CN109341989B - Bridge influence line identification method capable of eliminating vehicle power effect - Google Patents
Bridge influence line identification method capable of eliminating vehicle power effect Download PDFInfo
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Abstract
The invention belongs to the technical field of structural safety detection, and discloses a bridge influence line identification method capable of eliminating vehicle dynamic effect, which comprises the following steps: (1) preprocessing bridge response data measured by a sensor by adopting empirical mode decomposition; (2) and identifying the preprocessed bridge response by adopting a quasi-static method. The dynamic superposition items of the bridge response can be removed by adopting the empirical mode decomposition to preprocess the bridge response, so that the quasi-static response of the bridge is obtained, and the method can be further used for influence line identification. Meanwhile, the method does not need to know any bridge structure information in advance, can process the structural response data measured by the sensors on various bridge types, and has good engineering application prospect.
Description
Technical Field
The invention belongs to the technical field of structural safety detection, and particularly relates to a bridge influence line identification method capable of eliminating a vehicle dynamic effect.
Technical Field
In recent years, with the continuous development of high-speed railway technology and the improvement of the requirement of people on travel efficiency, the total mileage of the built high-speed railway in the world is improved unprecedentedly. As most of the operating mileage of the high-speed railway is on the viaduct, the safety condition of the high-speed railway bridge is more and more concerned by people. The high-speed train bridge bears the power loading of the high-speed train continuously, the load effect of the high-speed train bridge is more complex than that of a common highway bridge, and how to monitor the service performance of the high-speed train bridge in real time becomes a great problem about the life and property safety of passengers.
It is also worth noting that in recent years a method of damage identification based on influence line changes has emerged. The bridge influence line is a response curve of a specific position of a bridge when unit load passes through the bridge, and is used as a static characteristic of the bridge, and the influence line contains a large amount of structural information. As an index of structural damage, the influence line can directly reflect the rigidity and flexibility information of the bridge. In addition, the influence line also contains the position information of the bridge, and the damage can be positioned through the change of the influence line. The railway bridge is particularly suitable for identifying the influence line of the railway bridge due to the single load running track. However, the dynamic effect caused when a high-speed train passes a bridge can cause the bridge response to generate larger deviation compared with the quasi-static bridge response when a low-speed train passes the bridge, and how to identify the influence line from the dynamic effect becomes a difficult problem of identifying the influence line of the high-speed railway bridge.
Aiming at the quasi-static loading condition of the low-speed loading train bridge crossing, the method for identifying the influence line has a plurality of research achievements. OBerin first proposed a matrix method to identify the influence lines from the measured data. The method establishes a matrix related to train information, and the bridge influence line is identified by inverting the matrix. The Sio-Song Long deduces an influence line identification formula by adopting a maximum likelihood estimation method, and the method can simultaneously consider the effects of a plurality of loading vehicles, thereby providing more accurate influence line estimation. Chen firstly adopts a regularization method to eliminate the fluctuation of the influence lines which are obtained by identification and do not accord with the physical meaning.
Recognizing that the bridge response can be represented as a convolution of the influence line and the train information function, the influence line is identified by a fast fourier transform deconvolution method. The method can greatly improve the efficiency of the influence line identification and has certain guarantee on the identification precision. Under the condition of considering dynamic effect, Wangningbo et al respectively fit the static effect and the dynamic effect by a polynomial and a sine function, and identify and obtain influence lines of certain specific bridge types. But for other more general cases, further research is awaited.
Disclosure of Invention
The invention aims to provide a bridge influence line identification method capable of eliminating a vehicle power effect.
The technical scheme of the invention is as follows:
a bridge influence line identification method capable of eliminating vehicle power effects comprises the following steps:
step 1, preprocessing bridge response information measured by a sensor by adopting empirical mode decomposition
(1) Finding out local maximum and local minimum of the bridge response signal R (t);
(2) respectively calculating a minimum value interpolation function and a maximum value interpolation function by utilizing a cubic spline interpolation method, and obtaining a corresponding signal envelope emin(t) and emax(t)；
(3) Calculating a local mean m (t) of the maximum and minimum envelopes of the signalmin(t)+emax(t))/2。
(4) Subtracting the local mean value from the bridge response signal to obtain an oscillation signal H (t) ═ R (t) — M (t);
(5) when H (t) satisfies the condition of the eigenmode function, H (t) becomes imf (t); otherwise, replacing R (t) in the step (1) with H (t) and repeating the process from the step (1); an eigenmode function must satisfy 2 conditions: 1) in the whole time range of the function, the number of local extreme points and zero-crossing points is equal or at most one difference; 2) at any point in time, the average of the envelope of the local maxima and the envelope of the local minima must be 0;
(6) let imf1(t) H (t), then imf1(t) is the first imf (t), corresponding to the margin Res1(t)＝R(t)-imf1(t), mixing Res1(t) repeating all the steps as a new signal to obtain a second imf (t) component, and so on to obtain Res1(t)-imf2(t)＝Res2(t)，…Resn-1(t)-imfn(t)＝Resn(t); wherein, imfi(t) is the ith imf (t) component, Resi(t) is the ith residue; stopping the screening process when the eigenmode function can not be extracted any more;
(7) through the above decomposition process, R (t) is finally decomposed into n imf (t) components and a margin Resn(t); the original signal R (t) is represented as
The processed residual Resn(t) is the bridge quasi-static response;
After pretreatment of empirical mode decomposition, the dynamic response of the bridge is converted into the response of the bridge under the quasi-static condition, and a classical quasi-static influence line identification model is adopted for identification.
The invention has the beneficial effects that:
(1) compared with the original influence line identification method, the bridge influence line identification method can identify the bridge structure response when the high-speed train is loaded, and provides a theoretical basis for identifying the high-speed train influence line;
(2) the bridge influence line identification method has strict theoretical basis, synchronously acquires information based on bridge load and bridge response, and combines an advanced optimization identification algorithm to ensure that the influence line identified by the system has higher precision;
(3) the bridge influence line identification method is simple to use, bridge structure information does not need to be known in advance, and collected bridge dynamic response data can be directly processed, so that the influence line is obtained.
Drawings
FIG. 1 is a flow chart of an implementation of an algorithm employed in the present invention;
FIG. 2 is a simulated vibration model of a loaded vehicle bridge according to an embodiment of the method of the present invention;
FIG. 3 illustrates bridge dynamic responses resulting from loading a vehicle in an embodiment of a method of the present invention;
FIG. 4 is a diagram illustrating a quasi-static response obtained after empirical mode decomposition processing in an embodiment of the method of the present invention;
FIG. 5 is a diagram illustrating influence lines obtained by identifying processed quasi-static responses in an embodiment of the method of the present invention;
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and a numerical example.
The influence line identification method comprises two steps of preprocessing bridge response information measured by a sensor by adopting empirical mode decomposition and identifying the bridge response after preprocessing by adopting a quasi-static method, the specific implementation modes are given above, and the using method and the characteristics of the invention are explained by combining an example.
Carrying out calculation: 72km/h double-shaft loading vehicle bridge crossing influence line identification
In the numerical example, a biaxial vehicle with the speed of 72km/h is simulated to pass through a simple beam, and the deflection response of a midspan is analyzed by establishing an axle coupling vibration model. The wheelbase of the double-axle vehicle is 4m, and the length of the simply supported beam is 16 m. Simplify the automobile body into three quality pieces, the quality is respectively: m is1＝524kg；m2＝297kg；m36451 kg. The details of the model are shown in FIG. 2.
In the present embodiment, a differential equation of motion of the four-degree-of-freedom vehicle and the simply supported beam of the distributed mass system is established, and a time course of midspan response of the simply supported beam is obtained by solving an ordinary differential equation set, as shown in fig. 3. The bridge deflection response of the high-speed train passing the bridge can be divided into a quasi-static response part and a power superposition part, the two parts can be decomposed through empirical mode decomposition, and the decomposition result is shown in figure 4.
After the dynamic effect is eliminated, the influence lines can be identified by a quasi-static influence line identification method, and in the present embodiment, a regularization LSQR iteration method is adopted. The identified influence lines are compared with the real influence lines in figure 5. It can be seen that the identified influence lines substantially coincide with the true values. The method is an effective method for identifying the bridge influence line in dynamic loading.
Claims (1)
1. A bridge influence line identification method capable of eliminating vehicle power effects is characterized by comprising the following steps:
step 1, preprocessing bridge response information measured by a sensor by adopting empirical mode decomposition
(1) Finding out local maximum and local minimum of the bridge response signal R (t);
(2) respectively calculating a minimum value interpolation function and a maximum value interpolation function by utilizing a cubic spline interpolation method, and obtaining a corresponding signal envelope emin(t) and emax(t)；
(3) Calculating a local mean m (t) of the maximum and minimum envelopes of the signalmin(t)+emax(t))/2；
(4) Subtracting the local mean value from the bridge response signal to obtain an oscillation signal H (t) ═ R (t) — M (t);
(5) when H (t) satisfies the condition of the eigenmode function, H (t) becomes imf (t); otherwise, replacing R (t) in the step (1) with H (t) and repeating the process from the step (1); an eigenmode function must satisfy 2 conditions: 1) in the whole time range of the function, the number of local extreme points and zero-crossing points is equal or at most one difference; 2) at any point in time, the average of the envelope of the local maxima and the envelope of the local minima must be 0;
(6) let imf1(t) H (t), then imf1(t) is the first imf (t), corresponding to the margin Res1(t)＝R(t)-imf1(t), mixing Res1(t) repeating all the steps as a new signal to obtain a second imf (t) component, and so on to obtain Res1(t)-imf2(t)＝Res2(t)，…Resn-1(t)-imfn(t)＝Resn(t); wherein, imfi(t) is the ith imf (t) component, Resi(t) is the ith residue; stopping the screening process when the eigenmode function can not be extracted any more;
(7) through the above decomposition process, R (t) is finally decomposed into n imf (t) components and a margin Resn(t); the original signal R (t) is represented as
The processed residual Resn(t) is the bridge quasi-static response;
step 2, adopting a quasi-static method to carry out influence line identification on the preprocessed bridge response
After pretreatment of empirical mode decomposition, the dynamic response of the bridge is converted into the response of the bridge under the quasi-static condition, and a classical quasi-static influence line identification model is adopted for identification.
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| CN112485030B (en) * | 2020-11-09 | 2023-03-14 | 深圳市桥博设计研究院有限公司 | Bridge structure dynamic monitoring method, system and equipment based on frequency coupling |
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| CN101532917B (en) * | 2009-04-15 | 2011-08-03 | 江苏省交通科学研究院股份有限公司 | Quick load test method for bridge carrying capacity |
| CN102509005B (en) * | 2011-10-27 | 2015-02-25 | 招商局重庆交通科研设计院有限公司 | Bridge bearing capacity evaluation method based on field tested influence line |
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