The environment in cold regions undergoes significant changes that manifest in rising temperatures and melting ice caps. These processes allow access to new areas for shipping and the installation of structures. However, the occurring changes are not solely a reduction of ice, but also waves increasingly occur in cold regions contributing to ice break up in the Marginal Ice Zone (MIZ) and the transport of ice towards the open sea. Much work has been done to combine the single topic disciplines: wave-hydrodynamics, ice mechanics and structure mechanics to wave-structure (WSI) and ice-structure interaction (ISI). The changing environment in cold regions and the increased wave activity form a new combined discipline: wave-ice-structure interaction (WISI). This paper addresses existing knowledge gaps of the future loading scenario WISI that need to be addressed in engineering to ensure safety for future operations in Polar Regions. The knowledge gaps however, do not only refer to the discipline interfaces, i.e. challenges in combining them, but also to knowledge gaps within them. Wave statistics and the cross-effect between wave and ice are widely unknown which limits the definition of a design wave-scenario. Structures in such environments are exposed to subzero temperatures and neither their impact on properties nor on fatigue life is fully understood. While most phenomena of these two disciplines and their implementation into numerical models are established the ice mechanics appear as the weakest link. Ice is a complex material and not all aspects of its mechanical behaviour are understood and if – the implementation into (numerical) models has not been successful yet. Ice pieces that are energetically charged by waves and collide with structures at high velocities and for such high impact loads the governing ice mechanics are hardly covered by the state of the art or not at all.

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Ice material models often limit the accuracy of ice related simulations. The reasons for this are manifold, e.g. complex ice properties. One issue is linking experimental data to ice material modeling, where the aim is to identify patterns in the data that can be used by the models. However, numerous parameters that influence ice behavior lead to large, high dimensional data sets which are often fragmented. Handling the data manually becomes impractical. Machine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and small scale experimental database is established.