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Shahab Shamsirband


Nabi Mehri Khansari


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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.8 P.585-608


Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models

Author(s):  Shahab Shamsirband, Nabi Mehri Khansari

Affiliation(s):  Future Technology Research Center, College of Future, Yunlin University of Science and Technology, Yunlin 64002, China; more

Corresponding email(s):   shamshirbands@yuntech.edu.tw

Key Words:  Damage detection, Machine learning (ML), Composite structure, Micro-mechanics of damage, Deep learning (DL)

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Shahab Shamsirband, Nabi Mehri Khansari. Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models[J]. Journal of Zhejiang University Science A, 2021, 22(8): 585-608.

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%T Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
%A Shahab Shamsirband
%A Nabi Mehri Khansari
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%P 585-608
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%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000408

T1 - Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
A1 - Shahab Shamsirband
A1 - Nabi Mehri Khansari
J0 - Journal of Zhejiang University Science A
VL - 22
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SP - 585
EP - 608
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000408

A loss of integrity and the effects of damage on mechanical attributes result in macro/micro-mechanical failure, especially in composite structures. As a progressive degradation of material continuity, predictions for any aspects of the initiation and propagation of damage need to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides material design, structural integrity and health need to be monitored carefully. Among the most powerful methods for the detection of damage are machine learning (ML) and deep learning (DL). In this paper, we review state-of-the-art ML methods and their applications in detecting and predicting material damage, concentrating on composite materials. The more influential ML methods are identified based on their performance, and research gaps and future trends are discussed. Based on our findings, DL followed by ensemble-based techniques has the highest application and robustness in the field of damage diagnosis.



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


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