Estimating attached mortar paste on the surface of recycled aggregates based on deep learning and mineralogical models
Andrea Bisciotti, Derek Jiang, Yu Song, Giuseppe Cruciani
Abstract
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.