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  • 标题:A Real-Time Structural Damage Detection method for High-Pile Wharf Foundations
  • 本地全文:下载
  • 作者:Yongcheng Liu ; Yonglai Zheng ; Yujue Zhou
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2021
  • 卷号:283
  • 页码:1-4
  • DOI:10.1051/e3sconf/202128301022
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.
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