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.

无损损坏识别是一项重要任务，例如在航空，土木和海军工程中。基于（物理）模型的识别方法依赖于前向模型的预测准确性，并且通常会遭受因普遍存在的建模错误和不确定性而导致的影响。本文考虑了在不确定质量密度分布下梁和板中缺陷的识别，并使用合成数据提供了一些示例。我们表明，在存在此类不确定性的情况下，即使仅使用几个参数就可以对实际损坏进行参数化/描述，常规的最大似然法和常规的最大后验方法也可能产生不可行的估计。为了部分补偿质量密度的不确定性，