Enhanced Recognition of Sustainable Wood Building Materials Based on Deep Learning and Augmentation

Wei Gan, Shengbiao Li, Jinyu Li, Shuqi Peng, Ruoxi Li, Lan Qiu, Baofeng Li and Yi He ()
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Wei Gan: School of Design, Huazhong University of Science and Technology, Wuhan 430074, China
Shengbiao Li: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Jinyu Li: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Shuqi Peng: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Ruoxi Li: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Lan Qiu: Architecture and Urban Planning Design and Research Institute of Huazhong University of Science and Technology Co., Ltd., Wuhan 430074, China
Baofeng Li: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
Yi He: School of Design, Huazhong University of Science and Technology, Wuhan 430074, China

Sustainability, 2025, vol. 17, issue 15, 1-26

Abstract: The accurate identification of wood patterns is critical for optimizing the use of sustainable wood building materials, promoting resource efficiency, and reducing waste in construction. This study presents a deep learning-based approach for enhanced wood material recognition, combining EfficientNet architecture with advanced data augmentation techniques to achieve robust classification. The augmentation strategy incorporates geometric transformations (flips, shifts, and rotations) and photometric adjustments (brightness and contrast) to improve dataset diversity while preserving discriminative wood grain features. Validation was performed using a controlled augmentation pipeline to ensure realistic performance assessment. Experimental results demonstrate the model’s effectiveness, achieving 88.9% accuracy (eight out of nine correct predictions), with further improvements from targeted image preprocessing. The approach provides valuable support for preliminary sustainable building material classification, and can be deployed through user-friendly interfaces without requiring specialized AI expertise. The system retains critical wood pattern characteristics while enhancing adaptability to real-world variability, supporting reliable material classification in sustainable construction. This study highlights the potential of integrating optimized neural networks with tailored preprocessing to advance AI-driven sustainability in building material recognition, contributing to circular economy practices and resource-efficient construction.

Keywords: sustainable wood materials; deep learning; EfficientNet; data augmentation; building material classification; resource efficiency; circular construction; computer vision (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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