CN110688963A - Clustering algorithm-based large-span bridge vortex-induced vibration automatic identification method - Google Patents

Clustering algorithm-based large-span bridge vortex-induced vibration automatic identification method Download PDF

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CN110688963A
CN110688963A CN201910939086.7A CN201910939086A CN110688963A CN 110688963 A CN110688963 A CN 110688963A CN 201910939086 A CN201910939086 A CN 201910939086A CN 110688963 A CN110688963 A CN 110688963A
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vortex
induced vibration
vibration
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黎善武
赖马树金
李惠
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Harbin Institute of Technology
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Abstract

The invention discloses a method for identifying vortex-induced vibration of a large-span bridge based on a clustering algorithm. Aiming at the problem of identification of the vortex-induced vibration of the large-span bridge, the invention constructs a bridge vortex-induced vibration identification feature space and realizes the automatic processing of the whole process of feature extraction, vortex-induced vibration sample identification, vortex-induced vibration event identification and result display of the vibration of the prototype bridge under the excitation of a complex environment. The method improves the automation, the intellectualization, the accuracy and the robustness of the identification of the vortex-induced vibration of the large-span bridge, and provides a solution for the automatic identification of the vortex-induced vibration of the civil engineering bridge.

Description

Clustering algorithm-based large-span bridge vortex-induced vibration automatic identification method
Technical Field
The invention relates to the field of bridge wind engineering, in particular to a clustering algorithm-based method for automatically identifying vortex-induced vibration of a large-span bridge.
Background
With the rapid development of national economy and traffic in China, a large-span bridge becomes the development trend of bridge engineering. More and more large-span bridges play a crucial role in traffic. The increase of the span of the bridge enables the flexibility of the structure to be increased and the natural frequency to be reduced, so that the structure is more sensitive to wind, the wind speed induced by vortex-induced vibration is reduced, and finally the vortex-induced vibration of the large-span bridge is frequent. Since divergent vibration such as flutter is absolutely avoided in the bridge design stage, vortex-induced vibration almost becomes the largest wind-induced vibration mode of modern large-span bridges. The large-amplitude vibration not only causes fatigue damage to the bridge structure, but also can threaten driving safety. Therefore, research on the vortex-induced vibration of the large-span bridge is sufficiently necessary and urgent.
With the rapid development of the structural health monitoring system, prototype monitoring taking full-scale structure and real environment as advantages has become one of the important research methods for wind-induced vibration of large-span bridges. The girder vibration monitoring module integrated in the structural health monitoring system accumulates massive girder vibration data through long-term online monitoring, wherein the mass girder vibration data comprise various vibration modes such as vehicle-induced vibration, wind-induced buffeting and wind-induced vortex-induced vibration. In order to research vortex-induced vibration, the automatic, efficient and accurate identification and extraction of bridge vortex-induced vibration from mass vibration data become the primary task of research.
The traditional prototype bridge vortex-induced vibration identification method basically stays at a full-manual stage: checking the amplitude of the vibration signal in a segmented manner according to time; selecting a sample with larger amplitude according to experience to further analyze the frequency characteristic of the vibration signal; samples with large amplitudes and relatively single frequency components are empirically identified as vortex-induced vibrations. Obviously, the traditional method has two defects: (1) a large amount of time is required for processing mass data; (2) the identification process lacks powerful basis, and the identification result is too dependent on personal experience and is unstable and unreliable.
Disclosure of Invention
Based on the defects, the invention provides the automatic identification method of the vortex-induced vibration of the large-span bridge based on the clustering algorithm, which can be used for first off-line identification and second on-line identification, and improves the efficiency of the identification of the vortex-induced vibration of the bridge and the stability and reliability of the identification result.
The technical steps adopted by the invention are as follows: a method for automatically identifying vortex-induced vibration of a large-span bridge based on a clustering algorithm comprises the following steps:
step one, calculating characteristics of a girder vibration sample: dividing a long-term monitored main beam vibration acceleration time-course signal into a plurality of samples in a period of time by taking the period of time as a basic time interval; calculating the acceleration root mean square value of each sample
Figure BDA0002222390560000011
As a characteristic of the amplitude of the girder; calculating an acceleration power spectrum of each sample, picking up the first two large peak values in the power spectrum, and taking the ratio R of the second large peak value to the first large peak value as the characteristic for representing the single degree of the vibration frequency; finally, each vibration sample is composed of the acceleration root mean square value
Figure BDA0002222390560000021
And representing the peak value ratio R of the acceleration power spectrum;
step two, identifying vortex-induced vibration samples based on a clustering algorithm: considering each vibration sample as being composed of the acceleration root mean square value
Figure BDA0002222390560000022
Calculating the distance d between every two sample points of one sample point in a two-dimensional characteristic space formed by the acceleration power spectrum peak value ratio RijAnd calculating the local density rho of each sample point according to the local density rhoiAnd self distance deltaiGenerating a local density ρiAnd self distance deltaiA decision diagram of the horizontal and vertical coordinate axes; self-distance delta in decision diagramiAutomatically identifying the abnormal large points as cluster center points, wherein each cluster center point represents a cluster class; according to local density rhoiSequentially distributing each non-cluster point to the local density rho from big to smalliCluster class where the larger and closest sample point is located; returning to the vibration characteristic space, and identifying all clusters with the acceleration power spectrum peak value ratio R near 0 as vortex-induced vibration clustersWherein the sample is a vortex-induced vibration sample;
step three, identifying vortex-induced vibration events: because the duration time of the vortex-induced vibration event of the prototype bridge is usually longer than the analysis time interval, and can reach dozens of minutes or even hours, the vortex-induced vibration sample identified in the step two is usually one section of a certain bridge vortex-induced vibration event, so that the vortex-induced vibration samples which are continuous on the time axis are spliced to form the complete bridge vortex-induced vibration event.
The invention also has the following technical characteristics:
1. in step one as described above, the acceleration rms value
Figure BDA0002222390560000023
And the calculation formula of the acceleration power spectrum peak value ratio R is as follows:
Figure BDA0002222390560000024
in the formula (I), the compound is shown in the specification,
Figure BDA0002222390560000026
the vertical acceleration of a certain section of the main beam, and N is the sampling number within a period of time as a basic time interval; p is a radical of2And p1The second and first large peaks in the acceleration power spectrum, respectively.
2. In step two as described above, the distance d between every two sample pointsijLocal density p of each sample pointiAnd the self-distance delta of each sample pointiThe calculation formula of (2) is as follows:
Figure BDA0002222390560000027
Figure BDA0002222390560000028
Figure BDA0002222390560000029
in the formula (d)cFor a given cutoff distance, the size is taken to be the total distance dijThe 10 th% value is ranked from small to large; for the sample point with the highest local density in the whole sample, since there is no sample point with a local density greater than that, its own distance is defined as:
aiming at the problem of identification of the vortex-induced vibration of the large-span bridge, the invention constructs a bridge vortex-induced vibration identification feature space and realizes the automatic processing of the whole process of feature extraction, vortex-induced vibration sample identification, vortex-induced vibration event identification and result display of the vibration of the prototype bridge under the excitation of a complex environment. The method is convenient and accurate, and improves the efficiency of bridge vortex-induced vibration identification and the stability and reliability of identification results. The whole identification process is completed at one time by running the corresponding program codes, manual intervention is not needed, and full automation is realized. In addition, the method can meet the requirements of online early warning and real-time data processing of bridge vortex-induced vibration identification, namely directly classifying real-time monitoring vibration samples through the result of offline identification in advance, thereby realizing online identification and data processing of vortex-induced vibration. The method improves the automation, the intellectualization, the accuracy and the robustness of the identification of the vortex-induced vibration of the large-span bridge, and provides a solution for the automatic identification of the vortex-induced vibration of the civil engineering bridge.
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FIG. 1 is a flow chart of a large-span bridge vortex-induced vibration identification based on a clustering algorithm;
FIG. 2 is a schematic diagram of acceleration power spectrum peak ratio calculation;
FIG. 3 is a cluster analysis decision diagram;
FIG. 4 illustrates an acceleration time course of a bridge vortex induced vibration event.
Detailed Description
The invention will be further illustrated by way of example in the accompanying drawings of the specification:
example 1:
the embodiment is developed under the MATLAB environment, can be directly used for monitoring data of the vertical vibration of the girder of the large-span bridge, has high identification precision, high speed and low cost, can be used for first off-line identification and second on-line identification, and improves the automation, the intellectualization, the accuracy and the robustness of the vortex-induced vibration identification of the large-span bridge.
As shown in fig. 1, a large-span bridge vortex-induced vibration identification process based on a clustering algorithm is as follows:
step one, calculating the characteristics of a girder vibration sample: dividing a girder vibration acceleration time-course signal monitored for a long time into a plurality of 10-minute samples by taking 10 minutes as a basic time interval; calculating the acceleration root mean square value of each sample according to the formula (1)
Figure BDA0002222390560000032
As a characteristic of the amplitude of the girder; calculating the acceleration power spectrum of each sample according to the formula (2), picking up the first two large peaks in the power spectrum, and taking the ratio R of the second large peak to the first large peak as the characteristic for representing the single degree of the vibration frequency, as shown in FIG. 2; finally, each vibration sample is composed of the acceleration root mean square value
Figure BDA0002222390560000033
And the peak value ratio R of the acceleration power spectrum.
Figure BDA0002222390560000034
Figure BDA0002222390560000041
In the formula (I), the compound is shown in the specification,
Figure BDA0002222390560000042
the vertical acceleration of a certain section of the main beam is shown, and N is the sampling number within the basic time interval of 10 minutes; p is a radical of2And p1Are respectivelyThe second and first large peaks in the acceleration power spectrum.
Secondly, identifying vortex-induced vibration samples based on a clustering algorithm: considering each vibration sample as being composed of the acceleration root mean square value
Figure BDA0002222390560000043
And calculating the distance d between every two sample points according to the formula (3) at one sample point in a two-dimensional characteristic space formed by the acceleration power spectrum peak value ratio RijAnd calculating the local density rho of each sample point according to the formula (4) and the formula (5) respectivelyiAnd self distance deltaiGenerating a local density ρiAnd self distance deltaiA decision diagram of the horizontal and vertical axes, as shown in fig. 3; self-distance delta in decision diagramiAutomatically identifying the abnormal large points as cluster center points, wherein each cluster center point represents a cluster class; according to local density rhoiSequentially distributing each non-cluster point to the local density rho from big to smalliCluster class where the larger and closest sample point is located; returning to the vibration characteristic space, and identifying all clusters with the acceleration power spectrum peak value ratio R near 0 as vortex-induced vibration clusters, wherein the samples are vortex-induced vibration samples.
Figure BDA0002222390560000044
Figure BDA0002222390560000045
Figure BDA0002222390560000046
In the formula (d)cFor a given cutoff distance, the size is taken to be the total distance dijThe 10 th% value is ranked from small to large; for the sample point with the highest local density in the whole sample, since there is no sample point with a local density greater than that, its own distance is defined as:
Figure BDA0002222390560000047
thirdly, identifying vortex-induced vibration events: the vortex-induced vibration samples which are continuous on the time axis are spliced to form a plurality of complete bridge vortex-induced vibration events, and a vibration acceleration power spectrum can be obtained, as shown in fig. 4.

Claims (3)

1. A method for identifying vortex-induced vibration of a large-span bridge based on a clustering algorithm is characterized by comprising the following steps:
step one, calculating characteristics of a girder vibration sample: dividing a long-term monitored main beam vibration acceleration time-course signal into a plurality of samples in a period of time by taking the period of time as a basic time interval; calculating the acceleration root mean square value of each sample
Figure FDA0002222390550000011
As a characteristic of the amplitude of the girder; calculating an acceleration power spectrum of each sample, picking up the first two large peak values in the power spectrum, and taking the ratio R of the second large peak value to the first large peak value as the characteristic for representing the single degree of the vibration frequency; finally, each vibration sample is composed of the acceleration root mean square value
Figure FDA0002222390550000012
And representing the peak value ratio R of the acceleration power spectrum;
step two, identifying vortex-induced vibration samples based on a clustering algorithm: considering each vibration sample as being composed of the acceleration root mean square value
Figure FDA0002222390550000013
Calculating the distance d between every two sample points of one sample point in a two-dimensional characteristic space formed by the acceleration power spectrum peak value ratio RijAnd calculating the local density rho of each sample point according to the local density rhoiAnd self distance deltaiGenerating a local density ρiAnd self distance deltaiA decision diagram of the horizontal and vertical coordinate axes; self-distance delta in decision diagramiAbnormally large points are automatically identified as cluster points, each representing oneA cluster class; according to local density rhoiSequentially distributing each non-cluster point to the local density rho from big to smalliThe cluster type of the larger and nearest sample point completes the category distribution of all the sample points; returning to the vibration characteristic space, and identifying all clusters with the acceleration power spectrum peak value ratio R near 0 as vortex-induced vibration clusters, wherein the samples are vortex-induced vibration samples;
step three, identifying vortex-induced vibration events: and splicing the continuous vortex-induced vibration samples on the time axis to form a complete bridge vortex-induced vibration event.
2. The method for identifying the vortex-induced vibration of the large-span bridge based on the clustering algorithm according to claim 1, wherein the method comprises the following steps: in step one, the mean square root value of the acceleration
Figure FDA0002222390550000014
And the calculation formula of the acceleration power spectrum peak value ratio R is as follows:
in the formula (I), the compound is shown in the specification,
Figure FDA0002222390550000017
the vertical acceleration of a certain section of the main beam, and N is the sampling number within a period of time as a basic time interval; p is a radical of2And p1The second and first large peaks in the acceleration power spectrum, respectively.
3. The method for identifying the vortex-induced vibration of the large-span bridge based on the clustering algorithm according to claim 1, wherein the method comprises the following steps: in step two, the distance d between every two sample pointsijLocal density p of each sample pointiAnd the self-distance of each sample pointδiThe calculation formula of (2) is as follows:
Figure FDA0002222390550000021
in the formula (d)cFor a given cutoff distance, the size is taken to be the total distance dijThe 10 th% value is ranked from small to large;
for the sample point with the highest local density in the whole sample, since there is no sample point with a local density greater than that, its own distance is defined as:
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111829747A (en) * 2020-07-18 2020-10-27 扬州大学 Coastal bridge vortex-induced resonance wind field monitoring system based on fixed-wing unmanned aerial vehicle
CN113343541A (en) * 2021-07-08 2021-09-03 石家庄铁道大学 Vortex-induced vibration early warning method, device and terminal for long and large span bridge
CN114777910A (en) * 2022-04-02 2022-07-22 东衢智慧交通基础设施科技(江苏)有限公司 Cable multimode vortex-induced vibration monitoring method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102254068A (en) * 2010-12-01 2011-11-23 东南大学 Multi-scale analyzing method for buffeting response of large-span bridge
CN107491783A (en) * 2017-07-31 2017-12-19 广东电网有限责任公司惠州供电局 Based on the transformer fault genre classification methods for improving density peaks clustering algorithm
CN108318129A (en) * 2018-02-01 2018-07-24 石家庄铁道大学 The true and false discriminating method of bridge structure modal parameter and terminal device

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102254068A (en) * 2010-12-01 2011-11-23 东南大学 Multi-scale analyzing method for buffeting response of large-span bridge
CN107491783A (en) * 2017-07-31 2017-12-19 广东电网有限责任公司惠州供电局 Based on the transformer fault genre classification methods for improving density peaks clustering algorithm
CN108318129A (en) * 2018-02-01 2018-07-24 石家庄铁道大学 The true and false discriminating method of bridge structure modal parameter and terminal device

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
SHAN WULI 等: "Cluster analysis of winds and wind-induced vibrations on a long-span bridge based on long-term field monitoring data", 《ENGINEERING STRUCTURES》 *
秦浩等: "大跨度变截面连续钢箱梁桥涡激振动线性分析法", 《振动工程学报》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111829747A (en) * 2020-07-18 2020-10-27 扬州大学 Coastal bridge vortex-induced resonance wind field monitoring system based on fixed-wing unmanned aerial vehicle
CN113343541A (en) * 2021-07-08 2021-09-03 石家庄铁道大学 Vortex-induced vibration early warning method, device and terminal for long and large span bridge
CN113343541B (en) * 2021-07-08 2023-07-25 石家庄铁道大学 Vortex-induced vibration early warning method, device and terminal for long and large bridge span
CN114777910A (en) * 2022-04-02 2022-07-22 东衢智慧交通基础设施科技(江苏)有限公司 Cable multimode vortex-induced vibration monitoring method

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Application publication date: 20200114