Horseracing Simulation Algorithm for evaluation of small failure probabilities

Over the past decade, the civil engineering community has ever more realized the importance and perspective of the reliability-based design optimization (RBDO), the selection of a design that satisfies performance constraints with a specified probability. Since then several stochastic simulation algorithms for computing small failure probabilities encountered in reliability analysis of engineering systems have been developed: Subset Simulation [1], Line Sampling [2], The Auxiliary Domain Method [3], ALIS [4], etc. In this paper we propose a novel advanced stochastic simulation algorithm for solving highdimensional reliability problems, called Horseracing Simulation (HRS). The key idea behind HS is as follows. Although the reliability problem itself is high-dimensional, the limit-state function maps this high-dimensional parameter space into a one-dimensional real line. This mapping transforms a high-dimensional random parameter vector, which may represent the input load as well as the uncertain structural parameters, into a random variable with unknown distribution, that represents the uncertain structural response. It turns out, that the corresponding cumulative distribution function (CDF) of this random variable of interest can be accurately approximated by empirical CDFs constructed from specially designed samples. The accuracy and efficiency of the new method is demonstrated with a real-life wind engineering example.