Predicting the strength reduction of particleboard subjected to various climatic conditions in Japan using artificial neural networks

Particleboard specimens were subjected to various climatic conditions in Japan, and the relationships between climatic factors and internal bond strength (IB) were investigated using multiple regression analysis (MRA) or artificial neural networks (ANN). At low- and middle-temperature sites, the IB predicted using MRA (IB_MRA) and ANN (IB_ANN) decreased linearly with increasing exposure time. In addition, at high-temperature sites, with increasing exposure time, IB_MRA decreased linearly, whereas IB_ANN decreased exponentially. The trend of IB_ANN was almost identical to that of the measured IB of the specimens subjected to various climatic conditions. Moreover, IB_MRA and IB_ANN for 1-, 3-, and 5-year exposures were predicted using nationwide climatic factors. The minimum IB is zero when the particleboard is deteriorated; however, negative IB was predicted using MRA when the exposure time increased in the high-temperature area. In addition, the IB for 1-year exposure in the low-temperature area near site 1 was higher than the initial IB of 0.833 MPa. MRA is not always valid because of the assumption of linearity. However, negative IB even for 5-year exposure in the high-temperature area and high IB even for 1-year exposure in the low-temperature area were not predicted using ANN. The IB reduction was predicted correctly using ANN, and the correct IB reduction could be mapped.