Abstract
Non-linear finite element models (FEM) are commonly used to perform analysis in the time domain to simulate a limited number of stochastic loading scenarios that a slender marine structure may undergo, requiring a high computational time effort. Analytical equations and frequency domain analysis can be used to speed up these simulations, but they are not a convenient choice when high non-linearities are present in the dynamic system. Alternative models can be developed to reduce the simulation time while maintaining a good accuracy level of the system's response. This work proposes different strategies to develop artificial neural networks (ANN) architectures, based on deep learning algorithms, which can predict multiple structural node responses at once, in time and space, significantly reducing the total training time when a great number of structural nodes are considered. A novel classification concept of ANN-based models is introduced for this application: the NodeNet and the LengthNet class types. In the first approach, the model predictor focuses on a single structural node, while in the latter the model focuses on a length (segment) comprising many structural nodes. The work also extends the response predictions of such marine structures from the top region down to the Touchdown Zone (TDZ).
Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression