Automatic detection and classification of EOL-concrete and resulting recovered products by hyperspectral imaging

Roberta Palmieri; Giuseppe Bonifazi; Silvia Serranti

doi:10.1117/12.2049399

22 May 2014 Automatic detection and classification of EOL-concrete and resulting recovered products by hyperspectral imaging

Roberta Palmieri, Giuseppe Bonifazi, Silvia Serranti

Author Affiliations +

Proceedings Volume 9106, Advanced Environmental, Chemical, and Biological Sensing Technologies XI; 91060D (2014) https://doi.org/10.1117/12.2049399
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

The recovery of materials from Demolition Waste (DW) represents one of the main target of the recycling industry and the its characterization is important in order to set up efficient sorting and/or quality control systems. End-Of-Life (EOL) concrete materials identification is necessary to maximize DW conversion into useful secondary raw materials, so it is fundamental to develop strategies for the implementation of an automatic recognition system of the recovered products. In this paper, HyperSpectral Imaging (HSI) technique was applied in order to detect DW composition. Hyperspectral images were acquired by a laboratory device equipped with a HSI sensing device working in the near infrared range (1000-1700 nm): NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Acquired spectral data were analyzed adopting the PLS_Toolbox (Version 7.5, Eigenvector Research, Inc.) under Matlab® environment (Version 7.11.1, The Mathworks, Inc.), applying different chemometric methods: Principal Component Analysis (PCA) for exploratory data approach and Partial Least Square- Discriminant Analysis (PLS-DA) to build classification models. Results showed that it is possible to recognize DW materials, distinguishing recycled aggregates from contaminants (e.g. bricks, gypsum, plastics, wood, foam, etc.). The developed procedure is cheap, fast and non-destructive: it could be used to make some steps of the recycling process more efficient and less expensive.

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Roberta Palmieri, Giuseppe Bonifazi, and Silvia Serranti "Automatic detection and classification of EOL-concrete and resulting recovered products by hyperspectral imaging", Proc. SPIE 9106, Advanced Environmental, Chemical, and Biological Sensing Technologies XI, 91060D (22 May 2014); https://doi.org/10.1117/12.2049399

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