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Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

Zounemat Kermani, M ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1016/j.asoc.2008.09.006
  3. Publisher: 2009
  4. Abstract:
  5. The process of local scour around bridge piers is fundamentally complex due to the three-dimensional flow patterns interacting with bed materials. For geotechnical and economical reasons, multiple pile bridge piers have become more and more popular in bridge design. Although many studies have been carried out to develop relationships for the maximum scour depth at pile groups under clear-water scour condition, existing methods do not always produce reasonable results for scour predictions. It is partly due to the complexity of the phenomenon involved and partly because of limitations of the traditional analytical tool of statistical regression. This paper addresses the latter part and presents an alternative to the regression in the form of artificial neural networks, ANNs, and adaptive neuro-fuzzy inference system, ANFIS. Two ANNs model, feed forward back propagation, FFBP, and radial basis function, RBF, were utilized to predict the depth of the scour hole. Two combinations of input data were used for network training; the first input combination contains six-dimensional variables, which are flow depth, mean velocity, critical flow velocity, grain mean diameter, pile diameter, distance between the piles (gap), besides the number of piles normal to the flow and the number of piles in-line with flow, while the second combination contains seven non-dimensional parameters which is a composition of dimensional parameters. The training and testing experimental data on local scour at pile groups are selected from several precious references. Networks' results have been compared with the results of empirical methods that are already considered in this study. Numerical tests indicate that FFBP-NN model provides a better prediction than the other models. Also a sensitivity analysis showed that the pile diameter in dimensional variables and ratio of pile spacing to pile diameter in non-dimensional parameters are the most significant parameters on scour depth. © 2008 Elsevier B.V. All rights reserved
  6. Keywords:
  7. Artificial intelligence ; Backpropagation ; Bridge piers ; Erosion ; Feedforward neural networks ; Flow patterns ; Fuzzy inference ; Fuzzy neural networks ; Fuzzy systems ; Neural networks ; Piers ; Pile driving ; Piles ; Radial basis function networks ; Sensitivity analysis ; Speech recognition ; Three dimensional ; Back propagation (BP) ; Bed materials ; Bridge designs ; Feed forward (FF) ; Flow depths ; Inference systems ; Local scouring ; Mean diameter ; Mean velocities ; Network training ; Neural network ; Neuro-fuzzy ; Numerical testing ; Paper addresses ; Pile diameter ; Pile group ; Pile spacing ; Radial-basis function (RBF) ; Scour depth ; Scour holes ; Scour
  8. Source: Applied Soft Computing Journal ; Volume 9, Issue 2 , 2009 , Pages 746-755 ; 15684946 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S1568494608001324