将囊式智能集成式基于EMI技术与深度学习相结合,用于在混凝土中进行压力评估
Quoc-Bao Ta1, Quang-Quang Pham1,2, Ngoc-Lan Pham1
1Department of Ocean Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Republic of Korea.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了一种新的混凝土应力监测技术,使用1D CNN对智能聚合传感器电机阻抗 (EMI) 信号的深度学习. 该方法准确地估计了混凝土应力,即使有噪音数据.
科学领域:
- 结构健康监测 结构健康监测
- 深度学习应用程序
- 材料科学 材料科学 材料科学
背景情况:
- 混凝土结构需要可靠的应力监测,以确保安全和维护.
- 现有的方法在现实条件下可能缺乏精度或稳定性.
- 智能聚合传感器为嵌入式结构监控提供了一个有希望的方法.
研究的目的:
- 开发和验证一种基于深度学习的方法,用于使用电机阻抗 (EMI) 数据进行混凝土应力监测.
- 评估1D卷积神经网络 (CNN) 模型用于压力估计的性能.
- 评估拟议方法对噪声和未经训练的压力条件的稳定性.
主要方法:
- 一个囊式智能聚合物 (CSA) 传感器原型被开发用于EMI测量.
- 为嵌入在混凝土中的CSA传感器建立了一个2度自由度 (2DOF) EMI模型.
- 一个1D CNN深度回归模型被设计用于处理原始的EMI信号.
- 在CSA嵌入的混凝土气上,在不同的压力负荷下进行了实验性测试.
主要成果:
- 1D CNN模型有效地估计了来自原始EMI信号的混凝土应力.
- 该方法在处理受噪声污染的EMI数据方面证明了可行性和稳定性.
- 该模型甚至在未经训练的压力水平上也显示出有希望的性能.
结论:
- 拟议的1D CNN深度学习方法提供了一种有效和强大的方法,用于使用基于CSA的EMI信号进行具体的压力监测.
- 这种技术有可能实现混凝土结构的实时结构健康监测.
- 进一步的研究可以探索先进的深度学习架构和传感器集成.
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