Prediction of local scour depth around bridge piers: modelling based on machine learning approaches

Abstract

Scouring is a threat to the foundations of bridges, and it frequently results in bridge collapse. Recently, machine-learning approaches have become quite popular in solving hydraulic and hydrologic problems. The present study adopted adaptive neuro-fuzzy inference systems (ANFIS) and gene expression programming (GEP) to model bridge pier scour depth. A temporal scour depth relationship has been proposed based on ANFIS and GEP approaches. In this paper, 500 data sets have been used to model the temporal scour depth using ANFIS and GEP models, in which 80% (400) data sets for training and 20% (100) datasets for testing purposes are used to develop the model. The performance of the GEP model is validated by comparing the results of the developed ANFIS model and other existing empirical equations. It is found that the GEP-based scour depth prediction model is effective in training and validation. The present GEP model accurately predicts the scour depth with a mean absolute percentage error (MAPE) value of less than 12% and an R2 value greater than 0.85. Thus, the GEP model can be utilized to predict scour depth around the bridge pier for unsteady flow conditions.