Finite-difference equations of quasistatic motion of the shallow concrete shells in nonlinear setting

Abstract

<jats:title>Abstract</jats:title><jats:p>The study solves a system of finite difference equations for flexible shallow concrete shells while taking into account the nonlinear deformations. All stiffness properties of the shell are taken as variables, <jats:italic>i.e.</jats:italic>, stiffness surface and through-thickness stiffness. Differential equations under consideration were evaluated in the form of algebraic equations with the finite element method. For a reinforced shell, a system of 98 equations on a 8×8 grid was established, which was next solved with the approximation method from the nonlinear plasticity theory. A test case involved computing a 1×1 shallow shell taking into account the nonlinear properties of concrete. With nonlinear equations for the concrete creep taken as constitutive, equations for the quasi-static shell motion under constant load were derived. The resultant equations were written in a differential form and the problem of solving these differential equations was then reduced to the solving of the Cauchy problem. The numerical solution to this problem allows describing the stress-strain state of the shell at each point of the shell grid within a specified time interval.</jats:p>

Retracted: An unmanned aerial vehicles navigation system on the basis of pattern recognition applications—Review of implementation options and prospects for development

“…For example, stereo cameras and monocular cameras are used to collect three‐dimensional information about the environment, 7 whereas infrared sensors help UAVs to navigate in the dark 8 . Programs and devices have also appeared that might recognize various objects: faces, license plates, and other necessary information.…”

Improving the quality of sorting wood chips by scanning and machine vision technology

“…After that, a decision was made to sort the image. The use of convolution neural networks and manually created descriptors to classify the fine-grained images in the work of Duissenbekov et al (2020) showed that neural networks demonstrate good performance but are not reliable or accurate methods for recognizing fixed textures. The sorting program developed in this paper differs from previous methods by its simple architecture, and the use of the Otsu method allows speeding up the process of defect detection, which ensures fast and accurate sorting.…”

Experimental Analysis of the Stress State of a Prestressed Cylindrical Shell with Various Structural Parameters

“…The need to operate such structures for oil storage and transportation does not exclude the occurrence of accidents or unforeseen technical shutdowns as a result of problems caused by malfunctions during operation, aging of the structure and various deformations, which demonstrated the need for additional studies of the strength of steel shells and the implementation of new engineering solutions. The works [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] present the results of studies, including the modeling of shell structures under various static and dynamic loads, where studies were also carried out taking into account the deformations of the shell body (pipelines, reservoirs, cisterns, etc.). In the studies [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], work was carried out to study the effect of damage and defects in steel wrappings on the stiffness and natural frequencies of prestressed pipes as well as the characteristics of metals.…”

“…The results of the above works [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] show the general issues of construction and design of steel structures with and without prestressing shells.…”