An optimal statistical regression model for predicting wave-induced equilibrium scour depth in sandy and silty seabeds beneath pipelines

Journal article

Zhang, Yaqi, Wu, Jinran, Zhang, Shaotong, Li, Guangxue, Jeng, Dong-Sheng, Xu, Jishang, Tian, Zhuangcai and Xu, Xingyu. (2022). An optimal statistical regression model for predicting wave-induced equilibrium scour depth in sandy and silty seabeds beneath pipelines. Ocean Engineering. 258, p. Article 111709. https://doi.org/10.1016/j.oceaneng.2022.111709
AuthorsZhang, Yaqi, Wu, Jinran, Zhang, Shaotong, Li, Guangxue, Jeng, Dong-Sheng, Xu, Jishang, Tian, Zhuangcai and Xu, Xingyu
Abstract

Equilibrium scour depth (S) of seabed is critical to the safety of offshore pipelines which is one of the most important topics in ocean engineering. Compared to sands, few experiments have been done for silty seabed. In the present work, scour experiments under wave-only action were performed for both sandy and silty seabeds. Together with the data from literature, the most abundant dataset at the present stage is established. Based on this, two practical formulas for S were obtained with adaptive robust regression (ARR) from a data-driven perspective. One is for sands only that is related to the Keulegan–Carpenter (KC) number, pipeline-seabed gap and grain size of sands. The other is a more generalized model for both sands and silts, which is related to the KC number and sediment type that is distinguished by introducing a dummy variable. The formulas outperform the commonly-used process-based and data-driven models while also showing good interpretations in physical meaning. For silts from the Yellow River Delta, the S in silts is generally 1.2 times of that in sands. The better performance is attributed to (1) the outliers in the dataset are effectively handled with ARR; (2) the most abundant dataset.

Keywordswave-flume experiments; outliers; adaptive robust regression; dummy variable; Yellow River delta
Year2022
JournalOcean Engineering
Journal citation258, p. Article 111709
PublisherElsevier Ltd
ISSN0029-8018
Digital Object Identifier (DOI)https://doi.org/10.1016/j.oceaneng.2022.111709
Scopus EID2-s2.0-85132515255
Open accessPublished as green open access
Page range1-14
FunderIntergovernmental Key Special Project, China
Natural Science Foundation of Shandong Province, China
National Natural Science Foundation of China (NSFC)
Project of Taishan Scholar, China
Ocean University of China
Shandong Province, China
Author's accepted manuscript
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Output statusPublished
Publication dates
Online23 Jun 2022
Publication process dates
Accepted02 Jun 2022
Deposited11 Jul 2023
Grant ID2017YFE0133500
ZR2019BD009
41976198
41807229
201801026
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