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Artificial neural network modeling for predicting elastic strain of white birch disks during drying

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Abstract

Elastic strain is one of the most important parameters associated with drying stresses. The research presented in this paper attempts to develop an artificial neural network based model for predicting elastic strain in white birch (Betula platyphylla Suk) disks during drying as a function of temperature, moisture content, relative humidity and distance from the pith. The data set was obtained by using image analysis method under two drying schedules and divided into three subsets for training (60%), validation (20%) and test (20%). According to the results, the values of determination coefficient (R2) obtained were greater than 0.97, 0.96 and 0.95 for the training, validation and test sets, respectively.

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Acknowledgements

This study is financially supported by the Fundamental Research Funds for the Central Universities of China (Grant No. 2572015AB08).

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Authors and Affiliations

  1. Key Laboratory of Bio-based Material Science and Technology (Ministry of Education), College of Material Science and Engineering, Northeast Forestry University, Harbin, 150040, People’s Republic of China

    Zongying Fu, Jingyao Zhao & Yingchun Cai

  2. Department of Wood Science, University of British Columbia, Vancouver, Canada

    Zongying Fu & Stavros Avramidis

Authors
  1. Zongying Fu
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  2. Stavros Avramidis
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  3. Jingyao Zhao
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  4. Yingchun Cai
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Correspondence to Yingchun Cai.

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Fu, Z., Avramidis, S., Zhao, J. et al. Artificial neural network modeling for predicting elastic strain of white birch disks during drying. Eur. J. Wood Prod. 75, 949–955 (2017). https://doi.org/10.1007/s00107-017-1183-x

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