Dynamic analyses of mooring line systems are computationally expensive. Over the last decades an extensive variety of methods to reduce this computational cost have been suggested. One method that has shown promising preliminary results is a hybrid method which combines finite element analysis and artificial neural networks (ANN). The present study presents a novel strategy for selecting, arranging and normalizing training data for an ANN. With this approach one ANN can be trained to perform high speed dynamic response prediction for all fatigue relevant sea states and cover both wave frequency motion and slow drift motion. The method is tested on a mooring line system of a floating offshore platform. After training a full fatigue analysis is carried out. The results show that the ANN with high precision provides top tension force histories two orders of magnitude faster than a full dynamic analysis.

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