Characterization and Modeling of Corn Stalk Fibers tied with Clay using Support Vector Regression Algorithms

Several research groups are recently focusing on natural fibers as components of construction materials, contributing to the search for sustainable solutions that reduce the ecological footprint of buildings. Many of these fibers are proposed as acoustic absorbers to replace man-made fibers widely u...

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Main Authors: Giuseppe Ciaburro, Virginia Puyana-Romero, Gino Iannace, Wilson Andres Jaramillo-Cevallos
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:Journal of Natural Fibers
Subjects:
Online Access:http://dx.doi.org/10.1080/15440478.2021.1944427
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author Giuseppe Ciaburro
Virginia Puyana-Romero
Gino Iannace
Wilson Andres Jaramillo-Cevallos
author_facet Giuseppe Ciaburro
Virginia Puyana-Romero
Gino Iannace
Wilson Andres Jaramillo-Cevallos
author_sort Giuseppe Ciaburro
collection DOAJ
description Several research groups are recently focusing on natural fibers as components of construction materials, contributing to the search for sustainable solutions that reduce the ecological footprint of buildings. Many of these fibers are proposed as acoustic absorbers to replace man-made fibers widely used to reduce reverberation in rooms, such as fiberglass and stone wool, which consume a lot of energy in their production and are not biodegradable. In this article, the acoustic absorption of fiber panels composed of corn stalk fibers and clay – both environmentally friendly materials – is studied, considering samples of 6 mm, 12 mm, and 24 mm thickness. Three percentages of water were used for the kneading of the clay. A support vector machine model has been calculated to predict the behavior of this composite material. 24 mm sample with 6% of water returns values of the acoustic absorption coefficient between 0.6 and 0.8 in the frequency range from 750 to 1600 Hz. 6 mm samples with 16% and 26% of water result in values of the acoustic absorption coefficient near one at 4500 Hz and 4750 Hz, respectively. The simulation performed with the support vector machine model returned Pearson’s correlation coefficient values of 0.997, demonstrating excellent generalization and prediction ability of the model.
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spelling doaj.art-5f9e7e392f2742a3b95ae6b14745208e2023-09-20T13:04:28ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2022-12-0119137141715610.1080/15440478.2021.19444271944427Characterization and Modeling of Corn Stalk Fibers tied with Clay using Support Vector Regression AlgorithmsGiuseppe Ciaburro0Virginia Puyana-Romero1Gino Iannace2Wilson Andres Jaramillo-Cevallos3Università Degli Studi Della Campania Luigi VanvitelliUniversidad De Las AméricasUniversità Degli Studi Della Campania Luigi VanvitelliUniversidad De Las AméricasSeveral research groups are recently focusing on natural fibers as components of construction materials, contributing to the search for sustainable solutions that reduce the ecological footprint of buildings. Many of these fibers are proposed as acoustic absorbers to replace man-made fibers widely used to reduce reverberation in rooms, such as fiberglass and stone wool, which consume a lot of energy in their production and are not biodegradable. In this article, the acoustic absorption of fiber panels composed of corn stalk fibers and clay – both environmentally friendly materials – is studied, considering samples of 6 mm, 12 mm, and 24 mm thickness. Three percentages of water were used for the kneading of the clay. A support vector machine model has been calculated to predict the behavior of this composite material. 24 mm sample with 6% of water returns values of the acoustic absorption coefficient between 0.6 and 0.8 in the frequency range from 750 to 1600 Hz. 6 mm samples with 16% and 26% of water result in values of the acoustic absorption coefficient near one at 4500 Hz and 4750 Hz, respectively. The simulation performed with the support vector machine model returned Pearson’s correlation coefficient values of 0.997, demonstrating excellent generalization and prediction ability of the model.http://dx.doi.org/10.1080/15440478.2021.1944427天然材料吸声系数向量机回归模型声学测量
spellingShingle Giuseppe Ciaburro
Virginia Puyana-Romero
Gino Iannace
Wilson Andres Jaramillo-Cevallos
Characterization and Modeling of Corn Stalk Fibers tied with Clay using Support Vector Regression Algorithms
Journal of Natural Fibers
天然材料
吸声系数
向量机回归模型
声学测量
title Characterization and Modeling of Corn Stalk Fibers tied with Clay using Support Vector Regression Algorithms
title_full Characterization and Modeling of Corn Stalk Fibers tied with Clay using Support Vector Regression Algorithms
title_fullStr Characterization and Modeling of Corn Stalk Fibers tied with Clay using Support Vector Regression Algorithms
title_full_unstemmed Characterization and Modeling of Corn Stalk Fibers tied with Clay using Support Vector Regression Algorithms
title_short Characterization and Modeling of Corn Stalk Fibers tied with Clay using Support Vector Regression Algorithms
title_sort characterization and modeling of corn stalk fibers tied with clay using support vector regression algorithms
topic 天然材料
吸声系数
向量机回归模型
声学测量
url http://dx.doi.org/10.1080/15440478.2021.1944427
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AT virginiapuyanaromero characterizationandmodelingofcornstalkfiberstiedwithclayusingsupportvectorregressionalgorithms
AT ginoiannace characterizationandmodelingofcornstalkfiberstiedwithclayusingsupportvectorregressionalgorithms
AT wilsonandresjaramillocevallos characterizationandmodelingofcornstalkfiberstiedwithclayusingsupportvectorregressionalgorithms