Damage identification using inverse methods

By Michael I. Friswell. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2007

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A guided Bayesian inference approach for detection of multiple flaws in structures using the extended finite element method (2015, background): “During the past two decades, with the advances in the area of structural health monitoring (SHM), a wide range of techniques for flaw detection and quantification have been proposed and validated numerically and/or experimentally [1–3].”
An introduction to structural health monitoring (2007, background): “As such, numerous articles in this theme issue are devoted to the feature extraction portion of SHM (Fassois & Sakellariou 2007; Friswell 2007; Mal et al. 2007; Staszewski & Robertson 2007).”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “Such an inverse problem is not well-posed(6) and requires regularization and simplification.(7) An alternative to a model-driven SHM system is a data-driven model.”
Experimental structural damage localization in beam structure using spatial continuous wavelet transform and mode shape curvature methods (2017, background): “Extensive literature reviews on this topic can be found in [3,4].”