Inhaul cable fatigue damage position and degree identification method based on optical fiber technology

Abstract

The invention discloses an inhaul cable fatigue damage position and degree identification method based on an optical fiber technology, and the method comprises the steps: determining fatigue test parameters, carrying out an inhaul cable fatigue test to obtain corrected fatigue performance, and determining an inhaul cable fatigue model according to the corrected fatigue performance, inputting a to-be-identified stay cable working condition and a corresponding fatigue working condition into the stay cable fatigue model to obtain stay cable fatigue prediction data and determine a monitoring wavelength threshold value, performing abnormal screening and abnormal classification on a to-be-identified monitoring wavelength to determine a fatigue damage abnormal wavelength, and performing stay cable fatigue damage positioning according to the fatigue damage abnormal wavelength. A fatigue damage cable force model is constructed according to the damage positioning result to determine cable forces before and after fatigue damage, and the fatigue damage degree of the inhaul cable is determined according to the damage positioning result and the cable forces before and after fatigue damage. According to the method, the fatigue damage position and degree of the inhaul cable can be timely and accurately identified, and meanwhile, the method has important significance for guaranteeing structural safety and prolonging the service life.

Description

Technical Field

[0001] The invention relates to the field of nondestructive testing, and in particular to a method for identifying the position and degree of fatigue damage of a cable based on optical fiber technology. Background Art

[0002] As a key load-bearing component, the safety and durability of bridge cables are crucial to the stability of the entire bridge structure. During long-term service, bridge cables are prone to fatigue damage due to environmental erosion, fatigue loads, excessive stress and other factors, resulting in performance degradation and even structural failure. Therefore, timely and accurate identification of the fatigue damage location and degree of cables is of great significance to ensure structural safety and extend service life.

[0003] Traditional cable fatigue damage detection methods mostly rely on manual inspection, ultrasonic testing or magnetic particle testing. Although these methods can detect damage to a certain extent, they have problems such as long detection cycle, low efficiency and high cost, and it is difficult to accurately locate the damage position and accurately assess the degree of damage. Fiber optic sensors have the advantages of high sensitivity, strong anti-interference ability, and distributed measurement. They are widely used in the field of structural health monitoring, and are particularly suitable for structural damage monitoring in complex environments. Based on the above background, the present invention proposes a method for identifying the location and degree of cable fatigue damage based on fiber optic technology. The method monitors the cable in real time based on fiber optic sensors, combines fatigue test data and model prediction, and realizes rapid and accurate identification of the location and degree of cable fatigue damage, overcoming the shortcomings of existing methods for identifying the location and degree of cable fatigue damage. This method not only improves detection efficiency and reduces detection costs, but also provides strong support for structural safety assessment and maintenance decisions. Summary of the invention

[0004] The purpose of the present invention is to provide a method for identifying the position and degree of fatigue damage of a cable based on optical fiber technology.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] Determine fatigue test parameters, carry out cable fatigue test according to the fatigue test parameters, and obtain corrected fatigue performance according to the cable fatigue test results; the cable fatigue test includes a cable fatigue test test and a cable fatigue simulation test;

[0008] Determining a cable fatigue model according to the modified fatigue performance and the fatigue test parameters;

[0009] Inputting the cable working condition to be identified and the corresponding fatigue working condition into the cable fatigue model to obtain cable fatigue prediction data, determining the monitoring wavelength threshold according to the cable fatigue prediction data, and performing abnormal screening and abnormal classification on the monitoring wavelength to be identified to determine the fatigue damage abnormal wavelength;

[0010] Perform cable fatigue damage location according to the fatigue damage abnormal wavelength, and construct a fatigue damage cable force model based on the damage location result;

[0011] The cable forces before and after fatigue damage are determined according to the fatigue damage cable force model, and the fatigue degree of the cable is determined according to the damage location result and the cable forces before and after fatigue damage.

[0012] Furthermore, the method for determining the fatigue test parameters includes:

[0013] Determine the common cable size range and cable fatigue condition range in engineering projects, and use the Monte Carlo method to perform random sampling of parameters from the common cable size range and cable fatigue condition range in engineering projects to obtain multiple cable size and fatigue condition combinations;

[0014] The expected parameter combination is set, the sampling results are evaluated, the sampling loss function is set according to the evaluation results, the sampling parameters are adjusted by the gradient descent method, multiple iterative samplings are performed until the loss function is minimized and the iteration is stopped, and multiple groups of parameter design combinations, i.e., fatigue test parameters, are output; the step of evaluating the sampling results includes preliminary analysis and representative evaluation; the sampling loss function is used to measure the difference between the sampling parameter combination and the expected parameter combination.

[0015] Furthermore, the method for obtaining the corrected fatigue performance comprises the following steps:

[0016] According to the sampled fatigue test parameters, a cable fatigue tensile test is carried out to obtain a fatigue monitoring wavelength. The fatigue monitoring wavelength, the material parameters and the size parameters of the corresponding cable are calculated through the cable fatigue relationship to obtain a fatigue performance test value. The cable fatigue theoretical expression is:

[0017]

[0018] where ε F To consider the fatigue strain under fatigue conditions, ε i is the strain of fatigue stage i when considering fatigue conditions, g is the number of fatigue stages, ε max is the ultimate strain when considering fatigue conditions, N i,fat is the maximum number of cycles in fatigue stage i when fatigue conditions are considered, N u,fat The limit cycle number of the cable under fatigue conditions is obtained by Miner's law, K ε,fbg is the optical fiber strain sensitivity coefficient, ρ is the optical fiber strain transmission rate; Em is the elastic modulus of the cable material, Δλ B,i is the wavelength change in fatigue stage i under fatigue condition;

[0019] According to the sampled fatigue test parameters and the corresponding cable material parameters, a cable fatigue tensile finite element simulation test is carried out to obtain the fatigue performance simulation value;

[0020] The fatigue performance test value and the fatigue performance parameter simulation value are fitted to obtain the fitting correction coefficient k * The fitting correction coefficient is used to correct the fatigue performance simulation value to obtain the corrected fatigue performance.

[0021] Furthermore, the method for determining the abnormal fatigue damage data includes:

[0022] Define fatigue response as the objective function, define fatigue working condition and cable working condition as fatigue variables, and construct a fatigue response model as the cable fatigue model by performing response surface fitting based on fatigue response and fatigue variables;

[0023] Inputting the to-be-identified cable working condition and fatigue working condition into the cable fatigue model to obtain cable fatigue prediction data; the cable fatigue prediction data includes fatigue monitoring wavelength prediction value and fatigue performance prediction value;

[0024] The monitoring wavelength threshold is searched according to the fatigue monitoring wavelength prediction value, fatigue performance prediction value, cable working condition and fatigue working condition to determine the monitoring wavelength threshold, the monitoring wavelength to be identified is screened for abnormalities according to the monitoring wavelength threshold to obtain abnormal monitoring wavelengths, and the abnormal data of abnormal monitoring wavelengths is classified by a decision tree to determine the fatigue damage abnormal wavelength;

[0025] The specific steps to determine the monitoring wavelength threshold are:

[0026] Set the initial monitoring wavelength threshold, the expression is:

[0027]

[0028] where λ 0,sta is the initial monitoring wavelength threshold, is the maximum safety factor, Safe min =1 is the minimum safety factor, λ i,max is the maximum value of the monitored wavelength among all the measuring points, λ i is the monitoring wavelength of measuring point i, and h is the total number of measuring points;

[0029] According to the initial monitoring wavelength threshold fitness, determine whether to update the position and speed of the fatigue particle, recalculate the fitness of the monitoring wavelength threshold according to the updated position and speed of the fatigue particle, repeat the above operation until the fitness of the current monitoring wavelength threshold is the optimal fitness or the maximum search number is reached, stop searching, and output the monitoring wavelength threshold λ sta .

[0030] Furthermore, a method for locating cable fatigue damage according to the abnormal wavelength of fatigue damage includes:

[0031] Directly locate the smart reinforcement according to the measuring point corresponding to the abnormal wavelength of fatigue damage, determine the fatigue damage position of the cross section according to the layout position of the smart reinforcement and determine the fatigue damage depth, perform damage judgment on the distributed measuring points of the smart reinforcement to determine the longitudinal fatigue damage position, and determine the fatigue damage point and fatigue damage length from the longitudinal fatigue damage position;

[0032] The specific rules for damage determination are: define the measuring point corresponding to the fatigue damage abnormal wavelength as a fatigue abnormal point, directly determine the point as a fatigue damage point when there is no adjacent fatigue abnormal point, and when there are multiple adjacent fatigue abnormal points, perform abnormal screening on the adjacent fatigue abnormal points to determine the fatigue damage point;

[0033] When the number of adjacent fatigue abnormal points is less than or equal to 8, the fatigue condition is judged according to the fatigue damage abnormal wavelength of each fatigue abnormal point; when the number of adjacent fatigue abnormal points is greater than 8, the comprehensive similarity Sim between each fatigue abnormal point i and the remaining fatigue abnormal points is calculated. i , calculate the correlation Rel between each fatigue abnormal point i and the corresponding fatigue state i , calculate the fatigue abnormality Fati of each fatigue abnormal point i i =Rel i +(1-Sim i ), preliminarily screen the adjacent fatigue abnormal points according to the fatigue abnormality degree, and judge the fatigue condition according to the fatigue damage abnormal wavelength of the fatigue abnormal points after the preliminarily screening;

[0034] The specific steps for judging fatigue conditions based on the abnormal wavelength of fatigue damage are:

[0035] According to the monitoring wavelength threshold λ sta The monitoring wavelength samples are divided into normal monitoring wavelengths and abnormal monitoring wavelengths. The relevant data of fatigue abnormal points are scaled and standardized to determine the factor correlation matrix. Based on the factor correlation matrix, the normal skewness coefficient Ske of the normal monitoring wavelength of the fatigue abnormal point is calculated. i,nor and normal kurtosis coefficient Pek i,nor , calculate the abnormal skewness coefficient Ske of the abnormal monitoring wavelength of the fatigue abnormal point i,abn and abnormal kurtosis coefficient Pek i,abn, thereby determining the abnormal skewness coefficient change ΔSke i =|Ske i,nor -Ske i,abn | and the change in abnormal kurtosis coefficient ΔPek i =|Pek i,nor -Pek i,abn |, DS evidence theory is used to calculate the basic probability distribution m(i) of fatigue abnormal points;

[0036] Select the maximum value of basic probability distribution m(i) MAX The corresponding fatigue abnormal point is the fatigue center position. The basic probability distribution deviation between the fatigue abnormal point and the adjacent fatigue abnormal point is calculated respectively. The fatigue abnormal point with a basic probability distribution deviation less than 20% is classified into the fatigue area. The fatigue damage length is determined according to the length of the fatigue area, and the fatigue damage point position is determined according to the fatigue abnormal point in the fatigue area.

[0037] Furthermore, a method for constructing a fatigue damaged cable force model based on the damage location result includes:

[0038] The damage location results include fatigue damage depth, fatigue damage point and fatigue damage length;

[0039] The fatigue damage cable force model is constructed based on the damage location results, and the expression is:

[0040]

[0041] Where F is the cable force corresponding to the change in cable wavelength, E m is the elastic modulus of the cable reinforcement, A cab is the cross-sectional area of ​​the cable reinforcement, γ i is the weight of the fatigue damage location result, μ i is the standard deviation of the fatigue damage location results on the entire cable, K ε,fbg is the optical fiber strain sensitivity coefficient, Δλ D,j is the monitoring wavelength change of the jth fatigue damage point, J is the number of fatigue damage points on the entire cable, and when the cable has no fatigue damage J is the number of measuring points of the entire cable, Δλ D,j =Δλ nor,j , Δλ nor,j Monitor wavelength changes for normal monitoring points.

[0042] Furthermore, the method for determining the fatigue damage degree of the cable includes:

[0043] Calculate the normal fatigue cable force F based on the fatigue damage cable force model nor and fatigue damage cable force F D , calculate the ultimate bearing capacity F of the cable in the intact state according to the cable material propertiesmax , combined with the damage location results to determine the fatigue damage degree of the cable:

[0044]

[0045] Among them Ψ D is the fatigue degree of the cable, k * is the fitting correction coefficient, which is obtained by fitting the fatigue performance test value and the fatigue performance parameter simulation value, ΔF = F D -F nor The cable force changes before and after cable fatigue damage.

[0046] In the second aspect, a cable based on optical fiber technology comprises: ordinary reinforcement material, smart reinforcement material and HDPE protective layer; the HDPE protective layer is wrapped around the outside of the ordinary reinforcement material and the smart reinforcement material; the smart reinforcement material comprises distributed optical fiber Bragg grating, adhesive and base reinforcement material; a spiral groove is provided on the base reinforcement material; the distributed optical fiber Bragg grating is wound and arranged inside the spiral groove; a plurality of grating areas are engraved on the distributed optical fiber Bragg grating, and each grating area corresponds to a measuring point; the distributed optical fiber Bragg grating is fixed to the base reinforcement material through the adhesive; the adhesive is located between two adjacent grating areas, and the distance between the two adhesives is the monitoring section of a measuring point; the port of the optical fiber is connected to an external optical fiber Bragg grating demodulator.

[0047] The beneficial effects of the present invention are:

[0048] The present invention is a method for identifying the location and degree of fatigue damage of a cable based on optical fiber technology. Compared with the prior art, the present invention has the following technical effects:

[0049] The present invention can improve the data preprocessing capability and enhance the model adaptability in the identification of the position and degree of fatigue damage of cables through the steps of data correction, model construction, threshold search, damage location, cable force calculation and fatigue degree calculation, thereby improving the efficiency and accuracy of the identification of the position and degree of fatigue damage of cables, and optimizing the technology of identifying the position and degree of fatigue damage of cables, which can greatly save resources, improve work efficiency, realize the identification of the position and degree of fatigue damage of cables, and provide strong support for the safety assessment and maintenance decision-making of bridge structures, which is of great significance to the safety and sustainable development of infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flowchart of the steps of a method for identifying the location and degree of fatigue damage of a cable based on optical fiber technology of the present invention;

[0051] Figure 2 A schematic diagram of a cross-sectional view of a cable based on optical fiber technology provided by the present invention;

[0052] Figure 3A schematic diagram of a cross section of an intelligent tendon in a cable based on optical fiber technology provided by the present invention;

[0053] Figure 4 A schematic diagram of the longitudinal structure of the intelligent tendons in the cables based on the optical fiber technology provided by the present invention;

[0054] In the figure: 1-ordinary reinforcement; 2-smart reinforcement; 3-HDPE protective layer; 4-distributed fiber Bragg grating; 5-adhesive; 6-base reinforcement; 7-spiral groove; 8-grating area; L-monitoring section. DETAILED DESCRIPTION

[0055] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0056] The present invention provides a method for identifying the position and degree of fatigue damage of a cable based on optical fiber technology, comprising the following steps:

[0057] like Figure 1 As shown, in this embodiment, the following steps are included:

[0058] Determine fatigue test parameters, carry out cable fatigue test according to the fatigue test parameters, and obtain corrected fatigue performance according to the cable fatigue test results; the cable fatigue test includes a cable fatigue test test and a cable fatigue simulation test;

[0059] Determining a cable fatigue model according to the modified fatigue performance and the fatigue test parameters;

[0060] Inputting the cable working condition to be identified and the corresponding fatigue working condition into the cable fatigue model to obtain cable fatigue prediction data, determining the monitoring wavelength threshold according to the cable fatigue prediction data, and performing abnormal screening and abnormal classification on the monitoring wavelength to be identified to determine the fatigue damage abnormal wavelength;

[0061] Perform cable fatigue damage location according to the fatigue damage abnormal wavelength, and construct a fatigue damage cable force model based on the damage location result;

[0062] The cable forces before and after fatigue damage are determined according to the fatigue damage cable force model, and the fatigue degree of the cable is determined according to the damage location result and the cable forces before and after fatigue damage.

[0063] In this embodiment, the method for determining the fatigue test parameters includes:

[0064] Determine the common cable size range for engineering projects, including the number of cable reinforcement wires, reinforcement radius and cable length; determine the common cable fatigue conditions range for engineering projects, including vibration, bending stress, moving load, structural load, environmental factors, cable manufacturing process and anchoring effect;

[0065] Monte Carlo method is used to randomly sample parameters from the common cable size range and cable fatigue condition range in engineering to obtain multiple cable size and fatigue condition combinations;

[0066] Setting an expected parameter combination, evaluating the sampling results, setting a sampling loss function according to the evaluation results, adjusting the sampling parameters by using a gradient descent method, performing multiple iterative samplings until the loss function is minimized and then stopping the iteration, and outputting multiple sets of parameter design combinations, i.e., fatigue test parameters; the step of evaluating the sampling results includes preliminary analysis and representative evaluation; the sampling loss function is used to measure the difference between the sampling parameter combination and the expected parameter combination;

[0067] The preliminary analysis includes calculating the statistical characteristics of the sampling results and performing visual display; the representative evaluation indicators include coverage, uniformity and correlation analysis; the coverage indicator is used to observe the coverage area of ​​the sampling parameters and judge the omissions or errors of the sampling; the uniformity indicator is used to check the uniformity of the sampling dimension and avoid oversampling of the area; the correlation analysis is used to understand the dependence and influence relationship of different sampling parameters;

[0068] The expression of the sampling loss function is:

[0069]

[0070] Among them, Loss sam is the total sampling loss function, α1, α2, α3, α4 are loss weight coefficients, N c is the number of sampling parameter groups, L pre,i is the error loss of the combination of the i-th group of sampling parameters and the expected parameters, L param,i is the parameter range coverage loss of the i-th group of sampling parameters, L cond,i is the fatigue condition coverage loss of the i-th group of sampling parameters, L reason,i is the rationality loss of the sampling parameters of the i-th group;

[0071] In the actual evaluation, the common cable size range of the project is determined as follows: the number of stressed tendons is 7 to 253, the radius of the stressed tendons is 5 to 30 mm, and the cable length is 10 to 500 m. The common cable fatigue condition range of the project is determined as follows: the frequency range is 0.1 to 10 Hz, the upper limit of the stress is 0.42 to 0.6 times the ultimate stress, the bending stress is 0 to 1000 MPa, the bridge moving load is 1 to 5000 kN, and the structural load is 20 to 200 kN / m. 2 , ambient temperature -40~80℃, humidity 0%~100%, cable making process (cold casting, hot casting, prestressed), anchoring type (friction type, pressure type, combined type), anchoring strength 500~5000kN;

[0072] The expected parameters are set as follows: 121 steel bars, 7 mm steel bar radius, 150 m cable length, 1 Hz frequency, 1 mm amplitude, 50 MPa bending stress, 500 kN moving load, 100 kN / m structural load. 2 2. Using cold cast anchors with an anchoring strength of 2000kN, the Monte Carlo method is used to perform random sampling of parameters from the common cable size range and cable fatigue condition range in engineering projects to obtain multiple cable size and fatigue condition combinations;

[0073] Take two groups of cable fatigue tensile test conditions as examples: 1. (7 stress bars, 7 mm radius of stress bars, 5 m cable length, 4 Hz frequency, upper stress limit of 0.42 times the ultimate stress, fatigue stress of 200 MPa, and cold cast anchors with an anchoring strength of 2000 kN), 2. (21 stress bars, 7 mm radius of stress bars, 5 m cable length, 4 Hz frequency, upper stress limit of 0.60 times the ultimate stress, fatigue stress of 200 MPa, and cold cast anchors with an anchoring strength of 2000 kN).

[0074] In this embodiment, the method for obtaining the corrected fatigue performance comprises the following steps:

[0075] According to the sampled fatigue test parameters, a cable fatigue tensile test is carried out to obtain a fatigue monitoring wavelength. The fatigue monitoring wavelength, the material parameters and the size parameters of the corresponding cable are calculated through the cable fatigue relationship to obtain a fatigue performance test value. The cable fatigue theoretical expression is:

[0076]

[0077] where ε F To consider the fatigue strain under fatigue conditions, ε i is the strain of fatigue stage i when considering fatigue conditions, g is the number of fatigue stages, ε max is the ultimate strain when considering fatigue conditions, N i,fat is the maximum number of cycles in fatigue stage i when fatigue conditions are considered, N u,fat The limit cycle number of the cable under fatigue conditions is obtained by Miner's law, K ε,fbg is the optical fiber strain sensitivity coefficient, ρ is the optical fiber strain transmission rate; E m is the elastic modulus of the cable material, Δλ B,i is the wavelength change in fatigue stage i under fatigue condition;

[0078] According to the sampled fatigue test parameters and the corresponding cable material parameters, a cable fatigue tensile finite element simulation test is carried out to obtain the fatigue performance simulation value;

[0079] The fatigue performance test value and the fatigue performance parameter simulation value are fitted to obtain the fitting correction coefficient k* , the fatigue performance simulation value is corrected by using the fitting correction coefficient to obtain the corrected fatigue performance;

[0080] In the actual evaluation, the single cable reinforcement material is carbon fiber reinforced composite material, the theoretical breaking force is 516.74 kN, the elastic modulus is 181.5 GPa, and the optical fiber strain sensitivity coefficient K is ε,fbg =1.22pm / με, optical fiber strain transmission rate ρ=0.85, the maximum number of cycles is determined to be 100 million according to Miner's law, and the cable fatigue tensile test is carried out according to the above two groups of cable fatigue tensile test conditions to obtain test data, and the fatigue strain is calculated according to the cable fatigue theory:

[0081] Table 1 shows the monitoring wavelengths Δλ at different fatigue stages obtained from the fatigue tensile test of one of the smart tendons in the first group of cables. B and the calculated fatigue strain ε, corresponding to the limit strain ε max =12999με, Table 2 shows the monitoring wavelengths Δλ at different fatigue stages obtained from the fatigue tensile test of one of the smart tendons in the second group of cables B and the calculated fatigue strain ε, corresponding to the limit strain ε max =12827με, where the fatigue stage is distinguished by the number of fatigue cycles, in ten thousand cycles;

[0082] Table 1

[0083]

[0084] Table 2

[0085]

[0086] The fatigue performance test value and the fatigue performance parameter simulation value are fitted to obtain the fitting correction coefficient k * They are 1.15 and 1.21 respectively.

[0087] In this embodiment, the method for determining the cable fatigue model includes:

[0088] The fatigue response is defined as the objective function, the fatigue working condition and the cable working condition are defined as fatigue variables, and a fatigue response model is constructed as a cable fatigue model by performing response surface fitting according to the fatigue response and the fatigue variables; the fatigue response includes a fatigue monitoring wavelength and a corrected fatigue performance; the cable working condition includes material parameters and size parameters of the cable;

[0089] The expression of fatigue response model constructed by response surface method is:

[0090]

[0091] where y kis the objective function, k∈[1,K] corresponds to different fatigue response parameters, β0, β j , β jj and β ij are polynomial coefficients, x j 、x i is the fatigue variable, m is the dimension of the fatigue variable;

[0092] The polynomial coefficients are calculated using the elastic net according to the least squares principle:

[0093] β=[X T X+χ(δL1+ζL2)] -1 X T y

[0094] Where β is the polynomial coefficient vector, X is the fatigue variable matrix, y is the fatigue response parameter vector, χ is the regularization strength, L1 is the L1 regularization matrix, δ is the L1 regularization matrix scale parameter, L2 is the L2 regularization matrix, ζ is the L2 regularization matrix scale parameter, and δ+ζ=1.

[0095] In this embodiment, the method for determining the abnormal fatigue damage data includes:

[0096] Inputting the to-be-identified cable working condition and fatigue working condition into the cable fatigue model to obtain cable fatigue prediction data; the cable fatigue prediction data includes fatigue monitoring wavelength prediction value and fatigue performance prediction value;

[0097] The monitoring wavelength threshold is searched according to the fatigue monitoring wavelength prediction value, fatigue performance prediction value, cable working condition and fatigue working condition to determine the monitoring wavelength threshold, the monitoring wavelength to be identified is screened for abnormalities according to the monitoring wavelength threshold to obtain abnormal monitoring wavelengths, and the abnormal data of abnormal monitoring wavelengths is classified by a decision tree to determine the fatigue damage abnormal wavelength;

[0098] The specific steps to determine the monitoring wavelength threshold are:

[0099] Set the initial monitoring wavelength threshold, the expression is:

[0100]

[0101] where λ 0,sta is the initial monitoring wavelength threshold, is the maximum safety factor, Safe min =1 is the minimum safety factor, λ i,max is the maximum value of the monitored wavelength among all the measuring points, λ i is the monitoring wavelength of measuring point i, and h is the total number of measuring points;

[0102] According to the initial monitoring wavelength threshold fitness, it is determined whether to update the position and velocity of the fatigue particle. The position and velocity update expressions of the fatigue particle are:

[0103]

[0104] in is the speed of the i-th fatigue particle at the t+1-th search, is the velocity of the i-th fatigue particle at the t-th search, is the accuracy factor, T is the maximum number of searches, r1 and r2 are random numbers in (0,1), c1 and c2 are acceleration constants, is the position of the i-th fatigue particle at the t-th search, X i,best The historical optimal position of the i-th fatigue particle at the t-th search, X p,best is the historical optimal position of all fatigue particles in the t-th search, Unif(·) is the uniform perturbation term, is the position of the i-th fatigue particle at the t+1-th search, Γ(·) is the probability density function, θ is the jump characteristic parameter, c3 and c4 are learning factors, and Gaus(·) is the Gaussian perturbation term;

[0105] According to the updated position and velocity of the fatigue particle, the fitness of the monitoring wavelength threshold is recalculated, and the expression is:

[0106]

[0107] Where Fitness is the fitness of the monitoring wavelength threshold during the t-th search, err is the classification error rate, w1 is the monitoring wavelength weight, and λ pre To monitor the wavelength prediction value, is the monitoring wavelength threshold during the t-th search, w2 is the fatigue performance weight, o1 is the number of fatigue performance indicators, is the score of the lth fatigue performance index, w3 is the weight of the cable condition, o2 is the number of cable condition indicators, is the score of the lth cable working condition index, w4 is the fatigue working condition weight, o3 is the number of fatigue working condition indexes, is the score of the first fatigue condition index, w5 is the particle velocity weight, V t The fatigue particle velocity during the t-th search;

[0108] Repeat the above operation until the fitness of the current monitoring wavelength threshold is the optimal fitness or the maximum search number is reached, and the search is stopped, and the monitoring wavelength threshold λ is output. sta ;

[0109] In the actual evaluation, a group of cable working conditions and fatigue conditions of the cables to be identified are obtained (7 wires of stress reinforcement, 7mm radius of stress reinforcement, 5m length of cable, 4Hz frequency, 0.42 times of ultimate stress upper limit, 200MPa fatigue stress, 2000kN anchor strength of cold cast anchor, and initial wavelengths of each measuring point are 1535.0774 / 1539.9944 / 1545.8755 / 1534.8212 / 1540.2400 / 1545.4635nm respectively). The cable fatigue model is input to obtain the monitoring wavelength prediction value and fatigue strain, and the monitoring wavelength threshold search is performed to determine that the monitoring wavelength threshold of each measuring point is 1535.4774 / 1540.3944 / 1546.2755 / 1535.2212 / 1540.6400 / 1545.8635nm.

[0110] The monitoring wavelengths of the cables to be identified are screened and classified for abnormalities, and the monitoring wavelengths of measuring points 1, 2, 3, and 4 are determined to be fatigue damage abnormal wavelengths, and measuring point 6 is determined to be a sensing abnormal wavelength.

[0111] In this embodiment, the method for locating fatigue damage of a cable according to the abnormal wavelength of fatigue damage includes:

[0112] Directly locate the smart reinforcement according to the measuring point corresponding to the abnormal wavelength of fatigue damage, determine the fatigue damage position of the cross section according to the layout position of the smart reinforcement and determine the fatigue damage depth, perform damage judgment on the distributed measuring points of the smart reinforcement to determine the longitudinal fatigue damage position, and determine the fatigue damage point and fatigue damage length from the longitudinal fatigue damage position;

[0113] The specific rules for damage determination are: define the measuring point corresponding to the fatigue damage abnormal wavelength as a fatigue abnormal point, directly determine the point as a fatigue damage point when there is no adjacent fatigue abnormal point, and when there are multiple adjacent fatigue abnormal points, perform abnormal screening on the adjacent fatigue abnormal points to determine the fatigue damage point;

[0114] When the number of adjacent fatigue abnormal points is less than or equal to 8, the fatigue condition is judged according to the fatigue damage abnormal wavelength of each fatigue abnormal point; when the number of adjacent fatigue abnormal points is greater than 8, the comprehensive similarity Sim between each fatigue abnormal point i and the remaining fatigue abnormal points is calculated. i , calculate the correlation Rel between each fatigue abnormal point i and the corresponding fatigue state i , calculate the fatigue abnormality Fati of each fatigue abnormal point i i =Rel i +(1-Sim i ), preliminarily screen the adjacent fatigue abnormal points according to the fatigue abnormality degree, and judge the fatigue condition according to the fatigue damage abnormal wavelength of the fatigue abnormal points after the preliminarily screening;

[0115] Relationship iThe calculation method is:

[0116]

[0117] Among them, Rel i is the correlation between fatigue abnormal point i and the corresponding fatigue state, x i is the data set X corresponding to fatigue abnormal point i i The element of x i′ is the data set X corresponding to the fatigue state of fatigue abnormal point i i′ The fatigue state includes fatigue condition and smart tendon position, p(x i ,x i′ ) is x i and x i′ The joint probability density, p(x i ) is x i The marginal probability distribution, p(x i′ ) is x i′ The marginal probability distribution of i,abn is the monitoring wavelength of fatigue abnormal point i, is the average value of the monitoring wavelength of the two adjacent grid areas of fatigue abnormal point i;

[0118] The specific steps for judging fatigue conditions based on the abnormal wavelength of fatigue damage are:

[0119] The relevant data of the fatigue abnormal point are scaled and standardized to determine the factor correlation matrix; the relevant data of the fatigue abnormal point includes the fatigue damage abnormal wavelength, the fatigue working condition and the fatigue abnormal point position;

[0120] Calculate the skewness coefficient Ske of fatigue abnormal points based on the factor correlation matrix i and kurtosis coefficient Pek i , the expression is:

[0121]

[0122] Among them, Ske i is the skewness coefficient of fatigue abnormal point i, Pek i is the kurtosis coefficient of fatigue abnormal point i, Q is the number of sample data, is the pth wavelength data of fatigue abnormal point i, is the mean value of wavelength data in the sample;

[0123] According to the monitoring wavelength threshold λ sta The monitoring wavelength samples are divided into normal monitoring wavelengths and abnormal monitoring wavelengths, and the normal skewness coefficient Ske of the fatigue abnormal point is calculated based on the normal monitoring wavelength samples. i,nor and normal kurtosis coefficient Pek i,nor, calculate the abnormal skewness coefficient Ske of the fatigue abnormal point according to the abnormal monitoring wavelength sample i,abn and abnormal kurtosis coefficient Pek i,abn , thereby determining the abnormal skewness coefficient change ΔSke i =|Ske i,nor -Ske i,abn | and the change in abnormal kurtosis coefficient ΔPek i =|Pek i,nor -Pek i,abn |, using DS evidence theory to perform data fusion to determine the damage location:

[0124]

[0125] Where m(i) is the basic probability distribution of fatigue abnormal point i, m1(i,Z U,i ) is the change in the abnormal skewness coefficient ΔSke i For the basic probability distribution of fatigue abnormal point i, m2(i,Z U,i ) is the change in abnormal kurtosis coefficient ΔPek i For the basic probability distribution of fatigue abnormal point i, A is the set of all fatigue abnormal points;

[0126] Select the maximum value of basic probability distribution m(i) MAX The corresponding fatigue abnormal point is the fatigue center position, and the basic probability distribution deviation between the fatigue abnormal point and the adjacent fatigue abnormal point is calculated respectively. The fatigue abnormal point with a basic probability distribution deviation of less than 20% is classified into the fatigue area, and the fatigue damage length is determined according to the fatigue area length, and the fatigue damage point position is determined according to the fatigue abnormal point in the fatigue area;

[0127] In the actual evaluation, the intelligent tendon where the above-mentioned fatigue abnormal point is located is located in the outermost layer of the cable, the fatigue depth is 7mm, and the length of each monitoring section is L=500mm. At this time, there are 4 adjacent fatigue abnormal points. The adjacent fatigue abnormal points are directly screened for abnormalities to determine that the fatigue damage points are measuring points 1 and 2. The fatigue damage length is the sum of the monitoring section lengths of measuring points 1 and 2, that is, 1m.

[0128] In this embodiment, the method for constructing a fatigue damaged cable force model based on the damage location result includes:

[0129] The damage location results include fatigue damage depth, fatigue damage point and fatigue damage length;

[0130] The fatigue damage cable force model is constructed based on the damage location results, and the expression is:

[0131]

[0132] Where F is the cable force corresponding to the change in cable wavelength, E m is the elastic modulus of the cable reinforcement, A cab is the cross-sectional area of ​​the cable reinforcement, γ i is the weight of the fatigue damage location result, μ i is the standard deviation of the fatigue damage location results on the entire cable, K ε,fbg is the optical fiber strain sensitivity coefficient, Δλ D,j is the monitoring wavelength change of the jth fatigue damage point, J is the number of fatigue damage points on the entire cable, and when the cable has no fatigue damage J is the number of measuring points of the entire cable, Δλ D,j =Δλ nor,j , Δλ nor,j Monitor wavelength changes for normal monitoring points.

[0133] In this embodiment, the method for determining the fatigue damage degree of the cable includes:

[0134] Calculate the normal fatigue cable force F based on the fatigue damage cable force model nor and fatigue damage cable force F D , calculate the ultimate bearing capacity F of the cable in the intact state according to the cable material properties max , combined with the damage location results to determine the fatigue damage degree of the cable:

[0135]

[0136] Among them Ψ D is the fatigue degree of the cable, k * is the fitting correction coefficient, which is obtained by fitting the fatigue performance test value and the fatigue performance parameter simulation value, ΔF = F D -F nor is the cable force change before and after cable fatigue damage;

[0137] In actual assessment, the normal fatigue cable force F is calculated according to the fatigue damage cable force model. nor =47.9kN and fatigue damage cable force F D =210.9kN, the ultimate bearing capacity of the cable in the intact state F max =516.74kN, k * =1.2, calculate the fatigue damage degree of the cable Ψ D =0.3699.

[0138] like Figures 2 to 4As shown, in this embodiment, a cable based on optical fiber technology includes: ordinary reinforcement material 1, smart reinforcement 2 and HDPE protective layer 3; the HDPE protective layer 3 is wrapped around the outside of the ordinary reinforcement material 1 and the smart reinforcement 2; the smart reinforcement 2 includes a distributed optical fiber Bragg grating 4, an adhesive 5 and a base reinforcement 6; a spiral groove 7 is provided on the base reinforcement; the distributed optical fiber Bragg grating 4 is wound and arranged inside the spiral groove 7; a plurality of grating areas 8 are engraved on the distributed optical fiber Bragg grating 4, and each grating area 8 corresponds to a measuring point; the distributed optical fiber Bragg grating 4 is fixed to the base reinforcement 6 by the adhesive 5; the adhesive 5 is located between two adjacent grating areas 8, and the distance between the two adhesives 5 is the monitoring section L of a measuring point; the port of the optical fiber is connected to an external optical fiber Bragg grating demodulator.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying the location and degree of fatigue damage of a cable based on optical fiber technology, characterized in that: The following steps are involved: S1. Determine fatigue test parameters, carry out cable fatigue test according to the fatigue test parameters, and obtain corrected fatigue performance according to the cable fatigue test results; the cable fatigue test includes a cable fatigue test test and a cable fatigue simulation test; S2. determining a cable fatigue model according to the modified fatigue performance and the fatigue test parameters; S3, inputting the cable working condition to be identified and the corresponding fatigue working condition into the cable fatigue model to obtain cable fatigue prediction data, determining the monitoring wavelength threshold according to the cable fatigue prediction data, and performing abnormal screening and abnormal classification on the monitoring wavelength to be identified to determine the fatigue damage abnormal wavelength; S4, locating fatigue damage of the cable according to the abnormal wavelength of fatigue damage, and constructing a fatigue damage cable force model based on the damage location result; S5. Determine the cable forces before and after fatigue damage according to the fatigue damage cable force model, and determine the fatigue degree of the cable according to the damage location result and the cable forces before and after fatigue damage.

2. According to claim 1, a method for identifying the location and degree of cable fatigue damage based on optical fiber technology is characterized in that: The method for determining the fatigue test parameters comprises: Determine the common cable size range and cable fatigue condition range in engineering projects, and use the Monte Carlo method to perform random sampling of parameters from the common cable size range and cable fatigue condition range in engineering projects to obtain multiple cable size and fatigue condition combinations; The expected parameter combination is set, the sampling results are evaluated, the sampling loss function is set according to the evaluation results, the sampling parameters are adjusted by the gradient descent method, multiple iterative samplings are performed until the loss function is minimized and the iteration is stopped, and multiple groups of parameter design combinations, i.e., fatigue test parameters, are output; the step of evaluating the sampling results includes preliminary analysis and representative evaluation; the sampling loss function is used to measure the difference between the sampling parameter combination and the expected parameter combination.

3. According to claim 1, a method for identifying the location and degree of cable fatigue damage based on optical fiber technology is characterized in that: The method for obtaining the modified fatigue performance comprises the following steps: According to the sampled fatigue test parameters, a cable fatigue tensile test is carried out to obtain a fatigue monitoring wavelength. The fatigue monitoring wavelength, the material parameters and the size parameters of the corresponding cable are calculated through the cable fatigue relationship to obtain a fatigue performance test value. The cable fatigue theoretical expression is: where ε F To consider the fatigue strain under fatigue conditions, ε i is the strain of fatigue stage i when considering fatigue conditions, g is the number of fatigue stages, ε max is the ultimate strain when considering fatigue conditions, N i,fat is the maximum number of cycles in fatigue stage i when fatigue conditions are considered, N u,fat The limit cycle number of the cable under fatigue conditions is obtained by Miner's law, K ε,fbg is the optical fiber strain sensitivity coefficient, ρ is the optical fiber strain transmission rate; E m is the elastic modulus of the cable material, Δλ B,i is the wavelength change in fatigue stage i under fatigue condition; According to the sampled fatigue test parameters and the corresponding cable material parameters, a cable fatigue tensile finite element simulation test is carried out to obtain the fatigue performance simulation value; The fatigue performance test value and the fatigue performance parameter simulation value are fitted to obtain the fitting correction coefficient k * The fitting correction coefficient is used to correct the fatigue performance simulation value to obtain the corrected fatigue performance.

4. According to claim 3, a method for identifying the location and degree of cable fatigue damage based on optical fiber technology is characterized in that: The method for determining the abnormal fatigue damage data comprises: Define fatigue response as the objective function, define fatigue working condition and cable working condition as fatigue variables, and construct a fatigue response model as the cable fatigue model by performing response surface fitting based on fatigue response and fatigue variables; Inputting the to-be-identified cable working condition and fatigue working condition into the cable fatigue model to obtain cable fatigue prediction data; the cable fatigue prediction data includes fatigue monitoring wavelength prediction value and fatigue performance prediction value; The monitoring wavelength threshold is searched according to the fatigue monitoring wavelength prediction value, fatigue performance prediction value, cable working condition and fatigue working condition to determine the monitoring wavelength threshold, the monitoring wavelength to be identified is screened for abnormalities according to the monitoring wavelength threshold to obtain abnormal monitoring wavelengths, and the abnormal data of abnormal monitoring wavelengths is classified by a decision tree to determine the fatigue damage abnormal wavelength; The specific steps to determine the monitoring wavelength threshold are: Set the initial monitoring wavelength threshold, the expression is: where λ 0,sta is the initial monitoring wavelength threshold, is the maximum safety factor, Safe min =1 is the minimum safety factor, λ i,max is the maximum value of the monitored wavelength among all the measuring points, λ i is the monitoring wavelength of measuring point i, and h is the total number of measuring points; According to the initial monitoring wavelength threshold fitness, determine whether to update the position and speed of the fatigue particle, recalculate the fitness of the monitoring wavelength threshold according to the updated position and speed of the fatigue particle, repeat the above operation until the fitness of the current monitoring wavelength threshold is the optimal fitness or the maximum search number is reached, stop searching, and output the monitoring wavelength threshold λ sta .

5. According to claim 3, a method for identifying the location and degree of cable fatigue damage based on optical fiber technology is characterized in that: The method for locating fatigue damage of a cable according to the abnormal wavelength of fatigue damage comprises: Directly locate the smart reinforcement according to the measuring point corresponding to the abnormal wavelength of fatigue damage, determine the fatigue damage position of the cross section according to the layout position of the smart reinforcement and determine the fatigue damage depth, perform damage judgment on the distributed measuring points of the smart reinforcement to determine the longitudinal fatigue damage position, and determine the fatigue damage point and fatigue damage length from the longitudinal fatigue damage position; The specific rules for damage determination are: define the measuring point corresponding to the fatigue damage abnormal wavelength as a fatigue abnormal point, directly determine the point as a fatigue damage point when there is no adjacent fatigue abnormal point, and when there are multiple adjacent fatigue abnormal points, perform abnormal screening on the adjacent fatigue abnormal points to determine the fatigue damage point; When the number of adjacent fatigue abnormal points is less than or equal to 8, the fatigue condition is judged according to the fatigue damage abnormal wavelength of each fatigue abnormal point; when the number of adjacent fatigue abnormal points is greater than 8, the comprehensive similarity Sim between each fatigue abnormal point i and the remaining fatigue abnormal points is calculated. i , calculate the correlation Rel between each fatigue abnormal point i and the corresponding fatigue state i , calculate the fatigue abnormality Fati of each fatigue abnormal point i i =Rel i +(1-Sim i ), preliminarily screen the adjacent fatigue abnormal points according to the fatigue abnormality degree, and judge the fatigue condition according to the fatigue damage abnormal wavelength of the fatigue abnormal points after the preliminarily screening; The specific steps for judging fatigue conditions based on the abnormal wavelength of fatigue damage are: According to the monitoring wavelength threshold λ sta The monitoring wavelength samples are divided into normal monitoring wavelengths and abnormal monitoring wavelengths. The relevant data of fatigue abnormal points are scaled and standardized to determine the factor correlation matrix. Based on the factor correlation matrix, the normal skewness coefficient Ske of the normal monitoring wavelength of the fatigue abnormal point is calculated. i,nor and normal kurtosis coefficient Pek i,nor , calculate the abnormal skewness coefficient Ske of the abnormal monitoring wavelength of the fatigue abnormal point i,abn and abnormal kurtosis coefficient Pek i,abn , thereby determining the abnormal skewness coefficient change ΔSke i =|Ske i,nor -Ske i,abn | and the change in abnormal kurtosis coefficient ΔPek i =|Pek i,nor -Pek i,abn |, DS evidence theory is used to calculate the basic probability distribution m(i) of fatigue abnormal points; Select the maximum value of basic probability distribution m(i) MAX The corresponding fatigue abnormal point is the fatigue center position. The basic probability distribution deviation between the fatigue abnormal point and the adjacent fatigue abnormal point is calculated respectively. The fatigue abnormal point with a basic probability distribution deviation less than 20% is classified into the fatigue area. The fatigue damage length is determined according to the length of the fatigue area, and the fatigue damage point position is determined according to the fatigue abnormal point in the fatigue area.

6. According to claim 1, a method for identifying the location and degree of fatigue damage of a cable based on optical fiber technology is characterized in that: The method for constructing a fatigue damaged cable force model based on the damage location result comprises: The damage location results include fatigue damage depth, fatigue damage point and fatigue damage length; The fatigue damage cable force model is constructed based on the damage location results, and the expression is: Where F is the cable force corresponding to the change in cable wavelength, E m is the elastic modulus of the cable reinforcement, A cab is the cross-sectional area of ​​the cable reinforcement, γ i is the weight of the fatigue damage location result, μ i is the standard deviation of the fatigue damage location results on the entire cable, K ε,fbg is the optical fiber strain sensitivity coefficient, Δλ D,j is the monitoring wavelength change of the jth fatigue damage point, J is the number of fatigue damage points on the entire cable, and when the cable has no fatigue damage J is the number of measuring points of the entire cable, Δλ D,j =Δλ nor,j , Δλ nor,j Monitor wavelength changes for normal monitoring points.

7. The method for identifying the location and degree of fatigue damage of a cable based on optical fiber technology according to claim 1, characterized in that: The method for determining the fatigue damage degree of the cable comprises: Calculate the normal fatigue cable force F based on the fatigue damage cable force model nor and fatigue damage cable force F D , calculate the ultimate bearing capacity F of the cable in the intact state according to the cable material properties max , combined with the damage location results to determine the fatigue damage degree of the cable: Among them Ψ D is the fatigue degree of the cable, k * is the fitting correction coefficient, which is obtained by fitting the fatigue performance test value and the fatigue performance parameter simulation value, ΔF = F D -F nor The cable force changes before and after cable fatigue damage.

8. A cable based on optical fiber technology, used to perform the method according to any one of claims 1 to 8, characterized in that: include: Ordinary reinforcement material, smart reinforcement and HDPE protective layer; the HDPE protective layer is wrapped around the ordinary reinforcement material and the outside of the smart reinforcement; the smart reinforcement includes distributed optical fiber Bragg grating, adhesive and base reinforcement; the base reinforcement is provided with a spiral groove; the distributed optical fiber Bragg grating is wound and arranged inside the spiral groove; a plurality of grating areas are engraved on the distributed optical fiber Bragg grating, and each grating area corresponds to a measuring point; the distributed optical fiber Bragg grating is fixed to the base reinforcement through the adhesive; the adhesive is located between two adjacent grating areas, and the distance between the two adhesives is the monitoring section of a measuring point; the port of the optical fiber is connected to an external optical fiber Bragg grating demodulator.

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