Prediction of time variation of scour depth around spur dikes using neural networks

Prediction of Scour Depth around Pile Group Using Ann

Prediction of the scour around a group of pile in the field exposed to oscillatory waves is very important for many offshore structure and coastal engineering projects. Conventional predictive formulas for the geometric properties of scour hole, however, are not able to provide sufficiently accurate results. In this paper the ANNs approach is used to predict the scour depth around pile group us...

Prediction of scour depth around bridge piers using Gaussian process

A reliable prognostics framework is essential to prevent catastrophic failure of bridges due to scour. In the U.S., scour accounts for almost 60% of bridge failures. Currently available techniques in the literature for predicting scour are mostly based on empirical equations and deterministic regression models, like Neural Networks and Support Vector Machines, and do not predict the evolution o...

investigation on time variation of scour around t shape spur dike in a 90 degree bend

spur dike is an important element for protection of river banks. spur dike causes variations in flow field, sediment transport and bed topography. the mechanism of flow and sediment transport in a channel bend is very complex, especially when a spur dike is constructed in a bend. in this paper the time variations of dimension of the scour hole and the bed’s topography were considered around a t...

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

Prediction of Gas Lift Parameters Using Artificial Neural Networks