A Data-Driven Model for Ice-Breaking Resistance of Structure Based on Non-Smooth Discrete Element Method and Artificial Neural Network Method

Abstract

In this paper, a data-driven model based on the Non-smooth Discrete Element Method (NDEM) and Artificial Neural Network Method (ANN) is proposed for the computation of the ice-breaking resistance of the structure. The idea of so-called “meta-modelling”, which means establishing an Artificial Neural Network (ANN) model based on a pre-computed ice failure database to avoid the time-consuming direct resolving of the ice fracture process, was integrated in the non-smooth discrete element method (NDEM). The developed model was validated by simulating the ice-breaking process of the cone structure, and the computational results match well with the experimental ones in the literature. After that, the effects of various parameters on the ice-breaking resistance were analyzed by the developed model. It was found that the factors that have great influence on the resistance of cone structure in level ice condition are the cone angle, navigation velocity and ice thickness.