Machine learning-based prediction of internal moisture variation in kiln-dried timber-Reference-Cited by-同舟云学术

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Machine learning-based prediction of internal moisture variation in kiln-dried timber

Published:2023-10-09 Issue: Volume: Page:1-12
ISSN:1748-0272
Container-title:Wood Material Science & Engineering
language:en
Short-container-title:Wood Material Science & Engineering

Author:

Rahimi Sohrab¹²,Avramidis Stavros²,Sassani Farrokh³,Nasir Vahid⁴

Affiliation:

1. FPInnovations, Vancouver, Canada

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

3. Department of Mechanical Engineering, University of British Columbia, Vancouver, Canada

4. Department of Wood Science and Engineering, Oregon State University, Corvallis, OR, USA

Publisher

Informa UK Limited

Subject

General Materials Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/17480272.2023.2263422

Reference49 articles.

1. Avramidis, S., Lazarescu, C., and Rahimi, S., 2023. Basics of wood drying. In: Niemz Peter, Alfred Teischinger and Dick Sandberg, eds. Springer handbook of wood science and technology. Cham: Springer, 679–706.

2. Influence of pre-steaming on kiln drying of thick hem-fir lumber;Avramidis S.;Forest Products Journal,1993

3. Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression

4. Impact of Wood Variability on the Drying Rate at Different Moisture Content Levels

5. A Decade of Improved Lumber Drying Technology

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