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...

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Bibliographic Details
Published in:European journal of wood and wood products 2017-05, Vol.75 (3), p.385-396
Main Authors: Korai, Hideaki, Watanabe, Ken
Format: Article
Language:English
Subjects:
Artificial neural networks
Biomedical and Life Sciences
Bond strength
Bonding strength
Ceramics
Climatic conditions
Composites
Exposure
Glass
High temperature
Life Sciences
Linearity
Low temperature
Machines
Manufacturing
Multiple regression analysis
Natural Materials
Neural networks
Original
Particle board
Processes
Reduction
Wood Science & Technology
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Summary: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.
ISSN:0018-3768
1436-736X
DOI:10.1007/s00107-016-1056-8