Novelty Detection in a Cantilever Beam using Extreme Function Theory

Damage detection and localisation in beam-like structures using mode shape features is well-established in the research community. It is known that by inserting a localised anomaly in a cantilever beam, such as a crack, its mode shapes diverge from the usual deflection path. These novelties can hence be detected by a machine-learner trained exclusively on the modal data taken from the pristine beam.

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A Transfer Learning Application to FEM and Monitoring Data for Supporting the Classification of Structural Condition States

One of the main problems concerning the field of Structural Health Monitoring (SHM) is the unavai...lability of data from different structural conditions. This is especially true for civil structures, where the collection of data from different damage states is often infeasible or economically inconvenient, particularly when dealing with architectural heritage structures.

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Use of the cointegration strategies to remove environmental effects from data acquired on historical buildings

The theory of cointegration, usually employed in econometric studies, has proved very powerful in the context of Structural Health Monitoring (SHM), where it can be used to distinguish operational and environmental changes of dynamic features from those related to the evolution of damage. The different nature of the effects imposed by operational and environmental variations on structural response required here an extension of the theory of cointegration from the linear to the nonlinear field. For this purpose, a nonlinear multivariate regression has been developed.

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