Research Papers: Piper and Riser Technology

Classification and Regression Trees Approach for Predicting Current-Induced Scour Depth Under Pipelines

[+] Author and Article Information
R. Yasa

School of Civil Engineering,
Iran University of Science and Technology,
Narmak, Tehran, Iran
e-mail: reza_yasa@civileng.iust.ac.ir

A. Etemad-Shahidi

Griffith School of Engineering,
Griffith University,
QLD, 4222, Australia
e-mail: a.etemadshahidi@griffith.edu.au

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received January 18, 2013; final manuscript received August 8, 2013; published online November 28, 2013. Assoc. Editor: Colin Leung.

J. Offshore Mech. Arct. Eng 136(1), 011702 (Nov 28, 2013) (8 pages) Paper No: OMAE-13-1009; doi: 10.1115/1.4025654 History: Received January 18, 2013; Revised August 08, 2013

Reliable prediction of scour depth is important in engineering analysis concerned with pipeline stability. The aim of this study is to develop an accurate formula for prediction of the current-induced scour depth under pipelines. Previous experimental data are collected and used as a database by which to study the effect of different parameters on the scour depth. Decision tree and nonlinear regression approaches are used to develop engineering design formulae for estimation of the current induced scour depth in both live bed and clear water conditions. It is demonstrated that the proposed formulas are more accurate than previous ones in predicting the scour depth in all conditions. Probabilistic formulas are also presented for different levels of risk, aimed at safe and economic design of submerged pipelines.

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Grahic Jump Location
Fig. 1

Comparison between the measured and predicted dimensionless scour depths using different equations

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Fig. 2

Dimensionless scour depth against Fry, Sumer and Fredsøe [5]

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Fig. 3

Dimensionless scour depth against Fry, using the collected data set

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Fig. 4

The dimensionless scour depth against different initial gaps (e/D) by using MA and Ye data

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Fig. 5

The classification of the data set by using the CART algorithm

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Fig. 6

Comparison between the measured and predicted dimensionless scour depth, (a) test data, (b) all data




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