Browsing by Author "Kovrov, Oleksandr"

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    Determining the Influence of Physical Nonlinearity of Soil Strength Properties on the Estimated Base Resistance
    (Private company "Technology center", 2019) Shashenko, Oleksandr; Shapoval, Volodymyr; Kovrov, Oleksandr; Skobenko, Alexandr; Tiutkin, Oleksii L.; Babii, Kateryna; Samorodov, Oleksandr; Slobodyanyuk, Sergey
    ENG: The paper has examined the potential of using nonlinear models of strength in determining the initial critical load on soil, as well as the standardized and estimated base resistance, which makes it possible to reduce labor intensity in the process of determining the strength properties of soils.Based on the analysis and generalization of results from theoretical studies into geomechanical processes using analytical mathematical methods, the formula modifications have been derived that are intended for determining the initial critical load on soil, as well as the standardized and estimated base resistances.We have established interrelation between strength, in particular specific cohesion, and the angle of internal friction, which are included in the strength conditions by Mohr-Coulomb and A. Shashenko, thereby making it possible to improve the procedure for calculating external loads on soil.The dependences of critical loads on base on the mean pressure under the sole of the foundation haven been analyzed in the range of pressure Р = 100...500 kPa using the strength conditions by Mohr-Coulomb and A. Shashenko.It has been established that when using generally accepted estimation formulae to determine the critical loads on base, it is required that the pressure range should be taken into consideration at which the properties of soil strength were determined. In this case, using the Shashenko failure criterion to determine critical loads on base makes it possible to properly consider the impact exerted by the mean pressure on them under the sole of the foundation.In contrast to dependences used currently in the Ukrainian, Belarusian, Russian regulatory documents, as well as in other countries’ standards, the resulting formulae make it possible to take into consideration the dependences of soil strength properties on the mean pressure on soil under the sole of the foundation. The results obtained make it possible to improve the reliability of determining the initial critical load on soil, as well as the standardized and estimated base resistances. This is achieved by taking into consideration the nonlinearity of the Mohr limiting circles’ envelope using the strength condition by A. Shashenko.
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    Machine Learning-Based Monitoring of War-Damaged Water Bodies in Ukraine Using Satellite Images
    (CEUR-WS Team, Aachen, Germany, 2024) Sergieieva, K.; Kavats, Olena O.; Vasyliev, Volodymyr; Kavats, Yurii V.; Kovrov, Oleksandr
    ENG: Water resources are Ukraine's strategic environmental asset. As a result of the destruction caused by the Russo-Ukrainian War, critical water infrastructure has been severely damaged. This makes it essential to effectively manage and conserve water resources in the face of increasing anthropogenic impact. The use of machine learning methods to monitor water bodies' conditions based on optical and Synthetic Aperture Radar (SAR) satellite images allows for automating analysis processes and providing more accurate and timely results, which is important for making reasonable management decisions. In this study, information tools for mapping and assessing the dynamics of surface water body changes were developed based on Sentinel-1 and Sentinel-2 data using a convolutional neural network. They were used for the mapping of surface water bodies in the Lower Dnipro sub-basin affected by the destruction of the Kakhovka Hydropower Plant dam. To improve the result of satellite image mixed pixels classification in shallow areas of swampy water bodies at the bottom of the destroyed Kakhovka Reservoir, it is proposed to use a block data model and a probabilistic approach to assess the presence of "water" and "ground" class objects in the images, which allows to achieve mapping accuracy of up to 96%.