Estimation of scour depth around circular piers: applications of model tree

View/ Open
Author(s)
Etemad-Shahidi, Amir
Bonakdar, Lisham
Jeng, D-S
Year published
2015
Metadata
Show full item recordAbstract
Scour around bridge piers is one of the main causes of bridge failures and is of great importance for hydraulic engineers and scientists. Prediction of the scour depth around piers is complicated, and accurate results are rarely achieved by the existing models. Recently, data mining approaches such as artificial neural networks and fuzzy inference systems have been applied successfully to predict scour depth around hydraulic structures. In this study, an alternative robust data mining approach was used for the predictions of the scour depth around piers, and the results were compared with those of three empirical approaches. ...
View more >Scour around bridge piers is one of the main causes of bridge failures and is of great importance for hydraulic engineers and scientists. Prediction of the scour depth around piers is complicated, and accurate results are rarely achieved by the existing models. Recently, data mining approaches such as artificial neural networks and fuzzy inference systems have been applied successfully to predict scour depth around hydraulic structures. In this study, an alternative robust data mining approach was used for the predictions of the scour depth around piers, and the results were compared with those of three empirical approaches. Performances of developed models were tested by experimental data sets collected in laboratory experiments and field measurements, together with existing empirical approaches. Statistical measures indicate that the proposed M5′ model provides a better prediction of scour depth than the empirical approaches.
View less >
View more >Scour around bridge piers is one of the main causes of bridge failures and is of great importance for hydraulic engineers and scientists. Prediction of the scour depth around piers is complicated, and accurate results are rarely achieved by the existing models. Recently, data mining approaches such as artificial neural networks and fuzzy inference systems have been applied successfully to predict scour depth around hydraulic structures. In this study, an alternative robust data mining approach was used for the predictions of the scour depth around piers, and the results were compared with those of three empirical approaches. Performances of developed models were tested by experimental data sets collected in laboratory experiments and field measurements, together with existing empirical approaches. Statistical measures indicate that the proposed M5′ model provides a better prediction of scour depth than the empirical approaches.
View less >
Journal Title
Journal of Hydroinformatics
Volume
17
Issue
2
Copyright Statement
© IWA Publishing 2015. This is the author-manuscript version of this paper. The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics, Volume 17, Issue 2, Pages 226-238; DOI: 10.2166/hydro.2014.151, and is available at www.iwapublishing.com
Subject
Civil Engineering not elsewhere classified
Civil Engineering