基于物理的深度学习用于结构振动识别及其在基准结构上的应用
Minte Zhang1, Tong Guo1, Guodong Zhang1
1School of Civil Engineering, Southeast University, Nanjing 210096, People's Republic of China.
概括
这项研究引入了一种新的基于物理的深度学习 (PIDL) 框架,用于结构振动识别. PIDL将深度学习与结构动态相结合,提高了用于民用结构监测的激发和参数识别的准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 机器学习 机器学习
- 结构动力学 结构动力学
背景情况:
- 结构振动识别对于土木工程至关重要,它依赖于传感器数据.
- 在没有监测的地点和有限的数据中预测响应是具有挑战性的.
- 目前的深度学习方法很难将物理定律纳入振动分析中.
研究的目的:
- 为结构振动识别提供一种新的基于物理的深度学习 (PIDL) 框架.
- 将深层次的生成网络与结构动态知识相结合.
- 提高民用结构中振动分析的准确性和可靠性.
主要方法:
- 开发了一个PIDL框架,集成一个卷积神经网络和一个基于物理的变化自编码器.
- 使用显式时间域 (ETD) 分析与生成的单位冲动响应 (UIR) 信号.
- 将ETD方法辅助的卷积函数纳入UIR信号对齐的损失函数中.
主要成果:
- PIDL框架有效地提取了与物理相关的动态特征.
- 在各种损伤模式中,精确识别了激发信号和潜在物理参数.
- 与传统的深度学习方法相比,证明了更好的准确性和可靠性.
结论:
- 该PIDL框架成功地将结构动态知识整合到用于振动分析的深度学习中.
- 该研究强调了PIDL在建筑结构监测和健康评估中的潜力.
- 这项研究通过结合基于物理的机器学习来推进结构振动识别.
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