期刊
SENSORS
卷 19, 期 24, 页码 -出版社
MDPI
DOI: 10.3390/s19245372
关键词
tendon ducts; grouting defects; wavelet packet transform; Bayes classifier; piezoelectric transducers
资金
- National Natural Science Foundation of China [51708164]
- China Postdoctoral Science Foundation [2018M632523]
- Fundamental Research Funds for the Central Universities in China [JZ2017HGBZ0954, JZ2019HGTB0083]
The grouting quality of tendon ducts is very important for post-tensioning technology in order to protect the prestressing reinforcement from environmental corrosion and to make a smooth stress distribution. Unfortunately, various grouting defects occur in practice, and there is no efficient method to evaluate grouting compactness yet. In this study, a method based on wavelet packet transform (WPT) and Bayes classifier was proposed to evaluate grouting conditions using stress waves generated and received by piezoelectric transducers. Six typical grouting conditions with both partial grouting and cavity defects of different dimensions were experimentally investigated. The WPT was applied to explore the energy of received stress waves at multi-scales. After that, the Bayes classifier was employed to identify the grouting conditions, by taking the traditionally used total energy and the proposed energy vector of WPT components as input, respectively. The experimental results demonstrated that the Bayes classifier input with the energy vector could identify different grouting conditions more accurately. The proposed method has the potential to be applied at key spots of post-tensioning tendon ducts in practice.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据