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CLC number: TU43

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2021-07-30

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Nguyen Tien Khiem

https://orcid.org/0000-0001-5195-2704

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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.8 P.657-671

http://doi.org/10.1631/jzus.A2000402


Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network


Author(s):  Nguyen Tien Khiem, Tran Van Lien, Ngo Trong Duc

Affiliation(s):  Institute of Mechanics, Vietnam Academy of Science and Technology, Hanoi 10072, Vietnam; more

Corresponding email(s):   LienTV@nuce.edu.vn

Key Words:  Crack identification, Functionally graded material (FGM), Neural network (NN), Stationary wavelet transform (SWT), Dynamic stiffness method


Nguyen Tien Khiem, Tran Van Lien, Ngo Trong Duc. Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network[J]. Journal of Zhejiang University Science A, 2021, 22(8): 657-671.

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number="8",
pages="657-671",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000402"
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%T Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network
%A Nguyen Tien Khiem
%A Tran Van Lien
%A Ngo Trong Duc
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000402

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T1 - Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network
A1 - Nguyen Tien Khiem
A1 - Tran Van Lien
A1 - Ngo Trong Duc
J0 - Journal of Zhejiang University Science A
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%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000402


Abstract: 
In this paper, an integrated procedure is proposed to identify cracks in a portal framed structure made of functionally graded material (FGM) using stationary wavelet transform (SWT) and neural network (NN). Material properties of the structure vary along the thickness of beam elements by the power law of volumn distribution. Cracks are assumed to be open and are modeled by double massless springs with stiffness calculated from their depth. The dynamic stiffness method (DSM) is developed to calculate the mode shapes of a cracked frame structure based on shape functions obtained as a general solution of vibration in multiple cracked FGM Timoshenko beams. The SWT of mode shapes is examined for localization of potential cracks in the frame structure and utilized as the input data of NN for crack depth identification. The integrated procedure proposed is shown to be very effective for accurately assessing crack locations and depths in FGM structures, even with noisy measured mode shapes and a limited amount of measured data.

使用稳定小波转换和神经网络识别功能梯度材料框架结构裂纹

目的:功能梯度材料(FGM)框架结构的裂纹识别.
创新点:1. 可接收多裂纹FGM结构在任意高频带中的精确模态.2. 提出了一种使用稳定小波转换(SWT)模态和神经网络识别FGM框架结构裂纹的一体化程序.
方法:使用动态刚度方法并结合与频率相关的形状函数,填补有限元方法的空白.这些形状函数被认为是频域内振动问题的精确解.
结论:1. 神经网络与SWT模态振型方法相结合,即使在测得的模态噪声很大的情况下,也能准确评估FGM结构的裂纹深度.2. 本项研究中提出的FGM框架多裂纹识别一体化程序也适用于有限测量数据的情况,且这些数据不仅局限于模态,还包括结构的静态或动态挠度.

关键词:裂纹识别;功能梯度材料;神经网络;平稳小波变换;动刚度法

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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