Performance evaluation method and system for cable specimens under low-speed impact conditions

Abstract

The present invention relates to the field of performance evaluation technology, and in particular to a method and system for evaluating the performance of cable specimens under low-speed impact conditions. The present invention proposes the following scheme: force data is obtained through a drop hammer impact test, and the maximum load and anchoring performance are calculated through a data analysis model, and the evaluation data is integrated and optimized through a quantitative relationship model; the present invention can comprehensively analyze the overall mechanical properties of the cable under low-speed impact conditions, and through a multi-layer data analysis model, starting from multiple aspects such as dynamic response, anchoring stability, and energy absorption efficiency, ensure the accuracy and applicability of the evaluation results to meet the safety requirements of different engineering applications.

Description

Technical Field

[0001] The present invention relates to the technical field of performance evaluation, and in particular to a method and system for evaluating the performance of a cable specimen under low-speed impact conditions. Background Art

[0002] As modern bridges, buildings, and engineering structures grow in size, cable systems such as stay cables are increasingly used in these structures. However, in practical applications, cable structures are often subject to various external shocks, such as accidental collisions with vehicles and earthquakes caused by natural disasters. Especially under low-velocity impact conditions, the load-bearing capacity, stability, and fatigue resistance of the cable system are particularly important. These impacts can cause fatigue damage, reduced load-bearing capacity, or even fracture of the cables, posing a threat to the safety and durability of the entire structure. Therefore, effectively evaluating the performance of cable specimens under low-velocity impact conditions has become a critical and urgent issue in structural design and safety assurance.

[0003] At present, traditional cable performance evaluation methods are mainly based on static tensile tests and fatigue tests, and the focus is mostly on the tensile, fracture and deformation behavior of cables under static and cyclic loads. However, these methods are difficult to effectively reflect the complex mechanical behavior of cables under transient load conditions such as low-speed impact. Cable specimens under low-speed impact conditions not only have to withstand large instantaneous impact forces, but also involve complex dynamic response, energy absorption and stability evaluation of the anchoring structure. Traditional methods cannot fully reflect the dynamic response characteristics under impact loads. Therefore, how to comprehensively evaluate the performance of cables and their anchoring structures under low-speed impact environments has become a technical problem in research.

[0004] For example, the Chinese patent application with authorization announcement number CN108225906B discloses a computer vision-based cable corrosion monitoring, identification and fatigue life assessment method. It takes images of corroded high-strength steel wires and extracts image features from them. It then builds a corrosion degree evaluation model, and then builds a fatigue life characteristic index evaluation model. It matches the steel wire corrosion fatigue performance degradation state under artificial accelerated corrosion tests, and finally completes the corrosion state identification and fatigue life assessment of in-service cables. The invention has high recognition accuracy, fast speed and low cost. The invention can also meet the real-time data processing requirements of online monitoring and early warning of cable corrosion fatigue, that is, it does not update the data set and directly recognizes the images captured by ordinary consumer cameras. The invention improves the automation, intelligence, accuracy and robustness of cable corrosion monitoring, identification and fatigue life assessment, and provides a solution for the automatic monitoring and identification of corrosion fatigue of bridge structure cables.

[0005] The above patents have the problems raised by this background technology: it is difficult to effectively reflect the transient dynamic response characteristics under low-speed impact conditions, and there is a lack of comprehensive analysis of key performance indicators such as the stability and energy absorption efficiency of the anchoring structure, resulting in insufficient accuracy and applicability of the evaluation results. In order to solve the above problems, this application designs a performance evaluation method and system for cable specimens under low-speed impact conditions. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the shortcomings of the existing technology and provide a method and system for evaluating the performance of cable specimens under low-speed impact conditions. The method obtains force data in combination with a drop hammer impact test, and calculates the maximum load and anchoring performance through a data analysis model. The evaluation data is integrated and optimized through a quantitative relationship model, so that the method can comprehensively analyze the overall mechanical properties of the cable under low-speed impact conditions. Specifically, the method uses a multi-layer data analysis model to start from multiple aspects such as dynamic response, anchoring stability, and energy absorption efficiency to ensure the accuracy and applicability of the evaluation results and meet the safety requirements of different engineering applications.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for evaluating cable specimen performance under low-speed impact conditions, the method comprising:

[0009] The cable specimens were tested by drop hammer impact test to obtain the stress data of the cable specimens under different impact conditions;

[0010] Analyzing the force data to calculate the maximum load of the cable specimen;

[0011] Constructing a data analysis model, analyzing the stability and bearing capacity of the anchoring structure of the cable specimen through the data analysis model, and calculating the anchoring performance of the cable specimen;

[0012] A quantitative relationship model is established based on the maximum load and the anchoring performance, and design parameters of the cable and its anchoring structure are optimized based on the quantitative relationship model.

[0013] The drop hammer impact test is performed on the cable specimen by using a drop hammer impact test device, and the drop hammer impact test device includes:

[0014] The drop hammer assembly is placed directly above the cable specimen and is used to release the hammer from a preset height to generate an impact force, simulating the low-speed impact that may occur in actual environments;

[0015] The semi-cylindrical hammer head is installed at the end of the drop hammer, which can evenly transmit the impact force of the drop hammer to the surface of the cable specimen, thereby ensuring the stability and uniformity of the impact effect;

[0016] The cable specimen fixing system includes steel plates, fixing devices and support columns arranged at both ends of the cable specimen to ensure that the cable specimen remains stable during the impact process and does not produce lateral displacement;

[0017] The force sensor is installed near the impact area to measure the instantaneous impact force on the cable specimen during the impact process and transmit the instantaneous impact force to the data acquisition system;

[0018] Displacement sensors are installed at key locations on the cable specimen to record the displacement changes of the cable during the impact process in real time to obtain deformation information.

[0019] The data acquisition system is connected to the force sensor and displacement sensor to collect and store the force and displacement data collected during the test for subsequent data analysis and evaluation.

[0020] The analyzing the force data includes:

[0021] Connecting the force data to generate a force curve based on a time series, and extracting key dynamic response points based on the force curve;

[0022] Dividing the stress curve into a buffer section, a load-bearing section, an attenuation section, and a recovery section according to the key dynamic response points;

[0023] Performing adaptive polynomial segmented fitting on the force curves corresponding to the buffer section, the load-bearing section, the attenuation section, and the recovery section, respectively, to generate fitting curves that meet the force characteristics of each stage;

[0024] According to the fitting curve, a hierarchical grey correlation analysis model is constructed, and the grey correlation parameters of each stage are calculated by the hierarchical grey correlation analysis model. The initial buffering capacity, peak bearing capacity, energy absorption efficiency and residual strength recovery of the cable specimen are calculated based on the grey correlation parameters.

[0025] The hierarchical grey relational analysis model includes:

[0026] Map the fitting curves of each stage into the grey relational space, compare the fitting curves of each stage with the ideal curve, and calculate the difference sequence;

[0027] Calculating dynamic weights according to the stages corresponding to the difference sequence, assigning the dynamic weights to the difference sequence, and obtaining grey relational parameters, wherein the grey relational parameters include nonlinear adjustment factors, and the nonlinear adjustment factors include the nonlinear response and geometric characteristics of the cable specimen;

[0028] According to the grey correlation parameters of each stage, a correlation difference matrix between stages is constructed. According to the grey correlation parameters and the correlation difference matrix, the initial buffering capacity, peak bearing capacity, energy absorption efficiency and residual strength recovery force of the cable specimen are calculated.

[0029] The calculation of the maximum load of the cable specimen includes:

[0030] The parameters of initial buffering capacity, peak load-bearing capacity, energy absorption efficiency and residual strength recovery were standardized;

[0031] The normalized parameters are combined by weighted averaging to calculate the dynamic response index;

[0032] According to the frequency response of the cable specimen under different impact conditions, the residual strength restoring force is subjected to Fourier transform to calculate the nonlinear recovery coefficient;

[0033] The maximum load of the cable specimen is calculated according to the dynamic response index and the nonlinear recovery coefficient. The calculation formula of the maximum load is:

[0034] ;

[0035] in, represents the maximum load of the cable specimen, represents the dynamic correction coefficient, represents the reference load of the cable specimen, represents the dynamic response coefficient, represents the nonlinear restitution coefficient, represents the peak bearing capacity of the cable specimen, represents the energy absorption efficiency of the cable specimen, Represents the material property coefficient of the cable specimen.

[0036] The data analysis model includes:

[0037] The parameter extraction layer is used to perform multidimensional parameter decomposition on the stress data of the cable specimen and convert the original stress data from the time domain to the wavelet domain and principal component domain;

[0038] The parameter clustering layer divides the force data under different impact conditions into characteristic clusters based on the original force data in the wavelet domain and the principal component domain through a clustering algorithm, and calculates the clustering results based on the characteristic clusters;

[0039] The modal decomposition layer is used to apply the modal decomposition technology to the clustering results, extract the eigenmodes of the anchor structure, and analyze the spectral response of the eigenmodes to obtain the natural frequency, damping ratio and modal shape of the cable specimen in different modes.

[0040] The calculation formula of the anchoring performance is:

[0041] ;

[0042] in, represents the anchoring performance of the cable specimen, n represents a single characteristic mode of the anchoring structure, N represents the total number of characteristic modes of the anchoring structure, represents the stiffness coefficient of the nth eigenmode, represents the mode shape function of the nth eigenmode, represents the damping ratio of the nth eigenmode, represents the natural frequency of the nth characteristic mode, t represents the response time after the impact, represents the cosine function, represents the damping coefficient of the nth eigenmode, It represents the time rate of change of the nth eigenmode shape and is used to reflect the instantaneous velocity effect.

[0043] The quantitative relationship model includes:

[0044] The parameter coupling layer is used to couple the maximum load and anchorage performance of the cable specimen and calculate the comprehensive performance score;

[0045] a target optimization layer, which optimizes the design parameters of the cable specimen and its anchoring structure through an optimization algorithm based on the comprehensive performance score, wherein the optimization includes maximizing the impact strength of the structure, minimizing material consumption and weight, and extending fatigue life;

[0046] The feedback adjustment layer verifies and adjusts the optimized design parameters through a closed-loop feedback mechanism, and adjusts the objective function of the optimization algorithm based on the verification results.

[0047] A cable specimen performance evaluation system under low-speed impact conditions, the system comprising a data acquisition module, a performance analysis module, and a design optimization module;

[0048] The data acquisition module collects force and displacement data of the cable specimen by simulating low-speed impact conditions in actual environments through a drop hammer impact test;

[0049] The performance analysis module is configured with a force data analysis strategy, which is used to perform multi-dimensional performance analysis on the output of the data acquisition module and calculate the maximum load and anchoring performance of the cable specimen;

[0050] The design optimization module applies the results of the performance analysis to the design optimization of the cable specimens and anchoring structures based on the quantitative relationship model. Through the optimization algorithm, the design parameters are adjusted to maximize the impact strength, reduce the weight and improve the fatigue life.

[0051] The performance analysis module includes:

[0052] A data preprocessing unit is used to preprocess the force and displacement data output by the data acquisition module and perform denoising, filtering and standardization operations;

[0053] The maximum load analysis unit identifies and extracts the peak load-bearing capacity of the cable specimen under impact through time series analysis and segmented fitting of the force data. It then combines the frequency response and energy absorption characteristics to obtain the ultimate load-bearing capacity of the cable specimen.

[0054] Anchorage performance evaluation unit, which evaluates the stability and bearing capacity of anchorage structures under impact loads through modal analysis and dynamic response analysis methods;

[0055] The force data analysis strategy includes dynamic response extraction logic and modal feature analysis logic;

[0056] The dynamic response extraction logic is configured in the maximum load analysis unit and is used to extract dynamic response features from the time series;

[0057] The modal characteristic analysis logic is configured in the anchoring performance evaluation unit and is used to decompose the modal characteristics of the anchoring structure, including natural frequency, damping ratio and modal shape.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. This invention can comprehensively analyze the stress characteristics of cable specimens under impact loads. Through dynamic response analysis, energy absorption efficiency evaluation, and anchoring performance stability calculation, the accuracy and reliability of the evaluation results are improved;

[0060] 2. The present invention optimizes the design parameters through a quantitative relationship model and a target optimization algorithm, so that the cable and its anchoring structure achieve an optimal balance in terms of impact strength, weight reduction, and fatigue life, better meeting the safety and economy requirements in engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0062] Figure 1 Schematic diagram of the process of the cable specimen performance evaluation method under low-speed impact conditions in Example 1 of the present invention;

[0063] Figure 2 This is a flow chart of force data analysis in Example 1 of the present invention;

[0064] Figure 3 This is a flowchart of grey relational analysis in Example 1 of the present invention;

[0065] Figure 4 This is a structural diagram of the data analysis model of Example 1 of the present invention;

[0066] Figure 5 This is a module diagram of the cable specimen performance evaluation system under low-speed impact conditions in Example 2 of the present invention. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0068] Example 1

[0069] See also Figure 1 The present invention provides an embodiment of a method for evaluating the performance of a cable specimen under low-speed impact conditions, wherein the method comprises the following specific steps:

[0070] S1: Test the cable specimen through a drop hammer impact test to obtain the force data of the cable specimen under different impact conditions;

[0071] In this step, the magnitude and direction of the impact force are adjusted by setting different drop weight heights and release positions to comprehensively evaluate the performance of the specimen under various impact conditions. Force sensors and displacement sensors are installed at key locations on the specimen to monitor the instantaneous changes in impact force and the specimen's deformation response in real time. The data collected by these sensors is recorded and transmitted in real time by a data acquisition system, providing a basis for subsequent data analysis and performance evaluation. Through multiple tests and data collection under different impact conditions, the complete force characteristics and deformation of the cable specimen under low-speed impact conditions can be obtained.

[0072] S2: Analyze the force data and calculate the maximum load of the cable specimen;

[0073] S3: Construct a data analysis model, analyze the stability and bearing capacity of the anchoring structure of the cable specimen through the data analysis model, and calculate the anchoring performance of the cable specimen;

[0074] In this step, a method for evaluating anchor performance based on modal analysis and dynamic response is developed, combining the geometric and material characteristics of the anchor structure. The data analysis model uses modal characteristics extracted from the stress data (such as natural frequency, damping ratio, and modal shape) as input to comprehensively evaluate the dynamic response of the specimen under impact conditions. Modal decomposition identifies the response characteristics of the anchor structure at different frequencies, allowing analysis of its stiffness, stability, and energy dissipation capacity under impact forces.

[0075] S4: Based on the maximum load and anchoring performance, a quantitative relationship model is established, and the design parameters of the cable and its anchoring structure are optimized according to the quantitative relationship model;

[0076] In this step, a quantitative relationship model is established based on the specimen's maximum load and anchorage performance to correlate different performance parameters and structural characteristics. This quantitative relationship model couples multiple performance indicators, such as maximum load, anchorage stiffness, and fatigue life, to generate a comprehensive performance score that fully reflects the cable specimen's performance under impact conditions. The quantitative relationship model uses nonlinear fitting technology to balance various performance indicators with dynamically assigned weight coefficients, making the score more scientific and objective.

[0077] The drop hammer impact test is performed on the cable specimen by using a drop hammer impact test device, and the drop hammer impact test device includes:

[0078] The drop hammer assembly, positioned directly above the cable specimen, is used to release the force from a preset height to generate an impact force, simulating the low-speed impact that might occur in a real-world environment. This allows the specimen to exhibit a stress state similar to that experienced in actual working conditions. The height and weight of the drop hammer assembly can be adjusted to vary the impact energy, thereby testing the specimen's performance under different impact conditions.

[0079] The semi-cylindrical hammer head, installed at the end of the drop hammer assembly, is used to evenly transmit the impact force of the drop hammer to the specimen surface. Its semi-cylindrical design makes the impact force more evenly distributed, reduces local stress concentration, and thus simulates a more realistic impact distribution in actual stress environments.

[0080] The cable specimen, located in the center of the drop weight test apparatus, is directly impacted by the drop weight assembly. This specimen represents the actual cable in use, and its performance will be evaluated under impact loads. Steel plates and fixtures connect the specimen at both ends to ensure stability during impact, preventing lateral slippage that could cause test errors.

[0081] The cable specimen fixing assembly includes steel plates, fixing devices and support columns arranged at both ends of the cable specimen to ensure that the cable specimen remains stable during the impact process and does not produce lateral displacement;

[0082] The force sensor is installed below or near the impact area to measure the instantaneous impact force on the specimen during the impact process. The instantaneous impact force refers to the force acting on the object in a very short period of time. This force is characterized by a very short duration but can be very strong, and usually occurs during a rapid collision or impact. In a drop hammer impact test, the instantaneous force generated when the drop hammer hits the specimen is the instantaneous impact force. The force sensor transmits the captured data to the data acquisition system in real time to ensure that the peak value and change curve of the impact force can be accurately recorded. This data is key to the subsequent analysis of the specimen's strength and energy absorption characteristics;

[0083] Displacement sensors are installed at key locations on the specimen, specifically the deformation areas of the specimen, such as the impact center and its surroundings, or high-stress areas near the cable fixtures. They are used to monitor and record the specimen's displacement during the impact in real time. Displacement sensors can capture specimen deformation data, particularly deformation under impact, which is crucial for evaluating the specimen's elastic and plastic deformation properties.

[0084] Strain sensors are installed in certain areas of the specimen to measure the strain of the specimen under impact. They provide detailed strain distribution data, helping to study the stress distribution characteristics of the cable material during impact.

[0085] Resistive displacement sensors are used to measure displacement changes over a wider range. When used in conjunction with displacement sensors, they can provide specimen displacement data to ensure test accuracy.

[0086] A data acquisition system, connected to the force sensor and displacement sensor, is used to collect and store the force and displacement data collected during the test for subsequent data analysis and evaluation;

[0087] See also Figure 2 , the force data analysis flow chart of the embodiment of the present invention, the specific steps of S2 are as follows:

[0088] S2.1: Connect the force data to generate a force curve based on the time series, and extract key dynamic response points based on the force curve;

[0089] The force data obtained from the drop hammer impact test device is first converted into a time series, that is, the data points are connected one by one along the time axis to generate a complete force curve. This force curve shows the force changes of the specimen at different time points during the impact process, which can intuitively reflect the response process of the cable specimen under the impact force.

[0090] By processing the stress data using time series analysis techniques, key dynamic response points in the curve are extracted, including peak points, valley points, rising points, and falling points. These points represent the important mechanical responses of the cable specimen during the impact process and can show the stress state and structural response of the specimen at different time periods.

[0091] S2.2: Divide the force curve into a buffer section, a load section, a decay section, and a recovery section according to the key dynamic response points;

[0092] Based on the extracted key dynamic response points, the stress curve is divided into four main stages: buffer stage, load-bearing stage, attenuation stage, and recovery stage. Each stage corresponds to the performance of the specimen under different stress conditions. The buffer stage is the stage when the specimen is just beginning to be impacted, the load-bearing stage is the stage when the specimen bears the maximum load, the attenuation stage is the stage when the impact force begins to weaken, and the recovery stage is the process of the specimen gradually returning to the equilibrium state. The curve is automatically divided based on the position of different dynamic response points and the changing trend of the curve. Through segmented analysis, the response characteristics of the specimen at each stage can be carefully observed, laying the foundation for subsequent fitting analysis and correlation calculation;

[0093] S2.3: Performing adaptive polynomial segment-wise fitting on the force curves corresponding to the buffer segment, the load-bearing segment, the attenuation segment, and the recovery segment, respectively, to generate fitting curves that meet the force characteristics of each stage;

[0094] Specifically, according to the stress characteristics of each segment, the appropriate polynomial order is selected for fitting, thereby generating a fitting curve that meets the stress characteristics of each stage. For example, a high-order polynomial is used for fitting the rapidly changing buffer segment, while a low-order polynomial is selected for the stable recovery segment to simplify the model.

[0095] Adaptive polynomial fitting can decompose complex impact response curves into simple mathematical expressions, facilitating subsequent model calculation and analysis. At the same time, the fitting curve retains the characteristics of the original force data, ensuring that the model can reflect the actual force characteristics of the specimen.

[0096] S2.4: Construct a hierarchical grey relational analysis model based on the fitting curve, calculate grey relational parameters at each stage using the hierarchical grey relational analysis model, and calculate the initial buffering capacity, peak bearing capacity, energy absorption efficiency, and residual strength recovery of the cable specimen based on the grey relational parameters;

[0097] By calculating the grey correlation parameters, the stress performance of the specimen at different stages can be quantitatively evaluated, providing a basis for cable design and optimization. This analysis model not only improves data utilization but also effectively evaluates the bearing capacity of the specimen under complex impact conditions.

[0098] See also Figure 3 Flowchart of the gray relational analysis of an embodiment of the present invention. The core of the hierarchical gray relational analysis model is to compare the different stress stages of the cable specimen with the ideal state through staged gray relational analysis to quantify its performance at each stage. This model accurately reflects the response characteristics of the cable under low-speed impact conditions through dynamic weights, gray relational degrees, and nonlinear adjustment factors. The hierarchical gray relational analysis model includes:

[0099] The fitting curves of each stage are mapped into the grey relational space, and the fitting curves of each stage are compared with the ideal curve to calculate the difference sequence. The difference sequence is calculated point by point by calculating the difference between the fitting curve and the ideal curve to form a sequence that is used to characterize the degree of deviation between the actual stress state of the specimen and the ideal state at that stage.

[0100] Different dynamic weights are assigned based on the value range of the difference sequence and the importance of each stage in the load-bearing process. The dynamic weight takes into account the relative importance of the stage in the overall mechanical properties, so that the key characteristics exhibited in different stages can have an appropriate impact on the final grey relational degree parameters. For example, the dynamic weight of the load-bearing section may be higher to highlight its influence on the maximum load performance of the specimen;

[0101] Dynamic weighting is designed to assign different weight coefficients to different stages based on their importance, ensuring that key stages have a greater impact on the final performance evaluation. For example, in impact testing, the load-bearing stage is often the key stage for evaluating the maximum load-bearing capacity of the structure, while the cushioning and attenuation stages are mainly used to observe energy absorption efficiency and stability. Therefore, different stages have different importance, and fixed weights cannot fully reflect the impact of each stage on the overall performance.

[0102] Dynamic weights are automatically assigned based on the numerical characteristics of the difference sequence. Specifically, the model statistically analyzes the difference sequence of each stage to identify which stages are more important when experiencing the greatest impact force, thereby assigning higher weights to these stages. Furthermore, by analyzing the mechanical response characteristics of each stage, the weights are further modified to dynamically adapt to different impact conditions.

[0103] Dynamic weights are applied to the difference sequence to obtain the grey relational parameter. The grey relational parameter quantitatively reflects the degree of matching between each stage and the ideal model by comprehensively considering the difference sequence and weight distribution.

[0104] Under low-speed impact conditions, the stress behavior of the cable specimen has significant nonlinear characteristics, such as material yielding and plastic deformation. In order to adapt to these nonlinear responses, the model introduces a nonlinear adjustment factor to modify the gray correlation parameters so that the analysis results can better reflect the actual mechanical properties. The nonlinear adjustment factor is calculated based on the geometric characteristics and material properties of the specimen. For example, the nonlinear stress-strain relationship of the material can be reflected in the gray correlation parameters through the adjustment factor, making the parameters more sensitive to changes in stress. The geometric characteristics are reflected in the influence of the shape of the structure on the distribution of force. The model is dynamically adjusted through the adjustment factor to adapt to specimens of different shapes;

[0105] According to the difference sequence and the preset correlation function, the grey correlation parameter is calculated. The closer the grey correlation parameter is to 1, the closer the actual stress curve is to the ideal state.

[0106] The grey correlation parameters for each stage are arranged sequentially to form a correlation difference matrix, which is used to systematically evaluate the correlation differences between different stages. The correlation difference matrix is a quantitative tool used to systematically demonstrate the differences in mechanical properties between different stages. By comparing the grey correlation parameters for each stage, the correlation distribution of different stages can be clearly seen, thereby more comprehensively evaluating the overall performance of the specimen.

[0107] The gray correlation parameters of the buffer, load-bearing, attenuation, and recovery segments are arranged sequentially into a matrix, with each matrix element representing the correlation of a particular stage. The rows and columns of the matrix correspond to different mechanical stages, and the values of the matrix elements reflect the differences in correlation between the stages. For example, when the difference between the load-bearing and buffer segments is large, the value of this element will be significantly lower, prompting designers to focus on improving load-bearing performance.

[0108] Based on the correlation difference matrix, the initial buffering capacity, peak load-bearing capacity, energy absorption efficiency, and residual strength recovery of the specimen are further calculated. By decomposing and analyzing the parameter values in the matrix, the different mechanical performance indicators of the specimen during the impact process are quantified;

[0109] Specifically, assume that in an actual impact test, the cable specimen's gray correlation coefficient is 0.8 in the buffer phase, 0.9 in the load phase, 0.7 in the decay phase, and 0.85 in the recovery phase. Analysis of the correlation coefficient difference matrix shows that the specimen's performance during the load phase is close to ideal, indicating good peak load-bearing capacity. Meanwhile, the lower gray correlation coefficient in the decay phase indicates room for improvement in impact energy dissipation.

[0110] This analysis model can quickly identify the mechanical performance of the specimen at each stage and determine whether its energy absorption characteristics need to be strengthened or its peak load capacity needs to be increased. This multi-stage, hierarchical analysis method significantly improves the accuracy and detail of performance evaluation, providing in-depth analysis that cannot be achieved by traditional single load testing methods.

[0111] These processed parameters are combined by weighted averaging to form a dynamic response index, thereby evaluating the overall load-bearing capacity of the cable specimen under different impact conditions. Based on the frequency response characteristics, the residual strength and restoring force parameters are Fourier transformed to calculate the nonlinear recovery coefficient. The maximum load of the cable specimen is calculated by combining the dynamic response index and the nonlinear recovery coefficient. The calculation of the maximum load of the cable specimen includes:

[0112] The initial buffering capacity, peak load-bearing capacity, energy absorption efficiency, and residual strength recovery parameters are standardized. This process eliminates the influence of different indicator dimensions, allowing all indicators to be compared on the same scale. The standardized parameters reflect the relative strength of each performance throughout the impact process, making the final dynamic response index more accurate and unified.

[0113] Under low-speed impact conditions, the performance of a specimen is not determined by a single factor. For example, peak load capacity only reflects its load-bearing capacity in a short period of time, while ignoring important indicators such as cushioning performance and energy absorption efficiency. Therefore, the standardized parameters are combined through weighted averaging to calculate the dynamic response index. The weight of each parameter is dynamically adjusted according to its impact on the overall performance. The higher the final calculated dynamic response index, the better the specimen performs during the impact and the stronger its impact resistance.

[0114] Cable specimens may exhibit nonlinear recovery properties such as viscoelasticity or plastic deformation under low-velocity impact conditions. These properties prevent the specimen from returning to its original state immediately after impact, but rather gradually. This recovery ability is crucial for determining the durability of the structure.

[0115] Based on the frequency response of the cable specimen under different impact conditions, the residual strength restoring force is Fourier transformed, the time series of the restoring force is converted to the frequency domain, the frequency characteristics are extracted, and the nonlinear recovery coefficient is calculated. This coefficient describes the restoring force characteristics of the specimen after being subjected to force and is a parameter that can reflect the nonlinear recovery characteristics. The nonlinear recovery coefficient is used to describe the nonlinear characteristics of the specimen's restoring force after impact. Because cables may experience multiple repeated impacts under actual working conditions, their recovery characteristics directly affect the long-term stability and durability of the structure.

[0116] The maximum load of the cable specimen is calculated according to the dynamic response index and the nonlinear recovery coefficient. The calculation formula of the maximum load is:

[0117] ;

[0118] in, represents the maximum load of the cable specimen, Indicates the dynamic correction factor, which is a coefficient used to adjust the influence of the reference load. Especially under dynamic impact conditions, the influence of the reference load on the maximum load needs to be adjusted to adapt to the actual impact environment. represents the reference load of the cable specimen, represents the dynamic response coefficient, represents the nonlinear restitution coefficient, The peak bearing capacity of the cable specimen is the maximum instantaneous force that the specimen can withstand under impact load. This is an important mechanical performance indicator of the specimen, which directly reflects the strength of the material. Through the drop hammer impact test, the force time series curve of the specimen is recorded. The peak bearing capacity is the maximum value in the curve, which reflects the bearing capacity of the specimen under extreme stress conditions. The energy absorption efficiency of the cable specimen is the ratio of the energy absorbed by the specimen during the impact to the total input energy. It is an important indicator for measuring the impact resistance of the material. By integrating the force curve, the energy dissipation during the impact can be calculated. The material property coefficient of the cable specimen is used to quantify the influence of material properties on the overall bearing capacity. It specifically reflects the material's hardness, ductility, energy absorption capacity and other characteristics.

[0119] See also Figure 4 The data analysis model structure diagram of an embodiment of the present invention is designed to comprehensively extract the stress characteristics of cable specimens under low-speed impact conditions through hierarchical multidimensional data processing. The entire model consists of three main layers: parameter extraction layer, parameter clustering layer, and modal decomposition layer. Each layer undertakes different analysis tasks, gradually converting the specimen stress data into characteristic parameters that can be used to evaluate anchoring performance. The data analysis model includes:

[0120] The parameter extraction layer is used to perform multidimensional parameter decomposition on the stress data of the cable specimen and convert the original stress data from the time domain to the wavelet domain and the principal component domain. The wavelet domain is used to extract transient characteristics and detailed changes in the data, while the principal component domain is used to extract the main trends and patterns in the data.

[0121] Wavelet transform decomposition can decompose the original force data into sub-bands of different frequencies and times, revealing the impact response of the specimen at different time points. At the same time, principal component analysis (PCA) is used to extract the principal components of the data, reducing the dimensionality and retaining the most important data information to reduce computational complexity.

[0122] Through the combination of wavelet transform and principal component analysis, the parameter extraction layer can accurately extract the core factors that affect the stress characteristics of the specimen, ensuring the accuracy of subsequent cluster analysis and modal decomposition. At the same time, data dimensionality reduction processing greatly improves analysis efficiency;

[0123] The parameter clustering layer divides the stress data under different impact conditions into characteristic clusters based on the original stress data in the wavelet domain and the principal component domain through a clustering algorithm. The clustering results are calculated based on the characteristic clusters to identify the groups of specimens that exhibit similar stress characteristics under the same impact conditions.

[0124] Perform preliminary clustering on the data in the principal component domain according to the DBSCAN clustering algorithm to obtain core points, and determine the number of cluster center clusters according to the number of core points;

[0125] In this step, the data of the principal component domain is first obtained. The data of the principal component domain forms multiple functional element data points. The core of DBSCAN is to cluster based on the density of data points, without predefining the number of clusters. By setting the minimum number of points (MinPts) and the radius parameter (Eps), the DBSCAN algorithm can find high-density areas as cluster centers, while sparse data points are marked as noise points. The purpose of this step is to extract high-density areas (i.e., core points) from the demand description. The number of core points directly determines the number of initial cluster center clusters of the subsequent K-means algorithm;

[0126] The kernel points after DBSCAN clustering are used as the initial center clusters of the K-means clustering algorithm, and the Euclidean distance from the wavelet domain data to each of the initial center clusters is calculated;

[0127] The K-means algorithm is a distance-based partitioning clustering algorithm. The core idea is to optimize the clustering effect by minimizing the distance within the cluster. Each wavelet domain data will be assigned to the cluster center closest to it.

[0128] Perform clustering according to the Euclidean distance, calculate the average clustering error of the initial central cluster, and update the central cluster according to the average clustering error;

[0129] Define a convergence condition, determine whether the updated center cluster meets the convergence condition, if not, continue to update the center cluster, if it meets the convergence condition, output the clustering result;

[0130] The optimal partitioning of functional elements is ensured by iteratively updating cluster centers and calculating errors. In the K-means algorithm, the convergence condition is a common criterion, which is when the change in cluster center position or the error in intra-cluster distance no longer decreases significantly. After the convergence condition is set, the algorithm terminates and outputs the final module group for system architecture design.

[0131] Specifically, by combining the two clustering algorithms, DBSCAN and K-means, the data is clustered and analyzed, and more accurate clustering results are obtained by iteratively optimizing the central cluster. DBSCAN is a density clustering algorithm used to identify closely connected data points in space and divide them into clusters. DBSCAN can be used for preliminary clustering to obtain core points. Core points refer to the core area containing at least a specified number of data points within a given radius. Since each core point may represent a cluster, the number of core points can be used to preliminarily determine the number of central clusters of the cluster. Through cluster analysis, cable specimens with similar stress characteristics can be identified and grouped. Such clustering results facilitate the subsequent analysis of characteristic modes and simplify the complexity of modal decomposition. Clustering also helps identify the distribution pattern of stress characteristics under different impact conditions, making it easier for engineering designers to optimize the specimen structure in a targeted manner.

[0132] The modal decomposition layer is used to apply modal decomposition technology to the clustering results. The purpose of modal decomposition is to determine the natural frequency and damping ratio of the structure under different impact conditions. The natural frequency represents the inherent vibration characteristics of the structure, while the damping ratio describes the attenuation rate of the vibration. Through modal decomposition, the vibration mode of the cable under impact can be determined, thereby evaluating the stability of the anchoring structure. For example, the spectrum peak obtained by FFT analysis in the frequency domain can be used to calculate the natural frequency; and the damping ratio can be obtained by exponential fitting of the vibration attenuation curve, extracting the characteristic mode of the anchoring structure, and analyzing the spectrum response of the characteristic mode to obtain the natural frequency, damping ratio and modal shape of the cable specimen under different modes;

[0133] Modal analysis methods such as eigenvalue decomposition and spectrum analysis are used to extract the modal frequency and damping ratio of each characteristic cluster. For example, singular value decomposition (SVD) is used to determine the eigenfrequency of each mode, and spectrum analysis is combined to derive the spectral response of each mode under different impact conditions. The damping ratio is calculated through curve fitting in the time domain or frequency domain to measure the vibration attenuation capacity of the specimen under different modes.

[0134] The calculation formula of the anchoring performance is:

[0135] ;

[0136] in, represents the anchoring performance of the cable specimen, n represents a single characteristic mode of the anchoring structure, N represents the total number of characteristic modes of the anchoring structure, represents the stiffness coefficient of the nth eigenmode, represents the mode shape function of the nth eigenmode, represents the damping ratio of the nth eigenmode, represents the natural frequency of the nth characteristic mode, t represents the response time after the impact, represents the cosine function, represents the damping coefficient of the nth eigenmode, It represents the time rate of change of the nth characteristic mode shape, which is used to reflect the instantaneous velocity effect;

[0137] The quantitative relationship model is designed to quantitatively score the overall performance of cable specimens based on their maximum load and anchorage performance, and optimize design parameters through an optimization algorithm. The model consists of three main layers: parameter coupling layer, target optimization layer, and feedback adjustment layer. Each layer has different tasks, gradually analyzing, optimizing, and adjusting the specimen's performance. The quantitative relationship model includes:

[0138] The parameter coupling layer couples the cable specimen's maximum load and anchorage performance, two key performance indicators, to calculate a comprehensive performance score. Maximum load reflects the specimen's bearing capacity under impact loads, while anchorage performance characterizes the cable connection's stability and fatigue resistance.

[0139] A weighted algorithm quantifies the maximum load and anchoring performance and generates a comprehensive performance score. The weighting coefficient is adjusted according to the specific application scenario and performance requirements to balance load-bearing capacity and anchoring stability. For example, in applications with high impact resistance requirements, the weight of maximum load can be increased, while in applications focusing on long-term stability, the weight of anchoring performance can be increased.

[0140] a target optimization layer, which optimizes the design parameters of the cable specimen and its anchoring structure through an optimization algorithm based on the comprehensive performance score, wherein the optimization includes maximizing the impact strength of the structure, minimizing material consumption and weight, and extending fatigue life;

[0141] The optimization algorithm specifically includes:

[0142] defining an objective function that comprehensively considers the impact strength, material consumption and weight, and fatigue life of the cable and its anchoring structure;

[0143] The design parameter set is iteratively optimized through selection, crossover, and mutation operations. The selection operation selects the design solution with higher fitness based on the comprehensive performance score, the crossover operation is used to generate new design solutions, and the mutation operation is used to make small adjustments to the design parameters to increase the diversity of solutions.

[0144] Physical and engineering constraints are imposed on the design parameter set of the cables and their anchorage structures. These constraints include:

[0145] Impact strength constraint to ensure that the cable does not break under the maximum impact force;

[0146] Material and weight constraints, which limit the type, density, and overall weight of the selected materials;

[0147] Fatigue life constraints ensure that the cables maintain structural integrity within the designed number of fatigue cycles; these constraints are introduced into the objective function in the form of a penalty function to adjust the fitness score;

[0148] In each iteration, the algorithm generates a new set of design parameters based on the comprehensive performance score and verifies its performance through simulation or experiment. This process ensures that the optimization results achieve the best balance between multiple objectives.

[0149] Feedback adjustment layer verifies and adjusts the optimized design parameters through a closed-loop feedback mechanism, and adjusts the objective function of the optimization algorithm based on the verification results;

[0150] After each optimization step, the optimization results are verified through experiments or simulations, and deviation data is fed back into the optimization algorithm to adjust the parameter settings and weights of the objective function. This feedback mechanism enables the model to continuously self-correct, gradually converging to the optimal design solution.

[0151] Example 2

[0152] See also Figure 5 , the present invention provides an embodiment: a system for evaluating the performance of a cable specimen under low-speed impact conditions, the system comprising a data acquisition module, a performance analysis module and a design optimization module;

[0153] The data acquisition module collects force and displacement data of the cable specimen by simulating low-speed impact conditions in actual environments through a drop hammer impact test;

[0154] The performance analysis module is configured with a force data analysis strategy, which is used to perform multi-dimensional performance analysis on the output of the data acquisition module and calculate the maximum load and anchoring performance of the cable specimen;

[0155] The design optimization module applies the results of the performance analysis to the design optimization of the cable specimens and anchoring structures based on the quantitative relationship model. Through the optimization algorithm, the design parameters are adjusted to maximize the impact strength, reduce the weight and improve the fatigue life.

[0156] The performance analysis module includes:

[0157] A data preprocessing unit is used to preprocess the force and displacement data output by the data acquisition module and perform denoising, filtering and standardization operations;

[0158] The maximum load analysis unit identifies and extracts the peak load-bearing capacity of the cable specimen under impact through time series analysis and segmented fitting of the force data. It then combines the frequency response and energy absorption characteristics to obtain the ultimate load-bearing capacity of the cable specimen.

[0159] Anchorage performance evaluation unit, which evaluates the stability and bearing capacity of anchorage structures under impact loads through modal analysis and dynamic response analysis methods;

[0160] The force data analysis strategy includes dynamic response extraction logic and modal feature analysis logic;

[0161] The dynamic response extraction logic is configured in the maximum load analysis unit and is used to extract dynamic response features from the time series;

[0162] The modal characteristic analysis logic is configured in the anchoring performance evaluation unit and is used to decompose the modal characteristics of the anchoring structure, including natural frequency, damping ratio and modal shape.

[0163] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the performance of cable specimens under low-speed impact conditions, characterized in that: The method comprises: The cable specimens were tested by drop hammer impact test to obtain the stress data of the cable specimens under different impact conditions; Analyzing the force data to calculate the maximum load of the cable specimen; Constructing a data analysis model, analyzing the stability and bearing capacity of the anchoring structure of the cable specimen through the data analysis model, and calculating the anchoring performance of the cable specimen; Establishing a quantitative relationship model based on the maximum load and the anchoring performance, and optimizing design parameters of the cable and its anchoring structure based on the quantitative relationship model; The analyzing the force data includes: Connecting the force data to generate a force curve based on a time series, and extracting key dynamic response points based on the force curve; Dividing the stress curve into a buffer section, a load section, an attenuation section, and a recovery section according to the key dynamic response points; Performing adaptive polynomial segmented fitting on the force curves corresponding to the buffer section, the load-bearing section, the attenuation section, and the recovery section, respectively, to generate fitting curves that meet the force characteristics of each stage; According to the fitting curve, a hierarchical grey correlation analysis model is constructed, grey correlation parameters of each stage are calculated by the hierarchical grey correlation analysis model, and the initial buffering capacity, peak bearing capacity, energy absorption efficiency and residual strength recovery force of the cable specimen are calculated according to the grey correlation parameters; The data analysis model includes: The parameter extraction layer is used to perform multidimensional parameter decomposition on the stress data of the cable specimen and convert the original stress data from the time domain to the wavelet domain and principal component domain; The parameter clustering layer divides the force data under different impact conditions into characteristic clusters based on the original force data in the wavelet domain and the principal component domain through a clustering algorithm, and calculates the clustering results based on the characteristic clusters; A modal decomposition layer is used to apply modal decomposition technology to the clustering results to extract the eigenmodes of the anchor structure, analyze the spectral response of the eigenmodes, obtain the natural frequency, damping ratio and modal shape of the cable specimen in different modes, and calculate the anchor performance based on the natural frequency, damping ratio and modal shape; The quantitative relationship model includes: The parameter coupling layer is used to couple the maximum load and anchoring performance of the cable specimen and calculate the comprehensive performance score; a target optimization layer, which optimizes the design parameters of the cable specimen and its anchoring structure through an optimization algorithm according to the comprehensive performance score; The feedback adjustment layer verifies and adjusts the optimized design parameters through a closed-loop feedback mechanism, and adjusts the objective function of the optimization algorithm based on the verification results.

2. The method for evaluating cable specimen performance under low-speed impact conditions according to claim 1, characterized in that: The drop hammer impact test is performed on the cable specimen by using a drop hammer impact test device, and the drop hammer impact test device includes: The drop hammer assembly is placed directly above the cable specimen and is used to release the hammer from a preset height to generate an impact force, simulating the low-speed impact that occurs in actual environments; The semi-cylindrical hammer head is installed at the end of the drop hammer, which can evenly transmit the impact force of the drop hammer to the surface of the cable specimen, ensuring the stability and uniformity of the impact effect; The cable specimen fixing system includes steel plates, fixing devices and support columns arranged at both ends of the cable specimen to ensure that the cable specimen does not produce lateral displacement during the impact process; The force sensor is installed near the impact area to measure the instantaneous impact force on the cable specimen during the impact process and transmit the instantaneous impact force to the data acquisition system; Displacement sensors are installed at key locations on the cable specimen to record the displacement changes of the cable during the impact process in real time to obtain deformation information. The data acquisition system is connected to the force sensor and displacement sensor to collect and store the force and displacement data collected during the test.

3. The method for evaluating cable specimen performance under low-speed impact conditions according to claim 1, characterized in that: The hierarchical grey relational analysis model includes: Map the fitting curves of each stage into the grey relational space, compare the fitting curves of each stage with the ideal curve, and calculate the difference sequence; Calculating dynamic weights according to the stages corresponding to the difference sequence, assigning the dynamic weights to the difference sequence, and obtaining grey relational parameters, wherein the grey relational parameters include nonlinear adjustment factors, and the nonlinear adjustment factors include the nonlinear response and geometric characteristics of the cable specimen; According to the grey correlation parameters of each stage, a correlation difference matrix between stages is constructed. According to the grey correlation parameters and the correlation difference matrix, the initial buffering capacity, peak bearing capacity, energy absorption efficiency and residual strength recovery force of the cable specimen are calculated.

4. The method for evaluating cable specimen performance under low-speed impact conditions according to claim 3, characterized in that: The calculation of the maximum load of the cable specimen includes: The parameters of initial buffering capacity, peak load-bearing capacity, energy absorption efficiency and residual strength recovery were standardized; The normalized parameters are combined by weighted averaging to calculate the dynamic response index; According to the frequency response of the cable specimen under different impact conditions, the residual strength restoring force is subjected to Fourier transform to calculate the nonlinear recovery coefficient; The maximum load of the cable specimen is calculated based on the dynamic response index and the nonlinear recovery coefficient.

5. The method for evaluating cable specimen performance under low-speed impact conditions according to claim 1, characterized in that: The optimization of the design parameters of the cable specimen and its anchoring structure includes: Maximize the impact strength of the structure; Minimize material consumption and weight; Extend fatigue life.

6. A cable specimen performance evaluation system under low-speed impact conditions, for implementing the cable specimen performance evaluation method under low-speed impact conditions as claimed in any one of claims 1 to 5, characterized in that: The system includes a data acquisition module, a performance analysis module and a design optimization module; The data acquisition module collects force and displacement data of the cable specimen by simulating low-speed impact conditions in actual environments through a drop hammer impact test; The performance analysis module is configured with a force data analysis strategy, which is used to perform multi-dimensional performance analysis on the output of the data acquisition module and calculate the maximum load and anchoring performance of the cable specimen; The design optimization module applies the results of the performance analysis to the design optimization of the cable specimens and anchoring structures based on the quantitative relationship model. Through the optimization algorithm, the design parameters are adjusted to maximize the impact strength, reduce the weight and improve the fatigue life.

7. The cable specimen performance evaluation system under low-speed impact conditions according to claim 6, characterized in that: The performance analysis module includes: A data preprocessing unit is used to preprocess the force and displacement data output by the data acquisition module and perform denoising, filtering and standardization operations; The maximum load analysis unit identifies and extracts the peak load-bearing capacity of the cable specimen under impact through time series analysis and segmented fitting of the force data. It then combines the frequency response and energy absorption characteristics to obtain the ultimate load-bearing capacity of the cable specimen. Anchorage performance evaluation unit, which evaluates the stability and bearing capacity of anchorage structures under impact loads through modal analysis and dynamic response analysis methods; The force data analysis strategy includes dynamic response extraction logic and modal feature analysis logic; The dynamic response extraction logic is configured in the maximum load analysis unit and is used to extract dynamic response features from the time series; The modal characteristic analysis logic is configured in the anchoring performance evaluation unit and is used to decompose the modal characteristics of the anchoring structure, including natural frequency, damping ratio and modal shape.

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