Bayesian probabilistic approach to structural health monitoring

A Bayesian probabilistic methodology for structural health monitoring is pr esented. The method uses a sequence of identified modal parameter data sets to compute the probability that continually updated model stiffness parame ters are less than a specified fraction of the corresponding initial model stiffness parameters. In this approach, a high likelihood of reduction in m odel stiffness at a location is taken as a proxy for damage at the correspo nding structural location, The concept extends the idea of using as indicat ors of damage the changes in structural model parameters that are identifie d from modal parameter data sets when the structure is initially in an unda maged state and then later in a possibly damaged state. The extension is ne eded, since effects such as variation in the identified modal parameters in the absence of damage, as well as unavoidable model error, lead to uncerta inties in the updated model parameters that in practice obscure health asse ssment. The method is illustrated by simulating on-line monitoring, wherein specified modal parameters are identified on a regular basis and the proba bility of damage for each substructure is continually updated.