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Damage detection in offshore structures using neural networks

机译:使用神经网络的海上结构损伤检测

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摘要

This paper discusses the damage detection in offshore jacket platforms subjected to random loads using a combined method of random decrement signature and neural networks. The random decrement technique is used to extract the free decay of the structure from its online response while the structure is in service. The free decay and its time derivative are used as input for a neural network. The output of the neural network is used as an index for damage detection. It has been shown that function N is effective in damage detection in the members of an offshore structure. Experimental studies conducted on a reduced model for a real jacket structure with geometrical scale of 1:30 are used. The applied loads were random loads. Two different load spectra were used: White noise, and Pierson-Moskowitz.
机译:本文讨论了使用随机减量签名和神经网络相结合的方法对海上套管平台进行随机载荷的损伤检测。当结构在使用中时,随机减量技术用于从结构的在线响应中提取结构的自由衰减。自由衰减及其时间导数用作神经网络的输入。神经网络的输出用作损坏检测的指标。已经表明,功能N在海上结构的构件中的损伤检测中是有效的。使用对几何尺寸为1:30的真实外套结构的简化模型进行的实验研究。施加的载荷是随机载荷。使用了两种不同的负载谱:白噪声和Pierson-Moskowitz。

著录项

  • 来源
    《Marine Structures》 |2010年第1期|p.131-145|共15页
  • 作者单位

    Faculty of Engineering, Minufiya University, Egypt, and a Post Doctorate Fellow, Memorial University of Newfoundland, Canada;

    Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada;

    Faculty of Engineering, Architecture and Science, Ryerson University, Toronto, Ontario, Canada;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    damage detection; random decrement; neural networks; offshore jacket structures;

    机译:损坏检测;随机减量神经网络;离岸外套结构;
  • 入库时间 2022-08-18 01:47:06

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