基于时间电流图网络的结构损坏识别研究
Xiaoping Wu1, Chen Lan2, Changzhen Zhang1
1Engineering Research Center of Micro-Nano and Intelligent Manufacturing of Ministry of Education at Kaili University, Kaili, 556000, China.
Scientific reports
|February 2, 2026
概括
这项研究引入了一个基于物理的图形神经网络 (TPF-GNet) 来识别结构损伤. 它通过模拟无监督结构健康监测的能量流来提高准确性和可解释性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构健康监测 结构健康监测
- 人工智能的人工智能
背景情况:
- 用数据驱动的深度学习方法来识别结构损伤缺乏物理解释性和概括性.
- 现有的方法在无监督学习方面存在困难,需要标记损害数据.
研究的目的:
- 开发一个基于物理的图形神经网络框架,TPF-GNet,用于增强结构损伤识别.
- 提高结构性健康监测中的深度学习模型的物理解释性和概括能力.
- 为了实现无监督的损坏检测和定位,而不需要标记损坏数据.
主要方法:
- 提出了临时电力流量图网络 (TPF-GNet) 框架.
- 引入了临时功率流传播 (TPFP) 模块,将动态功率流嵌入到图形神经网络中.
- 利用多传感器加速响应,通过重建错误进行无监督损坏检测和定位.
主要成果:
- 与传统的GNN和LSTM模型相比,TPF-GNet显示出更高的准确性和物理解释性.
- TPFP模块有效地捕捉了由刚性降解或局部损伤引起的结构状态变化.
- 通过数值模拟和缩放基准框架测试来验证.
结论:
- TPF-GNet为结构性健康监测建立了一个物理限制的范式.
- 该框架为工程应用提供了更好的性能和可解释性,特别是在无监督的场景中.
- 整合动态动力流对于准确评估结构完整性至关重要.
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