| Autor: |
Andrea Bisciotti, Derek Jiang, Yu Song, Giuseppe Cruciani |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Cleaner Materials, Vol 11, Iss , Pp 100215- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2772-3976 |
| DOI: |
10.1016/j.clema.2023.100215 |
| Popis: |
Recycled aggregates, obtained from construction and demolition waste (C&DW), are currently underutilized in the production of new concrete given the incidence of widespread leftover cement paste adhering to the surface. C&DW sorting facilities based on optical technology can be developed and applied on an industrial scale, improving the overall quality of this secondary raw material. In this study, we present a novel approach based on image analysis and mineralogical laboratory methods to determine the residual attached mortar volume. Through clustering analysis, we classify C&DW samples with a comparable cement content determined by the image analysis. The leftover cement paste from these C&DW classes is mechanically extracted and examined using X-ray Powder Diffraction and Rietveld refinement. To estimate the attached mortar volume and the carbonation of the cement paste, we present a novel mathematical model based on the mineralogical data. To overcome the bottleneck associate with the image analysis, we further incorporate a deep learning model to automate the determination of the mortar volume, which enables high-throughput screening of C&DW in real production. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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