Project: Predicting the water vapor sorption of wood with machine learning methods
The water vapor sorption behavior of wood is a fundamental property that affects nearly all of its properties such as heat capacity, thermal conductivity, strength, dimensions, and resistance against decay. Prediction of the water sorption of wood with the existing numerical models is limited and their validity is under question, mainly due to the heterogeneity of wood structure (physically and chemically) at the cell wall level. This project aims at evaluating the applicability of machine learning methods for predicting the sorption behavior of wood species.