An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model

• Effects of heat treatment temperature and duration on CS were studied.
• CS values were predicted with the ANN and MLR models using the experimental data.
• CS values decreased with increasing heat treatment temperature and duration.
• ANN showed a better prediction performance compared to MLR.
• It was shown that the ANN model save time, and decrease the experimental costs.

This paper aims to design an artificial neural network model to predict compression strength parallel to grain of heat treated woods, without doing comprehensive experiments. In this study, the artificial neural network results were also compared with multiple linear regression results. The results indicated that artificial neural network model provided better prediction results compared to the multiple linear regression model. Thanks to the results of this study, strength properties of heat treated woods can be determined in a short period of time with low error rates so that usability of such wood species for structural purposes can be better understood.