JSV paper https://doi.org/10.1016/j.jsv.2024.118710
Year: 2024
Type: article
Abstract: This dataset corresponds to some of the data used in the publication: https://doi.org/10.1016/j.jsv.2024.118710The research describes the use of Finite Element (FE) models, combined with domain adaptation to deal with the lack of training data in data-based Structural Health Monitoring. The proposed methodology is showcased in two case studies, a Brake-Reußbeam, where damage scenarios correspond to different torque settings on a lap joint and a wingbox laboratory structure where damage is introduced as saw-cuts. Supervised learning algorithms in the form of Artificial Neural Networks (ANNs) and K-Nearest Neighbours (KNNs) are trained based on FE data after domain adaptation is applied and are then tested with the experimental data.
Source: Figshare
Author: Papatheou, Evangelos
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6921404311
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