Probabilistic modeling of system deterioration with inspection and monitoring data using Bayesian networks

To facilitate the estimation of the reliability of deteriorating structural systems conditional on inspection and monitoring results, we develop a modeling and computational framework based on Bayesian Networks (BNs). The framework enables accounting for dependence among deterioration at different system components, for dependence due to the structural system behavior, but also dependence introduced by information obtained on selected parts of the system, which effects the reliability estimates of other system parts. The proposed model and algorithm is applicable to aging structures, including offshore platforms, bridges, ships, aircraft structures, considering deterioration process such as corrosion and fatigue. To efficiently model dependence among component deterioration states, a hierarchical structure is defined. This structure facilitates the solution of the Bayesian model updating of the components in parallel. For illustration, a Daniels system subjected to fatigue is used as a case study. The computational efficiency of the proposed algorithm is compared with that of Markov Chain Monte Carlo and found to be orders of magnitude higher.