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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Shahab Shamsirband

https://orcid.org/0000-0001-6109-1311

Nabi Mehri Khansari

https://orcid.org/0000-0002-6232-5930

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

http://doi.org/10.1631/jzus.A2000408


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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pages="585-608",
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%A Shahab Shamsirband
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000408

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T1 - Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
A1 - Shahab Shamsirband
A1 - Nabi Mehri Khansari
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DOI - 10.1631/jzus.A2000408


Abstract: 
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.

基于机器学习和深度学习模型的微观力学损伤诊断方法

概要:本文总结了各向同性和正交各向异性材料微观力学损伤诊断开发的机器学习(ML)和深度学习(DL)技术的全面最新进展.材料中的微观力学损伤诊断对工业部件的安全具有重要作用.ML和DL作为一种智能方法,不仅能用于特定的损伤检测,还能用于其他多种类型材料的损伤检测,可以识别材料结构中的不连续性.可靠性和可持续性因素被当做ML和DL技术在损伤诊断领域的评价准则.DL和基于集成的技术在微观力学损伤诊断复杂性中应用最多且稳健性最显著.
关键词:损伤检测;机器学习;混合结构;微观损伤;深度学习

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

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