Deep learning-based structural health monitoring
This article provides a comprehensive review of deep learning-based structural health monitoring (DL-based SHM). It encompasses a broad spectrum of DL theories and applications including nondestructive approaches; computer vision-based methods, digital twins, unmanned aerial vehicles (UAVs), and the...
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| Published in | Automation in construction Vol. 161; p. 105328 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.05.2024
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| Subjects | |
| Online Access | Get full text |
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| Abstract | This article provides a comprehensive review of deep learning-based structural health monitoring (DL-based SHM). It encompasses a broad spectrum of DL theories and applications including nondestructive approaches; computer vision-based methods, digital twins, unmanned aerial vehicles (UAVs), and their integration with DL; vibration-based strategies including sensor fault and data recovery methods; and physics-informed DL approaches. Connections between traditional machine learning and DL-based methods as well as relations of local to global approaches including their extensive integrations are established. The state-of-the-art methods, including their advantages and limitations are presented. The review draws on current literature on the topic, also providing a synergistic analysis leading to the understanding of the evolution of DL as a basis for presenting the future research and development needs. Our overall finding is that despite the rapid progression of digital technology along with the progression of DL, the DL-based SHM appears to be in its infant stages with enormous potential for future developments to bring the SHM technology to a common practical use with wide scope applications, performance reliability, cost, and degree of automation. It is anticipated that this review paper will serve as a basic resource for readers seeking comprehensive and holistic understanding of the subject matter.
[Display omitted]
•First holistic review of deep learning based structural health monitoring (SHM).•Reviewed various advanced deep learning operations and networks used in SHM.•Reviewed broad and extensive subtopics of deep learning based SHM.•Provided perspectives and comprehensions of all subtopics and their cohesive connections.•Provided holistic evolution of deep learning based SHM with future works. |
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| AbstractList | This article provides a comprehensive review of deep learning-based structural health monitoring (DL-based SHM). It encompasses a broad spectrum of DL theories and applications including nondestructive approaches; computer vision-based methods, digital twins, unmanned aerial vehicles (UAVs), and their integration with DL; vibration-based strategies including sensor fault and data recovery methods; and physics-informed DL approaches. Connections between traditional machine learning and DL-based methods as well as relations of local to global approaches including their extensive integrations are established. The state-of-the-art methods, including their advantages and limitations are presented. The review draws on current literature on the topic, also providing a synergistic analysis leading to the understanding of the evolution of DL as a basis for presenting the future research and development needs. Our overall finding is that despite the rapid progression of digital technology along with the progression of DL, the DL-based SHM appears to be in its infant stages with enormous potential for future developments to bring the SHM technology to a common practical use with wide scope applications, performance reliability, cost, and degree of automation. It is anticipated that this review paper will serve as a basic resource for readers seeking comprehensive and holistic understanding of the subject matter.
[Display omitted]
•First holistic review of deep learning based structural health monitoring (SHM).•Reviewed various advanced deep learning operations and networks used in SHM.•Reviewed broad and extensive subtopics of deep learning based SHM.•Provided perspectives and comprehensions of all subtopics and their cohesive connections.•Provided holistic evolution of deep learning based SHM with future works. |
| ArticleNumber | 105328 |
| Author | Büyükӧztürk, Oral Cha, Young-Jin Lewis, John Ali, Rahmat |
| Author_xml | – sequence: 1 givenname: Young-Jin surname: Cha fullname: Cha, Young-Jin email: young.cha@umanitoba.ca organization: Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada – sequence: 2 givenname: Rahmat surname: Ali fullname: Ali, Rahmat organization: Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada – sequence: 3 givenname: John surname: Lewis fullname: Lewis, John organization: Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada – sequence: 4 givenname: Oral surname: Büyükӧztürk fullname: Büyükӧztürk, Oral organization: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA |
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| Keywords | Deep learning Automation Digital twin Infrastructure monitoring Damage identification Physics-informed Defect detection |
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