CN103837358B - The method for early warning of the overall lateral resistance behavior exception of long-span bridges - Google Patents

Abstract

The early warning method for the abnormality of the overall lateral force resistance of long-span bridge structures includes the following steps: (1) Collect data on the three-dimensional wind field and GPS displacement effect at the mid-span of the main beam; (2) Perform vector decomposition and mean value processing on the collected data , to obtain the static wind speed sequence and static displacement sequence of the cross bridge; (3) divide the static wind speed sequence and static displacement sequence by month, and use the wavelet packet cross-correlation coefficient to determine the main correlation sequence of the two in the same month; (4) sequentially Fourier series fitting is carried out on the main correlation sequence of each month, and the abnormal early warning of the overall lateral force resistance performance of the long-span bridge structure is carried out by using the monthly variation characteristics of the fitting parameter values. The present invention proposes an early warning method for the abnormality of the overall lateral force performance of long-span bridge structures based on the measured wind load effect, which makes up for the research gap in the early warning of the overall lateral force performance of long-span bridge structures, and can be used for long-span bridge structures. It provides an important reference for the monitoring and analysis of the overall lateral force resistance performance.

Description

Early warning method for abnormality of overall lateral force resistance of long-span bridge structures

technical field

The invention relates to an early warning method for abnormality of the overall lateral force resistance performance of a long-span bridge structure based on measured wind load effects.

Background technique

The overall lateral force resistance of long-span bridge structures under wind loads is directly related to the normal use and safety performance of the entire bridge structure during operation. During the 100-year design service period, due to the long-term influence of external factors such as climate, environment, and load, the structural materials of bridge components will be gradually corroded, aged, and accumulated damage, which will gradually degrade the overall lateral force resistance of the bridge structure. However, at present, there is little real-time monitoring and analysis of the degradation of the overall lateral force resistance of bridge structures. The overall lateral force resistance of bridge structures is generally considered to be in good condition at the initial stage of operation. The influencing factor of "degradation of lateral force resistance" is rarely taken into account. It can be seen that the engineering community lacks sufficient understanding of the overall lateral force performance degradation behavior of long-span bridge structures during service, and it is necessary to study the overall lateral force performance degradation analysis methods of long-span bridge structures. Since the long-span bridge structure mainly bears the wind load in the transverse bridge direction, the lateral displacement response of the main girder caused by the wind load reflects the lateral force resistance performance of the entire bridge structure. Force performance analysis work provides an opportunity.

In view of this, the present invention proposes an early warning method for abnormality of the overall lateral force resistance performance of a long-span bridge structure based on the measured wind load effect.

Contents of the invention

Technical problem: The present invention aims at the research gap in the prior art on the evaluation of the overall lateral force resistance performance of long-span bridge structures, and proposes an early warning method for the abnormality of the overall lateral force resistance performance of long-span bridge structures based on the measured wind load effect.

Technical solution: In order to solve the above-mentioned technical problems, the present invention adopts the following technical solution for the abnormal early warning method of the overall lateral force resistance performance of long-span bridge structures based on the measured wind load effect:

The present invention proposes a method for early warning of abnormality of the overall lateral force resistance performance of long-span bridge structures based on measured wind load effects. The method specifically includes the following steps:

Step (1): Collect data on the 3D wind field and GPS displacement effect at the mid-span of the main girder:

A three-dimensional ultrasonic anemometer and a GPS displacement monitoring station are installed at the middle of the main girder span of the long-span bridge, where the wind vector v(t) and displacement vector u(t) are monitored in real time and stored in time series, where v(t )=[v _r (t), α(t), β(t)], u(t)=[u _x (t), u _y (t), u _z (t)], v _r (t) , α(t), β(t) are the absolute wind speed, wind attack angle and wind direction angle respectively, u _x (t), u _y (t), u _z (t) are the three-axis direction displacement in the GPS coordinate system , t represents time, t=1,2,...,L, the unit is second, L represents the length of time;

Step (2): Perform vector decomposition and mean value processing on the collected data to obtain the static wind speed sequence and static displacement sequence of the cross bridge:

Use the following two formulas to decompose the time series v(t) and u(t) into vectors to obtain the time history of wind speed v _h (t) and the time history of displacement u _r (t) across the bridge:

v _h (t) = v _r (t)·cos(α(t))·sin(β(t))

u _r (t)＝u _x (t)·sin(γ)-u _y (t)·cos(γ)

In the formula, γ represents the angle between the x-axis in the GPS coordinate system and the longitudinal axis of the main girder; then divide L into n 10-min time periods, and use the following formula to calculate the corresponding v _h (t) and u _r in each time period (t) to get the static wind speed sequence v _m (k) and the static displacement sequence u _m (k):

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 599</span>}}{\overset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 600</span>}$

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 599</span>}}{\overset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 600</span>}$

In the formula, k=1,2,...,n;

Step (3): Divide the static wind speed sequence and static displacement sequence by month, and use the wavelet packet cross-correlation coefficient to determine the main correlation sequence of the two in the same month:

① Divide the static wind speed sequence and the static displacement sequence by month. Taking the division result of the qth month as an analysis example, the monthly static wind speed values are arranged in increasing order and divided into p segments at equal intervals. Among them, the static wind speed values before and after the arrangement are The position change is recorded as R(n ₁ ,n ₂ ), and R(n ₁ ,n ₂ ) specifically means that the n _1st static wind speed value is located at the n _2nd position after arrangement; in addition, according to R(n ₁ ,n ₂ ) Arrange the monthly static displacement values according to the arrangement rules and equally divide them into p segments;

② Taking the static wind speed value and static displacement value of the s segment as an example, the fourth-scale wavelet packet decomposition is performed on the static wind speed value and static displacement value of this segment, and 16 wavelet packet coefficients arranged according to the node positions are obtained for both of them. The following formula performs cross-correlation analysis one by one on the reconstructed sequence of wavelet packet coefficients at the same node position:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{<span\; class="notranslate">\; \&lsqb;</span>\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&rsqb;</span>}^{\mathrm{<span\; class="notranslate">\; 0.5</span>}}}$

In the formula, r _vu (g) represents the cross-correlation coefficient between the reconstructed static wind speed sequence and static displacement sequence of the gth wavelet packet coefficient, are respectively the n _gth reconstructed static wind velocity value and static displacement value of the gth wavelet packet coefficient, n _t (g) is the total number of reconstructed static wind velocity values of the gth wavelet packet coefficient, are the mean values of the reconstructed static wind speed sequence and static displacement sequence of the gth wavelet packet coefficient respectively, g=1,2,...,16;

③ Eliminate the wavelet packet coefficients corresponding to the absolute value of the cross-correlation coefficient less than 0.9 from the 16 wavelet packet coefficients, and then superimpose the reconstruction sequence of the remaining wavelet packet coefficients to obtain the reconstruction sequence of the static wind speed value and static displacement value of the s segment, According to this method, the reconstructed sequence of the static wind speed and static displacement of the p segment is recombined into the static wind speed and static displacement sequence of the qth month, which is used as the main correlation sequence of the static wind speed and static displacement in this month, Afterwards, the main correlation sequence between the static wind speed and static displacement of all months is obtained by traversing each month;

Step (4): Carry out Fourier series fitting on the main correlation sequence of each month in turn, and use the monthly variation characteristics of the fitted parameter values to give an abnormal early warning of the overall lateral force resistance performance of the long-span bridge structure:

Using the second-order Fourier series shown in the following formula, the least squares fitting is performed on the main correlation series of each month in turn and the parameter estimates of each month are determined:

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

In the formula, v _m represents the static wind speed value in the mth month main correlation sequence, u _m represents the static displacement value in the mth month main correlation sequence, a _e (m), b _x (m) and w (m ) are the parameter estimates of the Fourier series for the mth month, respectively. The monthly variation of the estimated value of each parameter is directly related to the overall lateral force resistance performance of the long-span bridge structure, so the ADF unit root test is carried out separately. (ADF unit root test is a statistical analysis method for judging the stationarity of time series, also known as Augmented Dickey-Fuller test, the test result can be obtained by calling the function command adftest in the MATLAB mathematical software, if the test result is not Rejecting the null hypothesis that there is a unit root indicates that the time series is non-stationary; if the test result rejects the null hypothesis that there is a unit root, it indicates that the time series is stationary.) For the ADF of the monthly change series of each parameter estimate The test results can be divided into the following four situations:

①If the test results of each parameter estimate reject the null hypothesis that there is a unit root, the overall lateral force resistance of the long-span bridge structure is in a good state; ②If there are 1 to 2 parameter estimates, the test results do not reject If there is a null hypothesis of a unit root, a yellow warning will be given to the overall lateral force resistance performance of the long-span bridge structure, and the ADF unit root test results of the monthly variation characteristics of the estimated values of each parameter will be closely tracked; ③ If there are 3 to 4 parameter estimates If the test result of the value does not reject the null hypothesis that there is a unit root, an orange warning will be given to the overall lateral force resistance of the long-span bridge structure, and bridge maintenance personnel will be sent to conduct on-site inspections on the key components and parts of the bridge structure. Countermeasures; ④ If the test results of more than 4 parameter estimates do not reject the null hypothesis that there is a unit root, a red warning will be given to the overall lateral force resistance of the long-span bridge structure, and bridge professionals will be sent to inspect the bridge structure. The overall lateral force resistance performance is used for safety assessment and decision-making.

Beneficial effects: The present invention proposes a method for early warning of anomalies in overall lateral force resistance of long-span bridge structures based on measured wind load effects. Correlation sequence, and use the monthly variation characteristics of the Fourier series parameter fitting value to carry out abnormal early warning of the overall lateral force resistance performance of long-span bridge structures, making up for the research on the abnormal early warning of overall lateral force resistance performance of long-span bridge structures It can provide an important reference for the monitoring and analysis of the overall lateral force performance of long-span bridge structures.

Description of drawings

Fig. 1 is the arrangement of monitoring points for lateral wind load effect of main girder of Sutong Bridge according to the embodiment of the present invention (unit: m);

Fig. 2 is the static wind speed sequence of the transverse bridge of the embodiment of the present invention;

Fig. 3 is the static displacement sequence of the horizontal bridge according to the embodiment of the present invention;

Fig. 4 is 11 segment correlation scatter plots between monthly static wind speed value and monthly static displacement value in the embodiment of the present invention;

Fig. 5 is 11 sections main correlation sequences of monthly static wind speed and monthly static displacement of the embodiment of the present invention;

Fig. 6 is the change time course of parameters a ₀ , a ₁ and a ₂ in the monthly variation characteristics of Fourier series fitting parameter values;

Fig. 7 is parameter b ₁ and b ₂ change time course in the month change characteristic of Fourier series fitting parameter value;

Fig. 8 is the change time course of parameter w in the monthly change feature of Fourier series fitting parameter value.

detailed description

Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.

A method for early warning of abnormality of the overall lateral force resistance performance of a long-span bridge structure based on the measured wind load effect of the present invention, the method specifically includes the following steps:

Step (1): Collect data on the 3D wind field and GPS displacement effect at the mid-span of the main girder:

A three-dimensional ultrasonic anemometer and a GPS displacement monitoring station are installed at the middle of the main girder span of the long-span bridge, where the wind vector v(t) and displacement vector u(t) are monitored in real time and stored in time series, where v(t )=[v _r (t), α(t), β(t)], u(t)=[u _x (t), u _y (t), u _z (t)], v _r (t) , α(t), β(t) are the absolute wind speed, wind attack angle and wind direction angle respectively, u _x (t), u _y (t), u _z (t) are the three-axis direction displacement in the GPS coordinate system , t represents time, t=1,2,...,L, the unit is second, L represents the length of time;

Step (2): Perform vector decomposition and mean value processing on the collected data to obtain the static wind speed sequence and static displacement sequence of the cross bridge:

Use the following two formulas to decompose the time series v(t) and u(t) into vectors to obtain the time history of wind speed v _h (t) and the time history of displacement u _r (t) across the bridge:

v _h (t) = v _r (t)·cos(α(t))·sin(β(t))

u _r (t)＝u _x (t)·sin(γ)-u _y (t)·cos(γ)

In the formula, γ represents the angle between the x-axis in the GPS coordinate system and the longitudinal axis of the main girder; then divide L into n 10-min time periods, and use the following formula to calculate the corresponding v _h (t) and u _r in each time period (t) to get the static wind speed sequence v _m (k) and the static displacement sequence u _m (k):

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 599</span>}}{\overset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 600</span>}$

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 599</span>}}{\overset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 600</span>}$

In the formula, k=1,2,...,n;

Step (3): Divide the static wind speed sequence and static displacement sequence by month, and use the wavelet packet cross-correlation coefficient to determine the main correlation sequence of the two in the same month:

① Divide the static wind speed sequence and the static displacement sequence by month. Taking the division result of the qth month as an analysis example, the monthly static wind speed values are arranged in increasing order and divided into p segments at equal intervals. Among them, the static wind speed values before and after the arrangement are The position change is recorded as R(n ₁ ,n ₂ ), and R(n ₁ ,n ₂ ) specifically means that the n _1st static wind speed value is located at the n _2nd position after arrangement; in addition, according to R(n ₁ ,n ₂ ) Arrange the monthly static displacement values according to the arrangement rules and equally divide them into p segments;

② Taking the static wind speed value and static displacement value of the s segment as an example, the fourth-scale wavelet packet decomposition is performed on the static wind speed value and static displacement value of this segment, and 16 wavelet packet coefficients arranged according to the node positions are obtained for both of them. The following formula performs cross-correlation analysis one by one for the reconstructed sequence of wavelet packet coefficients at the same node position:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{<span\; class="notranslate">\; \&lsqb;</span>\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&rsqb;</span>}^{\mathrm{<span\; class="notranslate">\; 0.5</span>}}}$

In the formula, r _vu (g) represents the cross-correlation coefficient between the reconstructed static wind speed sequence and static displacement sequence of the gth wavelet packet coefficient, are respectively the n _gth reconstructed static wind velocity value and static displacement value of the gth wavelet packet coefficient, n _t (g) is the total number of reconstructed static wind velocity values of the gth wavelet packet coefficient, are the mean values of the reconstructed static wind speed sequence and static displacement sequence of the gth wavelet packet coefficient respectively, g=1,2,...,16;

③ Eliminate the wavelet packet coefficients corresponding to the absolute value of the cross-correlation coefficient less than 0.9 from the 16 wavelet packet coefficients, and then superimpose the reconstruction sequence of the remaining wavelet packet coefficients to obtain the reconstruction sequence of the static wind speed value and static displacement value of the s segment, According to this method, the reconstructed sequence of the static wind speed and static displacement of the p segment is recombined into the static wind speed and static displacement sequence of the qth month, which is used as the main correlation sequence of the static wind speed and static displacement in this month, Afterwards, the main correlation sequence between the static wind speed and static displacement of all months is obtained by traversing each month;

Step (4): Carry out Fourier series fitting on the main correlation sequence of each month in turn, and use the monthly variation characteristics of the fitted parameter values to give an abnormal early warning of the overall lateral force resistance performance of the long-span bridge structure:

Using the second-order Fourier series shown in the following formula, the least squares fitting is performed on the main correlation series of each month in turn and the parameter estimates of each month are determined:

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

In the formula, v _m represents the static wind speed value in the mth month main correlation sequence, u _m represents the static displacement value in the mth month main correlation sequence, a _e (m), b _x (m) and w (m ) are the parameter estimates of the Fourier series in the mth month; the ADF unit root test is performed on the monthly variation characteristics of each parameter estimate, and if the test results of each parameter estimate reject the existence of a unit root Assuming that , the overall lateral force resistance of long-span bridge structures is in good condition; A yellow warning will be issued for the performance, and the ADF unit root test results of the monthly variation characteristics of the estimated values of each parameter will be closely tracked; The overall lateral force resistance performance will be given an orange warning, and bridge maintenance personnel will be sent to conduct on-site inspections of key components and parts of the bridge structure, and countermeasures will be taken according to the inspection results; if there are more than 4 parameter estimates, the inspection results do not reject the existence of a unit root The null hypothesis of the long-span bridge structure is given a red warning for the overall lateral force resistance performance, and bridge professionals are sent to conduct safety assessment and decision-making on the overall lateral force resistance performance of the bridge structure;

Example 1

Take Sutong Bridge as the object of analysis below to illustrate the concrete implementation process of the present invention:

(1) Sutong Bridge is a bridge spanning the Yangtze River connecting Nantong and Zhenjiang. It adopts a double-tower double-cable-plane cable-stayed bridge structure system, in which the main girder is in the form of a streamlined flat steel box girder, and the longitudinal design dimension of the main span reaches 1088m , this design dimension makes the main girder members have obvious lateral displacement effect at the mid-span under the action of wind load in the transverse bridge direction. Based on the bridge structural health monitoring system, long-term monitoring and data collection are carried out for the three-dimensional wind field and GPS displacement response at the mid-span of the main girder. The specific monitoring instrument layout is shown in Figure 1, and the sampling frequency of the instruments is set at 1 Hz;

(2) Based on step 2), vector decomposition and mean value processing are performed on the collected data, and the static wind speed sequence and static displacement sequence of the cross bridge are obtained as shown in Figure 2 and Figure 3 respectively (taken from August 1 to August 10, 2012 example);

(3) Based on step 3), the static wind speed series and static displacement series are divided by month. Taking the division results in August as an analysis example, the monthly static wind speed values are arranged in increasing order and divided into 11 segments at equal intervals. In addition, according to The arrangement rule of R(n ₁ ,n ₂ ) arranges the monthly static displacement values and equally divides them into 11 segments. The results of the division of 11 segments are represented by the correlation scatter diagram between the monthly static wind speed value and the monthly static displacement value as follows: As shown in Figure 4;

(4) Based on step 3), the fourth-scale wavelet packet decomposition is performed on each static wind speed value and static displacement value, and each static wind speed value and static displacement value can obtain 16 wavelet packet coefficients arranged according to the node position. The reconstruction sequence of wavelet packet coefficients at the same node position is analyzed one by one by cross-correlation, and the wavelet packet coefficients whose absolute value of cross-correlation coefficient is less than 0.9 are eliminated, and the remaining wavelet packet coefficients are reconstructed to obtain the static wind speed value and static displacement value of each segment. Reconstruction sequence, combining 11 segments of reconstruction sequence to obtain the main correlation sequence of static wind speed and static displacement in this month, as shown in Figure 5;

(5) Based on step 4), the second-order Fourier series fitting is performed on each monthly main correlation sequence. The monthly variation characteristics of each fitting parameter value are shown in Figure 6-8. The monthly variation characteristics of each parameter estimated value Carry out the ADF unit root test, and if the test results of two parameter estimates do not reject the null hypothesis of the existence of a unit root, a yellow warning will be given to the overall lateral force resistance of the long-span bridge structure, and the monthly changes in the estimated values of each parameter will be closely tracked The ADF unit root test results of the features.

Claims (3)

1. An early warning method for the abnormality of the overall lateral force performance of a long-span bridge structure, characterized in that the method may further comprise the steps: Step (1): Collect data on the 3D wind field and GPS displacement effect at the mid-span of the main girder: A three-dimensional ultrasonic anemometer and a GPS displacement monitoring station are installed at the middle of the main girder span of the long-span bridge, where the wind vector v(t) and displacement vector u(t) are monitored in real time and stored in time series, where v(t )=[v _r (t), α(t), β(t)], u(t)=[u _x (t), u _y (t), u _z (t)], v _r (t) , α(t), β(t) are the absolute wind speed, wind attack angle and wind direction angle respectively, u _x (t), u _y (t), u _z (t) are the three-axis direction displacement in the GPS coordinate system , t represents time, t=1,2,...,L, the unit is second, L represents the length of time; Step (2): Perform vector decomposition and mean value processing on the collected data to obtain the static wind speed sequence and static displacement sequence of the cross bridge: Use the following two formulas to decompose the time series v(t) and u(t) into vectors to obtain the time history of wind speed v _h (t) and the time history of displacement u _r (t) across the bridge: v _h (t) = v _r (t)·cos(α(t))·sin(β(t)) u _r (t)＝u _x (t)·sin(γ)-u _y (t)·cos(γ) In the formula, γ represents the angle between the x-axis in the GPS coordinate system and the longitudinal axis of the main girder; then divide L into n 10-min time periods, and use the following formula to calculate the corresponding v _h (t) and u _r in each time period (t) to get the static wind speed sequence v _m (k) and the static displacement sequence u _m (k): ${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 599</span>}}{\overset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 600</span>}$ ${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 599</span>}}{\overset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 600</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 600</span>}$ In the formula, k=1,2,...,n; Step (3): Divide the static wind speed sequence and static displacement sequence by month, and use the wavelet packet cross-correlation coefficient to determine the main correlation sequence of the two in the same month: ① Divide the static wind speed sequence and the static displacement sequence by month. Taking the division result of the qth month as an analysis example, the monthly static wind speed values are arranged in increasing order and divided into p segments at equal intervals. Among them, the static wind speed values before and after the arrangement are The position change is recorded as R(n ₁ ,n ₂ ), and R(n ₁ ,n ₂ ) specifically means that the n _1st static wind speed value is located at the n _2nd position after arrangement; in addition, according to R(n ₁ ,n ₂ ) Arrange the monthly static displacement values according to the arrangement rules and equally divide them into p segments; ② Taking the static wind speed value and static displacement value of the s segment as an example, the fourth-scale wavelet packet decomposition is performed on the static wind speed value and static displacement value of this segment, and 16 wavelet packet coefficients arranged according to the node positions are obtained for both of them. The following formula performs cross-correlation analysis one by one on the reconstructed sequence of wavelet packet coefficients at the same node position: ${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{{<span\; class="notranslate">\; \&lsqb;</span>\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&rsqb;</span>}^{\mathrm{<span\; class="notranslate">\; 0.5</span>}}}$ In the formula, r _vu (g) represents the cross-correlation coefficient between the reconstructed static wind speed sequence and static displacement sequence of the gth wavelet packet coefficient, are respectively the n _gth reconstructed static wind velocity value and static displacement value of the gth wavelet packet coefficient, n _t (g) is the total number of reconstructed static wind velocity values of the gth wavelet packet coefficient, are respectively the mean values of the reconstructed static wind speed sequence and static displacement sequence of the gth wavelet packet coefficient, g=1,2,...,16; ③ Eliminate the wavelet packet coefficients corresponding to the absolute value of the cross-correlation coefficient less than 0.9 from the 16 wavelet packet coefficients, and then superimpose the reconstruction sequence of the remaining wavelet packet coefficients to obtain the reconstruction sequence of the static wind speed value and static displacement value of the s segment, According to this method, the reconstructed sequence of the static wind speed and static displacement of the p segment is recombined into the static wind speed and static displacement sequence of the qth month, which is used as the main correlation sequence of the static wind speed and static displacement in this month, Afterwards, the main correlation sequence between the static wind speed and static displacement of all months is obtained by traversing each month; Step (4): Carry out Fourier series fitting on the main correlation sequence of each month in turn, and use the monthly variation characteristics of the fitting parameter values to give an abnormal early warning of the overall lateral force resistance performance of the long-span bridge structure: Using the second-order Fourier series shown in the following formula, the least squares fitting is performed on the main correlation series of each month in turn and the parameter estimates of each month are determined: ${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; e</span>}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$ In the formula, v _m represents the static wind speed value in the mth month main correlation sequence, u _m represents the static displacement value in the mth month main correlation sequence, a _e (m), b _x (m) and w (m ) are the parameter estimates of the Fourier series in the mth month; the ADF unit root test is performed on the monthly variation characteristics of each parameter estimate, and if the test results of each parameter estimate reject the existence of a unit root Assuming that , the overall lateral force resistance of long-span bridge structures is in good condition; A yellow warning will be issued for the performance, and the ADF unit root test results of the monthly variation characteristics of the estimated values of each parameter will be closely tracked; The overall lateral force resistance performance will be given an orange warning, and bridge maintenance personnel will be sent to conduct on-site inspections of key components and parts of the bridge structure, and countermeasures will be taken according to the inspection results; if there are more than 4 parameter estimates, the inspection results do not reject the existence of a unit root Based on the original hypothesis, a red warning will be given to the overall lateral force resistance of the long-span bridge structure, and bridge professionals will be sent to conduct safety assessment and decision-making on the overall lateral force resistance of the bridge structure. 2. the early warning method of a kind of long-span bridge structure overall lateral force resistance performance abnormality as claimed in claim 1, is characterized in that, the time length L described in step (1) should be the number of seconds of 10 months at least, and It should be an integer multiple of 600. 3. The early warning method for the abnormality of the overall lateral force resistance of a long-span bridge structure as claimed in claim 1, wherein the number of sections p described in step (3) should be between 8 and 11 sections, and each section The number of static wind speed values and static displacement values is the same.