Estimating piping potential in earth dams and levees using generalized neural networksXue, Xinhua (Avtor) Yang, Xingguo (Avtor) Chen, Xin (Avtor) , Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru (Lastnik avtorskih pravic) pipinggeneralized neural networkcross validationBP neural networkInternal erosion and piping in embankments and their foundations is the main cause of failures and accidents to embankment dams. To estimate the risks of dam failure phenomenon, it is necessary to understand this phenomenon and to develop scientifically derived analytical models that are simpler, easier to implement, and more accurate than traditional methods for evaluation of piping potential. In this study, a generalized regression neural network (GRNN) technique has been applied for the assessment of piping potential, as well, due to its ability to fit complex nonlinear models. The performance of GRNN has been cross validated using the k-fold cross validation method technique. The GRNN model is found to have very good predictive ability and is expected to be very reliable for evaluation of piping potential.20142018-06-14 12:10:26Znanstveno delo70823slFakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru