ANFIS- and GEP-based model for prediction of scour depth around bridge pier in clear-water scouring and live-bed scouring conditions

Abstract

Abstract Scour depth prediction is an important aspect of designing a bridge pier structure in a river. Proper modeling of scour depth ensures the sustainability of the structure. An attempt is made to develop a scour depth model for the bridge pier using an adaptive network-based fuzzy inference system (ANFIS) and gene expression programming (GEP). The scour depth is found to be influenced by various independent parameters such as pier diameter, flow depth, approach mean velocity, critical velocity, Froude number, bed sediment, and geometric standard deviation of bed particle size. Gamma tests are performed to identify the best input parameter combinations to predict scour depth. In the present study, two separate models have been developed for clear-water scouring (CWS) and live-bed scouring (LBS). For different ranges of input parameters, the scour depth ratio is computed and error analysis is performed. Results indicate that the ANFIS model (R2CWS = 0.95, MAPECWS = 9.39% and R2LBS = 0.95, MAPELBS = 5.29%) is the most accurate predictive model in both scour conditions as compared to the GEP model and existing models of previous researchers. However, for the low value of pier diameter (b) to flow depth (y) ratio (<0.25), the present ANFIS model apportioned unsatisfactory results for LBS only.