Genetic Programming to Predict River Pipeline Scour

Abstract

The process involved in the local scour below pipelines is so complex that makes it difficult to establish a general empirical model to provide an accurate estimation for scour. This technical note describes the use of genetic programming (GP) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in the training. The performance of GP was found to be more effective when compared with the results of regression equations and artificial neural networks modeling in predicting the scour depth around pipelines.