Prediction of River Pipeline Scour Depth Using Multivariate Adaptive Regression Splines
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Volume 8, Issue 1
Abstract
In this study, the multivariate adaptive regression splines (MARS) technique was applied to estimate scour depth around pipelines. To this purpose, 90 data sets related to effective dimensionless parameters on pipeline scouring phenomena were gathered from literature. A gamma test (GT) was used to define the most-effective parameters on scouring phenomena below pipelines. Performance of MARS model was compared with multilayer perceptron (MLP) neural network and empirical formulas. Results of the GT showed that , , and are the most important parameters for scour depth. Results of MARS model with coefficient of determination (0.91) and root-mean square error (0.05) indicated that this model has suitable performance for predicting scour depth under pipelines and results of this model are more accurate compared to empirical formulas. Comparing results of MARS model and MLP showed that accuracy of MARS model is slightly lower than that of the MLP.
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 8 • Issue 1 • February 2017
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© 2016 American Society of Civil Engineers.
History
Received: Oct 9, 2015
Accepted: Apr 11, 2016
Published online: Jul 20, 2016
Discussion open until: Dec 20, 2016
Published in print: Feb 1, 2017
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