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.
The construction sector produces more than one-third of the world's solid waste. Construction and demolition waste (CDWs) are generated from the construction, renovation and demolition of buildings, roads, bridges and other structures. Moreover, CDW include the materials that may suddenly be generated by natural disasters, such as earthquakes and floods. Post-earthquake building waste (PBW) is typically composed of a mixture of different materials, such as concrete, bricks, tiles, ceramics, wood, glass, gypsum and plastic. These materials represent, if properly separated, a high potential for recycling and reuse particularly the inert fraction, representing about 70% of the total. From this perspective, this work aims to develop an innovative strategy based on optical sensing in order to identify and classify different types of PBW coming from a post-earthquake site (Amatrice, Italy). A strategy based on hyperspectral imaging (HSI) working in the SWIR range (1000-2500 nm) was developed. The acquired hyperspectral images were analyzed using different chemometric methods: principal component analysis (PCA) for data exploration and partial least-squarediscriminant analysis (PLSDA) to build a classification model. Results showed that the proposed approach allows to recognize and classify inert fractions from contaminants (i.e., wood, plastics and drywall). The obtained results show how HSI could be particularly suitable to perform classification in complex scenarios as produced by earthquakes.
During an earthquake, a large amount of waste was generated, and many Asbestos-Containing Materials (ACM) were unintentionally destroyed. ACM is a mixture of cement matrix and asbestos fiber, widely used in construction materials, that causes serious diseases such as lung cancer, mesothelioma and asbestosis, as a consequence of inhalation of the asbestos fiber. In order to reuse and recycle Post-earthquake Building Waste (PBW) as secondary raw material, ACM must be separately collected and deposited from other wastes during the recycling process. The work aimed to develop a non-destructive, accurate and rapid method to detect ACM and recognize different types of PBW to obtain the best method to correctly identify and separate different types of material. The proposed approach is based on Hyperspectral Imaging (HSI) working in the short-wave infrared range (SWIR, 1000-2500 nm), followed by the implementation of a classification model based on hierarchical Partial Least Square Discriminant Analysis (hierarchical-PLS-DA). Micro-X-ray fluorescence (micro-XRF) analyses were carried out on the same samples in order to evaluate the reliability, robustness and analytical correctness of the proposed HSI approach. The results showed that the applied technology is a valid solution that can be implemented at the industrial level.
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