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A typical damage identification method for cable-stayed bridges

A damage identification and cable-stayed bridge technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as limitations in practical engineering applications and difficulty in obtaining, and achieve the effect of reasonable prediction methods and strong application promotion.

Active Publication Date: 2022-07-12
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

However, in the existing research, theoretical indicators such as frequency, vibration mode and curvature mode are mainly used as the output parameters of the neural network. There are limitations

Method used

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  • A typical damage identification method for cable-stayed bridges
  • A typical damage identification method for cable-stayed bridges
  • A typical damage identification method for cable-stayed bridges

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Embodiment Construction

[0034] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] like Figure 1 to Figure 5 As shown, the present invention discloses a typical damage identification method for a cable-stayed bridge, and the steps include:

[0036] Step S1, obtaining data samples: establishing a finite element numerical model of the cable-stayed bridge under typical damage conditions, and obtaining data samples under the bridge intact and typical damage conditions;

[0037] Step S2, obtaining sensitive parameter indexes: analyzing the obtained data samples to obtain parameter indexes that are more sensitive to typical damage;

[0038] Step S3, train and test the BP neural network: use the parameter index that is more sensitive to typical damage to train and test the BP neural network, and evaluate and correct the constructed BP neural network;

[0039] Step S4, damage identification: input the bridge actual ...

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Abstract

The invention discloses a typical damage identification method for a cable-stayed bridge. The steps include: step S1, acquiring data samples: establishing a finite element numerical model of the cable-stayed bridge under typical damage conditions, and acquiring data samples under the bridge intact and typical damage conditions ; Step S2, obtain sensitive parameter indicators: analyze the obtained data samples to obtain parameter indicators that are more sensitive to typical damage; Step S3, train and test the BP neural network: use the parameter indicators that are more sensitive to typical damage to perform BP neural network analysis. Train and test, evaluate and correct the constructed BP neural network; step S4, damage identification: input the bridge actual monitoring data into the corrected BP neural network to realize bridge damage identification. The invention has the advantages of more reasonable prediction mode, stronger application and popularization, and higher damage identification accuracy.

Description

technical field [0001] The invention relates to the field of service bridge safety assessment, in particular to a typical damage identification method of a cable-stayed bridge. Background technique [0002] Under the influence of factors such as unfavorable environment and external loads, some components of concrete cable-stayed bridges will be damaged to varying degrees, such as cable corrosion, main beam cracking, etc. The law of structural performance degradation is relatively complex. A large number of concrete cable-stayed bridges built in my country have accumulated a lot of damage in the process of use. In the safety assessment of the structure, if the damage is not considered enough, misjudgment will occur, which will bring major safety hazards. Therefore, it will have important theoretical and engineering significance to propose a damage identification method for concrete cable-stayed bridges under typical damage. [0003] At present, the damage identification of ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/13G06F30/23G06N3/08G06F119/14
CPCG06F30/13G06F30/23G06N3/084G06F2119/14Y04S10/50
Inventor 马亚飞彭安银鲁乃唯王磊张建仁
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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