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  5. Structural Health Monitoring in Composite Structures: A Comprehensive Review

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Article
English
2021

Structural Health Monitoring in Composite Structures: A Comprehensive Review

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English
2021
Sensors
Vol 22 (1)
DOI: 10.3390/s22010153

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Amir Gandomi
Amir Gandomi

University of Techology Sdyney

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Sahar Hassani
Mohsen Mousavi
Amir Gandomi

Abstract

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

How to cite this publication

Sahar Hassani, Mohsen Mousavi, Amir Gandomi (2021). Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors, 22(1), pp. 153-153, DOI: 10.3390/s22010153.

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Publication Details

Type

Article

Year

2021

Authors

3

Datasets

0

Total Files

0

Language

English

Journal

Sensors

DOI

10.3390/s22010153

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