@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}
}
@article{KokhanovskyLamareDanneetal.2019,
  author    = {Kokhanovsky, Alexander and Lamare, Maxim and Danne, Olaf and Brockmann, Carsten and Dumont, Marie and Picard, Ghislain and Arnaud, Laurent and Favier, Vincent and Jourdain, Bruno and Le Meur, Emmanuel and Di Mauro, Biagio and Aoki, Teruo and Niwano, Masashi and Rozanov, Vladimir and Korkin, Sergey and Kipfstuhl, Sepp and Freitag, Johannes and Hoerhold, Maria and Zuhr, Alexandra and Vladimirova, Diana and Faber, Anne-Katrine and Steen-Larsen, Hans Christian and Wahl, Sonja and Andersen, Jonas K. and Vandecrux, Baptiste and van As, Dirk and Mankoff, Kenneth D. and Kern, Michael and Zege, Eleonora and Box, Jason E.},
  title     = {Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument},
  series = {Remote sensing},
  volume    = {11},
  journal   = {Remote sensing},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs11192280},
  pages     = {43},
  year      = {2019},
  abstract  = {The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3\% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5\% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies-especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.},
  language  = {en}
}