A non-tuned machine learning method to simulate ice-seabed interaction process in clay

Abstract

Exploitation of oil and gas in the Arctic area is expected to expand in the coming years. These hydrocarbons are transferred through subsea pipelines from offshore to onshore; however, the marine pipelines are threatened by traveling icebergs where the seabed may be gouged by the moving masses in warmer months. Subsea trenching and backfilling are usually utilized to bury the subsea pipelines for physical protection against the ice scouring. Regarding the stress-based design methods, deformations and forces are generally the controlling design factors for the subsea assets. In this study, the subgouge clay displacements and the reaction forces were simulated using a non-tuned self-adaptive machine learning (ML) entitled “self-adaptive extreme learning machine” (SAELM). Initially, fifteen SAELM models were defined by means of the parameters affecting the ice-scoured features. Subsequently, 70% and 30% of the constructed dataset were respectively applied to train and test the ML models. After that, the optimum number of hidden layer neurons and the best activation function were selected for the SAELM network. By conducting a comprehensive sensitivity analysis, the premium SAELM models and the most influencing input parameters in estimation of the subgouge clay characteristics were introduced. Regarding the performed analyses, the horizontal load factor and the gouge depth ratio were identified as the most influential parameters to model the reaction forces, whereas the soil depth had a significant impact for simulation of the ice-induced clay deformations. Finally, a set of SAELM-based equations were presented to estimate the subgouge clay parameters.