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Shock and Vibration
Volume 2015, Article ID 789384, 11 pages
http://dx.doi.org/10.1155/2015/789384
Research Article

Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks

1Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Ehime 790-8577, Japan
2West Nippon Expressway Engineering Shikoku Company Ltd., Takamatsu, Kagawa 760-0072, Japan

Received 1 March 2015; Revised 6 April 2015; Accepted 7 April 2015

Academic Editor: Jiawei Xiang

Copyright © 2015 Pang-jo Chun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [4 citations]

The following is the list of published articles that have cited the current article.

  • Osama Abdeljaber, Onur Avci, Mustafa Serkan Kiranyaz, Boualem Boashash, Henry Sodano, and Daniel J. Inman, “1-D CNNs for Structural Damage Detection: Verification on a Structural Health Monitoring Benchmark Data,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  • Onur Avci, Osama Abdeljaber, Serkan Kiranyaz, and Daniel Inman, “Structural Damage Detection in Real Time: Implementation of 1D Convolutional Neural Networks for SHM Applications,” Structural Health Monitoring & Damage Detection, Volume 7, pp. 49–54, 2017. View at Publisher · View at Google Scholar
  • Osama Abdeljaber, Onur Avci, Serkan Kiranyaz, Moncef Gabbouj, and Daniel J. Inman, “Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks,” Journal of Sound and Vibration, vol. 388, pp. 154–170, 2017. View at Publisher · View at Google Scholar
  • Asif Khan, and Heung Soo Kim, “Assessment of Delaminated Smart Composite Laminates via System Identification and Supervised Learning,” Composite Structures, 2018. View at Publisher · View at Google Scholar