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Research on Response Parameters and Classification Identification Method of Concrete Vibration Process

The vibration process applied to fresh concrete is an important link in the construction process, but the lack of effective monitoring and evaluation methods results in the quality of the vibration process being difficult to control and, therefore, the structural quality of the resulting concrete st...

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Autores principales: Ma, Yuanshan, Tian, Zhenghong, Xu, Xiaobin, Liu, Hengrui, Li, Jiajie, Fan, Haoyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143475/
https://www.ncbi.nlm.nih.gov/pubmed/37109792
http://dx.doi.org/10.3390/ma16082958
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author Ma, Yuanshan
Tian, Zhenghong
Xu, Xiaobin
Liu, Hengrui
Li, Jiajie
Fan, Haoyue
author_facet Ma, Yuanshan
Tian, Zhenghong
Xu, Xiaobin
Liu, Hengrui
Li, Jiajie
Fan, Haoyue
author_sort Ma, Yuanshan
collection PubMed
description The vibration process applied to fresh concrete is an important link in the construction process, but the lack of effective monitoring and evaluation methods results in the quality of the vibration process being difficult to control and, therefore, the structural quality of the resulting concrete structures difficult to guarantee. In this paper, according to the sensitivity of internal vibrators to vibration acceleration changes under different vibration media, the vibration signals of vibrators in air, concrete mixtures, and reinforced concrete mixtures were collected experimentally. Based on a deep learning algorithm for load recognition of rotating machinery, a multi-scale convolution neural network combined with a self-attention feature fusion mechanism (SE-MCNN) was proposed for medium attribute recognition of concrete vibrators. The model can accurately classify and identify vibrator vibration signals under different working conditions with a recognition accuracy of up to 97%. According to the classification results of the model, the continuous working times of vibrators in different media can be further statistically divided, which provides a new method for accurate quantitative evaluation of the quality of the concrete vibration process.
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spelling pubmed-101434752023-04-29 Research on Response Parameters and Classification Identification Method of Concrete Vibration Process Ma, Yuanshan Tian, Zhenghong Xu, Xiaobin Liu, Hengrui Li, Jiajie Fan, Haoyue Materials (Basel) Article The vibration process applied to fresh concrete is an important link in the construction process, but the lack of effective monitoring and evaluation methods results in the quality of the vibration process being difficult to control and, therefore, the structural quality of the resulting concrete structures difficult to guarantee. In this paper, according to the sensitivity of internal vibrators to vibration acceleration changes under different vibration media, the vibration signals of vibrators in air, concrete mixtures, and reinforced concrete mixtures were collected experimentally. Based on a deep learning algorithm for load recognition of rotating machinery, a multi-scale convolution neural network combined with a self-attention feature fusion mechanism (SE-MCNN) was proposed for medium attribute recognition of concrete vibrators. The model can accurately classify and identify vibrator vibration signals under different working conditions with a recognition accuracy of up to 97%. According to the classification results of the model, the continuous working times of vibrators in different media can be further statistically divided, which provides a new method for accurate quantitative evaluation of the quality of the concrete vibration process. MDPI 2023-04-07 /pmc/articles/PMC10143475/ /pubmed/37109792 http://dx.doi.org/10.3390/ma16082958 Text en © 2023 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
Ma, Yuanshan
Tian, Zhenghong
Xu, Xiaobin
Liu, Hengrui
Li, Jiajie
Fan, Haoyue
Research on Response Parameters and Classification Identification Method of Concrete Vibration Process
title Research on Response Parameters and Classification Identification Method of Concrete Vibration Process
title_full Research on Response Parameters and Classification Identification Method of Concrete Vibration Process
title_fullStr Research on Response Parameters and Classification Identification Method of Concrete Vibration Process
title_full_unstemmed Research on Response Parameters and Classification Identification Method of Concrete Vibration Process
title_short Research on Response Parameters and Classification Identification Method of Concrete Vibration Process
title_sort research on response parameters and classification identification method of concrete vibration process
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143475/
https://www.ncbi.nlm.nih.gov/pubmed/37109792
http://dx.doi.org/10.3390/ma16082958
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