MODELLING WATER INTAKE PROPERTIES OF HEAT-TREATED BEECH AND SPRUCE WOOD TREATED AT DIFFERENT TEMPERATURES USING BY ARTIFICIAL NEURAL NETWORKS

Abstract

The aim of this study is the modelling the water intake rate of heat-treated oriental beech (Fagus orientalis Lipsky) and oriental spruce (Picea orientalis (L) Link) wood samples. For this purpose, all the needed data were obtained from the beech and spruce wood samples which have been subjected to heat treatment with four different temperatures (130, 150, 180 and 200 °C) and three different periods (2, 6 and 10 hour) and then which have been subjected to the water intake process at certain periods (2, 4, 8, 24, 48, 72, 168 and 336 hour). Data were modeled using artificial neural networks (ANN) method for both tree species in terms of water intake rate characteristics, seperately. Two different learning algorithms (Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG)) were used for the modeling process. In order to achieve the best model, all nodes between 1 and 25 were tested as hidden neuron. A total of 100 models were obtained and 2 models were chosen according to the performance of the models. For two wood species, LM learning algorithm had showed better results than SCG learning algorithm. The structures of the best models for beech and spruce were determined as 3-8-1 and 3-13-1 respectively. As a result, it has been concluded that ANN applications can be evaluated within the discipline of wood protection.

Keywords

• Heat treatment
• beech
• spruce
• water intake
• artificial neural networks

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