Knowledge Transfer between Oscillators and Real Vibrating Structures to Enrich Dynamic Monitoring Datasets
Year: 2024
Type: article
Abstract: Transfer learning (TL) techniques can be exploited in engineering structures to overcome the main limit of the data-driven approaches for dynamic monitoring, namely the lack of a labelled dataset for some structural configuration of the monitored systems. A variety of methods can be implemented, but those that enable heterogeneous TL based on domain adaptation have proven to be particularly useful, as they allow knowledge to be transferred within a population composed of a wider range of structures. Among them, the kernelized Bayesian transfer learning (KBTL) can be used to improve the knowledge of a less monitored structure exploiting the knowledge of a more monitored one. In this paper the KBTL is assumed to transfer information from an oscillator to a spatial frame and then from a laboratory masonry oscillator to an historical bell tower for which a limited set of dynamic monitoring observations is available.
DOI: 10.58286/30525
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404913858