Construction and demolition waste material library based on vision systems data.

Area of Science:

• Environmental Science
• Materials Science
• Data Science

Background:

• Construction and Demolition Wastes (CDWs) are a major solid waste stream in the EU, posing environmental challenges.
• The complex composition of CDWs hinders effective reuse and recycling, impeding the circular economy transition.
• Harmful contaminants in CDWs further complicate management and recovery efforts.

Purpose of the Study:

• To develop a comprehensive material library for Construction and Demolition Wastes (CDWs).
• To organize and provide data from advanced material characterization analyses using vision systems.
• To support the development of AI algorithms for optimizing CDW sorting and recycling.

Main Methods:

• Utilized infrared (IR) thermography and hyperspectral imaging (HSI) for material characterization.
• Collected and analyzed representative CDW samples from EU building demolition and renovation.
• Standardized and organized data for compatibility with statistical analysis and machine learning tools.

Main Results:

• Created a structured material library with data from IR thermography and HSI.
• Ensured data accuracy and reproducibility through rigorous sample preparation and analysis protocols.
• Facilitated data integration into predictive models for waste management and decision-making.

Conclusions:

• The material library is a significant contribution to CDW management and sustainable construction.
• It enables better material characterization, separation, and resource utilization.
• Provides a foundation for AI-driven innovations in optimizing CDW recycling processes.