A Robust and Efficient Computational Method for Fatigue Reliability Update Using Inspected Data

This paper presents a new methodology for reliability-based inspection planning focusing on robust and accurate computational strategies for fatigue-reliability updating using inspection results. The core of the proposed strategy is a conditioned sampling-based method, implemented by a Fast Probability Analyzer (FPA) software where efficiency is achieved by using the importance sampling principal. For a single component or limit state, FPA first generates Markov-Chain Monte Carlo (MCMC) samples in the failure domain, then applies an adaptive stratified importance sampling (ASIS) method to compute probability of failure (PoF) with error control. Once the MCMC samples have been created, solving a reliability updating problem is fairly straightforward and computationally robust relative to the conventional system reliability methods that rely on linearization of the limit states. The new approach is demonstrated using examples including stiffened panels of a ship-shaped vessel where reliability is updated using inspection results from 100 panel connections.