A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers
Abstract 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.
Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers
Abstract 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.
Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers
Ali, Ahmed Shakir Ali (author) / Günal, Mustafa (author)
2021
Article (Journal)
English
BKL:
56.30
Wasserbau
Neural network modeling for estimation of scour depth around bridge piers
| British Library Online Contents | 2007
Local Scour Around Bridge Piers
| ASCE | 2021
Artificial neural network study of observed pattern of scour depth around bridge piers
| Online Contents | 2010
Ultimate Depth of Scour Around Bridge Piers
| British Library Conference Proceedings | 1999