Genetic Programming to Predict River Pipeline Scour
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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.
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Acknowledgments
The writers wish to express their sincere gratitude to Universiti Sains Malaysia for funding a short-term grant to conduct this ongoing research (Grant No. UNSPECIFIED304.PREREDAC.6035262). The writers wish to thank Robert D. Jarrett, U.S. Geological Survey (USGS) for his suggestions in the preparation of this note and for his reviews.
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© 2010 ASCE.
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Received: Oct 31, 2009
Accepted: Apr 16, 2010
Published online: Apr 22, 2010
Published in print: Aug 2010
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