Estimation of pile group scour using neural networks

The interaction between ocean environment and pile structure is so complex that despite considerable laboratory as well as prototype studies estimation of scour depth and its geometry in a generalized and accurate form are still difficult to make. One of the reasons underlying this uncertainty could be the limitation of the statistical curve fitting technique, commonly employed to analyse the collected data. The present work therefore attempts to carry out scour data analysis using another technique of data mining: neural networks. Neural networks have ability to map a random input vector with the random output vector in a model-free manner unlike the model oriented non-linear regression methods. Different networks were developed to predict the scour depth as well as scour width for a group of piles supporting a pier situated at a coastal location off Japan using the input of wave height, wave period, water depth and pile diameter as well as pile Reynold’s number, maximum wave particle velocity, maximum shear velocity, Shield’s parameter and Keulegan–Carpenter number. The networks were of feed forward as well as recurrent type trained using back propagation and cascade correlation algorithms. The testing results showed that the neural network could provide a better alternative to the statistical curve fitting. Individual input parameters yielded better results than their grouped combinations. The depth of scour was predicted more accurately than its width. A matrix of weights is specified for use at any given location.