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 - TY - JOUR A1 - Kokhanovsky, Alexander A1 - Lamare, Maxim A1 - Danne, Olaf A1 - Brockmann, Carsten A1 - Dumont, Marie A1 - Picard, Ghislain A1 - Arnaud, Laurent A1 - Favier, Vincent A1 - Jourdain, Bruno A1 - Le Meur, Emmanuel A1 - Di Mauro, Biagio A1 - Aoki, Teruo A1 - Niwano, Masashi A1 - Rozanov, Vladimir A1 - Korkin, Sergey A1 - Kipfstuhl, Sepp A1 - Freitag, Johannes A1 - Hoerhold, Maria A1 - Zuhr, Alexandra A1 - Vladimirova, Diana A1 - Faber, Anne-Katrine A1 - Steen-Larsen, Hans Christian A1 - Wahl, Sonja A1 - Andersen, Jonas K. A1 - Vandecrux, Baptiste A1 - van As, Dirk A1 - Mankoff, Kenneth D. A1 - Kern, Michael A1 - Zege, Eleonora A1 - Box, Jason E. T1 - Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument JF - Remote sensing N2 - 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. KW - snow characteristics KW - optical remote sensing KW - snow grain size KW - specific surface area KW - albedo KW - Sentinel 3 KW - OLCI Y1 - 2019 U6 - https://doi.org/10.3390/rs11192280 SN - 2072-4292 VL - 11 IS - 19 PB - MDPI CY - Basel ER -