Prediction of Scour Depth at Bridge Abutments in Cohesive Bed Using Gene Expression Programming

The scour modelling in cohesive beds is relatively more complex than that in sandy beds and thus there is limited number of studies available on local scour at bridge abutments on cohesive sediment. Recently, a good progress has been made in the development of data-driven techniques based on artificial intelligence (AI). It has been reported that AI-based inductive modelling techniques are frequently used to model complex process due to their powerful and non-linear model structures and their increased capabilities to capture the cause and effect relationship of such complex processes. Gene Expression Programming (GEP) is one of the AI techniques that have emerged as a powerful tool in modelling complex phenomenon into simpler chromosomal architecture. This technique has been proved to be more accurate and much simpler than other AI tools. In the present study, an attempt has been made to implement GEP for the development of scour depth prediction model at bridge abutments in cohesive sediments using laboratory data available in literature. The present study reveals that the performance of GEP is better than nonlinear regression model for the prediction of scour depth at abutments in cohesive beds.