TL;DR: This paper makes a complete survey of stochastic models for sea state and wind time series, beginning with methods based on Gaussian processes, then non-parametric resampling methods for time series are introduced followed by various parametric models.

TL;DR: In this article, the authors proposed an approach to estimate the failure probability of a system under various design values using a single reliability analysis run, which can be regarded as an extension of that proposed by Au-Au SK.

TL;DR: In this article, an alternative univariate method for predicting component reliability of mechanical systems subject to random loads, material properties, and geometry is presented. But the method involves novel function decomposition at a most probable point that facilitates the univariate approximation of a general multivariate function in the rotated Gaussian space and one-dimensional integrations for calculating the failure probability.

TL;DR: In this paper, the authors developed a method that incorporates the information from approximate solutions to yield efficient and consistent reliability estimates, which is applied to studying first passage reliability of structures subjected to stochastic loadings.

TL;DR: In this article, a Gaussian process-based approach is proposed for estimating threshold exceedance in a sparsely-instrumented structure subject to uncertain excitation modeled by a linear dynamic model.

TL;DR: In this paper, the response of nonlinear systems driven by parametric Poissonian white noise is examined and the response sample function or the response statistics of a system driven by external white noise processes is completely defined.

TL;DR: In this article, a method for reliability assessment of the ultimate longitudinal strength of ship hulls in composite materials is described, and the reliability estimation is achieved by an improved first-order reliability algorithm.

TL;DR: An algorithm for the probabilistic analysis of concrete structures is proposed which considers material uncertainties and failure due to cracking, and is based on a stochastic training which uses wide spanned Latin hypercube sampling to generate the training samples.

TL;DR: In this paper, a method is presented to optimize the cost-effectiveness of inspection scheduling for single and multiple inspections during the expected lifetime, where the original crack depth is treated as a random variable with a known probability density function.

TL;DR: In this article, the authors introduce a new class of isotropic correlation functions, called Dagum, which allows to treat independently the Hausdorff-Besicovitch dimension and Hurst effect parameters.

TL;DR: In this paper, a reliability evaluation approach based on the development process of the structural nonlinearity is presented, where the traditional structural system reliability theory for structural safety regarding combination of failure modes is first revisited.

TL;DR: In this paper, a non-Gaussian stochastic linearization method was proposed for structural analysis of non-linear structural systems under white noise excitation, which is based on a modified A-type Gram-Charlier series approximation of the probability density function.

TL;DR: In this paper, a comparison of measured parallel tracks and synthetic parallel tracks, realized from a stochastic model, is made by using a bootstrap technique to estimate the uncertainty of vehicle fatigue indicated for the measured profile.

TL;DR: In this paper, a study of wave propagation in an infinite beam on a random Winkler foundation is presented, where the spatial variation of the foundation spring constant is modelled as a random field and the influence of the correlation length is studied.

TL;DR: In this paper, an analytical formulation is developed for determining the parameters of the multivariate Poisson process, which leads to the joint probability distribution of the extreme values of the non-Gaussian processes, over a given time duration.

TL;DR: In this paper, the dynamic shear modulus of the soil is modelled as a non-Gaussian random process that varies in the vertical direction and is characterized by a marginal probability density function and a correlation function.

TL;DR: In this paper, the load effect leading to fatigue damage is considered to be a nonlinear function of the vector of excitation loads and is thus non-Gaussian, and analytical expressions are developed for computing the level crossing statistics.

TL;DR: In this article, a new approach to cope with the non-stationary response of linear systems is presented, which provides a correction term determined as a pseudo-stochastic contribution of the equation governing either first-order or second-order statistics.

TL;DR: In this article, the buckling load of heterogeneous columns is found by applying the Functional Perturbation Method (FPM) directly to the Buckling (eigenvalue) Differential Equation (BDE).

TL;DR: In this article, a wave load induced fatigue damage accumulated by a vessel sailing along the North Atlantic route (NAr) is calculated based on the Palmgren-Miner additive rule and the rainflow cycle (RFC) count.

TL;DR: In this article, an efficient algorithm has been proposed for determining the probability of failure of structures containing flaws, based on a powerful generic equation, a central parameter in which is the conditional individual probability of initiating failure by a single flaw.

TL;DR: Stochastic analysis of failure of dump slopes (tailings) is addressed first, promoting the entire distribution function as an indispensable source of information to assess the quality of the structural system from the stability perspective.

TL;DR: In this paper, the evolution in time of the second moment properties of the output of linear systems subjected to fractional Brownian motion and fractional Gaussian noise, defined as the formal derivative of fractional brownian motion, is investigated.

TL;DR: In this paper, a detailed study of the structure and asymptotic behaviour of a second-order stochastic Volterra series model of the slow drift response of large volume compliant offshore structures subjected to random seas is presented.

TL;DR: Properties of binary processes are used to derive a hierarchy of upper bounds for any scalar process and the criteria for the validity of the approximation is revealed.

TL;DR: The Random Decrement algorithm was introduced in the late sixties by Henry Cole as a method of identification of structures under ambient loadings but the theory is still incomplete, and some errors of analysis are frequently made, which can lead to bias in estimations.

TL;DR: In this article, the shaping filter method is extended to non-Gaussian stochastic inputs that are not delta-correlated, and generalized moments are introduced as particular cases both the moments and cumulants.

TL;DR: In this paper, a conceptual representation of the mean points of failure and safety domains in reliability problems is presented, and a practical approach for approximating the mean point based on the first-order reliability method (FORM) is proposed.

TL;DR: It is shown that the system output at a time t can be approximated by a finite sum of deterministic functions of t with random coefficients given by equally spaced values of the input process over a window of finite width centered on t .