UNCERTAINTY ANALYSIS BY POINT ESTIMATE METHODS INCORPORATING MARGINALDISTRIBUTIONS

The model performance of an engineering system is affected by many var iables subject to uncertainty. Point estimate (PE) methods are practic al tools to assess the uncertainty features of a model involving multi variate stochastic parameters. Two PE methods have been developed for engineering applications. One is Rosenblueth's PE method, which preser ves the first three moments of random variables and the other is Harr' s PE method, which reduces the computations of Rosenblueth's method bu t only as appropriate for application to random variables with normal distributions. In this study, two algorithms are proposed to encompass the advantages of the two PE methods: computational practicality and the handling of mixture distributions. Through a numerical experiment, the proposed methods yielded mom accurate estimations than those of R osenblueth's method with about the same amount of computation as Harr' s method. The two proposed methods were also applied to estimate stati stical moments of a pier scouring model output to demonstrate their pe rformance in an engineering application.