Prediction of equilibrium moisture content and swelling of thermally modified hardwoods by Artificial Neural Networks

In this study artificial neural network (ANN) models were developed for predicting the effects of wood species, density, modifying time, and temperature on the equilibrium moisture content (EMC) and swelling of six different thermally modified hardwood species, as previously published by the authors. Lumber of Yellow-poplar (Liriodendron tulipifera), red oak (Quercus borealis), white ash (Fraxinus americana), red maple (Acer rubrum), hickory (Carya glabra), and black cherry (Prunus serotina) were selected. Treatment type, species, temperature, time, and density were used as inputs for the models. Using Keras and Pytorch libraries in Python, different feed forward and back propagation multilayer ANN models were created and tested. The best prediction models, determined based on the errors in training iterations, were selected and used for testing. Based on the performance analysis, the prediction ANN models were accurate, reliable, and effective tools in terms of time and cost-effectiveness, for predicting the EMC and swelling characteristics of thermally modified wood. The multiple-input model was more accurate than the single-input model and it provided a prediction with R2 of 0.9975, 0.92, and MAPE of 1.36, 7.77 for EMC and swelling.