Iceberg-seabed interaction evaluation in clay seabed using tree-based machine learning algorithms

Hamed Azimi

doi:10.1016/j.jpse.2022.100075

Iceberg-seabed interaction evaluation in clay seabed using tree-based machine learning algorithms

2022-12-01

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DOI：10.1016/j.jpse.2022.100075

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In Arctic offshore regions, the oil and gas hydrocarbons are transferred to the onshore basins through the subsea pipelines. However, the operational integrity of the subsea pipeline may be at risk of collision with traveling icebergs, which gouge the seabed in the Arctic shallow waters. Even though these sea bottom-founded structures are buried at a secure depth below the seafloor, the pipeline is still threatened by the ice scouring event extended much deeper than the ice tip due to the shear resistance of the seabed soil. Modeling the sub-gouge soil character-istics is a challenging problem that requires costly experimental and long-running finite element (FE) simulations. To overcome these challenges, in this paper, the reaction forces and sub-gouge soil deformations in clay were modeled using an advanced extra tree regression (ETR) algorithm, as a quick and cost-effective alternative for the early design phases of pipeline engineering projects. Eight ETR models, ETR 1 to ETR 8, were developed by using the input parameters governing the iceberg-seabed interaction problem. The collected data were randomly split into 70% for training the machine learning (ML) models and 30% for testing purposes. The most accurate ETR models and the most significant input parameters were identified by performing a sensitivity analysis. The com-parison of the most accurate ETR models and decision tree regression (DTR), random forest regression (RFR), and gradient boosting regression (GBR) algorithms proved that the ETR models had better performance to simulate the ice keel seabed interaction in clay.

Keyword:

Ice-seabed interaction process

Clay seabed

Extra tree regression (ETR)

Decision tree regression (DTR)

Random forest regression (RFR)

Gradient boosting regression (GBR)

Iceberg-seabed interaction evaluation in clay seabed using tree-based machine learning algorithms

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