An artificial neural network model for predicting mechanical strength of bamboo-wood composite based on layups configuration

Abstract

The transportation application of the bamboo–wood composite container flooring (BWCCF) has increased considerably. However, materials would be destroyed in the process of common mechanical evaluation, resulting in a waste of resources. Therefore, this paper aims to design artificial neural network (ANN) models to predict mechanical strength of BWCCF. The modulus of rupture (MOR) and the modulus of elasticity (MOE) of BWCCF were predicted by ANN models based on layups configuration, including directions, densities, and thicknesses of 21-layer BWCCF in each layer. According to results, the mean absolute percentage errors (MAPE) and the correlation coefficient (R) were determined as 16.93% and 0.619 in prediction of MOR, and 10.10% and 0.709 in prediction of MOE, respectively. The results indicated that ANN can be applied to predict mechanical properties of BWCCF.