- Tytuł:
- Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers
- Autorzy:
-
Shakir Ali Ali, Ahmed
Günal, Mustafa - Powiązania:
- https://bibliotekanauki.pl/articles/2097766.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
- Tematy:
-
artificial neural network
bridge pier
hydraulics
local scour - Opis:
- Local scour around bridge piers impairs the stability of bridges’ structures. Therefore, a delicate estimation of the local scour depth is vital in designing the bridge piers foundations. In this research, MATLAB software was used to train artificial neural network (ANN) models with four hundred laboratory datasets from different laboratory studies, including five parameters: pier diameter, flow depth flow velocity, critical sediment velocity, sediment particle size, and equilibrium local scour depth. The outcomes present that the ANN model with the Levenberg-Marquardt algorithm and 11 nodes in the single hidden layer gives an accurate estimation better than other ANN models trained with different training algorithms based on the regression results and mean squared error values. Besides, the ANN model accurately provides predicted local scour depth and is better than linear and nonlinear regression models. Furthermore, sensitivity analysis shows that removing pier diameter from training parameters diminishes the reliability of prediction.
- Źródło:
-
Archives of Hydro-Engineering and Environmental Mechanics; 2021, 68, 2; 87--101
1231-3726 - Pojawia się w:
- Archives of Hydro-Engineering and Environmental Mechanics
- Dostawca treści:
- Biblioteka Nauki
Artykuł