An efficient MDL-based construction of RBF networks
Neural Networks
Using adaptive neuro-fuzzy inference system for hydrological time series prediction
Applied Soft Computing
An adaptive neuro-fuzzy inference system for bridge risk assessment
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Characteristics forecasting of hydraulic valve based on grey correlation and ANFIS
Expert Systems with Applications: An International Journal
Advances in Engineering Software
Application of neural networks and fuzzy logic models to long-shore sediment transport
Applied Soft Computing
Support vector regression based modeling of pier scour using field data
Engineering Applications of Artificial Intelligence
Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model)
Engineering Applications of Artificial Intelligence
EMESEG'10 Proceedings of the 3rd WSEAS international conference on Engineering mechanics, structures, engineering geology
Prediction of scouring around an arch-shaped bed sill using Neuro-Fuzzy model
Applied Soft Computing
Engineering Applications of Artificial Intelligence
Artificial intelligence-based estimation of flushing half-cone geometry
Engineering Applications of Artificial Intelligence
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The mechanism of flow around a pier structure is so complicated that it is difficult to establish a general empirical model to provide accurate estimation for scour. Interestingly, each of the proposed empirical formula yields good results for a particular data set. Hence, in this study, alternative approaches, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), are proposed to estimate the equilibrium and time-dependent scour depth with numerous reliable data base. Two ANN models, multi-layer perception using back-propagation algorithm (MLP/BP) and radial basis using orthogonal least-squares algorithm (RBF/OLS), were used. The equilibrium scour depth was modeled as a function of five variables; flow depth, mean velocity, critical flow velocity, mean grain diameter and pier diameter. The time variation of scour depth was also modeled in terms of equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. The training and testing data are selected from the experimental data of several valuable references. Numerical tests indicate that MLP/BP model provide a better prediction of scour depth than RBF/OLS and ANFIS models as well as the previous empirical approaches. Finally, sensitivity analysis shows that pier diameter has a greater influence on equilibrium scour depth than the other independent parameters.