2D CONVOLUTIONAL NEURAL NETWORK IN THE DESIGN OF MONOLITHIC SELFSTRESSED SLABS ON BASE
DOI:
https://doi.org/10.36773/1818-1112-2023-132-3-54-60Keywords:
convolutional neural network, neurons, slabs on base, self-stressed concrete, hybridAbstract
The purpose of this paper is to demonstrate the capabilities of convolutional neural networks in mechanics-related problems, in particular, in the design of monolithic self-stressed slabs on the base. In order to simplify the procedure of designing and calculating the displacements of slabs on the base has been developed a method that combines the advantages of theoretical models, and neural network technologies. The paper shows the possibility of using "soft computing", and also points out the promising potential of convolutional neural networks in predicting forced displacements in slabs of different geometrical shape.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Brest State Technical University
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The work is provided under the terms of Creative Commons public license Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This license allows an unlimited number of persons to reproduce and share the Licensed Material in all media and formats. Any use of the Licensed Material shall contain an identification of its Creator(s) and must be for non-commercial purposes only. Users may not prevent other individuals from taking any actions allowed by the license.
