ГЕОМЕХАНІЧНЕ МОДЕЛЮВАННЯ СТІЙКОСТІ ЗЕМЛЯНОГО ПОЛОТНА У ВОЛОГОМУ СЕРЕДОВИЩІ НА ОСНОВІ ГЛИБОКОГО НАВЧАННЯ

Yuliia Balashova; Andrii Balashov

doi:10.30890/2567-5273.2025-38-02-034

Authors

Yuliia Balashova Ukrainian State University of Science and Technologies ESI «Prydniprovska State Academy of Civil Engineering and Architecture» https://orcid.org/0000-0002-2286-9263
Andrii Balashov Ukrainian State University of Science and Technologies ESI «Prydniprovska State Academy of Civil Engineering and Architecture»; University of Illinois Urbana-Champaign(UIUC)

DOI:

https://doi.org/10.30890/2567-5273.2025-38-02-034

Keywords:

deep learning, geomechanical modeling, subgrade stability, humid environments, soil mechanics, neural networks.

Abstract

The stability of roadbed subgrades in humid environments presents significant challenges due to complex soil behaviors under fluctuating hydrological conditions. Traditional geomechanical models often fail to capture the nonlinear interactions between soi

References

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Andrii Balashov / Attention-integrated convolutional neural networks for enhanced image classification: a comprehensive theoretical and empirical analysis /

International periodic scientific journal "Modern engineering and innovative technologies" ISSN 2567-5273, Issue №35, Part 2, October 2024, Karlsruhe, Germany, р. 18-27. / URL: https://doi.org/10.30890/2567-5273.2024-35-00-030

http:// www.moderntechno.de/index.php/meit/article/view/meit35-00-030

https://www.moderntechno.de/index.php/meit/issue/view/meit35-02/meit35-02