A method for predicting the static behavior of a bridge based on vibrations and response surfaces

Abstract

The application discloses a bridge static behavior prediction method based on vibration and response surface, comprising the following steps: collecting vibration characteristic information of an existing bridge and pre-processing data; establishing a structure analysis model of the bridge according to design data of the existing bridge; screening out key design parameters to be modified of the structure analysis model according to the structure analysis model and initial design parameters; establishing a training sample by using a Latin hypercube sampling method and constructing a corresponding response surface model; predicting actual values of the bridge structure design parameters based on the response surface model, substituting the actual values into the initial structure analysis model, and modifying the analysis model; realizing accurate prediction of the bridge static behavior based on the modified structure analysis model; and introducing a root mean square error evaluation index to evaluate the prediction result. The application has the advantages of low cost, high prediction result precision, fast calculation efficiency and the ability of realizing fast prediction of static behaviors of a large number of existing bridge structures.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge health monitoring and evaluation, and particularly relates to a bridge static behavior prediction method based on vibration and response surface. BACKGROUND

[0002] Bridge engineering plays an important role in the national transportation infrastructure construction. The safe operation of existing bridges is closely related to people's living environment and is one of the important indicators of national development. With the continuous increase of operating vehicles and the long-term influence of environmental factors, the gradual degradation of the structural performance of existing bridges directly affects the safe operation of the bridge structure. Since the bridges in China have shifted from the construction period to the construction and maintenance period, so far, China has built more than 900,000 highway bridges, and a large number of bridges built in the 50-70s need to be repaired and reinforced. The evaluation of the structural performance of the bridge is the premise of maintenance and reinforcement, and only by comprehensively mastering the bearing performance and use state of the bridge structure can the bridge be treated according to the symptoms and the service life of the bridge be extended. More importantly, the rapid evaluation of the structural performance of the bridge can help to find the safety hazards of the bridge as soon as possible and avoid causing huge and irreparable losses. At the same time, it can also improve the data of bridge maintenance and provide a basis for future design and reinforcement.

[0003] However, the traditional and effective method for evaluating the safety state of the bridge structure is usually through load test, but the load test has problems such as high cost, large workload, damage to the bridge structure, and influence on normal traffic. The dynamic load test in the load test has lower cost, shorter test time, less influence on traffic, and better safety compared with the static load test. Therefore, if the structural analysis model of the bridge can be corrected through the vibration data of the bridge, and the corrected analysis model can accurately predict the static behavior of the bridge, it will have important significance for the evaluation and repair and reinforcement of the bearing performance and use state of the bridge structure.

[0004] Therefore, researchers in the field are committed to proposing a bridge static behavior prediction method based on vibration and response surface, which can correct the structural analysis model and accurately predict the static behavior of the bridge based on the vibration characteristic information of the bridge and the response surface method. The prediction result of the method has high precision, low cost, and can realize the rapid prediction of the static behavior of a large number of existing bridges. SUMMARY

[0005] In view of the problems of high cost, long test time, need to close traffic and often inaccurate detection results in the current load test method of the technical state evaluation of the existing bridge structure, the application provides a bridge static behavior prediction method based on vibration and response surface. The method can realize the correction of the bridge structure analysis model by testing the relatively simple and convenient bridge vibration information, and realize the accurate prediction of the bridge static behavior by combining the response surface method. The method has low cost, high prediction result accuracy and fast calculation efficiency, and can realize the rapid prediction of the static behavior of a large number of existing bridge structures.

[0006] To achieve the above object, the application provides a bridge static behavior prediction method based on vibration and response surface, characterized in that the method comprises the following steps:

[0007] Step one, collecting and pre-processing the vibration characteristic information of the existing bridge;

[0008] Step two, determining the value of the bridge design parameter according to the design data of the existing bridge, establishing the structure analysis model of the bridge, and taking the structure analysis model as the initial structure analysis model for subsequent model correction;

[0009] Step three, determining the design parameter with large structure dynamic response according to the structure analysis model and the initial design parameter, and screening out the key design parameter to be corrected of the structure analysis model by using the sensitivity analysis method;

[0010] Step four, using the Latin hypercube sampling method to establish the training sample between the key design parameter to be corrected and the bridge structure vibration response, and constructing the corresponding response surface model;

[0011] Step five, predicting the actual value of the bridge structure design parameter based on the response surface model, and substituting the actual value into the initial structure analysis model to realize the correction of the analysis model;

[0012] Step six, realizing the accurate prediction of the bridge static behavior based on the corrected structure analysis model;

[0013] Step seven, introducing the root mean square error evaluation index to predict the bridge static behavior, and evaluating the prediction result, and the calculation formula of the root mean square error is as follows:

[0014]

[0015] Wherein, MSE is the mean square error; S 2 (x) is the variance; sigma is the standard deviation; F is the unit vector; T represents the meaning of transposition; R is the correlation matrix composed of the correlation function values between all known sample points; r is the correlation vector composed of the correlation function between the unknown point and all known sample points; F=[1, 1, 1...., 1]T .

[0016] Further, in step one, the actual vibration characteristic information of the bridge structure is obtained by using direct measurement method or indirect measurement method, and the collected vibration information is preprocessed by using signal processing method.

[0017] The direct measurement method is to arrange the vibration pickup directly on the bridge control section, such as 1 / 2, 1 / 4 and support point section position, collect the bridge vibration response signal by using the digital signal acquisition instrument, and read the bridge frequency and other responses by using the peak value of the power spectrum diagram and the time domain history curve recorded by the collection system.

[0018] The indirect measurement method is to install the sensor on the moving trolley, when the moving trolley drives through the bridge, the vehicle-bridge coupling effect occurs, the bridge dynamic characteristics are extracted from the acceleration response of the vehicle body, and the bridge frequency and other information are obtained.

[0019] The signal processing method is to obtain the frequency domain results of the bridge frequency characteristics by using Fourier transform on the time domain signal collected by the vibration pickup. The Fourier transform formula is:

[0020]

[0021] In the formula, j is a virtual unit, j^2=-1, and has no unit; T is the period, and the unit is second; X is the original function of x; t is the time, and the unit is second; ω is the frequency, and x(t) is the continuous time signal.

[0022] Further, in step two, the values of the bridge design parameters are determined according to the design data of the existing bridge, the initial structure analysis model of the bridge is established by using the numerical method, and the initial model is provided for the subsequent structure analysis model correction. The values of the bridge design parameters include the material elastic modulus, the material unit weight, the boundary condition and the load application, etc. The parameters of the model are determined according to the design data of the bridge, such as the size of the structure, the section parameter, and the material information provided in the design data of the bridge. The structure analysis model is established by using the finite element software, and the related algorithm and formula are not involved in the process.

[0023] Further, in step three, the sensitivity analysis method is used to analyze the sensitivity of the initially determined design parameters based on the structure analysis model established in step two, and the design parameters with greater influence on the vibration response of the bridge structure are selected as the design parameters to be corrected subsequently. When the sensitivity of the design parameters is analyzed, all the initial design parameters need to be analyzed one by one, and finally some key design parameters to be corrected are selected.

[0024] Specifically, based on the structural analysis model established in step two, the sensitivity of the design parameters is analyzed by using the Morris method, and the design parameters that have greater influence on the bridge vibration response are selected as the subsequent design parameters to be corrected. The Morris method causes the change of the output response through the change of a single factor, and its calculation formula is:

[0025]

[0026] In the formula: d i (j) is the base effect of the jth sample of the ith parameter, j = 1, 2, 3,... R (R is the number of repeated sampling), n is the number of parameters; x i is the ith parameter, Δ is the small change of a single parameter, and f(X) is the response output corresponding to the parameter group. Morris proposed two calculation indexes to judge the sensitivity of the parameters, i.e. the base effect mean μ and the standard deviation σ. Among them, μ represents the sensitivity of the parameters, and determines the sorting of the parameters, and σ represents the nonlinearity between the parameters. The key design parameters that need to be corrected are selected through the Morris calculation results.

[0027] Further, based on the key design parameters to be corrected selected in step three, the Latin hypercube sampling method is used to establish the training samples between the spatial full design parameters and the vibration response, and based on the training samples, the intelligent algorithm program is substituted to learn and train, and the response surface model between the bridge structure design parameters and the vibration response is constructed.

[0028] Specifically, the Latin hypercube sampling (LHS) method is used to sample efficiently from the distribution interval of the design parameters. For K variables x1, x2,..., x k , N samples are extracted, and the cumulative distribution of each variable is divided into the same N small intervals, a value is randomly selected from each interval, and N values of each variable are randomly combined with the values from other variables. This method can ensure full coverage of the range of each variable. The design parameters are used as input data, and the structural vibration response corresponding to each group of design parameters is used as output data to generate training samples. The Gaussian process response surface model predicts a new input quantity x N , t N ), t i is the corresponding target value of x i , and the corresponding target value t N+1 is obtained, and the training set is: N+1

[0029] R = {(X i , T i ), i = 1, 2, 3,..., i,..., N} (4); ​

[0030] The joint probability distribution of the training set is subject to a Gaussian distribution:

[0031] f(T N )~GP(m(x), K(x, x')) (5);

[0032] Wherein:

[0033] m(x) = E[f(x)]; (6)

[0034] K(x, x') = E[f(x)-m(x)(f(x')-m(x'))] (7);

[0035] Wherein, m(x) is the mean value; f(x) is the function about the sample point; E is the symbol of the mean value; K(x, x') is the covariance matrix;

[0036] By determining the mean value m(x) and the covariance matrix K(x, x'), the corresponding Gaussian process response surface model can be determined.

[0037] Further, based on the Gaussian process response surface model established in step four, the bridge vibration information obtained by the test in step one is input as an input parameter into the Gaussian process response surface model, so that a new set of design parameters x N+1 The predicted results of the design parameters are re-substituted into the bridge structure analysis model established in step two, so that the initial structure analysis model can be modified, and the modified analysis model is consistent with the actual bridge state.

[0038] The modification of the bridge structure analysis model actually refers to the modification of the bridge finite element model, and in this process, the predicted values of the design parameters are re-input into the initial finite element model to update the finite element model.

[0039] Further, the step six loads the bridge structure analysis model based on the modified bridge structure analysis model, loads the loading position and loading condition of the actual static load test in the structure analysis model, simulates the loading working condition of the bridge static load test, and calculates the deflection, stress and other results of the typical sections such as L / 8, L / 4, L / 2 and support point position under different loading working conditions. The results are the prediction results of the model modification.

[0040] The present application has the following beneficial effects:

[0041] (1) Based on the vibration characteristic information of the bridge, the response surface method is combined to realize accurate prediction of the static behavior of the existing bridge, which greatly improves the efficiency of the bridge performance evaluation and reduces the cost of the evaluation.

[0042] (2) Based on the response surface method, the accurate prediction of the static behavior of the bridge is realized, the prediction result is higher in accuracy than the traditional bridge load test result, and the overall analysis of the stress behavior of the bridge structure can be realized, and the limitation of the traditional load test result is overcome.

[0043] (3) The method has high calculation process efficiency, and can realize comprehensive prediction and analysis of the static behavior of a large number of existing bridges in urgent need of evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1a is a general flowchart of the bridge static behavior prediction method of the present application;

[0045] Figure 1b is a specific flowchart of the bridge static behavior prediction method of the present application;

[0046] Figure 2 is a time domain frequency spectrum diagram of actual bridge vibration information;

[0047] Figure 3 is a training sample set with 8 correction parameters (K1-K8) and two vibration response values (f1, f2);

[0048] Figure 4 is a set of constructed response surface model examples. DETAILED DESCRIPTION

[0049] The specific embodiments of the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application.

[0050] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0051] The present application will be described in detail below with reference to the accompanying drawings and in combination with exemplary embodiments.

[0052] The bridge static behavior prediction method based on vibration and response surface described in the present application is carried out according to the following steps:

[0053] Step 1: Collecting and pre-processing the vibration characteristic information of the existing bridge;

[0054] The actual vibration characteristic information of the bridge structure is obtained by using a direct measurement method (such as a dynamic load test method) or an indirect measurement method (such as a machine vision method), and the vibration information collected is processed by using a signal processing method. Specifically as follows:

[0055] A plurality of vibration sensors are arranged on the bridge, each group of sensors including displacement, velocity, acceleration sensors, or vibration characteristic information of the bridge is collected through a machine vision device, and the collected bridge vibration characteristic information (time domain information) is processed, such as Fourier transform to generate frequency domain information. The vibration information of the bridge, such as frequency, mode shape, damping ratio and the like, is obtained.

[0056] Specifically, low-frequency high-amplitude vibration pickups are arranged at key cross-section positions (such as L / 8, L / 4, L / 2 cross-sections) of the bridge, and the vibration pickups are provided with four gears of acceleration, small speed, medium speed and large speed, and the sensitivities thereof are respectively about 0.3V(m / s 2 ), about 20V(m / s), about 4V(m / s), and about 0.3V(m / s), and the corresponding frequency band ranges are respectively 0.2-100HZ, 1-100HZ, 0.5-100HZ, and 0.17-80HZ. The parameters are set according to actual test needs, and the vibration characteristic time domain information of the bridge is collected. The collected time domain information is converted into frequency domain information by using the Fourier transform method.

[0057] The Fourier transform formula is:

[0058] In the formula, j is a virtual unit, j^2=-1, and has no unit; T is a period, and has a unit of seconds; X is an original function of x; t is time, and has a unit of seconds; and ω is frequency, and x(t) is a continuous time signal.

[0059] After Fourier transform, the test results containing the dynamic characteristic information of the bridge frequency and the like are obtained. Figure 2 It is a time domain frequency spectrum diagram of actual bridge vibration information.

[0060] Step two, according to the design data of the existing bridge, the value of the bridge design parameter is determined, the structure analysis model of the bridge is established, and the structure analysis model is taken as the initial structure analysis model for subsequent model correction;

[0061] According to the design data of the existing bridge, the value of the bridge design parameter (such as material elastic modulus E, material unit weight γ, boundary condition, load application and the like) is determined, and after the design parameter is determined, the initial structure analysis model of the bridge is established in the finite element software, and the numerical method is usually used to establish. The initial model is provided for subsequent structure analysis model correction. The modeling process of the finite element is firstly to establish the geometric model of the bridge structure according to the design data, then to give the material properties to the geometric model and to divide the grid, and finally to complete the modeling by applying the boundary condition and the load.

[0062] Step three, according to the analysis model and the initial design parameter, the design parameters which have greater influence on the structure dynamic response are preliminarily determined, the sensitivity analysis method is used to screen out the key design parameters to be corrected of the structure analysis model;

[0063] In the sensitivity analysis of design parameters, based on the initial structure analysis model and the initial design parameter value, the change of each design parameter in the threshold range is calculated respectively, and the influence degree of each design parameter change on the structure response is calculated in turn. Further, the to-be-corrected design parameters of the structure analysis model are screened out.

[0064] The change range of the design parameter threshold value needs to be reduced according to the actual state of the bridge structure. The determination of the to-be-corrected parameters can also use the grey correlation degree method.

[0065] Specifically, the Morris method is used to analyze the sensitivity of the design parameters, and the design parameters that have greater influence on the bridge vibration response are selected as the subsequent to-be-corrected design parameters. The Morris method causes the change of the output response through the change of a single factor, and the calculation formula is:

[0066]

[0067] In the formula: d i (j) is the base effect of the i-th parameter in the j-th group of samples, j = 1, 2, 3, … R (R is the number of repeated sampling), n is the number of parameters; x i is the i-th parameter, Δ is the small change of a single parameter, and f(X) is the response output corresponding to the parameter group. Morris proposed two calculation indexes to judge the sensitivity of the parameters, namely the base effect mean μ and the standard deviation σ. Among them, μ represents the sensitivity of the parameter, and determines the sorting of the parameter, and σ represents the nonlinearity between parameters.

[0068] In the specific embodiment, within the threshold interval of the design parameter change, the value of one design parameter is changed at a time, and the influence of the change on the structure response is calculated.

[0069] Figure 1 supplements the parameter sensitivity analysis results in an embodiment. In the figure, K1-K5 and R1-R3 are design parameters, and f1 and f2 are the first two order frequencies of the bridge.

[0070] Step four, using the Latin hypercube sampling method, a space full of to-be-corrected key design parameters and bridge structure vibration response training samples are established, and a corresponding response surface model is constructed;

[0071] The Latin hypercube sampling method is used to establish a space full of design parameters and vibration response training samples.

[0072] In the embodiment, 8 design parameters of a bridge, i.e. K1-K5 and R1-R3, are used as correction parameters, and the first two order frequencies of the bridge are used as response values. 30 groups of experiments are designed by using the Latin hypercube method, i.e. U 30 (308 )As shown in Table 12, the training sample set between the input parameters and the output responses is constituted by 8 design parameters and 2 responses, and the training sample set is substituted into the Gaussian process model:

[0073] f(T N )~GP(m(x),K(x,x')) (5);

[0074] wherein:

[0075] m(x) = E[f(x)] (6);

[0076] K(x,x') = E[f(x)-m(x)(f(x')-m(x'))] (7);

[0077] wherein m(x) is the mean value; f(x) is the function about the sample point; E is the symbol of the mean value; K(x,x') is the covariance matrix;

[0078] The corresponding Gaussian process response surface model can be determined by determining the mean value m(x) and the covariance matrix K(x,x'). Figure 4 is the result graph of the Gaussian process response surface model constructed in an embodiment. Based on the Gaussian process response surface model, when the response values f1, f2 of the input bridge test frequency are input, the prediction results of the corresponding design parameters can be calculated. As shown in Figure 3 An example of the training sample set of 8 correction parameters (K1-K8) and two vibration response values (f1, f2) is given. Based on the training sample, the intelligent algorithm program is substituted to learn and train, and the response surface model between the bridge structure design parameters and the vibration response is constructed. The intelligent algorithms are: Gaussian process model algorithm, Kriging model algorithm. The Gaussian process model algorithm is adopted in this embodiment.

[0079] Step five, based on the response surface model, the actual value of the bridge structure design parameter is predicted, and substituted into the initial structure analysis model to realize the correction of the analysis model;

[0080] Based on the response surface model established in step four, the bridge vibration characteristic information obtained in step one is called as the input parameter, and the response surface model is substituted to predict the actual value of the structure design parameter. The predicted value of the design parameter is substituted into the initial structure analysis model established in step two to realize the correction of the bridge structure analysis model, and the corrected analysis model is consistent with the actual state of the existing bridge.

[0081] Step six, based on the corrected structure analysis model, the accurate prediction of the static behavior of the bridge is realized;

[0082] On the basis of the modified bridge structure analysis model, the static behaviors of the bridge in different states are calculated respectively by referring to the loading conditions of the loading cases in the static load test. The accurate prediction of the static behaviors of the bridge based on vibration and response surface method is realized.

[0083] Step seven, the root mean square error evaluation index is introduced to predict the static behaviors of the bridge,

[0084] and the prediction results are evaluated. The calculation formula of the root mean square error is as follows:

[0085]

[0086] Wherein, MSE is the mean square error; S 2 (x) is the variance; σ is the standard deviation; F is the unit vector; T represents the meaning of transposition; R is the correlation matrix composed of the correlation function values between all known sample points; r is the correlation vector composed of the correlation functions between the unknown point and all known sample points; F = [1, 1, 1...., 1] T . The mean square error can be used to guide the addition of new sample points to improve the accuracy of the surrogate model or to approximate the optimal solution of the optimization problem.

[0087] The deflection results of 14 measuring points of the bridge in the longitudinal direction are predicted in the embodiment, and the predicted results are compared and verified with the actual measured results. The theoretical analysis results, the predicted results and the measured results of the deflections of the measuring points are shown in Table 2. The deflections of three typical measuring points are selected for root mean square error analysis, and the analysis results are shown in Table 3. Among them, MSE1 is the error between the theoretical analysis results and the measured results, and MSE2 is the error between the predicted values and the theoretical analysis results.

[0088] Table 1 parameter design matrix

[0089]

[0090]

[0091] Table 2 theoretical value, predicted value and measured value of deflection measuring point

[0092]

[0093] Table 3 error analysis of deflection measuring point

[0094]

[0095] In the description of the present application, it is to be understood that the orientations or positional relationships indicated by the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like are based on the orientations or positional relationships shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the devices or elements indicated thereby must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0096] In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated thereby. Therefore, the features defined with "first", "second", etc. can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise explicitly specified and limited.

[0097] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected or in communication with each other; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication or interaction relationship of two elements, unless otherwise explicitly limited. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0098] In the present application, unless otherwise explicitly specified and limited, the first feature is "on" or "under" the second feature, which can be direct contact between the first and second features, or indirect contact between the first and second features through an intermediate medium. Moreover, the first feature "above", "above" and "above" the second feature can be directly above or obliquely above the first feature, or only indicate that the horizontal height of the first feature is higher than that of the second feature. The first feature "below", "below" and "below" the second feature can be directly below or obliquely below the first feature, or only indicate that the horizontal height of the first feature is less than that of the second feature.

[0099] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are contained in at least one embodiment or example of the present application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0100] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.

Claims

1. A method for predicting the static behavior of bridges based on vibration and response surfaces, characterized in that, Includes the following steps: Step 1: Collect vibration characteristic information of existing bridges and preprocess the data; Step 2: Based on the existing bridge design data, determine the values ​​of the bridge design parameters, establish a structural analysis model of the bridge, and use the structural analysis model as the initial structural analysis model for subsequent model corrections. Step 3: Based on the structural analysis model and initial design parameters, preliminarily determine the design parameters that respond to structural dynamics, and then use sensitivity analysis to screen out the key design parameters of the structural analysis model that need to be corrected. Step 4: Using the Latin hypercube sampling method, establish training samples between the key design parameters to be corrected and the vibration response of the bridge structure, which are spatially distributed, and construct the corresponding response surface model. Step 5: Based on the response surface model, predict the actual values ​​of the bridge structural design parameters, substitute them into the initial structural analysis model, and correct the analysis model. Step 6: Based on the modified structural analysis model, accurately predict the static behavior of the bridge; Step 7: Introduce the root mean square error (RMSE) evaluation index to predict the static behavior of the bridge and evaluate the prediction results. The formula for calculating the RMSE is as follows: Where MSE is the mean squared error; S 2 (x) represents the variance; σ represents the standard deviation; F is the unit vector; T indicates transpose; R is the correlation matrix, composed of the correlation function values ​​between all known sample points; r is the correlation vector, composed of the correlation functions between the unknown point and all known sample points; F = [1, 1, 1, ..., 1] T ; In step one, the actual vibration characteristics of the bridge structure are obtained by direct measurement or indirect measurement, and the collected vibration information is preprocessed by signal processing. Among them, the direct measurement method involves placing vibration pickups directly on the control section of the bridge, acquiring the bridge vibration response signal through a digital signal acquisition instrument, and reading the bridge frequency response through the peak value of the power spectrum and the time-domain history curve recorded by the acquisition system. Indirect measurement method: The sensor is installed on the moving trolley. When the moving trolley passes over the bridge, the vehicle-bridge coupling occurs. The dynamic characteristics of the bridge are extracted from the acceleration response of the vehicle body to obtain the bridge frequency information. Signal processing method: By performing a Fourier transform on the time-domain signal acquired by the vibration pickup, the frequency domain characteristics of the bridge can be obtained; the Fourier transform formula is: In the formula: j is the imaginary unit, j^2 = -1, which has no unit; T is the period, in seconds; X is the antiderivative of x; t is the time, in seconds; ω is the frequency; and x(t) is a continuous-time signal. Step 3 employs sensitivity analysis. Based on the structural analysis model established in Step 2, sensitivity analysis is performed on the initially determined design parameters to screen out the design parameters that have a greater impact on the vibration response of the bridge structure as design parameters to be corrected later. During the sensitivity analysis of design parameters, all initial design parameters need to be analyzed one by one, and finally, some key design parameters to be corrected are selected.

2. The method for predicting the static behavior of bridges based on vibration and response surfaces as described in claim 1, characterized in that: Based on the structural analysis model established in step two, the Morris method is used to perform sensitivity analysis on the design parameters, and the design parameters that have a significant impact on the bridge vibration response are selected as the design parameters to be corrected in the future. The Morris method calculates the change in output response caused by the change of a single factor, and its calculation formula is as follows: In the formula: d i (j) represents the base effect of the j-th sample group for the i-th parameter, j = 1, 2, 3, ..., R (R is the number of repeated samplings), and n is the number of parameters; x i Let f(.) be the i-th parameter, Δ be the small change of a single parameter, and f(.) be the response output of the corresponding parameter group. Morris proposed two calculation indicators to judge the sensitivity of the parameters, namely the mean of the base effect μ and the standard deviation σ. μ represents the sensitivity of the parameter and determines the order of the parameters, while σ represents the degree of nonlinearity between the parameters. The key design parameters that need to be corrected are screened out through the Morris calculation results.

3. The method for predicting the static behavior of bridges based on vibration and response surfaces as described in claim 1, characterized in that: Step 2: Based on existing bridge design data, determine the values ​​of bridge design parameters and establish an initial structural analysis model of the bridge using numerical methods to provide an initial model for subsequent structural analysis model correction. The values ​​of the bridge design parameters include the material elastic modulus, material unit weight, boundary conditions, and load application.

4. The method for predicting the static behavior of bridges based on vibration and response surfaces as described in claim 1, characterized in that: Step four, based on the key design parameters to be corrected selected in step three, uses the Latin hypercube sampling method to establish training samples between the spatially distributed design parameters and the vibration response. Based on these training samples, the intelligent algorithm program is substituted into the training program to construct a response surface model between the bridge structure design parameters and the vibration response.

5. The method for predicting the static behavior of bridges based on vibration and response surfaces as described in claim 4, characterized in that: Step four employs the Latin hypercube sampling method to efficiently sample from the distribution range of the design parameters. For a given set of K variables x1, x2, ..., x... k N samples are drawn from the dataset. The cumulative distribution of each variable is divided into N equal intervals. A value is randomly selected from each interval. The N values ​​of each variable are randomly combined with the values ​​of its dependent variables. This method ensures full coverage of the range of each variable. Training samples are generated using each design parameter as input data and the structural vibration response corresponding to each set of design parameters as output data. The Gaussian process response surface model is applied to the training sample sets (x1,t1), (x2,t2)...(x... N ,t N ), t i For x i The corresponding target value is used to predict a new set of input values ​​x. N+1 The corresponding target value t can be obtained. N+1 Its training set is: R={(X i ,T i ),i=1,2,3,...,i,...,N} (4); The joint probability distribution of the training set follows a Gaussian distribution: f(T N )~GP(m(x),K(x,x′)) (5); in: m(x)=E[fx)]; (6) K(x,x′)=E[f(x)-m(x)(f(x′)-m(x′))] (7); Where m(x) is the mean; f(x) is a function of the sample points; E is the sign of the mean; and K(x,x') is the covariance matrix. The corresponding Gaussian process response surface model can be determined by determining the mean m(x) and the covariance matrix K(x, x′).

6. The method for predicting the static behavior of bridges based on vibration and response surfaces as described in claim 5, characterized in that: Step 5, based on the response surface model established in Step 4, calls the bridge vibration characteristic information obtained in Step 1 as input parameters, substitutes it into the response surface model to predict the actual values ​​of the structural design parameters, substitutes the predicted values ​​of the design parameters into the initial structural analysis model established in Step 2, and realizes the correction of the bridge structural analysis model, so that the corrected analysis model matches the actual state of the existing bridge.

7. The method for predicting the static behavior of bridges based on vibration and response surfaces as described in claim 6, characterized in that: Step six, based on the modified bridge structural analysis model and referring to the loading conditions of the static load test, calculates the static behavior of the bridge under different states, thereby achieving accurate prediction of the bridge's static behavior based on vibration and response surface methodology.

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