Monitor concrete moisture level using percussion and machine learning
| Autor: | Linsheng Huo, Liqiong Zheng, Gangbing Song, Hao Cheng |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Moisture
Computer science business.industry Microphone 0211 other engineering and technologies Percussion 020101 civil engineering 02 engineering and technology Building and Construction Machine learning computer.software_genre Field (computer science) 0201 civil engineering Support vector machine 021105 building & construction Feature (machine learning) General Materials Science Mel-frequency cepstrum Artificial intelligence Underwater business computer Civil and Structural Engineering |
| Zdroj: | Construction and Building Materials. 229:117077 |
| ISSN: | 0950-0618 |
| DOI: | 10.1016/j.conbuildmat.2019.117077 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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