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-square-discriminant 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.
Earthquakes create significant volumes of rubbles and waste, strongly impacting the environment and posing serious health risks. In the last decade, earthquake occurred in several Italian areas, in particular the last sequence (Amatrice – Norcia, 2016-2017), still clearly visible in terms of destruction in the epicentral area, produced about 3 million tons of waste, mainly composed of Construction and Demolition Waste (CDW). Post-earthquake building waste is composed of heterogeneous materials, making difficult their separation and recovery as secondary raw materials. CDW recycling and reuse is of fundamental importance because it reduces the increase of landfilling, avoiding non-renewable raw materials exploitation and favoring circular economy. In this work, a sensor-based approach to classify different typologies of tile samples coming from buildings damaged by the Amatrice earthquake is proposed and investigated. Attention was focused on tiles, one of the most recycled masonry aggregates (RMA). This study presents a methodology based on a combination of two analytical techniques, HyperSpectral Imaging (HSI), working in the Short-Wave InfraRed (SWIR) range (1000-2500 nm), and micro-X-ray Fluorescence (micro-XRF), in order to discriminate different tile compositions and to detect the presence of cement mortar on the surface of the samples. The obtained results represent an important starting point to develop and introduce innovative strategies finalized to design, implement and set up automatic recognition and classification procedures of inert CDW fractions.
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