Using artificial neural networks in estimating wood resistance

  • Eder Pereira Miguel
  • Rafael Rodolfo de Melo
  • Laércio Serenini Junior
  • Cláudio Henrique Soares Del Menezzi
Keywords: 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.

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Published
2018-03-03
How to Cite
Pereira Miguel, E., Rodolfo de Melo, R., Serenini Junior, L., & Soares Del Menezzi, C. H. (2018). Using artificial neural networks in estimating wood resistance. Maderas. Ciencia Y Tecnología, 20(unassigned). Retrieved from http://revistas.ubiobio.cl/index.php/MCT/article/view/3203

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