Neural network and neuro-fuzzy assessments for scour depth around bridge piers
Engineering Applications of Artificial Intelligence
Neural Computing and Applications
Fuzzy control of a swells canal system
Applied Soft Computing
Modeling vibration frequencies of annular plates by regression based neural network
Applied Soft Computing
Structural topology optimization using ant colony optimization algorithm
Applied Soft Computing
Neural networks modeling of shear strength of SFRC corbels without stirrups
Applied Soft Computing
A memetic algorithm applied to the design of water distribution networks
Applied Soft Computing
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Predictions of long-shore sediment transport rate (LSTR) are a vital task for coastal engineers in the determination of erosion or accretion along coasts. Many scientists have tried to find empirical method for the estimation of LSTR in the past decades. However, due to the influence of significant number of parameters and randomness of the data, the existing empirical methods provide quite different results and have limited applications. In this paper, an alternative approach, fuzzy logic and neural network, is proposed to estimate LSTR. Six dominant variables on LSTR are considered in the present models, including wave breaking height (H"b"s), wave period (T), wave breaking angle (@a"b"s), beach slope (m), grain size (D) and sediment mass flow rate along shore (Q"s). A comprehensive comparison between both neural networks and fuzzy logic models and the existing empirical formulae will be presented to demonstrate capacity of fuzzy logic and artificial neural network.