Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood
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 es...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Universidad del Bío-Bío
2018-07-01
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| Series: | Maderas: Ciencia y Tecnología |
| Subjects: | |
| Online Access: | https://revistas.ubiobio.cl/index.php/MCT/article/view/3124 |
| _version_ | 1797354627135438848 |
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| author | Pamella Carolline Marques dos Reis Reis Agostinho Lopes de Souza Leonardo Pequeno Reis Ana Márcia Macedo Ladeira Carvalho Lucas Mazzei Lyvia Julienne Sousa Rêgo Helio Garcia Leite |
| author_facet | Pamella Carolline Marques dos Reis Reis Agostinho Lopes de Souza Leonardo Pequeno Reis Ana Márcia Macedo Ladeira Carvalho Lucas Mazzei Lyvia Julienne Sousa Rêgo Helio Garcia Leite |
| author_sort | Pamella Carolline Marques dos Reis Reis |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-03-08T13:53:10Z |
| format | Article |
| id | doaj.art-5c9f5847e79045bbba55ff1fbccf3a62 |
| institution | Directory Open Access Journal |
| issn | 0717-3644 0718-221X |
| language | English |
| last_indexed | 2024-03-08T13:53:10Z |
| publishDate | 2018-07-01 |
| publisher | Universidad del Bío-Bío |
| record_format | Article |
| series | Maderas: Ciencia y Tecnología |
| spelling | doaj.art-5c9f5847e79045bbba55ff1fbccf3a622024-01-15T18:36:06ZengUniversidad del Bío-BíoMaderas: Ciencia y Tecnología0717-36440718-221X2018-07-012033124Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle woodPamella Carolline Marques dos Reis ReisAgostinho Lopes de SouzaLeonardo Pequeno ReisAna Márcia Macedo Ladeira CarvalhoLucas MazzeiLyvia Julienne Sousa RêgoHelio Garcia Leite 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. https://revistas.ubiobio.cl/index.php/MCT/article/view/3124Artificial intelligencemodelingtimber potentialtropical woodwood technology |
| spellingShingle | Pamella Carolline Marques dos Reis Reis Agostinho Lopes de Souza Leonardo Pequeno Reis Ana Márcia Macedo Ladeira Carvalho Lucas Mazzei Lyvia Julienne Sousa Rêgo Helio Garcia Leite Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood Maderas: Ciencia y Tecnología Artificial intelligence modeling timber potential tropical wood wood technology |
| title | 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_short | 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 |
| topic | Artificial intelligence modeling timber potential tropical wood wood technology |
| url | https://revistas.ubiobio.cl/index.php/MCT/article/view/3124 |
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