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An Automated Classification of Recycled Aggregates for the Evaluation of Product Standard Compliance

Author

Listed:
  • Silvia Serranti

    (Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy)

  • Roberta Palmieri

    (Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy)

  • Giuseppe Bonifazi

    (Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy)

  • Riccardo Gasbarrone

    (Research and Service Center for Sustainable Technological Innovation (Ce.R.S.I.Te.S.), Sapienza University of Rome, 04100 Latina, Italy)

  • Gauthier Hermant

    (Centre Terre et Pierre, Chaussée d’Antoing 55, 7500 Tournai, Belgium)

  • Herve Bréquel

    (Höganäs Belgium SA, Ruelle Gros Pierre 10, 7800 Ath, Belgium)

Abstract

Nowadays, recycling of construction and demolition waste (C&DW) is a challenging opportunity for the management of such end-of-life (EOL) materials through alternative methods to environmentally unsustainable methods (i.e., landfilling). In order to make recycling processes more effective, quality control systems are needed. In this work, the possibility of developing a sensor-based procedure to recognize different demolition waste materials from a recycling perspective was explored. An automatic recognition of different predefined constituent classes of recyclables (i.e., concrete, mortar, natural stones, unbound aggregates, clay masonry units, bituminous materials) and contaminants (i.e., glass, metals, wood, cardboard, and gypsum plaster), as established by an European standard, was carried out using hyperspectral imaging (HSI) working in the short-wave infrared (SWIR) range (1000–2500 nm). The implemented classification strategies, starting from the collected hyperspectral images of the analyzed constituents, allowed for the identification of the different material categories. Two main models were built for identifying contaminants in recyclable materials and categorizing material groups based on technical specifications. The results showed accurate category identification with Sensitivity and Specificity values over 0.9 in all models. The possibility of performing a full detection of C&DW recycling products can dramatically contribute to increasing the quality of the final marketable products and their commercial value, at the same time reducing the amount of waste and the consumption of primary raw materials.

Suggested Citation

  • Silvia Serranti & Roberta Palmieri & Giuseppe Bonifazi & Riccardo Gasbarrone & Gauthier Hermant & Herve Bréquel, 2023. "An Automated Classification of Recycled Aggregates for the Evaluation of Product Standard Compliance," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:15009-:d:1262171
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    References listed on IDEAS

    as
    1. Paulo Miguel Pereira & Castorina Silva Vieira, 2022. "A Literature Review on the Use of Recycled Construction and Demolition Materials in Unbound Pavement Applications," Sustainability, MDPI, vol. 14(21), pages 1-28, October.
    2. Alessandra Bonoli & Sara Zanni & Francisco Serrano-Bernardo, 2021. "Sustainability in Building and Construction within the Framework of Circular Cities and European New Green Deal. The Contribution of Concrete Recycling," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    3. Herbert Sinduja Joseph & Thamilselvi Pachiappan & Siva Avudaiappan & Nelson Maureira-Carsalade & Ángel Roco-Videla & Pablo Guindos & Pablo F. Parra, 2023. "A Comprehensive Review on Recycling of Construction Demolition Waste in Concrete," Sustainability, MDPI, vol. 15(6), pages 1-27, March.
    4. Cyrine Mrad & Luís Frölén Ribeiro, 2022. "A Review of Europe’s Circular Economy in the Building Sector," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
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