No abstract

The normality polynomial and multi-linear regression approaches are revisited for estimating the reliability index, its precision, and other reliability-related values for coastal and structural engineering applications. In previous studies, neither the error in the reliability estimation is mathematically defined nor the adequacy of varying the tolerance is investigated. This is addressed in the present study. First, sets of given numbers of Monte Carlo simulations are obtained for three limit state functions and probabilities of failure are computed. Then, the normality polynomial approach is applied to each set and mean errors in estimating the reliability index are obtained, together with its associated uncertainty; this is defined mathematically. The data is also used to derive design points and sensitivity factors by multi-linear regression analysis for given tolerances. Results indicate that power laws define the mean error of the reliability index and its standard deviation as a function of the number of simulations for the normality polynomial approach. Results also indicate that the multi-linear regression approach accurately predicts reliability-related values if enough simulations are performed for a given tolerance. It is concluded that the revisited approaches are a valuable option to compute reliability-associated values with reduced simulations, by accepting a quantitative precision level.

Simultaneous occurrence of metocean variables can present a multihazard to maritime systems. However, simplified design approaches to assess simultaneous significant wave heights and wind velocities are lacking, especially if seasonality is considered. This is addressed in this study by using extreme significant wave heights and companion wind velocities recorded in the Gulf of Mexico. Time-dependent, generalized extreme value (GEV) models and classical regression are the basis to propose a simplified approach to estimate correlated extreme significant wave heights and wind velocities associated with given return periods, accounting for seasonality and including measures of uncertainty. It is found that the proposed approach is a new but simple method to adequately characterize the concurrent extreme metocean variables and their uncertainty. It is concluded that the method is an effective probabilistic design tool to determine simultaneous extreme significant wave heights and companion wind velocities for desired return periods and seasonality.

The adequate knowledge of the weather behavior is very important for the design and management of socioeconomical, environmental and sustainability human interests in the coasts and oceans.  In the present study an extreme value analysis of maximum significant waves recorded at a buoy located in the Pacific Ocean was carried out.  The analysis was carried out from two perspectives, by considering a Generalized Extreme Value (GEV) model with stationary distribution (i.e., the time variations are not accounted for), and by considering a non-stationary GEV model, which incorporates the monthly seasonality of maximum observed values in time increments; the maximum significant wave behavior was parameterized using harmonic functions for the distribution measures. Both approaches were compared for a single buoy. In the study a seasonality effect was found, which was also present at the Gulf of Mexico in previous studies, and which cannot be captured by a stationary model.