Damage identification under uncertain mass density distributions.

Nondestructive damage identification is a central task, for example, in aeronautical, civil and naval engineering. The identification approaches based on (physical) models rely on the predictive accuracy of the forward model, and typically suffer from effects caused by ubiquitous modeling errors and uncertainties. The present paper considers the identification of defects in beams and plates under uncertain mass density distribution and present some examples using synthetic data. We show that conventional maximum likelihood and conventional maximum a posteriori approaches can yield unfeasible estimates in the presence of such uncertainties even when the actual damage can be parameterized/described only with a few parameters. To partially compensate for mass density uncertainties, we adopt the Bayesian approximation error approach (BAE) for inverse problems which is based on (approximative) marginalization over the model uncertainties. • Modeling uncertainties cause negative impacts on damage identification. • We propose an approach for damage identification under uncertain mass density fields. • Bayesian approximation error is used for compensating uncertainties. • Standard maximum likelihood provides unfeasible estimates under uncertainties. • Approximate marginalization over uncertainties provides feasible estimates. [ABSTRACT FROM AUTHOR]