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

Maria Teresa Calcagni1, Giovanni Salerno1, Gloria Cosoli2

  • 1Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy.

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Summary

This study introduces a material library for Construction and Demolition Wastes (CDWs), using infrared thermography and hyperspectral imaging. This resource aids in optimizing recycling processes and promoting sustainable construction.

Keywords:
CDWsEmissivityHyperspectralMaterial spectral characterizationVision-based measurement

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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.