Genetic Programming to Predict Bridge Pier Scour
This article has been corrected.
VIEW CORRECTIONPublication: Journal of Hydraulic Engineering
Volume 136, Issue 3
Abstract
Bridge-pier scour is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by the writers), in the form of artificial neural networks (ANNs) and genetic programming (GP). There had been 398 data sets of field measurements that were collected from published literature and were 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 more effective when compared to regression equations and ANNs in predicting the scour depth at bridge piers.
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Acknowledgments
The first three writers express their sincere gratitude to Universiti Sains Malaysia for funding a short-term grant to conduct this on-going research (Grant No. UNSPECIFIED304.PREREDAC.6035262). The writers thank Robert D Jarrett, U.S. Geological Survey (USGS), for his suggestions in preparation of this manuscript and also reviews.
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Information
Published In
Journal of Hydraulic Engineering
Volume 136 • Issue 3 • March 2010
Pages: 165 - 169
Copyright
© 2010 ASCE.
History
Received: Jun 6, 2008
Accepted: Jul 7, 2009
Published online: Jul 13, 2009
Published in print: Mar 2010
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