Manufacturing, Characterization and Engineering Applications of Wood and Bamboo Bio-Based Engineered Materials

A Special Issue of Forests (ISSN 1999-4907) belonging to the section "Wood Science and Forest Products".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 151

Editor

College of Landscape Architecture, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China
Interests: cross-laminated timber; glued laminated timber; engineered bamboo; cross-laminated bamboo-timber; bamboo scrimber; laminated bamboo; manufacturing and processing; mechanical performance; durability performance

Special Issue Information

Dear Colleagues,

Bio-based engineered materials have received increasing attention in recent years due to the growing demand for sustainable development, low-carbon construction and the efficient utilization of renewable resources. Materials such as engineered bamboo, engineered timber and bamboo–wood composites offer clear advantages in terms of renewability, environmental performance and the potential for structural applications. At the same time, their broader engineering application still depends on continued advances in raw material utilization, manufacturing technologies, material characterization and engineering design.

This Special Issue of Forests focuses on the manufacturing, characterization and engineering applications of bio-based engineered materials. Contributions may address any aspect of the development and use of these materials, including raw material preparation, processing technologies, physical and mechanical properties and structural applications. Studies that improve the scientific understanding of these materials and support their broader use in engineering practice are particularly welcome.

Dr. Hao Li
Guest Editor

Manuscript Submission Information

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Keywords

  • bio-based engineered materials
  • low-carbon materials
  • engineered bamboo
  • engineered timber
  • bamboo-wood composites
  • manufacturing and processing
  • mechanical properties
  • sustainable construction
  • structural applications

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Published Papers (1 paper)

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Research

17 pages, 36223 KB  
Article
Ultimate Bearing Capacity of Engineered Bamboo Columns of Varying Lengths Under Eccentric Compression: Data-Driven Modeling
by Xin Zhang, Qing Guo, Guijuan Hu, Ben-Yue Li, Brad Jianhe Wang, Yihan Lan and Hao Li
Forests 2026, 17(9), 1122; https://doi.org/10.3390/f17091122 (registering DOI) - 20 Sep 2026
Abstract
This study investigates the ultimate bearing capacity of engineered bamboo columns of varying lengths under eccentric loading using a data-driven machine learning (ML) approach. Although previous studies have investigated engineered bamboo columns under axial or eccentric compression, few have systematically examined the combined [...] Read more.
This study investigates the ultimate bearing capacity of engineered bamboo columns of varying lengths under eccentric loading using a data-driven machine learning (ML) approach. Although previous studies have investigated engineered bamboo columns under axial or eccentric compression, few have systematically examined the combined effects of load eccentricity and column length. A database containing 432 records was established, with eccentricity, column length, cross-sectional area, compressive strength, and elastic modulus used as input features. Six tree-based ML algorithms were used to develop predictive models, all of which achieved satisfactory predictive accuracy. Shapley additive explanations analysis indicated that the trained random forest (RF) and extreme gradient boosting (XGBoost) models relied most strongly on eccentricity, followed by compressive strength, cross-sectional area, and column length, whereas elastic modulus made the smallest contribution within the range covered by the database. A new functional form was proposed for predicting the ultimate bearing capacity based on the eccentricity ratio, slenderness ratio, stability factor, compressive strength, and cross-sectional area. Two ML-assisted explicit models were developed using predictions generated by the trained RF and XGBoost models. Validation against both the developed database and an independent dataset showed that the explicit model derived from the XGBoost predictions achieved higher predictive accuracy than the RF-based model. Full article
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