Artificial neural network study of observed pattern of scour depth around bridge piers

An artificial neural networks (ANN) model is developed to study the observed pattern of local scour at bridge piers using an FHWA (Federal Highway Administration) data set composed of 380 measurements at 56 bridges in 13 states. Various ANN estimates of observed pier scour depth on different choices of input variables are examined. Reducing the number of variables from 14 to 9 has negligible effect on the coefficient of determination, R2, (0.73 vs. 0.72). Further sensitivity analysis indicates that pier scour depth can be estimated using only four variables: pier shape and skew, flow depth and velocity with a coefficient of determination of 0.81, suggesting that inclusion of some variables actually diminishes the quality of ANN predictions of short term observed pattern of scour. The ANN estimates indicate that flow depth and flow velocity make up 66% of the coefficient of determination.