A new Bayesian model updating approach#R##N#is presented for linear structural models. It is based on#R##N#the Gibbs sampler, a stochastic simulation method that#R##N#decomposes the uncertain model parameters into three#R##N#groups, so that the direct sampling from any one group is#R##N#possible when conditional on the other groups and the incomplete modal data. This means that even if the number#R##N#of uncertain parameters is large, the effective dimension for the Gibbs sampler is always three and so#R##N#high-dimensional parameter spaces that are fatal to most#R##N#sampling techniques are handled by the method, making#R##N#it more practical for health monitoring of real structures.#R##N#The approach also inherits the advantages of Bayesian#R##N#techniques: it not only updates the optimal estimate of#R##N#the structural parameters but also updates the associated uncertainties. The approach is illustrated by applying it to two examples of structural health monitoring problems, in which the goal is to detect and quantify any damage using incomplete modal data obtained#R##N#from small-amplitude vibrations measured before and#R##N#after a severe loading event, such as an earthquake or#R##N#explosion.
['Jianye Ching', 'Matthew Muto', 'James L. Beck']
Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler