Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood

Abstract: Timber from the second cutting cycle may make up the majority of future crop volumetric. However, there are few studies of the physical and mechanical properties of this timber, which are important to support the consolidation of new species. This study aimed to use Artificial Neural Networks to estimate the physical and mechanical properties of wood from the Amazon, based on basic density. The properties were: shrinkage (tangential, radial and volumetric), static bending, parallel and perpendicular to the fiber compression, parallel and transverse to the fibers, Janka hardness, traction, splitting and shear. The estimate followed the tendency of the data observed for the tangential, radial and volumetric shrinkage. The network estimated the mechanical properties with significant accuracy. Distribution of errors, static bending, parallel compression and perpendicular to the fiber compression also showed significant accuracy. Artificial Neural Networks can be used to estimate the physical and mechanical properties of wood from Amazon species.

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Main Authors: Reis,Pamella Carolline Marques dos Reis, Souza,Agostinho Lopes de, Reis,Leonardo Pequeno, Carvalho,Ana Márcia Macedo Ladeira, Mazzei,Lucas, Rêgo,Lyvia Julienne Sousa, Leite,Helio Garcia
Format: Digital revista
Language:English
Published: Universidad del Bío-Bío 2018
Online Access:http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2018000300343
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spelling oai:scielo:S0718-221X20180003003432018-09-26Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle woodReis,Pamella Carolline Marques dos ReisSouza,Agostinho Lopes deReis,Leonardo PequenoCarvalho,Ana Márcia Macedo LadeiraMazzei,LucasRêgo,Lyvia Julienne SousaLeite,Helio Garcia Artificial intelligence modeling timber potential tropical wood wood technology Abstract: Timber from the second cutting cycle may make up the majority of future crop volumetric. However, there are few studies of the physical and mechanical properties of this timber, which are important to support the consolidation of new species. This study aimed to use Artificial Neural Networks to estimate the physical and mechanical properties of wood from the Amazon, based on basic density. The properties were: shrinkage (tangential, radial and volumetric), static bending, parallel and perpendicular to the fiber compression, parallel and transverse to the fibers, Janka hardness, traction, splitting and shear. The estimate followed the tendency of the data observed for the tangential, radial and volumetric shrinkage. The network estimated the mechanical properties with significant accuracy. Distribution of errors, static bending, parallel compression and perpendicular to the fiber compression also showed significant accuracy. Artificial Neural Networks can be used to estimate the physical and mechanical properties of wood from Amazon species.info:eu-repo/semantics/openAccessUniversidad del Bío-BíoMaderas. Ciencia y tecnología v.20 n.3 20182018-07-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2018000300343en10.4067/S0718-221X2018005003501
institution SCIELO
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country Chile
countrycode CL
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access En linea
databasecode rev-scielo-cl
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region America del Sur
libraryname SciELO
language English
format Digital
author Reis,Pamella Carolline Marques dos Reis
Souza,Agostinho Lopes de
Reis,Leonardo Pequeno
Carvalho,Ana Márcia Macedo Ladeira
Mazzei,Lucas
Rêgo,Lyvia Julienne Sousa
Leite,Helio Garcia
spellingShingle Reis,Pamella Carolline Marques dos Reis
Souza,Agostinho Lopes de
Reis,Leonardo Pequeno
Carvalho,Ana Márcia Macedo Ladeira
Mazzei,Lucas
Rêgo,Lyvia Julienne Sousa
Leite,Helio Garcia
Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood
author_facet Reis,Pamella Carolline Marques dos Reis
Souza,Agostinho Lopes de
Reis,Leonardo Pequeno
Carvalho,Ana Márcia Macedo Ladeira
Mazzei,Lucas
Rêgo,Lyvia Julienne Sousa
Leite,Helio Garcia
author_sort Reis,Pamella Carolline Marques dos Reis
title Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood
title_short Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood
title_full Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood
title_fullStr Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood
title_full_unstemmed Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood
title_sort artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood
description Abstract: Timber from the second cutting cycle may make up the majority of future crop volumetric. However, there are few studies of the physical and mechanical properties of this timber, which are important to support the consolidation of new species. This study aimed to use Artificial Neural Networks to estimate the physical and mechanical properties of wood from the Amazon, based on basic density. The properties were: shrinkage (tangential, radial and volumetric), static bending, parallel and perpendicular to the fiber compression, parallel and transverse to the fibers, Janka hardness, traction, splitting and shear. The estimate followed the tendency of the data observed for the tangential, radial and volumetric shrinkage. The network estimated the mechanical properties with significant accuracy. Distribution of errors, static bending, parallel compression and perpendicular to the fiber compression also showed significant accuracy. Artificial Neural Networks can be used to estimate the physical and mechanical properties of wood from Amazon species.
publisher Universidad del Bío-Bío
publishDate 2018
url http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2018000300343
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