Compensation of model uncertainties in damage identification by means of the approximation error approach

Abstract

This work presents an application of the Approximation Error Approach (AEA) in the context of Structural Health Monitoring (SHM). Based on the Bayesian framework of statistical inversion, this approach allows one to compensate for errors caused by incorrect modeling of a physical system while still providing a relatively simple mathematical formulation. The application of different prior distributions of the unknown parameters is investigated. The AEA is compared to a traditional least-squares approach consisting of a forward model unable to compensate for modeling related errors.