图表特征精炼和融合在变压器中用于结构损坏检测.
Tianjie Hu1,2, Kejian Ma1,2, Jianchun Xiao1,2
1Research Center of Space Structures, Guizhou University, Guiyang 550025, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究介绍了用于结构损坏检测的CGsformer网络,有效地整合了结构响应数据的全球和当地信息. 这种新的方法实现了高精度和稳定性,优于现有的深度学习方法.
科学领域:
- 结构健康监测 结构健康监测
- 深度学习应用程序
- 图形神经网络的神经网络
背景情况:
- 检测结构损坏对于保持基础设施完整性至关重要.
- 数据驱动的深度学习显示出希望,但在全球-本地信息集成方面存在困难.
- 现有的方法缺乏对结构性响应数据关系的可靠分析.
研究的目的:
- 开发一个创新的深度学习网络,用于结构损坏检测.
- 从结构性响应数据中有效地融合全球和本地信息.
- 为了提高损坏识别的准确性和稳定性.
主要方法:
- 提出了卷积增强和图形特征融合在变压器 (CGsformer) 网络.
- 实现从全球到本地信息提取的层次学习.
- 集成了一个图形卷积网络用于噪声过和功能增强.
主要成果:
- 在实验数据上达到92.44%的损害识别准确度,在模拟数据上达到96.71%.
- 与传统的深度学习方法相比,表现出更高的性能.
- 在噪音条件下表现出显著的坚固性.
结论:
- 通过整合全球和地方特征,CGsformer网络有效地识别结构损害.
- 拟议的方法为结构健康监测提供了强大而准确的解决方案.
- 这种方法推进了结构工程中的深度学习应用.
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