Machine learning prediction of mechanical properties of bamboo by hemicelluloses removal

Abstract

The use of bamboo as a sustainable material is becoming increasingly prevalent; however, the optimisation of its mechanical properties remains a significant challenge. In this study, different concentrations of sodium hydroxide (NaOH) were used to remove the hemicellulose of bamboo and the effect of mechanical properties such as modulus of elasticity, flexural strength and elastic limit were evaluated. A total of 90 samples of data were collected and five machine learning algorithms including Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Ridge Regression (RR), Lasso Regression (LR) and Elastic Network Regression (ENR) were employed to build predictive models based on these properties. The models were trained and tested using 5-fold cross validation. The results showed that the mechanical properties of elastic modulus and elastic limit of bamboo medium were enhanced to 9.63 GPa and 66.44 MPa treated by 10 % NaOH, while the flexural strength of bamboo medium was up to 147.29 GPa with 5 % NaOH. The Ridge Regression algorithm was the best compared to other four algorithms. This methodological approach is not only offers an efficient way to optimize the mechanical properties of bamboo, but also enhances sustainable practices in the environmental sector.