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

Abstract

The method for early warning of the overall lateral resistance behavior exception of long-span bridges comprises following steps: (1) carries out data acquisition to the dimensional wind at girder span centre position and GPS displacement effect; (2) resolution of vectors and average value processing are carried out to image data, obtain the quiet wind series of direction across bridge and Static Correction sequence; (3) quiet wind series and Static Correction sequence were divided by month, and utilize wavelet packet cross-correlation coefficient to determine both main correlated serieses in same month; (4) successively Fourier series matching is carried out to main correlated series of each moon, utilize the overall lateral resistance behavior of the monthly changing characteristics of fit parameter values to long-span bridges to carry out abnormity early warning.A kind of long-span bridges overall lateral resistance behavior abnormity early warning method of carrying effect based on actual measurement wind that the present invention proposes, compensate for the research of long-span bridges in overall lateral resistance behavior abnormity early warning blank, the monitoring and the analytical work that can be the overall lateral resistance behavior of long-span bridges provide important references.

Description

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

Technical field

The present invention relates to a kind of long-span bridges overall lateral resistance behavior abnormity early warning method of carrying effect based on actual measurement wind.

Background technology

The overall lateral resistance behavior of the lower long-span bridges of the wind effect of carrying is directly connected to the normal use of whole bridge structure during runing and security performance.Within 100 years design military service phases, bridge member is owing to being subject to the impact of the extraneous factor such as weather, environment, load for a long time, and its structured material can be aging and form damage accumulation by burn into gradually, and the overall lateral resistance behavior of bridge structure is degenerated gradually.But, the Real-Time Monitoring of degenerating for the overall lateral resistance behavior of bridge structure at present and analytical work very few, generally the overall lateral resistance behavior of bridge structure is considered as all the time the serviceable condition at operation initial stage, and the Lateral Resistance design of bridge structure important component also seldom can take into account " lateral resistance behavior degeneration " this influence factor.Visible, engineering circles lacks for long-span bridges overall lateral resistance behavior degeneration behavior in-service to be enough familiar with, and is necessary the overall lateral resistance behavior degradation analysis method furtheing investigate long-span bridges.Because long-span bridges mainly bears wind action at direction across bridge, the girder lateral shift response magnitude caused by wind load reflects the lateral resistance behavior of whole bridge structure, and this point is that the overall lateral resistance behavior analytical work carrying out long-span bridges provides opportunity.

Given this, the present invention proposes a kind of long-span bridges overall lateral resistance behavior abnormity early warning method of carrying effect based on actual measurement wind.

Summary of the invention

Technical matters: the present invention is directed in prior art blank about the research of long-span bridges in overall lateral resistance behavior evaluation, proposes a kind of long-span bridges overall lateral resistance behavior abnormity early warning method of carrying effect based on actual measurement wind.

Technical scheme: for solving the problems of the technologies described above, a kind of long-span bridges overall lateral resistance behavior abnormity early warning method of carrying effect based on actual measurement wind of the present invention adopts following technical scheme:

A kind of long-span bridges overall lateral resistance behavior abnormity early warning method of carrying effect based on actual measurement wind that the present invention proposes, the method specifically comprises the steps:

Step (1): data acquisition is carried out to the dimensional wind at girder span centre position and GPS displacement effect:

At the girder span centre place of Loads of Long-span Bridges, three-D ultrasonic anemoscope and GPS displacement monitoring station are installed, to wind vector v (t) and motion vector u (t) carry out Real-Time Monitoring and store with time series, wherein v (t)=[v herein _r(t), α (t), β (t)], u (t)=[u _x(t), u _y(t), u _z(t)], v _rt (), α (t), β (t) are respectively true wind velocity, the wind angle of attack and wind angle, u _x(t), u _y(t), u _zt () is respectively three direction of principal axis displacements under gps coordinate system, t represents the time, t=1, and 2 ..., L, unit is second, and L represents time span;

Step (2): carry out resolution of vectors and average value processing to image data, obtains the quiet wind series of direction across bridge and Static Correction sequence:

Utilize following two formulas that time series v (t), u (t) are carried out resolution of vectors, obtain direction across bridge Wind Velocity History v _h(t) and displacement time-histories u _r(t):

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 formula, γ represents the angle of x-axis in gps coordinate system and girder longitudinal axis; After L is divided into n 10min time period, and utilize following formula to calculate corresponding v in each time period _h(t) and u _rt the mean value of (), obtains quiet wind series v _m(k) and Static Correction sequence u _m(k):

$v_m\left(k\right)=\left(\underset{t=600k-599}{\overset{t=600k}{\Σ}}{v}_h\right(t\left)\right)/600$

$u_m\left(k\right)=\left(\underset{t=600k-599}{\overset{t=600k}{\Σ}}{u}_r\right(t\left)\right)/600$

K=1 in formula, 2 ..., n;

Step (3): quiet wind series and Static Correction sequence were divided by month, and utilize wavelet packet cross-correlation coefficient to determine both main correlated serieses in same month:

1. divide by month quiet wind series and Static Correction sequence, with the division result in q month for analyzing example, by this month quiet air speed value by increasing progressively arrangement and being equidistantly divided into p section, the change in location wherein before and after quiet air speed value arrangement is designated as R (n ₁, n ₂), R (n ₁, n ₂) specifically represent n-th ₁individual quiet air speed value is positioned at n-th after arrangement ₂individual position; In addition according to R (n ₁, n ₂) queueing discipline the arrangement of this month Static Correction value is divided into p section equally;

2. for the quiet air speed value of s section and Static Correction value, carry out the 4th multi-scale wavelet bag to this section of quiet air speed value and Static Correction value to decompose, both all obtain, by 16 wavelet packet coefficients of site position arrangement, utilizing the reproducing sequence of following formula to same node position wavelet packet coefficient to carry out cross-correlation analysis one by one:

$r_{vu}\left(g\right)=\frac{\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}\left(\tilde{v}\right(g,n_g)-\stackrel{\‾}{v}(g\left)\right)\left(\tilde{u}\right(g,n_g)-\stackrel{\‾}{u}(g\left)\right)}{{\[\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}{\left(\tilde{v}\right(g,n_g)-\stackrel{\‾}{v}(g\left)\right)}^2\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}{\left(\tilde{u}\right(g,n_g)-\stackrel{\‾}{u}(g\left)\right)}^2\]}^{0.5}}$

In formula, r _vug () represents the cross-correlation coefficient between the quiet wind series of the reconstruct of g wavelet packet coefficient and Static Correction sequence, be respectively n-th of g wavelet packet coefficient _gthe quiet air speed value of individual reconstruct and Static Correction value, n _tg () is the total number of the quiet air speed value of reconstruct of g wavelet packet coefficient, be respectively the quiet wind series of reconstruct of g wavelet packet coefficient and Static Correction sequence average, g=1,2 ..., 16;

3. from 16 wavelet packet coefficients, weed out the wavelet packet coefficient that cross-correlation coefficient absolute value is less than 0.9 correspondence, afterwards the reproducing sequence superposition of residue wavelet packet coefficient is obtained to the reproducing sequence of the quiet air speed value of s section and Static Correction value, according to said method the reproducing sequence of the quiet air speed value of p section obtained and Static Correction value is reassembled into quiet wind speed and the Static Correction sequence in q month, in this, as quiet wind speed and the Static Correction main correlated series in this month, rear to each moon traversal obtain all months quiet wind speed and Static Correction between main correlated series;

Step (4): carry out Fourier series matching to main correlated series of each moon successively, utilizes the overall lateral resistance behavior of the monthly changing characteristics of fit parameter values to long-span bridges to carry out abnormity early warning:

Utilize the second-order Fourier gear progression shown in following formula, successively least square fitting carried out to main correlated series of each moon and determine each moon estimates of parameters:

$u_m\left(v_m\right)=\underset{e=0}{\overset{2}{\Σ}}\left(a_e\right(m\left)cos\right(e\·w\left(m\right)\·v_m\left)\right)+\underset{x=1}{\overset{2}{\Σ}}\left(b_x\right(m\left)sin\right(x\·w\left(m\right)\·v_m\left)\right)$

In formula, v _mrepresent the quiet air speed value in m month main correlated series, u _mrepresent the Static Correction value in m month main correlated series, a _e(m), b _xm () and w (m) are respectively the estimates of parameters of m month Fourier series.The monthly variation smooth performance of each estimates of parameters is directly connected to the overall lateral resistance behavior of long-span bridges, therefore carries out ADF unit root test to it respectively.(ADF unit root test is a kind of statistical analysis technique judging time series stationarity, be also called augmentation Dickey-Fu Le to check, specifically obtain assay by the function command adftest in Calling MATLAB mathematical software, if assay does not refuse the null hypothesis of an existence unit root, then show that time series has non-stationary; If the null hypothesis of an assay refusal existence unit root, then show that time series has stationarity.) following four kinds of situations can be divided into for the ADF assay of the monthly variation sequence of each estimates of parameters:

If 1. the assay of each estimates of parameters all refuses the null hypothesis of an existence unit root, then the overall lateral resistance behavior of long-span bridges is in shape; If the assay that 2. there is 1 to 2 estimates of parameters does not refuse the null hypothesis of an existence unit root, then yellow early warning is carried out to the overall lateral resistance behavior of long-span bridges, and closely follow the tracks of the ADF unit root test result of each estimates of parameters monthly changing characteristics; If the assay that 3. there are 3 to 4 estimates of parameters does not refuse the null hypothesis of an existence unit root, then orange early warning is carried out to the overall lateral resistance behavior of long-span bridges, and send bridge maintenance personnel to carry out Site Detection to bridge structure key member and position, take counter-measure according to testing result; If the assay that 4. there are more than 4 estimates of parameters does not refuse the null hypothesis of an existence unit root, then red early warning is carried out to the overall lateral resistance behavior of long-span bridges, and send the overall lateral resistance behavior of bridge professional to bridge structure to carry out safety assessment and decision-making.

Beneficial effect: a kind of long-span bridges overall lateral resistance behavior abnormity early warning method of carrying effect based on actual measurement wind that the present invention proposes, wavelet packet cross-correlation coefficient analytic approach is utilized to extract main correlated series between the quiet wind speed of direction across bridge and Static Correction, and utilize the overall lateral resistance behavior of the monthly variation characteristic of Fourier series parameter fitting value to long-span bridges to carry out abnormity early warning, compensate for the research of long-span bridges in overall lateral resistance behavior abnormity early warning blank, the monitoring and the analytical work that can be the overall lateral resistance behavior of long-span bridges provide important references.

Accompanying drawing explanation

Fig. 1 is that (unit: m) is arranged in embodiment of the present invention Su-Tong Brideg girder Lateral Wind effect monitoring point;

Fig. 2 is the quiet wind series of embodiment of the present invention direction across bridge;

Fig. 3 is embodiment of the present invention direction across bridge Static Correction sequence;

Fig. 4 be the embodiment of the present invention moon quiet air speed value and moon Static Correction value between 11 sections of correlativity scatter diagrams;

Fig. 5 be the embodiment of the present invention moon quiet wind speed and the moon Static Correction 11 sections of main correlated serieses;

Fig. 6 is parameter a in the monthly changing characteristics of Fourier series fit parameter values ₀, a ₁and a ₂change time-histories;

Fig. 7 is parameter b in the monthly changing characteristics of Fourier series fit parameter values ₁and b ₂change time-histories;

Fig. 8 is that in the monthly changing characteristics of Fourier series fit parameter values, parameter w changes time-histories.

Embodiment

Below in conjunction with the drawings and specific embodiments, illustrate the present invention further, these embodiments should be understood only be not used in for illustration of the present invention and limit the scope of the invention, after having read the present invention, the amendment of those skilled in the art to the various equivalent form of value of the present invention has all fallen within the application's claims limited range.

A kind of long-span bridges overall lateral resistance behavior abnormity early warning method of carrying effect based on actual measurement wind of the present invention, the method specifically comprises the steps:

Step (1): data acquisition is carried out to the dimensional wind at girder span centre position and GPS displacement effect:

At the girder span centre place of Loads of Long-span Bridges, three-D ultrasonic anemoscope and GPS displacement monitoring station are installed, to wind vector v (t) and motion vector u (t) carry out Real-Time Monitoring and store with time series, wherein v (t)=[v herein _r(t), α (t), β (t)], u (t)=[u _x(t), u _y(t), u _z(t)], v _rt (), α (t), β (t) are respectively true wind velocity, the wind angle of attack and wind angle, u _x(t), u _y(t), u _zt () is respectively three direction of principal axis displacements under gps coordinate system, t represents the time, t=1, and 2 ..., L, unit is second, and L represents time span;

Step (2): carry out resolution of vectors and average value processing to image data, obtains the quiet wind series of direction across bridge and Static Correction sequence:

Utilize following two formulas that time series v (t), u (t) are carried out resolution of vectors, obtain direction across bridge Wind Velocity History v _h(t) and displacement time-histories u _r(t):

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 formula, γ represents the angle of x-axis in gps coordinate system and girder longitudinal axis; After L is divided into n 10min time period, and utilize following formula to calculate corresponding v in each time period _h(t) and u _rt the mean value of (), obtains quiet wind series v _m(k) and Static Correction sequence u _m(k):

$v_m\left(k\right)=\left(\underset{t=600k-599}{\overset{t=600k}{\Σ}}{v}_h\right(t\left)\right)/600$

$u_m\left(k\right)=\left(\underset{t=600k-599}{\overset{t=600k}{\Σ}}{u}_r\right(t\left)\right)/600$

K=1 in formula, 2 ..., n;

Step (3): quiet wind series and Static Correction sequence were divided by month, and utilize wavelet packet cross-correlation coefficient to determine both main correlated serieses in same month:

1. divide by month quiet wind series and Static Correction sequence, with the division result in q month for analyzing example, by this month quiet air speed value by increasing progressively arrangement and being equidistantly divided into p section, the change in location wherein before and after quiet air speed value arrangement is designated as R (n ₁, n ₂), R (n ₁, n ₂) specifically represent n-th ₁individual quiet air speed value is positioned at n-th after arrangement ₂individual position; In addition according to R (n ₁, n ₂) queueing discipline the arrangement of this month Static Correction value is divided into p section equally;

2. for the quiet air speed value of s section and Static Correction value, carry out the 4th multi-scale wavelet bag to this section of quiet air speed value and Static Correction value to decompose, both all obtain, by 16 wavelet packet coefficients of site position arrangement, utilizing the reproducing sequence of following formula to same node position wavelet packet coefficient to carry out cross-correlation analysis one by one:

$r_{vu}\left(g\right)=\frac{\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}\left(\tilde{v}\right(g,n_g)-\stackrel{\‾}{v}(g\left)\right)\left(\tilde{u}\right(g,n_g)-\stackrel{\‾}{u}(g\left)\right)}{{\[\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}{\left(\tilde{v}\right(g,n_g)-\stackrel{\‾}{v}(g\left)\right)}^2\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}{\left(\tilde{u}\right(g,n_g)-\stackrel{\‾}{u}(g\left)\right)}^2\]}^{0.5}}$

In formula, r _vug () represents the cross-correlation coefficient between the quiet wind series of the reconstruct of g wavelet packet coefficient and Static Correction sequence, be respectively n-th of g wavelet packet coefficient _gthe quiet air speed value of individual reconstruct and Static Correction value, n _tg () is the total number of the quiet air speed value of reconstruct of g wavelet packet coefficient, be respectively the quiet wind series of reconstruct of g wavelet packet coefficient and Static Correction sequence average, g=1,2 ..., 16;

3. from 16 wavelet packet coefficients, weed out the wavelet packet coefficient that cross-correlation coefficient absolute value is less than 0.9 correspondence, afterwards the reproducing sequence superposition of residue wavelet packet coefficient is obtained to the reproducing sequence of the quiet air speed value of s section and Static Correction value, according to said method the reproducing sequence of the quiet air speed value of p section obtained and Static Correction value is reassembled into quiet wind speed and the Static Correction sequence in q month, in this, as quiet wind speed and the Static Correction main correlated series in this month, rear to each moon traversal obtain all months quiet wind speed and Static Correction between main correlated series;

Step (4): carry out Fourier series matching to main correlated series of each moon successively, utilizes the overall lateral resistance behavior of the monthly changing characteristics of fit parameter values to long-span bridges to carry out abnormity early warning:

Utilize the second-order Fourier gear progression shown in following formula, successively least square fitting carried out to main correlated series of each moon and determine each moon estimates of parameters:

$u_m\left(v_m\right)=\underset{e=0}{\overset{2}{\Σ}}\left(a_e\right(m\left)cos\right(e\·w\left(m\right)\·v_m\left)\right)+\underset{x=1}{\overset{2}{\Σ}}\left(b_x\right(m\left)sin\right(x\·w\left(m\right)\·v_m\left)\right)$

In formula, v _mrepresent the quiet air speed value in m month main correlated series, u _mrepresent the Static Correction value in m month main correlated series, a _e(m), b _xm () and w (m) are respectively the estimates of parameters of m month Fourier series; Carry out ADF unit root test to the monthly variation characteristic of each estimates of parameters respectively, if the assay of each estimates of parameters all refuses the null hypothesis of an existence unit root, then the overall lateral resistance behavior of long-span bridges is in shape; If the assay that there is 1 to 2 estimates of parameters does not refuse the null hypothesis of an existence unit root, then yellow early warning is carried out to the overall lateral resistance behavior of long-span bridges, and closely follow the tracks of the ADF unit root test result of each estimates of parameters monthly changing characteristics; If the assay that there are 3 to 4 estimates of parameters does not refuse the null hypothesis of an existence unit root, then orange early warning is carried out to the overall lateral resistance behavior of long-span bridges, and send bridge maintenance personnel to carry out Site Detection to bridge structure key member and position, take counter-measure according to testing result; If the assay that there are more than 4 estimates of parameters does not refuse the null hypothesis of an existence unit root, then red early warning is carried out to the overall lateral resistance behavior of long-span bridges, and send the overall lateral resistance behavior of bridge professional to bridge structure to carry out safety assessment and decision-making;

Embodiment 1

Be analytic target with Su-Tong Brideg below, specific embodiment of the invention process be described:

(1) Su-Tong Brideg be connect Nantong and Zhenjiang two city across the Yangtze Bridge, adopt double tower double plane cable stayed bridge structural system, wherein main beam member adopts streamlined Plate of Flat Steel Box Girder form, main span part longitudinal design size reaches 1088m, this design size makes main beam member under cross-bridges aweather carries effect, and span centre position there will be obvious lateral shift effect.Based on bridge health monitoring system, carry out long term monitoring and data acquisition to the dimensional wind at girder span centre position and GPS dynamic respond, concrete monitoring instrument is arranged as shown in Figure 1, and instrument sample frequency is all set as 1Hz;

(2) based on step 2) resolution of vectors and average value processing are carried out to image data, obtain the quiet wind series of direction across bridge and Static Correction sequence respectively as shown in Figures 2 and 3 (on August 1st, 2012 to August 10);

(3) based on step 3) divide by month to quiet wind series and Static Correction sequence, with the division result in August for analyzing example, by this month quiet air speed value by increasing progressively arrangement and being equidistantly divided into 11 sections, in addition according to R (n ₁, n ₂) queueing discipline the arrangement of this month Static Correction value is divided into 11 sections equally, 11 sections of division results adopt the correlativity scatter diagram between the quiet air speed value of the moon and moon Static Correction value to represent as shown in Figure 4;

(4) based on step 3) respectively the 4th multi-scale wavelet bag decomposition is carried out to every section of quiet air speed value and Static Correction value, every section of quiet air speed value and Static Correction value all obtain 16 wavelet packet coefficients by site position arrangement, cross-correlation analysis is one by one carried out to the reproducing sequence of same node position wavelet packet coefficient, weed out the wavelet packet coefficient that cross-correlation coefficient absolute value is less than 0.9, and the reconstruct of residue wavelet packet coefficient is obtained to the reproducing sequence of every section of quiet air speed value and Static Correction value, the combination of 11 sections of reproducing sequences is obtained quiet wind speed and Static Correction this month main correlated series as shown in Figure 5,

(5) based on step 4) second-order Fourier gear series approaching is carried out to main correlated series of each moon, the monthly changing characteristics of each fit parameter values as shown in figs 6-8, ADF unit root test is carried out to the monthly variation characteristic of each estimates of parameters, the assay that there are 2 estimates of parameters does not refuse the null hypothesis of an existence unit root, then yellow early warning is carried out to the overall lateral resistance behavior of long-span bridges, and closely follow the tracks of the ADF unit root test result of each estimates of parameters monthly changing characteristics.

Claims (3)

1. a method for early warning for the overall lateral resistance behavior exception of long-span bridges, it is characterized in that, the method comprises the steps:

Step (1): data acquisition is carried out to the dimensional wind at girder span centre position and GPS displacement effect:

At the girder span centre place of Loads of Long-span Bridges, three-D ultrasonic anemoscope and GPS displacement monitoring station are installed, to wind vector v (t) and motion vector u (t) carry out Real-Time Monitoring and store with time series, wherein v (t)=[v herein _r(t), α (t), β (t)], u (t)=[u _x(t), u _y(t), u _z(t)], v _rt (), α (t), β (t) are respectively true wind velocity, the wind angle of attack and wind angle, u _x(t), u _y(t), u _zt () is respectively three direction of principal axis displacements under gps coordinate system, t represents the time, t=1, and 2 ..., L, unit is second, and L represents time span;

Step (2): carry out resolution of vectors and average value processing to image data, obtains the quiet wind series of direction across bridge and Static Correction sequence:

Utilize following two formulas that time series v (t), u (t) are carried out resolution of vectors, obtain direction across bridge Wind Velocity History v _h(t) and displacement time-histories u _r(t):

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 formula, γ represents the angle of x-axis in gps coordinate system and girder longitudinal axis; After L is divided into n 10min time period, and utilize following formula to calculate corresponding v in each time period _h(t) and u _rt the mean value of (), obtains quiet wind series v _m(k) and Static Correction sequence u _m(k):

$v_m\left(k\right)=\left(\underset{t=600k-599}{\overset{t=600k}{\Σ}}{v}_h\right(t\left)\right)/600$

$u_m\left(k\right)=\left(\underset{t=600k-599}{\overset{t=600k}{\Σ}}{u}_r\right(t\left)\right)/600$

K=1 in formula, 2 ..., n;

Step (3): quiet wind series and Static Correction sequence were divided by month, and utilize wavelet packet cross-correlation coefficient to determine both main correlated serieses in same month:

1. divide by month quiet wind series and Static Correction sequence, with the division result in q month for analyzing example, by this month quiet air speed value by increasing progressively arrangement and being equidistantly divided into p section, the change in location wherein before and after quiet air speed value arrangement is designated as R (n ₁, n ₂), R (n ₁, n ₂) specifically represent n-th ₁individual quiet air speed value is positioned at n-th after arrangement ₂individual position; In addition according to R (n ₁, n ₂) queueing discipline the arrangement of this month Static Correction value is divided into p section equally;

2. for the quiet air speed value of s section and Static Correction value, carry out the 4th multi-scale wavelet bag to this section of quiet air speed value and Static Correction value to decompose, both all obtain, by 16 wavelet packet coefficients of site position arrangement, utilizing the reproducing sequence of following formula to same node position wavelet packet coefficient to carry out cross-correlation analysis one by one:

$r_{vu}\left(g\right)=\frac{\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}\left(\tilde{v}\left(g,n_g\right)-\stackrel{\‾}{v}\left(g\right)\right)\left(\tilde{u}\left(g,n_g\right)-\stackrel{\‾}{u}\left(g\right)\right)}{{\[\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}{\left(\tilde{v}\left(g,n_g\right)-\stackrel{\‾}{v}\left(g\right)\right)}^2\underset{n_g=1}{\overset{n_t\left(g\right)}{\Σ}}{\left(\tilde{u}\left(g,n_g\right)-\stackrel{\‾}{u}\left(g\right)\right)}^2\]}^{0.5}}$

In formula, r _vug () represents the cross-correlation coefficient between the quiet wind series of the reconstruct of g wavelet packet coefficient and Static Correction sequence, be respectively n-th of g wavelet packet coefficient _gthe quiet air speed value of individual reconstruct and Static Correction value, n _tg () is the total number of the quiet air speed value of reconstruct of g wavelet packet coefficient, be respectively the quiet wind series of reconstruct of g wavelet packet coefficient and the average of Static Correction sequence, g=1,2 ..., 16;

3. from 16 wavelet packet coefficients, weed out the wavelet packet coefficient that cross-correlation coefficient absolute value is less than 0.9 correspondence, afterwards the reproducing sequence superposition of residue wavelet packet coefficient is obtained to the reproducing sequence of the quiet air speed value of s section and Static Correction value, according to said method the reproducing sequence of the quiet air speed value of p section obtained and Static Correction value is reassembled into quiet wind speed and the Static Correction sequence in q month, in this, as quiet wind speed and the Static Correction main correlated series in this month, rear to each moon traversal obtain all months quiet wind speed and Static Correction between main correlated series;

Step (4): carry out Fourier series matching to main correlated series of each moon successively, utilizes the overall lateral resistance behavior of the monthly changing characteristics of fit parameter values to long-span bridges to carry out abnormity early warning:

Utilize the second-order Fourier gear progression shown in following formula, successively least square fitting carried out to main correlated series of each moon and determine each moon estimates of parameters:

$u_m\left(v_m\right)=\underset{e=0}{\overset{2}{\Σ}}\left(a_e\right(m\left)cos\right(e\·w\left(m\right)\·v_m\left)\right)+\underset{x=1}{\overset{2}{\Σ}}\left(b_x\right(m\left)sin\right(x\·w\left(m\right)\·v_m\left)\right)$

In formula, v _mrepresent the quiet air speed value in m month main correlated series, u _mrepresent the Static Correction value in m month main correlated series, a _e(m), b _xm () and w (m) are respectively the estimates of parameters of m month Fourier series; Carry out ADF unit root test to the monthly variation characteristic of each estimates of parameters respectively, if the assay of each estimates of parameters all refuses the null hypothesis of an existence unit root, then the overall lateral resistance behavior of long-span bridges is in shape; If the assay that there is 1 to 2 estimates of parameters does not refuse the null hypothesis of an existence unit root, then yellow early warning is carried out to the overall lateral resistance behavior of long-span bridges, and closely follow the tracks of the ADF unit root test result of each estimates of parameters monthly changing characteristics; If the assay that there are 3 to 4 estimates of parameters does not refuse the null hypothesis of an existence unit root, then orange early warning is carried out to the overall lateral resistance behavior of long-span bridges, and send bridge maintenance personnel to carry out Site Detection to bridge structure key member and position, take counter-measure according to testing result; If the assay that there are more than 4 estimates of parameters does not refuse the null hypothesis of an existence unit root, then red early warning is carried out to the overall lateral resistance behavior of long-span bridges, and send the overall lateral resistance behavior of bridge professional to bridge structure to carry out safety assessment and decision-making.

2. the method for early warning of the overall lateral resistance behavior exception of a kind of long-span bridges as claimed in claim 1, it is characterized in that, the time span L described in step (1) at least should be the number of seconds of 10 months, and should be the integral multiple of 600.

3. the method for early warning of the overall lateral resistance behavior exception of a kind of long-span bridges as claimed in claim 1, is characterized in that, the hop count p described in step (3) should between 8 ~ 11 sections, and every section of quiet air speed value is identical with the number of Static Correction value.