ARTIFICIAL NEURAL NETWORKS AS A NEW TOOL FOR ASSESSING AND MONITORING WOOD MOISTURE CONTENT

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dc.creatorAntônio José Vinha Zanuncio
dc.creatorAmélia Guimarães Carvalho
dc.creatorLiniker Fernandes da Silva
dc.creatorAngélica de Cássia Oliveira Carneiro
dc.creatorJorge Luiz Colodette
dc.date2016
dc.date.accessioned2026-07-07T03:15:35Z
dc.descriptionDrying of wood is necessary for its use and moisture control is important during this process. The aim of this study was to use artificial neural networks to evaluate and monitor the wood moisture content during drying. Wood samples of 2 × 2 × 4 cm were taken at 1.3 m above the ground, outside of radial direction, from seven 2-year-old materials and three 7-year-old materials. These samples were saturated and drying was evaluated until the equilibrium moisture content, then, the artificial neural networks were created. The materials with higher initial moisture reached equilibrium moisture content faster due to its higher drying rate. The basic density of all wood materials was inversely proportional at the beginning and directly proportional to the moisture at the end of drying. All artificial neural networks used in this work showed high accuracy to estimate the moisture, however, the neural network based on the basic density and drying days was the best. Therefore, artificial neural networks can be used to control the moisture content of wood during drying.
dc.formatapplication/pdf
dc.identifier0100-6762
dc.identifierhttps://www.redalyc.org/articulo.oa?id=48846415018
dc.identifier.urihttp://hdl.handle.net/123456789/428391
dc.languageen
dc.publisherUniversidade Federal de Viçosa
dc.relationhttp://www.redalyc.org/revista.oa?id=488
dc.rightsRevista Árvore
dc.sourceRevista Árvore (Brasil) Num.3 Vol.40
dc.subjectAgrociencias
dc.titleARTIFICIAL NEURAL NETWORKS AS A NEW TOOL FOR ASSESSING AND MONITORING WOOD MOISTURE CONTENT
dc.typeartículo científico

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