Predicting internal bond strength of particleboard under outdoor exposure based on climate data: comparison of multiple linear regression and artificial neural network

Abstract

The internal bond strength (IB) of a commercial particleboard put under various outdoor exposure conditions were modeled using a multiple linear regression (MLR) and an artificial neural network (ANN). The outdoor exposure data used in this study were collected from the results of past outdoor exposure tests conducted at eight locations across Japan from 2004 to 2011. The data from five locations were used to develop the MLR model and the ANN model for predicting the IB of particleboard under outdoor exposure based on climate data including exposure duration, annual mean temperature, annual sunshine duration, and annual precipitation. The performance of the models was assessed by comparing predicted IB values with measured ones for the remaining three locations. The MLR model gave a high R 2 of 0.87 and a low root mean square error (RMSE) of 0.07 MPa, while the ANN model gave an R 2 of 0.93 and an RMSE of 0.05 MPa. Thus, both models were demonstrated to be applicable to particleboards exposed at different locations in Japan. A statistical test of the MLR model revealed that the IB was influenced negatively by exposure duration, temperature, and precipitation. These influences were confirmed by the sensitivity analysis of the ANN model, and the analysis also showed an additional positive influence of sunshine duration on IB.