Abstract
In this paper, a methodological approach based on hyperspectral imaging (HSI) working in the short-wave infrared range (1000–2500 nm) was developed and applied for the recycling-oriented characterization of post-earthquake building waste. In more detail, the presence of residual cement mortar on the surface of tile fragments that can be recycled as aggregates was estimated. The acquired hyperspectral images were analyzed by applying different chemometric methods: principal component analysis (PCA) for data exploration and partial least-squares-discriminant analysis (PLS-DA) to build classification models. Micro-X-ray fluorescence (micro-XRF) maps were also obtained on the same samples in order to validate the HSI classification results. Results showed that it is possible to identify cement mortar on the surface of the recycled tile, evaluating its degree of liberation. The recognition is automatic and non-destructive and can be applied for recycling-oriented purposes at recycling plants.
Highlights
Academic Editors: Juan LuisNieves Gómez and MiguelA
Normal Variate (SNV) and Autoscale pre-processing, and (b) image of score representative of the classes set on the principal component analysis (PCA)
A methodology was developed and tested for assessing the degree of cement mortar from tile liberation observed in post-earthquake building waste, in order to improve the quality of recycled material
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The main difficulty in a post-earthquake flow stream is to evaluate the quality of recovered materials characterized by similar chemical composition, but presenting different mechanical proprieties, these latter strongly influencing their correct reuse: the presence of cement mortar in recovered inert products (i.e., aggregates, tiles, bricks, etc.) can dramatically influence the possibilities of their correct reuse as recycled aggregate. Few studies have been carried out on the monitoring of tiles coming from post-disaster building waste and on their possible reuse as RMA, despite their high presence in earthquake-damaged structures For this reason, in this work, the evaluation of the degree of liberation of tile fragments from cement mortar was performed by HSI coupled with a machine learning approach. The improvement of this strategy, able to recognize and topologically evaluate the detected contaminants, can represent a valid and efficient method for obtaining a higher quality of recycled tiles from building waste
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