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Prediction of water absorption and swelling of thermally modified fir wood by artificial neural network models

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Abstract

Classification is a useful tool for analyzing the quality control of thermally modified timber. This study aimed to predict water absorption and swelling of thermally modified solid fir wood (Abies sp.) by single and multiple input artificial neural networks (ANN) models based on the lightness difference (∆L*), the total color difference (∆E*), contact angle, and mass loss. Water absorption, swelling in longitudinal (αL), radial (αR), and tangential (αT) directions, as well as volumetric swelling (αV), were measured after 24 h of water soaking. The lowest mean absolute percentage error (MAPE) for the prediction of water absorption by the single input ANN model was 3.910%, based on mass loss. Moreover, for the prediction of αR, αT, and αV by the single input ANN model, it was 3.104%, 3.386%, and 2.755% based on ∆E*, mass loss, and contact angle, respectively. While MAPE values for the prediction of water absorption, αR, αT, and αV by multiple input ANN models were 2.424%, 3.152%, 3.115%, and 2.067%, respectively. These levels of errors are satisfactory for predicting water absorption and swelling of thermally modified fir wood by the ANN models (MAPE < 10%). The difference between MAPE values of the single and multiple input ANN models based on different predictors was smaller than 3%. In terms of time and cost measurement, simple measurement, single input ANN model with color change, especially ∆L* and ∆E* as a predictor, could be the best item. From this viewpoint, the next preferred predictor could be contact angle.

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Authors and Affiliations

  1. Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Akbar Rostampour Haftkhani

  2. Department of Wood and Paper Science, Faculty of Natural Resources, University of Tarbiat Modares, Noor, Islamic Republic of Iran

    Farshid Abdoli

  3. Canada Research Chair in Forest Genomics, Forest Research Centre, Université Laval, Quebec, QC, G1V 0A6, Canada

    Iman Rashidijouybari

  4. Centre de recherche sur les matériaux renouvelables, Département des sciences du bois et de la forêt, Faculté de foresterie, de géographie et de géomatique, Université Laval, Pavillon Gene-H.-Kruger, 2425 rue de la Terrasse, Quebec, QC, G1V 0A6, Canada

    Iman Rashidijouybari & Rosilei A. Garcia

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  1. Akbar Rostampour Haftkhani
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  2. Farshid Abdoli
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Haftkhani, A.R., Abdoli, F., Rashidijouybari, I. et al. Prediction of water absorption and swelling of thermally modified fir wood by artificial neural network models. Eur. J. Wood Prod. 80, 1135–1150 (2022). https://doi.org/10.1007/s00107-022-01839-x

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