In the reliability engineering and design of offshore structures, probabilistic approaches are frequently adopted. They require the estimation of extreme quantiles of oceanographic data based on the statistical information. Due to strong correlation between such random variables as, e.g., wave heights and wind speeds (WS), application of the multivariate, or bivariate in the simplest case, extreme value theory is sometimes necessary. The paper focuses on the extension of the average conditional exceedance rate (ACER) method for prediction of extreme value statistics to the case of bivariate time series. Using the ACER method, it is possible to provide an accurate estimate of the extreme value distribution of a univariate time series. This is obtained by introducing a cascade of conditioning approximations to the true extreme value distribution. When it has been ascertained that this cascade has converged, an estimate of the extreme value distribution has been obtained. In this paper, it will be shown how the univariate ACER method can be extended in a natural way to also cover the case of bivariate data. Application of the bivariate ACER method will be demonstrated for measured coupled WS and wave height data.

Apart from the mechanisms of ice structure interactions under various scenarios of sea ice conditions and structural configurations, the selection of probabilistic models is crucial in order to cope with the loading uncertainty. Sea ice is the primary contributor to design loads in cold regions. In many cases, ice loads present the highest magnitude. In recent years, the probabilistic study of ice thickness, ice strength coefficient, flexural strength, ice drift speed, etc., has significantly increased, corresponding to the increasing activity of offshore operations in cold regions as well as the development of instrument technology for sea ice observation. This paper reviews existing probabilistic modes of sea ice, which are used to estimate the uncertainty of ice loading with various types of offshore structures, including vertical structures, sloping structures, and stations with floating vessels. The ISO 19906 standard is employed for the probabilistic assessment of vertical and sloping structures. The interactions between ice and structures on sloping structures are considered in both the upward and downward directions. The ice resistance method is applied for station-keeping floating vessels in ice. The key parameters of sea ice properties to estimate the design loads are studied. The effect of correlation between the key parameters of ice loads is investigated. This review shows that most existing probabilistic models are proposed for the estimation of ice loading on the various types of offshore structures.

It is known that the mooring system response of floating production units subjected to environmental loads is nonlinear. Even though wave elevations can be assumed as Gaussian processes for short-term periods, corresponding line tension responses generally are not, due to second-order slow-drift floater motions and intrinsic nonlinearities of the system. In this work, short-term extreme responses are estimated based on two different approaches. In the first one, a number of probability distributions are fitted to the tension time histories’ peaks samples and classic order statistics is applied to determine the most probable extreme line tension corresponding to a short-time period (3-h) in order to identify the one with best performance. The effect of correlation between consecutive peaks in the extremes estimation is investigated through the one-step Markov chain condition by using a Nataf transformation-based model. In the second approach, a more robust and recently developed method named average conditional exceedance rate (ACER) is investigated, where dependencies between maxima can be easily taken into account. Additionally, effects of major parameters in dynamic analyses, such as simulation length and discretization level of the wave spectrum, are evaluated. All time-series-based extreme estimates are compared with the estimates directly obtained from a sample of epochal maxima (Gumbel method). Numerical examples cover two study cases for mooring lines belonging to Floating Production Storage and Offloading (FPSO) units installed offshore Brazil. It is shown that the consideration of dependence between peaks leads to lower extreme estimates and that both approaches return accurate results.

The ferry links crossing the fjords along the west coast of Norway are going to be replaced by fixed links. Among the proposed solutions is the construction of Submerged Floating Tunnels (SFT). A mooring system should be used to restrain the movements of these floating structures. Long period waves, such as swell waves, parametrically excite the system and might result in excessive vibrations in the cable. This paper presents the equations of motion of the mooring line and derives an analytical expression, which allows for a quick identification of the system’s stability. This expression is obtained through the Harmonic Balance Method (HBM) with two harmonic terms. The contribution of multiple modes of vibration is investigated for the case of small sag to span ratios. The expression is compared with the results from a numerical model, revealing the applicability of the proposed formulation.

Computation of the reliability of large technical systems is usually a very difficult problem for realistic systems, and it is generally not possible to calculate the exact reliability. There are many techniques for approximate calculations, but they are often complicated and difficult to implement. In this paper the development of a new method based on Monte Carlo simulation for efficient calculation of system reliability is described. Standard Monte Carlo simulation forms a simple and robust alternative for calculating system reliability. If one can generate large samples, the law of large numbers ensures that the estimated reliability will be accurate as well. This may, however, be a very time-consuming operation. The new method introduces a parameterized system that corresponds to the given system for a specific parameter value. By using regularity of the system reliability as a function of the introduced parameter, the system reliability for our original system can be predicted accurately from relatively small samples.