Determination of the deformation modulus of binary composite using artificial neural network
Using of existing methods of determining the characteristics of soils which are part of current regulatory documents and which are based on the hypothesis of normal character of distribution require considerable time and material costs. According to the results of conducted laboratory researches...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education
2024-06-01
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| Series: | Омский научный вестник |
| Subjects: | |
| Online Access: | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2024/%E2%84%962%20(190)%20%D0%9E%D0%9D%D0%92/153-162%20%D0%9A%D0%BB%D0%B8%D0%BC%D0%B0%D0%BD%D0%BE%D0%B2%D0%B0%20%D0%95.%20%D0%A1..pdf |
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| Summary: | Using of existing methods of determining the characteristics of soils which are part
of current regulatory documents and which are based on the hypothesis of normal
character of distribution require considerable time and material costs. According
to the results of conducted laboratory researches the hypothesis wasn’t confirmed.
In the paper it proposes to use trained artificial neural network for determination
of the deformation modulus of binary composite «sand — granules of expanded
polystyrene». Thus, it has been proven efficiency proposing method using trained
artificial neural network in compare classical regression equation for determination
of the deformation modulus of the binary composite. With a confidence probability
of P = 95 % the absolute value of the relative error is equal to 11,8 % the proposing
learning artificial neural network in 11 times less than the absolute value of the
relative error of classical regression equation. Also with a confidence probability of
P = 95 % the coefficient of determination is equal to 0,5641 and in 6,6 times less
than it of regression equation. Further research will be directed to the selection of
the values of the parameters of the artificial neural network program for increase
the accuracy of determining the deformation modulus of the binary composite. |
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| ISSN: | 1813-8225 2541-7541 |