Cecilia Surace

In recent years, the use of interferometric satellite data for Structural Health Monitoring has experienced a strong development. The urban environment confirms its fragility to adverse natural events, made even more severe by climate change. Hence, the need to carry out continuous monitoring of structures and artefacts appears increasingly urgent.

In recent years, entropy measures, and more specifically, spectral entropy have emerged as an eff...icient method for the damage assessment of both mechanical systems and civil structures. In the present work, entropy measures are applied as a damage-sensitive feature for the real-time structural health monitoring of buried pipelines. The management of these underground Fluids Distribution Systems (FDSs) is critical for supplying clean water, oil, gas, and other goods.

Mel-Frequency Cepstral Coefficients (MFCCs) have been proved to be via-ble to detect damage-induc...ed shifts in the frequency content of structures subjected to external forces. Nevertheless, the Melodic (Mel) Scale is a per-ceptual feature, roughly based on human perception and originally formulat-ed to resemble the biological mechanisms of the auditory apparatus. Thus, its straight application to non-auditive acquisition systems may be misleading.

The experimental acquisition of large vibrations presents various technical difficulties. Especially in the case of geometric nonlinearities, dealing with very flexible, very light structures causes minimal variations in mass or stiffness to affect severely the dynamical response. Thus, sensors' added masses change the behaviour of the structure with respect to the unloaded condition.

This work investigates the use of the inverse Finite Element Method (iFEM) for damage localisatio...n by reconstructing the damaged strain field of the structure. The iFEM is a proven technique for reconstructing the displacement field of a structure using surface strain measurements from discrete locations on the structure.

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.