridge health monitoring, in particular to a bridge support corrosion current field imaging monitoring method and system, and the method comprises the steps: presetting a matrix type electrochemical sensor on a support steel plate, and collecting the inductance data of a collector at a sampling frequency of 0.1-0.5 Hz; ohmic resistance drift caused by temperature is eliminated through adaptive quantum Kalman filtering, and a resistance-current density distribution diagram is constructed; constructing a current density distribution diagram of the cross section of the support by adopting a back propagation electrical impedance tomography technology; when the corrosion rate exceeds 0.15 mm / year, an early warning system is triggered, the bearing capacity attenuation rate of the support is calculated, and the support is guided to be replaced; a topological separation technology of a multi-dimensional state space and quantum heuristic adaptive parameter optimization effectively eliminate temperature interference; through current field topology reconstruction and multi-source data fusion, the imaging precision is improved; the quantitative relation between the corrosion rate and bearing capacity attenuation of the support is established, and a scientific basis is provided for bridge maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge health monitoring, and in particular to a bridge bearing corrosion current field imaging monitoring method and system thereof. Background Art

[0002] As a key connecting component between a bridge's superstructure and substructure, bridge bearings' performance directly impacts the overall safety and service life of the bridge. During bridge service, bearings are exposed to complex environments for extended periods, making them susceptible to corrosion. This reduces their bearing capacity and, in turn, impacts the overall safety of the bridge.

[0003] Currently, bridge bearing corrosion monitoring relies primarily on manual periodic inspections and ultrasonic thickness measurement. These traditional methods suffer from long monitoring cycles, low accuracy, and the inability to continuously monitor. For example, manual inspections are typically conducted once or twice a year, making it difficult to detect early-stage corrosion. While ultrasonic thickness measurement can measure metal thickness, it has limited measurement points and cannot fully reflect the corrosion status of bearings.

[0004] In recent years, electrochemical impedance spectroscopy (EIS) has gained some application in metal corrosion monitoring. However, this method still faces several technical challenges. First, ambient temperature fluctuations can cause ohmic resistance drift, seriously affecting monitoring accuracy. Second, single-point measurements cannot accurately capture the overall corrosion distribution of the bearing. Furthermore, the relationship between corrosion data and bearing capacity degradation has not yet been established, making it difficult to provide a scientific basis for maintenance decisions.

[0005] Therefore, there is an urgent need to develop a high-precision, continuous and real-time bridge bearing corrosion monitoring technology to achieve visual representation of the corrosion status and establish a correlation between the degree of corrosion and the attenuation of bearing capacity, so as to provide a scientific decision-making basis for bridge maintenance. Summary of the Invention

[0006] The purpose of the present invention is to provide a bridge bearing corrosion current field imaging monitoring method and system, aiming to solve the problems of low bearing corrosion monitoring accuracy, large environmental interference, inability to achieve continuous monitoring, and lack of scientific decision-making basis in the existing technology.

[0007] The present invention proposes a bridge bearing corrosion current field imaging monitoring method, comprising:

[0008] A matrix electrochemical sensor is pre-installed on the support steel plate to collect the inductance data of the electrode at a sampling frequency of 0.1 to 0.5 Hz;

[0009] Based on the collected inductance data, an adaptive quantum Kalman filter is used to eliminate the temperature-induced ohmic resistance drift and construct a resistance-current density distribution map.

[0010] Based on the constructed resistance-current density distribution map, the back propagation impedance tomography technique was used to construct the current density distribution map of the support cross section.

[0011] When the local corrosion rate of the support steel plate exceeds 0.15mm / year, the early warning system is triggered to calculate the support bearing capacity attenuation rate to guide the replacement of the monitoring section support.

[0012] Preferably, before pre-installing the matrix electrochemical sensor on the support steel plate, the method further comprises:

[0013] A finite element model of bearing corrosion was constructed. Combined with the data from strain gauges embedded in the bearing steel plates during the bridge load test, the bearing capacity and the evolution of rust spots on the bearing surface were simulated using finite element software, and the correlation between strain distribution and bearing flexural stiffness was analyzed.

[0014] Preferably, the correlation between the analysis strain distribution and the support bending stiffness includes:

[0015] Combining the measured response of the bearing with the parameters determined by finite element simulation, a parameter library of this type of bearing is constructed to provide a basis for subsequent strain-stress conversion;

[0016] The stress distribution of the support steel plate is obtained through stress-strain transformation, and the bending stiffness of the tensile side of the support is calculated using the principle of elastic strain energy.

[0017] Preferably, the matrix electrochemical sensor pre-installed on the support steel plate includes:

[0018] A matrix electrochemical sensor is pre-installed in the area corresponding to the tensile side of the support steel plate, and the sensor installation accuracy is controlled within 1mm;

[0019] The electrochemical sensor includes a working electrode, a reference electrode and an auxiliary electrode. The working electrode is a platinum electrode, the reference electrode is a silver / silver chloride electrode, and the auxiliary electrode is a graphite electrode.

[0020] Preferably, the electrochemical sensor is preset within the strain monitoring range of 2 / 3 to 3 / 4 of the upper flange of the support. The sensor is fixed to the steel plate by adhesive. The preset direction is perpendicular to the force direction, and the spacing between the sensors in the X and Y directions is 5 to 15 mm.

[0021] Preferably, the sampling frequency of 0.1 to 0.5 Hz is determined by comparing stress data and response data measured under static and dynamic conditions of the bridge, wherein:

[0022] Under normal monitoring conditions, the electrode inductance data was collected using a sampling frequency of 0.1 Hz;

[0023] When an abnormal change in resistance value is detected, the sampling frequency is automatically increased to 0.5Hz to capture the signal frequency change of local strain of the steel plate.

[0024] Preferably, eliminating the ohmic resistance drift caused by temperature by using an adaptive quantum Kalman filter comprises:

[0025] Based on the topological separation technology of multi-dimensional state space, the resistance signal is decomposed into corrosion current component and temperature drift component;

[0026] Using the uncertainty principle of quantum measurement theory, the system noise covariance and measurement noise covariance parameters of the filter are dynamically adjusted;

[0027] The sliding window method is used to calculate the statistical characteristics of short-term signals, evaluate the complexity of noise distribution, and dynamically optimize the Kalman gain coefficient;

[0028] Output filtered resistance change data to provide a basis for current density distribution reconstruction.

[0029] Preferably, constructing the resistance-current density distribution map includes:

[0030] Apply the resistance-current density calibration curve to the filtered resistance data for preliminary conversion;

[0031] Based on the currently collected temperature data, the temperature compensation coefficient is applied to eliminate the residual effect of temperature on the conversion relationship;

[0032] Nonlinear correction is performed in combination with the support material properties to handle the special response in high current density areas;

[0033] Output preliminary current density distribution data to provide input for back propagation electrical impedance tomography.

[0034] Preferably, the method of constructing a support cross-section current density distribution map using back propagation electrical impedance tomography technology includes:

[0035] Construct the support geometry model and unstructured mesh generation, and apply the finite element method to discretize the current field equations;

[0036] Set the initial current density distribution and forward calculate the simulated resistance measurement value;

[0037] Compare the simulated value with the actual measured value, calculate the residual, and backpropagate the residual to update the current density distribution;

[0038] Apply current field topology constraints to maintain the physical rationality of current distribution;

[0039] Combined with strain monitoring data, weighted correction is performed and image enhancement algorithms are applied to improve contrast and clarity;

[0040] The calculation is iterated until the residual is less than the set threshold, and the final support cross-section current density distribution diagram is output.

[0041] The system for implementing the bridge bearing corrosion current field imaging monitoring method is characterized by comprising:

[0042] A sensor network, including a matrix electrochemical sensor, a temperature sensor, and a strain sensor pre-installed on the support steel plate, is used to collect electrode inductance data, temperature data, and strain data;

[0043] Data acquisition and transmission unit, including a multi-channel synchronous data acquisition module and a data transmission network, for collecting and transmitting sensor data at a sampling frequency of 0.1 to 0.5 Hz;

[0044] Signal processing unit, including an adaptive quantum Kalman filter module, used to eliminate temperature-induced ohmic resistance drift and construct a resistance-current density distribution map;

[0045] A current field reconstruction unit, including a back-propagation electrical impedance tomography module, is used to construct a current density distribution map of the support cross section;

[0046] The analysis and decision-making unit is used to calculate the local corrosion rate of the support steel plate. When the corrosion rate exceeds 0.15mm / year, the early warning system is triggered, and the support bearing capacity attenuation rate is calculated to guide the replacement of the monitoring section support.

[0047] The present invention uses a matrix electrochemical sensor array, combined with adaptive quantum Kalman filtering technology and back-propagation electrical impedance tomography technology, to achieve high-precision real-time monitoring and visual characterization of the bearing corrosion state, and establishes a correlation between the degree of corrosion and the attenuation of the bearing bearing capacity, with the following beneficial effects:

[0048] 1. High-precision monitoring of bearing corrosion status is achieved. A matrix electrochemical sensor array and optimized sampling frequency design improve the spatial and temporal resolution of monitoring, achieving a corrosion rate detection accuracy of ±0.05mm / year, approximately 10 times higher than traditional methods.

[0049] 2. Effectively eliminates the impact of temperature on monitoring accuracy. Adopting adaptive quantum Kalman filtering technology, it innovatively solves the problem of ohmic resistance drift caused by temperature, significantly improving the reliability and stability of monitoring data.

[0050] 3. Visual representation of bearing corrosion status is achieved. Back-propagation electrical impedance tomography (BIT) technology is used to construct a current density distribution map, visually demonstrating the spatial distribution and development trend of bearing corrosion, with a spatial resolution of up to millimeters.

[0051] 4. Provides a scientific basis for maintenance decision-making. A quantitative relationship between the degree of corrosion and bearing capacity attenuation has been established. When the corrosion rate exceeds 0.15mm / year, the early warning system is triggered. When the bearing capacity attenuation rate exceeds 20%, replacement is determined to be necessary, providing a scientific basis for decision-making for bridge bearing maintenance.

[0052] 5. Achieves full-process support from monitoring to decision-making. The system integrates functional modules such as data acquisition, signal processing, current field reconstruction, and early warning decision-making, forming a complete technical chain, effectively improving the efficiency and scientificity of bridge maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of the overall architecture of the bridge bearing corrosion current field imaging monitoring system of the present invention;

[0054] Figure 2 Schematic diagram of the adaptive quantum Kalman filter processing flow of the present invention;

[0055] Figure 3 This is a flow chart of the support corrosion current field imaging monitoring method of the present invention;

[0056] Figure 4 It is a schematic diagram of the early warning decision support process of the present invention. DETAILED DESCRIPTION

[0057] Please refer to Figures 1-4 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] Reference Figure 3 As shown, the bridge bearing corrosion current field imaging monitoring method provided by the present invention mainly includes the following steps:

[0059] 1. Construction of finite element model of bearing corrosion:

[0060] Before pre-installing matrix electrochemical sensors on the bearing steel plates, a finite element model of bearing corrosion must be constructed. Specifically, data from strain gauges embedded in the bearing steel plates during bridge load tests is combined with finite element software (such as ANSYS or ABAQUS) to simulate the bearing's load-bearing capacity and the evolution of rust on the bearing surface. The correlation between strain distribution and bearing flexural stiffness is analyzed.

[0061] In one embodiment of the present invention, the finite element model of bearing corrosion is constructed using three-dimensional solid elements, with material properties set to Q345 steel, an elastic modulus of 210 GPa, and a Poisson's ratio of 0.3. Corroded areas are simulated using a material weakening method, which characterizes the impact of corrosion on the bearing's mechanical properties by reducing the elastic modulus and thickness of the corroded areas. The model boundary conditions are set based on the actual bearing installation situation, typically with the bottom fixed and a top load applied.

[0062] When analyzing the correlation between strain distribution and support bending stiffness, the measured response of the support is combined with finite element simulation to determine parameters and construct a parameter library for this type of support, providing a basis for subsequent strain-stress conversion. The stress distribution of the support steel plate is obtained through stress-strain conversion, and the bending stiffness of the tensile side of the support is calculated using the principle of elastic strain energy. The specific calculation method is as follows:

[0063] Assuming the stress is σ, the spacing between strain sensors is d, and the bending stiffness of the steel plate at a distance x from the support edge on the tension side is EI(x), the elastic strain energy in this area can be expressed as:

[0064] ,

[0065] Where: U is the elastic strain energy, σ is the stress, ε is the longitudinal tensile strain of the steel plate, M(x) is the bending moment, and EI(x) is the bending stiffness of the steel plate at a distance x from the support edge on the tension side.

[0066] The stress σ can be calculated from the strain ε:

[0067] ,

[0068] Where: E is the elastic modulus of steel, which is 210GPa.

[0069] The bending stiffness of the support on the tension side is calculated based on the strain sensor spacing d and the sensor data:

[0070] ,

[0071] Where: M(x) is the bending moment at x, d is the distance between strain sensors, and ε(x) is the strain value measured at x.

[0072] Through the above calculations, the distribution of bearing bending stiffness can be obtained, providing basic data for evaluating the impact of corrosion on the bearing capacity of the bearing.

[0073] 2. Matrix electrochemical sensor preset:

[0074] A matrix electrochemical sensor is pre-installed in the area corresponding to the tensile side of the support steel plate, and the sensor installation accuracy is controlled within 1mm. Figure 2 As shown, the electrochemical sensor is specifically preset within the strain monitoring range of 2 / 3 to 3 / 4 of the upper flange of the support. The sensor is fixed to the steel plate by epoxy resin adhesive. The preset direction is perpendicular to the force direction of the support, and the spacing between the sensors in the X and Y directions is 5 to 15 mm.

[0075] The electrochemical sensor used in the present invention comprises a working electrode, a reference electrode, and an auxiliary electrode, forming a three-electrode system. Preferably, the working electrode is a platinum electrode, the reference electrode is a silver / silver chloride electrode, and the auxiliary electrode is a graphite electrode. This three-electrode system provides a stable potential reference, ensuring the accuracy and reliability of electrochemical measurements.

[0076] The sensors are arranged in a regular grid, with appropriate density in high-stress areas to improve monitoring accuracy. For a typical plate-type support, 64 to 100 sensor nodes are typically deployed within a 1m² monitoring area, forming an 8x8 to 10x10 sensor matrix. Each sensor node has an effective monitoring radius of approximately 7.5mm, ensuring effective coverage of the entire monitoring area.

[0077] 3. Inductance data collection:

[0078] The electrode inductance data was collected at a sampling frequency of 0.1 to 0.5 Hz. This sampling frequency range, determined by comparing stress and response data measured under static and dynamic conditions, effectively captures the changing characteristics of the electrochemical process of bearing corrosion.

[0079] In a preferred embodiment of this invention, a hierarchical sampling strategy is employed: under normal monitoring conditions, electrode inductance data is collected at a sampling frequency of 0.1 Hz. When abnormal changes in resistance are detected (e.g., a resistance change rate exceeding 5% per hour), the sampling frequency is automatically increased to 0.5 Hz to capture the frequency variation in the signal of local strain in the steel plate. This hierarchical sampling strategy ensures continuous monitoring while providing data with higher temporal resolution in abnormal situations, while effectively limiting data storage capacity.

[0080] The raw inductance data collected is passed through a low-pass filter (cutoff frequency 2Hz) to remove high-frequency noise, and wavelet threshold denoising technology is used to eliminate random interference. At the same time, ambient temperature and support surface temperature data are collected to provide the necessary parameters for subsequent ohmic resistance drift elimination.

[0081] 4. Adaptive quantum Kalman filter processing:

[0082] Reference Figure 2 As shown in the figure, based on the collected inductance data, the temperature-induced ohmic resistance drift is eliminated through adaptive quantum Kalman filtering to construct a resistance-current density distribution map. This is one of the core innovations of the present invention and mainly includes the following processing steps:

[0083] First, based on a topological separation technique in a multidimensional state space, the resistance signal is decomposed into a corrosion current component and a temperature drift component. Specifically, a high-dimensional manifold representation of the resistance signal is constructed, and the time series data is embedded into the high-dimensional phase space using a delayed coordinate embedding method. The embedding dimension n is determined using the false nearest neighbor method, typically 5-8 dimensions. The embedding delay τ is calculated using the mutual information minimization method and dynamically adjusted based on the sampling frequency. In the reconstructed phase space, the temperature effect manifests primarily as low-frequency, large-scale structural changes, which can be separated using principal component analysis.

[0084] Secondly, the uncertainty principle of quantum measurement theory is used to dynamically adjust the filter's system noise covariance Q and measurement noise covariance R parameters. These parameters are treated as dynamic variables with quantum superposition properties and adaptively adjusted according to the system state. This is achieved by calculating short-term signal statistical characteristics, including signal entropy, Hurst exponent, and power spectral density, using a sliding window (typically 100 to 200 sampling points), to assess the complexity of the noise distribution and dynamically optimize the Kalman gain coefficient K.

[0085] The core algorithm of the adaptive quantum Kalman filter is as follows:

[0086] 1) State space representation:

[0087] ,

[0088] ,

[0089] in: is the state vector at time k, which contains the resistance value and its rate of change; is the state transfer matrix; is process noise, with mean 0 and covariance Normal distribution; is the observation vector at time k; is the observation matrix; is the observation noise, which has a mean of 0 and a covariance of Normal distribution.

[0090] 2) Time update (prediction):

[0091] ,

[0092] ,

[0093] in: is the prior state estimate at time k; is the prior estimation error covariance at time k; is the process noise covariance at time k, which is dynamically adjusted according to the signal characteristics.

[0094] 3) Measurement update (correction):

[0095] ,

[0096] ,

[0097] ,

[0098] in: is the Kalman gain at time k; is the posterior state estimate at time k; is the posterior estimation error covariance at time k; is the measurement noise covariance at time k, which is dynamically adjusted according to the signal characteristics; is the identity matrix.

[0099] 4) Adaptive parameter update:

[0100] ,

[0101] ,

[0102] in: and is the forgetting factor, and its value range is [0.9, 0.99]; and are the process noise covariance and measurement noise covariance estimated based on the current observation window, respectively.

[0103] This adaptive quantum Kalman filtering effectively eliminates temperature-induced ohmic resistance drift, outputting filtered resistance change data that provides the basis for reconstructing the current density distribution. In practical applications, this method can control the error in temperature-induced resistance drift to within ±0.5%, significantly improving monitoring accuracy.

[0104] 5. Resistance-current density distribution diagram construction:

[0105] Based on the filtered resistance data, the resistance-current density distribution diagram is constructed, which mainly includes the following steps:

[0106] First, the filtered resistance data is initially converted using a resistance-current density calibration curve. This calibration curve, obtained through laboratory testing of standard test specimens, establishes a relationship between resistance and current density. For Q345 steel, this calibration curve is typically nonlinear and can be fitted using a power function or polynomial function.

[0107] Secondly, based on the currently collected temperature data, a temperature compensation coefficient is applied to eliminate the residual effect of temperature on the conversion relationship. The temperature compensation coefficient α is usually in the range of 0.5% to 2% / °C and varies with the type of steel and the degree of corrosion. The temperature compensation calculation formula is:

[0108] ,

[0109] in: is the corrected current density at temperature T; To measure current density; is the temperature compensation coefficient; is the current temperature; is the reference temperature, usually 20°C.

[0110] Then, a nonlinear correction is performed based on the support material properties to address the special response in high current density areas. For high current density areas (usually greater than 1A / cm²), the traditional linear relationship is no longer applicable and a nonlinear correction factor needs to be applied for correction:

[0111] ,

[0112] in: is the corrected current density; is a nonlinear correction factor, usually about It is a piecewise function that is close to 1 in the low current density region and gradually increases in the high current density region.

[0113] Finally, preliminary current density distribution data is output to provide input for back propagation impedance tomography. The preliminary current density distribution data is represented in matrix form. The matrix dimensions are consistent with the sensor array layout, and each element corresponds to the current density value at a sensor location.

[0114] 6. Construction of current density distribution map:

[0115] Based on the constructed resistance-current density distribution map, the back propagation impedance tomography technique is used to construct the current density distribution map of the support cross section. This technique is another core innovation of the present invention and mainly includes the following steps:

[0116] First, a geometric model of the bearing and an unstructured mesh are constructed, and the current field equations are discretized using the finite element method. The bearing geometry is based on the actual bearing dimensions, typically using a 3D solid model. The mesh is generated using the Delaunay triangulation method, with adaptive refinement in critical corrosion areas, and a mesh size range of 1 to 5 mm.

[0117] Then, the initial current density distribution is set. Based on the preliminary current density distribution data obtained in the previous step, it is mapped to the finite element mesh through interpolation method as the initial iteration value. The forward calculation simulates the resistance measurement value. The calculation method is to solve the current field Poisson equation:

[0118] ,

[0119] in: is the gradient operator; is the conductivity; The boundary conditions are set according to the actual electrode arrangement, usually with a constant current density at the working electrode and a reference potential at the auxiliary electrode.

[0120] Next, the simulated values ​​are compared with the actual measured values, the residual is calculated, and the current density distribution is updated through the back-propagation algorithm. The back-propagation algorithm is based on the gradient descent method and optimizes the current density distribution by minimizing the objective function:

[0121] ,

[0122] in: is the objective function; is the measured potential of the i-th measuring point; Based on the current conductivity distribution Calculate the potential of the i-th measuring point; is the total number of measuring points; is the regularization parameter; is a regularization term used to constrain the smoothness of the solution.

[0123] The conductivity distribution is updated iteratively:

[0124] ,

[0125] in: is the conductivity distribution of the kth iteration; is the learning rate, usually 0.01-0.1; The objective function is about gradient.

[0126] During the back propagation process, the current field topology constraints are applied to maintain the physical rationality of the current distribution. The main constraints include: current continuity conditions ( ), positive value constraint ( ) and smoothness constraints. These constraints are implemented by adding corresponding penalty terms to the objective function.

[0127] Furthermore, weighted correction is performed in combination with strain monitoring data to enhance the accuracy of current field reconstruction. The weighted correction method is:

[0128] ,

[0129] in: is the corrected conductivity; is the weighting coefficient, usually 0.1-0.5; is the normalized strain value, reflecting the local stress state.

[0130] Then, an image enhancement algorithm is applied to improve the contrast and clarity of the current density distribution map. Local contrast enhancement and multi-scale edge enhancement techniques are mainly used to highlight the corrosion boundaries and abnormal areas.

[0131] Finally, the calculation is iterated until the residual is less than a set threshold (usually 1% of the initial residual), and the final current density distribution map of the support cross section is output. The final distribution map is presented as a pseudo-color image, with different colors representing different current density levels, making it easy to intuitively identify corrosion areas and severity.

[0132] 7. Corrosion rate calculation and early warning decision-making:

[0133] Based on the current density distribution diagram, the local corrosion rate of the support steel plate is calculated. According to Faraday's law of electrolysis, the corrosion rate is proportional to the current density:

[0134] ,

[0135] in: is the corrosion rate in mm / year; is the unit conversion constant. When the corrosion rate unit is mm / year, ; is the current density, in A / cm²; is the atomic weight of the metal, which is 55.85 g / mol for iron; is the valence of the metal ion, usually 2 for iron; is the density of metals, iron is 7.87 g / cm³; is the Faraday constant, 96485 C / mol.

[0136] When the local corrosion rate of the bearing steel plate exceeds 0.15 mm / year, the early warning system is triggered. This threshold is based on the fatigue life of conventional bearing failure (5×10 7~5×10 8 times) analysis, taking into account the heavy-load and overload traffic environment of high-grade highways, a 10% decrease in fatigue life caused by corrosion is used as the warning threshold, and a corrosion rate threshold of 0.15mm / year is set after 20 years of service in the bearing monitoring section.

[0137] Furthermore, the bearing capacity attenuation rate is calculated to guide the replacement of bearings in the monitoring section. The bearing capacity attenuation rate is calculated as follows:

[0138] ,

[0139] in: is the bearing capacity attenuation rate; is the bending moment actually measured in the load test; The bending moment is calculated theoretically.

[0140] When the bearing capacity decay rate exceeds 20%, the bearings in the monitored section are deemed to need replacement. This threshold is determined based on bearing safety margins and bridge design specifications to ensure the safety of the bridge in both normal use and extreme conditions.

[0141] Reference Figure 1 As shown, the bridge bearing corrosion current field imaging monitoring system provided by the present invention mainly includes the following components:

[0142] The sensor network 1 includes a matrix electrochemical sensor 11, a temperature sensor 12 and a strain sensor 13 pre-installed on a support steel plate, and is used to collect electrode inductance data, temperature data and strain data.

[0143] The matrix electrochemical sensor 11 utilizes a three-electrode system, consisting of a working electrode (platinum), a reference electrode (silver / silver chloride), and an auxiliary electrode (graphite). The sensors are arranged within the strain monitoring range, located between 2 / 3 and 3 / 4 of the upper flange of the support, with spacing of 5 to 15 mm in the X and Y directions, forming a regular grid.

[0144] The temperature sensor 12 adopts a PT100 platinum resistance temperature sensor with an accuracy of ±0.1°C. It is installed on the support surface and the environment respectively to monitor temperature changes in real time.

[0145] The strain sensor 13 uses a resistance strain gauge with an accuracy of 1με (microstrain) and is installed at the key stress points of the support steel plate to monitor the strain state of the support.

[0146] The data acquisition and transmission unit 2 includes a multi-channel synchronous data acquisition module and a data transmission network, which is used to collect and transmit sensor data at a sampling frequency of 0.1 to 0.5 Hz.

[0147] The multi-channel synchronous data acquisition module supports 32 to 128 channels of synchronous data acquisition, with 16-bit sampling accuracy and an adjustable sampling frequency from 0.1 to 10 Hz. The module features a built-in low-noise preamplifier (signal-to-noise ratio >80dB) and multi-stage filtering circuits to effectively remove power frequency interference and high-frequency noise.

[0148] The data transmission network supports multiple wireless communication methods, including 4G / 5G / LoRa, and provides RS485 / Ethernet wired interfaces as backup. The network utilizes a dual-channel redundant design to ensure reliable data transmission. The transmission module utilizes a low-power design, with battery life exceeding six months.

[0149] The signal processing unit 3 includes an adaptive quantum Kalman filter module for eliminating the ohmic resistance drift caused by temperature and constructing a resistance-current density distribution map.

[0150] The adaptive quantum Kalman filter module implements the filtering algorithm described in Example 1, including dynamic separation of temperature-resistance coupling, quantum-inspired adaptive parameter optimization, and state estimation and prediction. This module uses a real-time processing architecture with a processing delay of less than 1 second, meeting the needs of near-real-time monitoring.

[0151] The current field reconstruction unit 4 includes a back propagation impedance tomography module for constructing a current density distribution diagram of the support cross section.

[0152] The backpropagation electrical impedance tomography module implements the current field reconstruction algorithm described in Example 1, including support geometry model construction, current field equation solution, backpropagation optimization, and image enhancement. This module uses GPU-accelerated computing to improve the speed and accuracy of current field reconstruction.

[0153] The analysis and decision-making unit 5 is used to calculate the local corrosion rate of the bearing steel plate. When the corrosion rate exceeds 0.15 mm / year, the early warning system is triggered, and the bearing capacity attenuation rate is calculated to guide the replacement of the bearing in the monitoring section.

[0154] The unit includes a corrosion rate calculation module, an early warning trigger module, and a bearing capacity attenuation assessment module. The corrosion rate calculation module calculates the corrosion rate of each area of ​​the bearing based on the current density distribution map; the early warning trigger module sets a multi-level early warning mechanism, triggering the corresponding level of warning when the corrosion rate exceeds the threshold; and the bearing capacity attenuation assessment module calculates the bearing capacity attenuation rate. When the attenuation rate exceeds 20%, it determines that the bearing needs to be replaced.

[0155] The entire system adopts a modular design, with each functional unit connected via standard interfaces for easy maintenance and upgrades. The system supports remote monitoring and management, providing web and mobile user interfaces for real-time monitoring data viewing, historical data analysis, and early warning information push.

[0156] Example 3:

[0157] The bridge bearing corrosion current field imaging monitoring method and system of the present invention have been applied to the plate bearings of a large highway bridge. The bridge is located in a coastal area with strong atmospheric corrosion, and the bearings began to show signs of corrosion after 10 years of service.

[0158] During the application process, a finite element model was first constructed based on the bearing's dimensions and structural characteristics to identify key monitoring areas. An 8x8 electrochemical sensor matrix was then installed between 2 / 3 and 3 / 4 of the bearing's upper flange, with a sensor spacing of 10 mm, covering an area of ​​approximately 0.64 m². Four temperature sensors and 12 strain sensors were also installed to form a complete monitoring network.

[0159] The system continuously collects data at a base sampling rate of 0.1Hz, automatically increasing to 0.5Hz when anomalies are detected. The collected data is processed by an adaptive quantum Kalman filter, effectively eliminating resistance drift caused by temperature variations (daily fluctuations of approximately 15°C and seasonal fluctuations of approximately 40°C), limiting temperature effects to within ±0.5%.

[0160] Using backpropagation electrical impedance tomography (EIT) technology, the system successfully constructed a current density distribution map of the bearing cross section, clearly demonstrating the spatial distribution and development trend of corrosion. During monitoring, the corrosion rate in the southeast corner of the bearing reached 0.18 mm / year, exceeding the warning threshold of 0.15 mm / year, triggering an automatic warning. Further calculations revealed a 17% reduction in bearing capacity, which falls short of the 20% replacement threshold. However, the system recommends increasing monitoring frequency and addressing the issue during the next maintenance cycle.

[0161] Application results demonstrate that the monitoring method and system of this invention can accurately identify the corrosion status of bridge bearings, proactively identify potential risks, provide a scientific basis for bridge maintenance, and effectively extend the safe service life of bridges. Compared with traditional manual inspections, this system shortens the inspection cycle from six months to real-time monitoring and improves the corrosion identification accuracy from millimeters to microns, significantly improving the efficiency and accuracy of bridge bearing health monitoring.

[0162] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. The bridge support corrosion current field imaging monitoring method is characterized by: include: A matrix electrochemical sensor is pre-installed on the support steel plate to collect the inductance data of the electrode at a sampling frequency of 0.1 to 0.5 Hz; Based on the collected inductance data, an adaptive quantum Kalman filter is used to eliminate the temperature-induced ohmic resistance drift and construct a resistance-current density distribution map. Based on the constructed resistance-current density distribution map, the back propagation impedance tomography technique was used to construct the current density distribution map of the support cross section. When the local corrosion rate of the support steel plate exceeds 0.15mm / year, the early warning system is triggered to calculate the support bearing capacity attenuation rate to guide the replacement of the monitoring section support.

2. The bridge bearing corrosion current field imaging monitoring method according to claim 1 is characterized in that: Before pre-installing the matrix electrochemical sensor on the support steel plate, the method further includes: A finite element model of bearing corrosion was constructed. Combined with the data from strain gauges embedded in the bearing steel plates during the bridge load test, the bearing capacity and the evolution of rust spots on the bearing surface were simulated using finite element software, and the correlation between strain distribution and bearing flexural stiffness was analyzed.

3. The bridge bearing corrosion current field imaging monitoring method according to claim 2 is characterized in that: The correlation between the analytical strain distribution and the support bending stiffness includes: Combining the measured response of the bearing with the parameters determined by finite element simulation, a parameter library of this type of bearing is constructed to provide a basis for subsequent strain-stress conversion; The stress distribution of the support steel plate is obtained through stress-strain transformation, and the bending stiffness of the tensile side of the support is calculated using the principle of elastic strain energy.

4. The bridge bearing corrosion current field imaging monitoring method according to claim 1 is characterized in that: The matrix electrochemical sensor pre-installed on the support steel plate includes: A matrix electrochemical sensor is pre-installed in the area corresponding to the tensile side of the support steel plate, and the sensor installation accuracy is controlled within 1mm; The electrochemical sensor includes a working electrode, a reference electrode and an auxiliary electrode. The working electrode is a platinum electrode, the reference electrode is a silver / silver chloride electrode, and the auxiliary electrode is a graphite electrode.

5. The bridge bearing corrosion current field imaging monitoring method according to claim 4 is characterized in that: The electrochemical sensor is specifically preset within the strain monitoring range of 2 / 3 to 3 / 4 of the upper flange of the support. The sensor is fixed to the steel plate by adhesive. The preset direction is perpendicular to the force direction, and the spacing between the sensors in the X and Y directions is 5 to 15 mm.

6. The bridge bearing corrosion current field imaging monitoring method according to claim 1 is characterized in that: The sampling frequency of 0.1 to 0.5 Hz is determined by comparing the stress data and response data measured under static and dynamic conditions of the bridge, wherein: Under normal monitoring conditions, the electrode inductance data was collected using a sampling frequency of 0.1 Hz; When an abnormal change in resistance value is detected, the sampling frequency is automatically increased to 0.5Hz to capture the signal frequency change of local strain of the steel plate.

7. The bridge bearing corrosion current field imaging monitoring method according to claim 1 is characterized in that: Eliminating the ohmic resistance drift caused by temperature by using an adaptive quantum Kalman filter includes: Based on the topological separation technology of multi-dimensional state space, the resistance signal is decomposed into corrosion current component and temperature drift component; Using the uncertainty principle of quantum measurement theory, the system noise covariance and measurement noise covariance parameters of the filter are dynamically adjusted; The sliding window method is used to calculate the statistical characteristics of short-term signals, evaluate the complexity of noise distribution, and dynamically optimize the Kalman gain coefficient; Output filtered resistance change data to provide a basis for current density distribution reconstruction.

8. The bridge bearing corrosion current field imaging monitoring method according to claim 1 is characterized in that: The constructing of the resistance-current density distribution diagram includes: Apply the resistance-current density calibration curve to the filtered resistance data for preliminary conversion; Based on the currently collected temperature data, the temperature compensation coefficient is applied to eliminate the residual effect of temperature on the conversion relationship; Nonlinear correction is performed in combination with the support material properties to handle the special response in high current density areas; Output preliminary current density distribution data to provide input for back propagation electrical impedance tomography.

9. The bridge bearing corrosion current field imaging monitoring method according to claim 1 is characterized in that: The method of constructing the support cross-section current density distribution map using the back propagation electrical impedance tomography technology includes: Construct the support geometry model and unstructured mesh generation, and apply the finite element method to discretize the current field equations; Set the initial current density distribution and forward calculate the simulated resistance measurement value; Compare the simulated value with the actual measured value, calculate the residual, and backpropagate the residual to update the current density distribution; Apply current field topology constraints to maintain the physical rationality of current distribution; Combined with strain monitoring data, weighted correction is performed and image enhancement algorithms are applied to improve contrast and clarity; The calculation is iterated until the residual is less than the set threshold, and the final support cross-section current density distribution diagram is output.

10. A system for implementing the bridge bearing corrosion current field imaging monitoring method according to any one of claims 1 to 9, characterized in that: include: The sensor network includes a matrix electrochemical sensor, a temperature sensor, and a strain sensor pre-installed on the support steel plate, which is used to collect electrode inductance data, temperature data, and strain data; Data acquisition and transmission unit, including a multi-channel synchronous data acquisition module and a data transmission network, for collecting and transmitting sensor data at a sampling frequency of 0.1 to 0.5 Hz; Signal processing unit, including an adaptive quantum Kalman filter module, used to eliminate temperature-induced ohmic resistance drift and construct a resistance-current density distribution map; A current field reconstruction unit, including a back-propagation electrical impedance tomography module, is used to construct a current density distribution map of the support cross section; The analysis and decision-making unit is used to calculate the local corrosion rate of the support steel plate. When the corrosion rate exceeds 0.15mm / year, the early warning system is triggered, and the support bearing capacity attenuation rate is calculated to guide the replacement of the monitoring section support.

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