Using artificial neural networks in estimating wood resistance
Abstract: The purpose of this research was to evaluate the potential of Artificial Neural Networks in estimating the properties of wood resistance. In order to do so, a hybrid of eucalyptus (Eucalyptus urograndis) planted in the Northern Region of the State of Mato Grosso was selected and ten trees were collected. Then, four samples of each tree were removed, totaling 40 samples, which were later subjected to non-destructive testing of apparent density, ultrasonic wave propagation velocity, dynamic modulus of elasticity obtained by ultrasound, and Janka hardness. These properties were used as estimators of resistance and compressive strength parallel to fibers, and hardness. Multilayer Perceptron networks were also employed, training 100 of them for each of the evaluated parameters. The obtained results indicated that the use of Artificial Neural Networks is an efficient tool for predicting wood resistance.
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Universidad del Bío-Bío
2018
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oai:scielo:S0718-221X20180004005312019-02-14Using artificial neural networks in estimating wood resistanceMiguel,Eder PereiraMelo,Rafael Rodolfo deSerenini Junior,LaércioMenezzi,Cláudio Henrique Soares Del Artificial intelligence Eucalyptus urograndis hardness mechanical properties non-destructive testing. Abstract: The purpose of this research was to evaluate the potential of Artificial Neural Networks in estimating the properties of wood resistance. In order to do so, a hybrid of eucalyptus (Eucalyptus urograndis) planted in the Northern Region of the State of Mato Grosso was selected and ten trees were collected. Then, four samples of each tree were removed, totaling 40 samples, which were later subjected to non-destructive testing of apparent density, ultrasonic wave propagation velocity, dynamic modulus of elasticity obtained by ultrasound, and Janka hardness. These properties were used as estimators of resistance and compressive strength parallel to fibers, and hardness. Multilayer Perceptron networks were also employed, training 100 of them for each of the evaluated parameters. The obtained results indicated that the use of Artificial Neural Networks is an efficient tool for predicting wood resistance.info:eu-repo/semantics/openAccessUniversidad del Bío-BíoMaderas. Ciencia y tecnología v.20 n.4 20182018-10-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2018000400531en10.4067/S0718-221X2018005004101 |
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Miguel,Eder Pereira Melo,Rafael Rodolfo de Serenini Junior,Laércio Menezzi,Cláudio Henrique Soares Del |
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Miguel,Eder Pereira Melo,Rafael Rodolfo de Serenini Junior,Laércio Menezzi,Cláudio Henrique Soares Del Using artificial neural networks in estimating wood resistance |
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Miguel,Eder Pereira Melo,Rafael Rodolfo de Serenini Junior,Laércio Menezzi,Cláudio Henrique Soares Del |
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Miguel,Eder Pereira |
| title |
Using artificial neural networks in estimating wood resistance |
| title_short |
Using artificial neural networks in estimating wood resistance |
| title_full |
Using artificial neural networks in estimating wood resistance |
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Using artificial neural networks in estimating wood resistance |
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Using artificial neural networks in estimating wood resistance |
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using artificial neural networks in estimating wood resistance |
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Abstract: The purpose of this research was to evaluate the potential of Artificial Neural Networks in estimating the properties of wood resistance. In order to do so, a hybrid of eucalyptus (Eucalyptus urograndis) planted in the Northern Region of the State of Mato Grosso was selected and ten trees were collected. Then, four samples of each tree were removed, totaling 40 samples, which were later subjected to non-destructive testing of apparent density, ultrasonic wave propagation velocity, dynamic modulus of elasticity obtained by ultrasound, and Janka hardness. These properties were used as estimators of resistance and compressive strength parallel to fibers, and hardness. Multilayer Perceptron networks were also employed, training 100 of them for each of the evaluated parameters. The obtained results indicated that the use of Artificial Neural Networks is an efficient tool for predicting wood resistance. |
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Universidad del Bío-Bío |
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2018 |
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http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-221X2018000400531 |
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