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Diss. (sammanfattning) Helsingfors : Aalto-universitetet, 2024

Due to their large cargo carrying capacities, ships are an environmentally and economically efficient mode for transporting cargo. During winters, the ships must navigate in ice-prone waters and hence shipping in ice-covered waters is an important engineering topic. To protect human lives and the environment during the shipping operations, we must understand the forces the ship hull sustains during the shipping operations. However, because of many uncertainties in the ice microstructure, and the ice breaking process, the forces required for breaking the ice cannot be resolved deterministically, but rather statistically. This work analyzed full-scale ice load data and the measured covariate conditions at the instant the load occurred to examine how the load distribution changes in different conditions. The work used Bayesian hierarchical models with Gaussian process priors to infer how the load probability distribution parameters and their uncertainties change as a function of condition covariates. The ice thickness was modeled using a Gaussian process model with a Student-t error model, whereas the ship speed was interpolated linearly. Different ice load models were compared via their posterior predictions, and the Weibull model was found to have the best posterior predictions.