.png){kind=icon}
返回
Monitor concrete moisture level using percussion and machine learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.conbuildmat.2019.117077.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The durability of underwater and hydraulic concrete structures is highly dependent on their moisture content, which makes the evaluation of moisture contents of great significance in ensuring the proper functioning of these structures. This paper develops a novel percussion-based method to identify the moisture level of concrete. The method of percussion refers to tapping and listening. As a popular acoustic feature used in the field of speech recognition, the Mel-Frequency Cepstral Coefficients (MFCCs) are used in this paper as the features of impact-induced sound. In addition, a microphone was employed to obtain the impact-induced sound signals and a support vector machine (SVM) based machine learning were utilized to classify the different moisture content in concrete. The experimental results demonstrate that the proposed percussion method can identify different moisture levels in concrete with accuracy more than 98%. In comparison to traditional methods for evaluation of moisture content, the proposed percussion method is easy to operate and requires no sensor installation. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Concrete
Moisture content
Percussion
Mel-frequency cepstral coefficients (MFCCs)
Support vector machine (SVM)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8
论文数:
4.5W
被引数:
27.9W
机构
引用论文
Concrete Moisture Content Measurement Using Interdigitated Near-Field Sensors使用交叉近场传感器测量混凝土水分含量.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏分享收藏
查看更多内容