Reliability updating with measurements in spatially distributed systems using stochastic simulation

A recently developed method for reliability updating with equality information [Straub D., Probabilistic Engineering Mechanics, under review] is investigated for application to spatially distributed systems. In contrast to existing ones, the novel method enables the use of stochastic simulation (e.g. Monte Carlo or importance sampling) for reliability updating with any type of information. The main benefit of this approach over existing methods is that it is robust and calculations can thus be automated. This is of particular relevance for spatially distributed systems subject to uncertain information (e.g. though measurements, monitoring, observations of system performances), which require a large number of conditional reliability calculations. In the current paper it is shown that for such systems, stochastic simulation in combination with the novel method can provide accurate results for a large number of locations simultaneously.