Intelligent evaluation method and system for bearing capacity of bridge cable in corrosion environment

Abstract

The invention relates to the technical field of bridge cable health monitoring, in particular to an intelligent evaluation method and system for the bearing capacity of a bridge cable in a corrosion environment, and the method achieves the real-time monitoring of a microcrack and a stress state through the deployment of a nano sensor and a strain sensor on the surface of the cable. After data acquisition, noise is eliminated and missing data is complemented through a data preprocessing module, so that the accuracy of the data is ensured; then, the system performs coupling analysis on the microcrack data and the stress data by using a multi-dimensional data fusion algorithm to generate a stress distribution diagram, and evaluates the influence of microcrack propagation on the bearing capacity; and based on the corrosion fatigue model, predicting the change trend of the bearing capacity of the inhaul cable in a period of time in the future, and generating a detailed evaluation report. When the bearing capacity is reduced to a safety threshold value, early warning is automatically triggered, and maintenance suggestions are provided, so that effective support is provided for safety management of the bridge; according to the method, the safety and the operation efficiency of the bridge are improved, and the accident risk caused by structural failure is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge cable health monitoring, and in particular to an intelligent evaluation method and system for the bearing capacity of bridge cables in a corrosive environment. Background Art

[0002] With the acceleration of urbanization, the safety and reliability of bridges as important transportation infrastructure have received increasing attention. Microcracks refer to tiny cracks on the surface or inside of materials caused by factors such as stress, corrosion or fatigue. These cracks are usually in the range of a few microns to a few millimeters. Although they are small in size, they can expand rapidly under long-term load and environmental influences, seriously affecting the bearing capacity of the material. Especially in corrosive environments, bridge cables are prone to corrosion and fatigue damage due to long-term erosion by loads and environmental factors, resulting in a gradual decrease in the bearing capacity of the cables. Corrosion damage to cables will not only affect the overall safety of the bridge, but may also lead to structural failure, thus posing a threat to traffic safety.

[0003] However, many bridge monitoring technologies currently rely on traditional manual inspections and regular maintenance, which often fail to detect potential problems in a timely manner and make it difficult to accurately assess the actual bearing capacity changes of cables. The deficiencies of existing technologies in data collection and analysis have led to a lag in monitoring corrosion-induced microcracks and their impact on bearing capacity. In addition, existing technologies lack the ability to effectively integrate and analyze multiple data sources (such as microcrack data and stress data), making it impossible to comprehensively assess the status of bridge cables. Summary of the invention

[0004] The invention provides a method and system for intelligently evaluating the bearing capacity of bridge cables in a corrosive environment.

[0005] An intelligent evaluation method for the bearing capacity of bridge cables in a corrosive environment comprises the following steps:

[0006] S1, nanosensor deployment: nanosensors are uniformly deployed on the surface of bridge cables. The nanosensors monitor the formation and expansion process of corrosion-induced microcracks on the cable surface in real time. The nanosensors detect the generation of cracks through changes in self-induced electric fields and transmit data to a data analysis unit in real time.

[0007] S2, stress data collection: strain sensors are arranged at key locations of bridge cables to monitor the stress distribution of the cables under different load conditions in real time. The stress data collected by the strain sensors are transmitted to the data analysis unit via a wireless network;

[0008] S3, microcrack detection and data collection: the nanosensor collects microcrack data in real time, and the microcrack data includes the size, position and expansion speed of the microcrack;

[0009] S4, data preprocessing: preprocess the collected microcrack data and stress data, use noise filtering and data completion algorithms to remove environmental noise interference and complete the lost data to ensure the integrity and accuracy of the data;

[0010] S5, coupled analysis of stress distribution and microcrack extension: through the multi-dimensional data fusion algorithm, the pre-processed microcrack data is coupled with the stress data to generate the stress distribution map of the cable surface, and the influence of microcrack extension on the local stress of the cable is analyzed;

[0011] S6, bearing capacity prediction and safety assessment: Based on the trend of corrosion-induced microcrack extension and stress distribution diagram, a corrosion fatigue model is constructed to evaluate the impact of corrosion on the bearing capacity of the cables, and predict the changing trend of the bearing capacity in the future. A comprehensive assessment is made on the safety of the overall bridge structure. When the bearing capacity drops to the preset safety threshold, an early warning is automatically triggered and maintenance recommendations are generated.

[0012] Optionally, the S1 specifically includes:

[0013] S11, sensor layout design: Determine the layout of nanosensors based on the structural characteristics, corrosion environment and load distribution of bridge cables;

[0014] S12, sensor installation preparation: treat the cable surface to ensure that the nanosensor is in close contact with the cable surface. The treatment method includes removing surface dirt, corrosion and moisture;

[0015] S13, sensor installation: according to the designed layout plan, the nanosensors are evenly deployed at the key parts of the bridge cables (such as the connection between the cables and the anchors);

[0016] S14, sensor calibration: after completing the installation, perform preliminary calibration on the nanosensor;

[0017] S15, data transmission module connection: connecting the installed nanosensor to the data transmission unit, and transmitting the real-time collected microcrack data (including crack generation and expansion) to the data analysis unit via a wireless network or wired mode;

[0018] S16, adaptively adjust the acquisition frequency: dynamically adjust the acquisition frequency of the nanosensor according to the changes in the corrosion environment of the bridge cables.

[0019] Optionally, the S2 specifically includes:

[0020] S21, strain sensor layout planning: determine the layout location of the strain sensor according to the structure and load distribution of the bridge cable;

[0021] S22, strain sensor installation preparation: clean the cable surface where the strain sensor is installed to remove surface impurities, corrosion and oil stains;

[0022] S23, strain sensor installation: according to the layout plan, the strain sensors are installed at key locations on the surface of the bridge cables;

[0023] S24, strain sensor calibration: after the strain sensor is installed, perform sensor calibration;

[0024] S25, real-time collection of stress data: after the strain sensor is installed and calibrated, the stress data of the cable under different working conditions is collected in real time;

[0025] S26, data transmission and storage: The stress data collected by the strain sensor is transmitted to the data analysis unit via a wireless network or a wired connection.

[0026] Optionally, the S3 specifically includes:

[0027] S31, Microcrack size monitoring: The nanosensor monitors the size changes of microcracks on the cable surface in real time. The sensor determines the length and width of the crack by sensing the electric field changes in the microcrack area;

[0028] S32, microcrack location positioning: through the collaborative work of multiple nanosensors, the specific location of the microcrack is determined using a triangulation positioning algorithm;

[0029] S33, microcrack growth rate calculation: based on the real-time monitoring data of microcrack size by nanosensors, the crack growth rate is calculated;

[0030] S34, real-time update of microcrack status: the nanosensor transmits the collected microcrack size, position and expansion speed data to the data analysis unit in real time.

[0031] Optionally, the S4 specifically includes:

[0032] S41, Application of noise filtering algorithm: Noise filtering of microcrack data and stress data collected by nanosensors;

[0033] S42, lost data completion algorithm: A completion algorithm based on Kalman filtering is used to complete the lost data caused by sensor failure or data transmission interruption;

[0034] S43, data de-redundancy processing: in the data pre-processing process, data de-redundancy technology is used to identify and remove duplicate or invalid data;

[0035] S44, data smoothing: using a low-pass filter to smooth the noise-filtered and completed data;

[0036] S45, data integrity check: after the preprocessing is completed, perform a data integrity check;

[0037] S46, data preprocessing result storage: the data that has undergone integrity check is stored in the data analysis unit in the order of timestamps.

[0038] Optionally, the S5 specifically includes:

[0039] S51, multi-dimensional data fusion model construction: construct a multi-dimensional data fusion model to comprehensively process microcrack data (including size, location and expansion speed) and stress data (including stress distribution under different working conditions);

[0040] S52, stress distribution map generation: using the preprocessed microcrack data and stress data, the stress distribution on the cable surface is calculated using a data fusion model, and a stress distribution map is generated using visualization software;

[0041] S53, Correlation analysis between crack extension and stress change: Through stress distribution diagram, quantitative analysis of the correlation between crack extension and stress change is carried out;

[0042] S54, Identification of local stress concentration areas: Identify key areas of local stress concentration through stress distribution diagrams and correlation analysis, and generate stress concentration reports;

[0043] S55, stress distribution and crack growth trend prediction: Through the data fusion algorithm, the historical micro-crack growth and stress data are regressed and analyzed to predict the trend of the impact of crack growth on local stress distribution in the future;

[0044] S56, data analysis result output and storage: by performing regression analysis on historical microcrack growth data and stress data, a crack growth trend graph is generated, and the generated crack growth trend graph, stress distribution graph and stress concentration report are stored in the data analysis unit.

[0045] Optionally, the S6 includes:

[0046] S61, Construction of corrosion fatigue model: Based on the preprocessed microcrack data and stress distribution diagram, a corrosion fatigue model is established;

[0047] S62, bearing capacity impact assessment: using the constructed corrosion fatigue model, analyze the impact of corrosion-induced microcracks on the cable bearing capacity, calculate the current bearing capacity by inputting real-time microcrack data and stress data, and output the bearing capacity assessment results;

[0048] S63, storage and analysis of assessment results: the bearing capacity assessment results are stored in the data analysis unit to facilitate subsequent query and tracking, and a corresponding assessment report is generated based on the assessment results.

[0049] Optionally, S6 further includes:

[0050] S64, prediction of bearing capacity change trend: after completing the construction of the corrosion fatigue model, based on the real-time data input into the model, the time series analysis method is used to predict the bearing capacity change trend of the cable in the future;

[0051] S65, Trend analysis and report generation: Generate a bearing capacity change trend chart based on the prediction results, display the dynamic changes of future bearing capacity, identify potential danger points, and generate an assessment report, which includes the prediction of future bearing capacity values, the expected trend of crack extension, and the analysis of the impact on bearing capacity;

[0052] S66, Risk Assessment and Recommendations: Based on the analysis of the bearing capacity change trend, potential risks are assessed and corresponding maintenance and management recommendations are put forward.

[0053] Optionally, the S6 further includes:

[0054] S67, Comprehensive safety assessment: Based on the bearing capacity assessment results and bearing capacity change trend of the corrosion fatigue model, a comprehensive safety assessment of the overall structure of the bridge is conducted. During the assessment, the status of each cable, the extension degree of microcracks and their impact on the overall structure are considered, and a safety report is formed;

[0055] S68, safety threshold monitoring: set the bearing capacity safety threshold, monitor the relationship between the current bearing capacity and the preset threshold in real time, and when the bearing capacity drops to the safety threshold, the system will automatically trigger the early warning mechanism;

[0056] S69, early warning system and maintenance suggestion generation: When the early warning mechanism is triggered, detailed early warning information is automatically generated, including the current bearing capacity status, crack propagation trend and stress distribution, and corresponding maintenance suggestions are put forward.

[0057] An intelligent assessment system for the bearing capacity of bridge cables in a corrosive environment is used to implement the above-mentioned intelligent assessment method for the bearing capacity of bridge cables in a corrosive environment, and includes the following modules:

[0058] Nanosensor module: Nanosensors are evenly deployed on the surface of bridge cables to monitor the formation and expansion of corrosion-induced microcracks in real time. The sensors detect microcracks through changes in self-induced electric fields and transmit data to the data analysis unit;

[0059] Strain sensor module: strain sensors are placed at key locations of bridge cables (such as the connection with anchors, contact points with bridge towers or piers) to monitor the stress distribution of the cables under different load conditions in real time and transmit the stress data to the data analysis unit:

[0060] Data acquisition module: The nanosensor collects microcrack data in real time, and the microcrack data includes the size, position and expansion speed of the microcrack;

[0061] Data preprocessing module: noise filtering, data completion and redundancy removal for microcrack data and stress data;

[0062] Data analysis unit: Through the multi-dimensional data fusion algorithm, the pre-processed microcrack data and stress data are coupled and analyzed to generate stress distribution diagrams, and the influence of microcrack extension on local stress is analyzed;

[0063] Bearing capacity prediction and safety assessment module: Based on the corrosion fatigue model, it evaluates the impact of corrosion on the bearing capacity of the cable and predicts the change trend of the future bearing capacity;

[0064] User interface and report generation module: provides an intuitive user interface to display real-time monitoring data, bearing capacity trend chart, stress distribution chart and evaluation report.

[0065] Beneficial effects of the present invention:

[0066] The present invention realizes real-time monitoring of microcracks and stress states by deploying nanosensors and strain sensors on the surface of bridge cables. This real-time data acquisition and analysis capability can timely detect potential problems at the early stage of corrosion-induced microcrack formation, ensure early warning before the bearing capacity drops to the safety threshold, significantly reduce the risk of structural failure, and improve the safety of bridges.

[0067] The present invention uses a multi-dimensional data fusion algorithm of a data preprocessing module and a data analysis unit to enable the system to efficiently process microcrack data and stress data. This high-precision data analysis not only generates an accurate stress distribution map, but also quantifies the effect of microcrack extension on local stress, thereby providing a more accurate bearing capacity assessment. Such an assessment result provides a reliable basis for the daily maintenance and management of bridges, and helps to extend the service life of the structure.

[0068] The load-bearing capacity prediction and safety assessment module of the present invention is based on the corrosion fatigue model, which can predict the load-bearing capacity change trend of the cable in the future and generate a detailed assessment report. This function enables bridge managers to take appropriate maintenance and management measures before potential risks appear, optimize maintenance strategies, reduce operating costs, and improve the overall operation efficiency and safety of the bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0071] Figure 2 Schematic diagram of a system flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0073] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0074] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0075] like Figure 1 As shown, a method for intelligently evaluating the bearing capacity of bridge cables in a corrosive environment comprises the following steps:

[0076] S1, nanosensor deployment: nanosensors are evenly deployed on the surface of bridge cables. The nanosensors monitor the formation and expansion of corrosion-induced microcracks on the cable surface in real time. The nanosensors detect the generation of cracks through changes in self-induced electric fields and transmit the data to the data analysis unit in real time.

[0077] S2, stress data acquisition: strain sensors are arranged at key locations of bridge cables (such as the connection between cables and anchors, and the contact points between cables and bridge towers or piers) to monitor the stress distribution of cables under different load conditions in real time. The stress data collected by the strain sensors are transmitted to the data analysis unit via a wireless network for subsequent coupled analysis of stress and microcrack extension.

[0078] S3, microcrack detection and data acquisition: Nanosensors collect microcrack data in real time. Microcrack data includes the size, location and expansion speed of microcracks. Nanosensors capture microscopic corrosion damage in the early stage of microcrack formation, ensuring high-precision monitoring of the dynamic development of microcracks.

[0079] S4, data preprocessing: preprocess the collected microcrack data and stress data, use noise filtering and data completion algorithms to remove environmental noise interference and complete the lost data to ensure the integrity and accuracy of the data;

[0080] S5, coupled analysis of stress distribution and microcrack extension: through the multi-dimensional data fusion algorithm, the pre-processed microcrack data is coupled with the stress data to generate the stress distribution map of the cable surface, and the influence of microcrack extension on the local stress of the cable is analyzed;

[0081] S6, bearing capacity prediction and safety assessment: Based on the trend of corrosion-induced microcrack extension and stress distribution diagram, a corrosion fatigue model is constructed to evaluate the impact of corrosion on the bearing capacity of the cables, and predict the changing trend of the bearing capacity in the future. A comprehensive assessment is made on the safety of the overall bridge structure. When the bearing capacity drops to the preset safety threshold, an early warning is automatically triggered and maintenance recommendations are generated.

[0082] S1 specifically includes:

[0083] S11, sensor layout design: Based on the structural characteristics, corrosion environment and load distribution of the bridge cables, determine the layout of the nanosensors. The plan must ensure that the nanosensors are evenly distributed on the surface of the cables, especially at the connection between the cables and anchors, the contact points between the cables and bridge towers or piers, and other areas where stress concentration may occur, so as to effectively capture the early formation and expansion of corrosion-induced microcracks;

[0084] S12, sensor installation preparation: treat the cable surface to ensure that the nanosensor is in close contact with the cable surface. The treatment method includes removing surface dirt, corrosion and moisture to ensure that the installation area is clean and dry. At the same time, select appropriate adhesive materials to firmly attach the nanosensor to the cable surface and ensure that it has good durability in harsh environments such as high humidity and high salt spray;

[0085] S13, sensor installation: according to the designed layout plan, the nano sensors are evenly deployed at the key parts of the bridge cables (such as the connection between the cables and the anchors). During the installation process, ensure that the arrangement direction of the sensors is consistent with the stress direction of the cables to avoid damage to the sensors during the installation process. After the installation is completed, check the physical state of each sensor to ensure its stable operation;

[0086] S14, sensor calibration: after installation, the nanosensor is preliminarily calibrated. The calibration step includes testing the response sensitivity of the nanosensor in a controlled corrosion environment to ensure that it can accurately monitor the generation and expansion process of microcracks. By comparing with standard crack samples, the sensor's induction electric field range is adjusted to ensure the accuracy of the monitoring data.

[0087] S15, data transmission module connection: connecting the installed nanosensor to the data transmission unit, and transmitting the real-time collected microcrack data (including crack generation and expansion) to the data analysis unit via a wireless network or wired mode;

[0088] S16, adaptively adjust the acquisition frequency: dynamically adjust the acquisition frequency of the nanosensor according to the changes in the corrosion environment of the bridge cable. When the corrosion risk is high (such as extreme weather or increased concentration of corrosive substances), the system automatically increases the acquisition frequency; when the corrosion risk is low, the acquisition frequency is reduced accordingly to reduce energy consumption;

[0089] Through detailed sensor layout, installation, calibration and data transmission, it is ensured that the nanosensors can accurately and in real time monitor the corrosion-induced microcracks on the cable surface, and optimize the monitoring efficiency and resource utilization by adaptively adjusting the acquisition frequency.

[0090] S2 specifically includes:

[0091] S21, strain sensor layout planning: According to the structure and load distribution of the bridge cable, the layout location of the strain sensor is determined, and it is preferably arranged at the key parts of the bridge cable, including the connection between the cable and the anchor, the contact point between the cable and the bridge tower or pier, and other stress concentration areas, so as to capture the stress distribution of the cable under different load conditions to the greatest extent;

[0092] S22, strain sensor installation preparation: clean the cable surface where the strain sensor is installed, remove surface impurities, corrosion and oil, ensure that the strain sensor can fit tightly with the cable surface, and avoid stress monitoring errors caused by unclean surface;

[0093] S23, strain sensor installation: according to the layout plan, strain sensors are installed on key parts of the surface of the bridge cables. The installation of strain sensors needs to consider the alignment of their direction with the direction of stress change of the cables to ensure that the strain sensors can accurately sense the stress changes of the cables. After installation, the physical state and signal transmission function of each sensor need to be checked to ensure that it is firmly installed and the data transmission is normal;

[0094] S24, strain sensor calibration: After the strain sensor is installed, the sensor is calibrated. The calibration steps include applying a known load and using a calibration tool to detect the relationship between the stress data output by the sensor and the actual load to ensure that the accuracy of the strain sensor meets the requirements. The calibration also needs to consider the impact of external factors such as ambient temperature and humidity on the sensor to ensure its measurement accuracy in different environments;

[0095] S25, real-time collection of stress data: After the strain sensor is installed and calibrated, it starts to collect the stress data of the cable under different working conditions in real time. According to the dynamic load conditions of the bridge, such as traffic flow, wind force and temperature changes, the collection frequency of the sensor is adjusted in real time to ensure the representativeness and timeliness of the collected data;

[0096] S26, data transmission and storage: The stress data collected by the strain sensor is transmitted to the data analysis unit via a wireless network or a wired connection. All stress data are stored in the data analysis unit in chronological order to provide data support for the subsequent coupled analysis of stress and microcrack extension.

[0097] S3 specifically includes:

[0098] S31, Micro-crack size monitoring: Nano sensors monitor the size changes of micro-cracks on the cable surface in real time. The sensor determines the length and width of the crack by sensing the electric field changes in the micro-crack area. Through high-precision nano-level detection technology, the sensor can accurately measure the crack size to the micron level, ensuring accurate monitoring of the early stage of micro-crack formation;

[0099] S32, microcrack location: Through the collaborative work of multiple nano-sensors, the specific location of the microcrack is determined using a triangulation positioning algorithm. Each sensor transmits data based on the electric field change and time difference. The location of the microcrack is calculated as:

[0100]

[0101] Among them, L crack Indicates the center position of the microcrack, L1, L2, ..., L n It represents the crack location information detected by different sensors, and n represents the number of sensors involved in the positioning calculation;

[0102] S33, calculation of microcrack growth rate: Based on the real-time monitoring data of microcrack size by nanosensors, the crack growth rate is calculated and expressed as:

[0103]

[0104] Among them, V crack is the growth rate of microcracks, ΔL is the change in crack size over a period of time, and Δt is the time interval. This calculation can reflect the dynamic process of crack growth in real time and help to accurately evaluate the development trend of microcracks.

[0105] S34, real-time update of microcrack status: the nanosensor transmits the collected microcrack size, position and expansion speed data to the data analysis unit in real time. The data analysis unit updates the microcrack status in real time by monitoring the microcrack changes at each time point, and generates a time series diagram of crack expansion to ensure continuous monitoring of crack development;

[0106] Through the high-precision detection of nanosensors, the dynamic changes of corrosion damage can be captured in the early stage of microcrack formation. Through real-time data transmission and processing, high-precision monitoring of the dynamic development of microcracks is ensured, laying an important data foundation for subsequent bearing capacity assessment.

[0107] S4 specifically includes:

[0108] S41, Application of noise filtering algorithm: The noise of microcrack data and stress data collected by nanosensors is filtered out by using a noise suppression algorithm based on wavelet transform. The calculation of noise filtering is expressed as:

[0109] X(t)=∑ k c k ·ψ k (t);

[0110] Where X(t) is the filtered signal, c k is the wavelet coefficient, ψ k (t) is the wavelet basis function. This method effectively removes the high-frequency noise generated by environmental factors (such as electromagnetic interference and wind noise) and retains the effective components of microcracks and stress signals;

[0111] S42, missing data completion algorithm: A completion algorithm based on Kalman filtering is used to complete the lost data caused by sensor failure or data transmission interruption. Kalman filtering predicts and completes the lost data through dynamically updated state estimation. Its state prediction formula is expressed as:

[0112]

[0113] in, is the state prediction of the current time step, A is the state transfer matrix, is the state estimate of the previous time step, B is the control matrix, u k To control the input, the missing stress and microcrack data are effectively supplemented through dynamic estimation of the previous and next data to ensure data continuity;

[0114] S43, data de-redundancy processing: In the data pre-processing process, data de-redundancy technology is used to identify and remove duplicate or invalid data. Through the correlation analysis of data in adjacent time periods, the system marks highly similar or duplicate sensor data as redundant data and automatically removes them to reduce the impact of data redundancy on subsequent analysis;

[0115] S44, data smoothing: A low-pass filter is used to smooth the data after noise filtering and completion. The smoothing algorithm eliminates mutation points in the data by local averaging the data to ensure the smoothness and continuity of the data, which is expressed as:

[0116]

[0117] Among them, y[i] is the smoothed data value, x[j] is the original data, and n is the window size;

[0118] S45, data integrity check: after the preprocessing is completed, a data integrity check is performed, which includes verifying whether the data of all sensors are complete, ensuring that the microcrack and stress data in each time period have a reasonable value range, and no data anomalies or missing data occur;

[0119] S46, data preprocessing result storage: the data after integrity check is stored in the data analysis unit in the order of timestamps. All preprocessed data will serve as the basis for subsequent stress and microcrack extension coupling analysis to ensure the accuracy and integrity of the data;

[0120] Through detailed data noise filtering, missing data completion, de-redundancy processing and smoothing processing, the high quality and continuity of microcrack and stress data are ensured, the impact of environmental noise on data analysis is reduced, and the reliability of data is ensured through integrity checks, providing a solid data foundation for subsequent bearing capacity assessment.

[0121] S5 specifically includes:

[0122] S51, multi-dimensional data fusion model construction: construct a multi-dimensional data fusion model, and comprehensively process the microcrack data (including size, position and expansion speed) and stress data (including stress distribution under different working conditions). The multi-dimensional data fusion model uses a linear regression algorithm to spatially couple the data of different sensors at the same time point, which is expressed as:

[0123] F total (x,y,t)=α1·S crack (x,y,t)+α2·σ(x,y,t)+α3·v crack (x,y,t);

[0124] Among them, F total (x, y, t) represents the total stress distribution at position (x, y) and time r, S crack (x, y, t) is the microcrack size, σ(x, y, t) is the stress value, v crack (x, y, t) is the crack growth rate, α1, α2, α3 are the weight coefficients of data fusion;

[0125] S52, stress distribution map generation: The stress distribution on the cable surface is calculated using the preprocessed microcrack data and stress data using a data fusion model, and a stress distribution map is generated using visualization software to show the effect of microcrack extension on the local stress of the cable, especially in areas where crack extension is fast or stress is concentrated. The stress distribution map intuitively represents the stress magnitude of different areas through color gradients, which is convenient for analyzing the stress status of various parts of the cable surface;

[0126] S53, Correlation analysis between crack extension and stress change: Through the stress distribution diagram, the correlation between crack extension and stress change is quantitatively analyzed and expressed as:

[0127]

[0128] Where R is the parallel coefficient of crack extension and stress change, Δσ is the change in local stress, and ΔL crack The correlation analysis can help identify the areas where crack extension has a significant impact on the local stress distribution and provide a basis for risk assessment.

[0129] S54, Identification of local stress concentration areas: Through stress distribution diagrams and correlation analysis, key areas of local stress concentration (such as the connection between cables and anchors, and the contact points between cables and bridge towers or piers) are identified. These areas are often where cracks propagate and stress changes drastically, and are high-risk areas for structural damage. The system automatically marks these high-risk areas according to the degree of stress concentration and generates a stress concentration report.

[0130] S55, stress distribution and crack growth trend prediction: Through the data fusion algorithm, the historical micro-crack growth and stress data are regressed and analyzed to predict the trend of the impact of crack growth on local stress distribution in the future. This trend analysis helps to warn of potential structural damage in advance and take repair measures in time. The prediction formula is expressed as:

[0131] σfuture (t+Δt)=σ(t)+β·v crack Δt;

[0132] Among them, σ future (t+Δt) is the stress value at the future moment, σ(t) is the current stress value, β is the expansion influence coefficient, and v crack is the crack growth rate, Δt is the predicted time interval;

[0133] S56, data analysis result output and storage: by performing regression analysis on historical micro-crack growth data and stress data, a crack growth trend graph is generated to show the crack growth path, speed and its influence on local stress distribution in the future period, and the generated crack growth trend graph, stress distribution graph and stress concentration report are stored in the data analysis unit for subsequent inspection and analysis. At the same time, the system generates an automated analysis report to provide support for subsequent bearing capacity assessment;

[0134] Through multi-dimensional data fusion models and stress distribution maps, the system can accurately identify stress concentration areas and predict future stress distribution change trends, thereby providing real-time assessment and early warning for structural safety.

[0135] S6 includes:

[0136] S61, Construction of corrosion fatigue model: Based on the preprocessed microcrack data and stress distribution diagram, a corrosion fatigue model is established. The model expression is:

[0137]

[0138] Among them, σ f is fatigue stress, σ0 is initial stress, N is number of cycles, N0 is fatigue limit, β is stress attenuation coefficient. By associating historical crack growth data with current stress state, model parameters are dynamically adjusted to adapt to actual corrosion conditions and load changes.

[0139] S62, bearing capacity impact assessment: using the constructed corrosion fatigue model, analyze the impact of corrosion-induced microcracks on the cable bearing capacity, calculate the current bearing capacity by inputting real-time microcrack data and stress data, and output the bearing capacity assessment results, which include the current bearing capacity value, potential fatigue damage and its impact on the overall structure;

[0140] S63, storage and analysis of assessment results: the bearing capacity assessment results are stored in the data analysis unit for subsequent query and tracking, and a corresponding assessment report is generated based on the assessment results to record the current status and changes in corrosion conditions, providing data support for subsequent decision-making;

[0141] By establishing a corrosion fatigue model and evaluating the impact of corrosion on the bearing capacity of cables, an accurate assessment of the health status of the structure is ensured, providing an important basis for subsequent safety warnings and maintenance decisions.

[0142] The S6 also includes:

[0143] S64, prediction of bearing capacity change trend: After the construction of the corrosion fatigue model is completed, the time series analysis method is used to predict the bearing capacity change trend of the cable in the future based on the real-time data input into the model; specifically, the dynamic law of bearing capacity change is determined by regression analysis of historical bearing capacity data, and the prediction formula is expressed as:

[0144] P future (t+Δt)=P(t)+ΔP(t);

[0145] Among them, P future (t+Δt) represents the bearing capacity at a future time, P(t) is the current bearing capacity value, and ΔP(t) represents the change in bearing capacity based on historical data and crack growth prediction;

[0146] S65, Trend analysis and report generation: Generate a bearing capacity change trend chart based on the prediction results, display the dynamic changes of future bearing capacity, identify potential danger points, and generate an assessment report, which includes the prediction of future bearing capacity values, the expected trend of crack extension, and the analysis of the impact on bearing capacity;

[0147] S66, Risk Assessment and Recommendations: Based on the analysis of the bearing capacity change trend, potential risks are assessed and corresponding maintenance and management recommendations are put forward. If the forecast shows that the bearing capacity will be lower than the safety threshold at some point in the future, an alarm is triggered and corresponding maintenance recommendations are provided;

[0148] By predicting the trend of changes in bearing capacity, it helps identify potential risks and provides a scientific basis for subsequent maintenance and repair measures to ensure the long-term safety of the bridge structure.

[0149] S6 further includes:

[0150] S67, Comprehensive safety assessment: Based on the bearing capacity assessment results and bearing capacity change trend of the corrosion fatigue model, a comprehensive safety assessment of the overall structure of the bridge is conducted. During the assessment, the status of each cable, the extension degree of microcracks and their impact on the overall structure are considered, and a safety report is formed;

[0151] S68, safety threshold monitoring: set the bearing capacity safety threshold, monitor the relationship between the current bearing capacity and the preset threshold in real time, and when the bearing capacity drops to the safety threshold, the system will automatically trigger the early warning mechanism;

[0152] S69, early warning system and maintenance suggestion generation: When the early warning mechanism is triggered, detailed early warning information is automatically generated. The early warning information includes the current bearing capacity status, crack extension trend and stress distribution, and corresponding maintenance suggestions are put forward. The early warning information is sent to relevant maintenance personnel via the wireless network to ensure timely response.

[0153] like Figure 2 As shown, a bridge cable bearing capacity intelligent assessment system in a corrosive environment is used to implement the above-mentioned bridge cable bearing capacity intelligent assessment method in a corrosive environment, and includes the following modules:

[0154] Nanosensor module: Nanosensors are evenly deployed on the surface of bridge cables to monitor the formation and expansion of corrosion-induced microcracks in real time. The sensors detect microcracks through changes in self-induced electric fields and transmit data to the data analysis unit;

[0155] Strain sensor module: strain sensors are placed at key locations of bridge cables (such as the connection with anchors, contact points with bridge towers or piers) to monitor the stress distribution of the cables under different load conditions in real time and transmit the stress data to the data analysis unit:

[0156] Data acquisition module: The nanosensor collects microcrack data in real time, and the microcrack data includes the size, position and expansion speed of the microcrack;

[0157] Data preprocessing module: performs noise filtering, data completion and redundancy removal on microcrack data and stress data to ensure data integrity and accuracy, and provide high-quality data support for subsequent analysis;

[0158] Data analysis unit: Through the multi-dimensional data fusion algorithm, the pre-processed microcrack data and stress data are coupled and analyzed to generate a stress distribution diagram and analyze the impact of microcrack extension on local stress. The data analysis unit is also responsible for generating a bearing capacity assessment report and recording the current status and changes in corrosion conditions;

[0159] Bearing capacity prediction and safety assessment module: Based on the corrosion fatigue model, it evaluates the impact of corrosion on the bearing capacity of the cables, predicts the future change trend of the bearing capacity, and provides risk assessment, alarm triggering and maintenance suggestions to ensure the long-term safety of the bridge structure;

[0160] User interface and report generation module: provides an intuitive user interface to display real-time monitoring data, bearing capacity trend chart, stress distribution chart and evaluation report. The system can automatically generate detailed evaluation reports to facilitate decision-making and maintenance by relevant personnel.

[0161] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0162] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent evaluation method for the bearing capacity of bridge cables in a corrosive environment, characterized in that: The following steps are involved: S1, nanosensor deployment: nanosensors are uniformly deployed on the surface of bridge cables. The nanosensors monitor the formation and expansion process of corrosion-induced microcracks on the cable surface in real time. The nanosensors detect the generation of cracks through changes in self-induced electric fields and transmit data to a data analysis unit in real time. S2, stress data collection: strain sensors are arranged at key locations of bridge cables to monitor the stress distribution of the cables under different load conditions in real time. The stress data collected by the strain sensors are transmitted to the data analysis unit via a wireless network; S3, microcrack detection and data collection: the nanosensor collects microcrack data in real time, and the microcrack data includes the size, position and expansion speed of the microcrack; S4, data preprocessing: preprocess the collected microcrack data and stress data, use noise filtering and data completion algorithms to remove environmental noise interference and complete the lost data; S5, coupled analysis of stress distribution and microcrack extension: through the multi-dimensional data fusion algorithm, the pre-processed microcrack data is coupled with the stress data to generate the stress distribution map of the cable surface, and the influence of microcrack extension on the local stress of the cable is analyzed; S6, bearing capacity prediction and safety assessment: Based on the trend of corrosion-induced microcrack extension and stress distribution diagram, a corrosion fatigue model is constructed to evaluate the impact of corrosion on the bearing capacity of the cables, and predict the changing trend of the bearing capacity in the future. A comprehensive assessment is made on the safety of the overall bridge structure. When the bearing capacity drops to the preset safety threshold, an early warning is automatically triggered and maintenance recommendations are generated.

2. The intelligent evaluation method for the bearing capacity of bridge cables in a corrosive environment according to claim 1 is characterized in that: The S1 specifically includes: S11, sensor layout design: Determine the layout of nanosensors based on the structural characteristics, corrosion environment and load distribution of bridge cables; S12, sensor installation preparation: treat the cable surface to ensure that the nanosensor is in close contact with the cable surface. The treatment method includes removing surface dirt, corrosion and moisture; S13, sensor installation: according to the designed layout plan, the nanosensors are evenly deployed at the key positions of the bridge cables; S14, sensor calibration: after completing the installation, perform preliminary calibration on the nanosensor; S15, data transmission module connection: connecting the installed nanosensor to the data transmission unit, and transmitting the real-time collected microcrack data to the data analysis unit via a wireless network or a wired method; S16, adaptively adjust the acquisition frequency: dynamically adjust the acquisition frequency of the nanosensor according to the changes in the corrosion environment of the bridge cables.

3. The intelligent evaluation method for the bearing capacity of bridge cables in a corrosive environment according to claim 2 is characterized in that: The S2 specifically includes: S21, strain sensor layout planning: determine the layout location of the strain sensor according to the structure and load distribution of the bridge cable; S22, strain sensor installation preparation: clean the cable surface where the strain sensor is installed to remove surface impurities, corrosion and oil stains; S23, strain sensor installation: according to the layout plan, the strain sensors are installed at key locations on the surface of the bridge cables; S24, strain sensor calibration: after the strain sensor is installed, perform sensor calibration; S25, real-time collection of stress data: after the strain sensor is installed and calibrated, the stress data of the cable under different working conditions is collected in real time; S26, data transmission and storage: The stress data collected by the strain sensor is transmitted to the data analysis unit via a wireless network or a wired connection.

4. The intelligent evaluation method for the bearing capacity of bridge cables in a corrosive environment according to claim 3 is characterized in that: The S3 specifically includes: S31, Microcrack size monitoring: The nanosensor monitors the size changes of microcracks on the cable surface in real time. The sensor determines the length and width of the crack by sensing the electric field changes in the microcrack area; S32, microcrack location positioning: through the collaborative work of multiple nanosensors, the specific location of the microcrack is determined using a triangulation positioning algorithm; S33, microcrack growth rate calculation: based on the real-time monitoring data of microcrack size by nanosensors, the crack growth rate is calculated; S34, real-time update of microcrack status: the nanosensor transmits the collected microcrack size, position and expansion speed data to the data analysis unit in real time.

5. The intelligent evaluation method for the bearing capacity of bridge cables in a corrosive environment according to claim 4 is characterized in that: The S4 specifically includes: S41, Application of noise filtering algorithm: Noise filtering of microcrack data and stress data collected by nanosensors; S42, lost data completion algorithm: A completion algorithm based on Kalman filtering is used to complete the lost data caused by sensor failure or data transmission interruption; S43, data de-redundancy processing: in the data pre-processing process, data de-redundancy technology is used to identify and remove duplicate or invalid data; S44, data smoothing: using a low-pass filter to smooth the noise-filtered and completed data; S45, data integrity check: after the preprocessing is completed, perform a data integrity check; S46, data preprocessing result storage: the data that has undergone integrity check is stored in the data analysis unit in the order of timestamps.

6. The intelligent evaluation method for the bearing capacity of bridge cables in a corrosive environment according to claim 5 is characterized in that: The S5 specifically includes: S51, multi-dimensional data fusion model construction: construct a multi-dimensional data fusion model to comprehensively process microcrack data and stress data; S52, stress distribution map generation: using the preprocessed microcrack data and stress data, the stress distribution on the cable surface is calculated using a data fusion model, and a stress distribution map is generated using visualization software; S53, Correlation analysis between crack extension and stress change: Through stress distribution diagram, quantitative analysis of the correlation between crack extension and stress change is carried out; S54, Identification of local stress concentration areas: Identify key areas of local stress concentration through stress distribution diagrams and correlation analysis, and generate stress concentration reports; S55, stress distribution and crack growth trend prediction: Through the data fusion algorithm, the historical micro-crack growth and stress data are regressed and analyzed to predict the impact trend of crack growth on local stress distribution in the future; S56, data analysis result output and storage: by performing regression analysis on historical microcrack growth data and stress data, a crack growth trend graph is generated, and the generated crack growth trend graph, stress distribution graph and stress concentration report are stored in the data analysis unit.

7. The intelligent evaluation method for the bearing capacity of bridge cables in a corrosive environment according to claim 6 is characterized in that: The S6 includes: S61, Construction of corrosion fatigue model: Based on the preprocessed microcrack data and stress distribution diagram, a corrosion fatigue model is established; S62, bearing capacity impact assessment: using the constructed corrosion fatigue model, analyze the impact of corrosion-induced microcracks on the cable bearing capacity, calculate the current bearing capacity by inputting real-time microcrack data and stress data, and output the bearing capacity assessment results; S63, storage and analysis of assessment results: storing the bearing capacity assessment results in a data analysis unit, and generating a corresponding assessment report based on the assessment results.

8. The intelligent evaluation method for bridge cable bearing capacity in a corrosive environment according to claim 7 is characterized in that: The S6 further includes: S64, prediction of bearing capacity change trend: after completing the construction of the corrosion fatigue model, based on the real-time data input into the model, the time series analysis method is used to predict the bearing capacity change trend of the cable in the future; S65, Trend analysis and report generation: Generate a bearing capacity change trend chart based on the forecast results to show the dynamic changes of future bearing capacity and generate an evaluation report; S66, Risk Assessment and Recommendations: Based on the analysis of the bearing capacity change trend, potential risks are assessed and corresponding maintenance and management recommendations are put forward.

9. The intelligent evaluation method for bridge cable bearing capacity in a corrosive environment according to claim 8 is characterized in that: The S6 further comprises: S67, Comprehensive safety assessment: Based on the bearing capacity assessment results and bearing capacity change trend of the corrosion fatigue model, conduct a comprehensive safety assessment of the overall bridge structure and form a safety report; S68, safety threshold monitoring: set the bearing capacity safety threshold, monitor the relationship between the current bearing capacity and the preset threshold in real time, and when the bearing capacity drops to the safety threshold, the system will automatically trigger the early warning mechanism; S69, early warning system and maintenance suggestion generation: When the early warning mechanism is triggered, early warning information is automatically generated. The early warning information includes the current bearing capacity status, crack propagation trend and stress distribution, and corresponding maintenance suggestions are put forward.

10. An intelligent assessment system for the bearing capacity of bridge cables in a corrosive environment, used to implement the intelligent assessment method for the bearing capacity of bridge cables in a corrosive environment as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Nanosensor module: Nanosensors are evenly deployed on the surface of bridge cables to monitor the formation and expansion of corrosion-induced microcracks in real time. The sensors detect microcracks through changes in self-induced electric fields and transmit data to the data analysis unit; Strain sensor module: strain sensors are arranged at key locations of bridge cables to monitor the stress distribution of the cables under different load conditions in real time and transmit the stress data to the data analysis unit: Data acquisition module: The nanosensor collects microcrack data in real time, and the microcrack data includes the size, position and expansion speed of the microcrack; Data preprocessing module: noise filtering, data completion and redundancy removal for microcrack data and stress data; Data analysis unit: Through the multi-dimensional data fusion algorithm, the pre-processed microcrack data and stress data are coupled and analyzed to generate stress distribution diagrams, and the influence of microcrack extension on local stress is analyzed; Bearing capacity prediction and safety assessment module: Based on the corrosion fatigue model, it evaluates the impact of corrosion on the bearing capacity of the cable and predicts the change trend of the future bearing capacity; User interface and report generation module: provides an intuitive user interface to display real-time monitoring data, bearing capacity trend chart, stress distribution chart and evaluation report.

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