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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| Format: | Article |
| Language: | English |
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Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education
2024-06-01
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| Series: | Омский научный вестник |
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| 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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| author | E. S. Klimanova |
| author_facet | E. S. Klimanova |
| author_sort | E. S. Klimanova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ab4284d31fbc488599911c9a8a48cdd3 |
| institution | Kabale University |
| issn | 1813-8225 2541-7541 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education |
| record_format | Article |
| series | Омский научный вестник |
| spelling | doaj-art-ab4284d31fbc488599911c9a8a48cdd32025-02-03T00:48:59ZengOmsk State Technical University, Federal State Autonoumos Educational Institution of Higher EducationОмский научный вестник1813-82252541-75412024-06-012 (190)15316210.25206/1813-8225-2024-190-153-162Determination of the deformation modulus of binary composite using artificial neural networkE. S. Klimanova0Omsk State Technical UniversityUsing 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. 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..pdfrelative error in determining the characteristiccoefficient of determinationregression equationartificial neural networksandgranules of expanded polystyrene |
| spellingShingle | E. S. Klimanova Determination of the deformation modulus of binary composite using artificial neural network Омский научный вестник relative error in determining the characteristic coefficient of determination regression equation artificial neural network sand granules of expanded polystyrene |
| title | Determination of the deformation modulus of binary composite using artificial neural network |
| title_full | Determination of the deformation modulus of binary composite using artificial neural network |
| title_fullStr | Determination of the deformation modulus of binary composite using artificial neural network |
| title_full_unstemmed | Determination of the deformation modulus of binary composite using artificial neural network |
| title_short | Determination of the deformation modulus of binary composite using artificial neural network |
| title_sort | determination of the deformation modulus of binary composite using artificial neural network |
| topic | relative error in determining the characteristic coefficient of determination regression equation artificial neural network sand granules of expanded polystyrene |
| url | 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 |
| work_keys_str_mv | AT esklimanova determinationofthedeformationmodulusofbinarycompositeusingartificialneuralnetwork |