Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation

In a full Bayesian probabilistic framework for “robust” system identification, structural response predictions and performance reliability are updated using structural test data 𝒟 by considering the predictions of a whole set of possible structural models that are weighted by their updated probability. This involves integrating h(θ)p(θ|𝒟) over the whole parameter space, where θ is a parameter vector defining each model within the set of possible models of the structure, h(θ) is a model prediction of a response quantity of interest, and p(θ|𝒟) is the updated probability density for θ, which provides a measure of how plausible each model is given the data 𝒟. The evaluation of this integral is difficult…

• NS
  National Science Foundation