Structural reliability methods have limitations in near-real time applications, in which probability estimates should be updated with new information. The combination of structural reliability with discrete Bayesian networks can overcome such problems. This requires the discretization of the continuous basic random variables. We develop an efficient discretization scheme, which is based on finding an optimal discretization for the linear FORM approximation of the limit state function. Since the objective is a good approximation of the probability estimate under all possible future information scenarios, the discretization scheme is optimized with respected to the expected posterior error     «

Structural reliability methods have limitations in near-real time applications, in which probability estimates should be updated with new information. The combination of structural reliability with discrete Bayesian networks can overcome such problems. This requires the discretization of the continuous basic random variables. We develop an efficient discretization scheme, which is based on finding an optimal discretization for the linear FORM approximation of the limit state function. Since...     »