Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks

Abstract

In this study, MOR and MOE of the heat-treated wood were predicted by artificial neural networks (ANNs). For this purpose, samples were prepared from beech wood (Fagus orientalis Lipsky.) and spruce wood (Picea orientalis (L.) Link.). The samples were exposed to heat treatment at varying temperatures (125, 150, 175 and 200°C) for varying durations (3, 5, 7 and 9h). According to the results, the mean absolute percentage errors (MAPE) were determined as 0.74%, 1.01% and 1.04% in prediction of MOR values, and 1.14%, 2.21% and 2.13%, in prediction of MOE values for training, validation and testing data sets, respectively. In the prediction of MOR and MOE, values of R2 were obtained greater than 0.99 for all data sets with the proposed ANN models. The results show that ANN can be used successfully for predicting MOR and MOE of heat-treated wood.