Monitoring structural health using a probabilistic measure

A Bayesian probabilistic methodology for structural health monitoring is pr esented. The method uses a sequence of identified modal parameter data sets to continually compute the probability of damage. In this approach, a high likelihood of a reduction in model stiffness at a location is taken as a p roxy for damage at the corresponding structural location. The concept exten ds the idea of using as indicators of damage the changes in model parameter s identified using a linear finite-element model and modal parameter data s ets from the structure in undamaged and possibly, damaged states. This exte nsion is needed because of uncertainties in the updated model parameters th at in practice obscure health assessment. These uncertainties arise due to effects such as variation in the identified modal parameters in the absence of damage, as well as unavoidable model error. The method is illustrated b y simulating on-line monitoring, wherein specified modal parameters are ide ntified on a regular basis and the probability of damage for each substruct ure is continually updated. Examples are given for abrupt onset of damage a nd progressive deterioration.