Initial assessment of bridge backwater using an artificial neural network approach

Abstract

The assessment of backwater resulting from extra energy losses on flood flows caused by bridge constrictions is of vital interest in hydraulic engineering due to its importance in the design of waterways and management of flooding. Although many detailed methods for estimating bridge backwater have been developed, an initial estimate of the magnitude of bridge backwater using a practical model, such as the multiple linear regression (MLR) technique, has a crucial importance for rapid evaluation of flood damages upstream of the bridge structure. In the current study, first, two artificial neural network (ANN) models using the same amount of input data as that of an MLR approach were developed, and then the ability of these ANN models versus the MLR models was investigated for the initial assessment of bridge backwater, both models having been based on the comprehensive laboratory data of the Hydraulic Research Wallingford in UK. The comparison of the results by the MLR and the ANN approaches revealed that the ANN model gave better predictions than those of the MLR model when applied to these laboratory data. United States Geological Survey (USGS) field data were also used for the validation and comparison of these methods. The results showed that ANN approaches yielded more accurate results than those of the MLR models when applied to these field data including actual flood profiles through many bridges.