The influence of damage breach sampling process on the direct assessment of ship survivability

The current damage stability regulatory framework for passenger and dry cargo ships allows addressing vessel survivability after flooding due to collisions with probabilistic requirements. This methodology also applies to other hazards responsible for the flooding of a ship such as bottom and side groundings. Traditionally, the application of Monte Carlo sampling of pertinent distributions allows for assessing ship survivability. Such a method introduces randomness in the process, leading to a dispersion of the attained survivability index within multiple sets of generated damages. The present work investigates sampling methods alternative to Monte Carlo, based on Latin Hypercube and Randomised Quasi-Monte Carlo processes. The sampling methods application for collisions, side and bottom groundings on a reference barge available in the literature for benchmark purposes shows that the Randomised Quasi-Monte Carlo method based on multidimensional Sobol sequences grants lower dispersion of the final survivability index data within samples of equivalent size. Finally, the application on a sample cruise ship of Monte Carlo and Randomised Quasi-Monte Carlo methods highlights the possibility to reduce the number of damage breaches necessary to evaluate the survivability index within an engineering confidence interval, thus improving accuracy and efficiency in the amplification of probabilistic damage stability methods by the industry.