@article{OeschWeiseBruno2020,
  author    = {Oesch, Tyler and Weise, Frank and Bruno, Giovanni},
  title     = {Detection and quantification of cracking in concrete aggregate through virtual data fusion of X-ray computed tomography images},
  series = {Materials},
  volume    = {13},
  journal   = {Materials},
  number    = {18},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1996-1944},
  doi       = {10.3390/ma13183921},
  pages     = {27},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{LeonardZhangKrebsetal.2020,
  author    = {L{\´e}onard, Fabien and Zhang, Zhen and Krebs, Holger and Bruno, Giovanni},
  title     = {Structural and morphological quantitative 3D characterisation of ammonium nitrate prills by X-ray computed tomography},
  series = {Materials},
  volume    = {13},
  journal   = {Materials},
  number    = {5},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1996-1944},
  doi       = {10.3390/ma13051230},
  pages     = {16},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}