Long-Term Extreme Response Prediction of Mooring Lines Using Subset Simulation

Rigorous methods of probabilistic evaluations on long-term extremes are integral components in reliability research of offshore structures against overload events. Assessment across all conceivable sea states requires accounting for variabilities of long-term environmental loads and short-term stochastics, traditionally captured through extensive sampling or numerical expectation integration. The amount of environmental load variables render numerical integrations across high dimensions computationally prohibitive, while industry requirements of high return periods demand large Monte Carlo samples of timedomain dynamic analyses. Subset simulation offers a promising alternative to classic methods of statistical analysis, dividing ultra-low probability problems into subsets of intermediate probabilities. The methodology is uniquely advantageous for the assessment of heavy-tail overload events, which are unpredictably severe and occur at exceedingly rare frequencies. Subset simulation is experimented on a mooring case study situated in the hurricane-prone Gulf of Mexico, with the structure exposed to a joint-probabilistic description of wave, wind and current loads. The devised methodology is found to successfully evaluate hurricane-stimulated extreme events at ultra-low probabilities, beyond the feasible reach of Monte Carlo simulation at reasonable lead times.