Reliability analysis of complex structures based on Bayesian inference

Abstract

This paper outlines a logical process based on Bayesian inference to exploit monitoring observations to improve the accuracy in the reliability estimates of civil structures that are retrofitted during their service lives. The proposed approach is particularly effective for complex structures, whose response prediction is affected by structural parameters with significant uncertainty; typical examples are prestressed concrete bridges, heavily influenced by the inherent uncertainties of the material’s creep and shrinkage. The methodology is applied to a real-life complex bridge in Italy, as opposed to academic problems and laboratory tests; the structural model developed for this study includes the original creep law of the Model B3 proposed by Bazant rather than linear approximation or response surfaces. The Bayesian inference is performed using a Markov Chain Monte Carlo (MCMC) method, while the reliability assessment utilises importance sampling (IS) for computational efficiency. The results show the extent to which structural health monitoring (SHM) and Bayesian inference reduce the uncertainty in structural parameters and the prediction of the structural demand. At the same time, they increase structural reliability. Particular attention is given to computational effort, structural modelling and algorithm optimisation.