Structure damage degree identification method based on convolutional neural network

A convolutional neural network and structural damage technology, applied in the field of neural networks, can solve problems such as interference, damage identification effect, bad, etc., to achieve the effect of fast calculation speed and reduce calculation parameters

Pending Publication Date: 2019-09-24
GUANGDONG UNIV OF TECH
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Problems solved by technology

[0003] The damage characteristics of the structure are usually manifested in the change of the inherent properties of the structure, but due to the influence of the compl...

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  • Structure damage degree identification method based on convolutional neural network
  • Structure damage degree identification method based on convolutional neural network
  • Structure damage degree identification method based on convolutional neural network

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Embodiment 1

[0038] A method for identifying the degree of structural damage based on convolutional neural networks, such as figure 1 , including the following steps:

[0039] S1: Construct a simply supported beam structure and collect training sample data according to the simply supported beam structure. The simply supported beam structure includes 36 units, such as figure 2 , each unit has a different modal strain energy, the training sample data includes the modal strain energy of each unit, and the training sample data is to collect damage of 20%, 30%, 40%, 50% of each unit respectively , 60% of the damage data, a total of 36×5=180 cases, in each case the modal strain energy of 36 units can be obtained, the damage data of 20%, 30%, 40%, 50% of each unit damage and The corresponding modal strain energy is used as the training set, and the damage data of 60% of each unit damage and the corresponding modal strain energy are used as the test set;

[0040] S2: Construct an input matrix a...

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Abstract

The invention discloses a structure damage degree identification method based on a convolutional neural network. The structure damage degree identification method comprises the following steps: S1, constructing a simply-supported beam structure, collecting training sample data according to the simply-supported beam structure, wherein the simply-supported beam structure comprises N units, and each unit has different modal strain energy, and the training sample data comprise the modal strain energy of each unit; S2, constructing an input matrix according to the modal strain energy of each unit; S3, constructing a model of the convolutional neural network according to the input matrix; S4, training the convolutional neural network by using the training sample data comprising the modal strain energy of each unit, and storing the trained convolutional neural network; and S5, predicting unknown simply supported beam structure damage by using the trained convolutional neural network. According to the structure damage degree identification method, a convolutional neural network weight sharing method is utilized, so that calculation parameters are reduced, and the calculation speed is high, and positive significance is achieved for application of a large-scale structure.

Description

technical field [0001] The invention relates to the field of neural networks, and more specifically, to a method for identifying the degree of structural damage based on a convolutional neural network. Background technique [0002] Structural damage recognition is of great significance in the field of damage detection. Structural damage not only threatens people's lives, but also causes huge national property losses. Structure is an important support in people's life. Only by ensuring the safety of structure can people's daily life go smoothly. Therefore, the damage identification of structure is particularly important. How to accurately identify the degree of damage is an important problem currently facing. [0003] The damage characteristics of the structure are usually manifested in the change of the inherent properties of the structure, but due to the influence of the complexity of the structure, the environment, the data processing method, etc., there will be a lot of ...

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Application Information

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IPC IPC(8): G06T7/00G06N3/04
CPCG06T7/0002G06T2207/20081G06T2207/20084G06N3/045
Inventor 陈贡发腾帅
Owner GUANGDONG UNIV OF TECH
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