Precisely modeling offshore jacket structures considering model parameters uncertainty using Bayesian updating

Abstract

Digital twin (DT) is recognized as a promising tool to perform reliability analysis for offshore platforms. An open issue in developing the DT reliability analysis tool is to establish a high-fidelity highly-precise virtual model for the offshore platform of interest, where the virtual model is able to update its model parameters using actual sensory measurements to eliminate/reduce the effect of parameters uncertainty. To address this challenging issue, this paper proposes a Markov Chain Monte Carlo (MCMC)-based Bayesian updating method to build the requested virtual model for an offshore jacket platform. In this new method, the natural frequencies and mode shapes of the offshore jacket platform, extracted both from the structural dynamic responses of a finite element (FE) model and the sensory measurements in the physical system, are used to construct the likelihood function of the Bayesian inference. Then, the posterior probability distribution function (PDF) of the uncertain parameters is derived under the assumption of uniform prior distribution, which will be used to generate the Markov Chains by implementing the Metropolis-Hastings sampling. Hence, the most probable values of the uncertain parameters can be obtained from the Markov Chains for updating the FE model. A numerical study demonstrates high effectiveness of the proposed model updating method, even with high-level measurement noise and model uncertainty.