A new AI system that can automatically identify contaminated construction and demolition wood waste has been developed by researchers from Monash University and Charles Darwin University (CDU).
Published in Resources, Conservation and Recycling, the study presents the first real-world image dataset of contaminated wood waste—a major step toward smarter recycling and sustainable construction.
The research team, led by Madini De Alwis with Dr. Milad Bazli (CDU), under the supervision of Associate Professor Mehrdad Arashpour, Head of Construction Engineering at Monash, trained and tested cutting-edge deep learning models to detect contamination types in wood waste using images.
Contaminated wood from construction and demolition sites often ends up in landfill due to the difficulty of sorting it manually. But by applying AI models the team found strong precision and recall across six types of wood contamination.
“We curated the first real-world image dataset of contaminated construction and demolition wood waste,” said Madini, a Ph.D. candidate at Monash’s Department of Civil and Environmental Engineering.
“This new system could be deployed via camera-enabled sorting lines, drones or handheld tools to support on-site decision-making.”
While computer vision has been explored in general waste streams, its application to contaminated wood waste has remained limited, until now.
“By fine-tuning state-of-the-art deep learning models, including CNNs and Transformers, we showed that these tools can automatically recognize contamination types in wood using everyday RGB images,” Dr. Bazli said.
Wood waste is one of the largest components of construction waste globally. Most of it can be recycled, but contamination from paint, chemicals, metals and other construction residues makes sorting difficult and costly.
“This opens the door to scalable, AI-driven solutions that support wood waste reuse, recycling and reclamation,” Dr. Bazli said.
By integrating AI with waste management practices, the study supports Australia’s circular economy goals and the global push for greener construction.
“This is a practical, scalable solution for a global waste problem. By enabling automated sorting, we’re giving recyclers and contractors a powerful tool to recover valuable resources and reduce landfill dependency,” Madini said.
More information:
A. Madini Lakna De Alwis et al, Automated recognition of contaminated construction and demolition wood waste using deep learning, Resources, Conservation and Recycling (2025). DOI: 10.1016/j.resconrec.2025.108278
Monash University
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AI detects contaminated construction wood with 91% accuracy (2025, June 3)
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