A probabilistic framework for model updating and structural health monitoring

Abstract

This paper addresses the problem of model updating and structural health monitoring utilizing measured dynamic data. In order to explicitly treat the uncertainties arising from measurement noise, modeling error, and an inherent nonuniqueness of model updating, the proposed methodology follows a Bayesian framework for model updating. A set of damage index parameters are introduced to measure the possible damage within the structure. The proposed health monitoring methodology aims in calculating the joint probability density function (PDF) of these damage indices utilizing the posterior PDF of the stiffness parameters for both the "healthy" and "damaged" structure. This approach allows for a probabilistic assessment of damage based on the measured data and any prior information. The proposed health monitoring methodology is illustrated with a numerical example