TY  - JOUR
A1  - Oesch, Tyler
A1  - Weise, Frank
A1  - Bruno, Giovanni
T1  - Detection and quantification of cracking in concrete aggregate through virtual data fusion of X-ray computed tomography images
JF  - Materials
N2  - In this work, which is part of a larger research program, a framework called "virtual data fusion" was developed to provide an automated and consistent crack detection method that allows for the cross-comparison of results from large quantities of X-ray computed tomography (CT) data. A partial implementation of this method in a custom program was developed for use in research focused on crack quantification in alkali-silica reaction (ASR)-sensitive concrete aggregates. During the CT image processing, a series of image analyses tailored for detecting specific, individual crack-like characteristics were completed. The results of these analyses were then "fused" in order to identify crack-like objects within the images with much higher accuracy than that yielded by any individual image analysis procedure. The results of this strategy demonstrated the success of the program in effectively identifying crack-like structures and quantifying characteristics, such as surface area and volume. The results demonstrated that the source of aggregate has a very significant impact on the amount of internal cracking, even when the mineralogical characteristics remain very similar. River gravels, for instance, were found to contain significantly higher levels of internal cracking than quarried stone aggregates of the same mineralogical type.
KW  - X-ray computed tomography (CT)
KW  - concrete
KW  - alkali-silica reaction (ASR)
KW  - ASR-sensitive aggregate
KW  - solubility test
KW  - specific surface area
KW  - crack
KW  - detection
KW  - automated image processing
KW  - damage quantification
Y1  - 2020
U6  - https://doi.org/10.3390/ma13183921
SN  - 1996-1944
VL  - 13
IS  - 18
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Léonard, Fabien
A1  - Zhang, Zhen
A1  - Krebs, Holger
A1  - Bruno, Giovanni
T1  - Structural and morphological quantitative 3D characterisation of ammonium nitrate prills by X-ray computed tomography
JF  - Materials
N2  - The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually referred to as ANFO, is extensively used in the mining industry as a bulk explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the complex pore structure of the AN prills. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. Importantly, oil retention measurements (qualifying the efficiency of ANFO as explosives) correlate well with the specific surface area determined by XCT. XCT can therefore be employed non-destructively; it can accurately evaluate and characterise porosity in ammonium nitrate prills, and even predict their efficiency.
KW  - ANFO
KW  - explosives
KW  - specific surface area
KW  - porosity
KW  - XCT
KW  - data processing
Y1  - 2020
U6  - https://doi.org/10.3390/ma13051230
SN  - 1996-1944
VL  - 13
IS  - 5
PB  - MDPI
CY  - Basel
ER  -