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Improved bridge damage identification method based on neural network

A neural network and damage identification technology, applied in the field of bridge damage identification, can solve the problem of low accuracy of damage identification and achieve the effect of improving accuracy

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

[0005] The purpose of the present invention is to provide an improved 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

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

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Embodiment 1

[0209] Embodiment 1 of the present invention: the bridge damage identification method based on neural network, comprises the following steps:

[0210] S1, constructing sample data: use the general finite element calculation software ANSYS to establish a solid finite element model of the whole bridge, obtain the simulated strain data of the bridge in good condition and under different damage conditions, and use the strain change rate as the sample data of the BP neural network; The strain rate of change is:

[0211] 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;

[0212] The described acquisition of the simulated strain data under the intact and different damage conditions of the bridge includes: using ANSYS software to analyze the model, utilizing the Block Lanczos method to extract the natural frequency and the mode shape of the freq...

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Abstract

The invention discloses an improved bridge damage identification method based on a neural network. The improved bridge damage identification method comprises the steps of: S1, constructing sample data; S2, determining a network topology; S3, training and testing; S4, identifying damages, including inputting real-time strain data of the bridge into a trained BP neural network, and thereby identifying the damages of the bridge; the real-time strain data of the bridge is obtained by an optimally distributed sensor, the number of minimum unidentifiable models Ymin is adopted as a target function, and the distribution position of the sensor corresponding to the Ymin is the optimal sensor distribution. According to the improved bridge damage identification method based on the neural network, various possible damages of the structure can be maximally distinguished by using the fewest sensors, and meanwhile the identification result can have high accuracy and tend to be stable.

Description

technical field [0001] The invention relates to an improved 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 of...

Claims

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Application Information

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IPC IPC(8): G06N3/02
Inventor 吴朝霞金伟李俞成黄艳南胡利朋
Owner NORTHEASTERN UNIV
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