000205109 001__ 205109
000205109 005__ 20190317000109.0
000205109 0247_ $$2doi$$a10.1115/OMAE2015-42387
000205109 037__ $$aCONF
000205109 245__ $$aApplication of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers
000205109 269__ $$a2015
000205109 260__ $$c2015
000205109 336__ $$aConference Papers
000205109 520__ $$aScour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge piers is complex so that deriving a theoretical model for predicting the exact equilibrium depth of local scour seems to be near impossible. On the other hand, the assessment of empirical models highly depends on local conditions, which is usually too conservative. In the present study, artificial neural networks are used to estimate the equilibrium depth of the local scour around bridge piers. Assuming such equilibrium depth is a function of five vari- ables, and using experimental data, a neural network model is trained to predict this equilibrium depth. Multilayer neural net- works with backpropagation algorithm with different learning rules are investigated and implemented. Different methods of data normalization besides the effect of initial weightings and overtraining phenomenon are addressed. The results show well adoption of the neural network predictions against experimental data in comparison with the estimation of empirical models.
000205109 700__ $$0248176$$g239523$$aSarshari, Ehsan
000205109 700__ $$0241652$$g105941$$aMüllhaupt, Philippe
000205109 7112_ $$dMay 31-June 5$$cSt. John’s, Newfoundland, Canada$$aASME 2015 34th International Conference on Ocean, Offshore & Arctic Engineering OMAE2015
000205109 773__ $$tProceedings of the ASME 2015 34th International Conference on Ocean, Offshore & Arctic Engineering$$kOMAE2015-42387$$qV007T06A061
000205109 8564_ $$uhttp://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=2465896&resultClick=1$$zURL
000205109 8564_ $$uhttps://infoscience.epfl.ch/record/205109/files/V007T06A061-OMAE2015-42387-2.pdf$$zPublisher's version$$s2084478$$yPublisher's version
000205109 909C0 $$0252053$$pLA
000205109 909CO $$pSTI$$ooai:infoscience.tind.io:205109$$qGLOBAL_SET$$pconf
000205109 917Z8 $$x105941
000205109 917Z8 $$x239523
000205109 917Z8 $$x239523
000205109 917Z8 $$x239523
000205109 937__ $$aEPFL-CONF-205109
000205109 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000205109 980__ $$aCONF