Efficient model‐correction based reliability analysis of uncertain dynamical systems

In this contribution, a model-correction-based strategy is applied for efficient reliability analysis of uncertain dynamical systems based on low-fidelity (LF) model whose outcomes are corrected in probabilistic sense to represent the more realistic high-fidelity (HF) model. model-correction approach utilized, LF calibrated HF close so-called most probable point (MPP) standard normal space, which allows assessment considered complex system. Since only few expensive limit state function evaluations required, an enabled. application example, describes existing single span railway bridge modelled as simply supported Euler-Bernoulli beam subjected moving forces representing axle loads train. The modelling accounts bridge-train interaction by passing train mass-spring-damper (MSD) system, however increasing computational effort evaluations. failure probabilities evaluated with contrasted and discussed sophisticated It shown that provides reliable probability prediction while leading significant reduction effort.