Prediction of iceberg-seabed interaction using machine learning algorithms

• Main Author: Azimisiahchaghaei, Hamed
• Format: Thesis
• Language: English
• Published: Memorial University of Newfoundland 2023
• Subjects: Arctic; Iceberg*; North Atlantic
• Online Access: https://research.library.mun.ca/16132/; https://research.library.mun.ca/16132/1/converted.pdf

Every year thousands of icebergs are born out of glaciers in the Arctic zone and carried away by the currents and winds into the North Atlantic. These icebergs may touch the sea bottom in shallow waters and scratch the seabed, an incident called “ice-gouging”. Ice-gouging may endanger the integrity of the buried subsea pipelines and power cables because of subgouge soil displacement. In other words, the shear resistance of the soil causes the subgouge soil displacement to extend much deeper than the ice keel tip. This, in turn, may cause the displacement of the pipelines and cables buried deeper than the most possible gouge depth. Determining the best burial depth of the pipeline is a key design aspect and needs advanced continuum numerical modeling and costly centrifuge tests. Empirical equations suggested by design codes may be also used but they usually result in an over-conservative design. Iceberg management, i.e., iceberg towing and re-routing, is currently the most reliable approach to protect the subsea and offshore structures, where the approaching icebergs are hooked and towed in a safe direction. Iceberg management is costly and involves a range of marine fleets and advanced subsea survey tools to determine the iceberg draft, etc. The industry is constantly looking for cost-effective and quick alternatives to predict the iceberg draft and subgouge soil displacements. In this study, powerful machine learning (ML) algorithms were used as an alternative cost-effective approach to first screen the threatening icebergs by determining their drafts and then to predict the subgouge soil displacement to be fed into the structural integrity analysis. Developing a reliable solution to predict the iceberg draft and subgouge soil displacement requires a profound understanding of the problem's dominant parameters. Therefore, the present study started with dimensional analyses to identify the dimensionless groups of key parameters governing the physics of the problem. Two comprehensive datasets were constructed ...