Dynamic reliability calculation of random structures by conditional probability method

Reliability is sometimes computed as the likelihood of achieving an intended function in the presence of uncertainties, and this is known as dynamic reliability by the conditional probability approach. These techniques can produce incredibly accurate reliability estimates. This work uses the dynamic response spanning action Markov hypothesis for the composite random reliability problem. Two steps are needed to describe conditional probability: first, the Taylor expansion approach is used to derive a 2nd-order approximate formula for determining the dynamic reliability of the random structure. The second step is to come up with a mathematical sampling strategy based on the statistical analysis's Kriging model. The Kriging interpolation model's sampling process satisfies the nonlinear association between structural random boundaries and dynamic reliability. Consequently, the finite element results can be used immediately to anatomize the impact of random structural parameters on dynamic reliability, bypassing the arduous and time-consuming theoretical derivation. The numerical example results show that the sampling method based on the Kriging model is unconcerned about the ratio used to represent dispersion and provides extra benefits in computational verisimilitude and calculation productivity.