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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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

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

Method used

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  • Bridge damage identification method based on neural network
  • Bridge damage identification method based on neural network
  • Bridge damage identification method based on neural network

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Experimental program
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Effect test

Embodiment 1

[0193] Embodiment 1 of the present invention: the bridge damage identification method based on neural network, comprises the following steps:

[0194] 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: Among them, ε uj is the strain data of the jth position in the undamaged condition, ε sj is the strain data of the jth position under the damage condition; 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, and utilizing the Block Lanczos method to extract the natural frequency under the undamaged condition The modal shape of frequency and frequency, sel...

Embodiment 2

[0200] Embodiment 2: the bridge damage identification method based on neural network, comprises the following steps:

[0201] S1, Constructing sample data: Using the finite element method to build a bridge model refers to using the general finite element calculation software ANSYS to build a solid finite element model of the whole bridge; use ANSYS software to analyze the model, and use the Block Lanczos method to extract the inherent Frequency and frequency mode shape, select the damage location according to the size of the modal displacement in the mode shape and the installation position of the actual bridge sensor; use the method of reducing the elastic modulus to simulate different degrees of damage at different positions, that is, the damage location Different degrees of damage can be obtained by modifying the elastic modulus of the material at the location; then use the *get command in the APDL language to extract the strain data of different degrees of damage at differe...

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Abstract

The invention discloses a bridge damage identification method based on a neural network. The method includes the following steps of firstly, constructing sample data, wherein a bridge model is established with a finite element method, simulation strain data are obtained under the condition that a bridge is complete and under the condition that the bridge is differently damaged, and the strain change rates serve as the sample data of the BP neural network; secondly, determining a network topology structure, wherein the number of hidden layers of the BP neural network and the number of nerve cells contained on each layer are determined, and meanwhile the weight threshold value of the neural network is initialized; thirdly, conducting training and testing, wherein the BP neural network is trained through a gradient descent momentum algorithm, and the neural network is tested through a testing sample; fourthly, identifying the damage, wherein the damage of the bridge is identified by inputting the real-time train data of the bridge into the trained BP neural network. The bridge is identified through stress parameters, and therefore bridge damage identification accuracy is improved.

Description

technical field [0001] The invention relates to a bridge damage identification method based on a neural network, belonging 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 the brid...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06K9/66G06N3/02
Inventor 吴朝霞赵玉倩金伟崔宝影樊红黄艳南
Owner NORTHEASTERN UNIV
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