Abstract
Hyperspectral imaging (HSI) based sensing devices were utilized to develop nondestructive, rapid, and low cost analytical strategies finalized to detect and characterize materials constituting demolition waste. In detail, HSI was applied for quality control of high-grade recycled aggregates obtained from end-of-life concrete. The described HSI quality control approach is based on the utilization of a platform working in the near-infrared range (1000–1700 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: principal component analysis for data exploration and partial least-square-discriminant analysis to build classification models. Results showed that it is possible to recognize the recycled aggregates from different contaminants (e.g., brick, gypsum, plastic, wood, foam, and so on), allowing the quality control of the recycled flow stream.
Highlights
Demolition waste (DW) recycling is an interesting option to reduce the exploitation of the natural resources and the environmental impacts (CO2 emissions) associated with the construction sector.[1]
In order to set up effective sorting and/or a quality control system, material characterization is a crucial step and endof-life (EOL) concrete identification is important to make DW conversion into useful secondary raw materials easier
The hyperspectral imaging (HSI) system is composed of an integrated hardware and software architecture able to digitally capture and handle spectra, as they proceed along a predefined alignment on a surface sample that is properly energized.[2,3]
Summary
Demolition waste (DW) recycling is an interesting option to reduce the exploitation of the natural resources and the environmental impacts (CO2 emissions) associated with the construction sector.[1]. In order to set up effective sorting and/or a quality control system, material characterization is a crucial step and endof-life (EOL) concrete identification is important to make DW conversion into useful secondary raw materials easier. In this perspective, the development of strategies for automatic recognition of recovered products and the possibility to utilize efficient, reliable, and low cost sensing technologies that are able to perform detection/control actions during all recycling steps, are very important. HSI is a type of multivariate imaging. A typical multivariate image is an image of I rows and J columns measured for K variables.
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