An intelligent assessment method and system for bridge cable bearing capacity in a corrosive environment

Abstract

The present invention relates to the technical field of bridge cable health monitoring, and specifically to a method and system for intelligently assessing the bearing capacity of bridge cables in a corrosive environment. By deploying nanosensors and strain sensors on the surface of the cables, real-time monitoring of microcracks and stress states is achieved. After data acquisition, the system uses a data preprocessing module to eliminate noise and complete missing data to ensure data accuracy. Then, the system uses a multi-dimensional data fusion algorithm to perform coupled analysis on microcrack data and stress data, generate a stress distribution map, and evaluate the impact of microcrack expansion on bearing capacity. Based on a corrosion fatigue model, the system predicts the bearing capacity change trend of the cables in the future and generates a detailed assessment report. When the bearing capacity drops to a safety threshold, an early warning is automatically triggered and maintenance recommendations are provided, thereby providing effective support for bridge safety management. The present invention helps to improve the safety and operational efficiency of bridges and reduce the risk of accidents caused by structural failure.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge cable health monitoring, and in particular to an intelligent assessment 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, are receiving increasing attention. Microcracks refer to tiny cracks on the surface or inside a material caused by factors such as stress, corrosion, or fatigue. These cracks typically range from a few microns to several millimeters. Although small in size, they can rapidly expand under long-term load and environmental influences, seriously affecting the material's load-bearing capacity. 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 reduction in the cable's load-bearing capacity. Corrosion damage to cables not only affects the overall safety of the bridge but can also lead to structural failure, posing a threat to traffic safety.

[0003] However, many current bridge monitoring technologies still rely on traditional manual inspections and regular maintenance, which often fail to detect potential problems promptly and make it difficult to accurately assess changes in the actual load-bearing capacity of cables. Inadequate data collection and analysis techniques have resulted in delayed monitoring of corrosion-induced microcracks and their impact on load-bearing capacity. Furthermore, existing technologies lack the ability to effectively integrate and analyze multiple data sources (such as microcrack and stress data), hindering comprehensive assessment of the condition of bridge cables. Summary of the Invention

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

[0005] An intelligent assessment 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 to monitor the formation and expansion of corrosion-induced microcracks on the cable surface in real time. The nanosensors detect the occurrence of cracks through changes in the self-induced electric field and transmit the data in real time to the data analysis unit;

[0007] S2, stress data acquisition: strain sensors are placed at key locations on 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 is transmitted to the data analysis unit via a wireless network.

[0008] S3, microcrack detection and data acquisition: the nanosensor collects microcrack data in real time, including the size, location and expansion speed of the microcracks;

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

[0010] S5, coupled analysis of stress distribution and microcrack growth: Using a multi-dimensional data fusion algorithm, the pre-processed microcrack data is coupled with the stress data to generate a stress distribution map on the cable surface and analyze the effect of microcrack growth on the local stress of the cable;

[0011] S6, Bearing Capacity Prediction and Safety Assessment: Based on the trend of corrosion-induced microcrack propagation and stress distribution diagrams, a corrosion fatigue model is constructed to evaluate the impact of corrosion on the bearing capacity of the cables. The changing trend of bearing capacity in the future is predicted, and the safety of the overall bridge structure is comprehensively evaluated. When the bearing capacity drops to a 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 close contact between the nanosensor and the cable surface. The treatment method includes removing surface dirt, corrosion and moisture.

[0015] S13, sensor installation: according to the designed layout plan, nanosensors are evenly deployed at key locations 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 of the nanosensor;

[0017] S15, connecting the data transmission module: 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 strain sensors based on the structure and load distribution of bridge cables;

[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: Install strain sensors at key locations on the surface of bridge cables according to the layout plan;

[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, real-time collection of stress data of the cable under different working conditions begins;

[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: Nanosensors monitor the size changes of microcracks on the cable surface in real time. The sensors determine the length and width of the cracks by sensing the changes in the electric field in the microcrack area.

[0028] S32, microcrack location positioning: through the collaborative work of multiple nano-sensors, the triangulation positioning algorithm is used to determine the specific location of the microcrack;

[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: uses a Kalman filter-based completion algorithm to complete lost data due to sensor failure or data transmission interruption;

[0034] S43, data de-redundancy processing: During data pre-processing, 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 complemented 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 pre-processed 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 growth and stress change: quantitative analysis of the correlation between crack growth and stress change through stress distribution diagram;

[0042] S54, Identification of Local Stress Concentration Areas: Identify key areas of local stress concentration through stress distribution diagrams and correlation analysis, and generate a stress concentration report;

[0043] S55, stress distribution and crack growth trend prediction: Using data fusion algorithms to perform regression analysis on historical microcrack growth and stress data, the impact of crack growth on local stress distribution in the future is predicted;

[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 pre-processed microcrack data and stress distribution map, a corrosion fatigue model is established;

[0047] S62, Bearing Capacity Impact Assessment: Utilize the constructed corrosion fatigue model to analyze the impact of corrosion-induced microcracks on the cable bearing capacity. By inputting real-time microcrack data and stress data, the current bearing capacity is calculated and the bearing capacity assessment results are output;

[0048] 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.

[0049] Optionally, the S6 further includes:

[0050] S64, Bearing Capacity Trend Prediction: After the corrosion fatigue model is constructed, the bearing capacity trend of the cable will be predicted over the next period of time using time series analysis based on real-time data input into the model.

[0051] S65, Trend Analysis and Report Generation: Based on the prediction results, a bearing capacity trend chart is generated to show the dynamic changes in future bearing capacity, identify potential danger points, and generate an assessment report. The report includes the prediction of future bearing capacity values, the expected trend of crack growth, and the analysis of the impact on bearing capacity.

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

[0053] Optionally, the S6 further includes:

[0054] S67, Comprehensive Safety Assessment: Based on the bearing capacity assessment results and bearing capacity change trends of the corrosion fatigue model, a comprehensive safety assessment of the bridge's overall structure is conducted. During the assessment, the status of each cable, the extent of microcracks, and their impact on the overall structure are considered, and a safety report is generated.

[0055] S68, safety threshold monitoring: Set a safety threshold for bearing capacity and monitor the relationship between the current bearing capacity and the preset threshold in real time. When the bearing capacity drops to the safety threshold, the system will automatically trigger an 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 growth 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 the self-induced electric field and transmit the data to the data analysis unit;

[0059] Strain sensor module: Strain sensors are placed at key locations on bridge cables (such as the connection points with anchors, and the 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, including the size, location and expansion speed of the microcracks;

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

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

[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 cables and predicts the future trend of bearing capacity changes;

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

[0065] Beneficial effects of the present invention:

[0066] This invention uses nanosensors and strain sensors deployed on the surface of bridge cables to achieve real-time monitoring of microcracks and stress states. This real-time data acquisition and analysis capability can promptly detect potential problems caused by corrosion-induced microcracks at their earliest stages, ensuring early warning before the load-bearing capacity drops below a safe threshold, significantly reducing the risk of structural failure and improving bridge safety.

[0067] This system, through the multi-dimensional data fusion algorithm of the data preprocessing module and the data analysis unit, efficiently processes microcrack and stress data. This high-precision data analysis not only generates accurate stress distribution maps but also quantifies the impact of microcrack propagation on local stress, thereby providing a more accurate load-bearing capacity assessment. Such assessment results provide a reliable basis for routine bridge maintenance and management, helping to extend the service life of the structure.

[0068] The capacity prediction and safety assessment module, based on a corrosion fatigue model, predicts the cable's capacity over time and generates a detailed assessment report. This capability enables bridge managers to implement appropriate repair and management measures before potential risks materialize, optimizing maintenance strategies, reducing operating costs, and improving the overall 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0070] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

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

[0072] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

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

[0074] In general, terms can be understood, at least in part, from their 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 assessing the bearing capacity of bridge cables in a corrosive environment comprises the following steps:

[0076] S1, Nanosensor Deployment: Nanosensors are uniformly 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 occurrence of cracks through changes in the self-induced electric field and transmit the data in real time to the data analysis unit;

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

[0078] S3, Microcrack Detection and Data Acquisition: Nanosensors collect real-time microcrack data, including size, location, and growth rate. Nanosensors capture microscopic corrosion damage in the early stages 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, using noise filtering and data completion algorithms to remove environmental noise interference and complete the lost data to ensure data integrity and accuracy;

[0080] S5, coupled analysis of stress distribution and microcrack growth: Using a multi-dimensional data fusion algorithm, the pre-processed microcrack data is coupled with the stress data to generate a stress distribution map on the cable surface and analyze the effect of microcrack growth on the local stress of the cable;

[0081] S6, Bearing Capacity Prediction and Safety Assessment: Based on the trend of corrosion-induced microcrack propagation and stress distribution diagrams, a corrosion fatigue model is constructed to evaluate the impact of corrosion on the bearing capacity of the cables. The changing trend of bearing capacity in the future is predicted, and the safety of the overall bridge structure is comprehensively evaluated. When the bearing capacity drops to a 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 layout must ensure that the nanosensors are evenly distributed on the surface of the cables, especially at the connection points between the cables and anchors, the contact points between the cables and bridge towers or piers, and other areas where stress concentration may occur, to effectively capture the early formation and propagation of corrosion-induced microcracks.

[0084] S12, Sensor Installation Preparation: Treat the cable surface to ensure close contact between the nanosensor and the cable surface. This includes removing surface dirt, corrosion, and moisture, ensuring the installation area is clean and dry. Also, select a suitable adhesive material to firmly attach the nanosensor to the cable surface and ensure its durability in harsh environments such as high humidity and high salt spray.

[0085] S13, sensor installation: According to the designed layout plan, evenly deploy nanosensors at key locations of the bridge cables (such as the connection between the cables and the anchors). During installation, ensure that the arrangement direction of the sensors is consistent with the stress direction of the cables to avoid damage to the sensors during installation. After installation, check the physical condition of each sensor to ensure its stable operation.

[0086] S14, sensor calibration: After installation, the nanosensor is initially 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 of microcracks. By comparing with standard crack samples, the sensor's inductive electric field range is adjusted to ensure the accuracy of the monitoring data.

[0087] S15, connecting the data transmission module: 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 cables. 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, the nanosensors are ensured to accurately and in real time monitor corrosion-induced microcracks on the cable surface, and the monitoring efficiency and resource utilization are optimized by adaptively adjusting the acquisition frequency.

[0090] S2 specifically includes:

[0091] S21, Strain sensor layout planning: Determine the placement of strain sensors based on the structure and load distribution of the bridge cables, prioritizing placement at key locations of the bridge cables, including the connection between the cables and anchors, the contact points between the cables and bridge towers or piers, and other stress concentration areas, to maximize the capture of the stress distribution of the cables under different load conditions;

[0092] S22, strain sensor installation preparation: Clean the cable surface where the strain sensor is installed to remove surface impurities, corrosion, and oil stains. This ensures that the strain sensor fits tightly against the cable surface and avoids stress monitoring errors caused by surface uncleanliness.

[0093] S23, Strain Sensor Installation: Install strain sensors at key locations on the surface of the bridge cables according to the layout plan. The strain sensors must be aligned with the direction of cable stress changes to ensure they can accurately sense cable stress changes. After installation, check the physical condition and signal transmission function of each sensor to ensure it is securely installed and data transmission is normal.

[0094] S24, Strain Sensor Calibration: After the strain sensor is installed, perform sensor calibration. The calibration step includes applying a known load and using a calibration tool to test the relationship between the stress data output by the sensor and the actual load to ensure that the strain sensor meets the accuracy requirements. 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 stress data collection: After the strain sensor is installed and calibrated, it begins collecting real-time stress data of the cables under different working conditions. Based on the dynamic load conditions of the bridge, such as traffic flow, wind force, and temperature changes, the sensor collection frequency 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 wired connection. All stress data are stored in the data analysis unit in chronological order to provide data support for subsequent coupled analysis of stress and microcrack propagation.

[0097] S3 specifically includes:

[0098] S31, Microcrack Size Monitoring: Nanosensors monitor the size changes of microcracks on the cable surface in real time. The sensors determine the length and width of the cracks by sensing the changes in the electric field in the microcrack area. Using high-precision nanoscale detection technology, the sensors can accurately measure crack size down to the micron level, ensuring accurate monitoring of the early stages of microcrack formation.

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

[0100]

[0101] Among them, L crack Indicates the center position of the microcrack, L1, L2, ..., L n 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 data on microcrack size, location, and growth rate to the data analysis unit in real time. The data analysis unit updates the microcrack status in real time by monitoring the changes in microcracks at each time point, generating a time series diagram of crack growth 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 filtered noise 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, Lost Data Completion Algorithm: A Kalman filter-based completion algorithm is used to complete lost data due to sensor failure or data transmission interruption. The Kalman filter predicts and completes lost data through dynamically updated state estimation. The 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: During data pre-processing, data de-redundancy technology is used to identify and remove duplicate or invalid data. By analyzing the correlation between data in adjacent time periods, the system marks highly similar or duplicate sensor data as redundant data and automatically removes it to reduce the impact of data redundancy on subsequent analysis;

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

[0116]

[0117] Where 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. The check includes verifying whether the data of all sensors are complete, ensuring that the microcrack and stress data in each time period are within a reasonable value range, and there is no data anomaly or missing;

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

[0120] Through detailed data noise filtering, missing data completion, redundancy removal 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 the 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 to comprehensively process microcrack data (including size, location, 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 from 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 velocity, α1, α2, α3 are the weight coefficients of data fusion;

[0125] S52, stress distribution map generation: Using pre-processed microcrack and stress data, a data fusion model is used to calculate the stress distribution on the cable surface. Visualization software is then used to generate a stress distribution map that shows the impact of microcrack propagation on the local stress of the cable, particularly in areas with rapid crack propagation or stress concentration. The stress distribution map uses color gradients to visually represent the stress magnitude in different areas, facilitating analysis of the stress status of various parts of the cable surface.

[0126] S53, Correlation analysis between crack growth and stress change: The correlation between crack growth and stress change is quantitatively analyzed through stress distribution diagram, expressed as:

[0127]

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

[0129] S54, Identification of Local Stress Concentration Areas: Through stress distribution maps 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 dramatically, and are high-risk areas for structural damage. The system automatically marks these high-risk areas based on the degree of stress concentration and generates a stress concentration report.

[0130] S55, stress distribution and crack growth trend prediction: A data fusion algorithm is used to perform regression analysis on historical microcrack growth and stress data to predict the impact of crack growth on local stress distribution over a period of time. This trend analysis helps to provide early warning of potential structural damage and take timely repair measures. 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, 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 microcrack growth data and stress data, a crack growth trend graph is generated, showing the crack growth path and speed over a period of time, as well as its impact on the local stress distribution. 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 support 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 pre-processed microcrack data and stress distribution diagram, a corrosion fatigue model is established. The model expression is:

[0137]

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

[0139] S62, Bearing Capacity Impact Assessment: Utilizes the constructed corrosion fatigue model to analyze the impact of corrosion-induced microcracks on the cable bearing capacity. By inputting real-time microcrack and stress data, the current bearing capacity is calculated and output as a bearing capacity assessment result, including 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. A corresponding assessment report is generated based on the assessment results, recording 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 structural health status is ensured, providing an important basis for subsequent safety warnings and maintenance decisions.

[0142] The S6 also includes:

[0143] S64, Bearing Capacity Trend Prediction: After the corrosion fatigue model is constructed, the bearing capacity trend of the cable is predicted over the next period of time based on real-time data input into the model. Specifically, the dynamic law of bearing capacity change is determined by performing regression analysis on historical bearing capacity data. 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: Based on the prediction results, a bearing capacity trend chart is generated to show the dynamic changes in future bearing capacity, identify potential danger points, and generate an assessment report. The report includes the prediction of future bearing capacity values, the expected trend of crack growth, and the analysis of the impact on bearing capacity.

[0147] S66, Risk Assessment and Recommendations: Based on the analysis of load-bearing capacity trends, potential risks are assessed and corresponding maintenance and management recommendations are made. If the forecast shows that the load-bearing capacity will fall below the safety threshold at some point in the future, an alarm is triggered and corresponding maintenance recommendations are provided;

[0148] By predicting the changing trend of 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 trends of the corrosion fatigue model, a comprehensive safety assessment of the bridge's overall structure is conducted. During the assessment, the status of each cable, the extent of microcracks, and their impact on the overall structure are considered, and a safety report is generated.

[0151] S68, safety threshold monitoring: Set a safety threshold for bearing capacity and monitor the relationship between the current bearing capacity and the preset threshold in real time. When the bearing capacity drops to the safety threshold, the system will automatically trigger an 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 growth trend and stress distribution, and corresponding maintenance suggestions are made. The early warning information is sent to the 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, including 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 the self-induced electric field and transmit the data to the data analysis unit;

[0155] Strain sensor module: Strain sensors are placed at key locations on bridge cables (such as the connection points with anchors, and the 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, including the size, location and expansion speed of the microcracks;

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

[0158] Data Analysis Unit: Through a 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 propagation on local stress. The data analysis unit is also responsible for generating a bearing capacity assessment report, 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 cables and predicts future bearing capacity trends. It also provides risk assessment, alarm triggering, and maintenance recommendations to ensure the long-term safety of bridge structures.

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

[0161] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been 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 principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An intelligent assessment 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 to monitor the formation and expansion of corrosion-induced microcracks on the cable surface in real time. The nanosensors detect the occurrence of cracks through changes in the self-induced electric field and transmit the data in real time to the data analysis unit; S2, stress data acquisition: strain sensors are placed at key locations on 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 is transmitted to the data analysis unit via a wireless network. S3, microcrack detection and data acquisition: the nanosensor collects microcrack data in real time, including the size, location and expansion speed of the microcracks; 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 growth: Using a multi-dimensional data fusion algorithm, the pre-processed microcrack data is coupled with the stress data to generate a stress distribution map on the cable surface and analyze the effect of microcrack growth on the local stress of the cable; S6, Bearing Capacity Prediction and Safety Assessment: Based on the trend of corrosion-induced microcrack propagation and stress distribution diagrams, a corrosion fatigue model is constructed to evaluate the impact of corrosion on the bearing capacity of the cables. The changing trend of bearing capacity in the future is predicted, and the safety of the overall bridge structure is comprehensively evaluated. When the bearing capacity drops to a preset safety threshold, an early warning is automatically triggered and maintenance recommendations are generated.

2. The intelligent assessment method for the bearing capacity of bridge cables in a corrosive environment according to claim 1 is characterized in that: Said 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 close contact between the nanosensor and the cable surface. The treatment method includes removing surface dirt, corrosion and moisture. S13, sensor installation: nanosensors are evenly deployed at key locations on the bridge cables according to the designed layout plan; S14, sensor calibration: after completing the installation, perform preliminary calibration of 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 assessment 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 strain sensors based on the structure and load distribution of bridge cables; 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: Install strain sensors at key locations on the surface of bridge cables according to the layout plan; 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, real-time collection of stress data of the cable under different working conditions begins; 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 assessment 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: Nanosensors monitor the size changes of microcracks on the cable surface in real time. The sensors determine the length and width of the cracks by sensing the changes in the electric field in the microcrack area. S32, microcrack location positioning: through the collaborative work of multiple nano-sensors, the triangulation positioning algorithm is used to determine the specific location of the microcrack; 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 assessment 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: uses a Kalman filter-based completion algorithm to complete lost data due to sensor failure or data transmission interruption; S43, data de-redundancy processing: During data pre-processing, 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 complemented 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 assessment 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 pre-processed 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 growth and stress change: quantitative analysis of the correlation between crack growth and stress change through stress distribution diagram; S54, Identification of Local Stress Concentration Areas: Identify key areas of local stress concentration through stress distribution diagrams and correlation analysis, and generate a stress concentration report; S55, stress distribution and crack growth trend prediction: Using data fusion algorithms to perform regression analysis on historical microcrack growth and stress data, the impact of crack growth on local stress distribution in the future is predicted; 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 assessment method for bridge cable bearing capacity in a corrosive environment according to claim 6 is characterized in that: The S6 includes: S61, Construction of corrosion fatigue model: Based on pre-processed microcrack data and stress distribution map, a corrosion fatigue model is established; S62, Bearing Capacity Impact Assessment: Utilize the constructed corrosion fatigue model to analyze the impact of corrosion-induced microcracks on the cable bearing capacity. By inputting real-time microcrack data and stress data, the current bearing capacity is calculated and the bearing capacity assessment results are output; S63, storage and analysis of evaluation results: storing the bearing capacity evaluation results in the data analysis unit, and generating a corresponding evaluation report based on the evaluation results.

8. The intelligent assessment method for the bearing capacity of bridge cables in a corrosive environment according to claim 7 is characterized in that: The S6 further includes: S64, Bearing Capacity Trend Prediction: After the corrosion fatigue model is constructed, the bearing capacity trend of the cable will be predicted over the next period of time using time series analysis based on real-time data input into the model. S65, Trend Analysis and Report Generation: Based on the forecast results, generate a bearing capacity change trend chart to show the dynamic changes in future bearing capacity, and generate an assessment report; S66, Risk Assessment and Recommendations: Based on the analysis of the load-bearing capacity change trend, potential risks are assessed and corresponding maintenance and management recommendations are made.

9. The intelligent assessment method for bridge cable bearing capacity in a corrosive environment according to claim 8, characterized in that: Said S6 further comprises: S67, Comprehensive Safety Assessment: Based on the bearing capacity assessment results and bearing capacity change trends of the corrosion fatigue model, conduct a comprehensive safety assessment of the overall bridge structure and generate a safety report; S68, safety threshold monitoring: Set a safety threshold for bearing capacity and monitor the relationship between the current bearing capacity and the preset threshold in real time. When the bearing capacity drops to the safety threshold, the system will automatically trigger an 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 growth 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, for implementing 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 the self-induced electric field and transmit the data to the data analysis unit; Strain sensor module: Strain sensors are placed at key locations on 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, including the size, location and expansion speed of the microcracks; Data preprocessing module: performs noise filtering, data completion and redundancy removal on microcrack data and stress data; Data analysis unit: Through multi-dimensional data fusion algorithm, the pre-processed microcrack data and stress data are coupled and analyzed to generate stress distribution diagrams and analyze the impact of microcrack extension on local stress; Bearing capacity prediction and safety assessment module: Based on the corrosion fatigue model, it evaluates the impact of corrosion on the bearing capacity of cables and predicts the future trend of bearing capacity changes; User interface and report generation module: provides an intuitive user interface to display real-time monitoring data, bearing capacity change trend chart, stress distribution chart and evaluation report.

Images