Comprehensive performance evaluation method for bridge engineering ultrahigh-strength steel wire finished product

Abstract

The invention discloses a comprehensive performance evaluation method for a bridge engineering ultrahigh-strength steel wire finished product, and relates to the technical field of material performance evaluation, and the method comprises the following specific steps: microscopic characterization detection: detecting the bridge engineering ultrahigh-strength steel wire finished product, a steel wire bundle and a steel wire rope by adopting a microscopic morphology and component characterization technology and a microstructure characterization technology; according to the method, a quantitative correlation model is constructed, microstructure parameters, plating layer states, macroscopic performance parameters and long-term service stability are correlated, and the model defines reverse correlation of grain size, dislocation density, surface defect density and macroscopic performance and forward correlation of precipitated phase volume fraction, plating layer residual thickness and macroscopic performance. Meanwhile, based on the model, a preset interval of microstructure parameters and plating layer state variables can be reversely derived according to the target macroscopic performance, and an accurate regulation and control direction is provided for component design and plating assisting process optimization of the steel wires, the steel wire bundles and the steel wire ropes.

Description

Technical Field

[0001] This invention relates to the field of material performance evaluation technology, specifically a method for evaluating the comprehensive performance of finished ultra-high strength steel wire products for bridge engineering. Background Technology

[0002] In the field of bridge engineering, ultra-high strength steel wire and its further processed wire bundles and ropes are extremely critical core load-bearing components. Their performance directly determines the overall load-bearing capacity of the bridge and its service life during long-term use. As bridge engineering continues to develop towards larger spans and higher load-bearing capacities, the performance requirements for ultra-high strength steel wire and its products are becoming increasingly stringent. They not only need to have extremely high strength to withstand huge tensile forces, but also need to maintain good corrosion resistance in complex and variable service environments to ensure stable performance during long-term use and prevent safety hazards caused by performance degradation.

[0003] Currently, existing methods for evaluating the comprehensive performance of ultra-high strength steel wire, wire bundles, and ropes primarily focus on macroscopic performance testing. They determine whether product performance meets predetermined standards by detecting macroscopic indicators such as tensile strength, yield strength, elongation, and corrosion resistance. However, this evaluation method has technical limitations. On the one hand, it only obtains macroscopic performance results and cannot delve into the microscopic causes of performance formation. This makes it difficult to fundamentally explain the reasons for performance differences between different batches of products, or for changes in the performance of the same product at different stages of service, thus failing to provide precise directions for solving performance problems. On the other hand, macroscopic performance testing cannot effectively reflect the intrinsic relationship between microstructure, coating state and long-term service stability. It cannot deeply detect the damage mechanism of the coating after wear and corrosion, nor can it accurately predict the performance degradation law and corrosion resistance life of the product under long-term load and environmental erosion based on the evolution of coating state and microstructure. As a result, the existing evaluation methods cannot provide a scientific basis for the composition design of steel wires, wire bundles and ropes, lack precise guidance in the optimization of the plating process, and cannot provide reliable support for the long-term safe operation and maintenance of bridge projects. It is difficult to meet the higher requirements of modern bridge engineering for material performance evaluation. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a comprehensive performance evaluation method for ultra-high strength steel wire products used in bridge engineering. This method employs microscopic morphology and composition characterization techniques, as well as microscopic structure characterization techniques, to conduct microscopic-level testing of the product. This allows for the acquisition of information on coating loss after wear and corrosion, morphological changes, and microscopic structural parameters such as grain size and precipitate distribution. Subsequently, macroscopic performance tests are performed to obtain macroscopic mechanical and corrosion resistance parameters. Based on these microscopic and macroscopic parameters, and combined with service condition characteristics, a quantitative correlation model is constructed between microscopic structural parameters, coating state, macroscopic performance parameters, and long-term service stability. This model clarifies the direction of performance regulation, providing precise guidance for composition design and fluxing process optimization. Finally, by analyzing the influence mechanism of microscopic structure evolution and coating state changes on macroscopic performance, and combining this with the quantitative correlation model, a fundamental evaluation of the product's comprehensive performance is conducted, accurately predicting the estimated corrosion resistance life under service conditions and outputting a complete evaluation report. This method overcomes the technical bottlenecks of existing evaluation methods, which cannot guide process optimization and predict long-term corrosion resistance life.

[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: a method for evaluating the comprehensive performance of ultra-high strength steel wire products for bridge engineering, which includes the following specific steps: Microscopic characterization and testing: Microscopic morphology and composition characterization technology and microscopic structure characterization technology are used to test the finished products of ultra-high strength steel wire, steel wire bundles and ropes for bridge engineering. Macroscopic performance testing: Macroscopic performance testing is carried out on finished ultra-high strength steel wire products, wire bundles, and ropes to obtain macroscopic mechanical performance parameters and corrosion resistance performance parameters; Correlation Model Construction: Integrating various data obtained from microscopic characterization and macroscopic performance testing, and combining the operating conditions including service environment temperature and humidity, through multi-dimensional data processing and correlation analysis, incorporating the evolution law of microstructure and coating damage rate characteristics, a quantitative correlation model of microstructure parameters, coating state, macroscopic performance parameters, and long-term service stability is constructed. Confirmation of performance regulation direction: Based on the coating-related information obtained from microscopic characterization and the constructed quantitative correlation model, the coating damage mechanism and the influence of microstructure on performance are analyzed, and the performance regulation direction of steel wire, steel wire bundle, and rope composition design and fluxing process is clarified. Performance evaluation and corrosion resistance life prediction: Combining quantitative correlation models, we analyze the influence mechanism of microstructure evolution and coating state changes on macroscopic performance, complete the root cause evaluation of product comprehensive performance, and infer the performance degradation trend based on service condition characteristics, calculate and output the predicted corrosion resistance life and a complete evaluation report.

[0006] Furthermore, in the microscopic characterization and detection steps, the microscopic morphology and composition characterization techniques include scanning electron microscopy (SEM) and electron probe microanalysis (EPMA); the microscopic morphology and composition characterization techniques are used to obtain information on coating loss and coating morphology changes after wear and corrosion; the microscopic structure characterization techniques include electron microscopy and X-ray diffraction; the microscopic structure characterization techniques are used to obtain microscopic structural parameters including grain size, precipitate distribution, dislocation density, and interface transition region structure.

[0007] Furthermore, in the macroscopic performance testing step, the macroscopic mechanical performance parameters include at least one of tensile strength, yield strength, and elongation; the corrosion resistance performance parameters include at least one of corrosion rate and polarization resistance.

[0008] Furthermore, in the aforementioned correlation model construction step, the specific steps for constructing a quantitative correlation model between microstructure parameters, coating state, macroscopic performance parameters, and long-term service stability are as follows: Microstructure parameters obtained from microscopic characterization, including grain size and precipitate distribution, and coating state data, including coating loss range and morphological defects, are matched with mechanical and corrosion resistance performance parameters obtained from macroscopic performance testing, categorized and matched according to service time points and environmental conditions. Simultaneously, microstructure evolution data and coating damage dynamic data obtained through long-term tracking and monitoring are supplemented. All data are dimensionless to eliminate dimensional differences. Then, through multi-dimensional data feature extraction, the strong and weak correlation characteristics between microstructure parameters and performance parameters are identified, the contribution of coating state changes to performance degradation is clarified, and the correlation function between various parameters is established by combining the thermodynamic law of microstructure evolution and the kinetic mechanism of coating damage. Subsequently, based on the correlation coefficient obtained by data fitting, a mathematical expression containing microstructure parameters, coating state variables and macroscopic performance indicators is constructed. By adjusting the variable weights, the deviation between the calculation results and the measured data is controlled within a preset range, and finally a stable and reliable quantitative mapping relationship between microstructure-coating state-macroscopic performance is formed.

[0009] Furthermore, in the correlation model construction step, a mathematical expression is constructed that includes microstructural parameters, coating state variables, and macroscopic performance indicators, the formula of which is: ,in, It is a macroscopic performance indicator. It refers to the grain size. It is the volume fraction of the precipitated phase. It is dislocation density. It is the remaining thickness of the coating. It is the density of defects on the coating surface. It is a fundamental constant term. , , , and It is the correlation coefficient.

[0010] Furthermore, in the performance regulation direction confirmation step, based on the established quantitative mapping relationship between microstructure, coating state, and macroscopic performance, the preset ranges of the microstructure parameters and coating state variables corresponding to the target macroscopic performance are first determined. The target range to be achieved for each key parameter is determined through reverse derivation. For microstructure parameters including grain size, precipitate volume fraction, and dislocation density, the microstructure is directionally regulated by adjusting the heat treatment temperature, holding time, cooling rate, and alloy element addition ratio parameters during coating preparation, so that the relevant parameters are stable within the target range. For state variables including remaining coating thickness and surface defect density, combined with the characteristics of service conditions, the coating initial design thickness is optimized, surface pretreatment processes are used to reduce initial defects, or targeted protective measures are taken during service to slow down the wear and corrosion rate, so that the coating state variables meet the performance requirements. During the regulation process, the changes in microstructure parameters and coating state variables are monitored in real time. Based on the deviation between the monitoring data and the target range, the parameter settings of the regulation means are dynamically adjusted so that the macroscopic performance of the coating stably reaches the preset target.

[0011] Furthermore, in the performance evaluation and corrosion resistance life prediction steps, the established quantitative mapping relationship between microstructure, coating state, and macroscopic performance is used as the core basis. First, the current microstructure parameters of the coating, real-time coating state, and service environment parameters are integrated. A multi-index weighted evaluation method is adopted to convert the performance contribution of each parameter into a quantifiable comprehensive score. Combined with industry standards and actual application requirements, performance levels including excellent, qualified, and maintenance-required are defined to complete a systematic evaluation of the current comprehensive performance of the coating. Subsequently, based on long-term tracking data on the evolution of coating microstructure, damage rate change patterns, and performance decay curves under different environments, combined with the current performance evaluation results, a performance decay prediction model including the time dimension is constructed. By inputting simulation parameters of future service conditions, the time node when the comprehensive performance of the coating drops below the qualified threshold is estimated. At the same time, an environmental fluctuation coefficient is introduced to correct the prediction results. Finally, accelerated aging experiments are used to obtain performance change data of the coating under extreme conditions, which are compared and calibrated with the output results of the prediction model to form a complete evaluation report including the current performance level, remaining corrosion resistance life, and key performance decay nodes.

[0012] Furthermore, in the performance evaluation and corrosion resistance life prediction steps, a multi-index weighted evaluation method is adopted to convert the performance contribution of each parameter into a quantifiable comprehensive score, the formula of which is: ,in, It is a comprehensive performance rating of the coating. It is the first The weighting coefficients of the evaluation indicators It is the total number of evaluation indicators. It is the first The normalized score of each evaluation indicator It is a comprehensive evaluation correction item.

[0013] Furthermore, in the performance evaluation and corrosion resistance life prediction steps, a performance degradation prediction model incorporating the time dimension is constructed, and its model formula is as follows: ,when Corrosion resistance life ,in, yes The overall performance rating of the coating at any time. This is the current overall performance rating of the coating. It is the basic performance decay rate constant. It is the environmental fluctuation coefficient. It is the residual value of performance degradation. It is the overall performance qualification threshold. It is an estimated corrosion resistance life.

[0014] Compared with existing technologies, the comprehensive performance evaluation method for ultra-high strength steel wire products used in bridge engineering has the following advantages: I. This invention establishes a quantitative correlation model that links microstructural parameters, coating state, macroscopic performance parameters, and long-term service stability. The model clarifies the inverse correlation between grain size, dislocation density, surface defect density, and macroscopic performance, as well as the positive correlation between precipitate volume fraction, remaining coating thickness, and macroscopic performance. It also determines the weighted influence coefficients of each parameter on macroscopic performance indicators. Furthermore, based on this model, preset ranges for microstructural parameters and coating state variables can be derived from the target macroscopic performance, providing precise control directions for the composition design and fluxing process optimization of steel wires, wire bundles, and ropes. This makes process optimization more scientific and targeted, effectively improving product performance and quality stability.

[0015] Second, this invention uses the current comprehensive performance score as an initial value, combines it with the inherent performance degradation rate under standard operating conditions, and introduces an environmental fluctuation coefficient to correct for the impact of future service condition fluctuations on the degradation rate. This makes the predicted results more realistic. By setting the minimum residual value of performance degradation and the minimum score threshold for comprehensive performance qualification, the cumulative service time when the comprehensive performance of the coating drops below the qualification threshold is accurately calculated, solving the technical bottleneck of existing evaluation methods that are difficult to predict long-term corrosion resistance life. Simultaneously, accelerated aging tests are used to calibrate the predicted results, ensuring that the prediction error is controlled within a reasonable range. This provides a reliable basis for the safe operation and maintenance of bridge engineering, helps to formulate maintenance strategies in advance, and ensures the long-term safe use of bridges.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a flowchart of the comprehensive performance evaluation method for ultra-high strength steel wire finished products in bridge engineering; Figure 2 This is a flowchart of the steps involved in constructing the correlation model for the comprehensive performance evaluation method of ultra-high strength steel wire finished products in bridge engineering. Figure 3 This is a flowchart of the comprehensive performance evaluation method and corrosion resistance life prediction steps for ultra-high strength steel wire finished products in bridge engineering. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] This invention provides a comprehensive performance evaluation method for ultra-high strength steel wire products used in bridge engineering. By employing microscopic morphology and composition characterization techniques and microscopic structure characterization techniques, the method performs microscopic-level testing on the product, obtaining information on coating loss after wear and corrosion, morphological changes, and microscopic structural parameters such as grain size and precipitate distribution. Next, macroscopic performance tests are conducted to obtain macroscopic mechanical and corrosion resistance parameters. Then, based on the microscopic and macroscopic parameters, combined with service condition characteristics, a quantitative correlation model is constructed between microscopic structural parameters, coating state, macroscopic performance parameters, and long-term service stability. According to this model, the direction of performance regulation is clarified, providing precise guidance for composition design and fluxing process optimization. Finally, by analyzing the influence mechanism of microscopic structure evolution and coating state changes on macroscopic performance, combined with the quantitative correlation model, the comprehensive performance of the product is fundamentally evaluated, and the estimated corrosion resistance life under service conditions is accurately predicted, outputting a complete evaluation report. This method solves the technical bottlenecks of existing evaluation methods, which cannot guide process optimization and are difficult to predict long-term corrosion resistance life.

[0021] This invention provides a method for evaluating the comprehensive performance of finished ultra-high strength steel wire products for bridge engineering, such as... Figure 1As shown, the specific steps include the following: Microscopic characterization and testing: Microscopic morphology and composition characterization technology and microscopic structure characterization technology are used to test the finished products of ultra-high strength steel wire, steel wire bundles and ropes for bridge engineering. In one embodiment, representative samples were taken from different cross-sectional locations of the steel wire after service to ensure coverage of various states of the steel wire after service. After being fixed with resin, the samples were successively subjected to coarse grinding, fine grinding, and precision grinding until the surface was free of obvious scratches. Then, a diamond polishing agent was used for mirror polishing, and finally, a light etching agent was used to clearly reveal the microstructure. A high-resolution scanning electron microscope (SEM) was used to observe the coating from all angles. With appropriate acceleration voltage and magnification, the overall morphology of the coating at low magnification was recorded, as well as the details of corrosion pits and crack propagation paths at high magnification. The focus was on distinguishing between uniform corrosion and localized corrosion characteristics of the coating. Electron probe microanalysis (EPMA) was used simultaneously to analyze key areas such as the corrosion boundary of the coating and the substrate-coating interface. Line and area scans were performed to determine the migration patterns of elements during corrosion by analyzing the distribution gradient changes of characteristic elements, thereby clarifying the dominant corrosion mechanism of the coating. When analyzing the microstructure of the steel wire substrate using transmission electron microscopy (TEM), the sample was first processed into a thin disc, which was then thinned to an electron-penetrating thickness using a double-jet method. The grain morphology, size uniformity, and grain boundary state were observed under high magnification, and the distribution density and morphological changes of the strengthening precipitates were tracked, with particular attention paid to the accumulation and slip characteristics of dislocations. When performing phase analysis using X-ray diffraction, a reasonable scanning range and scanning speed were set. The position, intensity, and broadening of characteristic diffraction peaks were analyzed, and the dislocation density was calculated using the diffraction peak broadening method. The differences in diffraction patterns before and after service were compared to clarify the evolution of the microstructure.

[0022] Macroscopic performance testing: Macroscopic performance testing is carried out on finished ultra-high strength steel wire products, wire bundles, and ropes to obtain macroscopic mechanical performance parameters and corrosion resistance performance parameters; In one embodiment, referring to the testing specifications for steel wire used in bridges, macroscopic performance test specimens were prepared according to uniform dimensions. Multiple sets of parallel specimens were set for each test item to ensure the reliability of the results. Mechanical performance testing employed a high-precision electronic universal testing machine to conduct room-temperature tensile tests on the specimens. Before the test, the equipment's force and displacement accuracy were carefully calibrated. During loading, a constant loading rate was controlled, and stress-strain curves were recorded in real time. Core mechanical indicators such as tensile strength, yield strength, and elongation were extracted from the curves. The differences between the mechanical properties after service and the standard values ​​were analyzed in detail. Corrosion resistance performance was verified using a dual-verification scheme of simulated environmental testing and electrochemical testing. The simulated environmental testing employed a neutral salt spray test chamber, tailored to the location of the bridge. The regional climate characteristics were used to determine the salt spray concentration, spray volume, and test cycle. The formation and detachment of corrosion products on the sample surface were observed regularly, and the time points when obvious corrosion damage appeared were recorded. Electrochemical testing used an electrochemical workstation to construct a three-electrode system, using a solution simulating the composition of river fog as the electrolyte. Before testing, the sample surface was cleaned. Parameters such as self-corrosion potential and self-corrosion current density were obtained through polarization curve testing. Combined with AC impedance spectroscopy analysis, the charge transfer process on the electrode surface was analyzed to comprehensively evaluate the corrosion resistance level of the steel wire. All test data underwent error analysis, and the average value was taken after removing outliers as the final result, providing accurate macroscopic performance data support for the subsequent construction of correlation models.

[0023] Correlation Model Construction: Integrating various data obtained from microscopic characterization and macroscopic performance testing, and combining the operating conditions including service environment temperature and humidity, through multi-dimensional data processing and correlation analysis, incorporating the evolution law of microstructure and coating damage rate characteristics, a quantitative correlation model of microstructure parameters, coating state, macroscopic performance parameters, and long-term service stability is constructed. In one embodiment, such as Figure 2 As shown, the system collects full data from microscopic characterization and macroscopic performance testing, including coating state parameters (corrosion morphology, defect distribution, compositional changes), microstructure parameters (grain characteristics, precipitate state, dislocation density, interface structure), and macroscopic performance indicators (mechanical strength, elongation, corrosion rate, polarization parameters). It also integrates operating environment characteristics such as temperature fluctuation range, humidity variation range, and type and concentration of corrosive media. In the data preprocessing stage, standardization methods are used to eliminate numerical differences between parameters of different dimensions, transforming all parameters into dimensionless data of a uniform scale. The Grubbs criterion is used to eliminate abnormal data caused by sample preparation defects and testing operation errors, ensuring the integrity and reliability of the dataset. The model construction employs a multivariate statistical analysis algorithm based on a macroscopic performance-microstructure-coating state correlation model, using macroscopic performance indicators as output variables and microstructure and coating state parameters as input variables. Correlation analysis is used to initially screen key parameters that significantly affect macroscopic performance and eliminate redundant variables. The formula is: ,in, It is a macroscopic performance indicator. It refers to the grain size. It is the volume fraction of the precipitated phase. It is dislocation density. It is the remaining thickness of the coating. It is the density of defects on the coating surface. It is a fundamental constant term. , , , and This is a correlation coefficient. The correlation model determines the weighted correlation coefficients of each parameter through data fitting, clarifying the positive or negative correlation between different microstructural parameters, coating state variables, and macroscopic performance. Simultaneously, it introduces fundamental constants to calibrate the impact of testing environment and equipment system errors on the model. In the model validation phase, a portion of data not involved in the modeling is used as a validation set to compare the deviations between the model's predicted values ​​and actual test values, ensuring the model has good fitting accuracy and generalization ability. Ultimately, this forms a quantitative correlation model that accurately reflects the intrinsic correlation between the micro and macroscopic levels. Confirmation of performance regulation direction: Based on the coating-related information obtained from microscopic characterization and the constructed quantitative correlation model, the coating damage mechanism and the influence of microstructure on performance are analyzed, and the performance regulation direction of steel wire, steel wire bundle, and rope composition design and fluxing process is clarified. In one embodiment, by combining the results of microscopic characterization with the analytical conclusions of the correlation model between macroscopic performance, microstructure, and coating state, the core root causes of changes in steel wire performance are deeply analyzed: if SEM observation reveals localized peeling of the coating and EPMA shows abnormal interface composition, the model indicates that insufficient coating adhesion is the main cause of decreased corrosion resistance; if TEM observes significant grain growth and precipitate aggregation, the model analysis clarifies that microstructural degradation is the key factor in reduced mechanical strength. To address the need for production process optimization, the required microstructure and coating state parameter ranges for achieving the target macroscopic performance of the bridge service requirements are derived in reverse based on the correlation model. Specific process adjustments are proposed around microstructure control. Direction: To refine grains by lowering heat treatment temperature and extending holding time; to promote dispersed distribution of precipitates by optimizing the types and proportions of alloying elements; and to control dislocation density within a reasonable range by adjusting drawing deformation. For improving coating performance, the following improvement paths for the fluxing process are identified: optimizing pretreatment processes to enhance coating adhesion; adjusting zinc plating temperature and immersion time to control coating thickness uniformity; adding trace alloying elements to the flux to improve coating density; developing a real-time monitoring scheme; taking samples at fixed intervals during process adjustments; tracking parameter changes through microscopic detection and performance testing; dynamically correcting process parameters to ensure stable control of each indicator within the target range; and ultimately achieving precise control of macroscopic performance.

[0024] Performance evaluation and corrosion resistance life prediction: Combining quantitative correlation models, we analyze the influence mechanism of microstructure evolution and coating state changes on macroscopic performance, complete the root cause evaluation of product comprehensive performance, and infer the performance degradation trend based on service condition characteristics, calculate and output the predicted corrosion resistance life and a complete evaluation report.

[0025] In one embodiment, such as Figure 3 As shown, the comprehensive performance evaluation adopts a multi-index weighted evaluation method, transforming the performance contribution of each parameter into a quantifiable comprehensive score. A three-dimensional evaluation index system of microstructure, coating state, and macroscopic performance is constructed, covering core parameters such as grain size, precipitate distribution, dislocation density, coating defect density, remaining coating thickness, tensile strength, yield strength, and corrosion resistance rate. The weight of each index is determined using the analytic hierarchy process (AHP), with macroscopic mechanical strength and corrosion resistance rate having the highest weights as core indicators, followed by microstructure parameters. The sum of all index weights meets the normalization requirement. Then, each index is standardized: positive indicators are standardized using maximum values, and negative indicators are standardized using minimum values, ensuring that all index scores are positively correlated with comprehensive performance. Finally, the comprehensive performance score of the steel wire is calculated through weighted summation, while a correction term is introduced to calibrate the systematic error of the evaluation system. The formula is: ,in, It is a comprehensive performance rating of the coating. It is the first The weighting coefficients of the evaluation indicators It is the total number of evaluation indicators. It is the first The normalized score of each evaluation indicator It is a comprehensive evaluation correction item. Referring to the performance grading standard for main cable steel wires in bridge engineering, it is divided into four levels: excellent, qualified, need maintenance, and need to be replaced. It clarifies the current performance positioning of the steel wire and points out that local corrosion of the coating and slight coarse grains are the main potential risk points.

[0026] The corrosion resistance life prediction adopts a performance degradation prediction model. Using the current comprehensive performance score as the initial degradation starting point, and combining performance monitoring data from phased service operations, a combination of linear regression and curve fitting is used to obtain the basic performance degradation rate constant under standard operating conditions. In conjunction with the characteristics of the actual service environment, an environmental fluctuation coefficient reflecting the impact of complex actual operating conditions is calculated. The residual performance degradation value and the comprehensive performance qualification threshold are set. Substituting these parameters into the performance degradation prediction model, the model formula is as follows: ,when Corrosion resistance life ,in, yes The overall performance rating of the coating at any time. This is the current overall performance rating of the coating. It is the basic performance decay rate constant. It is the environmental fluctuation coefficient. It is the residual value of performance degradation. It is the overall performance qualification threshold. The method involves estimating the corrosion resistance life by analyzing the performance evolution curve over time. This calculation determines the cumulative service time during which the overall performance will drop below the acceptable threshold, thus providing a preliminary estimate of the corrosion resistance life. To improve the accuracy of the estimate, accelerated aging experiments are conducted. These experiments simulate extreme and harsh operating conditions to accelerate the degradation of the steel wire's performance. By comparing the actual degradation data with the model's prediction results, the environmental fluctuation coefficient is corrected. Ultimately, the remaining corrosion resistance life of the steel wire under actual service conditions is determined, and a complete evaluation report is output, including performance level determination, risk point analysis, remaining life conclusions, and targeted operation and maintenance recommendations.

[0027] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A comprehensive performance evaluation method for ultra-high strength steel wire finished products in bridge engineering, characterized in that, The method includes the following specific steps: Microscopic characterization and testing: Microscopic morphology and composition characterization technology and microscopic structure characterization technology are used to test the finished products of ultra-high strength steel wire, steel wire bundles and ropes for bridge engineering. Macroscopic performance testing: Macroscopic performance testing is carried out on finished ultra-high strength steel wire products, wire bundles, and ropes to obtain macroscopic mechanical performance parameters and corrosion resistance performance parameters; Correlation Model Construction: Integrating various data obtained from microscopic characterization and macroscopic performance testing, and combining the operating conditions including service environment temperature and humidity, through multi-dimensional data processing and correlation analysis, incorporating the evolution law of microstructure and coating damage rate characteristics, a quantitative correlation model of microstructure parameters, coating state, macroscopic performance parameters, and long-term service stability is constructed. Confirmation of performance regulation direction: Based on the coating-related information obtained from microscopic characterization and the constructed quantitative correlation model, the coating damage mechanism and the influence of microstructure on performance are analyzed, and the performance regulation direction of steel wire, steel wire bundle, and rope composition design and fluxing process is clarified. Performance evaluation and corrosion resistance life prediction: Combining quantitative correlation models, we analyze the influence mechanism of microstructure evolution and coating state changes on macroscopic performance, complete the root cause evaluation of product comprehensive performance, and infer the performance degradation trend based on service condition characteristics, calculate and output the predicted corrosion resistance life and a complete evaluation report.

2. The method for evaluating the comprehensive performance of ultra-high strength steel wire finished products for bridge engineering according to claim 1, characterized in that, In the microscopic characterization and detection steps, the microscopic morphology and composition characterization techniques include scanning electron microscopy (SEM) and electron probe microanalysis (EPMA); these techniques are used to obtain information on coating loss and morphological changes after wear and corrosion. The microscopic structure characterization techniques include electron microscopy and X-ray diffraction; these techniques are used to obtain microscopic structural parameters, including grain size, precipitate distribution, dislocation density, and interface transition region structure.

3. The method for evaluating the comprehensive performance of ultra-high strength steel wire finished products for bridge engineering according to claim 1, characterized in that, In the macroscopic performance testing step, the macroscopic mechanical performance parameters include at least one of tensile strength, yield strength, and elongation; the corrosion resistance performance parameters include at least one of corrosion rate and polarization resistance.

4. The method for evaluating the comprehensive performance of ultra-high strength steel wire finished products for bridge engineering according to claim 1, characterized in that, In the aforementioned correlation model construction step, the specific steps for constructing a quantitative correlation model of microstructure parameters, coating state, macroscopic performance parameters, and long-term service stability are as follows: the microstructure parameters obtained from microscopic characterization, including grain size and precipitate distribution, and the coating state data, including coating loss range and morphological defects, are matched with the mechanical and corrosion resistance performance parameters obtained from macroscopic performance testing, according to service time nodes and environmental conditions. Simultaneously, the microstructure evolution data and coating damage dynamic data obtained from long-term tracking and monitoring are supplemented, and all data are dimensionless to eliminate dimensional differences. Then, through multi-dimensional data feature extraction, the strong and weak correlation characteristics between microstructure parameters and performance parameters are identified, the contribution of coating state changes to performance degradation is clarified, and the correlation function between various parameters is established by combining the thermodynamic law of microstructure evolution and the kinetic mechanism of coating damage. Subsequently, based on the correlation coefficient obtained by data fitting, a mathematical expression containing microstructure parameters, coating state variables and macroscopic performance indicators is constructed. By adjusting the variable weights, the deviation between the calculation results and the measured data is controlled within a preset range, and finally a stable and reliable quantitative mapping relationship between microstructure-coating state-macroscopic performance is formed.

5. The method for evaluating the comprehensive performance of ultra-high strength steel wire products for bridge engineering according to claim 4, characterized in that, In the aforementioned correlation model construction step, a mathematical expression is constructed that includes microstructural parameters, coating state variables, and macroscopic performance indicators. The formula is as follows: ,in, It is a macroscopic performance indicator. It refers to the grain size. It is the volume fraction of the precipitated phase. It is dislocation density. It is the remaining thickness of the coating. It is the density of defects on the coating surface. It is a fundamental constant term. , , , and It is the correlation coefficient.

6. The method for evaluating the comprehensive performance of ultra-high strength steel wire finished products for bridge engineering according to claim 1, characterized in that, In the performance regulation direction confirmation step, based on the established quantitative mapping relationship between microstructure, coating state, and macroscopic performance, the preset ranges of the microstructure parameters and coating state variables corresponding to the target macroscopic performance are first determined. The target range to be achieved for each key parameter is then determined through reverse derivation. For microstructure parameters including grain size, precipitate volume fraction, and dislocation density, the microstructure is directionally regulated by adjusting the heat treatment temperature, holding time, cooling rate, and alloy element addition ratio during the coating preparation process, so that the relevant parameters are stabilized within the target range. For state variables including remaining coating thickness and surface defect density, combined with the characteristics of service conditions, the coating state variables are maintained in compliance with performance requirements by optimizing the initial design thickness of the coating, using surface pretreatment processes to reduce initial defects, or taking targeted protective measures during service to slow down the wear and corrosion rate. During the control process, the changes in microstructure parameters and coating state variables are monitored in real time. Based on the deviation between the monitoring data and the target range, the parameter settings of the control methods are dynamically adjusted to ensure that the macroscopic performance of the coating reaches the preset target.

7. The method for evaluating the comprehensive performance of ultra-high strength steel wire products for bridge engineering according to claim 1, characterized in that, In the performance evaluation and corrosion resistance life prediction steps, the established quantitative mapping relationship between microstructure, coating state and macro performance is used as the core basis. First, the current microstructure parameters of the coating, real-time coating state and service environment parameters are integrated. A multi-index weighted evaluation method is adopted to convert the performance contribution of each parameter into a quantifiable comprehensive score. Combined with industry standards and actual application requirements, performance levels including excellent, qualified and maintenance-required are defined to complete the systematic evaluation of the current comprehensive performance of the coating. Subsequently, based on long-term tracking data on the microstructure evolution of the coating, the law of damage rate change, and the performance degradation curves under different environments, combined with the current performance evaluation results, a performance degradation prediction model including the time dimension is constructed. By inputting simulation parameters of future service conditions, the time point when the overall performance of the coating drops below the qualified threshold is estimated. At the same time, an environmental fluctuation coefficient is introduced to correct the prediction results. Finally, accelerated aging experiments are used to obtain performance change data of the coating under extreme conditions, which are compared and calibrated with the output results of the prediction model to form a complete evaluation report including the current performance level, remaining corrosion resistance life, and key performance degradation nodes.

8. The method for evaluating the comprehensive performance of ultra-high strength steel wire products for bridge engineering according to claim 7, characterized in that, In the performance evaluation and corrosion resistance life prediction steps, a multi-index weighted evaluation method is adopted to convert the performance contribution of each parameter into a quantifiable comprehensive score. The formula is as follows: ,in, It is a comprehensive performance rating of the coating. It is the first The weighting coefficients of the evaluation indicators It is the total number of evaluation indicators. It is the first The normalized score of each evaluation indicator It is a comprehensive evaluation correction item.

9. The method for evaluating the comprehensive performance of ultra-high strength steel wire products for bridge engineering according to claim 7, characterized in that, In the performance evaluation and corrosion resistance life prediction steps, a performance degradation prediction model incorporating the time dimension is constructed, and its model formula is as follows: ,when Corrosion resistance life ,in, yes The overall performance rating of the coating at any time. This is the current overall performance rating of the coating. It is the basic performance decay rate constant. It is the environmental fluctuation coefficient. It is the residual value of performance degradation. It is the overall performance qualification threshold. It is an estimated corrosion resistance life.

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