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使用2D卷积神经网络模型对混凝土结构中的压力和损伤的综合估计学习了囊式智能聚合传感器的阻抗反应

Quoc-Bao Ta¹, Ngoc-Lan Pham¹, Jeong-Tae Kim¹

¹Department of Ocean Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Republic of Korea.

Sensors (Basel, Switzerland)

|October 26, 2024

概括

这项研究引入了一种新的2D CNN模型,使用囊式智能聚合物 (CSA) 来估计混凝土结构中的应力和损伤. 该方法通过分析电磁阻抗 (EMI) 响应来准确评估结构完整性,即使有噪声.

科学领域:

土木工程土木工程是指土木工程.
材料科学材料科学材料科学
结构健康监测结构健康监测

背景情况:

确保混凝土结构的安全性和性能需要准确的应力和损坏估计.
像囊的智能聚合物 (CSA) 技术显示出对内部混凝土损坏的早期检测有希望.

研究的目的:

提出一个2D卷积神经网络 (CNN) 模型,用于使用CSA传感器在混凝土结构中进行整体应力和损伤估计.
开发一种方法,从CSA学习电磁阻抗 (EMI) 响应,以评估结构条件.

主要方法:

在压缩负荷下建立了基于CSA的EMI损伤技术的理论框架.
一个2D CNN模型被设计用于从CSA的EMI响应中提取损坏敏感特征.
在CSA嵌入式混凝土气上进行了压缩实验,以记录应力损伤EMI反应.

主要成果:

2D CNN模型成功地从CSA的EMI响应中学习了对损坏敏感的特征.
该模型展示了估计应力和识别混凝土结构损伤水平的能力.
该模型的可行性在噪音条件下和未经训练的数据下得到验证.

结论:

开发的2D CNN模型可以同时准确地估计混凝土结构中的应力和损伤状态.
这种基于CSA的EMI技术为结构健康监测提供了可靠的方法.
这些发现支持使用智能聚合物和人工智能进行先进的混凝土结构评估.

关键词:

这是一个PZT传感器.混凝土结构结构是混凝土结构.机电阻抗方法的电机械阻抗方法.智能聚合物是一个智能聚合物.压力和损害监测和监控这是一个二维卷积神经网络.

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