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深度学习增强了使用Lamb波的板中的定量解锁评估.

Shuke Chen1, Ye Lu1

  • 1Department of Civil Engineering, Monash University, Melbourne, VIC, Australia.

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概括
此摘要是机器生成的。

这项研究使用深度学习和Lamb波来检测和绘制陶板中的脱节. 这种新的方法准确地识别了缺陷的大小和位置,以可靠地监测结构健康.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.羊羔的波浪在浪叫着.板面板板块的板.

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科学领域:

  • 结构健康监测 结构健康监测
  • 非破坏性的评价
  • 材料科学 材料科学 材料科学

背景情况:

  • 外部陶板容易脱节,损害结构完整性.
  • 对解锁的定量评估对于及时维护和安全至关重要.
  • 现有的非破坏性评估 (NDE) 方法在精度和范围上可能存在局限性.

研究的目的:

  • 开发一种新的,数据驱动的方法来定量评估外墙陶板中的脱.
  • 为了利用Lamb波信号和深度学习进行精确的缺陷表征.
  • 创建一个全面的二维损坏索引地图,用于识别解锁位置和大小.

主要方法:

  • 利用Lamb波信号并使用连续波束转换 (CWT) 将它们转换为时间频率图像.
  • 使用二维卷积神经网络 (2D-CNN) 来处理时间频率表示并提取解绑信息.
  • 结合模拟数据与实验验证,开发和测试损坏检测模型.

主要成果:

  • 2D-CNN有效地预测了陶的脱特性.
  • 创建了一个全面的二维损坏索引地图,准确识别了解锁缺陷的位置和大小.
  • 该模型在与结合实验数据的混合数据集进行训练时表现出卓越的性能.

结论:

  • 拟议的深度学习方法提供了一种可靠的方法,用于在结构中进行解锁的非破坏性评估.
  • 拉姆波和2D-CNN的整合为定量损害评估提供了一个强大的工具.
  • 这种技术具有显著的潜力,可以提高建筑立面的安全性和寿命.