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

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.

Automated summary

In this study, scour experiments were conducted on both sandy and silty seabeds, and a rich dataset was established by combining data from literature. Based on this, two practical formulas for equilibrium scour depth were obtained using adaptive robust regression. These formulas showed good interpretations in physical meaning and outperformed commonly used models.