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Optimized bridge damage identification method based on neural network

A neural network and damage recognition technology, applied in biological neural network models, character and pattern recognition, special data processing applications, etc., can solve the problem of low damage recognition accuracy, and achieve the effect of improving accuracy

Inactive Publication Date: 2014-12-10
NORTHEASTERN UNIV
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  • Description
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Problems solved by technology

[0005] The purpose of the present invention is to provide an optimized neural network-based bridge damage identification method, which can effectively solve the problems in the prior art, especially the problem of low damage identification accuracy

Method used

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  • Optimized bridge damage identification method based on neural network
  • Optimized bridge damage identification method based on neural network
  • Optimized bridge damage identification method based on neural network

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Embodiment Construction

[0298] Embodiments of the present invention: the bridge damage identification method based on neural network, comprises the following steps:

[0299] S1, Construct sample data: use the finite element method to establish a solid finite element model of the whole bridge, modify the solid finite element model, and use the revised solid finite element model to simulate the stress of different positions of the bridge under different load conditions, Obtain the simulated strain data under the intact and different damage situations of the bridge, and use the corresponding strain change rate as the sample data of the BP neural network; the described strain change rate is:

[0300] Among them, ε μj is the strain data of the jth position in the undamaged condition, ε sj is the strain data of the jth position under damage condition, S ij is the rate of strain change;

[0301] The described acquisition of the simulated strain data under the intact and different damage conditions of t...

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Abstract

The invention discloses an optimized bridge damage identification method based on a neural network. The optimized bridge damage identification method based on the neural network comprises the steps of S1, constructing sample data; S2, determining network topology; S3, conducting training and testing; S4, identifying damage, wherein real-time strain data of a bridge are input into a trained BP neural network, so that damage identification of the bridge is achieved, the real-time strain data of the bridge are obtained through sensors which are arranged optimally, the minimum number Ymin of unidentifiable models serves as an object function, and the arrangement positions of the sensors corresponding to Ymin is the optimal sensor arrangement. By the adoption of the optimized bridge damage identification method based on the neural network, various possible damage conditions of a structure can be identified to the maximum extent through the minimum number of sensors, and an identification result is high in precision and tends to be stable.

Description

technical field [0001] The invention relates to an optimized neural network-based bridge damage identification method, which belongs to the technical field of bridge damage identification. Background technique [0002] As a key point of transportation, bridges play an extremely important role in our daily life. It is precisely because of the existence of bridges that the national road and railway transportation network can be connected, forming a transportation system extending in all directions, and the importance of bridges for urban transportation is also increasing day by day. In recent years, with the rapid development of our country's economy, our country has made great achievements in bridge construction. At the same time, bridge engineering is a project related to the safety of people's lives and property. Therefore, the health of bridges needs to be highly valued. However, with the increase of the service period of the bridge, the internal mechanism and materials o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/02G06K9/66
Inventor 吴朝霞金伟王立夫赵玉倩邵元隆李俞成
Owner NORTHEASTERN UNIV
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