WO2020048090A1 - 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 PDF

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WO2020048090A1
WO2020048090A1 PCT/CN2019/073826 CN2019073826W WO2020048090A1 WO 2020048090 A1 WO2020048090 A1 WO 2020048090A1 CN 2019073826 W CN2019073826 W CN 2019073826W WO 2020048090 A1 WO2020048090 A1 WO 2020048090A1
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bridge
imf
response
res
influence line
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PCT/CN2019/073826
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伊廷华
杨东辉
郑旭
李宏男
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大连理工大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0008—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of bridges

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  • the invention belongs to the technical field of structural safety detection, and particularly relates to a method for identifying a bridge influence line capable of eliminating vehicle dynamic effects.
  • a bridge influence line is a response curve of a specific position of the bridge when a unit load passes through the bridge.
  • the influence line contains a large amount of structural information.
  • the influence line can directly reflect the stiffness and compliance information of the bridge.
  • the influence line also contains the position information of the bridge, and the damage can be located through the change of the influence line.
  • Railway bridges are particularly suitable for identifying their influence lines due to the single load trajectory.
  • the dynamic effects caused by high-speed trains passing bridges can cause large deviations in bridge responses compared with the quasi-static bridge responses when low-speed trains cross bridges. A difficult question.
  • OBerin For the quasi-static loading of low-speed loading trains across the bridge, there have been many research results on the method of line identification.
  • OBerin first proposed a matrix method to identify influence lines from measured data. This method establishes a matrix related to train information and identifies the influence line of the bridge by inverting the matrix.
  • Sio-Song Leng uses the maximum likelihood estimation method to derive an influence line identification formula. This method can consider the effects of multiple loaded vehicles at the same time, thereby giving a more accurate influence line estimation.
  • Chen first adopted a regularization method to eliminate the fluctuations of the influence lines that are not consistent with the physical meaning. It is recognized that the bridge response can be expressed as a convolution of the influence line and the train information function, and the influence line is identified by a fast Fourier variation deconvolution method.
  • the object of the present invention is a bridge influence line recognition method capable of eliminating vehicle dynamic effects.
  • a bridge influence line identification method capable of excluding vehicle dynamic effects the steps are as follows:
  • Step 1. Use empirical mode decomposition to preprocess bridge response information measured by sensors
  • H (t) When H (t) satisfies the conditions of the eigenmode function, H (t) becomes an imf (t); otherwise, replace R (t) in step (1) with H (t) and remove from step ( 1) Start repeating the above process; an eigenmode function must satisfy 2 conditions: 1) the number of local extreme points and zero-crossing points is equal, or at most one difference, over the entire time range; 2) at any time point The average value of the envelope of the local maximum and the envelope of the local minimum must be 0;
  • Step 2 Use quasi-static method to identify the influence line of the bridge response after pretreatment
  • the dynamic response of the bridge is transformed into a bridge response under quasi-static conditions, which are identified using a classic quasi-static influence line identification model.
  • the bridge influence line identification method can identify the bridge structure response when a high-speed train is loaded, and provides a theoretical basis for the identification of influence lines of high-speed railway bridges;
  • the method for identifying the influence line of a bridge of the present invention has a strict theoretical basis. Based on the synchronous collection of bridge load and bridge response information, combined with an advanced optimization identification algorithm, it can ensure that the influence line identified by the system has high accuracy;
  • the method for identifying the influence line of a bridge of the present invention is simple to use, and does not need to predict the structure information of the bridge, and can directly process the collected bridge dynamic response data to obtain the influence line.
  • FIG. 1 is a flowchart of an algorithm used in the present invention
  • FIG. 2 is a vibration model of a loaded vehicle crossing a bridge simulated in an embodiment of the method of the present invention
  • 3 is a bridge dynamic response caused by a loading vehicle in an embodiment of the method of the present invention.
  • the influence line recognition method of the present invention is divided into two steps: "pre-processing bridge response information measured by sensors using empirical mode decomposition” and "recognizing bridge response after pre-processing using a quasi-static method”. It has been given, and the following uses a calculation example to explain the use method and characteristics of the invention.
  • a differential equation of motion of a four-degree-of-freedom vehicle and a simply supported beam with a distributed mass system is established.
  • the time history of the mid-span response of the simply supported beam is obtained by solving ordinary differential equations, as shown in Figure 3.
  • the bridge deflection response when a high-speed train crosses a bridge can be divided into two parts: quasi-static response and dynamic superposition term.
  • the two parts can be decomposed by empirical mode decomposition. The decomposition results are shown in Figure 4.
  • the quasi-static influence line identification method can be used to identify the influence lines.
  • the comparison between the identified influence line and the real influence line is shown in Figure 5. It can be seen that the identified influence line is basically consistent with the real value.
  • This method is an effective method for identifying bridge influence lines during dynamic loading.

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Abstract

A bridge influence line identification method capable of eliminating a vehicle power effect, comprising: (1) preprocessing bridge response data measured by a sensor by using an empirical mode decomposition method; (2) identifying the preprocessed bridge response by using a quasi-static method. By preprocessing the bridge response using the empirical mode decomposition method, a dynamic overlay item of the bridge response can be eliminated to obtain a quasi-static response of a bridge, thereby being used for influence line identification. Moreover, the method can process structural response data measured by the sensor on various bridge types, without knowing any information on bridge structures in advance, and thus has a good engineering application prospect.

Description

一种能够剔除车辆动力效应的桥梁影响线识别方法Bridge influence line recognition method capable of eliminating vehicle dynamic effects 技术领域Technical field
本发明属于结构安全性检测技术领域，具体涉及一种能够剔除车辆动力效应的桥梁影响线识别方法。The invention belongs to the technical field of structural safety detection, and particularly relates to a method for identifying a bridge influence line capable of eliminating vehicle dynamic effects.
技术背景technical background
近些年来，随着高速铁路技术的不断发展和人们对出行效率要求的提升，世界上已建成的高速铁路总里程数前所未有的提高了。由于高速铁路的绝大多数运营里程都在高架桥上，高铁桥梁的安全状况也越来越受到人们的关注。高铁桥梁由于不断的承受高速列车的动力加载，其受到的荷载效应要比普通的公路桥梁更加复杂，如何实时的监测高铁桥梁的服役性能成为了一个关乎乘客生命财产安全的重大问题。In recent years, with the continuous development of high-speed railway technology and the improvement of people's requirements for travel efficiency, the total mileage of high-speed railways built in the world has increased unprecedentedly. Since most of the operating mileage of high-speed railways are on viaducts, the safety status of high-speed rail bridges has also received increasing attention. Because high-speed rail bridges are constantly subjected to the high-speed train's dynamic load, the load effects are more complicated than ordinary highway bridges. How to monitor the service performance of high-speed rail bridges in real time has become a major issue related to the safety of passengers' lives and property.
同时值得注意的是，近些年来出现了一种基于影响线变化的损伤识别方法。桥梁影响线是单位荷载通过桥梁时桥梁某一特定位置的响应曲线，作为桥梁的一种静力特征，影响线包含了大量的结构信息。作为一个结构损伤的指标，影响线可以直接反应桥梁的刚度和柔度信息。除此之外，影响线还包含了桥梁的位置信息，可以通过影响线的变化对损伤进行定位。铁路桥梁由于荷载运行轨迹单一，特别适合对其影响线进行识别。但高速列车过桥时造成的动力效应会使桥梁响应和低速列车过桥时的准静态桥梁响应相比产生较大的偏差，如何从动力效应中识别得到影响线成为了高速铁路桥梁影响线识别的一个难点问题。It is also worth noting that in recent years, a damage identification method based on changes in influence lines has emerged. A bridge influence line is a response curve of a specific position of the bridge when a unit load passes through the bridge. As a static feature of the bridge, the influence line contains a large amount of structural information. As an indicator of structural damage, the influence line can directly reflect the stiffness and compliance information of the bridge. In addition, the influence line also contains the position information of the bridge, and the damage can be located through the change of the influence line. Railway bridges are particularly suitable for identifying their influence lines due to the single load trajectory. However, the dynamic effects caused by high-speed trains passing bridges can cause large deviations in bridge responses compared with the quasi-static bridge responses when low-speed trains cross bridges. A difficult question.
针对低速加载列车过桥的准静态加载情况，影响线识别的方法已经有了很多研究成果。OBerin最先提出了一种矩阵法来从实测数据中识别影响线。这种方法建立了一种和列车信息相关的矩阵，通过对矩阵求逆来识别桥梁影响线。Sio-Song Leng采用极大似然估计方法推导出了一种影响线识别公式，这种方法 可以同时考虑多个加载车辆的作用，从而给出了一种更加准确的影响线估计。Chen首先采用了正则化方法对识别得到的影响线不符合物理意义的波动进行了消除。
Figure PCTCN2019073826-appb-000001
认识到桥梁响应可以表示成影响线和列车信息函数的卷积，通过快速傅立叶变化解卷积的方法识别影响线。这种方法能够大大提高影响线识别的效率，并且在识别精度上也有一定的保障。在考虑动力效应的情况下，王宁波等人通过多项式和正弦函数分别对静力效应和动力效应进行了拟合，识别得到了某些特定桥型的影响线。而对于其他更普遍的情况，有待于进一步的研究。 For the quasi-static loading of low-speed loading trains across the bridge, there have been many research results on the method of line identification. OBerin first proposed a matrix method to identify influence lines from measured data. This method establishes a matrix related to train information and identifies the influence line of the bridge by inverting the matrix. Sio-Song Leng uses the maximum likelihood estimation method to derive an influence line identification formula. This method can consider the effects of multiple loaded vehicles at the same time, thereby giving a more accurate influence line estimation. Chen first adopted a regularization method to eliminate the fluctuations of the influence lines that are not consistent with the physical meaning.
Figure PCTCN2019073826-appb-000001
It is recognized that the bridge response can be expressed as a convolution of the influence line and the train information function, and the influence line is identified by a fast Fourier variation deconvolution method. This method can greatly improve the efficiency of influence line recognition, and also has a certain guarantee in recognition accuracy. In consideration of dynamic effects, Wang Ningbo et al. Fitted the static effects and dynamic effects with polynomials and sine functions, respectively, and identified the influence lines of some specific bridge types. For other more general situations, further research is needed.
发明内容Summary of the Invention
本发明的目的是一种能够剔除车辆动力效应的桥梁影响线识别方法。The object of the present invention is a bridge influence line recognition method capable of eliminating vehicle dynamic effects.
本发明的技术方案：Technical solution of the present invention:
一种能够剔除车辆动力效应的桥梁影响线识别方法，步骤如下：A bridge influence line identification method capable of excluding vehicle dynamic effects, the steps are as follows:
步骤1、采用经验模式分解对传感器测得的桥梁响应信息进行预处理Step 1.Use empirical mode decomposition to preprocess bridge response information measured by sensors
(1)找出桥梁响应信号R(t)的局部极大值和局部极小值；(1) Find the local maximum and local minimum of the bridge response signal R (t);
(2)利用三次样条插值方法分别计算极小值插值和极大值插值函数，并得到对应信号包络e min(t)和e max(t)； (2) Use the cubic spline interpolation method to calculate the minimum value interpolation function and the maximum value interpolation function, respectively, and obtain the corresponding signal envelopes e min (t) and e max (t);
(3)计算信号极大值与极小值包络的局部均值M(t)＝(e min(t)+e max(t))/2。 (3) Calculate the local mean of the signal maximum and minimum envelope M (t) = (e min (t) + e max (t)) / 2.
(4)将原始输入信号减去局部均值得到震荡信号H(t)＝R(t)-M(t)；(4) Subtract the local mean value from the original input signal to obtain the oscillating signal H (t) = R (t) -M (t);
(5)当H(t)满足本征模函数的条件，H(t)变成一个imf(t)；否则，用H(t)替换步骤(1)中的R(t)并从步骤(1)开始重复上述过程；一个本征模函数必须满足2个条件：1)函数在整个时间范围内，局部极值点和过零点的数目相等，或最多差一个；2)在任一时间点上，局部最大值的包络线和局部最小值的包络线平均 值必须为0；(5) When H (t) satisfies the conditions of the eigenmode function, H (t) becomes an imf (t); otherwise, replace R (t) in step (1) with H (t) and remove from step ( 1) Start repeating the above process; an eigenmode function must satisfy 2 conditions: 1) the number of local extreme points and zero-crossing points is equal, or at most one difference, over the entire time range; 2) at any time point The average value of the envelope of the local maximum and the envelope of the local minimum must be 0;
(6)令imf 1(t)＝H(t)，则imf 1(t)为第一个imf(t)，对应的余量Res 1(t)＝R(t)-imf 1(t)，将Res 1(t)作为新的信号重复上述所有步骤，得到第二个imf(t)分量，以此类推，得到Res 1(t)-imf 2(t)＝Res 2(t)，…Res n-1(t)-imf n(t)＝Res n(t)；当本征模函数无法再继续提取时，停止筛分过程； (6) Let imf 1 (t) = H (t), then imf 1 (t) is the first imf (t), and the corresponding margin Res 1 (t) = R (t)-imf 1 (t) , Repeat all the above steps with Res 1 (t) as a new signal to get the second imf (t) component, and so on to get Res 1 (t)-imf 2 (t) = Res 2 (t), ... Res n-1 (t) -imf n (t) = Res n (t); when the eigenmode function cannot continue to extract, stop the screening process;
(7)经过上面的分解过程，R(t)最终被分解为n个imf(t)分量和一个余量Res n(t)；原始信号R(t)表示为
Figure PCTCN2019073826-appb-000002
经过处理后的余量Res n(t)就是桥梁准静态响应； (7) After the above decomposition process, R (t) is finally decomposed into n imf (t) components and a margin Res n (t); the original signal R (t) is expressed as
Figure PCTCN2019073826-appb-000002
The processed residual Res n (t) is the bridge quasi-static response;
步骤2、对经过预处理后的桥梁响应采用准静态方法进行影响线识别Step 2: Use quasi-static method to identify the influence line of the bridge response after pretreatment
经过经验模式分解的预处理后，桥梁动态响应转化成准静态情况下的桥梁响应，采用经典的准静态影响线识别模型进行识别。After the pre-processing of empirical mode decomposition, the dynamic response of the bridge is transformed into a bridge response under quasi-static conditions, which are identified using a classic quasi-static influence line identification model.
本发明的有益效果：The beneficial effects of the present invention:
(1)本发明的桥梁影响线识别方法和原有的影响线识别方法相比，可以对高速列车加载时的桥梁结构响应进行识别，为高铁桥梁影响线识别提供了理论基础；(1) Compared with the original influence line identification method of the present invention, the bridge influence line identification method can identify the bridge structure response when a high-speed train is loaded, and provides a theoretical basis for the identification of influence lines of high-speed railway bridges;
(2)本发明的桥梁影响线识别方法具备严格的理论基础，基于桥梁荷载和桥梁响应的同步采集信息，并结合先进优化识别算法，可保证通过该系统识别的影响线具有较高精度；(2) The method for identifying the influence line of a bridge of the present invention has a strict theoretical basis. Based on the synchronous collection of bridge load and bridge response information, combined with an advanced optimization identification algorithm, it can ensure that the influence line identified by the system has high accuracy;
(3)本发明的桥梁影响线识别方法使用简单，无需预知桥梁结构信息，可以对采集得到的桥梁动力响应数据进行直接处理，从而得到影响线。(3) The method for identifying the influence line of a bridge of the present invention is simple to use, and does not need to predict the structure information of the bridge, and can directly process the collected bridge dynamic response data to obtain the influence line.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明所采用算法的实现流程图；FIG. 1 is a flowchart of an algorithm used in the present invention;
图2为本发明方法实施例中模拟的加载车辆过桥的振动模型；2 is a vibration model of a loaded vehicle crossing a bridge simulated in an embodiment of the method of the present invention;
图3为本发明方法实施例中加载车辆引起的桥梁动态响应；3 is a bridge dynamic response caused by a loading vehicle in an embodiment of the method of the present invention;
图4为本发明方法实施例中通过经验模式分解处理后得到的准静态响应；4 is a quasi-static response obtained after empirical mode decomposition processing in an embodiment of the method of the present invention;
图5为本发明方法实施例中对处理后准静态响应进行识别得到的影响线；5 is an influence line obtained by identifying a quasi-static response after processing in an embodiment of the method of the present invention;
具体实施方式detailed description
下面结合附图和一个数值算例来对本发明做进一步的详细说明。The present invention will be further described in detail below with reference to the drawings and a numerical example.
本发明的影响线识别方法分“采用经验模式分解对传感器测得的桥梁响应信息进行预处理”和“对经过预处理后的桥梁响应采用准静态方法进行识别”两步，具体实施方式上文已经给出，接下来结合一个算例说明发明的使用方法和特点。The influence line recognition method of the present invention is divided into two steps: "pre-processing bridge response information measured by sensors using empirical mode decomposition" and "recognizing bridge response after pre-processing using a quasi-static method". It has been given, and the following uses a calculation example to explain the use method and characteristics of the invention.
实施算：72km/h双轴加载车辆过桥影响线识别Implementation calculation: 72km / h dual-axle-loaded vehicle crossing bridge influence line identification
在本数值算例中，我们模拟一个速度为72km/h的双轴车辆通过简支梁，通过建立车桥耦合振动模型来分析跨中的挠度响应。双轴车辆的轴距为4m，简支梁长度为16m。将车体简化成三个质量块，质量分别是：m 1＝524kg；m 2＝297kg；m 3＝6451kg。模型的具体情况见图2。 In this numerical example, we simulate a two-axle vehicle with a speed of 72km / h through a simply supported beam and establish a vehicle-bridge coupling vibration model to analyze the mid-span deflection response. The wheelbase of the two-axle vehicle is 4m, and the length of the simply supported beam is 16m. Simplify the car body into three masses, the masses are: m 1 = 524 kg; m 2 = 297 kg; m 3 = 6451 kg. The specific situation of the model is shown in Figure 2.
在本算例中，建立了四自由度车辆和分布质量体系简支梁的运动微分方程，通过求解常微分方程组获取了简支梁跨中响应的时程，如图3所示。高速列车过桥时的桥梁挠度响应可以分成准静态响应和动力叠加项两部分，通过经验模式分解可以对两部分进行分解，分解结果见图4。In this example, a differential equation of motion of a four-degree-of-freedom vehicle and a simply supported beam with a distributed mass system is established. The time history of the mid-span response of the simply supported beam is obtained by solving ordinary differential equations, as shown in Figure 3. The bridge deflection response when a high-speed train crosses a bridge can be divided into two parts: quasi-static response and dynamic superposition term. The two parts can be decomposed by empirical mode decomposition. The decomposition results are shown in Figure 4.
对动态效应剔除后，可以用准静态的影响线识别方法对影响线进行识别，在本算例中，我们采用的是正则化LSQR迭代法。识别得到的影响线与真实影响线对比见图5。可以看出，识别得到的影响线与真实值基本吻合。本方法是识别动态加载时桥梁影响线的一种有效方法。After removing the dynamic effects, the quasi-static influence line identification method can be used to identify the influence lines. In this example, we use the regularized LSQR iterative method. The comparison between the identified influence line and the real influence line is shown in Figure 5. It can be seen that the identified influence line is basically consistent with the real value. This method is an effective method for identifying bridge influence lines during dynamic loading.

Claims (1)

  1. 一种能够剔除车辆动力效应的桥梁影响线识别方法，其特征在于，步骤如下：A method for identifying a bridge influence line capable of excluding vehicle dynamic effects is characterized in that the steps are as follows:
    步骤1、采用经验模式分解对传感器测得的桥梁响应信息进行预处理Step 1.Use empirical mode decomposition to preprocess bridge response information measured by sensors
    (1)找出桥梁响应信号R(t)的局部极大值和局部极小值；(1) Find the local maximum and local minimum of the bridge response signal R (t);
    (2)利用三次样条插值方法分别计算极小值插值和极大值插值函数，并得到对应信号包络e min(t)和e max(t)； (2) Use the cubic spline interpolation method to calculate the minimum value interpolation function and the maximum value interpolation function, respectively, and obtain the corresponding signal envelopes e min (t) and e max (t);
    (3)计算信号极大值与极小值包络的局部均值M(t)＝(e min(t)+e max(t))/2； (3) Calculate the local mean of the maximum and minimum envelopes of the signal M (t) = (e min (t) + e max (t)) / 2;
    (4)将原始输入信号减去局部均值得到震荡信号H(t)＝R(t)-M(t)；(4) Subtract the local mean value from the original input signal to obtain the oscillating signal H (t) = R (t) -M (t);
    (5)当H(t)满足本征模函数的条件，H(t)变成一个imf(t)；否则，用H(t)替换步骤(1)中的R(t)并从步骤(1)开始重复上述过程；一个本征模函数必须满足2个条件：1)函数在整个时间范围内，局部极值点和过零点的数目相等，或最多差一个；2)在任一时间点上，局部最大值的包络线和局部最小值的包络线平均值必须为0；(5) When H (t) satisfies the conditions of the eigenmode function, H (t) becomes an imf (t); otherwise, replace R (t) in step (1) with H (t) and remove from step ( 1) Start repeating the above process; an eigenmode function must satisfy 2 conditions: 1) the number of local extreme points and zero-crossing points is equal, or at most one difference, over the entire time range; 2) at any time point The average value of the envelope of the local maximum and the envelope of the local minimum must be 0;
    (6)令imf 1(t)＝H(t)，则imf 1(t)为第一个imf(t)，对应的余量Res 1(t)＝R(t)-imf 1(t)，将Res 1(t)作为新的信号重复上述所有步骤，得到第二个imf(t)分量，以此类推，得到Res 1(t)-imf 2(t)＝Res 2(t)，…Res n-1(t)-imf n(t)＝Res n(t)；当本征模函数无法再继续提取时，停止筛分过程； (6) Let imf 1 (t) = H (t), then imf 1 (t) is the first imf (t), and the corresponding margin Res 1 (t) = R (t)-imf 1 (t) , Repeat all the above steps with Res 1 (t) as a new signal to get the second imf (t) component, and so on to get Res 1 (t)-imf 2 (t) = Res 2 (t), ... Res n-1 (t) -imf n (t) = Res n (t); when the eigenmode function cannot continue to extract, stop the screening process;
    (7)经过上面的分解过程，R(t)最终被分解为n个imf(t)分量和一个余量Res n(t)；原始信号R(t)表示为
    Figure PCTCN2019073826-appb-100001
    经过处理后的余量Res n(t)就是桥梁准静态响应；     (7) After the above decomposition process, R (t) is finally decomposed into n imf (t) components and a margin Res n (t); the original signal R (t) is expressed as
    Figure PCTCN2019073826-appb-100001
    The processed residual Res n (t) is the bridge quasi-static response;
    步骤2、对经过预处理后的桥梁响应采用准静态方法进行影响线识别Step 2: Use quasi-static method to identify the influence line of the bridge response after pretreatment
    经过经验模式分解的预处理后，桥梁动态响应转化成准静态情况下的桥梁 响应，采用经典的准静态影响线识别模型进行识别。After the pre-processing of empirical mode decomposition, the dynamic response of the bridge is transformed into a bridge response in a quasi-static situation, which is identified using a classic quasi-static influence line identification model.
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