Bridge pier scour prediction by gene expression programming

Abstract

Extensive research has been carried out to predict bridge pier scour, with laboratory and field data, using different modelling techniques. This study introduces a new soft computing technique called gene expression programming (GEP) for pier scour depth prediction using field data. A functional relationship has been established using GEP and its performance is compared with other inductive modelling techniques such as artificial neural networks (ANNs) and conventional regression-based techniques. Field data comprising 370 data sets were collected from the published literature and divided into calibration and validation (testing) data sets. The performance of GEP was found to be satisfactory and encouraging when compared with regression and ANN models in predicting bridge pier scour depth. GEP has the unique capability of providing a compact and explicit mathematical expression for computing bridge scour. This advantage of GEP over ANN is one of the main motivations for this work. The resulting GEP models add to the existing literature on artificial intelligence based inductive models that can be used effectively for bridge scour modelling.