Generative adversarial networks for labeled acceleration data augmentation for structural damage detection-Reference-Cited by-同舟云学术

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Generative adversarial networks for labeled acceleration data augmentation for structural damage detection

Published:2022-09-24 Issue:1 Volume:13 Page:181-198
ISSN:2190-5452
Container-title:Journal of Civil Structural Health Monitoring
language:en
Short-container-title:J Civil Struct Health Monit

Author:

Luleci Furkan,Catbas F. Necati^ORCID,Avci Onur

Funder

National Aeronautics and Space Administration

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Safety, Risk, Reliability and Quality,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s13349-022-00627-8.pdf

Reference54 articles.

1. Das S, Saha P, Patro SK (2016) Vibration-based damage detection techniques used for health monitoring of structures: a review. J Civ Struct Health Monit. https://doi.org/10.1007/s13349-016-0168-5

2. Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ (2021) A review of vibration-based damage detection in civil structures: from traditional methods to machine learning and deep learning applications. Mech Syst Signal Process 147:107077. https://doi.org/10.1016/J.YMSSP.2020.107077

3. Dong C-Z, Catbas FN (2021) A review of computer vision–based structural health monitoring at local and global levels. Struct Health Monit. https://doi.org/10.1177/1475921720935585

4. Gul M, Catbas FN (2008) Ambient vibration data analysis for structural identification and global condition assessment. J Eng Mech. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:8(650)

5. Yin T, Lam HF, Chow HM, Zhu HP (2009) Dynamic reduction-based structural damage detection of transmission tower utilizing ambient vibration data. Eng Struct. https://doi.org/10.1016/j.engstruct.2009.03.004

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