R !~ S U M EThe gray-green limestone from Agios Panteleimonas is studied in this paper after submitting it in special technological analyses in order to determine the possibility for quicklime production. Cubic test specimens with mean 50 mm edge length were calcined at 850, 950 and 1,050~ with 150 rain preheating time and 120 rain retention time at each calcination temperature. At the dissociation temperature of pure calcite (898~ only one half of the initial limestone has been calcined. The dissociation of the specimens started at 740~ and almost completed at 1,050~ Probably, the large edge length of the cubic specimens and the low retention time are responsible for the incomplete calcination at 1,050~ The dry apparent weight of the calcined limestone (1.577 g/cm3), its low shrinkage (0.1-0.3%), the 2% impurities content and the 24% value of the attrition and abrasion resistance, characterize this quicklime and classify it to the high quality products.
Dans cet article, le calcaire gris-vert d'Agios
The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and characterization in particular. During the last decade, the so-called Unmanned Aerial Vehicles (UAVs) have been evaluated for diverse applications such as 3D terrain analysis, slope stability, mass movement hazard and risk management. Their advantages of detailed data acquisition at a low cost and effective performance identifies them as leading platforms for site-specific 3D modelling. In this study, the proposed methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data resulted from UAV photogrammetry processing in order to take full advantage of the produced data. Two landslide case studies within the territory of Greece, with different geological and geomorphological characteristics, have been investigated in order to assess the developed landslide detection and characterization algorithm performance in distinct scenarios. The methodology outputs demonstrate the potential for an accurate characterization of individual landslide objects within this natural process based on ultra high-resolution data from close range photogrammetry and OBIA techniques for landslide conceptualization. This proposed study shows that UAV-based landslide modelling on the specific case sites provides a detailed characterization of local scale events in an automated sense with high adaptability on the specific case site.
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