Evaluation of ice-seabed interaction mechanism in sand by using self-adaptive evolutionary extreme learning machine

Abstract

Recent discovered oil and gases in the Arctic area have heightened the need for more attention to ice-seabed interaction during an ice scouring event. The seabed is gouged by these drifting icebergs in warmer months threatening the subsea pipelines transferring the hydrocarbons from offshore to onshore. The simulation of ice scouring seabed needs costly large deformation finite element analysis for the guaranteed operational integrity of the subsea pipelines. In this paper, a cost-effective alternative approach using the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm was taken to model the ice-induced seabed scour. Initially, using parameters governing the ice gouging process, 17 SaE-ELM models were developed. Then, a comprehensive dataset was established and properly allocated for training and testing of the developed models. The optimal number of hidden layer neurons and the best activation function were opted for the SaE-ELM network. The premium SaE-ELM models and the most influencing inputs were recognized by conducting a sensitivity analysis. The vertical component of load showed a significant impact on the reaction forces, rather the soil depth and berm height possessed a striking effect for modeling the soil displacements. Ultimately, a set of the SaE-ELM-based equations were presented to estimate the subgouge soil parameters.