Determination of the deformation modulus of binary composite using artificial neural network
https://doi.org/10.25206/1813-8225-2024-190-153-162
EDN: PFRQYJ
Abstract
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.
About the Author
E. S. Klimanova
Omsk State Technical University
Russian Federation
Russian Federation
Klimanova Ekaterina Sergeevna, Engineer, Assistant of Oil and Gas Engineering, Standardization and Metrology Department
Omsk
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Review
For citations:
Klimanova E.S. Determination of the deformation modulus of binary composite using artificial neural network. Omsk Scientific Bulletin. 2024;10(2):153-162. (In Russ.) https://doi.org/10.25206/1813-8225-2024-190-153-162. EDN: PFRQYJ
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