Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach

Sebahattin Tiryaki,Aytac Aydin,Gökay Nemli,Selahattin Bardak

doi:10.4067/s0718-221x2016005000043

Sebahattin Tiryaki, Aytac Aydin + Show 2 more

Open Access

https://doi.org/10.4067/s0718-221x2016005000043

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Abstract

Shrinkage and swelling characteristics of wood as a hygroscopic material affect negatively its effective utilization for a variety of applications. Heat treatment is widely used for minimizing the negative effects of volumetric swelling and shrinkage of wood. The present study aims to develop artificial neural network (ANN) models for predicting volumetric swelling and shrinkage of heat treated woods. For this purpose, wood samples were subjected to heat treatment at varying temperatures (130, 150, 170 and 190 oC) for varying durations (2, 4, 6 and 8 h). Experimental results have showed that volumetric swelling and shrinkage of wood decreased by heat treatment. Then, neural networks models capable of predicting the swelling and shrinkage of the treated woods were developed based on the resulting data. It was seen that ANN models allowed volumetric swelling and shrinkage of such woods to predict successfully with a limited set of experimental data. This approach was able to predict volumetric swelling and shrinkage of wood with a mean absolute percentage error equal to 2,599% and 2,647% in test phase, respectively. The developed models might thus serve as a robust tool to predict volumetric swelling and shrinkage with less number of experiments.

Highlights

Wood has been widely used as a building material due to its superior properties
The present study has focused on developing artificial neural network (ANN) models that are capable of predicting the amount of volumetric swelling and shrinkage of heat treated woods
The change in the volumetric swelling and shrinkage based on wood species, treatment temperature and exposure time was predicted by the ANN models developed in the MATLAB software package

Summary

INTRODUCTION

Wood has been widely used as a building material due to its superior properties. Many experimental studies for better understanding the impact of heat treatment on the amount of volumetric shrinkage and swelling of wood have been conducted so far (Esteves et al 2007, Gunduz et al 2008, Korkut and Budakci 2010). The present study has focused on developing ANN models that are capable of predicting the amount of volumetric swelling and shrinkage of heat treated woods. Among many different kinds of ANNs, the multi-layer perceptron (MLP) is known as the most useful type It is a feed-forward architecture that is capable of mapping the set of input data onto a set of proper outputs (Haghbakhsh et al 2013). It is possible to properly evaluate the performance of the established model (May et al 2010)

MATERIALS AND METHODS

Experimental results

CONCLUSIONS