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Forests
Special Issues
Wood Properties: Measurement, Modeling, and Future Needs
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Wood Properties: Measurement, Modeling, and Future Needs

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Wood Science and Forest Products".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 3171

Special Issue Editors

Dr. Rafael De Avila Delucis

E-Mail Website
Guest Editor
Postgraduate Program in Materials Science and Engineering (PPGCEM), Technology Development Center, Federal University of Pelotas (UFPel), Pelotas 96010-610, Brazil
Interests: wood properties; mechanical properties; material characterization; materials
Dr. Rene Herrera Diaz

E-Mail Website
Guest Editor
1 InnoRenew CoE, Livade 6A, 6310 Izola, Slovenia
2 Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia
Interests: wood modification; bio-based materials; analytical techniques (IR–Vis spectroscopy, hyperspectral images, chemometrics)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wood properties are crucial in various industries, including construction, furniture, and paper production. This Special Issue aims to explore advancements in measuring and modeling wood properties, addressing current challenges, and anticipating future needs in this field. Topics may include but are not limited to wood density, moisture content, mechanical properties, and the implications of these aspects for sustainability and innovation in wood-based industries.

This SI seeks to provide a comprehensive overview of wood property measurement techniques, modeling approaches, and future research directions, thereby fostering collaboration and knowledge exchange among researchers from different fields. This SI will feature research on innovative measurement and modeling techniques for wood properties, including advancements in ultrasound techniques, imaging technology, machine learning, and predictive modeling.

We invite submissions of original research papers, review articles, and methodological studies aimed at advancing the measurement, modeling, and comprehension of wood properties. Topics of interest encompass wood density, moisture content, mechanical properties, durability, and the diverse applications of these aspects across various fields.

Dr. Rafael De Avila Delucis
Dr. Rene Herrera Diaz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wood properties
  • measurement techniques
  • modeling
  • sustainability
  • innovation
  • forestry
  • timber engineering
  • material science
  • renewable resources
  • industry standards

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Published Papers (3 papers)

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Research

25 pages, 2704 KiB  
Article
Prediction of Heat-Treated Wood Adhesive Strength Using BP Neural Networks Optimized by Four Novel Metaheuristic Algorithms
by Ying Cao, Wei Wang and Yan He
Forests 2025, 16(2), 291; https://doi.org/10.3390/f16020291 - 8 Feb 2025
Cited by 1 | Viewed by 520
Abstract
This study integrates the Backpropagation (BP) Neural Network with several optimization algorithms, namely Hippopotamus Optimization (HO), Parrot Optimization (PO), Osprey Optimization Algorithm (OOA), and Goose Optimization (GO), to develop four predictive models for the adhesive strength of heat-treated wood: HO-BP, PO-BP, OOA-BP, and [...] Read more.
This study integrates the Backpropagation (BP) Neural Network with several optimization algorithms, namely Hippopotamus Optimization (HO), Parrot Optimization (PO), Osprey Optimization Algorithm (OOA), and Goose Optimization (GO), to develop four predictive models for the adhesive strength of heat-treated wood: HO-BP, PO-BP, OOA-BP, and GO-BP. These models were used to predict the adhesive strength of the wood that was heat-treated under multiple variables such as treatment temperature, time, feed rate, cutting speed, and abrasive particle size. The efficacy of the BP neural network models was assessed utilizing the coefficient of determination (R2), error rate, and CEC test dataset. The outcomes demonstrate that, relative to the other algorithms, the Hippopotamus Optimization (HO) method shows better search efficacy and convergence velocity. Furthermore, XGBoost was used to statistically evaluate and rank input variables, revealing that cutting speed (m/s) and treatment time (hours) had the most significant impact on model predictions. Taken together, these four predictive models demonstrated effective applicability in assessing adhesive strength under various processing conditions in practical experiments. The MAE, RMSE, MAPE, and R2 values of the HO-BP model reached 0.0822, 0.1024, 1.1317, and 0.9358, respectively, demonstrating superior predictive accuracy compared to other models. These findings support industrial process optimization for enhanced wood utilization. Full article
(This article belongs to the Special Issue Wood Properties: Measurement, Modeling, and Future Needs)
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15 pages, 3442 KiB  
Article
Parameter Estimations on Measurement Accuracy for Thermal Conductivity of Wood Using the Transient Plane Source Method
by Hongxu Meng, Xinxin Yu, Bonan Chen, Pengyuan Ren and Jingyao Zhao
Forests 2024, 15(10), 1820; https://doi.org/10.3390/f15101820 - 17 Oct 2024
Viewed by 858
Abstract
In order to enhance the reliability and accuracy of the results from the transient plane source (TPS) method for measuring the thermal conductivity of wood, this paper investigates setting parameters and measurement methods to improve measurement accuracy. Criteria are proposed to determine the [...] Read more.
In order to enhance the reliability and accuracy of the results from the transient plane source (TPS) method for measuring the thermal conductivity of wood, this paper investigates setting parameters and measurement methods to improve measurement accuracy. Criteria are proposed to determine the optimal parameters such as the power output, heating time, and time window. The measurement results of the TPS method and the HFM method are compared. The results show that the total to characteristic time, temperature increase in the probe, mean deviation, and temperature drift graph are valid indicators for evaluating the detection reliability of the TPS method. The optimal parameters for measuring the thermal conductivity of wood using the TPS method are as follows: power output of 0.05 or 0.1 W, heating time of 120 s, and time window covering 60% to 80% of the heating time. The thermal conductivity measured with the TPS method was higher than that measured by the steady-state method in all grain angle directions. The standard uncertainties after optimization were 18.9% to 59.5% lower than before optimization. The optimized TPS measurement method can be applied to other tree species as well. Full article
(This article belongs to the Special Issue Wood Properties: Measurement, Modeling, and Future Needs)
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28 pages, 4709 KiB  
Article
Prediction of Bonding Strength of Heat-Treated Wood Based on an Improved Harris Hawk Algorithm Optimized BP Neural Network Model (IHHO-BP)
by Yan He, Wei Wang, Ying Cao, Qinghai Wang and Meng Li
Forests 2024, 15(8), 1365; https://doi.org/10.3390/f15081365 - 5 Aug 2024
Cited by 3 | Viewed by 1178
Abstract
In this study, we proposed an improved Harris Hawks Optimization (IHHO) algorithm based on the Sobol sequence, Whale Optimization Algorithm (WOA), and t-distribution perturbation. The improved IHHO algorithm was then used to optimize the BP neural network, resulting in the IHHO-BP model. This [...] Read more.
In this study, we proposed an improved Harris Hawks Optimization (IHHO) algorithm based on the Sobol sequence, Whale Optimization Algorithm (WOA), and t-distribution perturbation. The improved IHHO algorithm was then used to optimize the BP neural network, resulting in the IHHO-BP model. This model was employed to predict the bonding strength of heat-treated wood under varying conditions of temperature, time, feed rate, cutting speed, and grit size. To validate the effectiveness and accuracy of the proposed model, it was compared with the original BP neural network model, WOA-BP, and HHO-BP benchmark models. The results showed that the IHHO-BP model reduced the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 51.16%, 40.38%, and 51.93%, respectively, while increasing the coefficient of determination (R2) by at least 10.85%. This indicates significant model optimization, enhanced generalization capability, and higher prediction accuracy, better meeting practical engineering needs. Predicting the bonding strength of heat-treated wood using this model can reduce production costs and consumption, thereby significantly improving production efficiency. Full article
(This article belongs to the Special Issue Wood Properties: Measurement, Modeling, and Future Needs)
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