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Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect
The measurement of stress in concrete structures is a complex issue. This paper presents a new measurement system called a self-acoustic system (SAS), which uses frequency measurements of acoustic waves to determine the condition of concrete structures. The SAS uses a positive feedback loop between...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347925/ https://www.ncbi.nlm.nih.gov/pubmed/34361310 http://dx.doi.org/10.3390/ma14154116 |
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author | Lalik, Krzysztof Kozek, Mateusz Dominik, Ireneusz |
author_facet | Lalik, Krzysztof Kozek, Mateusz Dominik, Ireneusz |
author_sort | Lalik, Krzysztof |
collection | PubMed |
description | The measurement of stress in concrete structures is a complex issue. This paper presents a new measurement system called a self-acoustic system (SAS), which uses frequency measurements of acoustic waves to determine the condition of concrete structures. The SAS uses a positive feedback loop between ultrasonic heads, which causes excitation to a stable limit cycle. The frequency of this cycle is related to the propagation time of an acoustic wave, which directly depends on stresses in the test object. The coupling mechanism between acoustic wave propagation speed and stress is the elastoacoustic effect described in this paper. Thus, the proposed system enables the coupling between the limit cycle frequency and the stress degree of the concrete structure. This paper presents a machine learning algorithm to analyse the frequency spectrum of the SAS system. The proposed solution is a real-time classifier that enables online analysis of the frequency spectrum from the SAS system. With this approach, an autonomous system for stress condition identification of concrete structures is built and described. |
format | Online Article Text |
id | pubmed-8347925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83479252021-08-08 Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect Lalik, Krzysztof Kozek, Mateusz Dominik, Ireneusz Materials (Basel) Article The measurement of stress in concrete structures is a complex issue. This paper presents a new measurement system called a self-acoustic system (SAS), which uses frequency measurements of acoustic waves to determine the condition of concrete structures. The SAS uses a positive feedback loop between ultrasonic heads, which causes excitation to a stable limit cycle. The frequency of this cycle is related to the propagation time of an acoustic wave, which directly depends on stresses in the test object. The coupling mechanism between acoustic wave propagation speed and stress is the elastoacoustic effect described in this paper. Thus, the proposed system enables the coupling between the limit cycle frequency and the stress degree of the concrete structure. This paper presents a machine learning algorithm to analyse the frequency spectrum of the SAS system. The proposed solution is a real-time classifier that enables online analysis of the frequency spectrum from the SAS system. With this approach, an autonomous system for stress condition identification of concrete structures is built and described. MDPI 2021-07-23 /pmc/articles/PMC8347925/ /pubmed/34361310 http://dx.doi.org/10.3390/ma14154116 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lalik, Krzysztof Kozek, Mateusz Dominik, Ireneusz Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect |
title | Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect |
title_full | Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect |
title_fullStr | Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect |
title_full_unstemmed | Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect |
title_short | Autonomous Machine Learning Algorithm for Stress Monitoring in Concrete Using Elastoacoustical Effect |
title_sort | autonomous machine learning algorithm for stress monitoring in concrete using elastoacoustical effect |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347925/ https://www.ncbi.nlm.nih.gov/pubmed/34361310 http://dx.doi.org/10.3390/ma14154116 |
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